Genome-Wide Associations with Cortical Thickness and Gray-White Matter Contrast
Poster No:
926
Submission Type:
Abstract Submission
Authors:
Nicholas Kim1, Nahian Chowdhury1, Kenneth Buetow2, Samuel Anderson1, Paul Thompson1, Andrei Irimia1
Institutions:
1University of Southern California, Los Angeles, CA, 2Arizona State University, Tempe, AZ
First Author:
Co-Author(s):
Introduction:
The increasing availability of genotyped samples paired with magnetic resonance imaging (MRI), such as in UK Biobank, enables genome-wide association studies (GWASs) to uncover genetic variants linked to structural brain traits. While many genetic determinants of cortical thickness (CT) have been identified, the genetic basis of gray-white matter contrast (GWC) remains less explored (Panizzon et al., 2012). CT, the distance between pial and white matter surfaces, has been associated with age-related atrophy and neurodegeneration, while GWC has similarly been linked to aging and neurodegeneration. Despite high heritability, CT and GWC are governed by distinct genetic mechanisms. In this study, we conducted a GWAS of CT and GWC across 43,002 UK Biobank participants. We identified 251 genetic variants linked to at least 1% of cortical locations: 42 shared between CT and GWC, 127 specific to CT, and 82 specific to GWC.
Methods:
Participants were derived from the UK Biobank with ethical approval and informed consent. The study included 43,030 cognitively normal individuals, aged 45–83 (mean 64.4). T1-weighted MRIs were processed using FreeSurfer to compute CT and GWC at 5,124 locations before being normalized and projected onto an atlas. 662,971 single nucleotide polymorphisms (SNPs) were analyzed. Genotypes were coded by reference alleles and mapped to genes using NHGRI-EBI GWAS catalog and GRCh37. Linear regression assessed associations between SNPs and local CT/GWC, controlling for age, sex, and population structure. Two tests were conducted: one for association strength and another using standardized z-scores to evaluate deviations. SNPs were ranked according to their most significant β coefficient. All rankings used Benjamini-Hochberg corrected p-values. For clarity, highest-ranked SNPs refer to the 10 SNPs featuring the most significant β coefficient, while top SNPs refer to SNPs ranked in the top 1%. Linkage disequilibrium was also performed to help identify spatial localization across those SNPs most strongly associated with CT and GWC respectively.
Results:
Our analyses identified 169 SNPs featuring significant (p < 0.05) genome-wide associations with CT, and 124 SNPs with significant associations with GWC. Manhattan plots specify the statistical significance of each SNP's most significant β coefficient across all regions for (A) CT and (B) GWC (Fig. 1).
Cortical maps display spatial patterns of SNP associations with CT and GWC for selected broadest-reaching SNPs (Fig. 2). Of the 10 broadest-reaching SNPs for CT, the rs13107325 SNP features the highest number of cortical locations significantly associated with CT. This SNP is located in the SLC39A8 gene (Sollis et al., 2022) and has previously been identified as a genetic risk locus for Parkinson's disease (Smeland et al., 2021). Similarly, the rs864736 SNP features the highest number of cortical locations significantly associated with GWC, being significantly associated with GWC for over 14% of cortical locations. This SNP has previously been associated with brain sulcal widening (Le Guen et al., 2019). Linkage disequilibrium across all SNPs identified the 10 most strongly CT-associated SNPs as being spatially localized within chromosomes 15 and 17, with chromosome 19 being the most common locality across the top 10 most strongly GWC-associated SNPs.
Cortical maps display spatial patterns of SNP associations with CT and GWC for selected broadest-reaching SNPs (Fig. 2). Of the 10 broadest-reaching SNPs for CT, the rs13107325 SNP features the highest number of cortical locations significantly associated with CT. This SNP is located in the SLC39A8 gene (Sollis et al., 2022) and has previously been identified as a genetic risk locus for Parkinson's disease (Smeland et al., 2021). Similarly, the rs864736 SNP features the highest number of cortical locations significantly associated with GWC, being significantly associated with GWC for over 14% of cortical locations. This SNP has previously been associated with brain sulcal widening (Le Guen et al., 2019). Linkage disequilibrium across all SNPs identified the 10 most strongly CT-associated SNPs as being spatially localized within chromosomes 15 and 17, with chromosome 19 being the most common locality across the top 10 most strongly GWC-associated SNPs.
Conclusions:
We identified 251 SNPs associated with MRI-derived cortical traits in at least 1% of cortical locations examined. Our findings expand the understanding of the genetic basis of CT and GWC. By uncovering patterns of association for variants in genes implicated in neurodegenerative disorders, this study provides new insights into the genetic factors shaping brain structures relevant to these conditions.
Genetics:
Genetic Association Studies 2
Lifespan Development:
Aging 1
Keywords:
Aging
Computational Neuroscience
MRI
STRUCTURAL MRI
White Matter
1|2Indicates the priority used for review
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2. Panizzon, M. S., et al. (2012). Genetic and Environmental Influences of White and Gray Matter Signal Contrast: A New Phenotype for Imaging Genetics? Neuroimage, 60(3), 1686–1695. https://doi.org/10.1016/j.neuroimage.2012.01.122
3. Smeland, O. B., et al. (2021). Genome-wide association analysis of Parkinson’s disease and schizophrenia reveals shared genetic architecture and identifies novel risk loci. Biological Psychiatry, 89(3), 227–235. https://doi.org/10.1016/j.biopsych.2020.01.026
4. Sollis, E., et al. (2022). The NHGRI-EBI GWAS Catalog: Knowledgebase and deposition resource. Nucleic Acids Research, 51(D1), D977–D985. https://doi.org/10.1093/nar/gkac1010