The power of genetic diversity in genome-wide association studies of lipids.
Journal
Nature
ISSN: 1476-4687
Titre abrégé: Nature
Pays: England
ID NLM: 0410462
Informations de publication
Date de publication:
12 2021
12 2021
Historique:
received:
15
09
2020
accepted:
27
09
2021
pubmed:
11
12
2021
medline:
16
4
2022
entrez:
10
12
2021
Statut:
ppublish
Résumé
Increased blood lipid levels are heritable risk factors of cardiovascular disease with varied prevalence worldwide owing to different dietary patterns and medication use
Identifiants
pubmed: 34887591
doi: 10.1038/s41586-021-04064-3
pii: 10.1038/s41586-021-04064-3
pmc: PMC8730582
mid: NIHMS1758809
doi:
Types de publication
Journal Article
Meta-Analysis
Langues
eng
Sous-ensembles de citation
IM
Pagination
675-679Subventions
Organisme : NIDDK NIH HHS
ID : R01 DK093757
Pays : United States
Organisme : Medical Research Council
ID : MC_UU_00006/1
Pays : United Kingdom
Organisme : BLRD VA
ID : I01 BX004821
Pays : United States
Organisme : NHLBI NIH HHS
ID : R01 HL109946
Pays : United States
Organisme : NICHD NIH HHS
ID : R01 HD030880
Pays : United States
Organisme : Medical Research Council
ID : MC_UU_12026/2
Pays : United Kingdom
Organisme : NHLBI NIH HHS
ID : R35 HL135824
Pays : United States
Organisme : Medical Research Council
ID : MR/R024227/1
Pays : United Kingdom
Organisme : NHLBI NIH HHS
ID : R35 HL135818
Pays : United States
Organisme : BLRD VA
ID : I01 BX003362
Pays : United States
Organisme : Wellcome Trust
ID : 202802/Z/16/Z
Pays : United Kingdom
Organisme : NHGRI NIH HHS
ID : R01 HG010297
Pays : United States
Organisme : NCCDPHP CDC HHS
ID : U01 DP006266
Pays : United States
Organisme : Wellcome Trust
ID : 212946/Z/18/Z
Pays : United Kingdom
Organisme : Medical Research Council
ID : MC_PC_19009
Pays : United Kingdom
Organisme : NHLBI NIH HHS
ID : R01 HL153805
Pays : United States
Organisme : NIDDK NIH HHS
ID : R01 DK072193
Pays : United States
Organisme : Medical Research Council
ID : MC_PC_20026
Pays : United Kingdom
Organisme : NIA NIH HHS
ID : R01 AG017917
Pays : United States
Organisme : NHGRI NIH HHS
ID : T32 HG000040
Pays : United States
Organisme : NHLBI NIH HHS
ID : R01 HL127564
Pays : United States
Organisme : Medical Research Council
ID : MC_PC_15018
Pays : United Kingdom
Organisme : NIDDK NIH HHS
ID : R01 DK062370
Pays : United States
Organisme : NHLBI NIH HHS
ID : R01 HL105756
Pays : United States
Organisme : NIA NIH HHS
ID : P30 AG010161
Pays : United States
Organisme : Medical Research Council
ID : MC_UU_00017/1
Pays : United Kingdom
Organisme : CSRD VA
ID : IK2 CX001780
Pays : United States
Organisme : Medical Research Council
ID : MC_U137686851
Pays : United Kingdom
Organisme : British Heart Foundation
ID : RG/14/5/30893
Pays : United Kingdom
Organisme : NHGRI NIH HHS
ID : U01 HG011723
Pays : United States
Organisme : NIDDK NIH HHS
ID : P30 DK020541
Pays : United States
Organisme : Medical Research Council
ID : G9521010
Pays : United Kingdom
Organisme : Medical Research Council
ID : MR/S011676/1
Pays : United Kingdom
Organisme : NIA NIH HHS
ID : U19 AG063893
Pays : United States
Organisme : Medical Research Council
ID : MC_UU_00007/10
Pays : United Kingdom
Organisme : NIDDK NIH HHS
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Pays : United States
Organisme : NHLBI NIH HHS
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Pays : United States
Organisme : Medical Research Council
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Pays : United Kingdom
Organisme : Medical Research Council
ID : MC_PC_13049
Pays : United Kingdom
Organisme : NIDDK NIH HHS
ID : P30 DK020572
Pays : United States
Organisme : NHLBI NIH HHS
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Pays : United States
Organisme : Medical Research Council
ID : MR/S019669/1
Pays : United Kingdom
Organisme : Medical Research Council
ID : MC_PC_14135
Pays : United Kingdom
Organisme : NHLBI NIH HHS
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Pays : United States
Organisme : NIA NIH HHS
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Pays : United States
Organisme : NHGRI NIH HHS
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Pays : United States
Organisme : NHLBI NIH HHS
ID : K01 HL135405
Pays : United States
Commentaires et corrections
Type : CommentIn
Type : ErratumIn
Informations de copyright
© 2021. The Author(s), under exclusive licence to Springer Nature Limited.
Références
Taddei, C. et al. Repositioning of the global epicentre of non-optimal cholesterol. Nature 582, 73–77 (2020).
doi: 10.1038/s41586-020-2338-1
Ference, B. A. et al. Low-density lipoproteins cause atherosclerotic cardiovascular disease. 1. Evidence from genetic, epidemiologic, and clinical studies. A consensus statement from the European Atherosclerosis Society Consensus Panel. Eur. Heart J. 38, 2459–2472 (2017).
pubmed: 28444290
pmcid: 5837225
doi: 10.1093/eurheartj/ehx144
Roth, G. A. et al. Global, regional, and national age-sex-specific mortality for 282 causes of death in 195 countries and territories, 1980–2017: a systematic analysis for the Global Burden of Disease Study 2017. Lancet 392, 1736–1788 (2018).
doi: 10.1016/S0140-6736(18)32203-7
Teslovich, T. M. et al. Biological, clinical and population relevance of 95 loci for blood lipids. Nature 466, 707–713 (2010).
pubmed: 20686565
pmcid: 3039276
doi: 10.1038/nature09270
Willer, C. J. et al. Discovery and refinement of loci associated with lipid levels. Nat. Genet. 45, 1274–1283 (2013).
pubmed: 24097068
pmcid: 3838666
doi: 10.1038/ng.2797
Liu, D. J. et al. Exome-wide association study of plasma lipids in >300,000 individuals. Nat. Genet. 49, 1758–1766 (2017).
pubmed: 29083408
pmcid: 5709146
doi: 10.1038/ng.3977
Lu, X. et al. Exome chip meta-analysis identifies novel loci and East Asian-specific coding variants that contribute to lipid levels and coronary artery disease. Nat. Genet. 49, 1722–1730 (2017).
pubmed: 29083407
pmcid: 5899829
doi: 10.1038/ng.3978
Kathiresan, S. et al. A genome-wide association study for blood lipid phenotypes in the Framingham Heart Study. BMC Med. Genet. 8, S17 (2007).
pubmed: 17903299
pmcid: 1995614
doi: 10.1186/1471-2350-8-S1-S17
Kathiresan, S. et al. Polymorphisms associated with cholesterol and risk of cardiovascular events. N. Engl. J. Med. 358, 1240–1249 (2008).
pubmed: 18354102
doi: 10.1056/NEJMoa0706728
Peloso, G. M. et al. Association of low-frequency and rare coding-sequence variants with blood lipids and coronary heart disease in 56,000 whites and blacks. Am. J. Hum. Genet. 94, 223–232 (2014).
pubmed: 24507774
pmcid: 3928662
doi: 10.1016/j.ajhg.2014.01.009
Hoffmann, T. J. et al. A large electronic-health-record-based genome-wide study of serum lipids. Nat. Genet. 50, 401–413 (2018).
pubmed: 29507422
pmcid: 5942247
doi: 10.1038/s41588-018-0064-5
Surakka, I. et al. The impact of low-frequency and rare variants on lipid levels. Nat. Genet. 47, 589–597 (2015).
pubmed: 25961943
pmcid: 4757735
doi: 10.1038/ng.3300
Klarin, D. et al. Genetics of blood lipids among ~300,000 multi-ethnic participants of the Million Veteran Program. Nat. Genet. 50, 1514–1523 (2018).
pubmed: 30275531
pmcid: 6521726
doi: 10.1038/s41588-018-0222-9
Holmen, O. L. et al. Systematic evaluation of coding variation identifies a candidate causal variant in TM6SF2 influencing total cholesterol and myocardial infarction risk. Nat. Genet. 46, 345–351 (2014).
pubmed: 24633158
pmcid: 4169222
doi: 10.1038/ng.2926
Asselbergs, F. W. et al. Large-scale gene-centric meta-analysis across 32 studies identifies multiple lipid loci. Am. J. Hum. Genet. 91, 823–838 (2012).
pubmed: 23063622
pmcid: 3487124
doi: 10.1016/j.ajhg.2012.08.032
Albrechtsen, A. et al. Exome sequencing-driven discovery of coding polymorphisms associated with common metabolic phenotypes. Diabetologia 56, 298–310 (2013).
pubmed: 23160641
doi: 10.1007/s00125-012-2756-1
Saxena, R. et al. Genome-wide association analysis identifies loci for type 2 diabetes and triglyceride levels. Science 316, 1331–1336 (2007).
pubmed: 17463246
doi: 10.1126/science.1142358
Iotchkova, V. et al. Discovery and refinement of genetic loci associated with cardiometabolic risk using dense imputation maps. Nat. Genet. 48, 1303–1312 (2016).
pubmed: 27668658
pmcid: 5279872
doi: 10.1038/ng.3668
Tachmazidou, I. et al. A rare functional cardioprotective APOC3 variant has risen in frequency in distinct population isolates. Nat. Commun. 4, 2872 (2013).
pubmed: 24343240
doi: 10.1038/ncomms3872
Tang, C. S. et al. Exome-wide association analysis reveals novel coding sequence variants associated with lipid traits in Chinese. Nat. Commun. 6, 10206 (2015).
pubmed: 26690388
doi: 10.1038/ncomms10206
van Leeuwen, E. M. et al. Genome of the Netherlands population-specific imputations identify an ABCA6 variant associated with cholesterol levels. Nat. Commun. 6, 6065 (2015).
pubmed: 25751400
doi: 10.1038/ncomms7065
Spracklen, C. N. et al. Association analyses of East Asian individuals and trans-ancestry analyses with European individuals reveal new loci associated with cholesterol and triglyceride levels. Hum. Mol. Genet. 26, 1770–1784 (2017).
pubmed: 28334899
pmcid: 6075203
doi: 10.1093/hmg/ddx062
Kanai, M. et al. Genetic analysis of quantitative traits in the Japanese population links cell types to complex human diseases. Nat. Genet. 50, 390–400 (2018).
pubmed: 29403010
doi: 10.1038/s41588-018-0047-6
Sirugo, G., Williams, S. M. & Tishkoff, S. A. The missing diversity in human genetic studies. Cell 177, 26–31 (2019).
pubmed: 30901543
pmcid: 7380073
doi: 10.1016/j.cell.2019.02.048
Khera, A. V. et al. Genome-wide polygenic scores for common diseases identify individuals with risk equivalent to monogenic mutations. Nat. Genet. 50, 1219–1224 (2018).
pubmed: 30104762
pmcid: 6128408
doi: 10.1038/s41588-018-0183-z
Duncan, L. et al. Analysis of polygenic risk score usage and performance in diverse human populations. Nat. Commun. 10, 3328 (2019).
pubmed: 31346163
pmcid: 6658471
doi: 10.1038/s41467-019-11112-0
Buniello, A. et al. The NHGRI–EBI GWAS Catalog of published genome-wide association studies, targeted arrays and summary statistics 2019. Nucleic Acids Res. 47, D1005–D1012 (2019).
pubmed: 30445434
doi: 10.1093/nar/gky1120
Tishkoff, S. A. et al. The genetic structure and history of Africans and African Americans. Science 324, 1035–1044 (2009).
pubmed: 19407144
pmcid: 2947357
doi: 10.1126/science.1172257
Mägi, R. et al. Trans-ethnic meta-regression of genome-wide association studies accounting for ancestry increases power for discovery and improves fine-mapping resolution. Hum. Mol. Genet. 26, 3639–3650 (2017).
pubmed: 28911207
pmcid: 5755684
doi: 10.1093/hmg/ddx280
Lee, S. H., Yang, J., Goddard, M. E., Visscher, P. M. & Wray, N. R. Estimation of pleiotropy between complex diseases using single-nucleotide polymorphism-derived genomic relationships and restricted maximum likelihood. Bioinformatics 28, 2540–2542 (2012).
pubmed: 22843982
pmcid: 3463125
doi: 10.1093/bioinformatics/bts474
Brown, B. C., Ye, C. J., Price, A. L. & Zaitlen, N. Transethnic genetic-correlation estimates from summary statistics. Am. J. Hum. Genet. 99, 76–88 (2016).
pubmed: 27321947
pmcid: 5005434
doi: 10.1016/j.ajhg.2016.05.001
Guo, J. et al. Quantifying genetic heterogeneity between continental populations for human height and body mass index. Sci. Rep. 11, 5240 (2021).
pubmed: 33664403
pmcid: 7933291
doi: 10.1038/s41598-021-84739-z
Ge, T., Chen, C.-Y., Ni, Y., Feng, Y.-C. A. & Smoller, J. W. Polygenic prediction via Bayesian regression and continuous shrinkage priors. Nat. Commun. 10, 1776 (2019).
pubmed: 30992449
pmcid: 6467998
doi: 10.1038/s41467-019-09718-5
Majara, L. et al. Low generalizability of polygenic scores in African populations due to genetic and environmental diversity. Preprint at bioRxiv https://doi.org/10.1101/2021.01.12.426453 (2021).
Lehmann, B. C. L., Mackintosh, M., McVean, G. & Holmes, C. C. High trait variability in optimal polygenic prediction strategy within multiple-ancestry cohorts. Preprint at bioRxiv https://doi.org/10.1101/2021.01.15.426781 (2021).
Shi, H. et al. Population-specific causal disease effect sizes in functionally important regions impacted by selection. Nat. Commun. 12, 1098 (2021).
pubmed: 33597505
pmcid: 7889654
doi: 10.1038/s41467-021-21286-1
Martin, A. R. et al. Clinical use of current polygenic risk scores may exacerbate health disparities. Nat. Genet. 51, 584–591 (2019).
pubmed: 30926966
pmcid: 6563838
doi: 10.1038/s41588-019-0379-x
Cavazos, T. B. & Witte, J. S. Inclusion of variants discovered from diverse populations improves polygenic risk score transferability. HGG Adv. 2, 100017 (2021).
pubmed: 33564748
Wojcik, G. L. et al. Genetic analyses of diverse populations improves discovery for complex traits. Nature 570, 514–518 (2019).
pubmed: 31217584
pmcid: 6785182
doi: 10.1038/s41586-019-1310-4
Bentley, A. R. et al. Multi-ancestry genome-wide gene–smoking interaction study of 387,272 individuals identifies new loci associated with serum lipids. Nat. Genet. 51, 636–648 (2019).
pubmed: 30926973
pmcid: 6467258
doi: 10.1038/s41588-019-0378-y
Taliun, D. et al. Sequencing of 53,831 diverse genomes from the NHLBI TOPMed Program. Nature 590, 290–299 (2021).
pubmed: 33568819
pmcid: 7875770
doi: 10.1038/s41586-021-03205-y
Kowalski, M. H. et al. Use of >100,000 NHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium whole genome sequences improves imputation quality and detection of rare variant associations in admixed African and Hispanic/Latino populations. PLoS Genet. 15, e1008500 (2019).
pubmed: 31869403
pmcid: 6953885
doi: 10.1371/journal.pgen.1008500
Das, S. et al. Next-generation genotype imputation service and methods. Nat. Genet. 48, 1284–1287 (2016).
pubmed: 27571263
pmcid: 5157836
doi: 10.1038/ng.3656
Baigent, C. et al. Efficacy and safety of cholesterol-lowering treatment: prospective meta-analysis of data from 90 056 participants in 14 randomised trials of statins. Lancet 366, 1267–1278 (2005).
pubmed: 16214597
doi: 10.1016/S0140-6736(05)67394-1
Loh, P.-R. et al. Efficient Bayesian mixed-model analysis increases association power in large cohorts. Nat. Genet. 47, 284–290 (2015).
pubmed: 25642633
pmcid: 4342297
doi: 10.1038/ng.3190
Zhou, W. et al. Efficiently controlling for case–control imbalance and sample relatedness in large-scale genetic association studies. Nat. Genet. 50, 1335–1341 (2018).
pubmed: 30104761
pmcid: 6119127
doi: 10.1038/s41588-018-0184-y
Winkler, T. W. et al. Quality control and conduct of genome-wide association meta-analyses. Nat. Protoc. 9, 1192–1212 (2014).
pubmed: 24762786
pmcid: 4083217
doi: 10.1038/nprot.2014.071
Feng, S., Liu, D., Zhan, X., Wing, M. K. & Abecasis, G. R. RAREMETAL: fast and powerful meta-analysis for rare variants. Bioinformatics 30, 2828–2829 (2014).
pubmed: 24894501
pmcid: 4173011
doi: 10.1093/bioinformatics/btu367
Willer, C. J., Li, Y. & Abecasis, G. R. METAL: fast and efficient meta-analysis of genomewide association scans. Bioinformatics 26, 2190–2191 (2010).
pubmed: 20616382
pmcid: 2922887
doi: 10.1093/bioinformatics/btq340
Loh, P.-R., Palamara, P. F. & Price, A. L. Fast and accurate long-range phasing in a UK Biobank cohort. Nat. Genet. 48, 811–816 (2016).
pubmed: 27270109
pmcid: 4925291
doi: 10.1038/ng.3571
Liu, X. et al. WGSA: an annotation pipeline for human genome sequencing studies. J. Med. Genet. 53, 111–112 (2016).
pubmed: 26395054
doi: 10.1136/jmedgenet-2015-103423
Cingolani, P. et al. A program for annotating and predicting the effects of single nucleotide polymorphisms, SnpEff: SNPs in the genome of Drosophila melanogaster strain w1118; iso-2; iso-3. Fly 6, 80–92 (2012).
pubmed: 22728672
pmcid: 3679285
doi: 10.4161/fly.19695
Wang, K., Li, M. & Hakonarson, H. ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data. Nucleic Acids Res. 38, e164 (2010).
pubmed: 20601685
pmcid: 2938201
doi: 10.1093/nar/gkq603
Quinlan, A. R. & Hall, I. M. BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics 26, 841–842 (2010).
pubmed: 20110278
pmcid: 2832824
doi: 10.1093/bioinformatics/btq033
Liu, D. J. et al. Meta-analysis of gene-level tests for rare variant association. Nat. Genet. 46, 200–204 (2014).
pubmed: 24336170
doi: 10.1038/ng.2852
Maller, J. B. et al. Bayesian refinement of association signals for 14 loci in 3 common diseases. Nat. Genet. 44, 1294–1301 (2012).
pubmed: 23104008
pmcid: 3791416
doi: 10.1038/ng.2435
Kass, R. E. & Raftery, A. E. Bayes factors. J. Am. Stat. Assoc. 90, 773–795 (1995).
doi: 10.1080/01621459.1995.10476572
Machiela, M. J. & Chanock, S. J. LDlink: a web-based application for exploring population-specific haplotype structure and linking correlated alleles of possible functional variants. Bioinformatics 31, 3555–3557 (2015).
pubmed: 26139635
pmcid: 4626747
doi: 10.1093/bioinformatics/btv402
McLaren, W. et al. The Ensembl Variant Effect Predictor. Genome Biol. 17, 122 (2016).
pubmed: 27268795
pmcid: 4893825
doi: 10.1186/s13059-016-0974-4
Sherry, S. T. et al. dbSNP: the NCBI database of genetic variation. Nucleic Acids Res. 29, 308–311 (2001).
pubmed: 11125122
pmcid: 29783
doi: 10.1093/nar/29.1.308
Giambartolomei, C. et al. Bayesian test for colocalisation between pairs of genetic association studies using summary statistics. PLoS Genet. 10, e1004383 (2014).
pubmed: 24830394
pmcid: 4022491
doi: 10.1371/journal.pgen.1004383
GTEx Consortium. The GTEx Consortium atlas of genetic regulatory effects across human tissues. Science 369, 1318–1330 (2020).
doi: 10.1126/science.aaz1776
Purcell, S. et al. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am. J. Hum. Genet. 81, 559–575 (2007).
pubmed: 17701901
pmcid: 1950838
doi: 10.1086/519795
Berisa, T. & Pickrell, J. K. Approximately independent linkage disequilibrium blocks in human populations. Bioinformatics 32, 283–285 (2016).
pubmed: 26395773
doi: 10.1093/bioinformatics/btv546
Finer, S. et al. Cohort Profile: East London Genes &Health (ELGH), a community-based population genomics and health study in British Bangladeshi and British Pakistani people. Int. J. Epidemiol. 49, 20–21i (2019).
pmcid: 7124496
doi: 10.1093/ije/dyz174
Moon, S. et al. The Korea Biobank Array: design and identification of coding variants associated with blood biochemical traits. Sci. Rep. 9, 1382 (2019).
pubmed: 30718733
pmcid: 6361960
doi: 10.1038/s41598-018-37832-9
Alexander, D. H., Novembre, J. & Lange, K. Fast model-based estimation of ancestry in unrelated individuals. Genome Res. 19, 1655–1664 (2009).
pubmed: 19648217
pmcid: 2752134
doi: 10.1101/gr.094052.109