Multi-ancestry and multi-trait genome-wide association meta-analyses inform clinical risk prediction for systemic lupus erythematosus

Chachrit Khunsriraksakul, Qinmengge Li, Havell Markus, Matthew T. Patrick, Renan Sauteraud, Daniel McGuire, Xingyan Wang, Chen Wang, Lida Wang, Siyuan Chen, Ganesh Shenoy, Bingshan Li, Xue Zhong, Nancy J. Olsen, Laura Carrel, Lam C. Tsoi, Bibo Jiang, Dajiang J. Liu

Research output: Contribution to journalArticlepeer-review

10 Scopus citations

Abstract

Systemic lupus erythematosus is a heritable autoimmune disease that predominantly affects young women. To improve our understanding of genetic etiology, we conduct multi-ancestry and multi-trait meta-analysis of genome-wide association studies, encompassing 12 systemic lupus erythematosus cohorts from 3 different ancestries and 10 genetically correlated autoimmune diseases, and identify 16 novel loci. We also perform transcriptome-wide association studies, computational drug repurposing analysis, and cell type enrichment analysis. We discover putative drug classes, including a histone deacetylase inhibitor that could be repurposed to treat lupus. We also identify multiple cell types enriched with putative target genes, such as non-classical monocytes and B cells, which may be targeted for future therapeutics. Using this newly assembled result, we further construct polygenic risk score models and demonstrate that integrating polygenic risk score with clinical lab biomarkers improves the diagnostic accuracy of systemic lupus erythematosus using the Vanderbilt BioVU and Michigan Genomics Initiative biobanks.

Original languageEnglish (US)
Article number668
JournalNature communications
Volume14
Issue number1
DOIs
StatePublished - Dec 2023

All Science Journal Classification (ASJC) codes

  • General Chemistry
  • General Biochemistry, Genetics and Molecular Biology
  • General Physics and Astronomy

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