Am J Hum Genet. 2022 Aug 4;109(8):1366-1387. doi: 10.1016/j.ajhg.2022.06.012.

A multi-layer functional genomic analysis to understand noncoding genetic variation in lipids 

Shweta Ramdas, Jonathan Judd, Sarah E Graham, Stavroula Kanoni , Yuxuan Wang , Ida Surakka, Brandon Wenz, Shoa L Clarke, Alessandra Chesi, Andrew Wells, Konain Fatima Bhatti, Sailaja Vedantam, Thomas W Winkler, Adam E Locke, Eirini Marouli, Greg J M Zajac, Kuan-Han H Wu, Ioanna Ntalla, Qin Hui, Derek Klarin... Christopher Oldmeadow, Han-Na Kim, Seungho Ryu, Paul R H J Timmers, Liubov Arbeeva, Rajkumar Dorajoo....

Abstract

A major challenge of genome-wide association studies (GWASs) is to translate phenotypic associations into biological insights. Here, we integrate a large GWAS on blood lipids involving 1.6 million individuals from five ancestries with a wide array of functional genomic datasets to discover regulatory mechanisms underlying lipid associations. We first prioritize lipid-associated genes with expression quantitative trait locus (eQTL) colocalizations and then add chromatin interaction data to narrow the search for functional genes. Polygenic enrichment analysis across 697 annotations from a host of tissues and cell types confirms the central role of the liver in lipid levels and highlights the selective enrichment of adipose-specific chromatin marks in high-density lipoprotein cholesterol and triglycerides. Overlapping transcription factor (TF) binding sites with lipid-associated loci identifies TFs relevant in lipid biology. In addition, we present an integrative framework to prioritize causal variants at GWAS loci, producing a comprehensive list of candidate causal genes and variants with multiple layers of functional evidence. We highlight two of the prioritized genes, CREBRF and RRBP1, which show convergent evidence across functional datasets supporting their roles in lipid biology.