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

TitleRare coding variants in 35 genes associate with circulating lipid levels-A multi-ancestry analysis of 170,000 exomes.
Publication TypeJournal Article
Year of Publication2022
AuthorsHindy, G, Dornbos, P, Chaffin, MD, Liu, DJ, Wang, M, Selvaraj, MSunitha, Zhang, D, Park, J, Aguilar-Salinas, CA, Antonacci-Fulton, L, Ardissino, D, Arnett, DK, Aslibekyan, S, Atzmon, G, Ballantyne, CM, Barajas-Olmos, F, Barzilai, N, Becker, LC, Bielak, LF, Bis, JC, Blangero, J, Boerwinkle, E, Bonnycastle, LL, Bottinger, E, Bowden, DW, Bown, MJ, Brody, JA, Broome, JG, Burtt, NP, Cade, BE, Centeno-Cruz, F, Chan, E, Chang, Y-C, Chen, Y-DI, Cheng, C-Y, Choi, WJung, Chowdhury, iv, R, Contreras-Cubas, C, Córdova, EJ, Correa, A, L Cupples, A, Curran, JE, Danesh, J, de Vries, PS, DeFronzo, RA, Doddapaneni, H, Duggirala, R, Dutcher, SK, Ellinor, PT, Emery, LS, Florez, JC, Fornage, M, Freedman, BI, Fuster, V, Garay-Sevilla, MEugenia, García-Ortiz, H, Germer, S, Gibbs, RA, Gieger, C, Glaser, B, Gonzalez, C, Gonzalez-Villalpando, MElena, Graff, M, Graham, SE, Grarup, N, Groop, LC, Guo, X, Gupta, N, Han, S, Hanis, CL, Hansen, T, He, J, Heard-Costa, NL, Hung, Y-J, Hwang, MYeong, Irvin, MR, Islas-Andrade, S, Jarvik, GP, Kang, HMin, Kardia, SLR, Kelly, T, Kenny, EE, Khan, AT, Kim, B-J, Kim, RW, Kim, YJin, Koistinen, HA, Kooperberg, C, Kuusisto, J, Kwak, SHeon, Laakso, M, Lange, LA, Lee, J, Lee, J, Lee, S, Lehman, DM, Lemaitre, RN, Linneberg, A, Liu, J, Loos, RJF, Lubitz, SA, Lyssenko, V, Ma, RCW, Martin, LWarsinger, Martínez-Hernández, A, Mathias, RA, McGarvey, ST, McPherson, R, Meigs, JB, Meitinger, T, Melander, O, Mendoza-Caamal, E, Metcalf, GA, Mi, X, Mohlke, KL, Montasser, ME, Moon, J-Y, Moreno-Macias, H, Morrison, AC, Muzny, DM, Nelson, SC, Nilsson, PM, O'Connell, JR, Orho-Melander, M, Orozco, L, Palmer, CNA, Palmer, ND, Park, CJoo, Park, KSoo, Pedersen, O, Peralta, JM, Peyser, PA, Post, WS, Preuss, M, Psaty, BM, Qi, Q, Rao, DC, Redline, S, Reiner, AP, Revilla-Monsalve, C, Rich, SS, Samani, N, Schunkert, H, Schurmann, C, Seo, D, Seo, J-S, Sim, X, Sladek, R, Small, KS, So, WYee, Stilp, AM, E Tai, S, Tam, CHT, Taylor, KD, Teo, YYing, Thameem, F, Tomlinson, B, Tsai, MY, Tuomi, T, Tuomilehto, J, Tusie-Luna, T, Udler, MS, van Dam, RM, Vasan, RS, Martinez, KAViaud, Wang, FFei, Wang, X, Watkins, H, Weeks, DE, Wilson, JG, Witte, DR, Wong, T-Y, Yanek, LR, Kathiresan, S, Rader, DJ, Rotter, JI, Boehnke, M, McCarthy, MI, Willer, CJ, Natarajan, P, Flannick, JA, Khera, AV, Peloso, GM
Corporate AuthorsAMP-T2D-GENES, Myocardial Infarction Genetics Consortium, NHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium, NHLBI TOPMed Lipids Working Group
JournalAm J Hum Genet
Volume109
Issue1
Pagination81-96
Date Published2022 01 06
ISSN1537-6605
KeywordsAlleles, Blood Glucose, Case-Control Studies, Computational Biology, Databases, Genetic, Diabetes Mellitus, Type 2, Exome, Genetic Predisposition to Disease, Genetic Variation, Genetics, Population, Genome-Wide Association Study, Humans, Lipid Metabolism, Lipids, Liver, Molecular Sequence Annotation, Multifactorial Inheritance, Open Reading Frames, Phenotype, Polymorphism, Single Nucleotide
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 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.

DOI10.1016/j.ajhg.2021.11.021
Alternate JournalAm J Hum Genet
PubMed ID34932938
PubMed Central IDPMC8764201
Grant ListR01 DK125490 / DK / NIDDK NIH HHS / United States
R01 HL105756 / HL / NHLBI NIH HHS / United States
R01 HL142711 / HL / NHLBI NIH HHS / United States
P30 DK079626 / DK / NIDDK NIH HHS / United States
R01 HL127564 / HL / NHLBI NIH HHS / United States