DOI: 10.5061/dryad.80gb5mkz6
title: Data for: Distinct impact modes of polygenic disposition to dyslexia in the adult brain

description: Dyslexia is a common and partially heritable condition that impacts
 reading ability. In a study of up to 35,231 adults, we explored the
 structural brain correlates of genetic disposition to dyslexia. Individual
 dyslexia-disposing genetic variants showed distinct patterns of
 association with brain structure. Independent component analysis revealed
 various brain networks that each had their own genomic profiles related to
 dyslexia susceptibility. Circuits involved in motor coordination, vision,
 and language were implicated. Polygenic scores for eight traits
 genetically correlated with dyslexia, including cognitive, behavioural,
 and reading-related psychometric measures, showed partial similarities to
 dyslexia in terms of brain-wide associations. Notably, the microstructure
 of the internal capsule was consistently implicated across all of these
 genetic dispositions, while the lower volume of the motor cortex was more
 specifically associated with dyslexia genetic disposition alone. These
 findings reveal genetic and neurobiological features that may contribute
 to dyslexia and its associations with other traits at the population
 level.
description: <strong>Experimental Design</strong><br>UK
 Biobank data were accessed following approval of application number 16066,
 P.I. Clyde Francks. UK Biobank is an in-depth investigation of more than
 500,000 volunteers in the UK who are assessed for health, lifestyle,
 genomics, and many other variables (1). Multimodal brain MRI data (2, 3)
 had also been released for approximately 10% of the individuals when the
 present study was initiated in 2022 (4). The UK Biobank received ethical
 approval from the National Research Ethics Service Committee North
 West-Haydock (reference 11/NW/0382), and all of their procedures were
 performed in accordance with the World Medical Association guidelines.
 Written informed consent was provided by all of the enrolled participants.
 Genotyping has been performed using either BiLEVE Axiom or Axiom arrays
 from Affymetrix, which target highly overlapping sets of ~800,000 genomic
 variants with more than 95% similarity (5). The UK Biobank has also
 released common genome-wide variants imputed to the Haplotype Reference
 Consortium and UK10K haplotypes (5). In this study, we focused on
 participants who also underwent brain MRI at one of the four imaging sites
 and for whom at least one usable T1-weighted and/or diffusion MRI (dMRI)
 scan had been produced (see the next section). The genetic analyses were
 focused on the largest ancestry group within this cohort, recorded as
 ‘white British’ using a combination of self-report and genomic principal
 component analysis (this group constitutes ~85% of the overall dataset:
 data field #22006). Pairs of genetically related subjects with kinship
 coefficients above 0.044 were identified in the target sample (70).
 Individuals related to the largest number of others were recursively
 removed until no two individuals were related at or above this kinship
 threshold, leaving 35,231 individuals (18,363 females). The resulting
 sample encompassed individuals aged from 45 to 82 years, with a mean age
 of 64.2 years and a standard deviation of 7.7 years. We then included
 bi-allelic genetic variants with minor allele frequency &gt;= 0.01,
 imputation quality score of higher than 0.7, and Hardy-Weinberg
 equilibrium P-value of greater than 10-7, yielding 8,366,177 autosomal
 single nucleotide variants (SNVs) and 1,092,696 short insertion-deletions
 (indels). We accessed minimally processed and brain-extracted T1-weighted
 brain MRI volumes of 42,798 individuals (2, 3) for tensor-based
 morphometry using symmetric image normalization (SyN) registration (6, 7).
 For the present study, we generated a study-specific average brain
 template in a randomly chosen subset of 1000 individuals. The template was
 generated through 11 consecutive Advanced Normalization Tools (ANTs
 v2.3.5) registrations that iteratively refined the template shape using
 rigid, affine, and diffeomorphic SyN transformations at incremental
 resolutions up to native resolution (i.e. 1 mm3).
 <strong>Statistical analysis</strong>
 <strong>Structural MRI: </strong>tensor-based
 morphometry<br>Thereafter, all individuals’ original T1-weighted
 brain volumes were histogram matched, winsorized at 1-99 percentiles, and
 non-linearly registered to our study-specific template using SyN.
 Registration parameters included a variance for a total field of three, a
 variance for an update field of zero, a resolution downsampling scheme of
 6×, 4×, 2×, and 1× (i.e. full resolution), and Gaussian smoothing at
 standard deviations of 4, 2, 1 and zero voxels. A cross-correlation metric
 with a radius of four voxels was used. The affine registration matrix was
 composed with the SyN deformation field and the final warps were
 subsequently converted to Jacobian determinant maps, which encode the
 amount of regional brain tissue ‘shrinkage’ or ‘expansion’ in the brain of
 each individual as compared to our study-specific, average T1 template.
 ANTs affine registrations failed in 2,098 individuals; instead of removing
 them, we opted to use a comparable linear registration method, FSL Flirt
 (8) to initialize SyN, while controlling for a potential batch effect in
 subsequent analyses as a binary covariate.
 <strong>Diffusion MRI: </strong>data preprocessing
 and fixel-based analysis<br>We retrieved minimally-preprocessed dMRI
 volumes of 37,930 subjects from the UK Biobank (2, 3). These data have
 been collected at 2 mm3 isotropic resolution across 100 different
 diffusion-encoding directions evenly distributed on two spherical shells
 at b-values of 1000 and 2000 s/mm2, as well as eight blip-reversed b≅0
 volumes. Diffusion images have been corrected for off-resonance warps,
 gradient non-linearity, Eddy currents, and head motion by the UK Biobank
 team (2, 3). For the present study, we reran these corrections on raw data
 for a first batch of 8,247 individuals whose corrected b-vector tables
 were not available, while accounting for a potential batch effect in the
 subsequent regression model fits through the use of a binary covariate.
 After data preprocessing, we constructed a study-specific fiber
 orientation density (FOD) template using MRTrix3 v3.0.3 (9) from a random
 subset of 890 individuals who passed registration quality control by
 visual inspection out of 1000. This procedure started with N4 bias field
 correction and intensity normalisation of the preprocessed diffusion
 volumes, and estimation of the average tract response function (10).
 Thereafter, spherical deconvolution was performed using the estimated
 response function to generate subject-wide FOD volumes. These volumes were
 subsequently non-linearly registered to a common space and an average FOD
 template was generated iteratively. The FOD template was then ‘fixelated’
 to identify the principal directions of white-matter tracts in each voxel.
 The same procedures were repeated in all 37,930 individuals to generate
 FOD volumes, which were then registered to the study-specific FOD template
 (9). FOD registrations passed quality control in 37,884 individuals
 following a visual inspection of each individual’s template-transformed
 zeroth-order harmonic map, representing average isotropic diffusion in
 each voxel. FOD volumes were segmented to obtain fixel-wise readouts,
 which were then transformed, rotated, and corresponded to the template’s
 fixel-wise space (9). We considered apparent fiber density (AFD) readouts
 as a measure of white-matter microstructure for subsequent analyses (11).
 In combination with genetic data, the sample available was 31,695 adult
 individuals (16,198 female). <strong>Optimizing
 polygenic scoring</strong><br>We first concatenated the
 voxel-wise Jacobian and fixel-wise AFD maps across all individuals and
 then applied MELODIC independent component analysis (12, 13) to extract
 imaging-derived phenotypes (IDPs). MELODIC was performed separately per
 each imaging modality and at various dimensions to extract IDPs at
 incremental levels of spatial detail, following a geometric series
 corresponding with dimensions 11, 18, 29, 47, 76, 124, 200, and 324. Due
 to the large size of this data matrix (6.2×1010 voxels in structural MRI),
 we used 8,000 internal eigenmaps for independent source decomposition
 (14). In addition, principal component analysis was performed on the same
 data, and the first 324 principal components were extracted as additional
 IDPs. Altogether, a total of 1,153 IDPs were extracted from voxel-wise
 Jacobian maps and an equal number of IDPs from the fixel-wise AFD data.
 These IDPs were derived for the purpose of optimizing our polygenic
 scoring, but they were not used for our voxel- or fixel-based imaging
 genetic analyses, nor our impact mode analysis, which forms the bulk of
 the findings in this study. We used summary statistics from the largest
 genome-wide association study (GWAS) of dyslexia that has been performed
 to date, carried out by 23andMe, Inc. (15). This GWAS was based on 51,800
 individuals of European ancestry who answered ‘Yes’ to the question ‘Have
 you been diagnosed with dyslexia?’, and 1,087,070 control individuals who
 answered ‘No’. The SNP-wise effect sizes from this GWAS were then applied
 to the genotype data of UK Biobank individuals, to estimate the polygenic
 disposition of each UK Biobank individual to dyslexia based on the
 combined effects of their autosome-wide genetic variants. Our primary
 approach for polygenic scoring was based on the Lassosum2 model (16). We
 observed a strong correlation between Lassosum2 PGS and two automated PGS
 methods, SBayesR (17) and PRS-CSauto (18). Lassosum2 generally explained
 the highest proportion of variance in brain IDPs (Supplementary Fig. S1)
 and was therefore used for the main analysis. This method fits a sparse
 elastic-net regression and optimizes two shrinkage penalties, including
 L1-norm (λ) and L2-norm (δ). A grid search across 30 λ and 10 δ values was
 utilized for optimization with respect to maximizing the top association
 with an IDP. The associations of dyslexia PGS were quantified with all
 1,153 IDPs in each imaging modality using linear regression. A set of
 confound covariates were controlled for, including subject age at imaging
 visit (data field #21003, instance 2), age2, sex (data field #31),
 age×sex, age2×sex, the first ten principal components of genomic ancestry
 (data field #22009), genotyping array (data field #22000, either BiLEVE or
 Axiom), three dummy covariates encoding four UK Biobank neuroimaging sites
 (data field #54, instance 2), and the number of days passed since MRI scan
 incepted at the site  (as a measure of slow drifts in MRI hardware
 performance; data field #53, instance 2).  For structural MRI data, the
 type of affine registration (i.e. ANTs or Flirt) was further controlled as
 a covariate. Structural MRI analysis was performed either without (main
 analysis) or with (secondary analysis) correction for head size as a
 confounding covariate (data field #25000). For diffusion MRI data, the
 batch effect associated with diffusion preprocessing (i.e. either
 performed by our team or by the UK Biobank) was added to the covariates,
 and the analyses were done without (main analysis) and with (secondary
 analysis) the global mean apparent fiber density per individual as an
 extra covariate. We found that high δ values in the range of 102-104
 slightly increased the accuracy of Lassosum2 over automated models
 PRS-CSauto and SBayesR, and λ in the range of 10-5-10-2 resulted in the
 highest accuracy of trait prediction (Supplementary Fig. S1). These
 shrinkage parameters were therefore used for subsequent
 analyses. <strong>Voxel- and fixel-wise brain
 associations with dyslexia polygenic scores</strong><br>We
 tested the brain-wide associations of dyslexia PGS with the voxel-wise and
 fixel-wise data in the UK Biobank. Both parametric (fsl_glm 6.0.3 (19))
 and non-parametric (randomise v2.9 (20)) linear regression models were
 fitted to the data, the former to yield t-value maps for visualization and
 impact mode analysis, and the latter to generate brain-wide multiple
 comparisons-corrected P-value maps. To reduce computation costs,
 voxel-wise permutations were performed at half (2 mm3 isotropic)
 resolution with a wall-time of 9 days for 5,000 permutations per
 statistical contrast. The Randomise C++ code was modified to prevent short
 integer overflows due to the study sample size. No cluster enhancement was
 applied. The same sets of covariates as the previous section were used for
 optimization. In all cases, we observed that a parametric t-value of
 &gt; 4.5 was equivalent to a non-parametric brain-wide corrected
 P-value of smaller than 0.05. To check the validity of our findings
 obtained with Lassosum2, we applied other methods for deriving PGS:
 SBayesR, PRS-CS, and PRS-CSauto. PRS-CS applies continuous shrinkage on
 variant-wise weights using Bayesian priors and is optimized using a single
 global shrinkage hyperparameter (ϕ). We explored four different ϕ values
 for optimizing PRS-CS, which were 10-6, 10-4, 0.01, and 1 (Supplementary
 Fig. S1). PRS-CSauto and SBayesR are automated polygenic scoring methods
 and therefore did not require hyperparameter optimization on an
 independent dataset. We found that dyslexia lassosum2 PGS was strongly
 correlated with dyslexia PGS derived from PRS-CSauto (Pearson’s r = 0.87
 and 0.93 following optimization on structural or diffusion-derived
 measures, respectively) and SBayesR (r = 0.74 and 0.84, same order).
 Compared to lassosum2, these additional PGS exhibited highly similar
 brain-wide associations (Supplementary Fig. S2). To describe the white
 matter tracts that run through regions where fixels showed significant
 associations of AFD with dyslexia PGS, we ran probabilistic fiber
 tractography using the second-order Integration over Fiber Orientation
 Distributions (iFOD2) algorithm in the template space (21).
 <strong>Dyslexia locus-based neuroimaging
 association</strong><br>42 individual genomic loci were
 significantly associated with dyslexia after genome-wide multiple testing
 correction in the 23andMe Inc. GWAS for dyslexia (15). 35 of these
 variants passed our genetic quality control process in the UK Biobank data
 (see the Materials and Methods section ‘Genetics’, above). At each of
 these 35 loci, the dosage of the dyslexia-disposing allele was calculated
 and used in separate linear regression models to find brain-wide
 associations with regional volume and white-matter microstructure (i.e.
 voxel-wise Jacobian values and fixel-wise AFD values, respectively), using
 the same approach and covariates as when testing voxel-wise and fixel-wise
 PGS associations. These covariates included age, age2, sex, age×sex,
 age2×sex, ten principal components of genomic ancestry, genotyping array,
 UK Biobank imaging site, the number of days passed since MRI scan incepted
 at the site, the type of affine registration (for structural MRI), and
 preprocessing being either performed by our team or by the UK Biobank team
 (for diffusion MRI). For each variant, we also performed secondary
 analyses in which head size or subject-average AFD across all fixels were
 additionally included as confound covariates, respectively in T1 and
 diffusion data modalities. <strong>Impact mode
 decomposition</strong><br>PGS approximates polygenic
 influences through a single scalar value. These models represent a
 weighted average of all disposing allele counts and are agnostic to
 variability in the brain-wide associations of genetic variants. We aimed
 to model the heterogeneity and the hidden covariance patterns in the
 brain-wide genomic associations. To achieve this, we initially created a
 brain-wide univariate association map (i.e. voxel-wise or fixel-wise
 t-value maps generated by a parametric regression) for each of the top
 independent 13,766 dyslexia GWAS loci, after clumping at a GWAS p-value
 threshold of less than 0.01, linkage disequilibrium r2 threshold of less
 than 0.1 and genomic window size of 500 kb (and using the same set of
 covariates as in all sections above). These voxel- or fixel-wise t-value
 maps were then concatenated across all 13,766 variants and decomposed by
 MELODIC into ten independent components, separately per each imaging
 modality. In order to enhance the sensitivity of ICA rotations to local
 effects rather than genetic associations with global measures, voxel-wise
 Jacobian determinant values were normalised to total brain volume before
 ICA. The default MELODIC ICA data transformations, including variance
 normalization and mean signal removal, were not applied as these momentums
 reflect meaningful signals in t-value maps (22). We refer to the extracted
 independent components as genomic impact modes, that reflect combinations
 of distinct genomic variants and spatial profiles through a limited number
 of features. <strong>Polygenic scores of
 additional traits related to dyslexia</strong><br>We first
 used LD score regression (23, 24) to confirm that we could detect
 previously reported genetic correlations between dyslexia and each of
 eight other behavioural, cognitive or education-related traits, based on
 summary statistics from the 23andMe dyslexia GWAS (15) and other
 large-scale GWAS studies: Attention-deficit/hyperactivity disorder (ADHD
 (25)), verbal numerical reasoning (a.k.a. fluid intelligence) (Pan-UKB
 team. https://pan.ukbb.broadinstitute.org. 2020.), the first principal
 component of school grades in mathematics and language (26), General
 Certificate of Secondary Education (GCSE) education (Pan-UKB team.
 https://pan.ukbb.broadinstitute.org. 2020.), word reading, non-word
 reading, spelling, and phonemic awareness (27). All of these traits showed
 significant genetic correlations |rg| &gt; 0.4 with dyslexia in our
 analysis (all P &lt; 10-23, Supplementary Fig. S4). In order to
 compare and contrast with dyslexia PGS, we then used Lassosum2 to generate
 PGS in the UK Biobank data for each of these eight additional traits, and
 mapped their brain-wide associations with the voxel-wise and fixel-wise
 data, using the same approach as for the dyslexia PGS.
 <strong>Further post hoc regression
 analyses</strong><br>We performed further regression analyses
 of the association between dyslexia PGS and voxel-wise volumes, this time
 using logarithm-transformed Jacobian determinant values to take allometry
 into account (28), rather than raw values, to assess whether this made a
 difference. In another post-hoc analysis, two extra covariates were added
 to assess voxel-wise and fixel-wise associations with dyslexia PGS
 independently of fluid intelligence and educational attainment: these
 covariates were ‘fluid intelligence’ (data-field #20016) and the number of
 years of education estimated from the data fields ‘qualifications’ (#6138)
 and ‘age completed full-time education’ (#845), following a previously
 published approach (29). Apart from the inclusion of these two covariates,
 the linear regression models were the same as the primary analyses
 described above in the Materials and Methods section ‘Voxel- and
 fixel-wise brain associations with dyslexia polygenic scores’.
description: # Data for: Distinct impact modes of polygenic disposition to dyslexia in
 the adult brain Access this dataset on Dryad:
 [https://doi.org/10.5061/dryad.80gb5mkz6](https://doi.org/10.5061/dryad.80gb5mkz6) ## **Descriptions** This dataset was generated as part of a research study and consists solely of group-level statistics. It does not include any individual-level information, such as individual brain scans or genetic data. The underlying UK Biobank data, which were used to generate these statistical maps, were collected with ethical approval from the National Research Ethics Service Committee North West-Haydock (reference: 11/NW/0382). All procedures adhered to the guidelines of the World Medical Association guidelines. Access to the UK Biobank data was granted following approval by the UK Biobank core (application number 16066, P.I. Clyde Francks). Written informed consent was obtained from all participants and their data have been accessed following de-identification. The dataset includes: -Data analysis scripts -Group-level IDPs: MRI phenotypes derived via ICA and PCA decompositions across various dimensions. -FBA: Fixel-Based Analysis \- ICA-X: \- melodic_IC.nii.gz: 4D z-transformed component volumes at dimension X. \- PCA-324: \- melodic_pca.nii.gz: First 324 PCA components ordered by increasing explained variance along the 4th dimension. -TBM: Tensor-Based Morphometry \- ICA-X: \- melodic_IC.nii.gz: 4D z-transformed component volumes at dimension X. \- PCA-324: \- melodic_pca.nii.gz: First 324 PCA components ordered by increasing explained variance along the 4th dimension. Impact Modes: Decomposed impact modes from variant-wise parametric t-maps. -FBA: Diffusion Impact Modes \- hist.svg: Histogram of variant-wise weight distribution for diffusion impact mode #10. \- melodic_mix: Variant-wise weights for diffusion impact mode #10. \- mix_plot.R: Heatmap plotting script for variant-wise impact mode weights. \- perm_rows.txt: Variant-row permutations (for visualization). \- weights.png: Visualization of variant-wise weights. -TBM: T1 Morphometry Impact Modes \- melodic_mix: Impact mode weights. \- variant-list.txt: rsIDs of all variants in impact mode decompositions. -PGS Maps: Brain-wide association maps of polygenic scores (PGS) for various traits. -FBA: Fixel-Based Analysis \- Trait X: \- parametric-tstat.nii.gz: GLM t-maps for trait X PGS. \- randomise-neg_vox_corrp_tstat1.nii.gz: Nonparametric GLM voxel-wise p-values (corrected with 5000 permutations). -TBM: T1 Tensor-Based Morphometry \- Trait X: \- parametric-tstat.nii.gz: GLM t-maps for trait X PGS. \- randomise-neg_vox_corrp_tstat1.nii.gz: Nonparametric GLM voxel-wise p-values (corrected with 5000 permutations). -Dyslexia: \- logJac: PGS GLM analyses for dyslexia, using log-transformed Jacobians as outcomes. \- parametric-tstat.nii.gz: Dyslexia PGS GLM. \- logJac-correctedFor-VNR-education: PGS GLM analyses for dyslexia, using log-transformed Jacobians as outcomes, adjusted for fluid intelligence and education \- Multiple outputs for pooled, sex-specific, and corrected maps. \- logJac-correctedFor-VNR-education-headSize: PGS GLM analyses for dyslexia, using log-transformed Jacobians as outcomes, adjusted for fluid intelligence, education, and head size. \- rawJac: Analyses using raw Jacobians. -Scripts \- edu_proc.sh: Educational attainment estimation. \- script-dMRI-fixel_based_analysis.sh: Diffusion MRI analysis script. \- script-pipeline.sh: Polygenic score analysis pipeline script. Templates: Templates and masks for visualization and transformation. \- analysis_voxel_mask.mif.gz: Fiber orientation density template mask. \- MNI-transform: Warps for transforming between study and MNI spaces. \- fixels: Template fixel directory for visualization in MRtrix. \- hybrid-T1-and-ADC-template-just-for-visualisation.nii.gz: T1 and diffusion ADC combined contrast. -Tractography Tractography streamlines initiated from dyslexia-associated clusters. \- dentate_seed.tck \- foceps_major_seed.tck \- internal_capsule_seed.tck \- SLF_seed.tck : superior longitudinal fasciculus -Variant Maps: association of allele dosage of genomic variants with MRI phenotypes. -FBA: Fixel-Wise Maps \- rs-IDs: \- parametric-t-stat.nii.gz: t-values for parametric GLM of allele dosage. \- randomise-pos_vox_corrp_tstat1.nii.gz: Nonparametric t-value maps of the positive contrast \- randomise-neg_vox_corrp_tstat1.nii.gz: Nonparametric t-value maps of the negative contrast -TBM: Voxel-Wise Maps \- rs-IDs: Similar outputs as FBA, specific to TBM. \- overlap: Spatial overlap among 35 genome-wide significant dyslexia variants. \- TBM-correctedFor-headSize: Adjusted maps for head size. ## **References** 1.      C. 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