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Approved research

Dissect the genetic architecture of various sociological traits through integrative analysis of GWAS and functional annotations

Principal Investigator: Dr Qiongshi Lu
Approved Research ID: 42148
Approval date: August 30th 2019

Lay summary

Genome-wide association studies (GWAS) have identified tens of thousands of genetic components for numerous diseases and traits. However, interpretation of GWAS findings remains challenging. The complex correlation structure of genetic variants and their weak effects on disease risk encumber us to identify biologically functional genetic variants. Despite these issues are ubiquitous for a variety of diseases and traits, it is particularly challenging to dissect the genetic basis of sociological traits due to their complex genetic architecture and particularly weak genetic effects. Leveraging rich data in UK biobank and various types of biological annotations of the human genome, this project aims to use sophisticated data integration techniques to study various sociological traits. Specifically, we will integrate sociological GWAS data from external resources (e.g. Wisconsin Longitudinal Study), GWAS data for a variety of traits in UK biobank, and biological annotation data in public repositories, to provide mechanistic insights into the genetic basis of many sociological traits. We expect the initial stage for data management and genome-wide association analysis for various traits to take 6~12 months to finish. Integrative analysis of GWAS, functional annotations, dense phenotypic information will take 12~18 months. Our results will provide fundamentally new insights into the genetic basis of sociological traits. Some research outcomes such as fine-mapped genetic variants, improved effect size estimates, and more accurate polygenic risk scores, will greatly benefit other researchers who are interested in similar phenotypes. In addition, our proposed study will provide a principled framework to model the genetics of sociological traits, analyze social-genomic data, and interpret findings via integration of functional annotations and multi-trait modeling. Methodological advancements in this proposed project will benefit researchers of broad interest. Finally, our results for psychological traits may guide future development of effective intervention/treatment strategies to improve population mental health.