| Application: | 27412, Boosting the power of GWAS using novel statistical tools |
| Title: | Vertex-wise multivariate genome-wide association study identifies 780 unique genetic loci associated with cortical morphology |
| Size: | 1.7 MB |
| Archived: | 2 Nov 2021 |
| Stability: | Complete |
| Personal: | No individual-level data |
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Notes
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.Application 27412
Boosting the power of GWAS using novel statistical tools
This proposal seeks to apply UK Biobank data to study the genetic architecture of human traits using novel statistical tools. We aim to investigate the relationship between mental disorders and co-morbid diseases such as cardiovascular disease, cancer and metabolic disease (as well as protective phenotypes). Genome-wide association studies (GWAS) have successfully identified many genetic variants influencing complex human traits. However, the identified genetic variants only explain a small portion of the heritability of these traits. To improve discovery of genetic variants in complex human traits, we have developed statistical tools building on a Bayesian statistical framework. This proposal seeks to increase discovery of genetic loci influencing a range of human traits and disorders. Identifying genetic factors that confer risk or protect against health-related traits is critical for understanding the causal mechanisms underlying disease, and the causal relationship shared between clinical conditions. Improved gene discovery might inform the development of genetic prediction tools and ultimately improve treatment strategies for large patient groups. Hence, the proposed research is entirely congruent with the stated aim of UK Biobank ?to improve the prevention, diagnosis and treatment of illness and the promotion of health throughout society?. We will analyze the GWAS data on complex traits in the UK Biobank cohort using novel statistical methodology. Using software and computational tools we are able to enhance gene discovery by integrating GWAS data with additional knowledge about genetic variants, including their association in related traits or their genomic position. To assess the replicability (i.e. the robustness of the results) of the identified variants, we will evaluate their association in independent GWAS cohorts. Finally, the results may inform the development of novel genetic prediction tools. We would wish to study the full UK Biobank cohort.
| Lead investigator: | Professor Ole Andreassen |
| Lead institution: | University of Oslo |
Enabling scientific discoveries that improve human health