Abstract
Large-scale gene sequencing studies for complex traits have the potential to identify causal genes with therapeutic implications. We performed gene-based association testing of blood lipid levels with rare (minor allele frequency < 1%) predicted damaging coding variation by using sequence data from >170,000 individuals from multiple ancestries: 97,493 European, 30,025 South Asian, 16,507 African, 16,440 Hispanic/Latino, 10,420 East Asian, and 1,182 Samoan. We identified 35 genes associated with circulating lipid levels; some of these genes have not been previously associated with lipid levels when using rare coding variation from population-based samples. We prioritize 32 genes in array-based genome-wide association study (GWAS) loci based on aggregations of rare coding variants; three (EVI5, SH2B3, and PLIN1) had no prior association of rare coding variants with lipid levels. Most of our associated genes showed evidence of association among multiple ancestries. Finally, we observed an enrichment of gene-based associations for low-density lipoprotein cholesterol drug target genes and for genes closest to GWAS index single-nucleotide polymorphisms (SNPs). Our results demonstrate that gene-based associations can be beneficial for drug target development and provide evidence that the gene closest to the array-based GWAS index SNP is often the functional gene for blood lipid levels.
Original language | English |
---|---|
Pages (from-to) | 81-96 |
Number of pages | 16 |
Journal | American journal of human genetics |
Volume | 109 |
Issue number | 1 |
DOIs | |
State | Published - Jan 6 2022 |
Keywords
- association
- cholesterol
- exome sequencing
- gene-based association
- lipid
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In: American journal of human genetics, Vol. 109, No. 1, 06.01.2022, p. 81-96.
Research output: Contribution to journal › Article › peer-review
TY - JOUR
T1 - Rare coding variants in 35 genes associate with circulating lipid levels—A multi-ancestry analysis of 170,000 exomes
AU - AMP-T2D-GENES, Myocardial Infarction Genetics Consortium
AU - NHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium
AU - NHLBI TOPMed Lipids Working Group
AU - Hindy, George
AU - Dornbos, Peter
AU - Chaffin, Mark D.
AU - Liu, Dajiang J.
AU - Wang, Minxian
AU - Selvaraj, Margaret Sunitha
AU - Zhang, David
AU - Park, Joseph
AU - Aguilar-Salinas, Carlos A.
AU - Antonacci-Fulton, Lucinda
AU - Ardissino, Diego
AU - Arnett, Donna K.
AU - Aslibekyan, Stella
AU - Atzmon, Gil
AU - Ballantyne, Christie M.
AU - Barajas-Olmos, Francisco
AU - Barzilai, Nir
AU - Becker, Lewis C.
AU - Bielak, Lawrence F.
AU - Bis, Joshua C.
AU - Blangero, John
AU - Boerwinkle, Eric
AU - Bonnycastle, Lori L.
AU - Bottinger, Erwin
AU - Bowden, Donald W.
AU - Bown, Matthew J.
AU - Brody, Jennifer A.
AU - Broome, Jai G.
AU - Burtt, Noël P.
AU - Cade, Brian E.
AU - Centeno-Cruz, Federico
AU - Chan, Edmund
AU - Chang, Yi Cheng
AU - Chen, Yii Der I.
AU - Cheng, Ching Yu
AU - Choi, Won Jung
AU - Chowdhury, Rajiv
AU - Contreras-Cubas, Cecilia
AU - Córdova, Emilio J.
AU - Correa, Adolfo
AU - Cupples, L. Adrienne
AU - Curran, Joanne E.
AU - Danesh, John
AU - de Vries, Paul S.
AU - DeFronzo, Ralph A.
AU - Doddapaneni, Harsha
AU - Duggirala, Ravindranath
AU - Dutcher, Susan K.
AU - Ellinor, Patrick T.
AU - Emery, Leslie S.
AU - Florez, Jose C.
AU - Fornage, Myriam
AU - Freedman, Barry I.
AU - Fuster, Valentin
AU - Garay-Sevilla, Ma Eugenia
AU - García-Ortiz, Humberto
AU - Germer, Soren
AU - Gibbs, Richard A.
AU - Gieger, Christian
AU - Glaser, Benjamin
AU - Gonzalez, Clicerio
AU - Gonzalez-Villalpando, Maria Elena
AU - Graff, Mariaelisa
AU - Graham, Sarah E.
AU - Grarup, Niels
AU - Groop, Leif C.
AU - Guo, Xiuqing
AU - Gupta, Namrata
AU - Han, Sohee
AU - Hanis, Craig L.
AU - Hansen, Torben
AU - He, Jiang
AU - Heard-Costa, Nancy L.
AU - Hung, Yi Jen
AU - Hwang, Mi Yeong
AU - Irvin, Marguerite R.
AU - Islas-Andrade, Sergio
AU - Jarvik, Gail P.
AU - Kang, Hyun Min
AU - Kardia, Sharon L.R.
AU - Kelly, Tanika
AU - Kenny, Eimear E.
AU - Khan, Alyna T.
AU - Kim, Bong Jo
AU - Kim, Ryan W.
AU - Kim, Young Jin
AU - Koistinen, Heikki A.
AU - Kooperberg, Charles
AU - Kuusisto, Johanna
AU - Kwak, Soo Heon
AU - Laakso, Markku
AU - Lange, Leslie A.
AU - Lee, Jiwon
AU - Lee, Juyoung
AU - Lee, Seonwook
AU - Lehman, Donna M.
AU - Lemaitre, Rozenn N.
AU - Linneberg, Allan
AU - Liu, Jianjun
AU - Loos, Ruth J.F.
AU - Lubitz, Steven A.
AU - Lyssenko, Valeriya
AU - Ma, Ronald C.W.
AU - Martin, Lisa Warsinger
AU - Martínez-Hernández, Angélica
AU - Mathias, Rasika A.
AU - McGarvey, Stephen T.
AU - McPherson, Ruth
AU - Meigs, James B.
AU - Meitinger, Thomas
AU - Melander, Olle
AU - Mendoza-Caamal, Elvia
AU - Metcalf, Ginger A.
AU - Mi, Xuenan
AU - Mohlke, Karen L.
AU - Montasser, May E.
AU - Moon, Jee Young
AU - Moreno-Macías, Hortensia
AU - Morrison, Alanna C.
AU - Muzny, Donna M.
AU - Nelson, Sarah C.
AU - Nilsson, Peter M.
AU - O'Connell, Jeffrey R.
AU - Orho-Melander, Marju
AU - Orozco, Lorena
AU - Palmer, Colin N.A.
AU - Palmer, Nicholette D.
AU - Park, Cheol Joo
AU - Park, Kyong Soo
AU - Pedersen, Oluf
AU - Peralta, Juan M.
AU - Peyser, Patricia A.
AU - Post, Wendy S.
AU - Preuss, Michael
AU - Psaty, Bruce M.
AU - Qi, Qibin
AU - Rao, D. C.
AU - Redline, Susan
AU - Reiner, Alexander P.
AU - Revilla-Monsalve, Cristina
AU - Rich, Stephen S.
AU - Samani, Nilesh
AU - Schunkert, Heribert
AU - Schurmann, Claudia
AU - Seo, Daekwan
AU - Seo, Jeong Sun
AU - Sim, Xueling
AU - Sladek, Rob
AU - Small, Kerrin S.
AU - So, Wing Yee
AU - Stilp, Adrienne M.
AU - Tai, E. Shyong
AU - Tam, Claudia H.T.
AU - Taylor, Kent D.
AU - Teo, Yik Ying
AU - Thameem, Farook
AU - Tomlinson, Brian
AU - Tsai, Michael Y.
AU - Tuomi, Tiinamaija
AU - Tuomilehto, Jaakko
AU - Tusié-Luna, Teresa
AU - Udler, Miriam S.
AU - van Dam, Rob M.
AU - Vasan, Ramachandran S.
AU - Viaud Martinez, Karine A.
AU - Wang, Fei Fei
AU - Wang, Xuzhi
AU - Watkins, Hugh
AU - Weeks, Daniel E.
AU - Wilson, James G.
AU - Witte, Daniel R.
AU - Wong, Tien Yin
AU - Yanek, Lisa R.
AU - Kathiresan, Sekar
AU - Rader, Daniel J.
AU - Rotter, Jerome I.
AU - Boehnke, Michael
AU - McCarthy, Mark I.
AU - Willer, Cristen J.
AU - Natarajan, Pradeep
AU - Flannick, Jason A.
AU - Khera, Amit V.
AU - Peloso, Gina M.
N1 - Funding Information: This work was supported by a grant from the Swedish Research Council ( 2016-06830 ) and grants from the National Heart, Lung, and Blood Institute (NHLBI): R01HL142711 and R01HL127564 . Please refer to the supplemental information for the full acknowledgements. Funding Information: This work was supported by a grant from the Swedish Research Council (2016-06830) and grants from the National Heart, Lung, and Blood Institute (NHLBI): R01HL142711 and R01HL127564. Please refer to the supplemental information for the full acknowledgements. The authors declare no competing interests for the present work. P.N. reports investigator-initiated grants from Amgen, Apple, and Boston Scientific; is a scientific advisor to Apple, Blackstone Life Sciences, and Novartis; and has spousal employment at Vertex, all unrelated to the present work. A.V.K. has served as a scientific advisor to Sanofi, Medicines Company, Maze Pharmaceuticals, Navitor Pharmaceuticals, Verve Therapeutics, Amgen, and Color; received speaking fees from Illumina, MedGenome, Amgen, and the Novartis Institute for Biomedical Research; received sponsored research agreements from the Novartis Institute for Biomedical Research and IBM Research; and reports a patent related to a genetic risk predictor (20190017119). C.J.W.?s spouse is employed at Regeneron. L.E.S. is currently an employee of Celgene/Bristol Myers Squibb. Celgene/Bristol Myers Squibb had no role in the funding, design, conduct, and interpretation of this study. M.E.M. receives funding from Regeneron unrelated to this work. E.E.K. has received speaker honoraria from Illumina, Inc and Regeneron Pharmaceuticals. B.M.P. serves on the Steering Committee of the Yale Open Data Access Project funded by Johnson & Johnson. L.A.C. has consulted with the Dyslipidemia Foundation on lipid projects in the Framingham Heart Study. P.T.E. is supported by a grant from Bayer AG to the Broad Institute focused on the genetics and therapeutics of cardiovascular disease. P.T.E. has consulted for Bayer AG, Novartis, MyoKardia, and Quest Diagnostics. S.A.L. receives sponsored research support from Bristol Myers Squibb/Pfizer, Bayer AG, Boehringer Ingelheim, Fitbit, and IBM and has consulted for Bristol Myers Squibb/Pfizer, Bayer AG, and Blackstone Life Sciences. The views expressed in this article are those of the author(s) and not necessarily those of the NHS, the NIHR, or the Department of Health. M.I.M. has served on advisory panels for Pfizer, NovoNordisk, and Zoe Global and has received honoraria from Merck, Pfizer, Novo Nordisk, and Eli Lilly and research funding from Abbvie, Astra Zeneca, Boehringer Ingelheim, Eli Lilly, Janssen, Merck, NovoNordisk, Pfizer, Roche, Sanofi Aventis, Servier, and Takeda. As of June 2019, M.I.M. is an employee of Genentech and a holder of Roche stock. M.E.J. holds shares in Novo Nordisk A/S. H.M.K. is an employee of Regeneron Pharmaceuticals; he owns stock and stock options for Regeneron Pharmaceuticals. M.E.J. has received research grants form Astra Zeneca, Boehringer Ingelheim, Amgen, and Sanofi. S.K. is founder of Verve Therapeutics. Publisher Copyright: © 2021 American Society of Human Genetics
PY - 2022/1/6
Y1 - 2022/1/6
N2 - Large-scale gene sequencing studies for complex traits have the potential to identify causal genes with therapeutic implications. We performed gene-based association testing of blood lipid levels with rare (minor allele frequency < 1%) predicted damaging coding variation by using sequence data from >170,000 individuals from multiple ancestries: 97,493 European, 30,025 South Asian, 16,507 African, 16,440 Hispanic/Latino, 10,420 East Asian, and 1,182 Samoan. We identified 35 genes associated with circulating lipid levels; some of these genes have not been previously associated with lipid levels when using rare coding variation from population-based samples. We prioritize 32 genes in array-based genome-wide association study (GWAS) loci based on aggregations of rare coding variants; three (EVI5, SH2B3, and PLIN1) had no prior association of rare coding variants with lipid levels. Most of our associated genes showed evidence of association among multiple ancestries. Finally, we observed an enrichment of gene-based associations for low-density lipoprotein cholesterol drug target genes and for genes closest to GWAS index single-nucleotide polymorphisms (SNPs). Our results demonstrate that gene-based associations can be beneficial for drug target development and provide evidence that the gene closest to the array-based GWAS index SNP is often the functional gene for blood lipid levels.
AB - Large-scale gene sequencing studies for complex traits have the potential to identify causal genes with therapeutic implications. We performed gene-based association testing of blood lipid levels with rare (minor allele frequency < 1%) predicted damaging coding variation by using sequence data from >170,000 individuals from multiple ancestries: 97,493 European, 30,025 South Asian, 16,507 African, 16,440 Hispanic/Latino, 10,420 East Asian, and 1,182 Samoan. We identified 35 genes associated with circulating lipid levels; some of these genes have not been previously associated with lipid levels when using rare coding variation from population-based samples. We prioritize 32 genes in array-based genome-wide association study (GWAS) loci based on aggregations of rare coding variants; three (EVI5, SH2B3, and PLIN1) had no prior association of rare coding variants with lipid levels. Most of our associated genes showed evidence of association among multiple ancestries. Finally, we observed an enrichment of gene-based associations for low-density lipoprotein cholesterol drug target genes and for genes closest to GWAS index single-nucleotide polymorphisms (SNPs). Our results demonstrate that gene-based associations can be beneficial for drug target development and provide evidence that the gene closest to the array-based GWAS index SNP is often the functional gene for blood lipid levels.
KW - association
KW - cholesterol
KW - exome sequencing
KW - gene-based association
KW - lipid
UR - http://www.scopus.com/inward/record.url?scp=85122004213&partnerID=8YFLogxK
U2 - 10.1016/j.ajhg.2021.11.021
DO - 10.1016/j.ajhg.2021.11.021
M3 - Article
C2 - 34932938
AN - SCOPUS:85122004213
SN - 0002-9297
VL - 109
SP - 81
EP - 96
JO - American journal of human genetics
JF - American journal of human genetics
IS - 1
ER -