Abstract
Brain morphology has been shown to be highly heritable, yet only a small portion of the heritability is explained by the genetic variants discovered so far. Here we extended the Multivariate Omnibus Statistical Test (MOSTest) and applied it to genome-wide association studies (GWAS) of vertex-wise structural magnetic resonance imaging (MRI) cortical measures from N=35,657 participants in the UK Biobank. We identified 695 loci for cortical surface area and 539 for cortical thickness, in total 780 unique genetic loci associated with cortical morphology robustly replicated in 8,060 children of mixed ethnicity from the Adolescent Brain Cognitive Development (ABCD) Study®. This reflects more than 8-fold increase in genetic discovery at no cost to generalizability compared to the commonly used univariate GWAS methods applied to region of interest (ROI) data. Functional follow up including gene-based analyses implicated 10% of all protein-coding genes and pointed towards pathways involved in neurogenesis and cell differentiation. Power analysis indicated that applying the MOSTest to vertex-wise structural MRI data triples the effective sample size compared to conventional univariate GWAS approaches. The large boost in power obtained with the vertex-wise MOSTest together with pronounced replication rates and highlighted biologically meaningful pathways underscores the advantage of multivariate approaches in the context of highly distributed polygenic architecture of the human brain.
Highlights
Variability in brain morphology is highly heritable, with twin studies estimating heritability for global measures at 89% for total surface area and 81% for mean cortical thickness (Panizzon et al, 2009) and regional measures at up to 46% for cortical area and 57% for thickness (Eyler et al, 2012)
We showed that applying Multivariate Omnibus Statistical Test (MOSTest) to cortical morphology region of interest (ROI) measures in the UK Biobank substantially increased loci discovery compared to the commonly applied mass univariate approach used by the ENIGMA consortium (Grasby et al, 2020), here referred to as the min-P approach
Using the vertex-wise MOSTest, we performed a multivariate genome-wide association studies (GWAS) of cortical morphology, such that the significance of each locus was estimated after aggregating its effects across all vertices (1284 data points each for thickness and area)
Summary
Variability in brain morphology is highly heritable, with twin studies estimating heritability for global measures at 89% for total surface area and 81% for mean cortical thickness (Panizzon et al, 2009) and regional measures (adjusting for whole brain measures) at up to 46% for cortical area and 57% for thickness (Eyler et al, 2012). The relatively low yield despite high heritabilities of brain morphology is likely due to high polygenicity and small effect size (discoverability) per locus (van der Meer et al, 2020). We showed that applying MOSTest to cortical morphology region of interest (ROI) measures in the UK Biobank substantially increased loci discovery (van der Meer et al, 2020) compared to the commonly applied mass univariate approach used by the ENIGMA consortium (Grasby et al, 2020), here referred to as the min-P approach. Uncovering the detailed genetic architecture of cortical area and thickness will provide insight into the underlying neurobiology of the human brain, and give a better understanding of brain-related human traits, such as cognition (Vuoksimaa et al, 2016), as well as neurological (Querbes et al, 2009) and psychiatric diseases (Rimol et al, 2010)
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