Multi-ancestral GWAS with the VA Million Veteran Program enables functional interpretation of rheumatoid arthritis alleles
Sakaue, S.; Yang, D.; Zhang, H.; Posner, D.; Rodriguez, Z.; Love, Z.; Cui, J.; Budu-Aggrey, A.; Ho, Y.-L.; Costa, L.; Monach, P.; Huang, S.; Ishigaki, K.; Melley, C.; Tanukonda, V.; Sangar, R.; Maripuri, M.; Sweet, S. M.; Panickan, V.; McDermott, G.; Hanberg, J. S.; Riley, T.; Laufer, V.; Okada, Y.; Scott, I.; Bridges, S. L.; Baker, J.; VA Million Veteran Program, ; Wilson, P. W.; Gaziano, J. M.; Hong, C.; Verma, A.; Cho, K.; Huffman, J. E.; Cai, T.; Raychaudhuri, S.; Liao, K. P.
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Introductory ParagraphRheumatoid arthritis (RA) is a heritable and common autoimmune condition. To date, most genetic associations were derived from individuals with either European or East Asian ancestries. Here, we applied a multimodal automated phenotyping strategy to define RA and performed a genome-wide association study (GWAS) of RA in the Million Veteran Program (MVP), including underrepresented African American (AFR) and Admixed American (AMR) populations. Meta-analyses with previous RA cohorts identified 152 autosomal genome-wide significant loci, of which 31 were novel. Inclusion of multi-ancestry data dramatically improved fine-mapping resolution. Functional characterization of these loci using single-cell transcriptomic and chromatin data suggested new RA genes such as CHD7 and CD247. We identified underappreciated functional roles of fine-grained immune cell states other than T cells, such as B cell and myeloid cell states. We observed that multi-ancestry polygenic risk scores using our data demonstrated better predictive ability, especially for AFR and AMR populations.
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