# Genome wide association study of incomplete hippocampal inversion on 40,000 subjects of the UK Biobank

Lisa Hemforth, Claire Cury, Kevin de Matos, Nemo Fournier, Arya

Yazdan-Panah, Olivier Colliot, Baptiste Couvy-Duchesne

► To cite this version:

Lisa Hemforth, Claire Cury, Kevin de Matos, Nemo Fournier, Arya Yazdan-Panah, et al.. Genome wide association study of incomplete hippocampal inversion on 40,000 subjects of the UK Biobank. 2026. [DOI: 10.55482/hal-05484275](https://doi.org/10.55482/hal-05484275)

HAL Id: hal-05484275

https://hal.science/hal-05484275v1

Preprint submitted on 29 Jan 2026

**HAL** is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_1.png" /></figure>

Distributed under a Creative Commons CC BY 4.0 - Attribution - International License
Genome wide association study of incomplete hippocampal
inversion on 40,000 subjects of the UK Biobank

Lisa Hemforth, PhD¹*, Claire Cury, PhD², Kevin De Matos, MD¹, Nemo Fournier, MSc¹,
Arya Yazdan-Panah, PhD¹, Olivier Colliot, PhD¹, Baptiste Couvy-Duchesne, PhD¹,³,⁴,⁵

1 Sorbonne Université, Institut du Cerveau – Paris Brain Institute - ICM, CNRS, Inria, Inserm, AP-HP, Hôpital de la Pitié-Salpêtrière, F-75013, Paris, France

2 University of Rennes, Inria, CNRS, Inserm, IRISA UMR 6074, Empenn ERL U-1228, Rennes, 35000, France

3 Institute for Molecular Bioscience, the University of Queensland, Brisbane, 4072, Australia

4 School of Biomedical Sciences, Queensland University of Technology, Brisbane, Australia

5 Brain & Mental Health Program, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia

* Corresponding author : Lisa Hemforth, Hôpital Pitié, 47 Bd de l'Hôpital, (+33)0611473213, hemforthl@gmail.com
Abstract

Incomplete hippocampal inversion (IHI) is an anatomical brain pattern found in about 20% of the population and may be associated with an increased risk of epilepsy and schizophrenia.

Previous attempts at elucidating the genetic underpinnings of IHI and its links with
disorders have been limited by sample size. We applied a deep-learning based automatic
rating method for IHI to conduct a genome-wide association study (GWAS) on 39,432

European UK Biobank participants. Automatic rating indicated an IHI prevalence of 16% in
the left hemisphere and 3% in the right. We performed GWAS using a mixed model with a
Leave One Chromosome Out approach. We identified six novel loci with six lead variants
associated with IHI in the left hemisphere and five loci with seven lead variants in the right
hemisphere, three variants of which replicated in an independent sample for left and right

IHI each. Functional annotation using gene-based tests such as mBAT-combo, h-MAGMA,
summary-data-based Mendelian randomization (multi-SMR) or known expression
quantitative trait loci (eQTLs) led to the discovery of genes implicated among others in the
pathogenesis of Alzheimer's disease, in schizophrenia, bipolar disorder and temporal lobe
epilepsy (DAAM1, GPR135, CREB3L4, SLC39A1, SLIT2, LEKR1, CCDC50, IL1RAP and
GMNC). By investigating gene expression over time, we identified several genes with
differential expression during the pre-natal development of the hippocampus (DAAM1 and
CCDC50). We reported heritability using twin studies ($H_{left}^{2} = 0.34(se = 0.05)$);

$H_{right}^{2} = 0.37(se = 0.05))$ as well as variant heritability ($h_{left}^{2} = 0.24(se = 0.014)$;

$h_{right}^{2} = 0.20(se = 0.014)$). The genetic correlation between IHI and bipolar disorder (BIP)

was nominally significant, and we did not observe a significant association with epilepsy and
schizophrenia. Our results give a first glimpse into the genetic underpinnings of IHI.

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

21

22

23
# 1 Introduction

Incomplete Hippocampal inversion (IHI) is an atypical anatomical brain pattern found in about 20% of the general population in the left and 9% in the right hemisphere (Hemforth et al. 2024). It is sometimes referred to as "hippocampal malformation" (Barsi et al. 2000) or "abnormal hippocampal formation" (Bernasconi et al. 2005). IHI were first noted in cohorts of participants with epilepsy (Baulac et al. 1998; N. Bernasconi et al. 2005; Dragan Bajic, Kumlien, et al. 2009; N. Bernasconi et al. 2005; Dragan Bajic, Kumlien, et al. 2009; Vaz, Teixeira, and Bertholdo 2022; Mutti et al. 2021) but have also been studied in association with schizophrenia (Roeske et al. 2021).

IHI likely occurs during prenatal development when most gyri are formed (gestational weeks 10 to 44) (Chi, Dooling, and Gilles 1977). Kier et al. 1997 showed that during pre-natal development the different parts of the hippocampal formation (dentate gyrus, cornu amonis, subiculum and parahippocampal gyrus) originate from the medial wall of the temporal horn. These structures are gradually infolded around the hippocampal sulcus which itself shifts position (R. Raininko and D. Bajic 2010). The allocortex, the older part of the cortex made of fewer layers than the neocortex, is displaced inferiorly, medially and internally, effectively leading to an inversion of the hippocampus during normal development, which may remain incomplete in some cases. The level of inversion is quantified by the IHI score studied in this work. We considered separate scores for the left and right hippocampi, as IHI is not always symmetrical, which may be in part due to the right hippocampus developing faster than the left (D. Bajic et al. 2012; Moskal et al. 2006). Furthering the idea that left and right hippocampi should be treated separately, the left and right hippocampal growths have been shown to be affected differently by maternal stress (Qiu et al. 2013). A difference in IHI prevalence according to the hemisphere can also be noted with IHI showing higher prevalence in the left hemisphere (15-20%) than in the right hemisphere (5-9%) over several studies (Dragan Bajic, C. Wang, et al. 2008; Cury, Toro, et al. 2015; Colenutt et al.

24

25

26

27

28

29

30

31

32

33

34

35

36

37

38

39

40

41

42

43

44

45

46

47

48

49
2018; Roeske et al. 2021; Fu et al. 2021). As hippocampi are formed during pre-natal development and develop differently in the left and right hemispheres, we can hypothesize IHI to be a developmental variation. A previous study (Cury, Toro, et al. 2015) suggested that IHI may be associated with more widespread changes in brain structure.

It is unclear if IHI impacts the hippocampus's function, although we can hypothesize that IHI likely influences the structure of the neighboring brain regions and the hippocampus's connectivity with the rest of the brain. This is of importance as the hippocampus is a central structure of the limbic system and has been implicated in many cognitive processes including the emotional stress response and memory (Vargha-Khadem et al. 1997; Axmacher et al. 2010). Hippocampi have also been linked to neurological disorders such as Alzheimer's disease (AD), where patients were often shown to present smaller hippocampi (Devanand et al. 2007; Barnes et al. 2009), suggesting they may indeed play a part in memory and cognitive decline. Overall, the hippocampal structure, and mostly its volume seem to be associated with many psychiatric disorders such as schizophrenia (SCZ) (Csernansky et al. 1998; for the ENIGMA Schizophrenia Working Group et al. 2016; McHugo et al. 2024), major depressive disorder (MDD) (Sheline, Gado, and Kraemer 2003) or post traumatic stress disorder (PTSD) (Thompson et al. 2020). Hippocampal volume can also be utilized to predict the response to antidepressants (Colle et al. 2015), making it an interesting drug target. However, IHI have been reported to negatively affect segmentation tools (Kim et al. 2012; Fragueiro, Committeri, and Cury 2023) which are often used in the previously mentioned studies. This may in turn impact the reported associations, as the reported volumes and shapes may differ from reality, confounding analyses.

Some studies have reported that patients with epilepsy may exhibit higher IHI prevalences (N. Bernasconi et al. 2005; Dragan Bajic, Kumlien, et al. 2009; Vaz, Teixeira, and Bertholdo 2022). However, other studies have suggested that IHI may be a normal anatomical variation without any link to epilepsy (Mutti et al. 2021; Fu et al. 2021). The
link with epilepsy hence remains unclear. Discussion on the association of IHI with epilepsy
in general or specific subtypes also arise with contradicting conclusions, some studies
suggesting associations to all sub-types, and some only to temporal lobe epilepsy or
malformations of the cortical development (Baulac et al. 1998; N. Bernasconi et al. 2005;
Dragan Bajic, Kumlien, et al. 2009). Roeske et al. have studied schizophrenia in association
with IHI, showing that populations of patients with schizophrenia have more severe and
higher prevalence of IHI (Roeske et al. 2021). Finally, IHI has been reported to be more
prevalent in subjects with chromosome 22q11.2 microdeletions (Andrade et al. 2013).
Overall, one of the main issues of the previously mentioned studies is their lack of statistical
power. Indeed the number of participants starts at a couple of dozen with only a few
surpassing the threshold of a couple thousand. This can lead to biased results and false
positives and could contribute to the lack of reproducibility of the published results.

The largest analysis of IHI to date included over 1400 subjects of the IMAGEN cohort
and the PING study (Cury, Scelsi, et al. 2020). This study was a genome-wide association
study (GWAS) which identified one associated variant (rs9952569), which led to the mapping
of two genes with substantial expression in the brain (AQP4 and KCTD1). This GWAS also
found top genetic associations to be either with intelligence or educational attainment and
found a heritability of 0.54 (se : 0.30). The statistical power of this study remained too low
for replication of all the found associations, highlighting the fact that larger samples are
required to progress our understanding of the genetic underpinnings of IHI.

In this work, we used an automatic rating method which we have previously trained and
tested on multiple manually annotated cohorts (Hemforth et al. 2024). We applied this
method to 41,630 T1-weighted images from the UK Biobank. Furthermore, we have manual
annotations on twin samples from the QTIM (Lachlan T. Strike, Blokland, et al. 2023) and
QTAB (Lachlan T. Strike, Narelle K. Hansell, et al. 2022) cohorts. We then performed the
largest GWAS to-date to elucidate the genetic underpinnings of incomplete hippocampal

76

77

78

79

80

81

82

83

84

85

86

87

88

89

90

91

92

93

94

95

96

97

98

99

100

101
inversion. Using the GWAS results, we sought to investigate the genetic correlations of IHI 102
with a range of traits and disorders, with well-powered GWAS, including epilepsy (The 103
International League Against Epilepsy Consortium on Complex Epilepsies et al. 2018), 104
Schizophrenia (Trubetskoy et al. 2022) and Alzheimer's disease (I. E. Jansen et al. 2019). 105
We further used twin samples to provide a more precise estimate of the heritability of IHI. 106

# 2 Materials and methods

107

## 2.1 QTIM and QTAB twin samples

108

We used two twin samples to estimate the twin-based heritability. Queensland Twin IMaging 109
(QTIM) brain MRI (Lachlan T Strike, Narelle K Hansell, Couvy-Duchesne, et al. 2019; 110
Lachlan T. Strike, Blokland, et al. 2023) were acquired using a 4 Tesla Bruker Medspec 111
scanner using an inversion recovery rapid gradient echo protocol (TI=700ms; TR=1500ms; 112
TE=3.35ms; flip angle=8°; resolution=0.94mm×0.98mm×0.98mm). Images in the 113
Queensland Twin Adolescent Brain Project (QTAB) (Lachlan T Strike, Narelle K Hansell, 114
Couvy-Duchesne, et al. 2019; Lachlan T Strike, Narelle K Hansell, Chuang, et al. 2022) were 115
acquired on a 3 Tesla Magnetom Prisma scanner (Siemens Medical Solutions, Erlangen) 116
using a 3D MP2RAGE sequence (TI=700ms; TR=4000ms; TE=2.99ms; flip angle=6°; 117
resolution=0.8mm×0.8mm×0.8mm). Both cohorts were acquired in Australia. 118

We studied a total of 1,006 individuals, including 98 complete pairs of monozygotic (MZ) 119
twins and 92 complete pairs of dizygotic (DZ) twins from QTAB (age 11.3, sd = 1.3, 48% 120
female) as well as 137 MZ and 176 complete DZ pairs from QTIM (age 22.6, sd = 2.72, 63%). 121
IHI in QTIM and QTAB were manually annotated by an expert rater, as described in a 122
previous publication (Hemforth et al. 2024). 123

123
## 2.2 UK Biobank

The UK Biobank (Miller et al. 2016; Sudlow et al. 2015; Bycroft et al. 2018) includes over 500,000 volunteers (aged between 40 and 69 years old at recruitment) from the UK, with genetic and detailed phenotypic data including demographics, lifestyle and history of disorders. At the time of extraction (Sept 2023), around 41,000 participants had been imaged with brain MRI. All subjects signed electronic consent forms. This research has been conducted using the UK Biobank Resource under Application Number 53185.

We included the 41,630 UK Biobank individuals with a T1-weighted MRI. The images were acquired on 3 Tesla Siemens Skyra scanners in 3 acquisition sites using a 3D MPRAGE sequence (TR=2000ms; TI=880ms; resolution=1mmx1mmx1mm). Of these, 52.5% were female, aged 64.1 years on average (sd = 7.7), at the time of imaging.

## 2.3 Variant genotyping and pre-processing

DNA was extracted from blood samples and genotyped by Affymetrix. The biobank-scale genotyping pipeline resulted in 489,212 individuals with genotypes called at 812,428 markers (Bycroft et al. 2018). Genotype imputation is a process in which variants, which are not directly genotyped, are predicted using a reference sample of sequencing data (here, the Haplotype Reference Consortium and the UK10K + 1000 Genomes reference panels). Using only subjects with an available T1-weighted (T1w) MRI image, we obtained a dataset of 41,553 subjects with 92,457,702 variants.

**Quality control:** We performed the quality control (QC) following the approach of Marees et al. (Marees et al. 2018) using plink v1.9 (Purcell et al. 2007). The number of individuals and variants at each QC step can be found in the supplementary table A1. Using the two-step approach recommended by Marees et al., we filter missing variants and individuals with a first threshold of 20% and a second of 2%. We removed participants with
discrepancies between reported sex and genetic sex. We excluded single nucleotide
polymorphisms (SNP)s, or variants, with a minor allele frequency (MAF) lower than 1% or
deviating from the Hardy-Weinberg equilibrium ($p < 1e^{-6}$), which can indicate genotyping
errors or SNPs under selection. We excluded individuals whose heterozygosity rates deviated
by more than three standard deviations from the sample mean. Finally, pairs of individuals
which were more than second-degree relatives (pi-hat>0.2) were filtered and the individual
with the lowest call rate of the pair was excluded. We calculated the genetic relatedness
matrix (GRM) of the sample using GCTA (v1.92.1).

**Population stratification:** We performed a multidimensional scaling (MDS) approach using a principal component analysis (PCA) on the non-imputed genotypes (same QC as the imputed data), using the 1KGenome (The 1000 Genomes Project Consortium et al. 2015) as a reference cohort for ancestry. We visually determined the PC cut-offs to select participants of European ancestry (*PC1* < 0.05 and *PC2* > -0.05 and < 0.05, see figure A1).

We have verified the overlap between our ancestry filtering and the caucasian UKBiobank genetic ancestry field. We found an overlap of 90%, indicating that the ancestry was well estimated despite the order of the steps. Note that this classification is not exactly the same as the European one but is the only genetic ancestry classification provided by the UK Biobank.

After excluding 752 non-European participants, we re-estimated the genetic principal components on the European subset that we will use in the rest of the analyses.

Our final UKB sample comprised 39,432 individuals (age=64.1, SD=7.7, 53.5% female) and 7,521,911 variants. We split the UKB sample into a GWAS discovery cohort of 29,234 (75%) and a replication cohort of 9,763 participants. We performed 500 random splits and selected the one with the largest p-value of a Kolmogoroff-Smirnoff test, to ensure the cohorts are similar in terms of age, sex and IHI scores.
## 2.4 Brain MRI processing and automatic rating of IHI

The UK Biobank performed a quality control (QC), and labeled some images with large motion, or artifacts "unusable" (Alfaro-Almagro, Jenkinson, et al. 2018), which we excluded from the analysis. We did not perform additional QC on the images.

We performed the MRI pre-processing from (Hemforth et al. 2024) and used the resulting cropped grey-matter maps for automatic rating.

We applied the automatic rating method for IHI, developed in a previous study (Hemforth et al. 2024) which consists of a convolutional neural network (with 5 convolutional layers and 3 fully connected layers) trained on manually annotated data from IMAGEN, QTIM and QTAB. This model returns 4 scores, each rating an anatomic criterion that indicates IHI (Cury, Toro, et al. 2015). The IHI criteria assess (1) the roundness of the hippocampus; (2) the verticality of the collateral sulcus; (3) the mediality of the hippocampus and (4) the depth of the occipito-temporal and collateral sulci. Each criterion is assessed on a scale ranging from 0 to 2. Each criterion was predicted independently and the obtained results were summed to form the composite IHI score in each hemisphere. A higher predicted IHI score represents a more pronounced form of IHI. Our trained model could predict IHI in the left hemisphere with performances on par with inter-human agreement. Performance was somewhat lower on the right, which may be due to the fact that phenotype is rarer. We found that 15% of the UK Biobank participants had an IHI in the left hemisphere (IHI score>3.75, as previously recommended (Cury, Toro, et al. 2015)), against 3% in the right hemisphere. This is similar to the prevalence reported in the literature (Dragan Bajic, C. Wang, et al. 2008; Cury, Toro, et al. 2015; Colenutt et al. 2018; Roeske et al. 2021).

## 2.5 Genome-wide association study

We performed genome-wide association studies using a linear mixed model and the leave-one-chromosome-out (LOCO) approach implemented in GCTA (Yang, Lee, et al. 2011;
Yang, Zaitlen, et al. 2014). The random effect models population structure, which minimises false positives, while LOCO avoids the double-fitting of a candidate variant as both fixed and random effect, thus increasing power. To speed up computation, we also fitted 10 genetic principal components as covariates, along with sex and age. Here we focused on IHI and did not explore the links between IHI and hippocampal volume, which has been reported at a phenotypic level. This relationship is of interest, as IHI has been linked to segmentation errors, which could induce bias in volume estimated from the standard processing software. Thus IHI could act as a confounder of hippocampal volume. Our automated rating tool and GWAS results should allow for a thorough investigation of the phenotypic and genetic link between IHI and hippocampus volume, which was outside the scope of this article. A sensitivity analysis with more covariates as suggested by Alfaro-Almagro, McCarthy, et al. 2021 was performed and can be read in supplementary A. We used a genome wide significance threshold of $p < 5e^{-8}$. We performed two GWAS: one for IHI in the left hemisphere and one for IHI in the right hemisphere. As we are performing two GWAS we could decrease the significance threshold, however this threshold of $p < 5e^{-8}$ is often seen as a convention nowadays, even-though it may not always be appropriate. While some teams change the threshold strictly to adapt it to multi-GWAS studies, we have mentioned loci with significance in the 2.5-5e-8 window with a "*" in table 1. The lead variants were tested in the replication cohort. Variants which reached significance (p-value < 0.05/ number of lead variants) were deemed replicated. We also tested variants of each locus which reached significance in the discovery cohort, and used a more stringent threshold for the replication p-value of 0.05/number of variants which reached significance in the discovery cohort. Two further GWAS on all data (i.e. both discovery and replication cohorts) were performed. We assessed the polgenicity of our results by investigating non-zero effect variants obtained through SBayesRC, looking into the LD score regression using ldsc and looking into associated variants using MiXeR (Frei et al. 2019;
Holland et al. 2020). We used multi-SNP-based conditional and joint association analysis (COJO) (Zhu, Zhili Zheng, et al. 2018), which can identify secondary associations by conditioning on the primary associated variant, to identify top associated variants. This method can also take into account longer range linkage disequilibrium (LD) interaction than plink-clump, for example, and hence better identify whether two loci are independent from each other. We performed COJO with a minimum MAF of 0.1 on our discovery cohort, using a significance threshold of $5e^{-8}$, assuming variants more than 10000 Kb away from each other are in complete linkage equilibrium and setting collinearity at 0.9. We identified corresponding loci by performing a clump (defined by $p < 5e^{-8}$, $R^2 > 0.2$ within a 1MB window). LD data was generated from the discovery cohort. Overlapping loci were merged but independent top variants determined by COJO are reported for all loci.

We estimated how much of the IHI score variance, can be accounted for by the loci identified using COJO. We fitted the top variants from COJO (as well as the covariates) in a multiple regression in the replication sample.

We further validated our results by constructing a Polygenic Risk Score (PRS) of IHI, which we evaluated in the replication sample. To do this, we used SBayesRC (Zheng et al. 2022), a Bayesian method which integrates functional annotations in the GWAS summary statistics, which improves polygenic prediction accuracy. The PRS performed by SBayesRC was fitted in a linear mode alongside covariates used during the GWAS and covariates used in our sensitivity analysis (10 first principal components, sex, age, center, batch, head motion, image scaling, position of the head and T1 intensity scaling) and evaluated using $R^2$ scores. Missing values were set to the mean value of the column. We established an LD reference calculated from 10,000 UK Biobank participants of European ancestry from our discovery cohort and fitted our covariates.

We used posterior inclusion probabilities provided by SBayesRC for fine-mapping.
## 2.6 Gene mapping

Most variants identified by GWAS are in intergenic regions of the genome, and additional analyses are required to identify the genes they tag.

### Single SNP mapping using FUnctionnal MAPPING Annotation (FUMA)

#### Gene based tests: mBAT-combo and h-MAGMA
Contrary to single-SNP analyses or enrichment, gene-based tests assess the joint association of all markers within a region. We used mBAT-combo (A. Li et al. 2023), the latest method capable of countering the issue of masking effects. This is an issue which arises when variant associations are aggregated without taking the direction of their effect into account. We apply mBAT-combo using LD approximated from our data, summary statistics and Ensemble coding genes (GrCH37).

During chromosome folding, segments of DNA which are far away from each other can be brought together to interact, a mechanism which is not taken into account when relying on the sole genetic position. We used Hi-C-coupled MAGMA (H-MAGMA) (Nancy Y. A. Sey et al. 2020), an improved version of the gene-based (burden) test MAGMA, with integrated chromatin interaction profiles (D. Wang et al. 2018). Here we use H-MAGMA using Hi-C annotations derived from the adult human brain (N. Sey, Pratt, and H. 2023).

While we report all identified genes in tables A3 and A4. We attempted to replicate the genes identified from gene-based testing using the discovery summary statistics by repeating the analysis using the summary statistics generated on the replication sample. For issues of conciseness, we chose to focus on genes identified by several methods or which replicated.

#### Causal inference on gene expression
We used SMR (Zhu, Zhang, et al. 2016), which combines GWAS summary statistics and eQTL data to identify genes whose expression is regulated by the significant GWAS loci. SMR uses Mendelian randomization to infer the causal effects of the loci on the gene expression, and implements the HEIDI test to filter out

249

250

251

252

253

254

255

256

257

258

259

260

261

262

263

264

265

266

267

268

269

270

271

272
pleiotropic effects. We used the eQTL data from BrainMeta v2 cis-eQTL summary data (Qi 273 et al. 2022), Westra eQTL hg1, hippocampus GTEx eQTLv8 summary data (Consortium 274 2020) and LD matrices we computed on 10,000 individuals of our discovery cohort with an 275 LD-window of 4000. We use the multi-SNP (Wu et al. 2018) approach in SMR which can 276 include several genetic instruments within each GWAS loci. 277

## 2.7 Gene expression during neuro-development, and gene reported associations 278

We used the GENE2FUNC method in FUMA, to investigate the gene expression across the 280 lifetime, for the genes mapped using the methods described above. We use gene expression 281 from the BrainSeq cohort( https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/ 282 study.cgi?study_id=phs000755.v2.p1), which has expression data from hippocampi and 283 dorsolateral prefrontal cortices. We present expression as a heat map from 8 weeks (pcw) 284 post conception to 40 years (yrs). Next, we used their gene cards (Stelzer et al. 2016; 285 Rappaport et al. 2017; Safran et al. 2021), and the data from the GWAS catalog (Sollis et al. 286 2023) to describe the reported gene functions, and associations with disorders/traits in the 287 literature. 288

## 2.8 Heritability 289

We estimated twin based heritability (Verweij et al. 2012) using the QTIM and QTAB 290 cohorts. We use ACE and ADE models implemented in the R package “openMx” (Neale 291 et al. 2016; Bates, Maes, and Neale 2019). The ACE model decomposes the trait variance 292 into additive genetic component (A - heritability), shared or familial environmental 293 contributions (C) and a residual term (E), which includes the contribution of the 294 environment and the measurement errors. We use age and sex as covariates. 295

We also estimated SNP heritability, which is a more conservative estimate of *h*², limited 296
to the variance accounted for by the common variants included in the GWAS. We used GCTA (GREML), which leverages cryptic genetic relatedness between unrelated individuals (*GRM* < 0.05, removing 1112 individuals in total, leaving 28395 individuals in the discovery and 9490 individuals in the replication cohort).

We also estimated SNP heritability using LD score regression, which relies on GWAS summary statistics and a reference LD structure. We used the ldsc python package (Gazal et al. 2017; Schizophrenia Working Group of the Psychiatric Genomics Consortium et al. 2015) and LD scores calculated from the 1KGenome (The 1000 Genomes Project Consortium et al. 2015).

We expect twin based *h*² to give larger *h*² estimates than SNP based heritability. The discrepancy is referred to as the "missing heritability", and is thought to reflect that twin-based *h*² includes the contribution of rare variants, variants not well tagged in SNP data, and possible unmodelled effects of the environment.

## 2.9 Genetic correlation

We used bivariate LDscore (Bulik-Sullivan et al. 2015) regression to investigated genetic correlations between IHI and a range of disorders that have been studied in GWAS, including panic disorder (Forstner et al. 2021), ADHD (Demontis et al. 2023), Epilepsy (The International League Against Epilepsy Consortium on Complex Epilepsies et al. 2018), ALS (Van Rheenen et al. 2021), bipolar disorder (Jiang et al. 2021), Schizophrenia (Trubetskoy et al. 2022), Alzheimer's disease (Bellenguez et al. 2022), ASD (Autism Spectrum Disorder Working Group of the Psychiatric Genomics Consortium et al. 2019), Parkinson's (Nalls et al. 2019), brain volume (P. R. Jansen et al. 2020), BMI (Koskeridis et al. 2022), educational attainment (Okbay et al. 2022) and intelligence (Sniekers et al. 2017). To maximise statistical power, we performed bivariate LD score regression (Bulik-Sullivan et al. 2015) using the IHI GWAS performed on all data
# 3 Results

## 3.1 Genome wide association study

Manhattan plots obtained from the results of our genome-wide association studies showed genome wide significant variants (figure 1). The QQ plots suggest some inflation of test statistics for IHI in both the left and right hemispheres (inflation factor, median $\chi^2/0.456 = 1.09$ for left IHI and 1.06 for right IHI). We attempted to reduce this inflation by including more genetic principal components as covariates or by rank normalising the IHI scores (after regressing out the covariates). Both approaches did not change the results (see supplementary material figure A4), which suggests the inflation may be attributable to polygenicity rather than unaccounted population stratification. Using SbayesRC, we determine the number of non-zero effect variants (nnz) to be 57614 for IHI in the left hemisphere and 58228 for IHI in the right hemisphere, suggesting an influence of polygenicity. Furthermore, we found the intercept of the LD score regression performed with *ldsc* to be 1.0085 for left IHI and 1.0065 for right IHI. This corresponds to a ratio of 0.026 for left and 0.054 for right IHI. According to *ldsc*, this ratio captures the inflation due to causes other than polygenic heritability. We can hence confirm the presence of polygenicity.

Finally, using MiXeR, we determined the number of associated variants to be 1941($sd = 128$) for IHI in the left hemisphere and 1106($sd = 224$) for IHI in the right hemisphere. The estimated $\pi$ was of $5.5e^{-4}$ for left IHI and $3.1e^{-4}$ for right IHI. While these results point to a limited polygenicity, we can use them to assess the power of our study. Indeed, we determine that we would need a sample of 220 000 participants to achieve 0.8 power. Our study hence remains underpowered.

For the left IHI, we identified 6 independently associated loci comprising 406 SNPs that
<figure><img src="image_2.png" /><figcaption>**Fig 1.** *Manhattan plot (L):* The y-axis depicts the -log10(p-value) of the association between variants and presence of IHI. The variants tested in the study are ordered along their chromosomal position on the x-axis. The red horizontal line shows the genome wide significance threshold (P = 5e-8). *QQ plot (R):* The y-axis shows the observed log-10 p value plotted against the expected value. Inflations are visible on both plots. Results for associations in the left hemisphere are shown on top and the right hemisphere on the bottom</figcaption></figure>

reached significance ($p < 5e^{-8}$). Each significant locus was linked to a single genetic signal, 346 according to the COJO analysis (figures A6 to A17). The top variants in two out of 6 loci 347 replicated ($p < 0.05/406$). When only considering the top variants in each locus, 3 replicated 348 ($p < 0.05/13$). These loci are the ones located in chromosomes (chr) 1, 4 and 14 (see table 1). 349 The direction of association of all top variants, along with other significant variants of each 350 locus was the same in discovery and replication cohorts, except for the locus located on 351 chromosome 3 (five significant variants). This locus also did not remain significant when 352 adjusting pvalues for two GWAS ($p < 2.5e^{-8}$). The most associated locus (chr 4, $p = 4.3e^{-16}$) 353 had the highest replication rate (76/134 (57%) variants), and is located downstream of gene 354 SLIT2 (Figure A10). In the locus on chromosome 14, 39/90 (43%) variants replicated. 355

For the right IHI, we identified 7 independent signals across five loci for a total of 302
<table><caption>**Table 1.** Description of risk loci associated with IHI in the left and right hemisphere. For each associated genetic locus, we show the chromosome, the variant name, the base-pair and position of the lead variant extracted from FUMA, the reference allele (the effect allele, A1), the other allele (A2), the frequency of the reference allele (f_A1), the total number (n_d and n_r) of significant variants in the risk locus in the discovery (p < 5e-8) and replication cohorts (p < 0.05/406 in the left and p < 0.05/302 in the right hemisphere) along with nominally significant variants (p < 0.05) as well as the p value of the lead variant of the discovery cohort in both the discovery and replication cohorts (p_d and p_r) and the conditional joint p-value obtained by COJO. Variants which did not surpass the corrected threshold for two GWAS (p < 2.5e-8) are marked with an *. Additional variants identified in the same locus by COJO are also reported. In bold are the p-values in the replication cohort which are significant at p < 0.05/13 (number of lead variants associated with IHI across left and right hemispheres).</caption><thead><tr><th rowspan="2">IHI</th><th rowspan="2">chr</th><th rowspan="2">Top variant</th><th rowspan="2">Base Pair</th><th rowspan="2">Loci</th><th rowspan="2">position</th><th rowspan="2">A1/A2</th><th rowspan="2">f A1</th><th rowspan="2">start:end</th><th colspan="3">Number of variants</th><th colspan="2">p GWAS</th><th colspan="2">p COJO</th><th colspan="2">Association (beta)</th></tr><tr><th>n d</th><th>n r (nom.)</th><th>p d</th><th>p r</th><th>p d</th><th>p r</th></tr></thead><tbody><tr><td>Left</td><td>1</td><td>rs6658111</td><td>47980916</td><td>intergenic</td><td>T/A</td><td>0.19</td><td>47944812:47993332</td><td>3</td><td>0 (3)</td><td>8.2e-10</td><td><strong>3.2e-3</strong></td><td>8.4e-10</td><td>-0.05</td><td>-0.04</td><td>-0.05</td><td>-0.04</td></tr><tr><td>Left</td><td>1</td><td>rs1781035</td><td>208458908</td><td>intergenic</td><td>T/G</td><td>0.48</td><td>208414033:208458908</td><td>14</td><td>0 (0)</td><td>4.6e-11</td><td>8.4e-2</td><td>4.7e-11</td><td>-0.05</td><td>-0.03</td><td>-0.05</td><td>-0.03</td></tr><tr><td>Left</td><td>3</td><td>rs7639236</td><td>56270302</td><td>intronic</td><td>G/A</td><td>0.17</td><td>55939201:56315452</td><td>5</td><td>0 (0)</td><td>3.2e-08*</td><td>7.3e-1</td><td>3.2e-8</td><td>-0.06</td><td>0.01</td><td>-0.06</td><td>-0.04</td></tr><tr><td>Left</td><td>3</td><td>rs12727448</td><td>190641109</td><td>intergenic</td><td>G/A</td><td>0.38</td><td>190591315:190678438</td><td>159</td><td>0 (159)</td><td>5.6e-12</td><td>6.7e-3</td><td>5.9e-12</td><td>-0.06</td><td>-0.04</td><td>-0.06</td><td>-0.06</td></tr><tr><td>Left</td><td>4</td><td>rs13150272</td><td>20121121</td><td>intergenic</td><td>A/G</td><td>0.27</td><td>20089210:20306941</td><td>134</td><td>76 (134)</td><td>4.3e-14</td><td><strong>1e-4</strong></td><td>4.3e-14</td><td>-0.07</td><td>-0.06</td><td>-0.07</td><td>-0.06</td></tr><tr><td>Left</td><td>14</td><td>rs2164950</td><td>59627631</td><td>intergenic</td><td>A/G</td><td>0.12</td><td>59449138:60027907</td><td>91</td><td>39 (60)</td><td>2.7e-13</td><td><strong>4.1e-07</strong></td><td>2.9e-13</td><td>0.09</td><td>0.11</td><td>-0.07</td><td>-0.07</td></tr><tr><td>Right</td><td>3</td><td>rs75482782</td><td>20162869</td><td>intronic</td><td>G/T</td><td>0.07</td><td>20091348:20545807</td><td>2</td><td>0 (0)</td><td>6.6e-09</td><td>2.3e-1</td><td>6.8e-09</td><td>-0.06</td><td>-0.02</td><td>-0.02</td><td>-0.03</td></tr><tr><td>Right</td><td>3</td><td>rs34620562</td><td>156774328</td><td>N/A</td><td>C/CCAAA</td><td>0.29</td><td>156577669:156789192</td><td>54</td><td>0 (45)</td><td>3.7e-11</td><td>9.6e-3</td><td>3.8e-11</td><td>-0.04</td><td>-0.03</td><td>-0.03</td><td>-0.03</td></tr><tr><td>Right</td><td>3</td><td>rs61067594</td><td>190650833</td><td>intergenic</td><td>A/G</td><td>0.39</td><td>190578566:190678438</td><td>164</td><td>164 (164)</td><td>5.5e-20</td><td><strong>6.1e-07</strong></td><td>1.5e-13</td><td>-0.05</td><td>-0.05</td><td>-0.05</td><td>-0.05</td></tr><tr><td>Right</td><td>7</td><td>rs9818981</td><td>190602087</td><td>intergenic</td><td>A/G</td><td>0.10</td><td></td><td></td><td></td><td>8.5e-15</td><td><strong>8.4e-10</strong></td><td>1.4e-09</td><td>0.08</td><td>0.10</td><td>0.10</td><td>0.10</td></tr><tr><td>Right</td><td>15</td><td>rs13075089</td><td>190578566</td><td>N/A</td><td>G/A</td><td>0.39</td><td></td><td></td><td></td><td>6.1e-8*</td><td>3e-2</td><td>3.8e-09</td><td>0.03</td><td>0.02</td><td>0.02</td><td>0.02</td></tr><tr><td>Right</td><td>15</td><td>rs11767221</td><td>19609643</td><td>ncRNA intronic</td><td>A/G</td><td>0.37</td><td>19549163:19647798</td><td>81</td><td>0 (81)</td><td>3.88e-09</td><td>1.78e-2</td><td>4.0e-09</td><td>0.04</td><td>0.02</td><td>0.02</td><td>0.04</td></tr><tr><td>Right</td><td>15</td><td>rs11636628</td><td>101788149</td><td>intronic</td><td>A/T</td><td>0.18</td><td>101699770:101794473</td><td>1</td><td>0 (1)</td><td>3.1e-08*</td><td><strong>1.1e-3</strong></td><td>3.2e-8</td><td>-0.04</td><td>-0.04</td><td>-0.04</td><td>-0.04</td></tr></tbody></table>
significant SNPs. COJO indicated 3 independent and conditional signals (Table 1) in the largest locus (164 variants, chr 3). This locus was the most strongly associated (lead variant $p = 5.5e^{-20}$, and all variants of the locus replicated, at $p < 0.05/302$. The locus plot (Figure A15) confirms that the GWAS locus consists of several haplotypes (small LD with top variant), near the GMNC gene. No variants replicated in the other loci (at $p < 0.05/302$), and only one top SNP replicated (at $p < 0.05/13$), which corresponded to the locus in chromosome 15. However, the top SNP of this locus did not surpass the significance threshold when controlling for two GWAS ($pvalue < 2.5e-8$). The direction of association was consistent for all 302 variants between the discovery and replication samples.

The GWAS performed on the combined discovery and replication cohorts (figure A18), identified 260 novel variants ($p < 5e-08$) for left IHI, although 5 variants of the discovery cohort were no longer associated. For IHI in the right hemisphere, the combined GWAS found an additional 157 associated variants ($p < 5e-08$).

COJO identified 12 lead variants for left IHI. The locus with five variants on chromosome 3 was not replicated but six new loci on chromosomes 1, 2, 6, 7, 10 and 20 were discovered. COJO identified 12 lead variants for right IHI, including four new loci on chromosomes 4, 6, 14 and 20.

## 3.2 Explained genetic variance

We investigated the predictive power of our GWAS results by constructing polygenic risk scores (PRS) using SBayesRC(Zheng et al. 2022). The PRS for the IHI score in the left hemisphere achieved a prediction of $r^2 = 0.058$ ($p = 2.09e^{-78}$), after controlling for covariates. Prediction of IHI in the right hemisphere was also significant ($r^2 = 0.082$, $p = 4.09e^{-123}$). We quantified the variance explained by the genome wide significant top variants found by COJO, that we fitted in a multiple regression in the replication cohort. The 6 variants associated with left IHI (Table 1) accounted for $r^2 = 0.029$ of the trait variance
$(p = 1.86e^{-28})$ and the 7 variants associated to right IHI explained $r^2 = 0.069$ ($p = 2.28e^{-95}$). 382

## 3.3 Functional annotation of significant loci

As many of the identified variants were intergenic (95% for left and 79% for right IHI), we 384 performed additional analyses to identify the genes associated to the found loci. 385

### 3.3.1 Gene-based tests: mBat-combo and h-MAGMA

We use mBAT-combo which improves power of gene-based tests by countering masking 387 effects, as well as h-MAGMA which integrates interactions due to chromatine folding into 388 the gene mapping. In total, we mapped 21 unique genes using these methods for IHI in the 389 left hemisphere. mBAT-combo identified 14 genes (DENND4B, JTB, SLC27A3, GMNC, 390 CREB3L4, RAB13, CRTC2, NUP210L, SLC39A1, GATAD2B, CYP26C1, DAAM1, SLIT2, 391 INTS3)($p < 2.7e-6$). Two genes (GMNC and DAAM1) replicated using the summary 392 statistics from the replication sample ($p < 0.05/17$). 393

H-MAGMA identified 9 genes for left IHI ($p < 9.49e^{-7}$: SFRP1, CREB3L4, IL1RAP, 394 MYOF, SLC39A1, KIAA0586, GPR135, CCDC50 and ENSG00000279379, five of which 395 replicated ($p < 0.05/18$) (ENSG00000279379, IL1RAP, SLC39A1, GPR135, CCDC50). 396

For IHI scores in the right hemisphere, we mapped a total of 11 genes. mBAT-combo 397 identified three genes (GMNC, LEKR1, NCKAP5) associated with right IHI ($p < 2.7e-6$). 398 The association with GMNC replicated ($p < 0.05/17$ in the replication sample). 399

H-MAGMA identified 9 associated genes for right IHI ($p < 9.49e^{-7}$: LRRK1, EQTN, 400 TIPARP-AS1, ENSG00000222499, HDAC9, TIPARP, LEKR1, IL1RAP, CCDC50), two of 401 which replicated ($p < 0.05/18$ IL1RAP, CCDC50) 402
### 3.3.2 Mendelian Randomisation with SMR:

We used multi-SMR which identifies genes whose expression levels are caused by the variants in the GWAS loci. For IHI in the left hemisphere, SMR identified two genes (AL606753.2 and DAAM1) but only DAAM1 replicated. No genes were significantly associated with right IHI using SMR.

403

404

405

406

407

### 3.3.3 Genes identified by several approaches

All the gene-based methods we applied identified statistically significant genes (figures A6 to A17 and tables A3 to A6) and we focus here on the genes that have been identified by several approaches, which gives more confidence that they are involved in IHI.

408

409

410

411

DAAM1 was identified by mBAT-combo and SMR (table 2 and A3), and is near the chromosome 14 locus also associated with IHI in the left hemisphere (figure A11). Ten variants of the locus are intronic to DAAM1 but their posterior inclusion probability (PIP) remained low (< 0.01). However, 86 variants of the locus, including the top variant, have been reported to be eQTL of DAAM1 (table A5), of which rs76341705 had a PIP of 1 (and replicated at $p = 2.05e^{-7} < 0.05/406$). Thus, rs76341705 could be the causal variant regulating DAAM1, even if h-MAGMA further identified GPR135 around the same locus, and variant rs76341705 (and neighboring variants) have also been reported to be eQTLs of RTN1, L3HYPDH, JKAMP.

412

413

414

415

416

417

418

419

420

In addition, CREB3L4 and SLC39A1 were identified as associated to left IHI by both h-MAGMA and mBAT-combo. These genes are more than 54 MB-pairs away from the closest significant locus of chromosome 1, although they are close to a locus (near top variant rs6690619) detected when we performed the GWAS on the full sample. (figure A18, figure A12, table 2 and A3).

421

422

423

424

425

SLIT2 was identified by mBAT-combo, and 75 variants in the locus associated with left IHI in chromosome 4 are known eQTLs (including the top variant, with PIP=1) (fig A10,

426

427
<table><caption>**Table 2.** *Main mapped genes.* Genes identified by at least two methods, or replicated. The hemisphere of the IHI is shown alongside the chromosome, lead variant of the closest locus, and probabilities in each of the methods (h-MAGMA, mBAT-combo and SMR). The number of eQTLs and intronic (int) variants is indicated. Significant probabilities are shown in bold.</caption><thead><tr><th rowspan="2">IHI</th><th rowspan="2">Gene</th><th colspan="2">Locus</th><th colspan="2">p h-MAGMA</th><th colspan="2">p mBAT-combo</th><th colspan="2">p SMR</th><th rowspan="2">number</th></tr><tr><th>chr</th><th>variant</th><th>discovery</th><th>replication</th><th>discovery</th><th>replication</th><th>discovery</th><th>replication</th><th>eQTL</th></tr></thead><tbody><tr><td>Left</td><td>DAAM1</td><td>14</td><td>rs2164950</td><td>2.6e-5</td><td>0.001</td><td><strong>1.8e-8</strong></td><td><strong>0.002</strong></td><td><strong>2.1e-6</strong></td><td><strong>0.003</strong></td><td>86</td><td>10</td></tr><tr><td>Left</td><td>GPR135</td><td>14</td><td>rs2164950</td><td><strong>5.6e-9</strong></td><td><strong>0.0002</strong></td><td>2.1e-5</td><td>0.08</td><td>0.19</td><td>0.15</td><td></td><td></td></tr><tr><td>Left</td><td>CREB3L4</td><td>1</td><td>-</td><td><strong>8.6e-7</strong></td><td>0.004</td><td><strong>5.1e-7</strong></td><td>0.03</td><td>1.4e-5</td><td>0.01</td><td></td><td></td></tr><tr><td>Left</td><td>SLC39A1</td><td>1</td><td>-</td><td><strong>6.3e-7</strong></td><td><strong>0.0007</strong></td><td><strong>4.1e-7</strong></td><td>0.03</td><td>7.2e-6</td><td>0.01</td><td></td><td></td></tr><tr><td>Left</td><td>SLIT2</td><td>4</td><td>rs13150272</td><td>4.7e-6</td><td>0.001</td><td><strong>1.1e-6</strong></td><td>0.006</td><td>0.001</td><td>0.006</td><td>75</td><td></td></tr><tr><td>Right</td><td>LEKR1</td><td>3</td><td>rs34620562</td><td><strong>2.6e-8</strong></td><td>0.3</td><td><strong>6.1e-8</strong></td><td>0.28</td><td>0.3</td><td>0.24</td><td></td><td>35</td></tr><tr><td>Right</td><td>CCDC50</td><td>3</td><td>rs61067594</td><td><strong>1.2e-13</strong></td><td><strong>3.6e-7</strong></td><td>0.33</td><td>0.54</td><td>0.6</td><td>0.8</td><td></td><td></td></tr><tr><td>Right</td><td>GMNC</td><td>3</td><td>rs61067594</td><td>7.4e-6</td><td>0.0002</td><td><strong>6.2e-23</strong></td><td><strong>1.0e-9</strong></td><td>0.006</td><td>0.02</td><td></td><td></td></tr><tr><td>Right</td><td>IL1RAP</td><td>3</td><td>rs61067594</td><td><strong>1.5e-14</strong></td><td><strong>2.1e-8</strong></td><td>0.29</td><td>0.75</td><td>0.54</td><td>0.51</td><td></td><td></td></tr><tr><td>Left</td><td>CCDC50</td><td>3</td><td>rs12727448</td><td><strong>2.7e-9</strong></td><td><strong>0.001</strong></td><td>0.24</td><td>0.31</td><td>0.07</td><td>0.52</td><td></td><td></td></tr><tr><td>Left</td><td>GMNC</td><td>3</td><td>rs12727448</td><td>7.6e-5</td><td>0.008</td><td><strong>5.3e-14</strong></td><td><strong>0.0005</strong></td><td>0.008</td><td>0.05</td><td></td><td></td></tr><tr><td>Left</td><td>IL1RAP</td><td>3</td><td>rs12727448</td><td><strong>7.3e-13</strong></td><td><strong>0.002</strong></td><td>0.04</td><td>0.13</td><td>0.71</td><td>0.55</td><td></td><td></td></tr></tbody></table>
table 2, A3 and A5).

Lastly, LEKR1 was identified by both mBAT-combo and H-MAGMA, and is near a locus
on chromosome 3 comprising 54 significant variants associated with right IHI (figure A14,
table 2 and A4). 35 variants (64%) of the loci were intronic for LEKR1, but we could not
locate the causal variant, as PIP remained low (< 0.13) and none of the significant variants
have been reported to be eQTLs of LEKR1. About 45 variants of the locus are known
eQTLs of TIPARP (table A6), but the gene was not detected in any of our analyses.

3.3.4 Genes identified by a single method

All other genes were only identified using a single approach, although the associations with
CCDC50, IL1RAP (h-MAGMA) and GMNC (mBAT-combo) replicated. All genes are near
the same locus (chromosome 3, associated with right IHI, with three independent signals
identified by COJO) (figure A15, table 2). rs9822973 exhibited the highest PIP(0.86) and
replicated (p = 2.15e-10) but we do not know if it is an eQTL of the genes identified. Of
note, these three genes were also found to be associated by the same methods with left IHI
(locus on chromosome 3, around top variant rs12727448)(figure A9, table 2 and A3).

**3.4 Gene expression during development**

We used the GENE2FUNC tool implemented in FUMA, to assess the expression levels of the
genes identified by two methods and/or which replicated during development (figure 2). The
analysis showed a higher gene expression for several genes including DAAM1, CCDC50 and
CREB3L4 during pre-natal development, particularly between post conception weeks (pcw)
13 and 21 when the hippocampus is formed and until pcw 36, when IHI prevalences decrease
from 24 to 14% (Kier et al. 1997; Dragan Bajic, Ewald, and Raili Raininko 2010).
<figure><img src="image_3.png" /><figcaption>**Fig 2.** *Gene expression* over development shown as a heatmap for each mapped gene in RPKM (Read Per Kilobase per Million). The developmental time stamps are expressed in post-conception weeks (pcw), months after birth (mos) and years (yrs)</figcaption></figure>

## 3.5 Heritability

We estimated SNP-based heritability of IHI scores to be 0.24 (se = 0.014) in the left and 0.20 (se = 0.014) in the right hemisphere. We found comparable results using gcta-GREML or LDscore regression, although SE were larger with LDSC, which is consistent with the lower statistical power of the method based on summary statistics (Table 3). SNP based heritability was comparable in the replication sample (as indicated by overlapping confidence intervals). Using an AE model, we estimated the twin-based heritability to be 0.34 (se = 0.05) for the left IHI and 0.37 (se = 0.05) for the right. When using an ACE model, that further models familial environment, the heritability of left IHI reduced to 0.18 (se = 0.12), and the model indicated a possible contribution of the shared environment (C = 0.14, se = 0.1), although it was not significantly different from 0, using the available sample size.

<table><caption>**Table 3.** *Heritability estimates:* We show SNP heritabilities obtained with GCTA-GREML and an LDscore regression (ldsc). We show the twin based heritability obtained on datasets QTIM and QTAB using and ACE/ADE model. Results are shown in the left (L) and right (R) hemisphere and for both discovery and replication cohorts. Results should be read as mean and 95% confidence intervals calculated as mean +− 2\*se.</caption><thead><tr><th rowspan="2">IHI</th><th colspan="3">GCTA-GREML</th><th colspan="3">ldsc</th><th>Twin heritability</th></tr><tr><th>discovery</th><th>replication</th><th>all</th><th>discovery</th><th>replication</th><th>all</th><th>QTIM/QTAB</th></tr></thead><tbody><tr><td>Left</td><td>0.24|0.22,0.26</td><td>0.23|0.15,0.31</td><td>0.23|0.21,0.25</td><td>0.24|0.20,0.28</td><td>0.19|0.11,0.27</td><td>0.22|0.18,0.26</td><td>0.34|0.24,0.44</td></tr><tr><td>Right</td><td>0.20|0.18,0.22</td><td>0.22|0.14,0.30</td><td>0.18|0.15,0.20</td><td>0.19|0.15,0.23</td><td>0.14|0.04,0.24</td><td>0.17|0.14,0.20</td><td>0.37|0.27,0.46</td></tr></tbody></table>
## 3.6 Genetic correlation

462

We estimated genetic correlations between IHI and a range of traits/disorders using bivariate LD score regression (table 4). We did not detect any significant genetic correlation ($rG$), after controlling for multiple testing. Of note, a handful of associations were nominally significant: between brain volume (BV) and left and right IHI ($rGleft = 0.12, se = 0.05, p = 0.017$ and $rGright = 0.13, se = 0.05, p = 0.020$), and between alzheimer's disease and right IHI ($rG = 0.11, se = 0.06, p = 0.04$). Interestingly, we detected a strong genetic correlation between left and right IHI: $rG = 0.83, se = 0.03, p = 2.3e^{-256}$. This correlation was stronger than phenotypic correlation found between left and right IHI ($r = 0.52, p = 0.0$).

463

464

465

466

467

468

469

470

As a further validation strategy, we investigated the genetic correlation between IHI summary statistics in the discovery and replication cohorts. We obtained strong genetic correlations, which are not significantly different from 1 (95% confidence intervals include 1) of $rGleft = 0.8, se = 0.13, 95\%CI : [0.54, 1]$ and $rGright = 0.89, se = 0.18, 95\%CI : [0.53, 1]$

471

472

473

474

## 4 Discussion and conclusion

475

We performed the largest GWAS on IHI to date, using 39,432 European, automatically annotated, structural brain MRI from the UK Biobank. We identified six risk loci associated with left IHI and five with IHI in the right hemisphere. Three of the top variants of each GWAS replicated in an independent sample. The largest associated locus with right IHI (164 genome wide significant variants, all replicated) was located on chromosome 3 (around rs61067594, Figure 1, Table 1) and contained 3 independent signals identified with COJO, two of which replicated.

476

477

478

479

480

481

482

The top variants identified by COJO accounted for $r^2 = 0.025$ of the trait variance ($p = 2.2e^{-42}$) for left IHI, and $r^2 = 0.065$ ($p = 1.8e^{-127}$), for right IHI, well below the SNP $h^2$, which we estimated to be 0.24 ($se = 0.01$) for left and 0.20 ($se = 0.01$) for right IHI (table 3).

483

484

485
<table><caption>**Table 4.** *Genetic correlations:* Genetic correlations of IHI in the left hemisphere with studies with public summary statistics obtained with an LD score regression. Results are shown alongside Z-scores, p-values and the *h*² heritability of the studied trait. Studied traits include ADHD, ALS, bipolar disorder, schizophrenia, ASD, brain volume, BMI and educational attainment</caption><thead><tr><th rowspan="2">Trait</th><th colspan="3">Left IHI</th><th colspan="3">Right IHI</th><th rowspan="2">h<sup>2</sup></th></tr><tr><th>rG</th><th>Z</th><th>p</th><th>rG</th><th>Z</th><th>p</th></tr></thead><tbody><tr><td>Panic</td><td>0.11 (0.08)</td><td>1.30</td><td>0.19</td><td>-0.03 (0.09)</td><td>-0.27</td><td>0.79</td><td>0.23</td></tr><tr><td>ADHD</td><td>-0.08 (0.05)</td><td>-1.87</td><td>0.06</td><td>-0.07 (0.05)</td><td>-1.49</td><td>0.14</td><td>0.08</td></tr><tr><td>Epilepsy</td><td>-0.04 (0.09)</td><td>-0.42</td><td>0.68</td><td>0.02 (0.09)</td><td>0.22</td><td>0.83</td><td>0.11</td></tr><tr><td>Amyotrophic lateral sclerosis</td><td>0.01 (0.07)</td><td>0.19</td><td>0.85</td><td>0.07 (0.07)</td><td>1.07</td><td>0.28</td><td>0.06</td></tr><tr><td>Bipolar</td><td>-0.02 (0.11)</td><td>-0.16</td><td>0.88</td><td>0.05 (0.11)</td><td>0.43</td><td>0.67</td><td>0.01</td></tr><tr><td>Schizophrenia</td><td>0.04 (0.03)</td><td>1.06</td><td>0.29</td><td>-0.0 (0.03)</td><td>-0.05</td><td>0.96</td><td>0.36</td></tr><tr><td>Alzheimer</td><td>0.01 (0.06)</td><td>-0.19</td><td>0.85</td><td>0.11 (0.06)</td><td>2.06</td><td><b>0.04</b></td><td>0.02</td></tr><tr><td>Autism Spectrum Disorder</td><td>-0.08 (0.07)</td><td>-1.20</td><td>0.23</td><td>-0.00 (0.06)</td><td>-0.018</td><td>0.99</td><td>0.16</td></tr><tr><td>Parkinson</td><td>0.06 (0.07)</td><td>0.85</td><td>0.39</td><td>-0.00 (0.07)</td><td>-0.06</td><td>0.95</td><td>0.02</td></tr><tr><td>Brain volume</td><td>0.12 (0.05)</td><td>2.40</td><td><b>0.02</b></td><td>0.13 (0.05)</td><td>2.33</td><td><b>0.02</b></td><td>0.19</td></tr><tr><td>Body Mass Index</td><td>-0.01 (0.03)</td><td>-0.31</td><td>0.76</td><td>-0.01 (0.03)</td><td>-0.17</td><td>0.86</td><td>0.21</td></tr><tr><td>Educational Attainment</td><td>0.05 (0.03)</td><td>0.91</td><td>0.36</td><td>0.04 (0.03)</td><td>1.29</td><td>0.20</td><td>0.13</td></tr><tr><td>Intelligence</td><td>-0.02 (0.04)</td><td>-0.53</td><td>0.60</td><td>-0.03 (0.05)</td><td>-0.62</td><td>0.54</td><td>0.17</td></tr></tbody></table>

We also obtain twin *h*² of 0.34(se = 0.05)/0.37(se = 0.05) for left/right IHI, which suggests IHI is also influenced by rarer variants, or variants not well captured using genotyping chips (e.g. CNVs, indels), but we cannot rule out a contribution from the familial (incl. in utero) environment (table A9). A previous GWAS on IHI had (Cury, Scelsi, et al. 2020) reported an SNP heritability of *h*² = 0.54(se = 0.30). This estimate is higher than ours but overlaps due to its large confidence intervals (estimation using 1381 subjects).

The most associated locus on chromosome 3 was associated with both left and right IHI (table 1), and neighboring genes (CCDC50, IL1RAP and GMNC) were detected to be associated with bilateral IHI. This is in line with our finding of a genetic correlation of 0.83 (se = 0.03, *p* = 2.3e-256), between left and right IHI. However, the confidence interval excludes 1, indicating some genetic differences between IHI in both hemisphere, which aligns with the differences in development and function reported for left and right hippocampi (D. Bajic et al. 2012; Moskal et al. 2006). We did not expect such a high genetic
overlap between left and right IHI, because of the differences in prevalence. However, this
result could inform future analyses, that may focus on a global IHI score averaged across left
and right, to boost detection of the common genetic variants.

Out of the 10 loci associated with left and/or right IHI, we identified candidate genes
becomes significant in the GWAS of the full sample>(tables A3 and A4). We decided to focus
on genes identified by several methods as we could not discuss all the findings. Note however
that all methods do not rely on the same assumptions and that it remains interesting to
investigate genes identified by a single method. Our analysis expands a previous GWAS on
1381 individuals, and we did not replicate the reported locus (Cury, Scelsi, et al. 2020). It
can be noted that this previous study was vastly under-powered and didn't replicate this
locus in their replication cohort.

DAAM1, was identified and replicated by mBAT-combo and SMR and linked to left IHI.
variant rs76341705 in the locus on chromosome 14 may be the causal variant, being a known
eQTL of DAMM1, with a fine-mapping PIP of 1. DAAM1 has been linked to brain
measurements including hippocampal subfield volumes (Liu et al. 2023) such as CA1. An
association between IHI and CA1 has already been noted by Colenutt et al. 2018. DAAM1
showed substantially higher expression during pre-natal development (8 post conception
weeks - 24 post conception weeks, figure 2), which corresponds to the formation of the
hippocampal structure (Kier et al. 1997). DAAM1 has been shown to be involved in the
asymmetric habenular morphogenesis in zebra fish (Colombo et al. 2013), a structure located
in the pineal gland which connects forebrain regions to the midbrain, suggesting it plays a
role in the growth of dendritic and axonal processes. The asymmetrical function of DAAM1
might explain why we only detected its association with left IHI, although larger studies are
warranted to conclude. Furthermore, DAAM1 has been associated with Amyloid β
synaptotoxicity in rats, an important neuro-biological mechanism of Alzheimer's disease
(AD) (Sellers et al. 2018). h-MAGMA detected and replicated G-protein coupled receptor 135 (GPR135) in both discovery and replication cohorts, which is located near DAAM1. It is currently unclear whether GPR135 also contributes to left IHI, of if it was detected due to LD in the region (figure A11). GPR135 has been associated with the hyperactivity component of ADHD in genetically modified mice (Cabana-Domínguez et al. 2023) and is a receptor which modulates the melatonergic system. While GPR135 is not able to bind with melatonin, it shows a reciprocal interaction with one of these receptors (Watkins and Orlandi 2020; Oishi et al. 2017).

SLC39A1 and CREB3L4, two genes which were not found near a locus, have been associated with left IHI, by mbAT-combo and h-MAGMA. They physically overlap, with opposing transcription directions, meaning that the gene burden tests we used are likely to identify both. Thus, more work and better powered studies are needed to clarify which is most likely to contribute to IHI. According to their gene cards, CREB3L4 and SLC39A1 have both been reported to be associated to cortical thickness and subcortical volume (Van Der Meer et al. 2020). SLC39A1 (a.k.a. ZIP1) is a zinc transporter associated with Alzheimer's disease. Indeed, zinc accelerates assembly of the Amyloid β peptides and tau proteins, and increased levels of ZIP1 have been found in the cortex of AD patients, and further increased during disease progression (Beyer et al. 2012). In addition, the expression of SLC39A1 in tumoral tissues has been linked to worse prognosis in gliomas (P. Wang et al. 2020). CREB3LA is a gene which encodes a CREB (cyclic-adenosine monophosphate (c-AMP) responsive element binding) protein. Signaling involving this protein plays a critical role in the formation of memories and, lower CREB levels have been reported in the prefrontal cortex of AD patients (Bartolotti, Bennett, and Lazarov 2016).

We identified LEKR1 (using mbAT-combo and h-MAGMA) to be associated with IHI in the right hemisphere. According to its gene card, LEKR1 has been reported in GWAS of cortical surface (Shadrin et al. 2021) and obsessive-compulsive eating disorders
(OCED) (Boraska et al. 2012).

551

In addition, we identified SLIT2 to be associated with left IHI using mBAT-combo.

552

Furthermore, 75 associated variants (of the chromosome 4 loci) were known eQTLs of SLIT2 and the GWAS top variant (rs13150272) exhibited a PIP of 1, thus is likely to be the causal variant. According to the GWAS catalog, this gene has been associated to brain morphology and hippocampal sub-volumes (Liu et al. 2023). SLIT2 has also been shown to guide dopaminergic axon growth growth in the midbrain in co-culture assays (Dugan et al. 2011).

553

554

555

556

557

Furthermore, SLIT2 over-expression was shown to improve the integrity of blood-brain barrier in ageing mice, leading to reduced Amyloid β in the brain (G. Li et al. 2018).

558

559

However, SLIT2 over-expression was also shown to increase depression and anxiety behaviours in adult mice, which the authors hypothesised to be due to damages in

560

561

hippocampal neurons (Huang et al. 2021). Finally, SLIT2 expression was found to be higher in neocortex tissue samples of temporal lobe epilepsy (TLE) patients than in controls (Fang et al. 2010). We found that the allele of rs13150272 that causes left IHI, was also associated with increased SLIT2 expression, which shed light on the reported link between IHI and epilepsy.

562

563

564

565

566

Lastly, CCDC50, IL1RAP and GMNC were identified by different methods, and all linked to the locus on chromosome 3 associated with IHI in both left and right hemispheres. More work is needed to clarify whether one or several of the genes are implicated, in a locus where COJO identified three independent signals. CCDC50 has been associated with changes of cortical thickness over time (Brouwer et al. 2022). A GWAS on tau levels in the CSF revealed 55 associated variants near CCDC50 (Cruchaga et al. 2013). Furthermore, the depletion of CCDC50 has been shown to impede neuronal development on human vectors (Min et al. 2020). CCD50 was also substantially more expressed during pre-natal development pointing to the hypothesis that IHI is a developmental variation. According to the GWAS catalog, IL1RAP was reported in a genome-wide gene-environment interaction

567

568

569

570

571

572

573

574

575

576
study (GWGEIS) about insomnia (Chu et al. 2021). IL1RAP has been suggested to be 577
associated to AD pathogenesis in GWAS (Ramanan et al. 2015), linked to tau levels and 578
amyloid burden. A further study of the genetic markers in and near IL1RAP showed 579
associations with disease intensity (Zettergren et al. 2019). Finally, IL1RAP has been used 580
to differentiate between patients with schizophrenia and patients with bipolar disorder, 581
patients with bipolar disorder presenting higher levels of the protein (Oraki Kohshour et al. 582
2022). ILIRAP has been linked to psychosis in a methylation study of brain tissues. 583
(Van Den Oord et al. 2016). According to the GWAS catalog, GMNC was found in GWAS of 584
brain morphology and node-level connectivity (Sha et al. 2023). Increased amounts of risk 585
alleles in GMNC were found to be associated with elevated total-tau levels in the CSF of AD 586
patients (Visser et al. 2022). GMNC was associated to neural cell proliferation and 587
differentiation in a GWAS on structural connectivity (Wainberg et al. 2024). 588

We used genetic correlation to explore the reported associations between IHI and 589
disorders such as epilepsy and schizophrenia (Vaz, Teixeira, and Bertholdo 2022; Roeske 590
et al. 2021). We only found a nominally significant correlation ($p < 0.05$, not significant after 591
Bonferroni correction) between right IHI and Alzheimer's disease (AD), which warrants 592
further investigation. Contrary to schizophrenia and epilepsy, this disorder has never been 593
linked to IHI, opening doors for investigation. Overall, our power to detect genetic 594
correlation remained limited by the size of the IHI GWAS, and we could not confirm or 595
infirm the link between IHI and epilepsy or schizophrenia. 596

Some limitations and potential future improvements are worth mentioning. Replication 597
may still be improved. This may be due to the limited power in the replication sample. 598
Another reason may be that the use of genome wide significance threshold that are too 599
lenient to account for the multiple testing among rarer variants, or the two traits considered. 600
The partial replication of lead loci may be due to winner's curse (overestimation of effect 601
among significant SNPs in the discovery) and the fact we used a smaller replication cohort. 602
We used a genome-wide significance threshold of 5e-8, which did not account for the fact 603 we analysed left and right IHI separately. In addition, such threshold may also be too lenient 604 when considering rarer variants (MAF<5%) (Fadista et al. 2016). This is why we performed 605 systematic replication to investigate our findings, either at a variant or gene level. The 606 replication rate and predictive ability of the loci we report give us confidence in the 607 associations we reported. 608

The pre-processing of the genetic data may not take into account some biases induced by 609 the order in which the pre-processing steps were applied. Here we chose to follow the exact 610 order described in Marees et al. 2018. However, it may be argued that missingness and HWE 611 should be applied on pre-imputation data. Furthermore, removal of SNPs due to HWE 612 before ancestry filtering is not recommended, as it can remove ancestrally informative 613 variants. We did also not apply our own post-imputation filters to verify its quality. 614

The moderate genetic correlation found between IHI in both hemispheres and brain 615 volume suggests that there may be structural covariance between IHI and overall brain 616 volume. We have not included brain volume as covariate and results should be treated 617 accordingly. 618

Automated IHI rating may contain measurement error (imperfect prediction, trained on 619 noisy labels), which can reduce power of GWAS, and decrease estimates of *h*² (as 620 measurement error is included in the contribution of the environment). Despite this, we 621 detected a SNP *h*² of 0.2, and several robust loci. 622

In addition, only four genes were mapped by several methods. The low agreement 623 between gene-based method may be due to their different approaches (burden test, causal 624 test on gene expression, chromatine folding, or known eQTLs) and specific limitations and 625 sensitivities. However, we cannot rule out that some loci are associated with several genes in 626 the region. More gene based methods and larger GWAS could help progress gene 627 identification. It is also possible that the detected loci do not regulate gene activity, but 628
instead tag the signal of an unmeasured coding variant in LD.

Underrepresented ancestry groups in genetics are of great interest. In this study, the
sample sizes were too low, but it would be of great interest to evaluate prevalence of IHI
across ancestry and whether GWAS results generalize.

Genome-wide genetic correlations may obscure localized effects. To counter this, in the
future, it may be interesting to perform studies using localized analyses such as LAVA (Local
Analysis of [co]Variant Annotation) (Werme et al. 2022).

While we do hypothesize IHI to be due to an atypical development, the lack of genetic
correlation with neurodevelopmental disorders may be an indication of an atypical
anatomical formation without strong association with neurodevelopmental disorders. As it
can be seen on table 4, this study did not have enough power to identify correlations below
0.2. Hence the association with neurodevelopmental disorders could be small, and even more
underpowered than for the other disorders as the sample sizes of their GWAS are often
inferior. However, while we did not find any genetic correlations, phenotypic correlations
remain to be investigated.

Our study represents the largest GWAS about IHI to date, and was made possible by our
use of an automated IHI rating method, created by our group. Our analysis of more than
39,000 genomes provides new and robust insights in the genetic loci contributing to IHI. In
addition we have robustly identified several genes linked to IHI, which have also been
implicated in disorders such as Alzheimer's and schizophrenia. Several genes showed
differential expression during the pre-natal development of the hippocampus. Our study
depicts IHI as a developmental variation which may share some genetic underpinnings with
neurological and psychiatric disorders, although larger GWAS and systematic analyses are
required to conclude.
# 5 Supporting information

653

## 5.1 Availability of data and materials

654

Code used to process the imaging data and perform automatic annotation can be found at https://github.com/LisaHemforth. Summary statistics from the GWAS conducted on the and combined sample of discovery and replication cohort will be made available in the GWAS catalog. This research has been conducted using the UK Biobank Resource under Application Number 53185.

655

656

657

658

659

## 5.2 Author Contributions

660

Lisa Hemforth had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

661

662

Study concepts and study design: Lisa Hemforth, Baptiste Couvy-Duchesne, Claire Cury, Olivier Colliot.

663

664

Acquisition, analysis or interpretation of data interpretation: all authors.

665

Manuscript drafting or manuscript revision for important intellectual content: all authors.

666

Approval of final version of submitted manuscript: all authors.

667

Literature research: Lisa Hemforth.

668

Statistical analysis: Lisa Hemforth.

669

Obtained funding: Baptiste Couvy-Duchesne, Claire Cury, Olivier Colliot.

670

Administrative, technical, or material support: Kevin de Matos, Nemo Fournier, Arya Yazdan Panah.

671

672

Study supervision: Lisa Hemforth, Baptiste Couvy-Duchesne, Claire Cury, Olivier Colliot

673
## 5.3 Declaration of Competing Interests and Funding

674

### 5.3.1 Disclosure statement

675

Competing financial interests related to the present article: none to disclose for all authors.

676

Competing financial interests unrelated to the present article: OC reports having received consulting fees from AskBio (2020) and Therapanacea (2022), and that his laboratory has received grants (paid to the institution) from Qynapse (2017-2022). Members from his laboratory have co-supervised a PhD thesis with Qynapse (2017-2022). OC's spouse is an employee of myBrainTechnologies (2015-present). OC holds a patent registered at the International Bureau of the World Intellectual Property Organization

677

678

679

680

681

682

(PCT/IB2016/0526993, Schiratti J-B, Allassonniere S, Colliot O, Durrleman S, A method for determining the temporal progression of a biological phenomenon and associated methods and devices) (2017).

683

684

685

### 5.3.2 Funding

686

The research leading to these results has received funding from the French government under management of Agence Nationale de la Recherche as part of the "Investissements d'avenir" program, reference ANR-19-P3IA-0001 (PRAIRIE 3IA Institute) and reference ANR-10-IAIHU-06 (Agence Nationale de la Recherche-10-IA Institut

687

688

689

690

Hospitalo-Universitaire-6). BCD is supported by INRIA and a CJ Martin fellowship (NHMRC app 1161356).

691

692

## 5.4 Ethical Approval

693

Informed consent was obtained from all UK Biobank participants. Procedures are controlled by a dedicated Ethics and Guidance Council (http://www.UKBiobank.ac.uk/thics), with the Ethics and Governance Framework available at

694

695

696
References

Alfaro-Almagro, Fidel, Mark Jenkinson, et al. (Feb. 2018). “Image processing and Quality Control for the first 10,000 brain imaging datasets from UK Biobank”. In: *NeuroImage* 166. Publisher: Elsevier BV, pp. 400–424. ISSN: 1053-8119. DOI: 10.1016/j.neuroimage.2017.10.034. URL: https://linkinghub.elsevier.com/retrieve/pii/S1053811917308613 (visited on 07/10/2025).

Alfaro-Almagro, Fidel, Paul McCarthy, et al. (Jan. 2021). “Confound modelling in UK Biobank brain imaging”. en. In: *NeuroImage* 224, p. 117002. ISSN: 10538119. DOI: 10.1016/j.neuroimage.2020.117002. URL: https://linkinghub.elsevier.com/retrieve/pii/S1053811920304882 (visited on 11/12/2024).

Andrade, Danielle M., Timo Krings, Eva W. C. Chow, Tim-Rasmus Kiehl, and
Anne S. Bassett (Sept. 2013). “Hippocampal Malrotation is Associated with Chromosome 22q11.2 Microdeletion”. en. In: *Canadian Journal of Neurological Sciences / Journal Canadien des Sciences Neurologiques* 40.5, pp. 652–656. ISSN: 0317-1671, 2057-0155. DOI: 10.1017/S0317167100014876. URL: https://www.cambridge.org/core/product/identifier/S0317167100014876/type/journal_article (visited on 03/17/2024).

Autism Spectrum Disorder Working Group of the Psychiatric Genomics Consortium et al. (Mar. 2019). “Identification of common genetic risk variants for autism spectrum disorder”. en. In: *Nature Genetics* 51.3, pp. 431–444. ISSN: 1061-4036, 1546-1718. DOI: 10.1038/s41588-019-0344-8. URL: https://www.nature.com/articles/s41588-019-0344-8 (visited on 04/01/2024).
Axmacher, Nikolai et al. (Feb. 2010). "Cross-frequency coupling supports multi-item working memory in the human hippocampus". en. In: *Proceedings of the National Academy of Sciences* 107.7, pp. 3228-3233. ISSN: 0027-8424, 1091-6490. DOI: 10.1073/pnas.0911531107. URL: https://pnas.org/doi/full/10.1073/pnas.0911531107 (visited on 03/17/2024).

Bajic, D., N. Canto Moreira, J. Wikström, and R. Raininko (Mar. 2012). "Asymmetric Development of the Hippocampal Region Is Common: A Fetal MR Imaging Study". en. In: *American Journal of Neuroradiology* 33.3, pp. 513-518. ISSN: 0195-6108, 1936-959X. DOI: 10.3174/ajnr.A2814. URL: http://www.ajnr.org/lookup/doi/10.3174/ajnr.A2814 (visited on 03/17/2024).

Bajic, Dragan, Uwe Ewald, and Raili Raininko (June 2010). "Hippocampal development at gestation weeks 23 to 36. An ultrasound study on preterm neonates". en. In: *Neuroradiology* 52.6, pp. 489-494. ISSN: 0028-3940, 1432-1920. DOI: 10.1007/s00234-010-0673-x. URL: http://link.springer.com/10.1007/s00234-010-0673-x (visited on 03/17/2024).

Bajic, Dragan, Eva Kumlien, et al. (Oct. 2009). "Incomplete hippocampal inversion—is there a relation to epilepsy?" en. In: *European Radiology* 19.10, pp. 2544-2550. ISSN: 0938-7994, 1432-1084. DOI: 10.1007/s00330-009-1438-y. URL: http://link.springer.com/10.1007/s00330-009-1438-y (visited on 03/17/2024).

Bajic, Dragan, Chen Wang, et al. (Jan. 2008). "Incomplete inversion of the hippocampus—a common developmental anomaly". en. In: *European Radiology* 18.1, pp. 138-142. ISSN: 0938-7994, 1432-1084. DOI: 10.1007/s00330-007-0735-6. URL: http://link.springer.com/10.1007/s00330-007-0735-6 (visited on 03/17/2024).

Barnes, Josephine et al. (Nov. 2009). "A meta-analysis of hippocampal atrophy rates in Alzheimer's disease". en. In: *Neurobiology of Aging* 30.11, pp. 1711-1723. ISSN: 01974580. DOI: 10.1016/j.neurobiolaging.2008.01.010. URL:
https://linkinghub.elsevier.com/retrieve/pii/S0197458008000262 (visited on
03/17/2024).

Barsi, P et al. (2000). “Hippocampal malrotation with normal corpus callosum: a new entity?” In: *Neuroradiology* 42.

Bartolotti, N, D A Bennett, and O Lazarov (Sept. 2016). "Reduced pCREB in Alzheimer's disease prefrontal cortex is reflected in peripheral blood mononuclear cells". en. In: *Molecular Psychiatry* 21.9, pp. 1158-1166. ISSN: 1359-4184, 1476-5578. DOI: 10.1038/mp.2016.111. URL: https://www.nature.com/articles/mp2016111 (visited on 04/21/2024).

Bates, Timothy C., Hermine Maes, and Michael C. Neale (Feb. 2019). “umx: Twin and Path-Based Structural Equation Modeling in R”. en. In: *Twin Research and Human Genetics* 22.1, pp. 27–41. ISSN: 1832-4274, 1839-2628. DOI: 10.1017/thg.2019.2. URL: https://www.cambridge.org/core/product/identifier/S1832427419000021/type/journal_article (visited on 04/09/2024).

Baulac, Michel et al. (Aug. 1998). “Hippocampal developmental changes in patients with partial epilepsy: Magnetic resonance imaging and clinical aspects”. en. In: *Annals of Neurology* 44.2, pp. 223–233. ISSN: 0364-5134, 1531-8249. DOI: 10.1002/ana.410440213. URL: https://onlinelibrary.wiley.com/doi/10.1002/ana.410440213 (visited on 03/17/2024).

Bellenguez, Céline et al. (Apr. 2022). “New insights into the genetic etiology of Alzheimer’s disease and related dementias”. en. In: *Nature Genetics* 54.4, pp. 412–436. ISSN: 1061-4036, 1546-1718. DOI: 10.1038/s41588-022-01024-z. URL: https://www.nature.com/articles/s41588-022-01024-z (visited on 11/13/2024).

Bernasconi, N, D Kinay, S Antel, and A Bernasconi (2005). “Analysis of shape and positioning of the hippocampal formation: an MRI study in patients with partial epilepsy and healthy controls”. In: *Brain* 128.
Bernasconi, N., D. Kinay, F. Andermann, S. Antel, and A. Bernasconi (Oct. 2005). "Analysis of shape and positioning of the hippocampal formation: an MRI study in patients with partial epilepsy and healthy controls". en. In: *Brain* 128.10, pp. 2442-2452. ISSN: 1460-2156, 0006-8950. DOI: 10.1093/brain/awh599. URL: http://academic.oup.com/brain/article/128/10/2442/274677/Analysis-of-shape-and-positioning-of-the (visited on 03/17/2024).

Beyer, Nancy et al. (Apr. 2012). "Zinc Transporter mRNA Levels in Alzheimer's Disease Postmortem Brain". en. In: *Journal of Alzheimer's Disease* 29.4, pp. 863-873. ISSN: 18758908, 13872877. DOI: 10.3233/JAD-2012-112105. URL: https://www.medra.org/servlet/aliasResolver?alias=iospress&doi=10.3233/JAD-2012-112105 (visited on 05/15/2024).

Boraska, Vesna et al. (Oct. 2012). "Genome-wide association analysis of eating disorder-related symptoms, behaviors, and personality traits". en. In: *American Journal of Medical Genetics Part B: Neuropsychiatric Genetics* 159B.7, pp. 803-811. ISSN: 1552-4841, 1552-485X. DOI: 10.1002/ajmg.b.32087. URL: https://onlinelibrary.wiley.com/doi/10.1002/ajmg.b.32087 (visited on 05/14/2024).

Brouwer, Rachel M. et al. (Apr. 2022). "Genetic variants associated with longitudinal changes in brain structure across the lifespan". en. In: *Nature Neuroscience* 25.4, pp. 421-432. ISSN: 1097-6256, 1546-1726. DOI: 10.1038/s41593-022-01042-4. URL: https://www.nature.com/articles/s41593-022-01042-4 (visited on 05/18/2024).

Bulik-Sullivan et al. (Nov. 2015). "An atlas of genetic correlations across human diseases and traits". en. In: *Nature Genetics* 47.11, pp. 1236-1241. ISSN: 1061-4036, 1546-1718. DOI: 10.1038/ng.3406. URL: https://www.nature.com/articles/ng.3406 (visited on 04/01/2024).
Bycroft, Clare et al. (Oct. 2018). "The UK Biobank resource with deep phenotyping and genomic data". en. In: *Nature* 562.7726, pp. 203-209. ISSN: 0028-0836, 1476-4687. DOI: 10.1038/s41586-018-0579-z. URL:

https://www.nature.com/articles/s41586-018-0579-z (visited on 04/08/2024).

Cabana-Domínguez, Judit et al. (Jan. 2023). "The translational genetics of ADHD and related phenotypes in model organisms". en. In: *Neuroscience & Biobehavioral Reviews* 144, p. 104949. ISSN: 01497634. DOI: 10.1016/j.neubiorev.2022.104949. URL:

https://linkinghub.elsevier.com/retrieve/pii/S0149763422004389 (visited on
04/21/2024).

Chi, Je G, Elizabeth C Dooling, and Floyd H. Gilles (Jan. 1977). "Gyral development of the human brain". en. In: *Annals of Neurology* 1.1, pp. 86-93. ISSN: 0364-5134, 1531-8249.

DOI: 10.1002/ana.410010109. URL:

https://onlinelibrary.wiley.com/doi/10.1002/ana.410010109 (visited on
03/17/2024).

Chu, Xiaomeng et al. (Sept. 2021). "Insomnia affects the levels of plasma bilirubin and protein metabolism: an observational study and GWGEIS in UK Biobank cohort". en. In: *Sleep Medicine* 85, pp. 184-190. ISSN: 13899457. DOI: 10.1016/j.sleep.2021.05.040.

URL: https://linkinghub.elsevier.com/retrieve/pii/S1389945721003191 (visited on 05/18/2024).

Colenutt, Jessica, Bryony McCann, Michael J. Knight, Elizabeth Coulthard, and Risto A. Kauppinen (July 2018). "Incomplete Hippocampal Inversion and Its Relationship to Hippocampal Subfield Volumes and Aging". en. In: *Journal of Neuroimaging* 28.4, pp. 422-428. ISSN: 1051-2284, 1552-6569. DOI: 10.1111/jon.12509. URL:

https://onlinelibrary.wiley.com/doi/10.1111/jon.12509 (visited on 03/17/2024).

Colle, Romain et al. (Feb. 2015). "Depressed suicide attempters have smaller hippocampus than depressed patients without suicide attempts". en. In: *Journal of Psychiatric Research*
61, pp. 13–18. ISSN: 00223956. DOI: 10.1016/j.jpsychires.2014.12.010. URL: https://linkinghub.elsevier.com/retrieve/pii/S0022395614003513 (visited on 03/17/2024).

Colombo, Alicia et al. (Oct. 2013). "Daam1a mediates asymmetric habenular morphogenesis by regulating dendritic and axonal outgrowth". en. In: *Development* 140.19, pp. 3997-4007. ISSN: 1477-9129, 0950-1991. DOI: 10.1242/dev.091934. URL: https://journals.biologists.com/dev/article/140/19/3997/45797/Daam1a-mediates-asymmetric-habenular-morphogenesis (visited on 04/21/2024).

Consortium, The GTEx (2020). "The GTEx Consortium atlas of genetic regulatory effects across human tissues". en. In: *GENETIC VARIATION*.

Cruchaga, Carlos et al. (Apr. 2013). "GWAS of Cerebrospinal Fluid Tau Levels Identifies Risk Variants for Alzheimer's Disease". en. In: *Neuron* 78.2, pp. 256-268. ISSN: 08966273. DOI: 10.1016/j.neuron.2013.02.026. URL: https://linkinghub.elsevier.com/retrieve/pii/S0896627313001840 (visited on 04/21/2024).

Csernansky, John G. et al. (Sept. 1998). "Hippocampal morphometry in schizophrenia by high dimensional brain mapping". en. In: *Proceedings of the National Academy of Sciences* 95.19, pp. 11406-11411. ISSN: 0027-8424, 1091-6490. DOI: 10.1073/pnas.95.19.11406. URL: https://pnas.org/doi/full/10.1073/pnas.95.19.11406 (visited on 03/17/2024).

Cury, Claire, Marzia Scelsi, et al. (2020). "Genome wide association study of incomplete hippocampal inversion in adolescents." In: *PLoS One*. 15.

Cury, Claire, Roberto Toro, et al. (2015). "Incomplete Hippocampal Inversion: A Comprehensive MRI Study of Over 2000 Subjects." In: *Front Neuroanat.* 9, p. 160.

Demontis, Ditte et al. (Feb. 2023). "Genome-wide analyses of ADHD identify 27 risk loci, refine the genetic architecture and implicate several cognitive domains". en. In: *Nature*
*Genetics* 55.2, pp. 198–208. ISSN: 1061-4036, 1546-1718. DOI:
10.1038/s41588-022-01285-8. URL:

https://www.nature.com/articles/s41588-022-01285-8 (visited on 04/01/2024).

Devanand, D. P. et al. (Mar. 2007). “Hippocampal and entorhinal atrophy in mild cognitive impairment: Prediction of Alzheimer disease”. en. In: *Neurology* 68.11, pp. 828–836. ISSN: 0028-3878, 1526-632X. DOI: 10.1212/01.wnl.0000256697.20968.d7. URL:

https://www.neurology.org/doi/10.1212/01.wnl.0000256697.20968.d7 (visited on 03/17/2024).

Dugan, James P., Andrea Stratton, Hilary P. Riley, W. Todd Farmer, and Grant S. Mastick (Jan. 2011). “Midbrain dopaminergic axons are guided longitudinally through the diencephalon by Slit/Robo signals”. en. In: *Molecular and Cellular Neuroscience* 46.1, pp. 347–356. ISSN: 10447431. DOI: 10.1016/j.mcn.2010.11.003. URL:

https://linkinghub.elsevier.com/retrieve/pii/S104474311000254X (visited on 05/24/2024).

Fadista, João, Alisa K Manning, Jose C Florez, and Leif Groop (Aug. 2016). “The (in)famous GWAS P-value threshold revisited and updated for low-frequency variants”. en. In: *European Journal of Human Genetics* 24.8, pp. 1202–1205. ISSN: 1018-4813, 1476-5438.

DOI: 10.1038/ejhg.2015.269. URL: https://www.nature.com/articles/ejhg2015269 (visited on 06/03/2024).

Fang, Min et al. (Apr. 2010). “Abnormal expression and spatiotemporal change of Slit2 in neurons and astrocytes in temporal lobe epileptic foci: A study of epileptic patients and experimental animals”. en. In: *Brain Research* 1324, pp. 14–23. ISSN: 00068993. DOI:
10.1016/j.brainres.2010.02.007. URL:

https://linkinghub.elsevier.com/retrieve/pii/S0006899310003513 (visited on 05/24/2024).
for the ENIGMA Schizophrenia Working Group et al. (Apr. 2016). "Subcortical brain volume abnormalities in 2028 individuals with schizophrenia and 2540 healthy controls via the ENIGMA consortium". en. In: *Molecular Psychiatry* 21.4, pp. 547-553. ISSN: 1359-4184, 1476-5578. DOI: 10.1038/mp.2015.63. URL: https://www.nature.com/articles/mp201563 (visited on 03/17/2024).

Forstner, Andreas J. et al. (Aug. 2021). "Genome-wide association study of panic disorder reveals genetic overlap with neuroticism and depression". en. In: *Molecular Psychiatry* 26.8, pp. 4179-4190. ISSN: 1359-4184, 1476-5578. DOI: 10.1038/s41380-019-0590-2. URL: https://www.nature.com/articles/s41380-019-0590-2 (visited on 04/01/2024).

Fragueiro, Agustina, Giorgia Committeri, and Claire Cury (Apr. 2023). *Incomplete Hippocampal Inversion and hippocampal subfield volumes: Implementation and inter-reliability of automatic segmentation*. ISBI 2023 - IEEE International Symposium on Biomedical Imaging. Poster. IEEE Signal Processing Society and the IEEE Engineering in Medicine and Biology Society. URL: https://www.hal.inserm.fr/inserm-04085099.

Frei, Oleksandr et al. (June 2019). "Bivariate causal mixture model quantifies polygenic overlap between complex traits beyond genetic correlation". en. In: *Nature Communications* 10.1, p. 2417. ISSN: 2041-1723. DOI: 10.1038/s41467-019-10310-0. URL: https://www.nature.com/articles/s41467-019-10310-0 (visited on 06/12/2025).

Fu, Ting-Ying et al. (Apr. 2021). "Hippocampal Malrotation: A Genetic Developmental Anomaly Related to Epilepsy?" en. In: *Brain Sciences* 11.4, p. 463. ISSN: 2076-3425. DOI: 10.3390/brainsci11040463. URL: https://www.mdpi.com/2076-3425/11/4/463 (visited on 03/31/2024).

Gazal, Steven et al. (Oct. 2017). "Linkage disequilibrium-dependent architecture of human complex traits shows action of negative selection". en. In: *Nature Genetics* 49.10,
pp. 1421-1427. ISSN: 1061-4036, 1546-1718. DOI: 10.1038/ng.3954. URL: https://www.nature.com/articles/ng.3954 (visited on 04/01/2024).

Hemforth, Lisa et al. (2024). "Automatic rating of incomplete hippocampal inversions evaluated across multiple cohorts". en. In: *MELBA*.

Holland, Dominic et al. (May 2020). "Beyond SNP heritability: Polygenicity and discoverability of phenotypes estimated with a univariate Gaussian mixture model". en. In: *PLOS Genetics* 16.5. Ed. by Xiaofeng Zhu, e1008612. ISSN: 1553-7404. DOI: 10.1371/journal.pgen.1008612. URL: https://dx.plos.org/10.1371/journal.pgen.1008612 (visited on 06/12/2025).

Huang, Guilan et al. (Feb. 2021). "Depression-/Anxiety-Like Behavior Alterations in Adult Slit2 Transgenic Mice". en. In: *Frontiers in Behavioral Neuroscience* 14, p. 622257. ISSN: 1662-5153. DOI: 10.3389/fnbeh.2020.622257. URL: https://www.frontiersin.org/articles/10.3389/fnbeh.2020.622257/full (visited on 05/24/2024).

Jansen, Iris E. et al. (Mar. 2019). "Genome-wide meta-analysis identifies new loci and functional pathways influencing Alzheimer's disease risk". en. In: *Nature Genetics* 51.3, pp. 404-413. ISSN: 1061-4036, 1546-1718. DOI: 10.1038/s41588-018-0311-9. URL: https://www.nature.com/articles/s41588-018-0311-9 (visited on 04/01/2024).

Jansen, Philip R. et al. (Nov. 2020). "Genome-wide meta-analysis of brain volume identifies genomic loci and genes shared with intelligence". en. In: *Nature Communications* 11.1, p. 5606. ISSN: 2041-1723. DOI: 10.1038/s41467-020-19378-5. URL: https://www.nature.com/articles/s41467-020-19378-5 (visited on 04/01/2024).

Jiang, Longda, Zhili Zheng, Hailing Fang, and Jian Yang (Nov. 2021). "A generalized linear mixed model association tool for biobank-scale data". In: *Nature genetics* 53, pp. 1616-1621.
Kier, E Leon, Jung H Kim, Robert K Fulbright, and Richard A Bronen (1997). "Embryology of the Human Fetal Hippocampus: MR Imaging, Anatomy, and Histology". en. In: *American Society of Neuroradiology*.

Kim, H et al. (2012). "Automatic hippocampal segmentation in temporal lobe epilepsy: impact of developmental abnormalities". In: *Neuroimage* 59.

Koskeridis, Fotios et al. (Nov. 2022). "Pleiotropic genetic architecture and novel loci for C-reactive protein levels". en. In: *Nature Communications* 13.1, p. 6939. ISSN: 2041-1723.
DOI: 10.1038/s41467-022-34688-6. URL:
https://www.nature.com/articles/s41467-022-34688-6 (visited on 11/13/2024).

Li, Ang et al. (Jan. 2023). "mBAT-combo: A more powerful test to detect gene-trait associations from GWAS data". en. In: *The American Journal of Human Genetics* 110.1, pp. 30-43. ISSN: 00029297. DOI: 10.1016/j.ajhg.2022.12.006. URL:
https://linkinghub.elsevier.com/retrieve/pii/S0002929722005407 (visited on 04/07/2024).

Li, Ge et al. (Aug. 2018). "Overexpression of Slit2 improves function of the paravascular pathway in the aging mouse brain". en. In: *International Journal of Molecular Medicine*. ISSN: 1107-3756, 1791-244X. DOI: 10.3892/ijmm.2018.3802. URL:
http://www.spandidos-publications.com/10.3892/ijmm.2018.3802 (visited on 05/24/2024).

Liu, Nana et al. (Aug. 2023). "Cross-ancestry genome-wide association meta-analyses of hippocampal and subfield volumes". In: *Nature genetics* 55.1.

Makowski, Carolina, Martin Lepage, and Alan C. Evans (Jan. 2019). "Head motion: the dirty little secret of neuroimaging in psychiatry". en. In: *Journal of Psychiatry & Neuroscience* 44.1, pp. 62-68. ISSN: 11804882. DOI: 10.1503/jpn.180022. URL:
http://www.jpn.ca/lookup/doi/10.1503/jpn.180022 (visited on 11/12/2024).
Marees, Andries T. et al. (June 2018). "A tutorial on conducting genome-wide association studies: Quality control and statistical analysis". en. In: *International Journal of Methods in Psychiatric Research* 27.2, e1608. ISSN: 1049-8931, 1557-0657. DOI: 10.1002/mpr.1608.
URL: https://onlinelibrary.wiley.com/doi/10.1002/mpr.1608 (visited on 03/29/2024).

McHugo, Maureen et al. (Jan. 2024). "Smaller anterior hippocampal subfields in the early stage of psychosis". en. In: *Translational Psychiatry* 14.1, p. 69. ISSN: 2158-3188. DOI: 10.1038/s41398-023-02719-5. URL:
https://www.nature.com/articles/s41398-023-02719-5 (visited on 03/17/2024).

Miller, Karla et al. (2016). "Multimodal population brain imaging in the UK Biobank prospective epidemiological study". In: *Nature neuroscience* 19.

Min, Ju-Sik et al. (Dec. 2020). "Coiled-coil domain containing 50-V2 protein positively regulates neurite outgrowth". en. In: *Scientific Reports* 10.1, p. 21295. ISSN: 2045-2322. DOI: 10.1038/s41598-020-78304-3. URL:
https://www.nature.com/articles/s41598-020-78304-3 (visited on 04/21/2024).

Moskal, Joseph R., Roger A. Kroes, Nigel J. Otto, Omid Rahimi, and Brenda J. Claiborne (Aug. 2006). "Distinct patterns of gene expression in the left and right hippocampal formation of developing rats". en. In: *Hippocampus* 16.8, pp. 629-634. ISSN: 1050-9631, 1098-1063. DOI: 10.1002/hipo.20198. URL:
https://onlinelibrary.wiley.com/doi/10.1002/hipo.20198 (visited on 03/17/2024).

Mutti, Carlotta et al. (Feb. 2021). "Incomplete hippocampal inversion and epilepsy: A systematic review and meta-analysis". en. In: *Epilepsia* 62.2, pp. 383-396. ISSN: 0013-9580, 1528-1167. DOI: 10.1111/epi.16787. URL:
https://onlinelibrary.wiley.com/doi/10.1111/epi.16787 (visited on 03/17/2024).
Nalls, Mike A et al. (Dec. 2019). "Identification of novel risk loci, causal insights, and heritable risk for Parkinson's disease: a meta-analysis of genome-wide association studies". en. In: *The Lancet Neurology* 18.12, pp. 1091-1102. ISSN: 14744422. DOI: 10.1016/S1474-4422(19)30320-5. URL: https://linkinghub.elsevier.com/retrieve/pii/S1474442219303205 (visited on 04/01/2024).

Neale, Michael C. et al. (June 2016). "OpenMx 2.0: Extended Structural Equation and Statistical Modeling". en. In: *Psychometrika* 81.2, pp. 535-549. ISSN: 0033-3123, 1860-0980. DOI: 10.1007/s11336-014-9435-8. URL: http://link.springer.com/10.1007/s11336-014-9435-8 (visited on 04/09/2024).

Oishi, Atsuro et al. (Aug. 2017). "Orphan GPR61, GPR62 and GPR135 receptors and the melatonin MT2 receptor reciprocally modulate their signaling functions". en. In: *Scientific Reports* 7.1, p. 8990. ISSN: 2045-2322. DOI: 10.1038/s41598-017-08996-7. URL: https://www.nature.com/articles/s41598-017-08996-7 (visited on 05/15/2024).

Okbay, Aysu et al. (Apr. 2022). "Polygenic prediction of educational attainment within and between families from genome-wide association analyses in 3 million individuals". en. In: *Nature Genetics* 54.4, pp. 437-449. ISSN: 1061-4036, 1546-1718. DOI: 10.1038/s41588-022-01016-z. URL: https://www.nature.com/articles/s41588-022-01016-z (visited on 04/01/2024).

Oraki Kohshour, Mojtaba et al. (Nov. 2022). "Comparative serum proteomic analysis of a selected protein panel in individuals with schizophrenia and bipolar disorder and the impact of genetic risk burden on serum proteomic profiles". en. In: *Translational Psychiatry* 12.1, p. 471. ISSN: 2158-3188. DOI: 10.1038/s41398-022-02228-x. URL: https://www.nature.com/articles/s41398-022-02228-x (visited on 04/21/2024).

Purcell, Shaun et al. (Sept. 2007). "PLINK: A Tool Set for Whole-Genome Association and Population-Based Linkage Analyses". en. In: *The American Journal of Human Genetics*
81.3, pp. 559–575. ISSN: 00029297. DOI: 10.1086/519795. URL:

https://linkinghub.elsevier.com/retrieve/pii/S0002929707613524 (visited on
03/17/2024).

Qi, Ting et al. (Sept. 2022). “Genetic control of RNA splicing and its distinct role in complex trait variation”. en. In: *Nature Genetics* 54.9, pp. 1355–1363. ISSN: 1061-4036, 1546-1718.

DOI: 10.1038/s41588-022-01154-4. URL:

https://www.nature.com/articles/s41588-022-01154-4 (visited on 04/21/2024).

Qiu, A et al. (Sept. 2013). “Maternal anxiety and infants’ hippocampal development: timing matters”. en. In: *Translational Psychiatry* 3.9, e306–e306. ISSN: 2158-3188. DOI:

10.1038/tp.2013.79. URL: https://www.nature.com/articles/tp201379 (visited on
03/17/2024).

Raininko, R. and D. Bajic (Apr. 2010). ““Hippocampal Malrotation”: No Real Malrotation and Not Rare”. en. In: *American Journal of Neuroradiology* 31.4, E39–E39. ISSN:

0195-6108, 1936-959X. DOI: 10.3174/ajnr.A2013. URL:

http://www.ajnr.org/lookup/doi/10.3174/ajnr.A2013 (visited on 03/17/2024).

Ramanan, Vijay K et al. (2015). “GWAS of longitudinal amyloid accumulation on ¹⁸F-florbetapir PET in Alzheimer’s disease implicates microglial activation gene *IL1RAP*”. en. In: *Brain* 138.10. ISSN: 0006-8950, 1460-2156. DOI: 10.1093/brain/awv231. URL:

https://academic.oup.com/brain/article-lookup/doi/10.1093/brain/awv231
(visited on 05/15/2024).

Rappaport, Noa et al. (Jan. 2017). “MalaCards: an amalgamated human disease compendium with diverse clinical and genetic annotation and structured search”. en. In: *Nucleic Acids Research* 45.D1, pp. D877–D887. ISSN: 0305-1048, 1362-4962. DOI: 10.1093/nar/gkw1012.

URL: https://academic.oup.com/nar/article-lookup/doi/10.1093/nar/gkw1012
(visited on 04/01/2024).
Roeske, Maxwell J. et al. (Sept. 2021). "Incomplete hippocampal inversion in schizophrenia: prevalence, severity, and impact on hippocampal structure". en. In: *Molecular Psychiatry* 26.9, pp. 5407-5416. ISSN: 1359-4184, 1476-5578. DOI: 10.1038/s41380-020-01010-z.
URL: https://www.nature.com/articles/s41380-020-01010-z (visited on 03/17/2024).

Safran, Marilyn et al. (2021). "The GeneCards Suite". en. In: *Practical Guide to Life Science Databases*. Ed. by Imad Abugessaisa and Takeya Kasukawa. Singapore: Springer Nature Singapore, pp. 27-56. DOI: 10.1007/978-981-16-5812-9_2. URL: https://link.springer.com/10.1007/978-981-16-5812-9_2 (visited on 04/01/2024).

Schizophrenia Working Group of the Psychiatric Genomics Consortium et al. (Mar. 2015). "LD Score regression distinguishes confounding from polygenicity in genome-wide association studies". en. In: *Nature Genetics* 47.3, pp. 291-295. ISSN: 1061-4036, 1546-1718. DOI: 10.1038/ng.3211. URL: https://www.nature.com/articles/ng.3211 (visited on 03/17/2024).

Sellers, Katherine J. et al. (Mar. 2018). "Amyloid B synaptotoxicity is Wnt-PCP dependent and blocked by fasudil". en. In: *Alzheimer's & Dementia* 14.3, pp. 306-317. ISSN: 1552-5260, 1552-5279. DOI: 10.1016/j.jalz.2017.09.008. URL: https://alz-journals.onlinelibrary.wiley.com/doi/10.1016/j.jalz.2017.09.008 (visited on 04/21/2024).

Sey, Nancy Y. A. et al. (Apr. 2020). "A computational tool (H-MAGMA) for improved prediction of brain-disorder risk genes by incorporating brain chromatin interaction profiles". en. In: *Nature Neuroscience* 23.4, pp. 583-593. ISSN: 1097-6256, 1546-1726. DOI: 10.1038/s41593-020-0603-0. URL: https://www.nature.com/articles/s41593-020-0603-0 (visited on 04/21/2024).

Sey, NYA, BM Pratt, and Won H. (2023). "Annotating genetic variants to target genes using H-MAGMA". en. In: *Nat Protoc*.
Sha, Zhiqiang, Dick Schijven, Simon E Fisher, and Clyde Francks (2023). "Genetic architecture of the white matter connectome of the human brain". en. In: *SCIENCE ADVANCES*.

Shadrin, Alexey A. et al. (Dec. 2021). "Vertex-wise multivariate genome-wide association study identifies 780 unique genetic loci associated with cortical morphology". en. In: *NeuroImage* 244, p. 118603. ISSN: 10538119. DOI: 10.1016/j.neuroimage.2021.118603. URL: https://linkinghub.elsevier.com/retrieve/pii/S1053811921008764 (visited on 05/18/2024).

Sheline, Yvette I, Mokhtar H Gado, and Helena C Kraemer (2003). "Untreated Depression and Hippocampal Volume Loss". en. In: *Am J Psychiatry*.

Sniekers, Suzanne et al. (July 2017). "Genome-wide association meta-analysis of 78,308 individuals identifies new loci and genes influencing human intelligence". en. In: *Nature Genetics* 49.7, pp. 1107-1112. ISSN: 1061-4036, 1546-1718. DOI: 10.1038/ng.3869. URL: https://www.nature.com/articles/ng.3869 (visited on 03/17/2024).

Sollis, Elliot et al. (Jan. 2023). "The NHGRI-EBI GWAS Catalog: knowledgebase and deposition resource". en. In: *Nucleic Acids Research* 51.D1, pp. D977-D985. ISSN: 0305-1048, 1362-4962. DOI: 10.1093/nar/gkac1010. URL: https://academic.oup.com/nar/article/51/D1/D977/6814460 (visited on 04/01/2024).

Stelzer, Gil et al. (June 2016). "The GeneCards Suite: From Gene Data Mining to Disease Genome Sequence Analyses". en. In: *Current Protocols in Bioinformatics* 54.1. ISSN: 1934-3396, 1934-340X. DOI: 10.1002/cpbi.5. URL: https://currentprotocols.onlinelibrary.wiley.com/doi/10.1002/cpbi.5 (visited on 04/01/2024).
Strike, Lachlan T, Narelle K Hansell, Kai-Hsiang Chuang, et al. (2022). "The Queensland Twin Adolescent Brain Project, a longitudinal study of adolescent brain development". en. In: *bioRxiv* pre-print.

Strike, Lachlan T, Narelle K Hansell, Baptiste Couvy-Duchesne, et al. (Mar. 2019). "Genetic Complexity of Cortical Structure: Differences in Genetic and Environmental Factors Influencing Cortical Surface Area and Thickness". en. In: *Cerebral Cortex* 29.3, pp. 952-962. ISSN: 1047-3211, 1460-2199. DOI: 10.1093/cercor/bhy002. URL: https://academic.oup.com/cercor/article/29/3/952/4824647 (visited on 04/09/2024).

Strike, Lachlan T., Gabriella A.M. Blokland, et al. (2023). *"Queensland Twin IMaging (QTIM)"*. DOI: doi:10.18112/openneuro ds004169.v1.0.7.

Strike, Lachlan T., Narelle K. Hansell, et al. (2022). *Queensland Twin Adolescent Brain (QTAB)*. DOI: doi:10.18112/openneuro ds004146.v1.0.0.

Sudlow, Cathie et al. (Mar. 2015). "UK Biobank: An Open Access Resource for Identifying the Causes of a Wide Range of Complex Diseases of Middle and Old Age". en. In: *PLOS Medicine* 12.3, e1001779. ISSN: 1549-1676. DOI: 10.1371/journal.pmed.1001779. URL: https://dx.plos.org/10.1371/journal.pmed.1001779 (visited on 03/29/2024).

The 1000 Genomes Project Consortium et al. (Oct. 2015). "A global reference for human genetic variation". en. In: *Nature* 526.7571, pp. 68-74. ISSN: 0028-0836, 1476-4687. DOI: 10.1038/nature15393. URL: https://www.nature.com/articles/nature15393 (visited on 04/01/2024).

The International League Against Epilepsy Consortium on Complex Epilepsies et al. (Dec. 2018). "Genome-wide mega-analysis identifies 16 loci and highlights diverse biological mechanisms in the common epilepsies". en. In: *Nature Communications* 9.1, p. 5269. ISSN: 2041-1723. DOI: 10.1038/s41467-018-07524-z. URL: https://www.nature.com/articles/s41467-018-07524-z (visited on 03/18/2024).
Thompson, P.M., N. Jahanshad, C.R.K. Ching, and et al. et (2020). "ENIGMA and global neuroscience: A decade of large-scale studies of the brain in health and disease across more than 40 countries". In: *Transl Psychiatry* 10.

Trubetskoy, Vassily et al. (Apr. 2022). "Mapping genomic loci implicates genes and synaptic biology in schizophrenia". en. In: *Nature* 604.7906, pp. 502–508. ISSN: 0028-0836, 1476-4687. DOI: 10.1038/s41586-022-04434-5. URL: https://www.nature.com/articles/s41586-022-04434-5 (visited on 04/01/2024).

Van Den Oord, Edwin J. C. G. et al. (July 2016). "A Whole Methylome CpG-SNP Association Study of Psychosis in Blood and Brain Tissue". en. In: *Schizophrenia Bulletin* 42.4, pp. 1018–1026. ISSN: 0586-7614, 1745-1701. DOI: 10.1093/schbul/sbv182. URL: https://academic.oup.com/schizophreniabulletin/article-lookup/doi/10.1093/schbul/sbv182 (visited on 05/15/2024).

Van Der Meer, Dennis et al. (July 2020). "Understanding the genetic determinants of the brain with MOSTest". en. In: *Nature Communications* 11.1, p. 3512. ISSN: 2041-1723. DOI: 10.1038/s41467-020-17368-1. URL: https://www.nature.com/articles/s41467-020-17368-1 (visited on 05/18/2024).

Van Rheenen, Wouter et al. (Dec. 2021). "Common and rare variant association analyses in amyotrophic lateral sclerosis identify 15 risk loci with distinct genetic architectures and neuron-specific biology". en. In: *Nature Genetics* 53.12, pp. 1636–1648. ISSN: 1061-4036, 1546-1718. DOI: 10.1038/s41588-021-00973-1. URL: https://www.nature.com/articles/s41588-021-00973-1 (visited on 04/01/2024).

Vargha-Khadem, F. et al. (July 1997). "Differential Effects of Early Hippocampal Pathology on Episodic and Semantic Memory". en. In: *Science* 277.5324, pp. 376–380. ISSN: 0036-8075, 1095-9203. DOI: 10.1126/science.277.5324.376. URL: https://www.science.org/doi/10.1126/science.277.5324.376 (visited on 03/17/2024).
Vaz, André, Bernardo Corrêa De Almeida Teixeira, and Debora Brighente Bertholdo (Aug. 2022). "Incomplete hippocampal inversion: diagnostic criteria and effect on epilepsy, seizure localization and therapeutic outcome in children". en. In: *Seizure: European Journal of Epilepsy* 100, pp. 67-75. ISSN: 10591311. DOI: 10.1016/j.seizure.2022.06.003. URL: https://linkinghub.elsevier.com/retrieve/pii/S1059131122001388 (visited on 03/17/2024).

Verweij, Karin J. H., Miriam A. Mosing, Brendan P. Zietsch, and Sarah E. Medland (2012). "Estimating Heritability from Twin Studies". en. In: *Statistical Human Genetics*. Ed. by Robert C. Elston, Jaya M. Satagopan, and Shuying Sun. Vol. 850. Series Title: Methods in Molecular Biology. Totowa, NJ: Humana Press, pp. 151-170. DOI: 10.1007/978-1-61779-555-8_9. URL: https://link.springer.com/10.1007/978-1-61779-555-8_9 (visited on 04/09/2024).

Visser, Pieter Jelle et al. (Dec. 2022). "*GMNC* risk alleles in individuals with Alzheimer's disease are associated with cerebrospinal fluid concentrations of proteins involved in neuronal plasticity and blood brain barrier function". en. In: *Alzheimer's & Dementia* 18.S4, e065570. ISSN: 1552-5260, 1552-5279. DOI: 10.1002/alz.065570. URL: https://alz-journals.onlinelibrary.wiley.com/doi/10.1002/alz.065570 (visited on 04/21/2024).

Wainberg, Michael et al. (Mar. 2024). "Genetic architecture of the structural connectome". en. In: *Nature Communications* 15.1, p. 1962. ISSN: 2041-1723. DOI: 10.1038/s41467-024-46023-2. URL: https://www.nature.com/articles/s41467-024-46023-2 (visited on 04/21/2024).

Wang, Daifeng et al. (Dec. 2018). "Comprehensive functional genomic resource and integrative model for the human brain". en. In: *Science* 362.6420, eaat8464. ISSN:
0036-8075, 1095-9203. DOI: 10.1126/science.aat8464. URL:

https://www.science.org/doi/10.1126/science.aat8464 (visited on 05/08/2024).

Wang, Peng, Jingjing Zhang, Shuai He, Boan Xiao, and Xiaobin Peng (Dec. 2020).

"SLC39A1 contribute to malignant progression and have clinical prognostic impact in gliomas". en. In: *Cancer Cell International* 20.1, p. 573. ISSN: 1475-2867. DOI:

10.1186/s12935-020-01675-0. URL:

https://cancerci.biomedcentral.com/articles/10.1186/s12935-020-01675-0
(visited on 04/21/2024).

Watkins, Lyndsay R. and Cesare Orlandi (June 2020). "Orphan G Protein Coupled

Receptors in Affective Disorders". en. In: *Genes* 11.6, p. 694. ISSN: 2073-4425. DOI:

10.3390/genes11060694. URL: https://www.mdpi.com/2073-4425/11/6/694 (visited on 04/21/2024).

Werme, Josefin, Sophie Van Der Sluis, Danielle Posthuma, and Christiaan A. De Leeuw

(Mar. 2022). "An integrated framework for local genetic correlation analysis". In: *Nature Genetics* 54.3, pp. 274-282. ISSN: 1061-4036, 1546-1718. DOI:

10.1038/s41588-022-01017-y. URL:

https://www.nature.com/articles/s41588-022-01017-y (visited on 12/23/2025).

Wu, Yang et al. (Mar. 2018). "Integrative analysis of omics summary data reveals putative mechanisms underlying complex traits". en. In: *Nature Communications* 9.1, p. 918. ISSN:

2041-1723. DOI: 10.1038/s41467-018-03371-0. URL:

https://www.nature.com/articles/s41467-018-03371-0 (visited on 04/21/2024).

Yang, Jian, S. Hong Lee, Michael E. Goddard, and Peter M. Visscher (Jan. 2011). "GCTA: A Tool for Genome-wide Complex Trait Analysis". en. In: *The American Journal of Human Genetics* 88.1, pp. 76-82. ISSN: 00029297. DOI: 10.1016/j.ajhg.2010.11.011. URL:

https://linkinghub.elsevier.com/retrieve/pii/S0002929710005987 (visited on 03/17/2024).
Yang, Jian, Noah A Zaitlen, Michael E Goddard, Peter M Visscher, and Alkes L Price (Feb. 2014). "Advantages and pitfalls in the application of mixed-model association methods". en. In: *Nature Genetics* 46.2, pp. 100–106. ISSN: 1061-4036, 1546-1718. DOI: 10.1038/ng.2876. URL: https://www.nature.com/articles/ng.2876 (visited on 04/09/2024).

Zettergren, Anna et al. (Feb. 2019). "Association of IL1RAP-related genetic variation with cerebrospinal fluid concentration of Alzheimer-associated tau protein". en. In: *Scientific Reports* 9.1, p. 2460. ISSN: 2045-2322. DOI: 10.1038/s41598-018-36650-3. URL: https://www.nature.com/articles/s41598-018-36650-3 (visited on 04/26/2024).

Zheng, Z et al. (2022). "Leveraging functional genomic annotations and genome coverage to improve polygenic prediction of complex traits within and between ancestries". en. In: *bioRxiv*. DOI: 10.1101/2022.10.12.510418.

Zhu, Zhihong, Futao Zhang, et al. (May 2016). "Integration of summary data from GWAS and eQTL studies predicts complex trait gene targets". en. In: *Nature Genetics* 48.5, pp. 481–487. ISSN: 1061-4036, 1546-1718. DOI: 10.1038/ng.3538. URL: https://www.nature.com/articles/ng.3538 (visited on 04/21/2024).

Zhu, Zhihong, Zhili Zheng, et al. (Jan. 2018). "Causal associations between risk factors and common diseases inferred from GWAS summary data". en. In: *Nature Communications* 9.1, p. 224. ISSN: 2041-1723. DOI: 10.1038/s41467-017-02317-2. URL: https://www.nature.com/articles/s41467-017-02317-2 (visited on 04/09/2024).
A Supplementary material

<table><caption>**Table A1.** *Number of individuals and variants:* The number of individuals and variants at each step of the QC and population stratification. The numbers are given at the end of each step. The QC steps follow the tutorial by Marees et al. (Marees et al. 2018)</caption><thead><tr><th>Step</th><th>Individuals</th><th>Variants</th></tr></thead><tbody><tr><td>Filter data with images</td><td>41.553</td><td>92.457.702</td></tr><tr><td>SNP missingness >0.2</td><td>41.553</td><td>92.212.787</td></tr><tr><td>Individual missingness >0.2 1</td><td>41.553</td><td>92.212.787</td></tr><tr><td>SNP missingness >0.02</td><td>41.553</td><td>90.455.995</td></tr><tr><td>Individual missingness >0.02</td><td>41.553</td><td>90.455.995</td></tr><tr><td>Filter sex discrepancy</td><td>41.450</td><td>90.455.995</td></tr><tr><td>MAF > 0.01</td><td>41.450</td><td>7.694.532</td></tr><tr><td>HWE > 1e-6</td><td>41.450</td><td>7.521.911</td></tr><tr><td>Heterozygosity</td><td>40.132</td><td>7.521.911</td></tr><tr><td>Pi-hat + PCA</td><td>39.432</td><td>7.521.911</td></tr><tr><td>Split discovery</td><td>29.574</td><td>7.521.911</td></tr><tr><td>Split replication</td><td>9.858</td><td>7.521.911</td></tr></tbody></table>
<figure><img src="image_4.png" /><figcaption>**Fig A1.** *Principal component analysis of the population stratification.* The second principal component (PC) is plotted against the first one. Subjects from the 1KGenome are colored according to their ethnic origin. All black data-points correspond to our data from the UKBiobank. Thresholds used to filter for population stratification (PC1<0.05 and -0.05<PC2<0.05) are plotted in dotted lines</figcaption></figure>
<figure><img src="image_5.png" /><figcaption>**Fig A2.** *Example of an input image for automatic IHI rating.* The raw MRI image has been pre-processed using the first step of the clinica T1-volume. Grey-matter maps are then cropped around the hippocampus and the surrounding sulci ([24:96,54:107,16:49] in MNI coordinates).</figcaption></figure>
**Sensitivity analysis with additional covariates** We performed GWAS sensitivity analyses(figure A3) using a larger list of covariates including genotyping batch, assessment center, and a range of putative MRI confounders suggested by Alfaro-Almagro, McCarthy, et al. 2021 (volumetric scaling from T1 head image to standard space (field 25000), x, y, and z positions of the head in the scanner (fields 25756-25758), T1 intensity scaling (field 25925) and head motion as approximated by head motion during resting state MRI (rfMRI), which has been shown to be a sufficient proxy (Makowski, Lepage, and Evans 2019)). Missing values were set to the mean of the column. This corresponded, over all participants included in this study, to 124 values of the T1 scaling, 1778 values for the head motion, 9 values for both x and y positions, 29 values for the z position and 125 values for the intensity scaling. Additional covariates did not change the GWAS results as indicated by a Pearson's correlation of association coefficients of r² = 0.97, with the same loci reaching significance (figure A3).

Furthermore, we investigated the association between IHI rating and head motion and obtained negligible *r* of [0.0163, 0.0347] (95% confidence intervals) for left IHI and [ 0.0506, 0.0690] for right IHI, indicating that IHI ratings are not contaminated by head motion.
<figure><img src="image_6.png" /><figcaption>**Fig A3.** *GWAS Sensitivity* analyses controlling for additional covariates (genotyping batch, assessment center, volumetric scaling from T1 head image to standard space (field 25000), x, y, and z positions of the head in the scanner (fields 25756-25758), T1 intensity scaling (field 25925) and head motion as approximated by head motion during resting state MRI (rfMRI)). On the left, we show fastGWAS results obtained using only the covariates used in the our main analysis. On the right, we show results obtained by adding further covariates (fastGWA approach in GCTA).</figcaption></figure>
<figure><img src="image_7.png" /><figcaption>**Fig A4.** *Attempts at reducing QQ inflation* a. Shows the IHI score distribution before (red) and after (blue) rank normalization. b. Shows the associated QQ plot which still shows inflation. c. The QQ plot using 20 pcs show a similar amount of inflation as the one produced with 10 pcs in figure 1</figcaption></figure>
<figure><img src="image_8.png" /><figcaption>**Fig A5.** Box plot of IHI scores obtained for variant values of top variants in left hemisphere (top) and right hemisphere (bottom). Boxplots are shown for variant values of 0,1 and 2 depending on the number of minor alleles present. The reference allele is named next to each variant.</figcaption></figure>
<figure><img src="image_9.png" /><figcaption>**Fig A6.** Locus plot of the locus on chromosome one associated with left IHI with three significant variants (obtained with Locus Zoom). An LD r² scale is provided and surrounding variants are colored accordingly. The lead variant is shown as a purple square. The post inclusion probability is plotted below the locus zoom. Purple lines give the limit of the locus. No gene was identified by gene based methods on this locus but the three variants, including top variant (maximal PIP=0.86 for rs61784835) were identified as eQTL of PDZK1IP1 and CMPK1, and two of then, including the one with the highest PIP as eQTLs of TAL1. rs61784835, was also found to be an eQTL of FOXE3.</figcaption></figure>
<figure><img src="image_10.png" /><figcaption>**Fig A7.** Locus plot of the locus on chromosome one with 14 significant variants associated to left IHI (obtained with Locus Zoom). An LD r² scale is provided and surrounding variants are colored accordingly. The lead variant is shown as a purple square. The post inclusion probability is plotted below the locus zoom. Purple lines give the limit of the locus. No genes were identified by gene based methods on this locus.</figcaption></figure>
<figure><img src="image_11.png" /><figcaption>**Fig A8.** Locus plot of the locus on chromosome 3 with five significant variants associated to left IHI (obtained with Locus Zoom). An LD r² scale is provided and surrounding variants are colored accordingly. The lead variant is shown as a purple square. The post inclusion probability is plotted below the locus zoom. Purple lines give the limit of the locus. The five significant variants, including the top variant of its locus, rs7639236 were intronic to ERC2 and its eQTLs. However, the highest PIP was of 0.05 and the variants did not replicate.</figcaption></figure>
<figure><img src="image_12.png" /><figcaption>**Fig A9.** Locus plot of the locus on chromosome 3 with 159 significant variants associated to left IHI (obtained with Locus Zoom). An LD r² scale is provided and surrounding variants are colored accordingly. The lead variant is shown as a purple square. The post inclusion probability is plotted below the locus zoom. Purple lines give the limit of the locus. h-MAGMA identified IL1RAP and CCDC50 as associated genes while mBAT-combo identified GMNC.</figcaption></figure>
<figure><img src="image_13.png" /><figcaption>**Fig A10.** Locus plot of the locus on chromosome 4 with 134 significant variants associated to left IHI (obtained with Locus Zoom). An LD r² scale is provided and surrounding variants are colored accordingly. The lead variant is shown as a purple square. The post inclusion probability is plotted below the locus zoom. Purple lines give the limit of the locus. 75 variants, including the top variant (PIP=1) were found to be eQTLs of SLIT2. mBAT-combo also identified SLIT2 in the discovery cohort but this result did not replicate.</figcaption></figure>
<figure><img src="image_14.png" /><figcaption>**Fig A11.** Locus plot of the locus on chromosome 14 with 91 significant variants associated to left IHI (obtained with Locus Zoom). An LD r² scale is provided and surrounding variants are colored accordingly. The lead variant is shown as a purple square. The post inclusion probability is plotted below the locus zoom. Purple lines give the limit of the locus. An eQTL locus plot generated by SMR is shown below this. DAAM1 was identified by mBAT-combo and SMR. In this locus ten variants were found to be intronic to DAAM1 but their post inclusion probability (PIP) remained low (< 0.01). However, 86 variants of the locus, including the top variant, have been reported to be eQTL of DAAM1. The PIP of one of the variants, rs76341705 was of 1, indicating that it is likely the causal variant, and it further replicated (p = 2.05e-7 < 0.05/406). In the same locus, h-MAGMA also identified GPR135.</figcaption></figure>
Locus plot: rs10494303

<figure><img src="image_15.png" /><figcaption>**Fig A12.** Plot of the region near CREB3L4 and SLC39A1 (obtained with Locus Zoom). An LD r² scale is provided and surrounding variants are colored accordingly. The lead variant is shown as a purple square. The post inclusion probability is plotted below the locus zoom. Purple lines give the limit of the locus. While this locus was not identified by COJO, CREB3L4 and SLC39A1 were mapped by h-MAGMA and mBAT-combo and a locus was identified by COJO on the GWAS of both discovery and replication cohorts.</figcaption></figure>
<figure><img src="image_16.png" /><figcaption>**Fig A13.** Locus plot of the locus on chromosome 3 with two significant variants associated with right IHI (obtained with Locus Zoom). An LD r² scale is provided and surrounding variants are colored accordingly. The lead variant is shown as a purple square. The post inclusion probability is plotted below the locus zoom. Purple lines give the limit of the locus. Both variants were intronic and eQTLs of KAT2B (PIP of top variant=0.97).</figcaption></figure>
<figure><img src="image_17.png" /><figcaption>**Fig A14.** Locus plot of the locus on chromosome 3 with 54 significant variants associated to right IHI (obtained with Locus Zoom). An LD r² scale is provided and surrounding variants are colored accordingly. The lead variant is shown as a purple square. The post inclusion probability is plotted below the locus zoom. Purple lines give the limit of the locus. LEKR1 was identified by both mBAT-combo and H-MAGMA. 35 variants of the locus were intronic for LEKR1, however their highest PIP was 0.1</figcaption></figure>
Locus plot: rs61067594

<figure><img src="image_18.png" /><figcaption>**Fig A15.** Locus plot of the locus on chromosome 3 with 164 significant variants and three lead variants associated with right IHI (obtained with Locus Zoom). An LD $r^2$ scale is provided and surrounding variants are colored accordingly. The lead variant is shown as a purple square. Other lead variants are circled in red. The post inclusion probability is plotted below the locus zoom. Purple lines give the limit of the locus. h-MAGMA identified IL1RAP and CCDC50 as associated genes while mBAT-combo identified GMNC.</figcaption></figure>
<figure><img src="image_19.png" /><figcaption>**Fig A16.** Locus plot of the locus on chromosome 7 with 81 significant variants associated with right IHI (obtained with Locus Zoom). An LD r² scale is provided and surrounding variants are colored accordingly. The lead variant is shown as a purple square. The post inclusion probability is plotted below the locus zoom. Purple lines give the limit of the locus. 81 variants, including one variant, rs4719574, with PIP=0.63 were eQTLs of TWIST1.</figcaption></figure>
<figure><img src="image_20.png" /><figcaption>**Fig A17.** Locus plot of the locus on chromosome 15 associated with right IHI (obtained with *Locus Zoom*). An LD $r^2$ scale is provided and surrounding variants are colored accordingly. The lead variant is shown as a purple square. The post inclusion probability is plotted below the locus zoom. Purple lines give the limit of the locus. The top variant which did not achieve a threshold of $p < 2.5e^{-08}$, but replicated, was identified to be an eQTL of CHSY1 and VIMP (PIP=0.01) and intronic to CHSY1.</figcaption></figure>
<figure><img src="image_21.png" /><figcaption>**Fig A18.** *Manhattan plot (L):* The y-axis depicts the -log10(p) of the association between variant and presence of IHI for data of the discovery and replication cohorts. The variants tested in the study are ordered along their chromosomal position on the x-axis. The red horizontal line denotes genome wide significance at the Bonferroni threshold (P = 5e-8). *QQ plot (R):* The y-axis shows the observed log-10 p value plotted against the expected value. Inflations are shown on both plots. Results for associations in the left hemisphere are shown on top and the right hemisphere on the bottom</figcaption></figure>
<table><caption>**Table A2.** variants with maximal post inclusion probability (PIP) in each locus The table shows the chromosome and top variant of each locus along with the variant with the highest PIP, the value of said PIP and its probability of association in the replication GWAS</caption><thead><tr><th rowspan="2">IHI</th><th colspan="2">Locus</th><th colspan="3">Highest PIP</th></tr><tr><th>chr</th><th>Top variant</th><th>variant</th><th>PIP</th><th>p replication</th></tr></thead><tbody><tr><td>Left</td><td>1</td><td>rs6658111</td><td>rs61784835</td><td>0.86</td><td>0.0006</td></tr><tr><td>Left</td><td>1</td><td>rs1781035</td><td>rs1781035</td><td>0.37</td><td>0.08</td></tr><tr><td>Left</td><td>3</td><td>rs7639236</td><td>rs2123184</td><td>0.05</td><td>0.73</td></tr><tr><td>Left</td><td>3</td><td>rs12727448</td><td>rs13065568</td><td>0.31</td><td>0.02</td></tr><tr><td>Left</td><td>4</td><td>rs13150272</td><td>rs13150272</td><td>1</td><td>0.0001</td></tr><tr><td>Left</td><td>14</td><td>rs2164950</td><td>rs76341705</td><td>1</td><td>2.0e-7</td></tr><tr><td>Right</td><td>3</td><td>rs75482782</td><td>rs75482782</td><td>0.97</td><td>0.88</td></tr><tr><td>Right</td><td>3</td><td>rs34620562</td><td>rs6774654</td><td>0.13</td><td>0.03</td></tr><tr><td>Right</td><td>3</td><td>rs61067594</td><td>rs9822973</td><td>0.86</td><td>0.001</td></tr><tr><td>Right</td><td>7</td><td>rs11767221</td><td>rs4719574</td><td>0.63</td><td>0.001</td></tr><tr><td>Right</td><td>15</td><td>rs11636628</td><td>rs9672951</td><td>0.49</td><td>0.001</td></tr></tbody></table>
<table><caption>**Table A3.** *Identified genes for left IHI:* Genes mapped by FUMA, h-MAGMA, mBAT-combo or SMR. p-values are shown for discovery and replication cohorts. If a gene was not part of an annotation method, the p-values are set to NaN.</caption><thead><tr><th>Gene</th><th>P_magma_disc</th><th>P_magma_rep</th><th>P_mBAT_disc</th><th>P_mBAT_rep</th><th>P_SMR_disc</th><th>P_SMR_rep</th></tr></thead><tbody><tr><td>PDZK1IP1</td><td>2.71E-02</td><td>6.59E-01</td><td>1.82E-01</td><td>5.34E-03</td><td>3.61E-01</td><td>1.82E-01</td></tr><tr><td>TAL1</td><td>7.38E-02</td><td>1.66E-01</td><td>1.35E-01</td><td>1.07E-02</td><td>5.27E-02</td><td>4.74E-03</td></tr><tr><td>CMPK1</td><td>2.29E-01</td><td>8.04E-02</td><td>6.39E-02</td><td>5.01E-02</td><td>1.08E-01</td><td>7.65E-02</td></tr><tr><td>FOXE3</td><td>3.97E-01</td><td>2.93E-01</td><td>1.12E-01</td><td>9.81E-02</td><td>2.81E-01</td><td>6.53E-02</td></tr><tr><td>TRABD2B</td><td>3.39E-01</td><td>5.43E-01</td><td>3.95E-02</td><td>9.42E-01</td><td>NaN</td><td>NaN</td></tr><tr><td>FLJ00388</td><td>1.26E-01</td><td>3.15E-02</td><td>NaN</td><td>NaN</td><td>NaN</td><td>NaN</td></tr><tr><td>C1orf116</td><td>4.29E-01</td><td>8.73E-01</td><td>2.48E-01</td><td>5.28E-01</td><td>NaN</td><td>NaN</td></tr><tr><td>YOD1</td><td>7.20E-01</td><td>6.24E-01</td><td>4.03E-01</td><td>5.95E-01</td><td>1.75E-01</td><td>5.50E-01</td></tr><tr><td>PFKFB2</td><td>8.22E-01</td><td>5.61E-01</td><td>2.92E-01</td><td>6.79E-01</td><td>1.45E-01</td><td>9.48E-01</td></tr><tr><td>C4BPB</td><td>7.05E-01</td><td>4.93E-01</td><td>4.51E-01</td><td>5.35E-01</td><td>NaN</td><td>NaN</td></tr><tr><td>C4BPA</td><td>5.15E-01</td><td>7.08E-01</td><td>4.64E-01</td><td>3.94E-01</td><td>7.00E-01</td><td>9.09E-01</td></tr><tr><td>CR2</td><td>5.31E-01</td><td>2.78E-01</td><td>6.41E-01</td><td>6.20E-01</td><td>5.91E-01</td><td>4.86E-01</td></tr><tr><td>CR1L</td><td>9.43E-02</td><td>6.43E-01</td><td>7.80E-01</td><td>5.73E-01</td><td>NaN</td><td>NaN</td></tr><tr><td>CD34</td><td>8.58E-01</td><td>2.87E-01</td><td>7.62E-01</td><td>4.51E-01</td><td>2.84E-01</td><td>3.22E-01</td></tr><tr><td>PLXNA2</td><td>1.12E-02</td><td>2.36E-01</td><td>1.80E-04</td><td>3.40E-01</td><td>9.69E-01</td><td>5.11E-01</td></tr><tr><td>CAMK1G</td><td>1.01E-01</td><td>8.90E-02</td><td>5.52E-01</td><td>2.27E-01</td><td>7.30E-01</td><td>3.77E-01</td></tr><tr><td>LAMB3</td><td>4.87E-01</td><td>5.91E-02</td><td>2.74E-01</td><td>2.11E-01</td><td>9.34E-01</td><td>4.28E-01</td></tr><tr><td>G0S2</td><td>7.99E-01</td><td>8.89E-01</td><td>2.25E-01</td><td>3.06E-01</td><td>4.37E-01</td><td>6.04E-01</td></tr><tr><td>HSD11B1</td><td>4.08E-02</td><td>1.03E-01</td><td>1.70E-01</td><td>5.35E-01</td><td>4.82E-01</td><td>7.96E-01</td></tr><tr><td>TRAF3IP3</td><td>7.34E-02</td><td>1.64E-01</td><td>1.83E-01</td><td>7.37E-01</td><td>9.38E-02</td><td>1.13E-01</td></tr><tr><td>C1orf74</td><td>2.89E-01</td><td>7.33E-01</td><td>1.62E-01</td><td>7.60E-01</td><td>NaN</td><td>NaN</td></tr><tr><td>IRF6</td><td>1.64E-01</td><td>9.52E-01</td><td>1.90E-01</td><td>7.68E-01</td><td>1.00E-01</td><td>7.27E-01</td></tr><tr><td>UTP25</td><td>3.22E-02</td><td>8.51E-01</td><td>1.25E-01</td><td>9.73E-01</td><td>7.05E-01</td><td>4.80E-01</td></tr><tr><td>CACNA2D3</td><td>4.57E-01</td><td>1.25E-01</td><td>9.77E-01</td><td>6.43E-01</td><td>9.61E-01</td><td>3.50E-01</td></tr><tr><td>LRTM1</td><td>1.85E-01</td><td>7.94E-01</td><td>5.39E-01</td><td>5.44E-01</td><td>NaN</td><td>NaN</td></tr><tr><td>WNT5A</td><td>2.41E-05</td><td>7.95E-01</td><td>3.12E-04</td><td>1.27E-01</td><td>1.94E-01</td><td>3.97E-01</td></tr><tr><td>ERC2</td><td>4.84E-03</td><td>4.85E-01</td><td>2.95E-06</td><td>7.57E-01</td><td>1.33E-01</td><td>8.50E-01</td></tr><tr><td>TASOR</td><td>2.31E-04</td><td>5.57E-01</td><td>3.82E-02</td><td>3.83E-01</td><td>3.86E-02</td><td>7.73E-01</td></tr><tr><td>TP63</td><td>7.46E-01</td><td>9.85E-01</td><td>5.14E-02</td><td>9.69E-01</td><td>NaN</td><td>NaN</td></tr><tr><td>LEPREL1</td><td>5.06E-01</td><td>2.76E-01</td><td>3.62E-01</td><td>9.14E-01</td><td>NaN</td><td>NaN</td></tr><tr><td>CLDN1</td><td>7.15E-01</td><td>2.87E-01</td><td>2.71E-01</td><td>1.02E-01</td><td>NaN</td><td>NaN</td></tr><tr><td>TMEM207</td><td>8.71E-02</td><td>4.52E-01</td><td>3.99E-02</td><td>5.89E-01</td><td>NaN</td><td>NaN</td></tr><tr><td>IL1RAP</td><td>7.32E-13</td><td>2.24E-03</td><td>3.77E-02</td><td>1.27E-01</td><td>7.07E-01</td><td>5.49E-01</td></tr><tr><td>GMNC</td><td>7.62E-05</td><td>7.56E-03</td><td>5.26E-14</td><td>4.68E-04</td><td>8.10E-03</td><td>5.24E-02</td></tr><tr><td>OSTN</td><td>1.48E-01</td><td>8.18E-01</td><td>3.37E-01</td><td>4.90E-01</td><td>4.52E-01</td><td>3.01E-01</td></tr><tr><td>UTS2B</td><td>3.46E-01</td><td>2.73E-01</td><td>3.70E-02</td><td>5.02E-01</td><td>9.38E-01</td><td>2.41E-01</td></tr><tr><td>CCDC50</td><td>2.68E-09</td><td>1.07E-03</td><td>2.44E-01</td><td>3.12E-01</td><td>6.52E-02</td><td>5.22E-01</td></tr></tbody></table>
<table><tr><td>PYDC2</td><td>5.43E-01</td><td>2.80E-01</td><td>1.57E-01</td><td>2.68E-01</td><td>NaN</td><td>NaN</td></tr><tr><td>FGF12</td><td>1.38E-01</td><td>4.50E-01</td><td>9.15E-01</td><td>5.67E-01</td><td>2.19E-01</td><td>5.52E-01</td></tr><tr><td>ATP13A5</td><td>7.16E-02</td><td>3.45E-01</td><td>9.01E-01</td><td>5.49E-01</td><td>7.31E-01</td><td>6.31E-01</td></tr><tr><td>ATP13A4</td><td>2.64E-01</td><td>2.11E-01</td><td>7.42E-01</td><td>9.94E-02</td><td>6.96E-01</td><td>3.77E-01</td></tr><tr><td>OPA1</td><td>7.37E-01</td><td>6.83E-02</td><td>7.19E-01</td><td>5.39E-02</td><td>2.99E-01</td><td>3.16E-02</td></tr><tr><td>DCAF16</td><td>9.27E-01</td><td>7.11E-01</td><td>7.44E-01</td><td>8.66E-01</td><td>1.40E-01</td><td>4.48E-01</td></tr><tr><td>NCAPG</td><td>9.34E-01</td><td>6.16E-01</td><td>8.00E-01</td><td>9.25E-01</td><td>NaN</td><td>NaN</td></tr><tr><td>SLIT2</td><td>4.78E-06</td><td>1.04E-03</td><td>1.11E-06</td><td>5.78E-03</td><td>1.50E-03</td><td>5.85E-03</td></tr><tr><td>ARMH4</td><td>3.33E-01</td><td>5.70E-01</td><td>9.58E-02</td><td>4.93E-02</td><td>4.72E-01</td><td>2.01E-01</td></tr><tr><td>ACTR10</td><td>3.29E-01</td><td>6.57E-01</td><td>7.10E-01</td><td>3.80E-01</td><td>5.67E-01</td><td>4.16E-01</td></tr><tr><td>AL132989.1</td><td>3.11E-01</td><td>5.75E-01</td><td>NaN</td><td>NaN</td><td>NaN</td><td>NaN</td></tr><tr><td>ARID4A</td><td>4.20E-01</td><td>7.03E-01</td><td>2.61E-01</td><td>4.92E-01</td><td>9.62E-02</td><td>2.95E-01</td></tr><tr><td>TOMM20L</td><td>5.32E-01</td><td>1.21E-02</td><td>4.88E-01</td><td>3.37E-01</td><td>NaN</td><td>NaN</td></tr><tr><td>TIMM9</td><td>2.44E-01</td><td>1.53E-01</td><td>7.99E-01</td><td>1.85E-01</td><td>NaN</td><td>NaN</td></tr><tr><td>KIAA0586</td><td>6.49E-08</td><td>1.03E-01</td><td>8.99E-01</td><td>1.91E-01</td><td>2.59E-01</td><td>9.97E-01</td></tr><tr><td>DACT1</td><td>5.95E-03</td><td>4.74E-01</td><td>NaN</td><td>1.06E-01</td><td>8.09E-01</td><td>1.61E-01</td></tr><tr><td>DAAM1</td><td>2.63E-05</td><td>1.01E-03</td><td>1.80E-08</td><td>2.36E-03</td><td>2.13E-06</td><td>3.20E-03</td></tr><tr><td>GPR135</td><td>5.57E-09</td><td>2.33E-04</td><td>2.08E-05</td><td>7.55E-02</td><td>1.93E-01</td><td>1.47E-01</td></tr><tr><td>L3HYPDH</td><td>3.76E-02</td><td>1.08E-01</td><td>6.82E-06</td><td>7.59E-02</td><td>2.50E-04</td><td>7.05E-02</td></tr><tr><td>JKAMP</td><td>3.70E-01</td><td>2.64E-01</td><td>7.99E-06</td><td>5.32E-02</td><td>3.22E-03</td><td>1.08E-02</td></tr><tr><td>CCDC175</td><td>7.21E-02</td><td>2.75E-01</td><td>2.07E-04</td><td>7.71E-02</td><td>7.05E-02</td><td>4.45E-01</td></tr><tr><td>RTN1</td><td>2.31E-01</td><td>3.76E-01</td><td>1.91E-03</td><td>1.02E-01</td><td>3.83E-01</td><td>1.68E-01</td></tr><tr><td>LRRC9</td><td>3.05E-01</td><td>3.44E-01</td><td>7.53E-01</td><td>2.49E-01</td><td>NaN</td><td>NaN</td></tr><tr><td>DHRS7</td><td>5.95E-01</td><td>5.95E-01</td><td>6.81E-01</td><td>5.28E-01</td><td>3.20E-01</td><td>3.66E-01</td></tr><tr><td>PPM1A</td><td>6.00E-01</td><td>5.80E-02</td><td>6.80E-01</td><td>3.63E-02</td><td>4.86E-01</td><td>3.89E-02</td></tr><tr><td>TRMT5</td><td>7.30E-02</td><td>3.11E-01</td><td>4.60E-01</td><td>1.19E-01</td><td>NaN</td><td>NaN</td></tr><tr><td>SLC38A6</td><td>2.49E-01</td><td>4.85E-01</td><td>5.67E-02</td><td>1.17E-01</td><td>4.99E-01</td><td>2.87E-02</td></tr><tr><td>TMEM30B</td><td>7.51E-01</td><td>1.31E-01</td><td>5.42E-01</td><td>7.65E-01</td><td>7.35E-01</td><td>5.39E-01</td></tr><tr><td>HIF1A</td><td>6.33E-01</td><td>9.30E-01</td><td>2.25E-01</td><td>4.84E-01</td><td>1.99E-01</td><td>1.91E-01</td></tr><tr><td>SNAPC1</td><td>2.11E-01</td><td>9.98E-01</td><td>2.16E-01</td><td>8.49E-01</td><td>2.83E-01</td><td>8.04E-01</td></tr><tr><td>INTS3</td><td>8.54E-04</td><td>9.33E-02</td><td>1.03E-06</td><td>3.49E-02</td><td>NaN</td><td>NaN</td></tr><tr><td>SLC27A3</td><td>2.28E-04</td><td>6.75E-02</td><td>5.51E-07</td><td>3.73E-02</td><td>8.84E-02</td><td>1.69E-01</td></tr><tr><td>GATAD2B</td><td>1.10E-06</td><td>1.07E-02</td><td>3.68E-07</td><td>1.73E-02</td><td>7.21E-06</td><td>9.98E-03</td></tr><tr><td>DENND4B</td><td>2.95E-04</td><td>2.40E-02</td><td>6.90E-07</td><td>3.37E-02</td><td>1.45E-01</td><td>4.44E-01</td></tr><tr><td>CRTC2</td><td>4.91E-04</td><td>3.05E-02</td><td>4.71E-07</td><td>4.29E-02</td><td>NaN</td><td>NaN</td></tr><tr><td>SLC39A1</td><td>6.54E-07</td><td>7.22E-04</td><td>4.09E-07</td><td>3.70E-02</td><td>7.26E-06</td><td>1.05E-02</td></tr><tr><td>CREB3L4</td><td>8.63E-07</td><td>4.14E-03</td><td>5.06E-07</td><td>3.17E-02</td><td>1.42E-05</td><td>1.32E-02</td></tr><tr><td>JTB</td><td>2.11E-04</td><td>7.64E-02</td><td>6.77E-07</td><td>2.62E-02</td><td>NaN</td><td>NaN</td></tr><tr><td>RAB13</td><td>2.21E-02</td><td>5.18E-01</td><td>4.78E-07</td><td>4.36E-02</td><td>NaN</td><td>NaN</td></tr><tr><td>RPS27</td><td>5.99E-04</td><td>8.82E-01</td><td>2.89E-07</td><td>7.18E-02</td><td>NaN</td><td>NaN</td></tr></table>
<table><tr><td>NUP210L</td><td>1.45E-02</td><td>4.87E-01</td><td>4.40E-07</td><td>1.37E-02</td><td>8.22E-02</td><td>3.87E-02</td></tr><tr><td>CYP26C1</td><td>1.57E-03</td><td>5.31E-02</td><td>2.28E-06</td><td>5.97E-01</td><td>3.73E-02</td><td>9.79E-01</td></tr><tr><td>SFRP1</td><td>8.81E-07</td><td>2.62E-01</td><td>1.04E-03</td><td>3.78E-01</td><td>4.34E-01</td><td>1.30E-01</td></tr><tr><td>MYOF</td><td>7.13E-07</td><td>1.94E-01</td><td>5.86E-04</td><td>1.29E-01</td><td>2.95E-01</td><td>5.73E-01</td></tr><tr><td>ENSG00000 279379</td><td>9.99E-08</td><td>7.34E-05</td><td>NaN</td><td>NaN</td><td>NaN</td><td>NaN</td></tr><tr><td>AL606753.2</td><td>NaN</td><td>NaN</td><td>NaN</td><td>NaN</td><td>1.38E-07</td><td>7.74E-02</td></tr></table>
<table><caption>**Table A4.** *Identified genes for right IHI:* Genes mapped by FUMA, h-MAGMA, mBAT-combo or SMR. p-values are shown for discovery and replication cohorts. If a gene was not part of an annotation method, the p-values are set to NaN.</caption><thead><tr><th>Gene</th><th>P_magma_disc</th><th>P_magma_rep</th><th>P_mBAT_disc</th><th>P_mBAT_rep</th><th>P_SMR_disc</th><th>P_SMR_rep</th></tr></thead><tbody><tr><td>KCNH8</td><td>6.53E-02</td><td>9.67E-02</td><td>4.29E-01</td><td>9.81E-01</td><td>NaN</td><td>NaN</td></tr><tr><td>EFHB</td><td>1.15E-03</td><td>2.00E-01</td><td>1.00E-01</td><td>2.32E-01</td><td>3.56E-02</td><td>2.14E-01</td></tr><tr><td>RAB5A</td><td>3.30E-03</td><td>1.86E-01</td><td>1.41E-01</td><td>3.16E-01</td><td>3.25E-01</td><td>4.78E-01</td></tr><tr><td>KAT2B</td><td>2.09E-04</td><td>1.76E-02</td><td>3.35E-05</td><td>2.17E-02</td><td>1.15E-01</td><td>6.47E-01</td></tr><tr><td>SGOL1</td><td>5.48E-05</td><td>5.01E-01</td><td>4.08E-05</td><td>3.82E-02</td><td>NaN</td><td>NaN</td></tr><tr><td>KCNAB1</td><td>6.09E-01</td><td>7.64E-01</td><td>8.56E-01</td><td>3.81E-01</td><td>6.35E-01</td><td>5.06E-01</td></tr><tr><td>SSR3</td><td>5.44E-02</td><td>7.59E-01</td><td>3.13E-01</td><td>4.96E-01</td><td>3.50E-01</td><td>3.96E-01</td></tr><tr><td>TIPARP</td><td>3.45E-08</td><td>5.41E-02</td><td>1.23E-03</td><td>1.33E-01</td><td>NaN</td><td>NaN</td></tr><tr><td>LEKR1</td><td>2.66E-08</td><td>3.01E-01</td><td>6.05E-08</td><td>2.83E-01</td><td>3.01E-02</td><td>2.45E-01</td></tr><tr><td>CCNL1</td><td>8.18E-01</td><td>4.74E-01</td><td>3.74E-01</td><td>7.32E-01</td><td>6.80E-01</td><td>4.70E-01</td></tr><tr><td>TP63</td><td>6.99E-01</td><td>9.90E-01</td><td>5.62E-01</td><td>9.72E-01</td><td>NaN</td><td>NaN</td></tr><tr><td>LEPREL1</td><td>8.29E-01</td><td>1.82E-01</td><td>4.01E-01</td><td>3.22E-01</td><td>NaN</td><td>NaN</td></tr><tr><td>CLDN1</td><td>8.56E-01</td><td>8.81E-03</td><td>5.33E-01</td><td>6.46E-03</td><td>NaN</td><td>NaN</td></tr><tr><td>TMEM207</td><td>6.75E-01</td><td>2.86E-02</td><td>4.97E-01</td><td>3.44E-01</td><td>NaN</td><td>NaN</td></tr><tr><td>IL1RAP</td><td>1.51E-14</td><td>2.15E-08</td><td>2.91E-01</td><td>7.53E-01</td><td>5.46E-01</td><td>5.10E-01</td></tr><tr><td>GMNC</td><td>7.39E-06</td><td>2.56E-04</td><td>6.22E-23</td><td>1.03E-09</td><td>6.15E-03</td><td>1.95E-02</td></tr><tr><td>OSTN</td><td>4.84E-01</td><td>3.97E-01</td><td>2.35E-01</td><td>1.17E-01</td><td>4.57E-01</td><td>5.42E-01</td></tr><tr><td>UTS2B</td><td>6.47E-01</td><td>4.30E-01</td><td>1.76E-01</td><td>3.16E-01</td><td>5.55E-01</td><td>2.46E-01</td></tr><tr><td>CCDC50</td><td>1.22E-13</td><td>3.59E-07</td><td>3.30E-01</td><td>5.40E-01</td><td>6.05E-01</td><td>8.01E-01</td></tr><tr><td>PYDC2</td><td>5.48E-01</td><td>8.32E-01</td><td>2.25E-01</td><td>6.54E-01</td><td>NaN</td><td>NaN</td></tr><tr><td>FGF12</td><td>4.26E-01</td><td>1.61E-01</td><td>5.45E-01</td><td>1.43E-01</td><td>4.13E-01</td><td>2.27E-01</td></tr><tr><td>ATP13A5</td><td>1.15E-02</td><td>2.40E-01</td><td>4.19E-01</td><td>4.44E-01</td><td>7.21E-01</td><td>3.07E-01</td></tr><tr><td>ATP13A4</td><td>6.72E-02</td><td>1.11E-01</td><td>5.08E-01</td><td>3.34E-02</td><td>7.47E-01</td><td>5.50E-01</td></tr><tr><td>OPA1</td><td>2.30E-01</td><td>6.82E-02</td><td>4.57E-01</td><td>5.03E-03</td><td>9.29E-01</td><td>4.67E-02</td></tr><tr><td>AGR2</td><td>8.02E-01</td><td>3.05E-01</td><td>5.76E-01</td><td>1.73E-01</td><td>NaN</td><td>NaN</td></tr><tr><td>AGR3</td><td>9.42E-01</td><td>7.41E-01</td><td>6.02E-01</td><td>3.66E-03</td><td>NaN</td><td>NaN</td></tr><tr><td>TWIST1</td><td>7.80E-01</td><td>8.05E-01</td><td>1.86E-01</td><td>5.21E-01</td><td>4.13E-01</td><td>6.53E-02</td></tr><tr><td>FERD3L</td><td>9.46E-01</td><td>1.70E-01</td><td>9.72E-01</td><td>3.15E-01</td><td>NaN</td><td>NaN</td></tr><tr><td>POLR1F</td><td>2.02E-01</td><td>7.23E-01</td><td>1.12E-01</td><td>8.59E-01</td><td>4.47E-01</td><td>4.91E-01</td></tr><tr><td>SP8</td><td>1.12E-02</td><td>4.18E-01</td><td>4.06E-01</td><td>7.94E-01</td><td>NaN</td><td>NaN</td></tr><tr><td>SYNM</td><td>9.48E-01</td><td>3.86E-01</td><td>8.67E-01</td><td>5.47E-01</td><td>8.99E-01</td><td>5.43E-01</td></tr><tr><td>TTC23</td><td>6.05E-01</td><td>3.91E-01</td><td>9.61E-01</td><td>5.25E-01</td><td>8.28E-01</td><td>7.37E-02</td></tr><tr><td>LRRC28</td><td>5.49E-01</td><td>3.80E-01</td><td>5.12E-01</td><td>4.05E-01</td><td>9.53E-01</td><td>1.64E-01</td></tr><tr><td>MEF2A</td><td>5.14E-01</td><td>2.64E-01</td><td>1.93E-01</td><td>2.78E-01</td><td>1.20E-01</td><td>6.22E-01</td></tr><tr><td>LYSMD4</td><td>1.88E-01</td><td>1.01E-01</td><td>4.17E-01</td><td>9.65E-02</td><td>8.93E-02</td><td>3.08E-01</td></tr><tr><td>ADAMTS17</td><td>6.04E-01</td><td>5.89E-01</td><td>6.66E-01</td><td>8.19E-01</td><td>6.78E-02</td><td>4.88E-01</td></tr><tr><td>LINS1</td><td>1.85E-01</td><td>3.83E-01</td><td>7.29E-01</td><td>5.86E-01</td><td>5.96E-01</td><td>8.67E-01</td></tr></tbody></table>
<table><tr><td>ASB7</td><td>3.16E-02</td><td>6.36E-01</td><td>7.20E-01</td><td>4.97E-01</td><td>NaN</td><td>NaN</td></tr><tr><td>ALDH1A3</td><td>1.27E-02</td><td>2.21E-01</td><td>1.06E-02</td><td>7.84E-01</td><td>7.56E-01</td><td>8.68E-01</td></tr><tr><td>LRRK1</td><td>8.51E-08</td><td>4.47E-03</td><td>7.08E-03</td><td>7.40E-01</td><td>7.28E-02</td><td>8.26E-01</td></tr><tr><td>CHSY1</td><td>1.29E-03</td><td>1.26E-01</td><td>4.85E-05</td><td>6.05E-02</td><td>5.40E-06</td><td>3.44E-03</td></tr><tr><td>VIMP</td><td>2.15E-01</td><td>6.56E-01</td><td>1.38E-04</td><td>6.68E-02</td><td>NaN</td><td>NaN</td></tr><tr><td>SNRPA1</td><td>5.61E-01</td><td>1.67E-01</td><td>2.06E-04</td><td>8.41E-02</td><td>5.07E-02</td><td>3.33E-01</td></tr><tr><td>TM2D3</td><td>4.02E-01</td><td>5.38E-01</td><td>5.56E-01</td><td>3.86E-01</td><td>2.11E-01</td><td>3.78E-01</td></tr><tr><td>TARS3</td><td>4.78E-01</td><td>5.85E-01</td><td>6.36E-01</td><td>4.12E-01</td><td>1.31E-01</td><td>6.49E-01</td></tr><tr><td>OR4F6</td><td>7.96E-01</td><td>4.64E-01</td><td>7.57E-01</td><td>2.27E-01</td><td>NaN</td><td>NaN</td></tr><tr><td>OR4F15</td><td>2.85E-01</td><td>2.28E-01</td><td>6.85E-01</td><td>1.71E-01</td><td>NaN</td><td>NaN</td></tr><tr><td>NCKAP5</td><td>1.90E-04</td><td>3.30E-01</td><td>1.56E-06</td><td>5.20E-02</td><td>1.10E-03</td><td>6.10E-02</td></tr><tr><td>HDAC9</td><td>4.48E-08</td><td>5.51E-03</td><td>1.68E-02</td><td>6.17E-01</td><td>8.69E-01</td><td>7.95E-01</td></tr><tr><td>EQTN</td><td>8.05E-07</td><td>9.01E-01</td><td>9.44E-03</td><td>9.16E-01</td><td>NaN</td><td>NaN</td></tr><tr><td>ENSG00000 222499</td><td>5.51E-07</td><td>6.91E-02</td><td>NaN</td><td>NaN</td><td>NaN</td><td>NaN</td></tr><tr><td>TIPARP-AS1</td><td>6.11E-09</td><td>3.66E-02</td><td>NaN</td><td>NaN</td><td>2.64E-01</td><td>6.81E-01</td></tr></table>
<table><caption>**Table A5.** *eQTLs identified by FUMA for IHI in the left hemisphere.* The variant rsID is shown alongside the topvariant of the locus, the probability of the eQTL (p), the symbol of the regulated gene, the PIP of the variant and the probability of the variant in the replication GWAS (p replication)</caption><thead><tr><th>variant</th><th>Topvariant</th><th>p</th><th>symbol</th><th>tissue</th><th>PIP</th><th>p replication</th></tr></thead><tbody><tr><td>rs61784835</td><td>rs6658111</td><td>8.78E-12</td><td>TAL1</td><td>blood (eQTLGen_cis)</td><td>8.59E-01</td><td>6.04E-04</td></tr><tr><td>rs6658111</td><td>rs6658111</td><td>4.30E-11</td><td>TAL1</td><td>blood (eQTLGen_cis)</td><td>1.64E-01</td><td>3.21E-03</td></tr><tr><td>rs13150272</td><td>rs13150272</td><td>3.12E-06</td><td>SLIT2</td><td>Muscle_Skeletal</td><td>1.00E+00</td><td>1.01E-04</td></tr><tr><td>rs613798</td><td>rs13150272</td><td>1.18E-12</td><td>SLIT2</td><td>Muscle_Skeletal</td><td>1.30E-02</td><td>1.06E-05</td></tr><tr><td>rs7658270</td><td>rs13150272</td><td>2.04E-07</td><td>SLIT2</td><td>blood (Fairfax_2014)</td><td>1.15E-02</td><td>8.95E-05</td></tr><tr><td>rs1821717</td><td>rs13150272</td><td>6.26E-06</td><td>SLIT2</td><td>Muscle_Skeletal</td><td>1.05E-02</td><td>4.89E-06</td></tr><tr><td>rs6842483</td><td>rs13150272</td><td>1.18E-06</td><td>SLIT2</td><td>blood (Fairfax_2014)</td><td>1.05E-02</td><td>1.03E-04</td></tr><tr><td>rs2575080</td><td>rs13150272</td><td>1.18E-12</td><td>SLIT2</td><td>Muscle_Skeletal</td><td>1.00E-02</td><td>1.28E-05</td></tr><tr><td>rs1485565</td><td>rs13150272</td><td>3.11E-07</td><td>SLIT2</td><td>Muscle_Skeletal</td><td>9.50E-03</td><td>9.68E-05</td></tr><tr><td>rs6828588</td><td>rs13150272</td><td>6.47E-06</td><td>SLIT2</td><td>Muscle_Skeletal</td><td>9.00E-03</td><td>5.00E-06</td></tr><tr><td>rs7660859</td><td>rs13150272</td><td>2.62E-08</td><td>SLIT2</td><td>Muscle_Skeletal</td><td>9.00E-03</td><td>3.47E-04</td></tr><tr><td>rs13118125</td><td>rs13150272</td><td>2.63E-08</td><td>SLIT2</td><td>blood (Fairfax_2014)</td><td>8.50E-03</td><td>1.69E-04</td></tr><tr><td>rs7655770</td><td>rs13150272</td><td>2.13E-08</td><td>SLIT2</td><td>Muscle_Skeletal</td><td>8.50E-03</td><td>2.52E-04</td></tr><tr><td>rs1165316</td><td>rs13150272</td><td>6.47E-06</td><td>SLIT2</td><td>Muscle_Skeletal</td><td>8.00E-03</td><td>5.00E-06</td></tr><tr><td>rs1344851</td><td>rs13150272</td><td>6.41E-06</td><td>SLIT2</td><td>Muscle_Skeletal</td><td>8.00E-03</td><td>1.87E-06</td></tr><tr><td>rs587668</td><td>rs13150272</td><td>1.56E-11</td><td>SLIT2</td><td>Muscle_Skeletal</td><td>8.00E-03</td><td>1.14E-05</td></tr><tr><td>rs12503674</td><td>rs13150272</td><td>1.06E-08</td><td>SLIT2</td><td>Muscle_Skeletal</td><td>7.50E-03</td><td>9.79E-05</td></tr><tr><td>rs1843363</td><td>rs13150272</td><td>3.11E-07</td><td>SLIT2</td><td>Muscle_Skeletal</td><td>7.50E-03</td><td>8.09E-05</td></tr><tr><td>rs2629711</td><td>rs13150272</td><td>6.26E-06</td><td>SLIT2</td><td>Muscle_Skeletal</td><td>7.50E-03</td><td>4.62E-06</td></tr><tr><td>rs4560407</td><td>rs13150272</td><td>1.03E-05</td><td>SLIT2</td><td>Muscle_Skeletal</td><td>7.50E-03</td><td>2.30E-06</td></tr><tr><td>rs4572880</td><td>rs13150272</td><td>6.41E-06</td><td>SLIT2</td><td>Muscle_Skeletal</td><td>7.50E-03</td><td>1.87E-06</td></tr><tr><td>rs7656520</td><td>rs13150272</td><td>2.86E-08</td><td>SLIT2</td><td>Muscle_Skeletal</td><td>7.50E-03</td><td>3.40E-04</td></tr><tr><td>rs934630</td><td>rs13150272</td><td>6.41E-06</td><td>SLIT2</td><td>Muscle_Skeletal</td><td>7.50E-03</td><td>1.87E-06</td></tr><tr><td>rs10034451</td><td>rs13150272</td><td>3.76E-12</td><td>SLIT2</td><td>Muscle_Skeletal</td><td>7.00E-03</td><td>1.78E-05</td></tr><tr><td>rs1344852</td><td>rs13150272</td><td>6.41E-06</td><td>SLIT2</td><td>Muscle_Skeletal</td><td>7.00E-03</td><td>1.87E-06</td></tr><tr><td>rs4579109</td><td>rs13150272</td><td>3.76E-07</td><td>SLIT2</td><td>Muscle_Skeletal</td><td>7.00E-03</td><td>1.08E-04</td></tr><tr><td>rs532059</td><td>rs13150272</td><td>1.18E-12</td><td>SLIT2</td><td>Muscle_Skeletal</td><td>7.00E-03</td><td>1.12E-05</td></tr><tr><td>rs677198</td><td>rs13150272</td><td>1.18E-12</td><td>SLIT2</td><td>Muscle_Skeletal</td><td>7.00E-03</td><td>1.06E-05</td></tr><tr><td>rs6852889</td><td>rs13150272</td><td>1.04E-08</td><td>SLIT2</td><td>Muscle_Skeletal</td><td>7.00E-03</td><td>1.02E-04</td></tr><tr><td>rs749349313</td><td>rs13150272</td><td>4.53E-07</td><td>SLIT2</td><td>Muscle_Skeletal</td><td>7.00E-03</td><td>1.21E-04</td></tr><tr><td>rs76600436</td><td>rs13150272</td><td>3.40E-06</td><td>SLIT2</td><td>Muscle_Skeletal</td><td>7.00E-03</td><td>1.19E-05</td></tr><tr><td>rs7685753</td><td>rs13150272</td><td>2.62E-08</td><td>SLIT2</td><td>Muscle_Skeletal</td><td>7.00E-03</td><td>3.47E-04</td></tr><tr><td>rs1165318</td><td>rs13150272</td><td>6.47E-06</td><td>SLIT2</td><td>Muscle_Skeletal</td><td>6.50E-03</td><td>4.77E-06</td></tr><tr><td>rs13148842</td><td>rs13150272</td><td>4.53E-07</td><td>SLIT2</td><td>Muscle_Skeletal</td><td>6.50E-03</td><td>9.21E-05</td></tr><tr><td>rs1485574</td><td>rs13150272</td><td>2.62E-08</td><td>SLIT2</td><td>Muscle_Skeletal</td><td>6.50E-03</td><td>2.63E-04</td></tr><tr><td>rs6846397</td><td>rs13150272</td><td>1.04E-08</td><td>SLIT2</td><td>Muscle_Skeletal</td><td>6.50E-03</td><td>4.27E-04</td></tr></tbody></table>
<table>
  <tr>
    <td>rs71605392</td>
    <td>rs13150272</td>
    <td>1.04E-08</td>
    <td>SLIT2</td>
    <td>Muscle_Skeletal</td>
    <td>6.50E-03</td>
    <td>9.88E-05</td>
  </tr>
  <tr>
    <td>4:20204557_CA_C</td>
    <td>rs13150272</td>
    <td>1.18E-12</td>
    <td>SLIT2</td>
    <td>Muscle_Skeletal</td>
    <td>6.00E-03</td>
    <td>1.58E-05</td>
  </tr>
  <tr>
    <td>rs1179096</td>
    <td>rs13150272</td>
    <td>6.26E-06</td>
    <td>SLIT2</td>
    <td>Muscle_Skeletal</td>
    <td>6.00E-03</td>
    <td>5.25E-06</td>
  </tr>
  <tr>
    <td>rs13142577</td>
    <td>rs13150272</td>
    <td>6.26E-06</td>
    <td>SLIT2</td>
    <td>Muscle_Skeletal</td>
    <td>6.00E-03</td>
    <td>5.00E-06</td>
  </tr>
  <tr>
    <td>rs1561123</td>
    <td>rs13150272</td>
    <td>6.41E-06</td>
    <td>SLIT2</td>
    <td>Muscle_Skeletal</td>
    <td>6.00E-03</td>
    <td>1.87E-06</td>
  </tr>
  <tr>
    <td>rs28678885</td>
    <td>rs13150272</td>
    <td>2.57E-07</td>
    <td>SLIT2</td>
    <td>blood (Fairfax_2014)</td>
    <td>6.00E-03</td>
    <td>9.15E-05</td>
  </tr>
  <tr>
    <td>rs4696947</td>
    <td>rs13150272</td>
    <td>1.38E-05</td>
    <td>SLIT2</td>
    <td>Muscle_Skeletal</td>
    <td>6.00E-03</td>
    <td>4.89E-06</td>
  </tr>
  <tr>
    <td>rs13103118</td>
    <td>rs13150272</td>
    <td>5.95E-05</td>
    <td>SLIT2</td>
    <td>Muscle_Skeletal</td>
    <td>5.50E-03</td>
    <td>1.35E-04</td>
  </tr>
  <tr>
    <td>rs13130457</td>
    <td>rs13150272</td>
    <td>2.62E-08</td>
    <td>SLIT2</td>
    <td>Muscle_Skeletal</td>
    <td>5.50E-03</td>
    <td>3.04E-04</td>
  </tr>
  <tr>
    <td>rs2322257</td>
    <td>rs13150272</td>
    <td>7.74E-09</td>
    <td>SLIT2</td>
    <td>Muscle_Skeletal</td>
    <td>5.50E-03</td>
    <td>9.30E-05</td>
  </tr>
  <tr>
    <td>rs34116790</td>
    <td>rs13150272</td>
    <td>2.19E-04</td>
    <td>SLIT2</td>
    <td>Muscle_Skeletal</td>
    <td>5.50E-03</td>
    <td>6.49E-05</td>
  </tr>
  <tr>
    <td>rs3906730</td>
    <td>rs13150272</td>
    <td>6.41E-06</td>
    <td>SLIT2</td>
    <td>Muscle_Skeletal</td>
    <td>5.50E-03</td>
    <td>1.87E-06</td>
  </tr>
  <tr>
    <td>rs7699297</td>
    <td>rs13150272</td>
    <td>7.17E-09</td>
    <td>SLIT2</td>
    <td>Muscle_Skeletal</td>
    <td>5.50E-03</td>
    <td>1.02E-04</td>
  </tr>
  <tr>
    <td>rs141828657</td>
    <td>rs13150272</td>
    <td>6.47E-06</td>
    <td>SLIT2</td>
    <td>Muscle_Skeletal</td>
    <td>5.00E-03</td>
    <td>4.89E-06</td>
  </tr>
  <tr>
    <td>rs1436293</td>
    <td>rs13150272</td>
    <td>5.35E-06</td>
    <td>SLIT2</td>
    <td>Muscle_Skeletal</td>
    <td>5.00E-03</td>
    <td>5.19E-06</td>
  </tr>
  <tr>
    <td>rs1561124</td>
    <td>rs13150272</td>
    <td>6.41E-06</td>
    <td>SLIT2</td>
    <td>Muscle_Skeletal</td>
    <td>5.00E-03</td>
    <td>1.87E-06</td>
  </tr>
  <tr>
    <td>rs35910295</td>
    <td>rs13150272</td>
    <td>6.47E-06</td>
    <td>SLIT2</td>
    <td>Muscle_Skeletal</td>
    <td>5.00E-03</td>
    <td>5.00E-06</td>
  </tr>
  <tr>
    <td>rs36021599</td>
    <td>rs13150272</td>
    <td>5.95E-05</td>
    <td>SLIT2</td>
    <td>Muscle_Skeletal</td>
    <td>5.00E-03</td>
    <td>7.13E-05</td>
  </tr>
  <tr>
    <td>rs4342186</td>
    <td>rs13150272</td>
    <td>6.47E-06</td>
    <td>SLIT2</td>
    <td>Muscle_Skeletal</td>
    <td>5.00E-03</td>
    <td>5.00E-06</td>
  </tr>
  <tr>
    <td>rs7680673</td>
    <td>rs13150272</td>
    <td>4.53E-08</td>
    <td>SLIT2</td>
    <td>blood (Fairfax_2014)</td>
    <td>5.00E-03</td>
    <td>2.88E-04</td>
  </tr>
  <tr>
    <td>rs7694349</td>
    <td>rs13150272</td>
    <td>6.47E-06</td>
    <td>SLIT2</td>
    <td>Muscle_Skeletal</td>
    <td>5.00E-03</td>
    <td>4.82E-06</td>
  </tr>
  <tr>
    <td>rs1185160</td>
    <td>rs13150272</td>
    <td>1.18E-12</td>
    <td>SLIT2</td>
    <td>Muscle_Skeletal</td>
    <td>4.50E-03</td>
    <td>1.09E-05</td>
  </tr>
  <tr>
    <td>rs11946155</td>
    <td>rs13150272</td>
    <td>6.26E-06</td>
    <td>SLIT2</td>
    <td>Muscle_Skeletal</td>
    <td>4.50E-03</td>
    <td>4.82E-06</td>
  </tr>
  <tr>
    <td>rs1369891</td>
    <td>rs13150272</td>
    <td>6.41E-06</td>
    <td>SLIT2</td>
    <td>Muscle_Skeletal</td>
    <td>4.50E-03</td>
    <td>1.87E-06</td>
  </tr>
  <tr>
    <td>rs1385642</td>
    <td>rs13150272</td>
    <td>2.62E-08</td>
    <td>SLIT2</td>
    <td>Muscle_Skeletal</td>
    <td>4.50E-03</td>
    <td>3.04E-04</td>
  </tr>
  <tr>
    <td>rs1601439</td>
    <td>rs13150272</td>
    <td>6.47E-06</td>
    <td>SLIT2</td>
    <td>Muscle_Skeletal</td>
    <td>4.50E-03</td>
    <td>4.84E-06</td>
  </tr>
  <tr>
    <td>rs36076574</td>
    <td>rs13150272</td>
    <td>6.26E-06</td>
    <td>SLIT2</td>
    <td>Muscle_Skeletal</td>
    <td>4.50E-03</td>
    <td>5.00E-06</td>
  </tr>
  <tr>
    <td>rs593770</td>
    <td>rs13150272</td>
    <td>1.56E-11</td>
    <td>SLIT2</td>
    <td>Muscle_Skeletal</td>
    <td>4.50E-03</td>
    <td>1.36E-05</td>
  </tr>
  <tr>
    <td>rs6814369</td>
    <td>rs13150272</td>
    <td>6.41E-06</td>
    <td>SLIT2</td>
    <td>Muscle_Skeletal</td>
    <td>4.50E-03</td>
    <td>2.53E-06</td>
  </tr>
  <tr>
    <td>rs71607006</td>
    <td>rs13150272</td>
    <td>6.26E-06</td>
    <td>SLIT2</td>
    <td>Muscle_Skeletal</td>
    <td>4.50E-03</td>
    <td>5.00E-06</td>
  </tr>
  <tr>
    <td>rs9684159</td>
    <td>rs13150272</td>
    <td>7.08E-05</td>
    <td>SLIT2</td>
    <td>Muscle_Skeletal</td>
    <td>4.50E-03</td>
    <td>8.66E-05</td>
  </tr>
  <tr>
    <td>rs13102750</td>
    <td>rs13150272</td>
    <td>8.56E-05</td>
    <td>SLIT2</td>
    <td>Muscle_Skeletal</td>
    <td>4.00E-03</td>
    <td>1.44E-04</td>
  </tr>
  <tr>
    <td>rs1385630</td>
    <td>rs13150272</td>
    <td>1.73E-09</td>
    <td>SLIT2</td>
    <td>Muscle_Skeletal</td>
    <td>4.00E-03</td>
    <td>5.63E-05</td>
  </tr>
  <tr>
    <td>rs6851843</td>
    <td>rs13150272</td>
    <td>8.56E-05</td>
    <td>SLIT2</td>
    <td>Muscle_Skeletal</td>
    <td>4.00E-03</td>
    <td>5.96E-05</td>
  </tr>
  <tr>
    <td>rs956244</td>
    <td>rs13150272</td>
    <td>6.47E-06</td>
    <td>SLIT2</td>
    <td>Muscle_Skeletal</td>
    <td>4.00E-03</td>
    <td>5.00E-06</td>
  </tr>
  <tr>
    <td>rs35880570</td>
    <td>rs13150272</td>
    <td>6.47E-06</td>
    <td>SLIT2</td>
    <td>Muscle_Skeletal</td>
    <td>3.50E-03</td>
    <td>4.54E-06</td>
  </tr>
  <tr>
    <td>rs7699070</td>
    <td>rs13150272</td>
    <td>7.17E-09</td>
    <td>SLIT2</td>
    <td>Muscle_Skeletal</td>
    <td>3.50E-03</td>
    <td>1.02E-04</td>
  </tr>
  <tr>
    <td>rs13108783</td>
    <td>rs13150272</td>
    <td>2.62E-08</td>
    <td>SLIT2</td>
    <td>Muscle_Skeletal</td>
    <td>3.00E-03</td>
    <td>3.28E-04</td>
  </tr>
  <tr>
    <td>rs1485575</td>
    <td>rs13150272</td>
    <td>4.00E-08</td>
    <td>SLIT2</td>
    <td>Muscle_Skeletal</td>
    <td>3.00E-03</td>
    <td>2.73E-04</td>
  </tr>
  <tr>
    <td>rs12647703</td>
    <td>rs13150272</td>
    <td>1.96E-09</td>
    <td>SLIT2</td>
    <td>Muscle_Skeletal</td>
    <td>2.00E-03</td>
    <td>1.27E-03</td>
  </tr>
</table>
<table>
  <tr>
    <td>rs13139355</td>
    <td>rs13150272</td>
    <td>5.95E-05</td>
    <td>SLIT2</td>
    <td>Muscle_Skeletal</td>
    <td>1.50E-03</td>
    <td>6.86E-05</td>
  </tr>
  <tr>
    <td>rs76341705</td>
    <td>rs2164950</td>
    <td>1.57E-07</td>
    <td>RTN1</td>
    <td>blood (eQTLGen_cis)</td>
    <td>9.99E-01</td>
    <td>2.05E-07</td>
  </tr>
  <tr>
    <td>rs4901906</td>
    <td>rs2164950</td>
    <td>1.40E-07</td>
    <td>RTN1</td>
    <td>blood (eQTLGen_cis)</td>
    <td>5.30E-02</td>
    <td>4.11E-07</td>
  </tr>
  <tr>
    <td>rs74826997</td>
    <td>rs2164950</td>
    <td>1.52E-07</td>
    <td>RTN1</td>
    <td>blood (eQTLGen_cis)</td>
    <td>1.65E-02</td>
    <td>1.98E-07</td>
  </tr>
  <tr>
    <td>rs112195026</td>
    <td>rs2164950</td>
    <td>7.39E-07</td>
    <td>RTN1</td>
    <td>blood (eQTLGen_cis)</td>
    <td>1.45E-02</td>
    <td>7.50E-07</td>
  </tr>
  <tr>
    <td>rs72724702</td>
    <td>rs2164950</td>
    <td>2.25E-08</td>
    <td>RTN1</td>
    <td>blood (eQTLGen_cis)</td>
    <td>1.15E-02</td>
    <td>3.13E-04</td>
  </tr>
  <tr>
    <td>rs56115079</td>
    <td>rs2164950</td>
    <td>3.64E-07</td>
    <td>RTN1</td>
    <td>blood (eQTLGen_cis)</td>
    <td>1.10E-02</td>
    <td>8.00E-07</td>
  </tr>
  <tr>
    <td>rs17833752</td>
    <td>rs2164950</td>
    <td>2.98E-08</td>
    <td>RTN1</td>
    <td>blood (eQTLGen_cis)</td>
    <td>1.05E-02</td>
    <td>4.56E-07</td>
  </tr>
  <tr>
    <td>rs2164950</td>
    <td>rs2164950</td>
    <td>1.43E-07</td>
    <td>RTN1</td>
    <td>blood (eQTLGen_cis)</td>
    <td>1.05E-02</td>
    <td>4.06E-07</td>
  </tr>
  <tr>
    <td>rs72724697</td>
    <td>rs2164950</td>
    <td>1.21E-08</td>
    <td>RTN1</td>
    <td>blood (eQTLGen_cis)</td>
    <td>1.05E-02</td>
    <td>1.11E-04</td>
  </tr>
  <tr>
    <td>rs73313052</td>
    <td>rs2164950</td>
    <td>4.19E-07</td>
    <td>RTN1</td>
    <td>blood (eQTLGen_cis)</td>
    <td>1.00E-02</td>
    <td>3.14E-07</td>
  </tr>
  <tr>
    <td>rs55649771</td>
    <td>rs2164950</td>
    <td>1.13E-07</td>
    <td>RTN1</td>
    <td>blood (eQTLGen_cis)</td>
    <td>9.50E-03</td>
    <td>2.62E-05</td>
  </tr>
  <tr>
    <td>rs8005394</td>
    <td>rs2164950</td>
    <td>6.35E-07</td>
    <td>RTN1</td>
    <td>blood (eQTLGen_cis)</td>
    <td>9.50E-03</td>
    <td>4.34E-07</td>
  </tr>
  <tr>
    <td>rs4901904</td>
    <td>rs2164950</td>
    <td>4.30E-08</td>
    <td>RTN1</td>
    <td>blood (eQTLGen_cis)</td>
    <td>9.00E-03</td>
    <td>2.42E-07</td>
  </tr>
  <tr>
    <td>rs113269922</td>
    <td>rs2164950</td>
    <td>3.28E-07</td>
    <td>RTN1</td>
    <td>blood (eQTLGen_cis)</td>
    <td>8.50E-03</td>
    <td>1.44E-06</td>
  </tr>
  <tr>
    <td>rs4901898</td>
    <td>rs2164950</td>
    <td>7.13E-07</td>
    <td>RTN1</td>
    <td>blood (eQTLGen_cis)</td>
    <td>8.50E-03</td>
    <td>3.12E-05</td>
  </tr>
  <tr>
    <td>rs76674533</td>
    <td>rs2164950</td>
    <td>2.51E-08</td>
    <td>RTN1</td>
    <td>blood (eQTLGen_cis)</td>
    <td>8.50E-03</td>
    <td>1.49E-06</td>
  </tr>
  <tr>
    <td>rs17255304</td>
    <td>rs2164950</td>
    <td>1.94E-08</td>
    <td>RTN1</td>
    <td>blood (eQTLGen_cis)</td>
    <td>8.00E-03</td>
    <td>2.11E-04</td>
  </tr>
  <tr>
    <td>rs117461235</td>
    <td>rs2164950</td>
    <td>4.48E-10</td>
    <td>RTN1</td>
    <td>blood (eQTLGen_cis)</td>
    <td>7.50E-03</td>
    <td>8.65E-06</td>
  </tr>
  <tr>
    <td>rs79814107</td>
    <td>rs2164950</td>
    <td>3.61E-07</td>
    <td>RTN1</td>
    <td>blood (eQTLGen_cis)</td>
    <td>7.50E-03</td>
    <td>1.49E-06</td>
  </tr>
  <tr>
    <td>rs143720575</td>
    <td>rs2164950</td>
    <td>1.57E-06</td>
    <td>RTN1</td>
    <td>blood (eQTLGen_cis)</td>
    <td>7.00E-03</td>
    <td>1.49E-06</td>
  </tr>
  <tr>
    <td>rs76787422</td>
    <td>rs2164950</td>
    <td>6.10E-09</td>
    <td>RTN1</td>
    <td>blood (eQTLGen_cis)</td>
    <td>7.00E-03</td>
    <td>2.38E-05</td>
  </tr>
  <tr>
    <td>rs76256424</td>
    <td>rs2164950</td>
    <td>1.81E-08</td>
    <td>RTN1</td>
    <td>blood (eQTLGen_cis)</td>
    <td>6.50E-03</td>
    <td>1.49E-06</td>
  </tr>
  <tr>
    <td>rs77168169</td>
    <td>rs2164950</td>
    <td>9.23E-07</td>
    <td>RTN1</td>
    <td>blood (eQTLGen_cis)</td>
    <td>6.50E-03</td>
    <td>4.64E-05</td>
  </tr>
  <tr>
    <td>rs17255395</td>
    <td>rs2164950</td>
    <td>4.47E-09</td>
    <td>RTN1</td>
    <td>blood (eQTLGen_cis)</td>
    <td>6.00E-03</td>
    <td>2.01E-05</td>
  </tr>
  <tr>
    <td>rs75255901</td>
    <td>rs2164950</td>
    <td>3.47E-09</td>
    <td>RTN1</td>
    <td>blood (eQTLGen_cis)</td>
    <td>6.00E-03</td>
    <td>1.70E-05</td>
  </tr>
  <tr>
    <td>rs78032956</td>
    <td>rs2164950</td>
    <td>2.39E-07</td>
    <td>RTN1</td>
    <td>blood (eQTLGen_cis)</td>
    <td>6.00E-03</td>
    <td>1.87E-06</td>
  </tr>
  <tr>
    <td>rs78349529</td>
    <td>rs2164950</td>
    <td>4.17E-07</td>
    <td>RTN1</td>
    <td>blood (eQTLGen_cis)</td>
    <td>6.00E-03</td>
    <td>7.50E-07</td>
  </tr>
  <tr>
    <td>rs79676525</td>
    <td>rs2164950</td>
    <td>2.60E-07</td>
    <td>RTN1</td>
    <td>blood (eQTLGen_cis)</td>
    <td>6.00E-03</td>
    <td>1.96E-06</td>
  </tr>
  <tr>
    <td>rs4901902</td>
    <td>rs2164950</td>
    <td>6.56E-08</td>
    <td>RTN1</td>
    <td>blood (eQTLGen_cis)</td>
    <td>5.50E-03</td>
    <td>4.12E-07</td>
  </tr>
  <tr>
    <td>rs79360654</td>
    <td>rs2164950</td>
    <td>5.48E-08</td>
    <td>RTN1</td>
    <td>blood (eQTLGen_cis)</td>
    <td>5.50E-03</td>
    <td>1.49E-06</td>
  </tr>
  <tr>
    <td>rs77618329</td>
    <td>rs2164950</td>
    <td>5.10E-08</td>
    <td>RTN1</td>
    <td>blood (eQTLGen_cis)</td>
    <td>5.00E-03</td>
    <td>1.46E-06</td>
  </tr>
  <tr>
    <td>rs72726305</td>
    <td>rs2164950</td>
    <td>1.94E-08</td>
    <td>RTN1</td>
    <td>blood (eQTLGen_cis)</td>
    <td>4.50E-03</td>
    <td>1.99E-04</td>
  </tr>
  <tr>
    <td>rs76850797</td>
    <td>rs2164950</td>
    <td>3.45E-07</td>
    <td>RTN1</td>
    <td>blood (eQTLGen_cis)</td>
    <td>4.00E-03</td>
    <td>8.00E-07</td>
  </tr>
  <tr>
    <td>rs10498489</td>
    <td>rs2164950</td>
    <td>1.54E-07</td>
    <td>RTN1</td>
    <td>blood (eQTLGen_cis)</td>
    <td>3.50E-03</td>
    <td>1.96E-06</td>
  </tr>
  <tr>
    <td>rs17255332</td>
    <td>rs2164950</td>
    <td>1.09E-07</td>
    <td>RTN1</td>
    <td>blood (eQTLGen_cis)</td>
    <td>3.50E-03</td>
    <td>1.73E-06</td>
  </tr>
  <tr>
    <td>rs72724695</td>
    <td>rs2164950</td>
    <td>1.04E-08</td>
    <td>RTN1</td>
    <td>blood (eQTLGen_cis)</td>
    <td>3.00E-03</td>
    <td>1.47E-04</td>
  </tr>
  <tr>
    <td>rs10498487</td>
    <td>rs2164950</td>
    <td>7.62E-09</td>
    <td>RTN1</td>
    <td>blood (eQTLGen_cis)</td>
    <td>2.50E-03</td>
    <td>1.97E-04</td>
  </tr>
  <tr>
    <td>rs78580207</td>
    <td>rs2164950</td>
    <td>1.44E-07</td>
    <td>RTN1</td>
    <td>blood (eQTLGen_cis)</td>
    <td>2.00E-03</td>
    <td>1.98E-06</td>
  </tr>
  <tr>
    <td>rs61784835</td>
    <td>rs6658111</td>
    <td>1.26E-52</td>
    <td>PDZK1IP1</td>
    <td>blood (eQTLGen_cis)</td>
    <td>8.59E-01</td>
    <td>6.04E-04</td>
  </tr>
</table>
<table>
  <tr>
    <td>rs6658111</td>
    <td>rs6658111</td>
    <td>1.43E-54</td>
    <td>PDZK1IP1</td>
    <td>blood (eQTLGen_cis)</td>
    <td>1.64E-01</td>
    <td>3.21E-03</td>
  </tr>
  <tr>
    <td>rs72688340</td>
    <td>rs6658111</td>
    <td>3.07E-27</td>
    <td>PDZK1IP1</td>
    <td>blood (eQTLGen_cis)</td>
    <td>1.55E-02</td>
    <td>1.62E-02</td>
  </tr>
  <tr>
    <td>rs76341705</td>
    <td>rs2164950</td>
    <td>4.39E-32</td>
    <td>L3HYPDH</td>
    <td>blood (eQTLGen_cis)</td>
    <td>9.99E-01</td>
    <td>2.05E-07</td>
  </tr>
  <tr>
    <td>rs4901906</td>
    <td>rs2164950</td>
    <td>6.94E-36</td>
    <td>L3HYPDH</td>
    <td>blood (eQTLGen_cis)</td>
    <td>5.30E-02</td>
    <td>4.11E-07</td>
  </tr>
  <tr>
    <td>rs74826997</td>
    <td>rs2164950</td>
    <td>4.65E-32</td>
    <td>L3HYPDH</td>
    <td>blood (eQTLGen_cis)</td>
    <td>1.65E-02</td>
    <td>1.98E-07</td>
  </tr>
  <tr>
    <td>rs112195026</td>
    <td>rs2164950</td>
    <td>2.64E-35</td>
    <td>L3HYPDH</td>
    <td>blood (eQTLGen_cis)</td>
    <td>1.45E-02</td>
    <td>7.50E-07</td>
  </tr>
  <tr>
    <td>rs11158250</td>
    <td>rs2164950</td>
    <td>5.18E-26</td>
    <td>L3HYPDH</td>
    <td>blood (eQTLGen_cis)</td>
    <td>1.15E-02</td>
    <td>3.80E-04</td>
  </tr>
  <tr>
    <td>rs72724702</td>
    <td>rs2164950</td>
    <td>9.24E-21</td>
    <td>L3HYPDH</td>
    <td>blood (eQTLGen_cis)</td>
    <td>1.15E-02</td>
    <td>3.13E-04</td>
  </tr>
  <tr>
    <td>rs56115079</td>
    <td>rs2164950</td>
    <td>5.59E-36</td>
    <td>L3HYPDH</td>
    <td>blood (eQTLGen_cis)</td>
    <td>1.10E-02</td>
    <td>8.00E-07</td>
  </tr>
  <tr>
    <td>rs17833752</td>
    <td>rs2164950</td>
    <td>2.08E-30</td>
    <td>L3HYPDH</td>
    <td>blood (eQTLGen_cis)</td>
    <td>1.05E-02</td>
    <td>4.56E-07</td>
  </tr>
  <tr>
    <td>rs2164950</td>
    <td>rs2164950</td>
    <td>4.14E-32</td>
    <td>L3HYPDH</td>
    <td>blood (eQTLGen_cis)</td>
    <td>1.05E-02</td>
    <td>4.06E-07</td>
  </tr>
  <tr>
    <td>rs72724697</td>
    <td>rs2164950</td>
    <td>5.54E-20</td>
    <td>L3HYPDH</td>
    <td>blood (eQTLGen_cis)</td>
    <td>1.05E-02</td>
    <td>1.11E-04</td>
  </tr>
  <tr>
    <td>rs73313052</td>
    <td>rs2164950</td>
    <td>6.84E-31</td>
    <td>L3HYPDH</td>
    <td>blood (eQTLGen_cis)</td>
    <td>1.00E-02</td>
    <td>3.14E-07</td>
  </tr>
  <tr>
    <td>rs17833596</td>
    <td>rs2164950</td>
    <td>3.76E-06</td>
    <td>L3HYPDH</td>
    <td>blood (Lepik_2017)</td>
    <td>9.50E-03</td>
    <td>3.47E-02</td>
  </tr>
  <tr>
    <td>rs55649771</td>
    <td>rs2164950</td>
    <td>2.65E-37</td>
    <td>L3HYPDH</td>
    <td>blood (eQTLGen_cis)</td>
    <td>9.50E-03</td>
    <td>2.62E-05</td>
  </tr>
  <tr>
    <td>rs8005394</td>
    <td>rs2164950</td>
    <td>2.81E-29</td>
    <td>L3HYPDH</td>
    <td>blood (eQTLGen_cis)</td>
    <td>9.50E-03</td>
    <td>4.34E-07</td>
  </tr>
  <tr>
    <td>rs4901904</td>
    <td>rs2164950</td>
    <td>5.54E-30</td>
    <td>L3HYPDH</td>
    <td>blood (eQTLGen_cis)</td>
    <td>9.00E-03</td>
    <td>2.42E-07</td>
  </tr>
  <tr>
    <td>rs56400494</td>
    <td>rs2164950</td>
    <td>1.45E-11</td>
    <td>L3HYPDH</td>
    <td>Adipose_Subcutaneous</td>
    <td>9.00E-03</td>
    <td>8.13E-05</td>
  </tr>
  <tr>
    <td>rs113269922</td>
    <td>rs2164950</td>
    <td>6.15E-36</td>
    <td>L3HYPDH</td>
    <td>blood (eQTLGen_cis)</td>
    <td>8.50E-03</td>
    <td>1.44E-06</td>
  </tr>
  <tr>
    <td>rs4901898</td>
    <td>rs2164950</td>
    <td>1.44E-28</td>
    <td>L3HYPDH</td>
    <td>blood (eQTLGen_cis)</td>
    <td>8.50E-03</td>
    <td>3.12E-05</td>
  </tr>
  <tr>
    <td>rs76674533</td>
    <td>rs2164950</td>
    <td>1.02E-35</td>
    <td>L3HYPDH</td>
    <td>blood (eQTLGen_cis)</td>
    <td>8.50E-03</td>
    <td>1.49E-06</td>
  </tr>
  <tr>
    <td>rs147959616</td>
    <td>rs2164950</td>
    <td>2.99E-10</td>
    <td>L3HYPDH</td>
    <td>Fairfax_2014_naive</td>
    <td>8.00E-03</td>
    <td>1.96E-06</td>
  </tr>
  <tr>
    <td>rs17255304</td>
    <td>rs2164950</td>
    <td>4.86E-22</td>
    <td>L3HYPDH</td>
    <td>blood (eQTLGen_cis)</td>
    <td>8.00E-03</td>
    <td>2.11E-04</td>
  </tr>
  <tr>
    <td>14:59627434_AC_A</td>
    <td>rs2164950</td>
    <td>2.39E-15</td>
    <td>L3HYPDH</td>
    <td>blood (Lepik_2017)</td>
    <td>7.50E-03</td>
    <td>2.02E-07</td>
  </tr>
  <tr>
    <td>rs117461235</td>
    <td>rs2164950</td>
    <td>5.78E-30</td>
    <td>L3HYPDH</td>
    <td>blood (eQTLGen_cis)</td>
    <td>7.50E-03</td>
    <td>8.65E-06</td>
  </tr>
  <tr>
    <td>rs79814107</td>
    <td>rs2164950</td>
    <td>5.70E-36</td>
    <td>L3HYPDH</td>
    <td>blood (eQTLGen_cis)</td>
    <td>7.50E-03</td>
    <td>1.49E-06</td>
  </tr>
  <tr>
    <td>rs143720575</td>
    <td>rs2164950</td>
    <td>1.07E-36</td>
    <td>L3HYPDH</td>
    <td>blood (eQTLGen_cis)</td>
    <td>7.00E-03</td>
    <td>1.49E-06</td>
  </tr>
  <tr>
    <td>rs76787422</td>
    <td>rs2164950</td>
    <td>1.40E-36</td>
    <td>L3HYPDH</td>
    <td>blood (eQTLGen_cis)</td>
    <td>7.00E-03</td>
    <td>2.38E-05</td>
  </tr>
  <tr>
    <td>rs76256424</td>
    <td>rs2164950</td>
    <td>2.51E-30</td>
    <td>L3HYPDH</td>
    <td>blood (eQTLGen_cis)</td>
    <td>6.50E-03</td>
    <td>1.49E-06</td>
  </tr>
  <tr>
    <td>rs77168169</td>
    <td>rs2164950</td>
    <td>5.19E-28</td>
    <td>L3HYPDH</td>
    <td>blood (eQTLGen_cis)</td>
    <td>6.50E-03</td>
    <td>4.64E-05</td>
  </tr>
  <tr>
    <td>rs142169263</td>
    <td>rs2164950</td>
    <td>2.09E-17</td>
    <td>L3HYPDH</td>
    <td>blood (Lepik_2017)</td>
    <td>6.00E-03</td>
    <td>3.26E-07</td>
  </tr>
  <tr>
    <td>rs142965864</td>
    <td>rs2164950</td>
    <td>1.69E-14</td>
    <td>L3HYPDH</td>
    <td>blood (Lepik_2017)</td>
    <td>6.00E-03</td>
    <td>4.07E-07</td>
  </tr>
  <tr>
    <td>rs17255395</td>
    <td>rs2164950</td>
    <td>5.91E-37</td>
    <td>L3HYPDH</td>
    <td>blood (eQTLGen_cis)</td>
    <td>6.00E-03</td>
    <td>2.01E-05</td>
  </tr>
  <tr>
    <td>rs75255901</td>
    <td>rs2164950</td>
    <td>5.03E-37</td>
    <td>L3HYPDH</td>
    <td>blood (eQTLGen_cis)</td>
    <td>6.00E-03</td>
    <td>1.70E-05</td>
  </tr>
  <tr>
    <td>rs78032956</td>
    <td>rs2164950</td>
    <td>4.23E-31</td>
    <td>L3HYPDH</td>
    <td>blood (eQTLGen_cis)</td>
    <td>6.00E-03</td>
    <td>1.87E-06</td>
  </tr>
  <tr>
    <td>rs78349529</td>
    <td>rs2164950</td>
    <td>3.45E-36</td>
    <td>L3HYPDH</td>
    <td>blood (eQTLGen_cis)</td>
    <td>6.00E-03</td>
    <td>7.50E-07</td>
  </tr>
  <tr>
    <td>rs79676525</td>
    <td>rs2164950</td>
    <td>1.94E-31</td>
    <td>L3HYPDH</td>
    <td>blood (eQTLGen_cis)</td>
    <td>6.00E-03</td>
    <td>1.96E-06</td>
  </tr>
  <tr>
    <td>rs4901902</td>
    <td>rs2164950</td>
    <td>6.23E-28</td>
    <td>L3HYPDH</td>
    <td>blood (eQTLGen_cis)</td>
    <td>5.50E-03</td>
    <td>4.12E-07</td>
  </tr>
  <tr>
    <td>rs79360654</td>
    <td>rs2164950</td>
    <td>3.14E-35</td>
    <td>L3HYPDH</td>
    <td>blood (eQTLGen_cis)</td>
    <td>5.50E-03</td>
    <td>1.49E-06</td>
  </tr>
  <tr>
    <td>rs77618329</td>
    <td>rs2164950</td>
    <td>5.50E-35</td>
    <td>L3HYPDH</td>
    <td>blood (eQTLGen_cis)</td>
    <td>5.00E-03</td>
    <td>1.46E-06</td>
  </tr>
</table>
<table>
  <tr>
    <td>rs72726305</td>
    <td>rs2164950</td>
    <td>5.31E-22</td>
    <td>L3HYPDH</td>
    <td>blood (eQTLGen_cis)</td>
    <td>4.50E-03</td>
    <td>1.99E-04</td>
  </tr>
  <tr>
    <td>rs76850797</td>
    <td>rs2164950</td>
    <td>2.40E-36</td>
    <td>L3HYPDH</td>
    <td>blood (eQTLGen_cis)</td>
    <td>4.00E-03</td>
    <td>8.00E-07</td>
  </tr>
  <tr>
    <td>14:59631075_GTTGT_G</td>
    <td>rs2164950</td>
    <td>2.39E-15</td>
    <td>L3HYPDH</td>
    <td>blood (Lepik_2017)</td>
    <td>3.50E-03</td>
    <td>4.62E-07</td>
  </tr>
  <tr>
    <td>rs10498489</td>
    <td>rs2164950</td>
    <td>8.12E-31</td>
    <td>L3HYPDH</td>
    <td>blood (eQTLGen_cis)</td>
    <td>3.50E-03</td>
    <td>1.96E-06</td>
  </tr>
  <tr>
    <td>rs17255332</td>
    <td>rs2164950</td>
    <td>3.45E-30</td>
    <td>L3HYPDH</td>
    <td>blood (eQTLGen_cis)</td>
    <td>3.50E-03</td>
    <td>1.73E-06</td>
  </tr>
  <tr>
    <td>rs72724695</td>
    <td>rs2164950</td>
    <td>4.88E-21</td>
    <td>L3HYPDH</td>
    <td>blood (eQTLGen_cis)</td>
    <td>3.00E-03</td>
    <td>1.47E-04</td>
  </tr>
  <tr>
    <td>rs10498487</td>
    <td>rs2164950</td>
    <td>6.58E-21</td>
    <td>L3HYPDH</td>
    <td>blood (eQTLGen_cis)</td>
    <td>2.50E-03</td>
    <td>1.97E-04</td>
  </tr>
  <tr>
    <td>rs78580207</td>
    <td>rs2164950</td>
    <td>6.21E-31</td>
    <td>L3HYPDH</td>
    <td>blood (eQTLGen_cis)</td>
    <td>2.00E-03</td>
    <td>1.98E-06</td>
  </tr>
  <tr>
    <td>rs76341705</td>
    <td>rs2164950</td>
    <td>3.22E-115</td>
    <td>JKAMP</td>
    <td>blood (eQTLGen_cis)</td>
    <td>9.99E-01</td>
    <td>2.05E-07</td>
  </tr>
  <tr>
    <td>rs4901906</td>
    <td>rs2164950</td>
    <td>8.43E-124</td>
    <td>JKAMP</td>
    <td>blood (eQTLGen_cis)</td>
    <td>5.30E-02</td>
    <td>4.11E-07</td>
  </tr>
  <tr>
    <td>rs77381622</td>
    <td>rs2164950</td>
    <td>4.87E-17</td>
    <td>JKAMP</td>
    <td>blood (eQTLGen_cis)</td>
    <td>1.70E-02</td>
    <td>8.57E-02</td>
  </tr>
  <tr>
    <td>rs74826997</td>
    <td>rs2164950</td>
    <td>4.40E-115</td>
    <td>JKAMP</td>
    <td>blood (eQTLGen_cis)</td>
    <td>1.65E-02</td>
    <td>1.98E-07</td>
  </tr>
  <tr>
    <td>rs112195026</td>
    <td>rs2164950</td>
    <td>9.87E-123</td>
    <td>JKAMP</td>
    <td>blood (eQTLGen_cis)</td>
    <td>1.45E-02</td>
    <td>7.50E-07</td>
  </tr>
  <tr>
    <td>rs79830598</td>
    <td>rs2164950</td>
    <td>1.57E-17</td>
    <td>JKAMP</td>
    <td>blood (eQTLGen_cis)</td>
    <td>1.25E-02</td>
    <td>4.01E-02</td>
  </tr>
  <tr>
    <td>rs11158250</td>
    <td>rs2164950</td>
    <td>1.65E-66</td>
    <td>JKAMP</td>
    <td>blood (eQTLGen_cis)</td>
    <td>1.15E-02</td>
    <td>3.80E-04</td>
  </tr>
  <tr>
    <td>rs72724702</td>
    <td>rs2164950</td>
    <td>6.93E-83</td>
    <td>JKAMP</td>
    <td>blood (eQTLGen_cis)</td>
    <td>1.15E-02</td>
    <td>3.13E-04</td>
  </tr>
  <tr>
    <td>rs56115079</td>
    <td>rs2164950</td>
    <td>9.72E-124</td>
    <td>JKAMP</td>
    <td>blood (eQTLGen_cis)</td>
    <td>1.10E-02</td>
    <td>8.00E-07</td>
  </tr>
  <tr>
    <td>rs17833752</td>
    <td>rs2164950</td>
    <td>3.08E-110</td>
    <td>JKAMP</td>
    <td>blood (eQTLGen_cis)</td>
    <td>1.05E-02</td>
    <td>4.56E-07</td>
  </tr>
  <tr>
    <td>rs2164950</td>
    <td>rs2164950</td>
    <td>1.57E-113</td>
    <td>JKAMP</td>
    <td>blood (eQTLGen_cis)</td>
    <td>1.05E-02</td>
    <td>4.06E-07</td>
  </tr>
  <tr>
    <td>rs72724697</td>
    <td>rs2164950</td>
    <td>7.10E-83</td>
    <td>JKAMP</td>
    <td>blood (eQTLGen_cis)</td>
    <td>1.05E-02</td>
    <td>1.11E-04</td>
  </tr>
  <tr>
    <td>rs17833663</td>
    <td>rs2164950</td>
    <td>8.10E-17</td>
    <td>JKAMP</td>
    <td>blood (eQTLGen_cis)</td>
    <td>1.00E-02</td>
    <td>7.31E-02</td>
  </tr>
  <tr>
    <td>rs73313052</td>
    <td>rs2164950</td>
    <td>5.72E-110</td>
    <td>JKAMP</td>
    <td>blood (eQTLGen_cis)</td>
    <td>1.00E-02</td>
    <td>3.14E-07</td>
  </tr>
  <tr>
    <td>rs150705768</td>
    <td>rs2164950</td>
    <td>1.64E-09</td>
    <td>JKAMP</td>
    <td>blood (eQTLGen_cis)</td>
    <td>9.50E-03</td>
    <td>3.16E-02</td>
  </tr>
  <tr>
    <td>rs17833596</td>
    <td>rs2164950</td>
    <td>2.14E-17</td>
    <td>JKAMP</td>
    <td>blood (eQTLGen_cis)</td>
    <td>9.50E-03</td>
    <td>3.47E-02</td>
  </tr>
  <tr>
    <td>rs17833602</td>
    <td>rs2164950</td>
    <td>3.49E-17</td>
    <td>JKAMP</td>
    <td>blood (eQTLGen_cis)</td>
    <td>9.50E-03</td>
    <td>3.64E-02</td>
  </tr>
  <tr>
    <td>rs55649771</td>
    <td>rs2164950</td>
    <td>2.08E-122</td>
    <td>JKAMP</td>
    <td>blood (eQTLGen_cis)</td>
    <td>9.50E-03</td>
    <td>2.62E-05</td>
  </tr>
  <tr>
    <td>rs78616162</td>
    <td>rs2164950</td>
    <td>4.81E-17</td>
    <td>JKAMP</td>
    <td>blood (eQTLGen_cis)</td>
    <td>9.50E-03</td>
    <td>8.29E-02</td>
  </tr>
  <tr>
    <td>rs79187540</td>
    <td>rs2164950</td>
    <td>1.67E-17</td>
    <td>JKAMP</td>
    <td>blood (eQTLGen_cis)</td>
    <td>9.50E-03</td>
    <td>3.64E-02</td>
  </tr>
  <tr>
    <td>rs8005394</td>
    <td>rs2164950</td>
    <td>2.49E-105</td>
    <td>JKAMP</td>
    <td>blood (eQTLGen_cis)</td>
    <td>9.50E-03</td>
    <td>4.34E-07</td>
  </tr>
  <tr>
    <td>rs4901904</td>
    <td>rs2164950</td>
    <td>2.65E-110</td>
    <td>JKAMP</td>
    <td>blood (eQTLGen_cis)</td>
    <td>9.00E-03</td>
    <td>2.42E-07</td>
  </tr>
  <tr>
    <td>rs56400494</td>
    <td>rs2164950</td>
    <td>4.52E-11</td>
    <td>JKAMP</td>
    <td>blood (Lepik_2017)</td>
    <td>9.00E-03</td>
    <td>8.13E-05</td>
  </tr>
  <tr>
    <td>rs113269922</td>
    <td>rs2164950</td>
    <td>7.52E-124</td>
    <td>JKAMP</td>
    <td>blood (eQTLGen_cis)</td>
    <td>8.50E-03</td>
    <td>1.44E-06</td>
  </tr>
  <tr>
    <td>rs1954003</td>
    <td>rs2164950</td>
    <td>5.53E-17</td>
    <td>JKAMP</td>
    <td>blood (eQTLGen_cis)</td>
    <td>8.50E-03</td>
    <td>8.29E-02</td>
  </tr>
  <tr>
    <td>rs4901898</td>
    <td>rs2164950</td>
    <td>1.89E-98</td>
    <td>JKAMP</td>
    <td>blood (eQTLGen_cis)</td>
    <td>8.50E-03</td>
    <td>3.12E-05</td>
  </tr>
  <tr>
    <td>rs75499021</td>
    <td>rs2164950</td>
    <td>4.81E-17</td>
    <td>JKAMP</td>
    <td>blood (eQTLGen_cis)</td>
    <td>8.50E-03</td>
    <td>8.29E-02</td>
  </tr>
  <tr>
    <td>rs76443390</td>
    <td>rs2164950</td>
    <td>5.12E-17</td>
    <td>JKAMP</td>
    <td>blood (eQTLGen_cis)</td>
    <td>8.50E-03</td>
    <td>8.29E-02</td>
  </tr>
  <tr>
    <td>rs76674533</td>
    <td>rs2164950</td>
    <td>1.32E-124</td>
    <td>JKAMP</td>
    <td>blood (eQTLGen_cis)</td>
    <td>8.50E-03</td>
    <td>1.49E-06</td>
  </tr>
  <tr>
    <td>rs17255304</td>
    <td>rs2164950</td>
    <td>1.77E-87</td>
    <td>JKAMP</td>
    <td>blood (eQTLGen_cis)</td>
    <td>8.00E-03</td>
    <td>2.11E-04</td>
  </tr>
  <tr>
    <td>14:59627434_AC_A</td>
    <td>rs2164950</td>
    <td>3.72E-12</td>
    <td>JKAMP</td>
    <td>blood (Lepik_2017)</td>
    <td>7.50E-03</td>
    <td>2.02E-07</td>
  </tr>
  <tr>
    <td>rs117461235</td>
    <td>rs2164950</td>
    <td>1.21E-93</td>
    <td>JKAMP</td>
    <td>blood (eQTLGen_cis)</td>
    <td>7.50E-03</td>
    <td>8.65E-06</td>
  </tr>
</table>
<table>
  <tr>
    <td>rs74742608</td>
    <td>rs2164950</td>
    <td>5.37E-17</td>
    <td>JKAMP</td>
    <td>blood (eQTLGen_cis)</td>
    <td>7.50E-03</td>
    <td>8.29E-02</td>
  </tr>
  <tr>
    <td>rs74746058</td>
    <td>rs2164950</td>
    <td>1.53E-17</td>
    <td>JKAMP</td>
    <td>blood (eQTLGen_cis)</td>
    <td>7.50E-03</td>
    <td>5.40E-02</td>
  </tr>
  <tr>
    <td>rs75035809</td>
    <td>rs2164950</td>
    <td>8.10E-17</td>
    <td>JKAMP</td>
    <td>blood (eQTLGen_cis)</td>
    <td>7.50E-03</td>
    <td>8.29E-02</td>
  </tr>
  <tr>
    <td>rs776935542</td>
    <td>rs2164950</td>
    <td>9.21E-05</td>
    <td>JKAMP</td>
    <td>Skin Not Sun Exposed Suprapubic</td>
    <td>7.50E-03</td>
    <td>8.83E-02</td>
  </tr>
  <tr>
    <td>rs79814107</td>
    <td>rs2164950</td>
    <td>8.55E-124</td>
    <td>JKAMP</td>
    <td>blood (eQTLGen_cis)</td>
    <td>7.50E-03</td>
    <td>1.49E-06</td>
  </tr>
  <tr>
    <td>rs143720575</td>
    <td>rs2164950</td>
    <td>2.94E-132</td>
    <td>JKAMP</td>
    <td>blood (eQTLGen_cis)</td>
    <td>7.00E-03</td>
    <td>1.49E-06</td>
  </tr>
  <tr>
    <td>rs17255157</td>
    <td>rs2164950</td>
    <td>1.03E-17</td>
    <td>JKAMP</td>
    <td>blood (eQTLGen_cis)</td>
    <td>7.00E-03</td>
    <td>3.84E-02</td>
  </tr>
  <tr>
    <td>rs17255192</td>
    <td>rs2164950</td>
    <td>2.03E-17</td>
    <td>JKAMP</td>
    <td>blood (eQTLGen_cis)</td>
    <td>7.00E-03</td>
    <td>3.51E-02</td>
  </tr>
  <tr>
    <td>rs76185024</td>
    <td>rs2164950</td>
    <td>6.33E-17</td>
    <td>JKAMP</td>
    <td>blood (eQTLGen_cis)</td>
    <td>7.00E-03</td>
    <td>8.57E-02</td>
  </tr>
  <tr>
    <td>rs76751566</td>
    <td>rs2164950</td>
    <td>3.18E-16</td>
    <td>JKAMP</td>
    <td>blood (eQTLGen_cis)</td>
    <td>7.00E-03</td>
    <td>9.06E-02</td>
  </tr>
  <tr>
    <td>rs76787422</td>
    <td>rs2164950</td>
    <td>2.87E-120</td>
    <td>JKAMP</td>
    <td>blood (eQTLGen_cis)</td>
    <td>7.00E-03</td>
    <td>2.38E-05</td>
  </tr>
  <tr>
    <td>rs79633637</td>
    <td>rs2164950</td>
    <td>4.77E-17</td>
    <td>JKAMP</td>
    <td>blood (eQTLGen_cis)</td>
    <td>7.00E-03</td>
    <td>8.29E-02</td>
  </tr>
  <tr>
    <td>rs112313693</td>
    <td>rs2164950</td>
    <td>1.67E-17</td>
    <td>JKAMP</td>
    <td>blood (eQTLGen_cis)</td>
    <td>6.50E-03</td>
    <td>3.77E-02</td>
  </tr>
  <tr>
    <td>rs17833651</td>
    <td>rs2164950</td>
    <td>3.02E-17</td>
    <td>JKAMP</td>
    <td>blood (eQTLGen_cis)</td>
    <td>6.50E-03</td>
    <td>5.04E-02</td>
  </tr>
  <tr>
    <td>rs76045330</td>
    <td>rs2164950</td>
    <td>4.71E-17</td>
    <td>JKAMP</td>
    <td>blood (eQTLGen_cis)</td>
    <td>6.50E-03</td>
    <td>8.29E-02</td>
  </tr>
  <tr>
    <td>rs76048834</td>
    <td>rs2164950</td>
    <td>1.03E-16</td>
    <td>JKAMP</td>
    <td>blood (eQTLGen_cis)</td>
    <td>6.50E-03</td>
    <td>7.18E-02</td>
  </tr>
  <tr>
    <td>rs76256424</td>
    <td>rs2164950</td>
    <td>1.85E-115</td>
    <td>JKAMP</td>
    <td>blood (eQTLGen_cis)</td>
    <td>6.50E-03</td>
    <td>1.49E-06</td>
  </tr>
  <tr>
    <td>rs77168169</td>
    <td>rs2164950</td>
    <td>5.47E-97</td>
    <td>JKAMP</td>
    <td>blood (eQTLGen_cis)</td>
    <td>6.50E-03</td>
    <td>4.64E-05</td>
  </tr>
  <tr>
    <td>14:59559810_TG_T</td>
    <td>rs2164950</td>
    <td>9.21E-05</td>
    <td>JKAMP</td>
    <td>Skin Not Sun Exposed Suprapubic</td>
    <td>6.00E-03</td>
    <td>1.07E-01</td>
  </tr>
  <tr>
    <td>rs142169263</td>
    <td>rs2164950</td>
    <td>4.38E-12</td>
    <td>JKAMP</td>
    <td>blood (Lepik_2017)</td>
    <td>6.00E-03</td>
    <td>3.26E-07</td>
  </tr>
  <tr>
    <td>rs142965864</td>
    <td>rs2164950</td>
    <td>5.16E-11</td>
    <td>JKAMP</td>
    <td>blood (Lepik_2017)</td>
    <td>6.00E-03</td>
    <td>4.07E-07</td>
  </tr>
  <tr>
    <td>rs17255213</td>
    <td>rs2164950</td>
    <td>1.34E-17</td>
    <td>JKAMP</td>
    <td>blood (eQTLGen_cis)</td>
    <td>6.00E-03</td>
    <td>4.71E-02</td>
  </tr>
  <tr>
    <td>rs17255241</td>
    <td>rs2164950</td>
    <td>3.00E-17</td>
    <td>JKAMP</td>
    <td>blood (eQTLGen_cis)</td>
    <td>6.00E-03</td>
    <td>5.04E-02</td>
  </tr>
  <tr>
    <td>rs17255395</td>
    <td>rs2164950</td>
    <td>6.29E-120</td>
    <td>JKAMP</td>
    <td>blood (eQTLGen_cis)</td>
    <td>6.00E-03</td>
    <td>2.01E-05</td>
  </tr>
  <tr>
    <td>rs75255901</td>
    <td>rs2164950</td>
    <td>4.21E-121</td>
    <td>JKAMP</td>
    <td>blood (eQTLGen_cis)</td>
    <td>6.00E-03</td>
    <td>1.70E-05</td>
  </tr>
  <tr>
    <td>rs75666025</td>
    <td>rs2164950</td>
    <td>3.11E-17</td>
    <td>JKAMP</td>
    <td>blood (eQTLGen_cis)</td>
    <td>6.00E-03</td>
    <td>5.37E-02</td>
  </tr>
  <tr>
    <td>rs78032956</td>
    <td>rs2164950</td>
    <td>6.15E-110</td>
    <td>JKAMP</td>
    <td>blood (eQTLGen_cis)</td>
    <td>6.00E-03</td>
    <td>1.87E-06</td>
  </tr>
  <tr>
    <td>rs78349529</td>
    <td>rs2164950</td>
    <td>5.42E-124</td>
    <td>JKAMP</td>
    <td>blood (eQTLGen_cis)</td>
    <td>6.00E-03</td>
    <td>7.50E-07</td>
  </tr>
  <tr>
    <td>rs79676525</td>
    <td>rs2164950</td>
    <td>2.88E-110</td>
    <td>JKAMP</td>
    <td>blood (eQTLGen_cis)</td>
    <td>6.00E-03</td>
    <td>1.96E-06</td>
  </tr>
  <tr>
    <td>rs4901902</td>
    <td>rs2164950</td>
    <td>3.76E-106</td>
    <td>JKAMP</td>
    <td>blood (eQTLGen_cis)</td>
    <td>5.50E-03</td>
    <td>4.12E-07</td>
  </tr>
  <tr>
    <td>rs75923883</td>
    <td>rs2164950</td>
    <td>5.58E-17</td>
    <td>JKAMP</td>
    <td>blood (eQTLGen_cis)</td>
    <td>5.50E-03</td>
    <td>8.29E-02</td>
  </tr>
  <tr>
    <td>rs78090044</td>
    <td>rs2164950</td>
    <td>4.67E-17</td>
    <td>JKAMP</td>
    <td>blood (eQTLGen_cis)</td>
    <td>5.50E-03</td>
    <td>8.29E-02</td>
  </tr>
  <tr>
    <td>rs78091436</td>
    <td>rs2164950</td>
    <td>1.52E-17</td>
    <td>JKAMP</td>
    <td>blood (eQTLGen_cis)</td>
    <td>5.50E-03</td>
    <td>5.46E-02</td>
  </tr>
  <tr>
    <td>rs79360654</td>
    <td>rs2164950</td>
    <td>5.85E-124</td>
    <td>JKAMP</td>
    <td>blood (eQTLGen_cis)</td>
    <td>5.50E-03</td>
    <td>1.49E-06</td>
  </tr>
  <tr>
    <td>14:59556988_TC_T</td>
    <td>rs2164950</td>
    <td>9.21E-05</td>
    <td>JKAMP</td>
    <td>Skin Not Sun Exposed Suprapubic</td>
    <td>5.00E-03</td>
    <td>8.29E-02</td>
  </tr>
  <tr>
    <td>rs17255220</td>
    <td>rs2164950</td>
    <td>2.15E-17</td>
    <td>JKAMP</td>
    <td>blood (eQTLGen_cis)</td>
    <td>5.00E-03</td>
    <td>5.40E-02</td>
  </tr>
  <tr>
    <td>rs17255248</td>
    <td>rs2164950</td>
    <td>4.94E-17</td>
    <td>JKAMP</td>
    <td>blood (eQTLGen_cis)</td>
    <td>5.00E-03</td>
    <td>8.29E-02</td>
  </tr>
  <tr>
    <td>rs17833632</td>
    <td>rs2164950</td>
    <td>2.23E-17</td>
    <td>JKAMP</td>
    <td>blood (eQTLGen_cis)</td>
    <td>5.00E-03</td>
    <td>4.95E-02</td>
  </tr>
  <tr>
    <td>rs74749999</td>
    <td>rs2164950</td>
    <td>1.62E-17</td>
    <td>JKAMP</td>
    <td>blood (eQTLGen_cis)</td>
    <td>5.00E-03</td>
    <td>3.66E-02</td>
  </tr>
  <tr>
    <td>rs75468739</td>
    <td>rs2164950</td>
    <td>1.83E-17</td>
    <td>JKAMP</td>
    <td>blood (eQTLGen_cis)</td>
    <td>5.00E-03</td>
    <td>3.66E-02</td>
  </tr>
</table>
<table>
  <tr>
    <td>rs75514756</td>
    <td>rs2164950</td>
    <td>6.93E-16</td>
    <td>JKAMP</td>
    <td>blood (eQTLGen_cis)</td>
    <td>5.00E-03</td>
    <td>8.29E-02</td>
  </tr>
  <tr>
    <td>rs77542809</td>
    <td>rs2164950</td>
    <td>1.81E-17</td>
    <td>JKAMP</td>
    <td>blood (eQTLGen_cis)</td>
    <td>5.00E-03</td>
    <td>3.64E-02</td>
  </tr>
  <tr>
    <td>rs77618329</td>
    <td>rs2164950</td>
    <td>2.45E-123</td>
    <td>JKAMP</td>
    <td>blood (eQTLGen_cis)</td>
    <td>5.00E-03</td>
    <td>1.46E-06</td>
  </tr>
  <tr>
    <td>rs17833614</td>
    <td>rs2164950</td>
    <td>1.49E-17</td>
    <td>JKAMP</td>
    <td>blood (eQTLGen_cis)</td>
    <td>4.50E-03</td>
    <td>3.66E-02</td>
  </tr>
  <tr>
    <td>rs72726305</td>
    <td>rs2164950</td>
    <td>1.19E-87</td>
    <td>JKAMP</td>
    <td>blood (eQTLGen_cis)</td>
    <td>4.50E-03</td>
    <td>1.99E-04</td>
  </tr>
  <tr>
    <td>rs117513981</td>
    <td>rs2164950</td>
    <td>1.33E-16</td>
    <td>JKAMP</td>
    <td>blood (eQTLGen_cis)</td>
    <td>4.00E-03</td>
    <td>8.29E-02</td>
  </tr>
  <tr>
    <td>rs76850797</td>
    <td>rs2164950</td>
    <td>1.29E-124</td>
    <td>JKAMP</td>
    <td>blood (eQTLGen_cis)</td>
    <td>4.00E-03</td>
    <td>8.00E-07</td>
  </tr>
  <tr>
    <td>14:59631075_GTTGT_G</td>
    <td>rs2164950</td>
    <td>3.72E-12</td>
    <td>JKAMP</td>
    <td>blood (Lepik_2017)</td>
    <td>3.50E-03</td>
    <td>4.62E-07</td>
  </tr>
  <tr>
    <td>rs10498489</td>
    <td>rs2164950</td>
    <td>3.30E-109</td>
    <td>JKAMP</td>
    <td>blood (eQTLGen_cis)</td>
    <td>3.50E-03</td>
    <td>1.96E-06</td>
  </tr>
  <tr>
    <td>rs17255332</td>
    <td>rs2164950</td>
    <td>4.45E-109</td>
    <td>JKAMP</td>
    <td>blood (eQTLGen_cis)</td>
    <td>3.50E-03</td>
    <td>1.73E-06</td>
  </tr>
  <tr>
    <td>rs75012551</td>
    <td>rs2164950</td>
    <td>6.28E-17</td>
    <td>JKAMP</td>
    <td>blood (eQTLGen_cis)</td>
    <td>3.50E-03</td>
    <td>8.29E-02</td>
  </tr>
  <tr>
    <td>rs75951218</td>
    <td>rs2164950</td>
    <td>2.06E-17</td>
    <td>JKAMP</td>
    <td>blood (eQTLGen_cis)</td>
    <td>3.50E-03</td>
    <td>4.66E-02</td>
  </tr>
  <tr>
    <td>rs78035000</td>
    <td>rs2164950</td>
    <td>7.83E-17</td>
    <td>JKAMP</td>
    <td>blood (eQTLGen_cis)</td>
    <td>3.50E-03</td>
    <td>8.29E-02</td>
  </tr>
  <tr>
    <td>rs78339200</td>
    <td>rs2164950</td>
    <td>6.42E-17</td>
    <td>JKAMP</td>
    <td>blood (eQTLGen_cis)</td>
    <td>3.50E-03</td>
    <td>8.29E-02</td>
  </tr>
  <tr>
    <td>rs72724695</td>
    <td>rs2164950</td>
    <td>1.77E-85</td>
    <td>JKAMP</td>
    <td>blood (eQTLGen_cis)</td>
    <td>3.00E-03</td>
    <td>1.47E-04</td>
  </tr>
  <tr>
    <td>rs10498487</td>
    <td>rs2164950</td>
    <td>1.12E-84</td>
    <td>JKAMP</td>
    <td>blood (eQTLGen_cis)</td>
    <td>2.50E-03</td>
    <td>1.97E-04</td>
  </tr>
  <tr>
    <td>rs117804823</td>
    <td>rs2164950</td>
    <td>6.47E-17</td>
    <td>JKAMP</td>
    <td>blood (eQTLGen_cis)</td>
    <td>2.50E-03</td>
    <td>8.29E-02</td>
  </tr>
  <tr>
    <td>rs78580207</td>
    <td>rs2164950</td>
    <td>3.06E-109</td>
    <td>JKAMP</td>
    <td>blood (eQTLGen_cis)</td>
    <td>2.00E-03</td>
    <td>1.98E-06</td>
  </tr>
  <tr>
    <td>rs61784835</td>
    <td>rs6658111</td>
    <td>1.16E-04</td>
    <td>FOXE3</td>
    <td>Artery_Tibial</td>
    <td>8.59E-01</td>
    <td>6.04E-04</td>
  </tr>
  <tr>
    <td>rs2123184</td>
    <td>rs7639236</td>
    <td>3.22E-05</td>
    <td>FAM208A</td>
    <td>Brain_Nucleus_ACCUMBENS_BASAL_GANGLIA</td>
    <td>5.10E-02</td>
    <td>7.29E-01</td>
  </tr>
  <tr>
    <td>rs7639236</td>
    <td>rs7639236</td>
    <td>3.22E-05</td>
    <td>FAM208A</td>
    <td>Brain_Nucleus_ACCUMBENS_BASAL_GANGLIA</td>
    <td>3.40E-02</td>
    <td>7.29E-01</td>
  </tr>
  <tr>
    <td>rs7641482</td>
    <td>rs7639236</td>
    <td>1.71E-05</td>
    <td>FAM208A</td>
    <td>Brain_Nucleus_ACCUMBENS_BASAL_GANGLIA</td>
    <td>1.95E-02</td>
    <td>5.69E-01</td>
  </tr>
  <tr>
    <td>rs1825561</td>
    <td>rs7639236</td>
    <td>3.39E-05</td>
    <td>FAM208A</td>
    <td>Brain_Nucleus_ACCUMBENS_BASAL_GANGLIA</td>
    <td>1.90E-02</td>
    <td>6.12E-01</td>
  </tr>
  <tr>
    <td>rs62253567</td>
    <td>rs7639236</td>
    <td>3.63E-05</td>
    <td>FAM208A</td>
    <td>Brain_Nucleus_ACCUMBENS_BASAL_GANGLIA</td>
    <td>1.05E-02</td>
    <td>6.17E-01</td>
  </tr>
  <tr>
    <td>rs2123184</td>
    <td>rs7639236</td>
    <td>1.72E-07</td>
    <td>ERC2</td>
    <td>Artery_Aorta</td>
    <td>5.10E-02</td>
    <td>7.29E-01</td>
  </tr>
  <tr>
    <td>rs7639236</td>
    <td>rs7639236</td>
    <td>1.72E-07</td>
    <td>ERC2</td>
    <td>Artery_Aorta</td>
    <td>3.40E-02</td>
    <td>7.29E-01</td>
  </tr>
  <tr>
    <td>rs7641482</td>
    <td>rs7639236</td>
    <td>1.29E-07</td>
    <td>ERC2</td>
    <td>Artery_Aorta</td>
    <td>1.95E-02</td>
    <td>5.69E-01</td>
  </tr>
  <tr>
    <td>rs1825561</td>
    <td>rs7639236</td>
    <td>1.20E-07</td>
    <td>ERC2</td>
    <td>Artery_Aorta</td>
    <td>1.90E-02</td>
    <td>6.12E-01</td>
  </tr>
  <tr>
    <td>rs62253567</td>
    <td>rs7639236</td>
    <td>1.72E-07</td>
    <td>ERC2</td>
    <td>Artery_Aorta</td>
    <td>1.05E-02</td>
    <td>6.17E-01</td>
  </tr>
  <tr>
    <td>rs76341705</td>
    <td>rs2164950</td>
    <td>4.47E-25</td>
    <td>DAAM1</td>
    <td>blood (eQTLGen_cis)</td>
    <td>9.99E-01</td>
    <td>2.05E-07</td>
  </tr>
  <tr>
    <td>rs4901906</td>
    <td>rs2164950</td>
    <td>2.77E-25</td>
    <td>DAAM1</td>
    <td>blood (eQTLGen_cis)</td>
    <td>5.30E-02</td>
    <td>4.11E-07</td>
  </tr>
  <tr>
    <td>rs77381622</td>
    <td>rs2164950</td>
    <td>3.48E-12</td>
    <td>DAAM1</td>
    <td>blood (eQTLGen_cis)</td>
    <td>1.70E-02</td>
    <td>8.57E-02</td>
  </tr>
  <tr>
    <td>rs74826997</td>
    <td>rs2164950</td>
    <td>5.00E-25</td>
    <td>DAAM1</td>
    <td>blood (eQTLGen_cis)</td>
    <td>1.65E-02</td>
    <td>1.98E-07</td>
  </tr>
  <tr>
    <td>rs112195026</td>
    <td>rs2164950</td>
    <td>2.29E-25</td>
    <td>DAAM1</td>
    <td>blood (eQTLGen_cis)</td>
    <td>1.45E-02</td>
    <td>7.50E-07</td>
  </tr>
  <tr>
    <td>rs79830598</td>
    <td>rs2164950</td>
    <td>1.88E-11</td>
    <td>DAAM1</td>
    <td>blood (eQTLGen_cis)</td>
    <td>1.25E-02</td>
    <td>4.01E-02</td>
  </tr>
  <tr>
    <td>rs11158250</td>
    <td>rs2164950</td>
    <td>2.02E-116</td>
    <td>DAAM1</td>
    <td>blood (eQTLGen_cis)</td>
    <td>1.15E-02</td>
    <td>3.80E-04</td>
  </tr>
  <tr>
    <td>rs72724702</td>
    <td>rs2164950</td>
    <td>1.78E-19</td>
    <td>DAAM1</td>
    <td>blood (eQTLGen_cis)</td>
    <td>1.15E-02</td>
    <td>3.13E-04</td>
  </tr>
  <tr>
    <td>rs56115079</td>
    <td>rs2164950</td>
    <td>2.45E-25</td>
    <td>DAAM1</td>
    <td>blood (eQTLGen_cis)</td>
    <td>1.10E-02</td>
    <td>8.00E-07</td>
  </tr>
  <tr>
    <td>rs17833752</td>
    <td>rs2164950</td>
    <td>9.36E-26</td>
    <td>DAAM1</td>
    <td>blood (eQTLGen_cis)</td>
    <td>1.05E-02</td>
    <td>4.56E-07</td>
  </tr>
  <tr>
    <td>rs2164950</td>
    <td>rs2164950</td>
    <td>1.65E-24</td>
    <td>DAAM1</td>
    <td>blood (eQTLGen_cis)</td>
    <td>1.05E-02</td>
    <td>4.06E-07</td>
  </tr>
</table>
<table>
  <tr>
    <td>rs72724697</td>
    <td>rs2164950</td>
    <td>6.54E-20</td>
    <td>DAAM1</td>
    <td>blood (eQTLGen_cis)</td>
    <td>1.05E-02</td>
    <td>1.11E-04</td>
  </tr>
  <tr>
    <td>rs17833663</td>
    <td>rs2164950</td>
    <td>2.57E-12</td>
    <td>DAAM1</td>
    <td>blood (eQTLGen_cis)</td>
    <td>1.00E-02</td>
    <td>7.31E-02</td>
  </tr>
  <tr>
    <td>rs73313052</td>
    <td>rs2164950</td>
    <td>2.26E-25</td>
    <td>DAAM1</td>
    <td>blood (eQTLGen_cis)</td>
    <td>1.00E-02</td>
    <td>3.14E-07</td>
  </tr>
  <tr>
    <td>rs150705768</td>
    <td>rs2164950</td>
    <td>1.30E-12</td>
    <td>DAAM1</td>
    <td>blood (eQTLGen_cis)</td>
    <td>9.50E-03</td>
    <td>3.16E-02</td>
  </tr>
  <tr>
    <td>rs17833596</td>
    <td>rs2164950</td>
    <td>1.91E-11</td>
    <td>DAAM1</td>
    <td>blood (eQTLGen_cis)</td>
    <td>9.50E-03</td>
    <td>3.47E-02</td>
  </tr>
  <tr>
    <td>rs17833602</td>
    <td>rs2164950</td>
    <td>2.15E-11</td>
    <td>DAAM1</td>
    <td>blood (eQTLGen_cis)</td>
    <td>9.50E-03</td>
    <td>3.64E-02</td>
  </tr>
  <tr>
    <td>rs55649771</td>
    <td>rs2164950</td>
    <td>4.44E-28</td>
    <td>DAAM1</td>
    <td>blood (eQTLGen_cis)</td>
    <td>9.50E-03</td>
    <td>2.62E-05</td>
  </tr>
  <tr>
    <td>rs78616162</td>
    <td>rs2164950</td>
    <td>3.54E-12</td>
    <td>DAAM1</td>
    <td>blood (eQTLGen_cis)</td>
    <td>9.50E-03</td>
    <td>8.29E-02</td>
  </tr>
  <tr>
    <td>rs79187540</td>
    <td>rs2164950</td>
    <td>1.59E-11</td>
    <td>DAAM1</td>
    <td>blood (eQTLGen_cis)</td>
    <td>9.50E-03</td>
    <td>3.64E-02</td>
  </tr>
  <tr>
    <td>rs8005394</td>
    <td>rs2164950</td>
    <td>6.87E-26</td>
    <td>DAAM1</td>
    <td>blood (eQTLGen_cis)</td>
    <td>9.50E-03</td>
    <td>4.34E-07</td>
  </tr>
  <tr>
    <td>rs4901904</td>
    <td>rs2164950</td>
    <td>1.40E-25</td>
    <td>DAAM1</td>
    <td>blood (eQTLGen_cis)</td>
    <td>9.00E-03</td>
    <td>2.42E-07</td>
  </tr>
  <tr>
    <td>rs56400494</td>
    <td>rs2164950</td>
    <td>4.65E-05</td>
    <td>DAAM1</td>
    <td>Nerve_Tibial</td>
    <td>9.00E-03</td>
    <td>8.13E-05</td>
  </tr>
  <tr>
    <td>rs113269922</td>
    <td>rs2164950</td>
    <td>1.44E-25</td>
    <td>DAAM1</td>
    <td>blood (eQTLGen_cis)</td>
    <td>8.50E-03</td>
    <td>1.44E-06</td>
  </tr>
  <tr>
    <td>rs1954003</td>
    <td>rs2164950</td>
    <td>3.52E-12</td>
    <td>DAAM1</td>
    <td>blood (eQTLGen_cis)</td>
    <td>8.50E-03</td>
    <td>8.29E-02</td>
  </tr>
  <tr>
    <td>rs4901898</td>
    <td>rs2164950</td>
    <td>5.71E-25</td>
    <td>DAAM1</td>
    <td>blood (eQTLGen_cis)</td>
    <td>8.50E-03</td>
    <td>3.12E-05</td>
  </tr>
  <tr>
    <td>rs75499021</td>
    <td>rs2164950</td>
    <td>3.54E-12</td>
    <td>DAAM1</td>
    <td>blood (eQTLGen_cis)</td>
    <td>8.50E-03</td>
    <td>8.29E-02</td>
  </tr>
  <tr>
    <td>rs76443390</td>
    <td>rs2164950</td>
    <td>3.21E-12</td>
    <td>DAAM1</td>
    <td>blood (eQTLGen_cis)</td>
    <td>8.50E-03</td>
    <td>8.29E-02</td>
  </tr>
  <tr>
    <td>rs76674533</td>
    <td>rs2164950</td>
    <td>7.39E-25</td>
    <td>DAAM1</td>
    <td>blood (eQTLGen_cis)</td>
    <td>8.50E-03</td>
    <td>1.49E-06</td>
  </tr>
  <tr>
    <td>rs17255304</td>
    <td>rs2164950</td>
    <td>1.18E-20</td>
    <td>DAAM1</td>
    <td>blood (eQTLGen_cis)</td>
    <td>8.00E-03</td>
    <td>2.11E-04</td>
  </tr>
  <tr>
    <td>14:59627434_AC_A</td>
    <td>rs2164950</td>
    <td>3.67E-06</td>
    <td>DAAM1</td>
    <td>T-cell_CD4 (Kasela_2017)</td>
    <td>7.50E-03</td>
    <td>2.02E-07</td>
  </tr>
  <tr>
    <td>rs117461235</td>
    <td>rs2164950</td>
    <td>2.43E-22</td>
    <td>DAAM1</td>
    <td>blood (eQTLGen_cis)</td>
    <td>7.50E-03</td>
    <td>8.65E-06</td>
  </tr>
  <tr>
    <td>rs74742608</td>
    <td>rs2164950</td>
    <td>3.83E-12</td>
    <td>DAAM1</td>
    <td>blood (eQTLGen_cis)</td>
    <td>7.50E-03</td>
    <td>8.29E-02</td>
  </tr>
  <tr>
    <td>rs74746058</td>
    <td>rs2164950</td>
    <td>1.26E-11</td>
    <td>DAAM1</td>
    <td>blood (eQTLGen_cis)</td>
    <td>7.50E-03</td>
    <td>5.40E-02</td>
  </tr>
  <tr>
    <td>rs75035809</td>
    <td>rs2164950</td>
    <td>3.27E-12</td>
    <td>DAAM1</td>
    <td>blood (eQTLGen_cis)</td>
    <td>7.50E-03</td>
    <td>8.29E-02</td>
  </tr>
  <tr>
    <td>rs79814107</td>
    <td>rs2164950</td>
    <td>1.01E-25</td>
    <td>DAAM1</td>
    <td>blood (eQTLGen_cis)</td>
    <td>7.50E-03</td>
    <td>1.49E-06</td>
  </tr>
  <tr>
    <td>rs143720575</td>
    <td>rs2164950</td>
    <td>4.51E-29</td>
    <td>DAAM1</td>
    <td>blood (eQTLGen_cis)</td>
    <td>7.00E-03</td>
    <td>1.49E-06</td>
  </tr>
  <tr>
    <td>rs17255157</td>
    <td>rs2164950</td>
    <td>2.10E-11</td>
    <td>DAAM1</td>
    <td>blood (eQTLGen_cis)</td>
    <td>7.00E-03</td>
    <td>3.84E-02</td>
  </tr>
  <tr>
    <td>rs17255192</td>
    <td>rs2164950</td>
    <td>2.05E-11</td>
    <td>DAAM1</td>
    <td>blood (eQTLGen_cis)</td>
    <td>7.00E-03</td>
    <td>3.51E-02</td>
  </tr>
  <tr>
    <td>rs76185024</td>
    <td>rs2164950</td>
    <td>5.91E-12</td>
    <td>DAAM1</td>
    <td>blood (eQTLGen_cis)</td>
    <td>7.00E-03</td>
    <td>8.57E-02</td>
  </tr>
  <tr>
    <td>rs76751566</td>
    <td>rs2164950</td>
    <td>4.48E-12</td>
    <td>DAAM1</td>
    <td>blood (eQTLGen_cis)</td>
    <td>7.00E-03</td>
    <td>9.06E-02</td>
  </tr>
  <tr>
    <td>rs76787422</td>
    <td>rs2164950</td>
    <td>1.55E-26</td>
    <td>DAAM1</td>
    <td>blood (eQTLGen_cis)</td>
    <td>7.00E-03</td>
    <td>2.38E-05</td>
  </tr>
  <tr>
    <td>rs79633637</td>
    <td>rs2164950</td>
    <td>3.75E-12</td>
    <td>DAAM1</td>
    <td>blood (eQTLGen_cis)</td>
    <td>7.00E-03</td>
    <td>8.29E-02</td>
  </tr>
  <tr>
    <td>rs112313693</td>
    <td>rs2164950</td>
    <td>1.55E-11</td>
    <td>DAAM1</td>
    <td>blood (eQTLGen_cis)</td>
    <td>6.50E-03</td>
    <td>3.77E-02</td>
  </tr>
  <tr>
    <td>rs17833651</td>
    <td>rs2164950</td>
    <td>1.36E-11</td>
    <td>DAAM1</td>
    <td>blood (eQTLGen_cis)</td>
    <td>6.50E-03</td>
    <td>5.04E-02</td>
  </tr>
  <tr>
    <td>rs76045330</td>
    <td>rs2164950</td>
    <td>3.67E-12</td>
    <td>DAAM1</td>
    <td>blood (eQTLGen_cis)</td>
    <td>6.50E-03</td>
    <td>8.29E-02</td>
  </tr>
  <tr>
    <td>rs76048834</td>
    <td>rs2164950</td>
    <td>2.98E-12</td>
    <td>DAAM1</td>
    <td>blood (eQTLGen_cis)</td>
    <td>6.50E-03</td>
    <td>7.18E-02</td>
  </tr>
  <tr>
    <td>rs76256424</td>
    <td>rs2164950</td>
    <td>4.39E-26</td>
    <td>DAAM1</td>
    <td>blood (eQTLGen_cis)</td>
    <td>6.50E-03</td>
    <td>1.49E-06</td>
  </tr>
  <tr>
    <td>rs77168169</td>
    <td>rs2164950</td>
    <td>1.33E-24</td>
    <td>DAAM1</td>
    <td>blood (eQTLGen_cis)</td>
    <td>6.50E-03</td>
    <td>4.64E-05</td>
  </tr>
  <tr>
    <td>rs142169263</td>
    <td>rs2164950</td>
    <td>8.70E-08</td>
    <td>DAAM1</td>
    <td>T-cell_CD4 (Kasela_2017)</td>
    <td>6.00E-03</td>
    <td>3.26E-07</td>
  </tr>
  <tr>
    <td>rs17255213</td>
    <td>rs2164950</td>
    <td>1.07E-11</td>
    <td>DAAM1</td>
    <td>blood (eQTLGen_cis)</td>
    <td>6.00E-03</td>
    <td>4.71E-02</td>
  </tr>
</table>