Publications

Abstract

Language is supported by a distributed network of brain regions with a particular contribution from the left hemisphere. A multi-level understanding of this network requires studying the genetic architecture of its functional connectivity and hemispheric asymmetry. We used resting state functional imaging data from 29,681 participants from the UK Biobank to measure functional connectivity between 18 left-hemisphere regions implicated in multimodal sentence-level processing, as well as their homotopic regions in the right-hemisphere, and interhemispheric connections. Multivariate genome-wide association analysis of this total network, based on common genetic variants (with population frequencies above 1%), identified 14 loci associated with network functional connectivity. Three of these loci were also associated with hemispheric differences of intrahemispheric connectivity. Polygenic dispositions to lower language-related abilities, dyslexia and left-handedness were associated with generally reduced leftward asymmetry of functional connectivity, but with some trait- and connection-specific exceptions. Exome-wide association analysis based on rare, protein-altering variants (frequencies < 1%) suggested 7 additional genes. These findings shed new light on the genetic contributions to language network connectivity and its asymmetry based on both common and rare genetic variants, and reveal genetic links to language-related traits and hemispheric dominance for hand preference.

Abstract

The highly polygenic and pleiotropic nature of behavioural traits, psychiatric disorders and structural and functional brain phenotypes complicate mechanistic interpretation of related genome-wide association study (GWAS) signals, thereby obscuring underlying causal biological processes. We propose genomic principal and independent component analysis (PCA, ICA) to decompose a large set of univariate GWAS statistics of multimodal brain traits into more interpretable latent genomic components. Here we introduce and evaluate this novel methods various analytic parameters and reproducibility across independent samples. Two UK Biobank GWAS summary statistic releases of 2240 imaging-derived phenotypes (IDPs) were retrieved. Genome-wide beta-values and their corresponding standard-error scaled z-values were decomposed using genomic PCA/ICA. We evaluated variance explained at multiple dimensions up to 200. We tested the inter-sample reproducibility of output of dimensions 5, 10, 25 and 50. Reproducibility statistics of the respective univariate GWAS served as benchmarks. Reproducibility of 10-dimensional PCs and ICs showed the best trade-off between model complexity and robustness and variance explained (PCs: |rz − max| = 0.33, |rraw − max| = 0.30; ICs: |rz − max| = 0.23, |rraw − max| = 0.19). Genomic PC and IC reproducibility improved substantially relative to mean univariate GWAS reproducibility up to dimension 10. Genomic components clustered along neuroimaging modalities. Our results indicate that genomic PCA and ICA decompose genetic effects on IDPs from GWAS statistics with high reproducibility by taking advantage of the inherent pleiotropic patterns. These findings encourage further applications of genomic PCA and ICA as fully data-driven methods to effectively reduce the dimensionality, enhance the signal to noise ratio and improve interpretability of high-dimensional multitrait genome-wide analyses.

Abstract

Handedness is a manifestation of brain hemispheric specialization. Left-handedness occurs at increased rates in neurodevelopmental disorders. Genome-wide association studies have identified common genetic effects on handedness or brain asymmetry, which mostly involve variants outside protein-coding regions and may affect gene expression. Implicated genes include several that encode tubulins (microtubule components) or microtubule-associated proteins. Here we examine whether left-handedness is also influenced by rare coding variants (frequencies ≤ 1%), using exome data from 38,043 left-handed and 313,271 right-handed individuals from the UK Biobank. The beta-tubulin gene TUBB4B shows exome-wide significant association, with a rate of rare coding variants 2.7 times higher in left-handers than right-handers. The TUBB4B variants are mostly heterozygous missense changes, but include two frameshifts found only in left-handers. Other TUBB4B variants have been linked to sensorineural and/or ciliopathic disorders, but not the variants found here. Among genes previously implicated in autism or schizophrenia by exome screening, DSCAM and FOXP1 show evidence for rare coding variant association with left-handedness. The exome-wide heritability of left-handedness due to rare coding variants was 0.91%. This study reveals a role for rare, protein-altering variants in left-handedness, providing further evidence for the involvement of microtubules and disorder-relevant genes.

Abstract

Recent studies of aging organisms have identified a systematic phenomenon, characterized by a negative correlation between gene length and their expression in various cell types, species, and diseases. We term this phenomenon gene-length-dependent transcription decline (GLTD) and suggest that it may represent a bottleneck in the transcription machinery and thereby significantly contribute to aging as an etiological factor. We review potential links between GLTD and key aging processes such as DNA damage and explore their potential in identifying disease modification targets. Notably, in Alzheimer’s disease, GLTD spotlights extremely long synaptic genes at chromosomal fragile sites (CFSs) and their vulnerability to postmitotic DNA damage. We suggest that GLTD is an integral element of biological aging.

Abstract

Dyslexia is a common condition that impacts reading ability. Identifying affected brain networks has been hampered by limited sample sizes of imaging case-control studies. We focused instead on brain structural correlates of genetic disposition to dyslexia in large-scale population data. In over 30,000 adults (UK Biobank), higher polygenic disposition to dyslexia was associated with lower head and brain size, and especially reduced volume and/or altered fiber density in networks involved in motor control, language and vision. However, individual genetic variants disposing to dyslexia often had quite distinct patterns of association with brain structural features. Independent component analysis applied to brain-wide association maps for thousands of dyslexia-disposing genetic variants revealed multiple impact modes on the brain, that corresponded to anatomically distinct areas with their own genomic profiles of association. Polygenic scores for dyslexia-related cognitive and educational measures, as well as attention-deficit/hyperactivity disorder, showed similarities to dyslexia polygenic disposition in terms of brain-wide associations, with microstructure of the internal capsule consistently implicated. In contrast, lower volume of the primary motor cortex was only associated with higher dyslexia polygenic disposition among all traits. These findings robustly reveal heterogeneous neurobiological aspects of dyslexia genetic disposition, and whether they are shared or unique with respect to other genetically correlated traits.