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Multi-ethnic Gene-trait Connection Landscape Using Electronic Health Record Linked Biobank Data

Authors: Binglan Li (1), Anastasia Lucas (2), Yuki Bradford(2), Shefali S. Verma(2), Anurag Verma(2), Hae Kyung Im (3), Marylyn D. Ritchie(1,2,4)    1.Genomics and Computational Biology Program, University of Pennsylvania   2.Department of Genetics, University of Pennsylvania 3. Department of Medicine, The University of Chicago 4. Institute for Biomedical Informatics, University of Pennsylvania


Abstract:Understanding the shared and ancestry-specific genetic factors of complex traits across ancestry groups holds a key to improving the overall health care quality for diverse populations in the US. In recent years, multiple electronic health record-linked (EHR-linked) biobanks have put in efforts to include participants of diverse ethnicity/ancestral backgrounds; make it possible to obtain phenome-wide association study (PheWAS) summary statistics in a genome-wide scale for different ancestry groups. Moreover, advancement in bioinformatics methods, such as transcriptome-wide association study (TWAS), can improve the translatability of basic discoveries by integrating GWAS summary statistics and quantitative trait locus (eQTL) data to identify complex trait-related genes. Here, we combined the advantages of multi-ethnic biobanks and TWAS approaches to investigate the multi-ethnic gene-trait connection landscapes. We designed and first applied a phenome-wide TWAS framework on eMERGE European American (EA, N = 68,813) and African American (AA, N = 12,763) populations, separately, across 516 disease phenotypes. Case/control disease phenotypes for each of the 516 diseases were derived using the rule of two based on phecodes to obtain clinical sensible disease status. Next, we meta-analyzed the EA and AA-specific TWAS results to identify gene-trait association shared across the two ancestry groups. FastENCLOC, an efficient colocalization algorithm, followed cross-ancestry TWAS to identify potential allelic heterogeneity underlying the shared gene-trait associations across ancestry groups. We replicated the phenome-wide TWAS analysis in PMBB. Phenome-wide TWAS identified many proof-of-concept gene-trait associations and numerous novel disease-associated genes. In short, the multi-ethnic gene-trait connection landscape provides rich resources for future multi-ethnic complex disease research and improve understanding of ancestry-specific genetic architecture of complex diseases.

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