# Contribution to Alzheimer's disease risk of rare variants in TREM_2, SORL_1, and ABCA_7 in 1779 cases and 1273 controls

Céline Bellenguez, Camille Charbonnier, Benjamin Grenier-Boley, Olivier Quenez, Kilan Le Guennec, Gaël Nicolas, Ganesh Chauhan, David Wallon, Stéphane Rousseau, Anne-Claire Richard, et al.

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Céline Bellenguez, Camille Charbonnier, Benjamin Grenier-Boley, Olivier Quenez, Kilan Le Guennec, et al. Contribution to Alzheimer's disease risk of rare variants in TREM_2, SORL_1, and ABCA_7 in 1779 cases and 1273 controls. Neurobiology of Aging, 2017, 59, pp.220.e1-220.e9. 10.1016/j.neurobiolaging.2017.07.001. hal-01760388

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Contribution to Alzheimer's disease risk of rare variants in *TREM2*, *SORL1* and *ABCA7* in 1,779 cases and 1,273 controls

Céline Bellenguez, Camille Charbonnier, Benjamin Grenier-Boley, Olivier Quenez, Kilan Le Guennec, Gaël Nicolas, Ganesh Chauhan, David Wallon, Stéphane Rousseau, Anne Claire Richard, Anne Boland, Guillaume Bourque, Hans Markus Munter, Robert Olaso, Vincent Meyer, Adeline Rollin-Sillaire, Florence Pasquier, Luc Letenneur, Richard Redon, Jean-François Dartigues, Christophe Tzourio, Thierry Frebourg, Mark Lathrop, Jean-François Deleuze, Didier Hannequin, Emmanuelle Genin, Philippe Amouyel, Stéphanie Debette, Jean-Charles Lambert, Dominique Campion

PII: S0197-4580(17)30232-4

DOI: 10.1016/j.neurobiolaging.2017.07.001

Reference: NBA 9979

To appear in: *Neurobiology of Aging*

Received Date: 22 May 2017

Revised Date: 3 July 2017

Accepted Date: 5 July 2017

Please cite this article as: Bellenguez, C., Charbonnier, C., Grenier-Boley, B., Quenez, O., Le Guennec, K., Nicolas, G., Chauhan, G., Wallon, D., Rousseau, S., Richard, A.C., Boland, A., Bourque, G., Munter, H.M., Olaso, R., Meyer, V., Rollin-Sillaire, A., Pasquier, F., Letenneur, L., Redon, R., Dartigues, J.-F., Tzourio, C., Frebourg, T., Lathrop, M., Deleuze, J.-F., Hannequin, D., Genin, E., Amouyel, P., Debette, S., Lambert, J.-C., Campion, D., CNR MAJ collaborators, Contribution to Alzheimer's disease risk of rare variants in *TREM2*, *SORL1* and *ABCA7* in 1,779 cases and 1,273 controls, *Neurobiology of Aging* (2017), doi: 10.1016/j.neurobiolaging.2017.07.001.

This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
Contribution to Alzheimer's disease risk of rare variants in TREM2, SORL1 and ABCA7 in
1,779 cases and 1,273 controls.

Céline Bellenguez<sup>a,b,c*</sup>, Camille Charbonnier<sup>d*</sup>, Benjamin Grenier-Boley<sup>a,b,c**</sup>, Olivier Quenez<sup>d**</sup>, Kilan Le Guennec<sup>d</sup>, Gaël Nicolas<sup>d</sup>, Ganesh Chauhan<sup>e</sup>, David Wallon<sup>f</sup>, Stéphane Rousseau<sup>d</sup>, Anne Claire Richard<sup>d</sup>, Anne Boland<sup>g</sup>, Guillaume Bourque<sup>h</sup>, Hans Markus Munter<sup>h</sup>, Robert Olaso<sup>g</sup>, Vincent Meyer<sup>g</sup>, Adeline Rollin-Sillaire<sup>i</sup>, Florence Pasquier<sup>i</sup>, CNR MAJ collaborators<sup>j</sup>, Luc Letenneur<sup>e</sup>, Richard Redon<sup>k</sup>, Jean-François Dartigues<sup>e</sup>, Christophe Tzourio<sup>e</sup>, Thierry Frebourg<sup>l</sup>, Mark Lathrop<sup>h</sup>, Jean-François Deleuze<sup>g</sup>, Didier Hannequin<sup>m</sup>, Emmanuelle Genin<sup>n</sup>, Philippe Amouyel<sup>a,b,c,o</sup>, Stéphanie Debette<sup>e</sup>, Jean-Charles Lambert<sup>a,b,c#</sup>, Dominique Campion<sup>d,p#</sup>

<sup>a</sup>Inserm, U1167, RID-AGE - Risk factors and molecular determinants of aging-related diseases, F-59000 Lille, France;

<sup>b</sup>Institut Pasteur de Lille, F-59000 Lille, France;

<sup>c</sup>University Lille, U1167 - Excellence Laboratory LabEx DISTALZ, F-59000 Lille, France;

<sup>d</sup>Normandie Univ, UNIROUEN, Inserm U1245 and Rouen University Hospital, Department of Genetics and CNR-MAJ, F 76000, Normandy Centre for Genomic and Personalized Medicine, Rouen, France

<sup>e</sup>Univ. Bordeaux, Inserm, Bordeaux Population Health Research Center, UMR1219, F-33076 Bordeaux, France

<sup>f</sup>Normandie Univ, UNIROUEN, Inserm U1245 and Rouen University Hospital, Department of Neurology and CNR-MAJ, F 76000, Normandy Centre for Genomic and Personalized Medicine, Rouen, France

<sup>g</sup>Centre National de Génotypage, Institut de Génomique, CEA, Evry, France

<sup>h</sup>McGill University and Génome Québec Innovation Centre, Montréal, Canada

<sup>i</sup>CNR-MAJ; and Department of Neurology, Université de Lille, CHU, Inserm UMR-S 1171, Lille, France

<sup>j</sup>see appendix for a full list of authors

<sup>k</sup>Inserm UMR-1087 / CNRS UMR 6291, l'institut du thorax, Univ. Nantes, Nantes, France

<sup>l</sup>Normandie Univ, UNIROUEN, Inserm U1245 and Rouen University Hospital, Department of Genetics, F 76000, Normandy Centre for Genomic and Personalized Medicine, Rouen, France

<sup>m</sup>Normandie Univ, UNIROUEN, Inserm U1245 and Rouen University Hospital, Department of Genetics, Department of Neurology and CNR-MAJ, F 76000, Normandy Centre for Genomic and Personalized Medicine, Rouen, France

<sup>n</sup>Inserm UMR-1078, CHRU Brest, Univ. Brest, Brest, France

<footnote>* co-first</footnote>

<footnote>** co second</footnote>

<footnote># co-last</footnote>
Correspondance to:

Dominique Campion, MD, PhD

INSERM U 1245

Faculté de médecine

22 Boulevard Gambetta

76000 Rouen

Tel : 00 33 (0)2 35 14 82 80

Fax : 00 33 (0)2 35 14 82 37

E-mail : dominique.campion@univ-rouen.fr

Jean-Charles Lambert, PhD

Inserm UMR1167

Institut Pasteur de Lille

1 rue du Pr. Calmette, 59019 Lille Cedex

Tel : 00 33 (0)3 20 87 73 91

Fax : 00 33 (0)3 20 87 78 94

E-mail : jean-charles.lambert@pasteur-lille.fr

<footnote>°Centre Hospitalier Universitaire de Lille, Epidemiology and Public Health Department, F-59000 Lille, France;</footnote>

<footnote>pDepartment of Research, Centre hospitalier du Rouvray, Sotteville-lès-Rouen, France</footnote>
**Abstract:**

We performed whole-exome and whole-genome sequencing in 927 late-onset Alzheimer Disease (LOAD) cases, 852 early-onset AD (EOAD) cases and 1,273 controls from France. We assessed evidence for gene-based association of rare variants with AD in 6 genes for which an association with such variants was previously claimed. When aggregating protein truncating and missense predicted damaging variants we found exome-wide significant association between EOAD risk and rare variants in *SORL1*, *TREM2* and *ABCA7*. No exome-wide significant signal was obtained in the LOAD sample and significance of the order of 10⁻⁶ was observed in the whole AD group for *TREM2*. Our study confirms previous gene-level results for *TREM2*, *SORL1* and *ABCA7* and provides a clearer insight into the classes of rare variants involved. Despite different effect sizes and varying cumulative minor allele frequencies, the rare protein truncating and missense predicted damaging variants in *TREM2*, *SORL1* and *ABCA7* contribute similarly to the heritability of EOAD and explain between 1.1 and 1.5 % of EOAD heritability each, compared with 9.12% for APOEε4.

**Keywords:** *SORL1*; *ABCA7*; *TREM2*; Alzheimer's disease.
# 1. Introduction

In Alzheimer's disease (AD) a substantial genetic component has been reported with heritability ranging from 58% to 79% (Gatz et al., 2006) and the advent of the genomic high throughput approaches has allowed deciphering a part of this genetic landscape. In particular, genome-wide association studies (GWAS) led to the discovery of more than 25 loci in which frequent variants are associated with AD risk (Harold et al., 2009; Hollingworth et al., 2011; Jun et al., 2016; J. C. Lambert et al., 2013; J.-C. Lambert et al., 2013; Lambert et al., 2009; Naj et al., 2011; Seshadri et al., 2010), some of them confirming previous candidate-gene results (e.g. *SORL1* (Rogaeva et al., 2007)).

However, even though our knowledge of the genetics of AD has progressed markedly over the last few years, it is still far from complete and as with other multifactorial diseases, deciphering the “missing heritability” is one of the main challenges. It has been suggested that rare variants, not captured by classical GWAS approaches, might explain a part of the missing heritability. With the development and generalisation of whole exome sequencing (WES) and whole genome sequencing (WGS) approaches, it is now possible to assess such a hypothesis. In the AD field, exome-wide significant association of rare variants in *TREM2*, *SORL1* and *ABCA7* with AD risk has been reported (Guerreiro et al., 2013; Jonsson et al., 2013, G. Nicolas et al., 2016; Steinberg et al., 2015). However, these seminal analyses have often been performed in populations of restricted size or with a limited panel of variants, restricting the possibility to fully estimate how these rare variant groups explain the susceptibility to AD and how they may influence a disease as heterogeneous as AD. Indeed, one can argue that, in particular, they may affect age at onset which can spread out over several decades from early 40's to very old age. In addition, as there are several types of rare variants, each leading to different biological consequences, deciphering for each gene whether they act through a loss or gain of function is of particular interest for establishing their pathophysiological mechanisms.

In order to assess these points, we took advantage of the ADES-FR dataset, one of the largest AD WES dataset available worldwide and made up of 1,779 AD cases and 1,273 controls, all originating from France. We focused on the *TREM2*, *SORL1* and *ABCA7* genes but also included in this study three other genes, namely *PLD3* (Cruchaga et al., 2014), *AKAP9* (Logue et al., 2014) and *UNC5C* (Wetzel-Smith et al., 2014) which association of rare variants with AD risk has recently been reported, albeit with lesser evidence. *PLD3* association reached exome-wide significance in the first study (Cruchaga et al., 2014), but this result was challenged by four different subsequent reports (Heilmann et al., 2015; Hooli et al., 2015; Lambert et al., 2015; van der Lee et al., 2015) and regarding *AKAP9* and *UNC5C*, the exome-wide threshold was not reached.
## 2. Methods

### 2.1 ADES-FR samples

The ADES-FR project combines WES and WGS data from AD cases and controls (Supplementary Table 1).

Lille cases were selected from the EADI dataset (Lambert et al., 2009). Clinical diagnosis of AD was established by neurologists according to the DSM-III-R and NINCDS-ADRDA criteria (McKhann et al., 1984).

Patient ascertainment of the Rouen cases is described in detail in (G. Nicolas et al., 2016). Briefly, unrelated cases with early-onset AD (age at onset <= 65 years) from France were recruited by the French National CNR-MAJ consortium. All diagnoses fulfilled the NIAA criteria (McKhann et al., 2011). The clinical examination included personal medical and family history assessment, neurologic examination, neuropsychological assessment, and neuroimaging. In addition, cerebrospinal fluid (CSF) biomarkers indicative of AD were available for 63% of cases. Cases with CSF biomarkers not consistent with an AD diagnostic were excluded. A positive family history (i.e. at least a secondary case among first-degree relative, whatever the age of onset) was present in 46% of cases.

The Three-City (3C) cases were selected among individuals with prevalent or incident AD in the 3C cohort, a population-based, prospective (12-years follow-up) study of the relationship between vascular factors and dementia carried out in three French cities (3C Study Group, 2003). Diagnostic was established according to the DSM-III-R and NINCDS-ADRDA criteria.

3C controls were selected among the 3C individuals not diagnosed with AD. Samples with known non-AD dementia were excluded from the ADES-FR project. Some other controls were obtained from the FREX consortium. These controls were specifically recruited in 6 French cities with the aim of studying the French population structure. Overall, from the 1,273 controls, 1,045 were above 50 years old and had normal cognitive function assessed by mini mental state examination (MMSE). The remaining FREX controls and the Rouen controls were younger healthy blood donors of various age.

Among the samples included in the ADES-FR project, none had contamination above 5% as estimated by VerifyBamID (Jun et al., 2012), and no cases carried known pathogenic mutations in one of the 23 dementia genes described in (Gaël Nicolas et al., 2016), including *APP*, *PSEN1* and *PSEN2*. At this stage, 1,785 AD cases and 1,273 controls were included in the ADES-FR project.
## 2.2 Data generation and quality control

**2.2.1 Sequencing:** WES was performed at the Centre National de Génotypage (CNG, Institut de Génomique, CEA), the McGill University and Genoscreen, while WGS was performed at the McGill University. Exomes were captured using the Agilent SureSelect All Exons Human V1, V2 or V3 kits (Agilent technologies, CA, USA) for 18 individuals, while the Agilent SureSelect All Exons Human V4, V4+UTR, V5 or V5+UTR kits were used for 2,085 other individuals. For the WGS sequencing of the remaining 955 individuals, libraries were generated using the TruSeq DNA PCR-Free Library Preparation Kit (Illumina Inc, CA, USA). Libraries were sequenced on a HiSeq2000 or 2500 from Illumina (Illumina Inc., CA, USA) with paired end mode, for 76, 100 or 125 base pairs (bp) reads. WES resulted in an average individual median depth of coverage on targeted exome kits of 102x, with 95 % of individual median depths between 49x and 169x. WGS average median depth of coverage was estimated at 34x, with 95 % of individual median depths between 28x and 38x.

**2.2.2 Read alignment and variant calling:** Read alignment and variant calling were performed in 4 different centers (Lille, Rouen, Bordeaux, and McGill University) using similar bioinformatics pipelines. Sequences were aligned on the GRCh37 reference genome with BWA (Li and Durbin, 2009) and duplicate reads were tagged with Picard MarkDuplicates. Realignment around InDels and base quality score recalibration (BQSR) were performed with GATK (McKenna et al., 2010). All centers performed single-sample calling using HaplotypeCaller from GATK 3.3 in GVCF mode with default band definition, except for the samples processed in the Bordeaux center, whose calling was generated in base-pair resolution. WES calling was done on the target intervals of each exome kit using a padding of 100 bp while WGS calling was restricted to the union of target intervals of several exome kits with padding including those used in the ADES-FR project. Multi-sample calling was performed from these single-sample GVCFs with the GenotypeGVCFs tool implemented in GATK 3.4. A quality score (VQSLOD) was then estimated for each variant with the VQSR function from GATK 3.4. Finally, genotypes with genotype quality (GQ) below 20 were set as missing.

**2.2.3 Sample quality control (QC):** After multi-sample calling, a sample QC was performed on the overall ADES-FR dataset using the bi-allelic sites and after filtering on VQSLOD, using a sensitivity threshold of 99.5% for Single Nucleotide Variants (SNVs) and 99% for InDels, as recommended in GATK best practices. Unless specified, the metrics were computed on the sequencing target interval of each sample. All samples had missingness below 15%, and none showed outlying values (outside mean +/- 6 standard deviations) of heterozygosity/homozygosity ratio (HetHom ratio) measured on "known" variants available in dbSNP147 (Sherry et al., 2001). Transition-transversion ratio (Ti/Tv) computed on those "known" variants were homogeneous across samples
sequenced on the same target intervals. No sample showed evidence of discordance between clinical and genetic sex as determined by homozygosity on the X-chromosome. Evidence of non-European ancestry was assessed through a principal component analysis (PCA) performed on the 1000G Phase 3 individuals (1000 Genomes Project Consortium et al., 2015) with the smartpca program from the EIGENSOFT software (Patterson et al., 2006). This analysis was restricted to variants covered by all target intervals of the ADES-FR exome kits and available in 1000G Phase 3, with minor allele frequency (MAF) above 0.5%, a p-value for the Hardy-Weinberg test above $1 \times 10^{-6}$, and missingness below 5%. Besides, variants in long-range linkage disequilibrium (LD) regions (Price et al., 2008) were excluded, and LD pruning was performed on the remaining variants with the R package Gaston (R Core Team, n.d.). ADES-FR samples were then projected on the first two axes defined by the 1000G PCA analysis. Another PCA was performed on the ADES-FR samples only on the same subset of variants. One individual clustering apart from the remaining of the ADES-FR samples in both PCA analyses was excluded. Relatedness between individuals was estimated by the PI_HAT statistic from Plink 1.9 (Chang et al., 2015) on this same subset of variants. Four pairs of individuals had a PI_HAT above 0.15, and the individual sequenced on the largest target intervals (or with the highest average quality if target intervals were identical) was kept from each pair. One additional individual was excluded because of an outlying number of private mutations among those covered by all target intervals. Six individuals were excluded following this sample quality control, leading to a final sample size of 1,779 AD cases and 1,273 controls. Note that 481 EOAD cases and 590 controls have already been included in previous WES studies (Le Guennec et al., 2016; G. Nicolas et al., 2016).

Detailed descriptive statistics of the final dataset are provided in Table 1, where AD cases are categorized into 927 late-onset AD cases (LOAD) and 852 early-onset AD cases (EOAD), using a cut-off of 65 for age at onset.

#### 2.2.4 Variant annotation and quality control (QC)
To include multi-allelic sites in the analysis, multi-allelic sites were converted into bi-allelic variants that were then left-aligned using bcftools 1.3. Allele balance annotations (ABHet and ABHom) were added using GATK for bi-allelic SNPs, and using custom scripts for InDels and variants extracted from multi-allelic sites. Variants were annotated with SnpEff 4.2 (Cingolani et al., 2012b) and SnpSift 4.2 (Cingolani et al., 2012a) software, using dbNSFP 2.9.1 (Liu et al., 2013) and Ensembl GRCh37.75 (Cunningham et al., 2015) databases. Variants were filtered according to their VQSLOD value, using a sensitivity threshold of 99.5% for SNVs and 99% for InDels. Variants with missingness > 5%, Hardy-Weinberg test p-value in controls < $1 \times 10^{-7}$, ABHet > 0.75, ABHet < 0.25 or ABHom < 0.9 were excluded from the analyses. We also excluded variants in low-complexity regions (LCR) as identified by the mdust program (Li, 2014), and variants in simple tandem repeat (STR) regions located by Tandem Repeats Finder (Benson, 1999) and retrieved using the UCSC Table browser (Karolchik et al., 2004). Besides, to prevent inconsistencies in the analysis, multi-allelic sites where more than one
alternate allele had a frequency below the frequency of the reference allele were excluded. Finally, variants with p-value for differential missingness between cases and controls below 1x10⁻⁷ were excluded. Note that this last filter is specific to the analysis performed, as some analyses are performed on a subset of cases.

## 2.3 Statistical tests

### 2.3.1 Gene-level association analyses
Variants with MAF ≤ 1% in the ADES-FR dataset or in the ExAC non-Finnish European dataset (Lek et al., 2016) were included in the association tests. However the MAF in ExAC non-Finnish European samples was only taken into account if the variant was called in at least 80% of the ExAC non-Finnish European individuals. The analyses were restricted to three classes of variants defined from *in silico* pathogenicity predictions: 1) protein truncating variants (PTVs), corresponding to the variants annotated as “LOF” by SnpEff based on the conservative definition provided by (MacArthur et al., 2012); 2) strictly damaging missense variants (SD variants), corresponding to missense variants unanimously predicted as damaging by MutationTaster (Ensembl69) (Schwarz et al., 2014), SIFT (Ensembl66) (Ng and Henikoff, 2001) and Polyphen-2 2.2.2 (Hum Div option) (Adzhubei et al., 2010) for at least one transcript; 3) broadly damaging missense variants (BD variants), corresponding to missense variants predicted as damaging by 1 or 2 of those 3 tools whatever the transcript. Note that except when another transcript was specifically affected, we provide amino acid changes referring to the canonical transcript (*TREM2*: NM_018965, *SORL1*: NM_003105 and *ABCA7*: NM_019112). Those variants were further classified into four different classes: 1) PTVs only; 2) PTVs + SD variants; 3) PTVs + SD + BD variants; 4) SD + BD variants. Although the c.5570+5G>C (rs200538373) variant of the *ABCA7* gene was not included in one of those groups according to this systematic annotation, it has been shown to affect splicing leading to a premature stop codon (Steinberg et al., 2015), and was thus included a *posteriori* as a PTV in the analyses (this variant passed all QC filters). For each gene, both burden (Asimit and Zeggini, 2010) and SKAT (Wu et al., 2011) tests were applied on each of those four variant groups using seqMeta1.6.6, using the default weights, i.e. unweighted burden test and beta weights for the SKAT test. Because effect sizes provided by seqMeta are approximate, odds-ratio (OR) and their corresponding confidence intervals were computed by logistic regression with R statistical software (R Core Team, n.d.). Analyses were adjusted on the first 3 principal components (PCs) computed on the ADES-FR individuals, using variants extracted from bi-allelic sites with MAF > 0.5% that passed the above QC filters, except for the differential missingness criterion and following LD pruning as described above.

Gene-level association analyses were run on all cases vs controls (all AD analysis), but also separately on EOAD cases vs controls (EOAD analysis) and on LOAD cases vs controls (LOAD
analysis). Although only 6 genes were under investigation we conservatively interpreted p-values in these analyses with an exome-wide significance threshold of 2.5x10⁻⁶ (Bonferroni correction for ~ 20,000 genes).

When a significant effect was detected within a single AD group for a specific class of rare variants, we systematically tested the difference in effect size between EOAD and LOAD on the one hand, and between familial EOAD and sporadic EOAD on the other hand, using the burden test. The EOAD vs LOAD analyses were performed on variants retained after variant QC for both the EOAD and LOAD analyses and after further exclusion of variants showing differential missingness (p<1x10⁻⁷) between EOAD and LOAD cases. Similarly, the familial EOAD vs sporadic EOAD analysis was restricted to variants retained after QC in the EOAD analysis, and further excluding variants with differential missingness (p<1x10⁻⁷) between familial and sporadic cases. Bonferroni correction for 6 independent tests (3 genes and 2 comparisons per gene) was used for the interpretation of this analysis (threshold of 8.33x10⁻³).

In addition, for each gene, when a significant association was specifically detected with PTVs + SD variants while no significant association could be detected with PTVs + SD + BD variants altogether or with PTVs alone, we systematically assessed differences in effect sizes between the PTVs + SD variants and BD variants on the one hand, and between PTVs and SD variants on the other hand using likelihood ratio tests for nested logistic regression models. In the end, Bonferroni correction for 6 independent tests (3 genes and 2 model comparisons per gene) was used for the interpretation of this analysis (threshold of 8.33x10⁻³).

### 2.3.2 Contribution of rare variants

Calculations of rare variant contributions to EOAD genetic risk were based on INDI-V software (Witte et al., 2014), assuming a 0.35% average lifetime risk of developing AD before the age of 65 years and a sibling relative lifetime risk by age 65 of 6.24. The former was averaged over the four lifetime risk values at age 65 provided for men and women in Genin et al. (Genin et al., 2011). The latter was estimated from a sample of 522 EOAD index cases with at least one sibling, negatively screened for *PSENs* and *APP* mutations and ascertained from consecutive admissions in Rouen. These parameters resulted in an estimated heritability of 49% for non-autosomal dominant EOAD. To guarantee consistency with a similar analysis involving common variants, *APOE ε4* parameters (allelic frequency of 14% for APOE ε4 and an odds ratio of 3.63 for ε3ε4 carriers) were borrowed from Cuyvers et al (Cuyvers and Sleegers, 2016).

## 3. Results

We gathered evidence for gene-based association of rare variants with AD in 6 genes (Table 2 and Supplementary Tables 2 and 3). All variants included in this analysis were observed at the
heterozygous state, except the *TREM2* p.R47H variant, for which one individual is homozygous. Exome-wide significance was achieved for *TREM2* (OR = 6.30 [2.86-13.86], p=2.28x10⁻⁷), *SORL1* (OR=3.41 [2.09-5.55], p=1.61x10⁻⁷) and *ABCA7* (OR=2.54 [1.72-3.76], p=9.33x10⁻⁷) on PTVs+SD variants in EOAD cases. In LOAD cases no exome-wide association signal was detected. In the whole AD sample, significance of the order of 10⁻⁶ was achieved for *TREM2* PTVs+SD variants. No association was detected in the other genes. Finally, none of the ADES-FR participants carried the rare p.A673T *APP* variant which has been reported as protective (Jonsson et al., 2012).

## 3.1 Close look at *TREM2*

Ten PTVs+SD variants (4 PTVs and 6 SD variants) were seen in *TREM2*, affecting 0.63 % of controls, 3.64 % of EOAD cases and 1.94 % of LOAD cases (Supplementary Table 6).

The four PTVs were observed only in cases and except for the p.R47H and p.T96K variants, all SD variants were seen at most twice and were restricted to cases.

We observed that the significant association of PTVs+SD variants with EOAD was robust to the exclusion of the p.R47H variant, previously described as significantly associated with AD risk (Jonsson et al., 2013), leading to a residual p-value of 1.00 x 10⁻⁴.

Although the association of *TREM2* PTVs+SD variants was 1.7 times larger in EOAD cases than the one observed in LOAD cases, this difference did not reach significance (p=6.29x10⁻², Supplementary Table 4).

We then compared the effect of the different classes of variants on EOAD risk (Supplementary Table 5). We observed that this association decreased as variants of reduced predicted pathogenicity were progressively included in the burden analysis (Table 2). Our nested model analysis confirmed that BD variants showed a significantly different magnitude of association with EOAD when compared to PTVs+SD variants (p=7.24x10⁻³). Notably, no residual association could be detected between BD variants alone and EOAD (OR=1.02 [0.34-3.03], p=9.7x10⁻¹). In contrast, SD variants showed a suggestive association with EOAD risk (OR = 5.69 [2.56 - 12.64], p=1.98x10⁻⁵). Despite the fact that only four cases (and no control) carried a PTV resulting in an infinite OR, we were not able to detect a difference between the association of PTVs and SD variants on EOAD risk (p=2.34x10⁻¹).

## 3.2 Close look at *SORL1*

Altogether, 77 distinct PTVs+SD variants were observed in *SORL1*, affecting 1.89% of controls, 6.10 % of EOAD cases and 2.91 % of LOAD cases (Supplementary Table 7). Of note, all 13 PTVs were singletons and were exclusively observed in EOAD cases, except for one InDel carried by a control of 75 years old. Among PTV carriers, eight out of the 10 cases with available family information had a positive family history of AD. Among the 64 SD variants, 52 were singletons whereas the most recurrent variant, namely p.V2097I was observed with a MAF of 0.12 % in controls and 0.23% in EOAD cases.
Magnitude of the *SORL1* PTV+SD variant association increased gradually from LOAD, sporadic EOAD and finally to familial EOAD cases (OR of 1.48 [0.87-2.53], 2.11 [1.04-4.09] and 4.83 [2.80 – 8.46] respectively), with significant difference in EOAD vs LOAD association and familial vs sporadic EOAD association (p=5.91x10⁻⁴ and p=3.17x10⁻³ respectively) (Supplementary Table 4).

Similarly to *TREM2*, a gradation of effect size was observed between BD, SD and PTV variants (OR=1.21 [0.85-1.72], OR = 2.84 [1.70 – 4.73], and OR = 16.73 [2.13–Inf], respectively). Nested model comparisons validated the difference of effect size between the BD and PTVs+SD variants (p=5.16x10⁻⁴) but the difference between SD variants and PTVs did not reach significance after correction for multiple testing (p=4.57x10⁻², Supplementary Table 5).

### 3.3 Close look at ABCA7

Seventy PTVs+SD variants were observed in *ABCA7* in our dataset, affecting 3.22 % of controls, 8.22 % of EOAD cases and 4.53% of LOAD cases (Supplementary Table 8).

We noted a slight but not significant increase in the burden of PTVs+SD variants in EOAD cases compared to LOAD cases (OR=2.54 [1.72 – 3.76] and OR=1.58 [1.04 – 2.38] respectively, p=2.34x10⁻², Supplementary Table 4).

Finally, similarly to what we observed for *TREM2* and *SORL1*, burden analysis of BD variants by themselves did not show any significant association with EOAD risk (OR=1.06 [0.72 – 1.55], p=7.85x10⁻¹), resulting in a significantly different magnitude of association when compared to PTVs+SD variants (p=1.85x10⁻³, Supplementary Table 5).

### 3.4 Individuals carrying more than one risk allele

Twelve individuals carried more than one risk allele. Three controls and 5 cases carried two hits in *SORL1* and *ABCA7*. Two cases carried one variant in *TREM2* and one variant in *ABCA7*. One case carried two risk alleles in *SORL1* and *TREM2* and lastly, one case carried two hits in *SORL1* and *TREM2* (Supplementary Table 9).

### 3.5 Contribution of *TREM2*, *SORL1* and *ABCA7* to the EOAD genetic component

Measures of genetic contributions are known to vary in their objective and scales, we therefore report hereafter the most conservative one, namely the proportion of heritability explained on the liability scale (Witte et al., 2014). With a cumulative MAF of 0.63% among controls and an OR of 6.30, *TREM2* rare PTVs+SD variants explained 1.17% of EOAD heritability. With an OR of 3.41 but a cumulative MAF of 1.89% among controls, *SORL1* rare PTVs+SD variants explained 1.42% of EOAD heritability. Finally, with an OR of 2.54 and a cumulative MAF of 3.22% among controls, *ABCA7* rare PTVs+SD variants explained 1.33% of EOAD heritability. In comparison, 9.12% of EOAD heritability was attributable to *APOEε4*. Values for other measures of genetic contributions are provided in Supplementary Fig 1.
In this study focusing on 6 genes, exome-wide significant association between EOAD risk and specific classes of rare variants was reached for 3 genes: *TREM2*, *SORL1* and *ABCA7*. Patients and controls were carefully matched for ethnic origin. The inclusion of 236 controls with age below 65 may at most reduce power to detect a risk factor.

Our findings on *TREM2* indicate that the association signal is due to a collection of rare PTVs+SD variants and is robust to the exclusion of the well-known p.R47H variant. Concerning *SORL1*, the exome-wide significant association of rare PTVs+SD variants can now be extended from EOAD cases with positive family history (G. Nicolas et al., 2016) to EOAD cases in general, substantiating our previous results as well as the work of Verheijen et al. (Verheijen et al., 2016). However, our data still suggest a stronger contribution of *SORL1* rare PTVs+SD variants to familial EOAD compared to sporadic EOAD. Sadly the absence of DNA from relatives prevented us from leading the segregation analyses that could help prioritize variants for future functional analyses.

For *SORL1* we observed that the magnitude of the association signal from the PTV+SD variants increased from LOAD to EOAD cases. Although the same trend was found for *TREM2* and *ABCA7*, a larger sample size would be required to formally validate a similar result for these two genes.

Beyond a confirmation of previous gene-level results for *TREM2*, *SORL1* and *ABCA7*, our data also provide a clearer insight into the classes of rare variants involved in AD risk, shedding light on the underlying biological mechanisms. Indeed, across all these three genes, we observed a difference between SD and BD missense variants, with SD variants contributing with PTVs to the association signal, while BD variants failed to show any association in our dataset. It is possible that the rarity of PTVs deprives our analysis from the power to detect a difference in the risk associated with SD variants compared to PTVs. Yet, the clear distinction in effect size between the PTV+SD variant group and the BD missense variants in our dataset has consequences on at least two levels. First, from a practical point of view, it is essential to take into account pathogenicity predictions (functional or *in silico*) to filter out potential neutral variants if one wants to unveil any association signal. Second, from a pathophysiological point of view, this observation suggests that most of the SD missense variants on these three genes likely share a common loss of function biological mechanism with PTVs.

The high odds ratios observed allowed the identification of associations between *TREM2*, *SORL1* and *ABCA7* PTVs+SD variants and EOAD risk from samples of limited size. By contrast, it is noticeable that we did not detect any association for the other genes (*PLD3*, *AKAP9* and *UNC5C*) under investigation. Note however that the two *AKAP9* variants originally reported as associated with AD in the African-American population were not observed in our samples. Besides for these three genes, it is obviously impossible to distinguish potential false positive previous reports from
true associations. Indeed, the power of our study might not be sufficient to identify an association
with any gene with smaller cumulative MAF and/or lower OR compared to the signals detected. In
particular, *TREM2* association was detected with a cumulative MAF of only 0.63 % among controls
combined with an OR of 6.30 among EOAD cases and 4.76 among EOAD+LOAD cases, *SORL1*
association was detected with a cumulative MAF of 1.89 % in controls combined with an OR of
3.41 in EOAD cases, while *ABCA7* association was detected with a cumulative MAF of 3.22 % in
controls combined with an OR of 2.54 in EOAD cases.

For similar reasons of limited power, it is impossible to exclude any association of *SORL1* and *ABCA7* rare variants with LOAD risk, as suggested by several reports (Cuyvers et al., 2015; Steinberg et al., 2015; Vardarajan et al., 2015). If these genes were to be associated with LOAD, our data suggest that the strength of association would be much weaker in LOAD than in EOAD. However we cannot exclude that the associations in LOAD were less apparent because of heterogeneity in LOAD forms, for example unknown comorbidities such as a vascular component.

**5. Conclusions**

Our study confirms previous gene-level results for *TREM2*, *SORL1* and *ABCA7*. We observe that despite differential strengths of association and varying cumulative MAFs, *TREM2*, *SORL1* and *ABCA7* PTVs+SD variants eventually contribute similarly to the genetic component of EOAD at the level of about 1 to 1.5% of EOAD heritability each, to be compared with the 9.12% of EOAD heritability attributable to *APOEε4*. Obviously, these cumulative contributions depend on our definition of PTVs+SD variants. Future functional and segregation analyses would be required to better circumvent damaging variants and provide more accurate estimations of *TREM2*, *SORL1* and *ABCA7* contributions to AD risk.
**Abbreviations**

3C: Three-City

AD: Alzheimer's disease

BD: broadly damaging missense variant

BQSR: base quality score recalibration

CSF: cerebrospinal fluid

EOAD: early-onset Alzheimer's disease

GQ: genotype quality

GWAS: genome-wide association study

HetHom ratio: heterozygosity/homozygosity ratio

LCR: low-complexity region

LD: linkage disequilibrium

LOAD: late-onset Alzheimer's disease

MAF: minor allele frequency

MMSE: mini mental state examination

OR: odds-ratio

PC: principal component

PCA: principal component analysis

PTV: protein truncating variant

QC: quality control

SD: strictly damaging missense variant

STR: simple tandem repeat

SNV: Single Nucleotide Variant

Ti/Tv: transition-transversion ratio

WES: whole-exome sequencing

WGS: whole-genome sequencing
**Declarations**

**Ethics approval and consent to participate:** All subjects enrolled in this study gave a signed consent. This study was approved by the Paris Necker Ethic Committee.

**Consent for publication:** Not applicable

**Availability of data and material:** The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.

**Competing interests:** The authors declare that they have no competing interests

**Funding:** This study was funded by grants from the Clinical Research Hospital Program from the French Ministry of Health (GMAJ, PHRC 2008/067), the CNR-MAJ, the JPND PERADES, the GENMED labex and the FP7 AgedBrainSysBio. This work was supported by the France Génomique National infrastructure, funded as part of the « Investissements d'Avenir » program managed by the Agence Nationale pour la Recherche (ANR-10-INBS-09), the Centre National de Génotypage, the National Foundation for Alzheimer's disease and related disorders, the Institut Pasteur de Lille, Inserm, FRC (Fondation pour la Recherche sur le Cerveau), the Lille Métropole Communauté Urbaine council and the French government's LABEX (laboratory of excellence program investment for the future) DISTALZ grant (Development of Innovative Strategies for a Transdisciplinary approach to Alzheimer's disease). The 3C Study supports are listed on the Study Website (www.three-city-study.com).

**Authors' contributions:** Acquisition and collection of data: DW, SR, ACR, AB, GB, HMM, RO, VM, ARS, FP, LL, RR, JFD, CT, TF, ML, JFD, DH, EG, PA, SD, JCL, DC and CNR MAJ collaborators. Analysis and interpretation of data: CB, CC, BGB, OQ, KLG, GN, GC, JCL, DC. Manuscript draft: CB, CC, KLG, GN, JCL, DC. Principal investigator and study supervision: JCL, DC.

**Acknowledgements:** We are indebted to the Banque d'ADN et de cellules-Institut du Cerveau et de la Moelle épinière (ICM-Inserm U1127-UPMC P6 UMR S 1127-CNRS UMR 7225).

CNR-MAJ collaborators: Didier Hannequin, Dominique Campion, David Wallon, Olivier Martinaud, Aline Zarea, Gaël Nicolas (CHU Rouen); Adeline Rollin-Sillaire, Stéphanie Bombois, Marie-Anne Mackowiak, Vincent Deramecourt, Florence Pasquier (CHU Lille); Agnès Michon, Isabelle Le Ber, Bruno Dubois, (CHU La Salpêtrière, Paris); Olivier Godefroy (CHU Amiens); Frédérique Etcharry-Bouyx, Valérie Chauviré (CHU Angers); Ludivine Chamard, Eric Berger, Eloi Magnin (CHU
Besançon); Jean-François Dartigues, Sophie Auriacombe, François Tison, (CHU Bordeaux); Vincent de la Sayette, (CHU Caen); Dominique Castan (CH Castres), Elsa Dionet (CHU Clermont-Ferrand); François Sellal (CH Colmar); Olivier Rouaud, Christel Thauvin (CHU Dijon); Olivier Moreaud, Mathilde Sauvée (CHU Grenoble); Maïté Formaglio, Hélène Mollion, Isabelle Roullet-Solignac, Alain Vighetto, Bernard Croisile (CHU Lyon); Mira Didic, Olivier Félician, Lejla Koric, Mathieu Ceccaldi (CHU Marseille); Audrey Gabelle, Cecilia Marelli, Pierre Labauge (CHU Montpellier); Thérèse Jonveaux (CHU Nancy); Martine Vercelletto, Claire Boutoleau-Bretonnière (CHU Nantes); Giovanni Castelnovo (CHU Nimes); Claire Paquet, Julien Dumurgier, Jacques Hugon (CHU Lariboisière, Paris); Foucauld De Boisgueheneuc (CHU Poitiers); Serge Belliard (CHU Rennes); Serge Bakchine (CHU de Reims); Marie Sarazin (CHU St Anne, Paris), Marie-Odile Barrellon, Bernard Laurent (CHU Saint-Etienne); Frédéric Blanc (CHU Strasbourg); Jérémie Pariente, (CHU Toulouse); Snejana Jurici (CHR Perpignan).

Additional files

Additional_file_1: Supplementary Tables 1-8. Contains additional information regarding sample size and results (.pdf 951 ko)
<table><caption>Table 1: Characteristics of the ADES-FR individuals</caption><thead><tr><th></th><th>EOAD</th><th>LOAD</th><th>Controls</th></tr></thead><tbody><tr><td>N</td><td>852</td><td>927</td><td>1273</td></tr><tr><td>N females (%)</td><td>523 (61.38)</td><td>602 (64.94)</td><td>744 (58.44)</td></tr><tr><td>Mean age (sd, range)</td><td>56.18 (5.32, 44-65)</td><td>78.35 (6.91, 66-99)</td><td>75.58 (14.28, 19-103)</td></tr><tr><td>N APOEε4 alleles (%)*</td><td></td><td></td><td></td></tr><tr><td>0 allele</td><td>380 (44.6)</td><td>525 (56.63)</td><td>990 (77.77)</td></tr><tr><td>1 allele</td><td>388 (45.54)</td><td>347 (37.43)</td><td>265 (20.82)</td></tr><tr><td>2 alleles</td><td>84 (9.86)</td><td>55 (5.93)</td><td>16 (1.26)</td></tr><tr><td>N APOEε2 alleles (%)*</td><td></td><td></td><td></td></tr><tr><td>0 allele</td><td>798 (93.66)</td><td>840 (90.61)</td><td>1084 (85.15)</td></tr><tr><td>1 allele</td><td>54 (6.34)</td><td>82 (8.85)</td><td>182 (14.3)</td></tr><tr><td>2 alleles</td><td>0 (0)</td><td>5 (0.54)</td><td>5 (0.39)</td></tr></tbody></table>

N: sample size; sd: standard deviation; EOAD: early-onset Alzheimer's disease; LOAD: late-onset disease
Alzheimer's
<table><caption>Table 2: TREM2, SORL1 and ABCA7 burden test results</caption><thead><tr><th rowspan="2">Pathogenicity class</th><th colspan="2">Controls (N=1273)</th><th colspan="4">EOAD (N=852)</th><th colspan="4">LOAD (N=927)</th><th colspan="4">All AD (N=1779)</th></tr><tr><th>Carriers (%)</th><th>n</th><th>Carriers (%)</th><th>OR [95% CI]</th><th>p-value</th><th>n</th><th>Carriers (%)</th><th>OR [95%CI]</th><th>p-value</th><th>n</th><th>%Carriers</th><th>OR [95%]</th><th>p-value</th></tr></thead><tbody><tr><td><strong>TREM2</strong></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>PTV</td><td>0 (0.00%)</td><td>3</td><td>3 (0.35%)</td><td>Inf [0.00-Inf]</td><td>3.19X10<sup>-2</sup></td><td>1</td><td>1 (0.11%)</td><td>Inf [0.00-Inf]</td><td>2.85X10<sup>-1</sup></td><td>4</td><td>4 (0.22%)</td><td>Inf [0.00-Inf]</td><td>9.08X10<sup>-2</sup></td></tr><tr><td>PTV + SD</td><td>8 (0.63%)</td><td>7</td><td>31 (3.64%)</td><td><strong>6.30 [2.86-13.86]</strong></td><td><strong>2.28X10<sup>-7</sup></strong></td><td>5</td><td>18 (1.94%)</td><td>3.71 [1.63-8.45]</td><td>6.59X10<sup>-4</sup></td><td>10</td><td>49 (2.75%)</td><td>4.76 [2.25-10.08]</td><td><strong>6.16X10<sup>-6</sup></strong></td></tr><tr><td>PTV + SD + BD</td><td>16 (1.26%)</td><td>16</td><td>36 (4.23%)</td><td><strong>3.55 [1.96-6.43]</strong></td><td><strong>7.91X10<sup>-6</sup></strong></td><td>16</td><td>27 (2.91%)</td><td>2.64 [1.43-4.89]</td><td>1.25X10<sup>-3</sup></td><td>23</td><td>63 (3.54%)</td><td>3.04 [1.76-5.26]</td><td><strong>2.70X10<sup>-5</sup></strong></td></tr><tr><td>SD + BD</td><td>16 (1.26%)</td><td>13</td><td>33 (3.87%)</td><td>3.24 [1.78-5.91]</td><td>4.73X10<sup>-5</sup></td><td>15</td><td>26 (2.80%)</td><td>2.56 [1.38-4.76]</td><td>1.94X10<sup>-3</sup></td><td>19</td><td>59 (3.32%)</td><td>2.85 [1.64-4.95]</td><td><strong>8.96X10<sup>-5</sup></strong></td></tr><tr><td><strong>SORL1</strong></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>PTV</td><td>1 (0.08%)</td><td>13</td><td>11 (1.29%)</td><td>16.22 [2.08-Inf]</td><td>3.43X10<sup>-4</sup></td><td>1</td><td>0 (0.00%)</td><td>0.00 [0.00-Inf]</td><td>3.42X10<sup>-1</sup></td><td>13</td><td>11 (0.62%)</td><td>7.22 [0.96-54.35]</td><td><strong>2.15X10<sup>-2</sup></strong></td></tr><tr><td>PTV + SD</td><td>24 (1.89%)</td><td>62</td><td>52 (6.10%)</td><td><strong>3.41 [2.09-5.55]</strong></td><td><strong>1.61X10<sup>-7</sup></strong></td><td>35</td><td>27 (2.91%)</td><td>1.48 [0.87-2.53]</td><td>1.45X10<sup>-1</sup></td><td>77</td><td>79 (4.44%)</td><td>2.34 [1.49-3.66]</td><td><strong>1.32X10<sup>-4</sup></strong></td></tr><tr><td>PTV + SD + BD</td><td>102 (8.01%)</td><td>93</td><td>108 (12.68%)</td><td>1.76 [1.34-2.30]</td><td>3.79X10<sup>-5</sup></td><td>71</td><td>77 (8.31%)</td><td>1.06 [0.79-1.41]</td><td>7.17X10<sup>-1</sup></td><td>124</td><td>185 (10.40%)</td><td>1.37 [1.08-1.75]</td><td><strong>8.74X10<sup>-3</sup></strong></td></tr><tr><td>SD + BD</td><td>101 (7.93%)</td><td>80</td><td>99 (11.62%)</td><td>1.60 [1.21-2.12]</td><td>9.06X10<sup>-4</sup></td><td>70</td><td>77 (8.31%)</td><td>1.07 [0.80-1.43]</td><td>6.64X10<sup>-1</sup></td><td>111</td><td>176 (9.89%)</td><td>1.31 [1.03-1.67]</td><td><strong>2.95X10<sup>-2</sup></strong></td></tr><tr><td><strong>ABCA7</strong></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>PTV</td><td>25 (1.96%)</td><td>20</td><td>40 (4.69%)</td><td>2.43 [1.46-4.04]</td><td>3.56X10<sup>-4</sup></td><td>12</td><td>15 (1.62%)</td><td>0.86 [0.44-1.65]</td><td>6.49X10<sup>-1</sup></td><td>23</td><td>55 (3.09%)</td><td>1.63 [1.01-2.63]</td><td><strong>3.85X10<sup>-2</sup></strong></td></tr><tr><td>PTV + SD</td><td>41 (3.22%)</td><td>53</td><td>70 (8.22%)</td><td><strong>2.54 [1.72-3.76]</strong></td><td><strong>9.33X10<sup>-7</sup></strong></td><td>46</td><td>42 (4.53%)</td><td>1.58 [1.04-2.38]</td><td>2.72X10<sup>-2</sup></td><td>69</td><td>111 (6.24%)</td><td>1.99 [1.40-2.84]</td><td><strong>7.34X10<sup>-5</sup></strong></td></tr><tr><td>PTV + SD + BD</td><td>104 (8.17%)</td><td>95</td><td>113 (13.26%)</td><td>1.64 [1.26-2.13]</td><td>1.58X10<sup>-4</sup></td><td>84</td><td>100 (10.79%)</td><td>1.36 [1.04-1.79]</td><td>2.25X10<sup>-2</sup></td><td>121</td><td>212 (11.92%)</td><td>1.49 [1.19-1.88]</td><td><strong>4.86X10<sup>-4</sup></strong></td></tr><tr><td>SD + BD</td><td>82 (6.44%)</td><td>75</td><td>77 (9.04%)</td><td>1.42 [1.04-1.93]</td><td>2.46X10<sup>-2</sup></td><td>72</td><td>87 (9.39%)</td><td>1.53 [1.13-2.07]</td><td>5.66X10<sup>-3</sup></td><td>98</td><td>163 (9.16%)</td><td>1.46 [1.12-1.91]</td><td><strong>4.02X10<sup>-3</sup></strong></td></tr></tbody></table>

N: sample size, n: number of distinct variants taken into account in a given AD group analysis, OR and 95% confidence intervals for the burden tests obtained from logistic regression adjusted on the first 3 principal components, p-values from burden tests adjusted on the first 3 principal components provided by seqMeta. Bold values indicate exome-wide significant p-values, italic values indicate p-values of the order of 10⁻⁶

PTV: protein truncating variants; SD: strictly damaging missense variants; BD: broadly damaging missense variants; AD: Alzheimer's disease; EOAD: early-onset AD; LOAD: late-onset AD
Bcftools: http://www.htslib.org/download/

Gaston: https://cran.r-project.org/web/packages/gaston/index.html

Picard: http://broadinstitute.github.io/picard

seqMeta: https://github.com/DavisBrian/seqMeta

INDI-V: http://cnsgenomics.com/shiny/INDI-V/

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Highlights

* We performed WES and WGS in 927 LOAD, 852 EOAD cases and 1,273 controls

* *TREM2*, *SORL1* and *ABCA7* rare variants were significantly associated with EOAD

* *TREM2*, *SORL1* and *ABCA7* contribute similarly to the heritability of EOAD

* These genes explain between 1.1 and 1.5 % of EOAD heritability each

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