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. 2022 Jan 6;109(1):81-96.
doi: 10.1016/j.ajhg.2021.11.021. Epub 2021 Dec 20.

Rare coding variants in 35 genes associate with circulating lipid levels-A multi-ancestry analysis of 170,000 exomes

George Hindy  1 Peter Dornbos  2 Mark D Chaffin  3 Dajiang J Liu  4 Minxian Wang  5 Margaret Sunitha Selvaraj  6 David Zhang  7 Joseph Park  7 Carlos A Aguilar-Salinas  8 Lucinda Antonacci-Fulton  9 Diego Ardissino  10 Donna K Arnett  11 Stella Aslibekyan  12 Gil Atzmon  13 Christie M Ballantyne  14 Francisco Barajas-Olmos  15 Nir Barzilai  16 Lewis C Becker  17 Lawrence F Bielak  18 Joshua C Bis  19 John Blangero  20 Eric Boerwinkle  21 Lori L Bonnycastle  22 Erwin Bottinger  23 Donald W Bowden  24 Matthew J Bown  25 Jennifer A Brody  19 Jai G Broome  26 Noël P Burtt  27 Brian E Cade  28 Federico Centeno-Cruz  15 Edmund Chan  29 Yi-Cheng Chang  30 Yii-Der I Chen  31 Ching-Yu Cheng  32 Won Jung Choi  33 Rajiv Chowdhury  34 Cecilia Contreras-Cubas  15 Emilio J Córdova  15 Adolfo Correa  35 L Adrienne Cupples  36 Joanne E Curran  20 John Danesh  37 Paul S de Vries  38 Ralph A DeFronzo  39 Harsha Doddapaneni  40 Ravindranath Duggirala  20 Susan K Dutcher  9 Patrick T Ellinor  41 Leslie S Emery  26 Jose C Florez  42 Myriam Fornage  43 Barry I Freedman  44 Valentin Fuster  45 Ma Eugenia Garay-Sevilla  46 Humberto García-Ortiz  15 Soren Germer  47 Richard A Gibbs  48 Christian Gieger  49 Benjamin Glaser  50 Clicerio Gonzalez  51 Maria Elena Gonzalez-Villalpando  52 Mariaelisa Graff  53 Sarah E Graham  54 Niels Grarup  55 Leif C Groop  56 Xiuqing Guo  31 Namrata Gupta  57 Sohee Han  58 Craig L Hanis  59 Torben Hansen  60 Jiang He  61 Nancy L Heard-Costa  62 Yi-Jen Hung  63 Mi Yeong Hwang  58 Marguerite R Irvin  64 Sergio Islas-Andrade  65 Gail P Jarvik  66 Hyun Min Kang  67 Sharon L R Kardia  18 Tanika Kelly  68 Eimear E Kenny  69 Alyna T Khan  26 Bong-Jo Kim  58 Ryan W Kim  33 Young Jin Kim  58 Heikki A Koistinen  70 Charles Kooperberg  71 Johanna Kuusisto  72 Soo Heon Kwak  73 Markku Laakso  72 Leslie A Lange  74 Jiwon Lee  75 Juyoung Lee  58 Seonwook Lee  33 Donna M Lehman  39 Rozenn N Lemaitre  19 Allan Linneberg  76 Jianjun Liu  77 Ruth J F Loos  78 Steven A Lubitz  41 Valeriya Lyssenko  79 Ronald C W Ma  80 Lisa Warsinger Martin  81 Angélica Martínez-Hernández  15 Rasika A Mathias  17 Stephen T McGarvey  82 Ruth McPherson  83 James B Meigs  84 Thomas Meitinger  85 Olle Melander  86 Elvia Mendoza-Caamal  15 Ginger A Metcalf  40 Xuenan Mi  68 Karen L Mohlke  87 May E Montasser  88 Jee-Young Moon  89 Hortensia Moreno-Macías  90 Alanna C Morrison  38 Donna M Muzny  40 Sarah C Nelson  26 Peter M Nilsson  91 Jeffrey R O'Connell  88 Marju Orho-Melander  91 Lorena Orozco  15 Colin N A Palmer  92 Nicholette D Palmer  24 Cheol Joo Park  33 Kyong Soo Park  93 Oluf Pedersen  55 Juan M Peralta  20 Patricia A Peyser  18 Wendy S Post  94 Michael Preuss  95 Bruce M Psaty  96 Qibin Qi  89 D C Rao  97 Susan Redline  28 Alexander P Reiner  98 Cristina Revilla-Monsalve  99 Stephen S Rich  100 Nilesh Samani  25 Heribert Schunkert  101 Claudia Schurmann  102 Daekwan Seo  33 Jeong-Sun Seo  33 Xueling Sim  103 Rob Sladek  104 Kerrin S Small  105 Wing Yee So  80 Adrienne M Stilp  26 E Shyong Tai  106 Claudia H T Tam  80 Kent D Taylor  31 Yik Ying Teo  107 Farook Thameem  108 Brian Tomlinson  109 Michael Y Tsai  110 Tiinamaija Tuomi  111 Jaakko Tuomilehto  112 Teresa Tusié-Luna  113 Miriam S Udler  114 Rob M van Dam  115 Ramachandran S Vasan  116 Karine A Viaud Martinez  117 Fei Fei Wang  26 Xuzhi Wang  118 Hugh Watkins  119 Daniel E Weeks  120 James G Wilson  121 Daniel R Witte  122 Tien-Yin Wong  32 Lisa R Yanek  17 AMP-T2D-GENES, Myocardial Infarction Genetics ConsortiumNHLBI Trans-Omics for Precision Medicine (TOPMed) ConsortiumNHLBI TOPMed Lipids Working GroupSekar Kathiresan  123 Daniel J Rader  124 Jerome I Rotter  31 Michael Boehnke  67 Mark I McCarthy  125 Cristen J Willer  126 Pradeep Natarajan  127 Jason A Flannick  2 Amit V Khera  3 Gina M Peloso  128
Affiliations

Rare coding variants in 35 genes associate with circulating lipid levels-A multi-ancestry analysis of 170,000 exomes

George Hindy et al. Am J Hum Genet. .

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.

Keywords: association; cholesterol; exome sequencing; gene-based association; lipid.

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Conflict of interest statement

Declaration of interests 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.

Figures

Figure 1
Figure 1
Study samples and design Flow chart of the different stages of the study. Exome sequence genotypes were derived from four major data sources: the Myocardial Infarction Genetics consortium (MIGen), the Trans-Omics from Precision Medicine (TOPMed), the UK Biobank, and the Type 2 Diabetes Genetics (AMP-T2D-GENES) consortium. Single-variant association analyses were performed by ancestry and case status in case-control studies and meta-analyzed. Single-variant summary estimates and covariance matrices were used in gene-based analyses with six different variant groups and in multi-ancestry and each of the five main ancestries. AFR, African ancestry; EAS, East Asian ancestry; EUR, European ancestry; HIS, Hispanic ancestry; SAM, Samoan ancestry; SAS, South Asian ancestry.
Figure 2
Figure 2
Exome-wide significant associations with blood lipid phenotypes (A) Circular plot highlighting the evidence of association between the exome-wide significant 35 genes with any of the six different lipid traits (p < 4.3 × 10−7). The most significant associations from any of the six different variant groups are plotted. For almost all of the genes, the most significant associations were obtained from the multi-ancestry meta-analysis. (B) Strength of association of the 35 exome-wide significant genes based on the most significant variant grouping and ancestry across the six lipid phenotypes studied. Beta (effect size) is obtained from the corresponding burden test for SKAT results. Most of the genes indicated associations with more than one phenotype. Sign(beta)−log10(p value) displayed for associations that reached a p < 4.3 × 10−7. When the Sign(beta)−log10(p) > 50, they were trimmed to 50.
Figure 3
Figure 3
Enrichment of Mendelian, GWAS, and drug target genes in the gene-based lipid associations Enrichment of gene sets of Mendelian genes (n = 21), GWAS loci for LDL-C (n = 487), HDL-C (n = 531), and triglycerides (TG) (n = 471) genes, and drug target genes (n = 53). Error bars denote 95% confidence intervals.

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