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; Austin 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; Christopher D. Brown

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 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 journal › Article › peer-review

1 Citation (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 language	English
Pages (from-to)	1366-1387
Number of pages	22
Journal	American Journal of Human Genetics
Volume	109
Issue number	8
Early online date	5 Aug 2022
DOIs	https://doi.org/10.1016/j.ajhg.2022.06.012
Publication status	Published - 5 Aug 2022

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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10.1016/j.ajhg.2022.06.012

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Ramdas, S., Judd, J., Graham, S. E., Kanoni, S., Wang, Y., Surakka, I., Wenz, B., Clarke, S. L., Chesi, A., Wells, A., Bhatti, K. F., Vedantam, S., Winkler, T. W., Locke, A. E., Marouli, E., Zajac, G. J. M., Wu, K-H. H., Ntalla, I., Hui, Q., ... Brown, C. D. (2022). A multi-layer functional genomic analysis to understand noncoding genetic variation in lipids. American Journal of Human Genetics, 109(8), 1366-1387. https://doi.org/10.1016/j.ajhg.2022.06.012

Ramdas, S, Judd, J, Graham, SE, Kanoni, S, Wang, Y, Surakka, I, Wenz, B, Clarke, SL, Chesi, A, Wells, A, Bhatti, KF, Vedantam, S, Winkler, TW, Locke, AE, Marouli, E, Zajac, GJM, Wu, K-HH, Ntalla, I, Hui, Q, Klarin, D, Hilliard, AT, Wang, Z, Xue, C, Thorleifsson, G, Helgadottir, A, Gudbjartsson, DF, Holm, H, Olafsson, I, Hwang, MY, Han, S, Akiyama, M, Sakaue, S, Terao, C, Kanai, M, Zhou, W, Brumpton, BM, Rasheed, H, Havulinna, AS, Veturi, Y, Pacheco, JA, Rosenthal, EA, Lingren, T, Feng, Q, Kullo, IJ, Narita, A, Takayama, J, Martin, HC, Hunt, KA, Trivedi, B, Black, C, Million Veterans Program, Global Lipids Genetics Consortium, Zhu, X & Brown, CD 2022, 'A multi-layer functional genomic analysis to understand noncoding genetic variation in lipids', American Journal of Human Genetics, vol. 109, no. 8, pp. 1366-1387. https://doi.org/10.1016/j.ajhg.2022.06.012

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title = "A multi-layer functional genomic analysis to understand noncoding genetic variation in lipids",

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.",

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",

author = "Shweta Ramdas and Jonathan Judd and Graham, {Sarah E} and Stavroula Kanoni and Yuxuan Wang and Ida Surakka and Brandon Wenz and Clarke, {Shoa L} and Alessandra Chesi and Andrew Wells and Bhatti, {Konain Fatima} and Sailaja Vedantam and Winkler, {Thomas W} and Locke, {Adam E} and Eirini Marouli and Zajac, {Greg J M} and Wu, {Kuan-Han H} and Ioanna Ntalla and Qin Hui and Derek Klarin and Hilliard, {Austin T} and Zeyuan Wang and Chao Xue and Gudmar Thorleifsson and Anna Helgadottir and Gudbjartsson, {Daniel F} and Hilma Holm and Isleifur Olafsson and Hwang, {Mi Yeong} and Sohee Han and Masato Akiyama and Saori Sakaue and Chikashi Terao and Masahiro Kanai and Wei Zhou and Brumpton, {Ben M} and Humaira Rasheed and Havulinna, {Aki S} and Yogasudha Veturi and Pacheco, {Jennifer Allen} and Rosenthal, {Elisabeth A} and Todd Lingren and QiPing Feng and Kullo, {Iftikhar J} and Akira Narita and Jun Takayama and Martin, {Hilary C} and Hunt, {Karen A} and Bhavi Trivedi and Corri Black and {Million Veterans Program} and {Global Lipids Genetics Consortium} and Xiang Zhu and Brown, {Christopher D.}",

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.",

year = "2022",

month = aug,

day = "5",

doi = "10.1016/j.ajhg.2022.06.012",

language = "English",

volume = "109",

pages = "1366--1387",

journal = "American Journal of Human Genetics",

issn = "0002-9297",

publisher = "Cell Press",

number = "8",

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TY - JOUR

T1 - A multi-layer functional genomic analysis to understand noncoding genetic variation in lipids

AU - Ramdas, Shweta

AU - Judd, Jonathan

AU - Graham, Sarah E

AU - Kanoni, Stavroula

AU - Wang, Yuxuan

AU - Surakka, Ida

AU - Wenz, Brandon

AU - Clarke, Shoa L

AU - Chesi, Alessandra

AU - Wells, Andrew

AU - Bhatti, Konain Fatima

AU - Vedantam, Sailaja

AU - Winkler, Thomas W

AU - Locke, Adam E

AU - Marouli, Eirini

AU - Zajac, Greg J M

AU - Wu, Kuan-Han H

AU - Ntalla, Ioanna

AU - Hui, Qin

AU - Klarin, Derek

AU - Hilliard, Austin T

AU - Wang, Zeyuan

AU - Xue, Chao

AU - Thorleifsson, Gudmar

AU - Helgadottir, Anna

AU - Gudbjartsson, Daniel F

AU - Holm, Hilma

AU - Olafsson, Isleifur

AU - Hwang, Mi Yeong

AU - Han, Sohee

AU - Akiyama, Masato

AU - Sakaue, Saori

AU - Terao, Chikashi

AU - Kanai, Masahiro

AU - Zhou, Wei

AU - Brumpton, Ben M

AU - Rasheed, Humaira

AU - Havulinna, Aki S

AU - Veturi, Yogasudha

AU - Pacheco, Jennifer Allen

AU - Rosenthal, Elisabeth A

AU - Lingren, Todd

AU - Feng, QiPing

AU - Kullo, Iftikhar J

AU - Narita, Akira

AU - Takayama, Jun

AU - Martin, Hilary C

AU - Hunt, Karen A

AU - Trivedi, Bhavi

AU - Black, Corri

AU - Million Veterans Program

AU - Global Lipids Genetics Consortium

AU - Zhu, Xiang

AU - Brown, Christopher D.

N1 - 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.

PY - 2022/8/5

Y1 - 2022/8/5

N2 - 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.

AB - 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.

KW - Chromatin/genetics

KW - Genome-Wide Association Study

KW - Genomics

KW - Humans

KW - Lipids/genetics

KW - Polymorphism, Single Nucleotide/genetics

KW - lipid biology

KW - fine-mapping

KW - functional genomics

KW - post-GWAS

KW - regulatory mechanism

KW - complex traits

KW - variant prioritization

UR - https://www.biorxiv.org/content/10.1101/2021.12.07.470215v1

U2 - 10.1016/j.ajhg.2022.06.012

DO - 10.1016/j.ajhg.2022.06.012

M3 - Article

C2 - 35931049

VL - 109

SP - 1366

EP - 1387

JO - American Journal of Human Genetics

JF - American Journal of Human Genetics

SN - 0002-9297

IS - 8

ER -

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

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Keywords

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