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 KlarinAustin T Hilliard, Zeyuan Wang, Chao Xue, Gudmar Thorleifsson, Anna Helgadottir, Daniel F Gudbjartsson, Hilma Holm, Isleifur Olafsson, Mi Yeong Hwang, Sohee Han, Masato Akiyama, Saori Sakaue, Chikashi Terao, Masahiro Kanai, Wei Zhou, Ben M Brumpton, Humaira Rasheed, Aki S Havulinna, Yogasudha Veturi, Jennifer Allen Pacheco, Elisabeth A Rosenthal, Todd Lingren, QiPing Feng, Iftikhar J Kullo, Akira Narita, Jun Takayama, Hilary C Martin, Karen A Hunt, Bhavi Trivedi, Corri Black, Million Veterans Program, Global Lipids Genetics Consortium, Xiang Zhu* (Corresponding Author), Christopher D. Brown* (Corresponding Author)

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

14 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)1366-1387
Number of pages22
JournalAmerican Journal of Human Genetics
Volume109
Issue number8
Early online date5 Aug 2022
DOIs
Publication statusPublished - 5 Aug 2022

Bibliographical note

Xiang Zhu is supported by the Stein Fellowship from Stanford University and Institute for Computational and Data Sciences Seed Grant from The Pennsylvania State University. C.D.B. is supported by the NIH (R01-HL133218). Funding for the Global Lipids Genetics Consortium was provided by the NIH (R01-HL127564). This research was conducted using the UK Biobank Resource under application number 24460. This research is based on data from the Million Veteran Program, Office of Research and Development, Veterans Health Administration, and was supported by awards 2I01BX003362-03A1 and 1I01BX004821-01A1. This publication does not represent the views of the Department of Veteran Affairs or the United States Government. We thank Bethany Klunder for administrative support. Study-specific acknowledgments are provided in the supplemental information.

Data Availability Statement

The accession number for the HLC Capture-C data reported in this paper is https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE189026.

Keywords

  • Chromatin/genetics
  • Genome-Wide Association Study
  • Genomics
  • Humans
  • Lipids/genetics
  • Polymorphism, Single Nucleotide/genetics
  • lipid biology
  • fine-mapping
  • functional genomics
  • post-GWAS
  • regulatory mechanism
  • complex traits
  • variant prioritization

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