Minority-centric meta-analyses of blood lipid levels identify novel loci in the Population Architecture using Genomics and Epidemiology (PAGE) study
Autoři:
Yao Hu aff001; Mariaelisa Graff aff002; Jeffrey Haessler aff001; Steven Buyske aff003; Stephanie A. Bien aff001; Ran Tao aff004; Heather M. Highland aff002; Katherine K. Nishimura aff001; Niha Zubair aff001; Yingchang Lu aff006; Marie Verbanck aff006; Austin T. Hilliard aff007; Derek Klarin aff008; Scott M. Damrauer aff011; Yuk-Lam Ho aff014; ; Peter W. F. Wilson aff011; Kyong-Mi Chang aff012; Philip S. Tsao aff017; Kelly Cho aff014; Christopher J. O’Donnell aff014; Themistocles L. Assimes aff017; Lauren E. Petty aff005; Jennifer E. Below aff005; Ozan Dikilitas aff021; Daniel J. Schaid aff022; Matthew L. Kosel aff022; Iftikhar J. Kullo aff021; Laura J. Rasmussen-Torvik aff023; Gail P. Jarvik aff024; Qiping Feng aff025; Wei-Qi Wei aff025; Eric B. Larson aff026; Frank D. Mentch aff027; Berta Almoguera aff027; Patrick M. Sleiman aff027; Laura M. Raffield aff028; Adolfo Correa aff029; Lisa W. Martin aff030; Martha Daviglus aff031; Tara C. Matise aff003; Jose Luis Ambite aff033; Christopher S. Carlson aff001; Ron Do aff006; Ruth J. F. Loos aff006; Lynne R. Wilkens aff034; Loic Le Marchand aff034; Chris Haiman aff035; Daniel O. Stram aff035; Lucia A. Hindorff aff036; Kari E. North aff002; Charles Kooperberg aff001; Iona Cheng aff037; Ulrike Peters aff001
Působiště autorů:
Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, United States of America
aff001; Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America
aff002; Department of Statistics and Biostatistics, Rutgers University, New Brunswick, New Jersey, United States of America
aff003; Department of Biostatistics, Vanderbilt University Medical Center, Nashville, Tennessee, United States of America
aff004; The Vanderbilt Genetics Institute, Division of Genetic Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, United States of America
aff005; The Charles Bronfman Institute for Personalized Medicine, The Icahn School of Medicine at Mount Sinai, New York, New York, United States of America
aff006; Palo Alto Veterans Institute for Research, VA Palo Alto Health Care System, Palo Alto, California, United States of America
aff007; Center for Genomic Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, United States of America
aff008; Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, United States of America
aff009; Boston VA Healthcare System, Boston, Massachusetts, United States of America
aff010; Emory Clinical Cardiovascular Research Institute, Atlanta, Georgia, United States of America
aff011; Corporal Michael Crescenz VA Medical Center, Philadelphia, Pennsylvania, United States of America
aff012; Department of Surgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
aff013; Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, Massachusetts, United States of America
aff014; Atlanta VA Medical Center, Decatur, Georgia, United States of America
aff015; Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
aff016; Department of Medicine, Stanford University School of Medicine, Stanford, California, United States of America
aff017; VA Palo Alto Health Care System, Palo Alto, California, United States of America
aff018; Department of Medicine, Harvard Medical School, Boston, Massachusetts, United States of America
aff019; Department of Epidemiology, Human Genetics & Environmental Sciences, University of Texas School of Public Health, Houston, Texas, United States of America
aff020; Department of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota, United states of America
aff021; Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota, United States of America
aff022; Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois, United States of America
aff023; Department of Medicine, University of Washington Medical Center, Seattle, Washington, United States of America
aff024; Department of Medicine, Division of Clinical Pharmacology, Vanderbilt University Medical Center, Nashville, Tennessee, United States of America
aff025; Kaiser Permanente Washington Health Research Institute, Seattle, Washington, United States of America
aff026; Center for Applied Genomics, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, United States of America
aff027; Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America
aff028; Departments of Medicine, Pediatrics, and Population Health Science, University of Mississippi Medical Center, Jackson, Mississippi, United States of America
aff029; School of Medicine and Health Sciences, George Washington University, Washington, District of Columbia, United States of America
aff030; Institute for Minority Health Research, University of Illinois at Chicago, Chicago, Illinois, United States of America
aff031; Department of Medicine, University of Illinois at Chicago, Chicago, Illinois, United States of America
aff032; Information Sciences Institute, University of Southern California, Marina del Rey, California, United States of America
aff033; Epidemiology Program, University of Hawaii Cancer Center, Honolulu, Hawaii, United States of America
aff034; Keck School of Medicine, University of Southern California, Los Angeles, California, United States of America
aff035; Division of Genomic Medicine, NIH National Human Genome Research Institute, Bethesda, Maryland, United States of America
aff036; Cancer Prevention Institute of California, Fremont, California, United States of America
aff037
Vyšlo v časopise:
Minority-centric meta-analyses of blood lipid levels identify novel loci in the Population Architecture using Genomics and Epidemiology (PAGE) study. PLoS Genet 16(3): e32767. doi:10.1371/journal.pgen.1008684
Kategorie:
Research Article
doi:
https://doi.org/10.1371/journal.pgen.1008684
Souhrn
Lipid levels are important markers for the development of cardio-metabolic diseases. Although hundreds of associated loci have been identified through genetic association studies, the contribution of genetic factors to variation in lipids is not fully understood, particularly in U.S. minority groups. We performed genome-wide association analyses for four lipid traits in over 45,000 ancestrally diverse participants from the Population Architecture using Genomics and Epidemiology (PAGE) Study, followed by a meta-analysis with several European ancestry studies. We identified nine novel lipid loci, five of which showed evidence of replication in independent studies. Furthermore, we discovered one novel gene in a PrediXcan analysis, minority-specific independent signals at eight previously reported loci, and potential functional variants at two known loci through fine-mapping. Systematic examination of known lipid loci revealed smaller effect estimates in African American and Hispanic ancestry populations than those in Europeans, and better performance of polygenic risk scores based on minority-specific effect estimates. Our findings provide new insight into the genetic architecture of lipid traits and highlight the importance of conducting genetic studies in diverse populations in the era of precision medicine.
Klíčová slova:
Europe – Genetic loci – Genome-wide association studies – Hispanic people – Lipids – Metaanalysis – Population genetics – Trait locus analysis
Zdroje
1. Emerging Risk Factors C, Di Angelantonio E, Sarwar N, Perry P, Kaptoge S, Ray KK, et al. Major lipids, apolipoproteins, and risk of vascular disease. JAMA. 2009;302(18):1993–2000. Epub 2009/11/12. doi: 10.1001/jama.2009.1619 19903920; PubMed Central PMCID: PMC3284229.
2. Qi Q, Liang L, Doria A, Hu FB, Qi L. Genetic predisposition to dyslipidemia and type 2 diabetes risk in two prospective cohorts. Diabetes. 2012;61(3):745–52. Epub 2012/02/09. doi: 10.2337/db11-1254 22315312; PubMed Central PMCID: PMC3282815.
3. Oresic M, Hyotylainen T, Kotronen A, Gopalacharyulu P, Nygren H, Arola J, et al. Prediction of non-alcoholic fatty-liver disease and liver fat content by serum molecular lipids. Diabetologia. 2013;56(10):2266–74. Epub 2013/07/05. doi: 10.1007/s00125-013-2981-2 23824212; PubMed Central PMCID: PMC3764317.
4. Kettunen J, Tukiainen T, Sarin AP, Ortega-Alonso A, Tikkanen E, Lyytikainen LP, et al. Genome-wide association study identifies multiple loci influencing human serum metabolite levels. Nat Genet. 2012;44(3):269–76. Epub 2012/01/31. doi: 10.1038/ng.1073 22286219; PubMed Central PMCID: PMC3605033.
5. Teslovich TM, Musunuru K, Smith AV, Edmondson AC, Stylianou IM, Koseki M, et al. Biological, clinical and population relevance of 95 loci for blood lipids. Nature. 2010;466(7307):707–13. Epub 2010/08/06. doi: 10.1038/nature09270 20686565; PubMed Central PMCID: PMC3039276.
6. Willer CJ, Schmidt EM, Sengupta S, Peloso GM, Gustafsson S, Kanoni S, et al. Discovery and refinement of loci associated with lipid levels. Nat Genet. 2013;45(11):1274–83. Epub 2013/10/08. doi: 10.1038/ng.2797 24097068; PubMed Central PMCID: PMC3838666.
7. Liu DJ, Peloso GM, Yu H, Butterworth AS, Wang X, Mahajan A, et al. Exome-wide association study of plasma lipids in >300,000 individuals. Nat Genet. 2017;49(12):1758–66. Epub 2017/10/31. doi: 10.1038/ng.3977 29083408; PubMed Central PMCID: PMC5709146.
8. Lu X, Peloso GM, Liu DJ, Wu Y, Zhang H, Zhou W, 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. 2017;49(12):1722–30. Epub 2017/10/31. doi: 10.1038/ng.3978 29083407; PubMed Central PMCID: PMC5899829.
9. Hoffmann TJ, Theusch E, Haldar T, Ranatunga DK, Jorgenson E, Medina MW, et al. A large electronic-health-record-based genome-wide study of serum lipids. Nat Genet. 2018;50(3):401–13. Epub 2018/03/07. doi: 10.1038/s41588-018-0064-5 29507422; PubMed Central PMCID: PMC5942247.
10. Bentley AR, Sung YJ, Brown MR, Winkler TW, Kraja AT, Ntalla I, et al. Multi-ancestry genome-wide gene-smoking interaction study of 387,272 individuals identifies new loci associated with serum lipids. Nat Genet. 2019;51(4):636–48. Epub 2019/03/31. doi: 10.1038/s41588-019-0378-y 30926973.
11. Deo RC, Reich D, Tandon A, Akylbekova E, Patterson N, Waliszewska A, et al. Genetic Differences between the Determinants of Lipid Profile Phenotypes in African and European Americans: The Jackson Heart Study. Plos Genetics. 2009;5(1). ARTN e1000342 10.1371/journal.pgen.1000342. WOS:000266221100024.
12. Bermudez OI, Velez-Carrasco W, Schaefer EJ, Tucker KL. Dietary and plasma lipid, lipoprotein, and apolipoprotein profiles among elderly Hispanics and non-Hispanics and their association with diabetes. American Journal of Clinical Nutrition. 2002;76(6):1214–21. WOS:000179414600005. doi: 10.1093/ajcn/76.6.1214 12450885
13. Klarin D, Damrauer SM, Cho K, Sun YV, Teslovich TM, Honerlaw J, et al. Genetics of blood lipids among ~300,000 multi-ethnic participants of the Million Veteran Program. Nat Genet. 2018. Epub 2018/10/03. doi: 10.1038/s41588-018-0222-9 30275531.
14. Matise TC, Ambite JL, Buyske S, Carlson CS, Cole SA, Crawford DC, et al. The Next PAGE in Understanding Complex Traits: Design for the Analysis of Population Architecture Using Genetics and Epidemiology (PAGE) Study. American Journal of Epidemiology. 2011;174(7):849–59. doi: 10.1093/aje/kwr160 WOS:000295166500011. 21836165
15. Bien SA, Wojcik GL, Zubair N, Gignoux CR, Martin AR, Kocarnik JM, et al. Strategies for Enriching Variant Coverage in Candidate Disease Loci on a Multiethnic Genotyping Array. Plos One. 2016;11(12):e0167758. Epub 2016/12/16. doi: 10.1371/journal.pone.0167758 27973554; PubMed Central PMCID: PMC5156387.
16. Surakka I, Horikoshi M, Magi R, Sarin AP, Mahajan A, Lagou V, et al. The impact of low-frequency and rare variants on lipid levels. Nat Genet. 2015;47(6):589–97. Epub 2015/05/12. doi: 10.1038/ng.3300 25961943; PubMed Central PMCID: PMC4757735.
17. Stanaway IB, Hall TO, Rosenthal EA, Palmer M, Naranbhai V, Knevel R, et al. The eMERGE genotype set of 83,717 subjects imputed to ~40 million variants genome wide and association with the herpes zoster medical record phenotype. Genet Epidemiol. 2019;43(1):63–81. Epub 2018/10/10. doi: 10.1002/gepi.22167 30298529.
18. Hoffmann TJ, Theusch E, Haldar T, Ranatunga DK, Jorgenson E, Medina MW, et al. A large electronic-health-record-based genome-wide study of serum lipids. Nature Genetics. 2018;50(3):401–+. doi: 10.1038/s41588-018-0064-5 WOS:000427933400016. 29507422
19. Taylor HA, Wilson JG, Jones DW, Sarpong DF, Srinivasan A, Garrison RJ, et al. Toward resolution of cardiovascular health disparities in African Americans: Design and methods of the Jackson Heart Study. Ethnic Dis. 2005;15(4):S4–S17. WOS:000233440100002.
20. Wojcik GL, Graff M, Nishimura KK, Tao R, Haessler J, Gignoux CR, et al. Genetic analyses of diverse populations improves discovery for complex traits. Nature. 2019;570(7762):514–8. Epub 2019/06/21. doi: 10.1038/s41586-019-1310-4 31217584.
21. Leipold E, Liebmann L, Korenke GC, Heinrich T, Giesselmann S, Baets J, et al. A de novo gain-of-function mutation in SCN11A causes loss of pain perception. Nat Genet. 2013;45(11):1399–404. Epub 2013/09/17. doi: 10.1038/ng.2767 24036948.
22. Wainberg M, Sinnott-Armstrong N, Mancuso N, Barbeira AN, Knowles DA, Golan D, et al. Opportunities and challenges for transcriptome-wide association studies. Nat Genet. 2019;51(4):592–9. Epub 2019/03/31. doi: 10.1038/s41588-019-0385-z 30926968.
23. Benner C, Spencer CCA, Havulinna AS, Salomaa V, Ripatti S, Pirinen M. FINEMAP: efficient variable selection using summary data from genome-wide association studies. Bioinformatics. 2016;32(10):1493–501. doi: 10.1093/bioinformatics/btw018 WOS:000376656900008. 26773131
24. Zubair N, Graff M, Ambite JL, Bush WS, Kichaev G, Lu YC, et al. Fine-mapping of lipid regions in global populations discovers ethnic-specific signals and refines previously identified lipid loci. Human Molecular Genetics. 2016;25(24):5500–12. doi: 10.1093/hmg/ddw358 WOS:000397063900019. 28426890
25. Davis JP, Vadlamudi S, Roman TS, Zeynalzadeh M, Iyengar AK, Mohlke KL. Enhancer deletion and allelic effects define a regulatory molecular mechanism at the VLDLR cholesterol GWAS locus. Hum Mol Genet. 2018. Epub 2018/11/18. doi: 10.1093/hmg/ddy385 30445632.
26. Liu X, White S, Peng B, Johnson AD, Brody JA, Li AH, et al. WGSA: an annotation pipeline for human genome sequencing studies. J Med Genet. 2016;53(2):111–2. Epub 2015/09/24. doi: 10.1136/jmedgenet-2015-103423 26395054; PubMed Central PMCID: PMC5124490.
27. Quang D, Chen YF, Xie XH. DANN: a deep learning approach for annotating the pathogenicity of genetic variants. Bioinformatics. 2015;31(5):761–3. doi: 10.1093/bioinformatics/btu703 WOS:000352268500019. 25338716
28. Dzitoyeva S, Manev H. Reduction of Cellular Lipid Content by a Knockdown of Drosophila PDP1 gamma and Mammalian Hepatic Leukemia Factor. J Lipids. 2013;2013:297932. Epub 2013/09/26. doi: 10.1155/2013/297932 24062952; PubMed Central PMCID: PMC3766575.
29. Ionita-Laza I, McCallum K, Xu B, Buxbaum JD. A spectral approach integrating functional genomic annotations for coding and noncoding variants. Nature Genetics. 2016;48(2):214–20. doi: 10.1038/ng.3477 WOS:000369043900021. 26727659
30. Gamazon ER, Segre AV, van de Bunt M, Wen XQ, Xi HS, Hormozdiari F, et al. Using an atlas of gene regulation across 44 human tissues to inform complex disease- and trait-associated variation. Nature Genetics. 2018;50(7):956–+. doi: 10.1038/s41588-018-0154-4 WOS:000437224400011. 29955180
31. Nelson CP, Goel A, Butterworth AS, Kanoni S, Webb TR, Marouli E, et al. Association analyses based on false discovery rate implicate new loci for coronary artery disease. Nature Genetics. 2017;49(9):1385–+. doi: 10.1038/ng.3913 WOS:000408672000017. 28714975
32. Holmen OL, Zhang H, Fan YB, Hovelson DH, Schmidt EM, Zhou W, et al. Systematic evaluation of coding variation identifies a candidate causal variant in TM6SF2 influencing total cholesterol and myocardial infarction risk. Nature Genetics. 2014;46(4):345–+. doi: 10.1038/ng.2926 WOS:000334510100009. 24633158
33. Ma J, Dempsey AA, Stamatiou D, Marshall KW, Liew CC. Identifying leukocyte gene expression patterns associated with plasma lipid levels in human subjects. Atherosclerosis. 2007;191(1):63–72. doi: 10.1016/j.atherosclerosis.2006.05.032 WOS:000244944100008. 16806233
34. Hussain MR, Hoessli DC, Fang M. N-acetylgalactosaminyltransferases in cancer. Oncotarget. 2016;7(33):54067–81. Epub 2016/06/21. doi: 10.18632/oncotarget.10042 27322213; PubMed Central PMCID: PMC5288242.
35. Stewart JD, Marchan R, Lesjak MS, Lambert J, Hergenroeder R, Ellis JK, et al. Choline-releasing glycerophosphodiesterase EDI3 drives tumor cell migration and metastasis. Proc Natl Acad Sci U S A. 2012;109(21):8155–60. Epub 2012/05/10. doi: 10.1073/pnas.1117654109 22570503; PubMed Central PMCID: PMC3361409.
36. Willer CJ, Li Y, Abecasis GR. METAL: fast and efficient meta-analysis of genomewide association scans. Bioinformatics. 2010;26(17):2190–1. Epub 2010/07/10. doi: 10.1093/bioinformatics/btq340 20616382; PubMed Central PMCID: PMC2922887.
37. Lin DY, Tao R, Kalsbeek WD, Zeng D, Gonzalez F, 2nd, Fernandez-Rhodes L, et al. Genetic association analysis under complex survey sampling: the Hispanic Community Health Study/Study of Latinos. Am J Hum Genet. 2014;95(6):675–88. Epub 2014/12/07. doi: 10.1016/j.ajhg.2014.11.005 25480034; PubMed Central PMCID: PMC4259979.
38. Yang J, Ferreira T, Morris AP, Medland SE, Genetic Investigation of ATC, Replication DIG, et al. Conditional and joint multiple-SNP analysis of GWAS summary statistics identifies additional variants influencing complex traits. Nat Genet. 2012;44(4):369–75, S1-3. Epub 2012/03/20. doi: 10.1038/ng.2213 22426310; PubMed Central PMCID: PMC3593158.
39. Shim H, Chasman DI, Smith JD, Mora S, Ridker PM, Nickerson DA, et al. A Multivariate Genome-Wide Association Analysis of 10 LDL Subfractions, and Their Response to Statin Treatment, in 1868 Caucasians. Plos One. 2015;10(4). UNSP e012075810.1371/journal.pone.0120758. WOS:000353212600006.
Článek vyšel v časopise
PLOS Genetics
2020 Číslo 3
- S diagnostikou Parkinsonovy nemoci může nově pomoci AI nástroj pro hodnocení mrkacího reflexu
- Proč při poslechu některé muziky prostě musíme tančit?
- Chůze do schodů pomáhá prodloužit život a vyhnout se srdečním chorobám
- Metamizol jako analgetikum první volby: kdy, pro koho, jak a proč?
- „Jednohubky“ z klinického výzkumu – 2024/44
Nejčtenější v tomto čísle
- Evidence of defined temporal expression patterns that lead a gram-negative cell out of dormancy
- The Lid/KDM5 histone demethylase complex activates a critical effector of the oocyte-to-zygote transition
- The alarmones (p)ppGpp are part of the heat shock response of Bacillus subtilis
- Modeling cancer genomic data in yeast reveals selection against ATM function during tumorigenesis
