Principal Investigator: Professor Hua Tang
Department: Stanford UniversityTags: 51628, eqtl, GWAS, polygenic risk score, trans-ethnic analysis
Genome-wide association studies (GWAS) have identified a myriad of genetic loci associated with complex traits and diseases. However, GWAS inform which loci influence phenotype, but do not tell us how these loci work. A majority of these loci lie in non-coding regions and therefore their functional impact is difficult to decipher. The research proposed in this application aims to fill this gap by bringing in additional relevant biological data, such as gene expression information from human tissues (Aim 1). Furthermore, an essential component of precision medicine is to integrate genomic information into individualized disease risk assessment. Yet to date, individuals of non-European descent are under-represented in most studies; it has been shown that genetic risk scores trained in one ethnicity often perform worse when applied to individuals in other ethnicities. Therefore, we propose to apply and extend a trans-ethnic algorithm that is designed to improve the accuracy of genetic risk prediction for minority individuals (Aim 2).
The UK Biobank has been designed to “improve the prevention, diagnosis, and treatment of a wide range of serious and life-threatening illnesses.” Our scientific rationale is that the UK Biobank offers a rich resource for identifying and characterizing molecular consequences of disease-associating variants in large, diverse and well-phenotyped human cohorts. The biological insights gained through Aim 1 will be essential for the design of individualized disease prevention and intervention strategies, while the research in Aim 2 ensures that individuals of all ethnicities benefit from the research in genomic medicine.