학술논문
Leveraging polygenic enrichments of gene features to predict genes underlying complex traits and diseases
Document Type
Article
Author
Weeks, Elle M.; Ulirsch, Jacob C.; Cheng, Nathan Y.; Trippe, Brian L.; Fine, Rebecca S.; Miao, Jenkai; Patwardhan, Tejal A.; Kanai, Masahiro; Nasser, Joseph; Fulco, Charles P.; Tashman, Katherine C.; Aguet, Francois; Li, Taibo; Ordovas-Montanes, Jose; Smillie, Christopher S.; Biton, Moshe; Shalek, Alex K.; Ananthakrishnan, Ashwin N.; Xavier, Ramnik J.; Regev, Aviv; Gupta, Rajat M.; Lage, Kasper; Ardlie, Kristin G.; Hirschhorn, Joel N.; Lander, Eric S.; Engreitz, Jesse M.; Finucane, Hilary K.
Source
Nature Genetics; August 2023, Vol. 55 Issue: 8 p1267-1276, 10p
Subject
Language
ISSN
10614036; 15461718
Abstract
Genome-wide association studies (GWASs) are a valuable tool for understanding the biology of complex human traits and diseases, but associated variants rarely point directly to causal genes. In the present study, we introduce a new method, polygenic priority score (PoPS), that learns trait-relevant gene features, such as cell-type-specific expression, to prioritize genes at GWAS loci. Using a large evaluation set of genes with fine-mapped coding variants, we show that PoPS and the closest gene individually outperform other gene prioritization methods, but observe the best overall performance by combining PoPS with orthogonal methods. Using this combined approach, we prioritize 10,642 unique gene–trait pairs across 113 complex traits and diseases with high precision, finding not only well-established gene–trait relationships but nominating new genes at unresolved loci, such as LGR4for estimated glomerular filtration rate and CCR7for deep vein thrombosis. Overall, we demonstrate that PoPS provides a powerful addition to the gene prioritization toolbox.