A phenome-wide association study (PheWAS) in the Population Architecture using Genomics and Epidemiology (PAGE) study reveals potential pleiotropy in African Americans
Autoři:
Sarah A. Pendergrass aff001; Steven Buyske aff002; Janina M. Jeff aff004; Alex Frase aff005; Scott Dudek aff005; Yuki Bradford aff005; Jose-Luis Ambite aff006; Christy L. Avery aff007; Petra Buzkova aff008; Ewa Deelman aff006; Megan D. Fesinmeyer aff009; Christopher Haiman aff010; Gerardo Heiss aff007; Lucia A. Hindorff aff012; Chun-Nan Hsu aff013; Rebecca D. Jackson aff014; Yi Lin aff015; Loic Le Marchand aff016; Tara C. Matise aff003; Kristine R. Monroe aff010; Larry Moreland aff017; Kari E. North aff007; Sungshim L. Park aff010; Alex Reiner aff018; Robert Wallace aff019; Lynne R. Wilkens aff016; Charles Kooperberg aff015; Marylyn D. Ritchie aff005; Dana C. Crawford aff020
Působiště autorů:
Genentech, Inc., South San Francisco, California, United States of America
aff001; Department of Statistics, Rutgers University, Piscataway, New Jersey, United States of America
aff002; Department of Genetics, Rutgers University, Piscataway, New Jersey, United States of America
aff003; Illumina, Inc., San Diego, California, United States of America
aff004; Department of Genetics, Institute for Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
aff005; Information Sciences Institute; University of Southern California, Marina del Rey, California, United States of America
aff006; Department of Epidemiology, University of North Carolina, Chapel Hill, North Carolina, United States of America
aff007; Department of Biostatistics, University of Washington, Seattle, Washington, United States of America
aff008; Amgen, Thousand Oaks, California, United States of America
aff009; Department of Preventive Medicine, Keck School of Medicine, University of Southern California/Norris Comprehensive Cancer Center, Los Angeles, California, United States of America
aff010; Carolina Center for Genome Sciences, University of North Carolina, Chapel Hill, North Carolina, United States of America
aff011; National Human Genome Research Institute, National Institutes of Health, Bethesda, Maryland, United States of America
aff012; Center for Research in Biological Systems, Department of Neurosciences, University of California, San Diego, La Jolla, California, United States of America
aff013; The Ohio State University, Columbus, Ohio, United States of America
aff014; Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, Washington, United States of America
aff015; Epidemiology Program, University of Hawaii Cancer Center, Honolulu, Hawaii, United States of America
aff016; University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
aff017; Department of Epidemiology, University of Washington, Seattle, Washington, United states of America
aff018; Departments of Epidemiology and Internal Medicine, University of Iowa, Iowa City, Iowa, United States of America
aff019; Cleveland Institute for Computational Biology, Cleveland, Ohio, United States of America
aff020; Departments of Population and Quantitative Health Sciences and Genetics and Genome Sciences, Case Western Reserve University, Cleveland, Ohio, United States of America
aff021
Vyšlo v časopise:
PLoS ONE 14(12)
Kategorie:
Research Article
prolekare.web.journal.doi_sk:
https://doi.org/10.1371/journal.pone.0226771
Souhrn
We performed a hypothesis-generating phenome-wide association study (PheWAS) to identify and characterize cross-phenotype associations, where one SNP is associated with two or more phenotypes, between thousands of genetic variants assayed on the Metabochip and hundreds of phenotypes in 5,897 African Americans as part of the Population Architecture using Genomics and Epidemiology (PAGE) I study. The PAGE I study was a National Human Genome Research Institute-funded collaboration of four study sites accessing diverse epidemiologic studies genotyped on the Metabochip, a custom genotyping chip that has dense coverage of regions in the genome previously associated with cardio-metabolic traits and outcomes in mostly European-descent populations. Here we focus on identifying novel phenome-genome relationships, where SNPs are associated with more than one phenotype. To do this, we performed a PheWAS, testing each SNP on the Metabochip for an association with up to 273 phenotypes in the participating PAGE I study sites. We identified 133 putative pleiotropic variants, defined as SNPs associated at an empirically derived p-value threshold of p<0.01 in two or more PAGE study sites for two or more phenotype classes. We further annotated these PheWAS-identified variants using publicly available functional data and local genetic ancestry. Amongst our novel findings is SPARC rs4958487, associated with increased glucose levels and hypertension. SPARC has been implicated in the pathogenesis of diabetes and is also known to have a potential role in fibrosis, a common consequence of multiple conditions including hypertension. The SPARC example and others highlight the potential that PheWAS approaches have in improving our understanding of complex disease architecture by identifying novel relationships between genetic variants and an array of common human phenotypes.
Klíčová slova:
Genome-wide association studies – Insulin – Smoking habits – Hypertension – Myocardial infarction – Cell binding assay – African American people – Hematocrit
Zdroje
1. Stearns FW. One Hundred Years of Pleiotropy: A Retrospective. Genetics. 2010;186(3):767–73. doi: 10.1534/genetics.110.122549 21062962
2. Paaby AB, Rockman MV. The many faces of pleiotropy. Trends in Genetics. 2013;29(2):66–73. doi: 10.1016/j.tig.2012.10.010 23140989
3. Tyler AL, Crawford DC, Pendergrass SA. The detection and characterization of pleiotropy: discovery, progress, and promise. Brief Bioinform. 2016;17(1):13–22. doi: 10.1093/bib/bbv050 26223525
4. Hindorff LA, Sethupathy P, Junkins HA, Ramos EM, Mehta JP, Collins FS, et al. Potential etiologic and functional implications of genome-wide association loci for human diseases and traits. Proceedings of the National Academy of Sciences. 2009;106(23):9362–7.
5. Sivakumaran S, Agakov F, Theodoratou E, Prendergast JG, Zgaga L, Manolio T, et al. Abundant pleiotropy in human complex diseases and traits. Am J Hum Genet. 2011;89(5):607–18. doi: 10.1016/j.ajhg.2011.10.004 22077970
6. Denny JC. PheWAS: demonstrating the feasibility of a phenome-wide scan to discover gene-disease associations. Bioinformatics. 2010;26. doi: 10.1093/bioinformatics/btq126 20335276
7. Pendergrass SA, Brown-Gentry K, Dudek SM, Torstenson ES, Ambite JL, Avery CL, et al. The use of phenome-wide association studies (PheWAS) for exploration of novel genotype-phenotype relationships and pleiotropy discovery. Genetic Epidemiology. 2011;35(5):410–22. doi: 10.1002/gepi.20589 21594894
8. Verma A, Bang L, Miller JE, Zhang Y, Lee MTM, Zhang Y, et al. Human-Disease Phenotype Map Derived from PheWAS across 38,682 Individuals. The American Journal of Human Genetics. 2019;104(1):55–64. doi: 10.1016/j.ajhg.2018.11.006 30598166
9. 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 21836165
10. Crawford DC, Goodloe R, Farber-Eger E, Boston J, Pendergrass SA, Haines JL, et al. Leveraging epidemiologic and clinical collections for genomic studies of complex traits. Human Heredity. 2015;79(3–4):137–46. doi: 10.1159/000381805 26201699
11. Buyske S, Wu Y, Carty CL, Cheng I, Assimes TL, Dumitrescu L, et al. Evaluation of the Metabochip Genotyping Array in African Americans and Implications for Fine Mapping of GWAS-Identified Loci: The PAGE Study. PLoS ONE. 2012;7(4):e35651. doi: 10.1371/journal.pone.0035651 22539988
12. Voight BF, Kang HM, Ding J, Palmer CD, Sidore C, Chines PS, et al. The Metabochip, a custom genotyping array for genetic studies of metabolic, cardiovascular, and anthropometric traits. PLoS Genet. 2012;8(8):e1002793. doi: 10.1371/journal.pgen.1002793 22876189
13. The Atherosclerosis Risk in Communities (ARIC) Study: design and objectives. The ARIC investigators. Am J Epidemiol. 1989;129(4):687–702. 2646917
14. Kolonel LN, Henderson BE, Hankin JH, Nomura AMY, Wilkens LR, Pike MC, et al. A Multiethnic Cohort in Hawaii and Los Angeles: Baseline Characteristics. American Journal of Epidemiology. 2000;151(4):346–57. doi: 10.1093/oxfordjournals.aje.a010213 10695593
15. The Women’s Health Initiative. Design of the Women’s Health Inititiative clinical trail and observational study. Control Clin Trials. 1998;19(1):61–109.
16. Reiner AP. Genome-wide association study of white blood cell count in 16,388 African Americans: the continental origins and genetic epidemiology network (COGENT). PLoS Genet. 2011;7. doi: 10.1371/journal.pgen.1002108 21738479
17. Chiba-Falek O, Linnertz C, Guyton J, Gardner SD, Roses AD, McCarthy JJ, et al. Pleiotropy and allelic heterogeneity in the TOMM40-APOE genomic region related to clinical and metabolic features of hepatitis C infection. Human Genetics. 2012;131(12):1911–20. doi: 10.1007/s00439-012-1220-0 22898894
18. Radwan ZH, Wang X, Waqar F, Pirim D, Niemsiri V, Hokanson JE, et al. Comprehensive Evaluation of the Association of APOE Genetic Variation with Plasma Lipoprotein Traits in U.S. Whites and African Blacks. PLOS ONE. 2014;9(12):e114618. doi: 10.1371/journal.pone.0114618 25502880
19. Pirim D, Radwan ZH, Wang X, Niemsiri V, Hokanson JE, Hamman RF, et al. Apolipoprotein E-C1-C4-C2 gene cluster region and inter-individual variation in plasma lipoprotein levels: a comprehensive genetic association study in two ethnic groups. PLOS ONE. 2019;14(3):e0214060. doi: 10.1371/journal.pone.0214060 30913229
20. 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):401–13. doi: 10.1038/s41588-018-0064-5 29507422
21. Verma A, Bradford Y, Verman SS, Pendergrass SA, Daar ES, Venuto C, et al. Multiphenotype association study of patients randomized to initiate antiretroviral regimens in AIDS Clinical Trials Group protocol A5202. Pharmacogenet Genomics. 2017;27(3):101–11. doi: 10.1097/FPC.0000000000000263 28099408
22. Takeuchi F, Isono M, Katsuya T, Yokota M, Yamamoto K, Nabika T, et al. Association of Genetic Variants Influencing Lipid Levels with Coronary Artery Disease in Japanese Individuals. PLOS ONE. 2012;7(9):e46385. doi: 10.1371/journal.pone.0046385 23050023
23. Burman D, Mente A, Hegele RA, Islam S, Yusuf S, Anand SS. Relationship of the ApoE polymorphism to plasma lipid traits among South Asians, Chinese, and Europeans living in Canada. Atherosclerosis. 2009;203(1):192–200. doi: 10.1016/j.atherosclerosis.2008.06.007 18656198
24. Larifla L, Armand C, Bangou J, Blanchet-Deverly A, Numeric P, Fonteau C, et al. Association of APOE gene polymorphism with lipid profile and coronary artery disease in Afro-Caribbeans. PLOS ONE. 2017;12(7):e0181620. doi: 10.1371/journal.pone.0181620 28727855
25. Natarajan P, Peloso GM, Zekavat SM, Montasser M, Ganna A, Chaffin M, et al. Deep-coverage whole genome sequences and blood lipids among 16,324 individuals. Nature Communications. 2018;9(1):3391. doi: 10.1038/s41467-018-05747-8 30140000
26. Sanna S, Li B, Mulas A, Sidore C, Kang HM, Jackson AU, et al. Fine Mapping of Five Loci Associated with Low-Density Lipoprotein Cholesterol Detects Variants That Double the Explained Heritability. PLOS Genetics. 2011;7(7):e1002198. doi: 10.1371/journal.pgen.1002198 21829380
27. Kanoni S, Masca NGD, Stirrups KE, Varga TV, Warren HR, Scott RA, et al. Analysis with the exome array identifies multiple new independent variants in lipid loci. Human Molecular Genetics. 2016;25(18):4094–106. doi: 10.1093/hmg/ddw227 27466198
28. Mh Chang, Ned ReM, Hong Y, Yesupriya A, Yang Q, Liu T, et al. Racial/Ethnic Variation in the Association of Lipid-Related Genetic Variants With Blood Lipids in the US Adult Population / Clinical Perspective. Circulation: Cardiovascular Genetics. 2011;4(5):523–33.
29. Talmud PJ, Drenos F, Shah S, Shah T, Palmen J, Verzilli C, et al. Gene-centric Association Signals for Lipids and Apolipoproteins Identified via the HumanCVD BeadChip. The American Journal of Human Genetics. 2009;85(5):628–42. doi: 10.1016/j.ajhg.2009.10.014 19913121
30. Lange Leslie A, Hu Y, Zhang H, Xue C, Schmidt Ellen M, Tang Z-Z, et al. Whole-Exome Sequencing Identifies Rare and Low-Frequency Coding Variants Associated with LDL Cholesterol. The American Journal of Human Genetics. 2014;94(2):233–45. doi.org/10.1016/j.ajhg.2014.01.010. 24507775
31. Chang MH, Yesupriya A, Ned RM, Mueller PW, Dowling NF. Genetic variants associated wtih fasting blood lipids in the US population: Third National Health and Nutrition Examination Survey. BMC Med Genet. 2010;11:62. doi: 10.1186/1471-2350-11-62 20406466
32. Rasmussen-Torvik LJ, Pacheco JA, Wilke RA, Thompson WK, Ritchie MD, Kho AN, et al. High Density GWAS for LDL Cholesterol in African Americans Using Electronic Medical Records Reveals a Strong Protective Variant in APOE. Clinical and Translational Science. 2012;5(5):394–9. doi: 10.1111/j.1752-8062.2012.00446.x 23067351
33. Chasman DI, Giulianini F, MacFadyen J, Barratt BJ, Nyberg F, Ridker PM. Genetic Determinants of Statin-Induced Low-Density Lipoprotein Cholesterol Reduction. Circulation: Cardiovascular Genetics. 2012;5(2):257–64.
34. Ciuculete DM, Bandstein M, Benedict C, Waeber G, Vollenweider P, Lind L, et al. A genetic risk score is significantly associated with statin therapy response in the elderly population. Clinical Genetics. 2017;91(3):379–85. doi: 10.1111/cge.12890 27943270
35. Lagos J, Zambrano T, Rosales A, Salazar LA. APOE polymorphisms contribute to reduced atorvastatin response in Chilean Amerindian subjects. Int J Mol Sci. 2015;16(4):7890–9. doi: 10.3390/ijms16047890 25860945
36. Thompson JF, Hyde CL, Wood LS, Paciga SA, Hinds DA, Cox DR, et al. Comprehensive Whole-Genome and Candidate Gene Analysis for Response to Statin Therapy in the Treating to New Targets (TNT) Cohort. Circulation: Cardiovascular Genetics. 2009;2(2):173–81. doi: 10.1161/CIRCGENETICS.108.818062 20031582
37. Mega JL, Morrow DA, Brown A, Cannon CP, Sabatine MS. Identification of Genetic Variants Associated With Response to Statin Therapy. Arteriosclerosis, Thrombosis, and Vascular Biology. 2009;29(9):1310–5. doi: 10.1161/ATVBAHA.109.188474 19667110
38. Morrison AC, Huang Z, Yu B, Metcalf G, Liu X, Ballantyne C, et al. Practical Approaches for Whole-Genome Sequence Analysis of Heart- and Blood-Related Traits. The American Journal of Human Genetics. 2017;100(2):205–15. doi.org/10.1016/j.ajhg.2016.12.009. 28089252
39. Kettunen J, Tukiainen T, Sarin A-P, Ortega-Alonso A, Tikkanen E, Lyytikäinen L-P, et al. Genome-wide association study identifies multiple loci influencing human serum metabolite levels. Nature Genetics. 2012;44:269. doi: 10.1038/ng.1073 22286219
40. Dumitrescu L, Carty CL, Taylor K, Schumacher FR, Hindorff LA, Ambite J-L, et al. Genetic Determinants of Lipid Traits in Diverse Populations from the Population Architecture using Genomics and Epidemiology (PAGE) Study. PLoS Genet. 2011;7(6):e1002138. doi: 10.1371/journal.pgen.1002138 21738485
41. Kathiresan S, Melander O, Guiducci C, Surti A, Burtt NP, Rieder MJ, et al. Six new loci associated with blood low-density lipoprotein cholesterol, high-density lipoprotein cholesterol or triglycerides in humans. Nat Genet. 2008;40(2):189–97. doi: 10.1038/ng.75 18193044
42. Fairoozy RH, White J, Palmen J, Kalea AZ, Humphries SE. Identification of the Functional Variant(s) that Explain the Low-Density Lipoprotein Receptor (LDLR) GWAS SNP rs6511720 Association with Lower LDL-C and Risk of CHD. PLOS ONE. 2016;11(12):e0167676. doi: 10.1371/journal.pone.0167676 27973560
43. Zubair N, Graff M, Luis Ambite J, Bush WS, Kichaev G, Lu Y, 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 28426890
44. Crosslin D, McDavid A, Weston N, Nelson S, Zheng X, Hart E, et al. Genetic variants associated with the white blood cell count in 13,923 subjects in the eMERGE Network. Human Genetics. 2012;131(4):639–52. doi: 10.1007/s00439-011-1103-9 22037903
45. Consortium GP, Auton A, Brooks LD, Durbin RM, Garrison EP, Kang HM, et al. A global reference for human genetic variation. Nature. 2015;526(7571):68–74. doi: 10.1038/nature15393 26432245
46. Wu Y, Waite LL, Jackson AU, Sheu WH-H, Buyske S, Absher D, et al. Trans-Ethnic Fine-Mapping of Lipid Loci Identifies Population-Specific Signals and Allelic Heterogeneity That Increases the Trait Variance Explained. PLoS Genet. 2013;9(3):e1003379. doi: 10.1371/journal.pgen.1003379 23555291
47. Craig WY, Palomaki GE, Haddow JE. Cigarette smoking and serum lipid and lipoprotein concentrations: an analysis of published data. British Medical Journal. 1989;298(6676):784–8. doi: 10.1136/bmj.298.6676.784 2496857
48. Nagy R, Boutin TS, Marten J, Huffman JE, Kerr SM, Campbell A, et al. Exploration of haplotype research consortium imputation for genome-wide association studies in 20,032 Generation Scotland participants. Genome Medicine. 2017;9(1):23. doi: 10.1186/s13073-017-0414-4 28270201
49. Kichaev G, Bhatia G, Loh P-R, Gazal S, Burch K, Freund MK, et al. Leveraging Polygenic Functional Enrichment to Improve GWAS Power. The American Journal of Human Genetics. 2019;104(1):65–75. doi: 10.1016/j.ajhg.2018.11.008 30595370
50. Giri A, Hellwege JN, Keaton JM, Park J, Qiu C, Warren HR, et al. Trans-ethnic association study of blood pressure determinants in over 750,000 individuals. Nature Genetics. 2019;51(1):51–62. doi: 10.1038/s41588-018-0303-9 30578418
51. Surendran P, Drenos F, Young R, Warren H, Cook JP, Manning AK, et al. Trans-ancestry meta-analyses identify rare and common variants associated with blood pressure and hypertension. Nat Genet. 2016;48(10):1151–61. doi: 10.1038/ng.3654 27618447
52. Liu X, Byrd JB. Cigarette Smoking and Subtypes of Uncontrolled Blood Pressure Among Diagnosed Hypertensive Patients: Paradoxical Associations and Implications. American Journal of Hypertension. 2017;30(6):602–9. doi: 10.1093/ajh/hpx014 28203691
53. Ward LD, Kellis M. HaploReg: a resource for exploring chromatin states, conservation, and regulatory motif alterations within sets of genetically linked variants. Nucleic Acids Research. 2012;40(D1):D930–D4. doi: 10.1093/nar/gkr917 22064851
54. Boyle AP, Hong EL, Hariharan M, Cheng Y, Schaub MA, Kasowski M, et al. Annotation of functional variation in personal genomes using RegulomeDB. Genome Research. 2012;22(9):1790–7. doi: 10.1101/gr.137323.112 22955989
55. Gamazon ER, Zhang W, Konkashbaev A, Duan S, Kistner EO, Nicolae DL, et al. SCAN: SNP and copy number annotation. Bioinformatics. 2010;26(2):259–62. doi: 10.1093/bioinformatics/btp644 19933162
56. Tishkoff SA, Reed FA, Friedlaender FR, Ehret C, Ranciaro A, Froment A, et al. The Genetic Structure and History of Africans and African Americans. Science. 2009;324(5930):1035–44. doi: 10.1126/science.1172257 19407144
57. Dumitrescu L, Restrepo NA, Goodloe R, Boston J, Farber-Eger E, Pendergrass SA, et al. Towards a phenome-wide catalog of human clinical traits impacted by genetic ancestry. BioData Mining. 2015;8(35). doi: 10.1186/s13040-015-0068-y 26566401
58. Bryc K, Durand E-á, Macpherson J-á, Reich D, Mountain J-á. The Genetic Ancestry of African Americans, Latinos, and European Americans across the United States. The American Journal of Human Genetics. 2015;96(1):37–53. doi: 10.1016/j.ajhg.2014.11.010 25529636
59. Baharian S, Barakatt M, Gignoux CR, Shringarpure S, Errington J, Blot WJ, et al. The Great Migration and African-American Genomic Diversity. PLoS Genet. 2016;12(5):e1006059. doi: 10.1371/journal.pgen.1006059 27232753
60. 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. Nature Genetics. 2018;50(11):1514–23. doi: 10.1038/s41588-018-0222-9 30275531
61. Evangelou E, Warren HR, Mosen-Ansorena D, Mifsud B, Pazoki R, Gao H, et al. Genetic analysis of over 1 million people identifies 535 new loci associated with blood pressure traits. Nature Genetics. 2018;50(10):1412–25. doi: 10.1038/s41588-018-0205-x 30224653
62. Lin BM, Nadkarni GN, Tao R, Graff M, Fornage M, Buyske S, et al. Genetics of Chronic Kidney Disease Stages Across Ancestries: The PAGE Study. Frontiers in Genetics. 2019;10(494). doi: 10.3389/fgene.2019.00494 31178898
63. Wyss AB, Sofer T, Lee MK, Terzikhan N, Nguyen JN, Lahousse L, et al. Multiethnic meta-analysis identifies ancestry-specific and cross-ancestry loci for pulmonary function. Nature Communications. 2018;9(1):2976. doi: 10.1038/s41467-018-05369-0 30061609
64. Locke AE, Kahali B, Berndt SI, Justice AE, Pers TH, Day FR, et al. Genetic studies of body mass index yield new insights for obesity biology. Nature. 2015;518(7538):197–206. doi: 10.1038/nature14177 25673413
65. 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. doi: 10.1038/s41586-019-1310-4 31217584
66. Fernández-Rhodes L, Malinowski JR, Wang Y, Tao R, Pankratz N, Jeff JM, et al. The genetic underpinnings of variation in ages at menarche and natural menopause among women from the multi-ethnic Population Architecture using Genomics and Epidemiology (PAGE) Study: A trans-ethnic meta-analysis. PLOS ONE. 2018;13(7):e0200486. doi: 10.1371/journal.pone.0200486 30044860
67. Hodonsky CJ, Schurmann C, Schick UM, Kocarnik J, Tao R, van Rooij FJA, et al. Generalization and fine mapping of red blood cell trait genetic associations to multi-ethnic populations: The PAGE study. American Journal of Hematology. 2018;93(8):1061–73. doi: 10.1002/ajh.25161 29905378
68. Kocarnik JM, Richard M, Graff M, Haessler J, Bien S, Carlson C, et al. Discovery, fine-mapping, and conditional analyses of genetic variants associated with C-reactive protein in multiethnic populations using the Metabochip in the Population Architecture using Genomics and Epidemiology (PAGE) study. Human Molecular Genetics. 2018;27(16):2940–53. doi: 10.1093/hmg/ddy211 29878111
69. Gong J, Nishimura KK, Fernandez-Rhodes L, Haessler J, Bien S, Graff M, et al. Trans-ethnic analysis of metabochip data identifies two new loci associated with BMI. International Journal Of Obesity. 2018;42:384. doi: 10.1038/ijo.2017.304 29381148
70. Bien SA, Pankow JS, Haessler J, Lu YN, Pankratz N, Rohde RR, et al. Transethnic insight into the genetics of glycaemic traits: fine-mapping results from the Population Architecture using Genomics and Epidemiology (PAGE) consortium. Diabetologia. 2017;60(12):2384–98. doi: 10.1007/s00125-017-4405-1 28905132
71. Ng MCY, Graff M, Lu Y, Justice AE, Mudgal P, Liu C-T, et al. Discovery and fine-mapping of adiposity loci using high density imputation of genome-wide association studies in individuals of African ancestry: African Ancestry Anthropometry Genetics Consortium. PLOS Genetics. 2017;13(4):e1006719. doi: 10.1371/journal.pgen.1006719 28430825
72. Fernández-Rhodes L, Gong J, Haessler J, Franceschini N, Graff M, Nishimura KK, et al. Trans-ethnic fine-mapping of genetic loci for body mass index in the diverse ancestral populations of the Population Architecture using Genomics and Epidemiology (PAGE) Study reveals evidence for multiple signals at established loci. Human Genetics. 2017;136(6):771–800. doi: 10.1007/s00439-017-1787-6 28391526
73. Avery CL, Wassel CL, Richard MA, Highland HM, Bien S, Zubair N, et al. Fine mapping of QT interval regions in global populations refines previously identified QT interval loci and identifies signals unique to African and Hispanic descent populations. Heart Rhythm. 2017;14(4):572–80. doi: 10.1016/j.hrthm.2016.12.021 27988371
74. Yoneyama S, Yao J, Guo X, Fernandez-Rhodes L, Lim U, Boston J, et al. Generalization and fine mapping of European ancestry-based central adiposity variants in African ancestry populations. International Journal Of Obesity. 2016;41:324. doi: 10.1038/ijo.2016.207 27867202
75. Evans DS, Avery CL, Nalls MA, Li G, Barnard J, Smith EN, et al. Fine-mapping, novel loci identification, and SNP association transferability in a genome-wide association study of QRS duration in African Americans. Human Molecular Genetics. 2016;25(19):4350–68. doi: 10.1093/hmg/ddw284 27577874
76. Franceschini N, Carty CL, Lu Y, Tao R, Sung YJ, Manichaikul A, et al. Variant Discovery and Fine Mapping of Genetic Loci Associated with Blood Pressure Traits in Hispanics and African Americans. PLOS ONE. 2016;11(10):e0164132. doi: 10.1371/journal.pone.0164132 27736895
77. Liu C-T, Raghavan S, Maruthur N, Kabagambe Edmond K, Hong J, Ng Maggie CY, et al. Trans-ethnic Meta-analysis and Functional Annotation Illuminates the Genetic Architecture of Fasting Glucose and Insulin. The American Journal of Human Genetics. 2016;99(1):56–75. doi: 10.1016/j.ajhg.2016.05.006 27321945
78. 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. Nature Genetics. 2019;51(4):636–48. doi: 10.1038/s41588-019-0378-y 30926973
79. Trombetta-Esilva J, Bradshwa AD. The function of SPARC as a mediator of fibrosis. Open Rheumatol J. 2012;6:146–55. doi: 10.2174/1874312901206010146 22802913
80. Atorrasagasti C, Onorato A, Gimeno María L, Andreone L, Garcia M, Malvicini M, et al. SPARC is required for the maintenance of glucose homeostasis and insulin secretion in mice. Clinical Science. 2019;133(2):351–65. doi: 10.1042/CS20180714 30626728
81. Kos K, Wilding JPH. SPARC: a key player in the pathologies associated with obesity and diabetes. Nature Reviews Endocrinology. 2010;6:225. doi: 10.1038/nrendo.2010.18 20195270
82. Preuss M, König IR, Thompson JR, Erdmann J, Absher D, Assimes TL, et al. Design of the Coronary ARtery DIsease Genome-Wide Replication And Meta-Analysis (CARDIoGRAM) Study. Circulation: Cardiovascular Genetics. 2010;3(5):475–83. doi: 10.1161/CIRCGENETICS.109.899443 20923989
83. Meisinger C, Prokisch H, Gieger C, Soranzo N, Mehta D, Rosskopf D, et al. A Genome-wide Association Study Identifies Three Loci Associated with Mean Platelet Volume. The American Journal of Human Genetics. 2009;84(1):66–71. doi: 10.1016/j.ajhg.2008.11.015 19110211
84. Soranzo N, Spector TD, Mangino M, Kühnel B, Rendon A, Teumer A, et al. A genome-wide meta-analysis identifies 22 loci associated with eight hematological parameters in the HaemGen consortium. Nature Genetics. 2009;41:1182. doi: 10.1038/ng.467 19820697
85. Gehwolf R, Wagner A, Lehner C, Bradshaw AD, Scharler C, Niestrawska JA, et al. Pleiotropic roles of the matricellular protein Sparc in tendon maturation and ageing. Scientific Reports. 2016;6:32635. doi: 10.1038/srep32635 27586416
86. Winkler CA, Nelson GW, Smith MW. Admixture Mapping Comes of Age. Annual Review of Genomics and Human Genetics. 2010;11(1):65–89.
87. Fish AE, Crawford DC, Capra John A, Bush WS. Local ancestry transitions modify SNP-trait associations. Pac Symp Biocomput. 2018;23:424–35. 29218902
88. Bryc K, Velez C, Karafet T, Moreno-Estrada A, Reynolds A, Auton A, et al. Genome-wide patterns of population structure and admixture among Hispanic/Latino populations. Proceedings of the National Academy of Sciences. 2010;107(Supplement 2):8954–61.
89. Bhatia G, Tandon A, Patterson N, Aldrich Melinda C, Ambrosone Christine B, Amos C, et al. Genome-wide Scan of 29,141 African Americans Finds No Evidence of Directional Selection since Admixture. The American Journal of Human Genetics. 2014;95(4):437–44. doi: 10.1016/j.ajhg.2014.08.011 25242497
90. Bush WS, Oetjens MT, Crawford DC. Unravelling the human genome-phenome relationship using phenome-wide association studies. Nat Rev Genet. 2016;17(3):129–45. doi: 10.1038/nrg.2015.36 26875678
91. Oetjens MT, Bush WS, Denny JC, Birdwell K, Kodaman N, Verma A, et al. Evidence for extensive pleiotropy among pharmacogenes. Pharmacogenomics. 2016;17(8):853–66. doi: 10.2217/pgs-2015-0007 27249515
92. Chami N, Chen M-H, Slater Andrew J, Eicher John D, Evangelou E, Tajuddin Salman M, et al. Exome Genotyping Identifies Pleiotropic Variants Associated with Red Blood Cell Traits. The American Journal of Human Genetics. 2016;99(1):8–21. doi: 10.1016/j.ajhg.2016.05.007 27346685
93. Safarova MS, Satterfield BA, Fan X, Austin EE, Ye Z, Bastarache L, et al. A phenome-wide association study to discover pleiotropic effects of PCSK9, APOB, and LDLR. NPJ Genom Med. 2019;4:3. doi: 10.1038/s41525-019-0078-7 30774981
94. Verma SS, Frase AT, Verma A, Pendergrass SA, Mahony SA, Haas DW, et al. Phenome-wide interaction study (PheWIS) in AIDS Clinical Trials Group Data (ACTG). Pac Symp Biocomput. 2016;(21):5768–68.
95. Verma A, Verma SS, Pendergrass SA, Crawford DC, Crosslin DR, Kuivaniemi H, et al. eMERGE Phenome-Wide Association Study (PheWAS) identifies clinical associations and pleiotropy for stop-gain variants. BMC Medical Genomics. 2016;9(1):19–25. doi: 10.1186/s12920-016-0191-8 27535653
96. Verma A, Basile AO, Bradford Y, Kuivaniemi H, Tromp G, Carey D, et al. Phenome-Wide Association Study to Explore Relationships between Immune System Related Genetic Loci and Complex Traits and Diseases. PLOS ONE. 2016;11(8):e0160573. doi: 10.1371/journal.pone.0160573 27508393
97. Verma A, Leader JB, Verma SS, Frase A, Wallace J, Dudek S, et al. Integrating clinical laboratory measures and ICD-9 code diagnoses in phenome-wide association studies. Pac Symp Biocomput. 2016;21:168–79. 26776183
98. Denny JC. Systematic comparison of phenome-wide association study of electronic medical record data and genome-wide association study data. Nat Biotechnol. 2013;31. doi: 10.1038/nbt.2749 24270849
99. Pendergrass SA, Crawford DC. Using Electronic Health Records To Generate Phenotypes For Research. Current Protocols in Human Genetics. 2019;100(1):e80. doi: 10.1002/cphg.80 30516347
100. Emdin CA, Khera AV, Kathiresan S. Mendelian RandomizationMendelian RandomizationMendelian Randomization. JAMA. 2017;318(19):1925–6.
101. Mailman MD, Feolo M, Jin Y, Kimura M, Tryka K, Bagoutdinov R, et al. The NCBI dbGaP database of genotypes and phenotypes. Nat Genet. 2007;39(10):1181–6. doi: 10.1038/ng1007-1181 17898773
102. Anderson GL, Manson J, Wallace R, Lund B, Hall D, Davis S, et al. Implementation of the Women’s Health Initiative study design. Ann Epidemiol. 2003;13(9 Suppl):S5–S17. doi: 10.1016/s1047-2797(03)00043-7 14575938
103. Giannoulatou E, Yau C, Colella S, Ragoussis J, Holmes CC. GenoSNP: a variational Bayes within-sample SNP genotyping algorithm that does not require a reference population. Bioinformatics. 2008;24(19):2209–14. doi: 10.1093/bioinformatics/btn386 18653518
104. Purcell S, Neale B, Todd-Brown K, Thomas L, Ferreira MA, Bender D, et al. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am J Hum Genet. 2007;81(3):559–75. Epub 559. doi: 10.1086/519795 17701901
105. Price AL, Patterson NJ, Plenge RM, Weinblatt ME, Shadick NA, Reich D. Principal components analysis corrects for stratification in genome-wide association studies. Nat Genet. 2006;38(8):904–9. doi: 10.1038/ng1847 16862161
106. Wolfe D, Dudek S, Ritchie M, Pendergrass S. Visualizing genomic information across chromosomes with PhenoGram. BioData Mining. 2013;6(1):18. doi: 10.1186/1756-0381-6-18 24131735
107. Lonsdale J, Thomas J, Salvatore M, Phillips R, Lo E, Shad S, et al. The Genotype-Tissue Expression (GTEx) project. Nat Genet. 2013;45(6):580–5. doi: 10.1038/ng.2653 23715323
108. MacArthur J, Bowler E, Cerezo M, Gil L, Hall P, Hastings E, et al. The new NHGRI-EBI Catalog of published genome-wide association studies (GWAS Catalog). Nucleic Acids Research. 2017;45(D1):D896–D901. doi: 10.1093/nar/gkw1133 27899670
109. Alexander DH, Novembre J, Lange K. Fast model-based estimation of ancestry in unrelated individuals. Genome Research. 2009;19(9):1655–64. doi: 10.1101/gr.094052.109 19648217
110. Baran Y, Pasaniuc B, Sankararaman S, Torgerson DG, Gignoux C, Eng C, et al. Fast and accurate inference of local ancestry in Latino populations. Bioinformatics. 2012;28(10):1359–67. doi: 10.1093/bioinformatics/bts144 22495753
Článok vyšiel v časopise
PLOS One
2019 Číslo 12
- Metamizol jako analgetikum první volby: kdy, pro koho, jak a proč?
- Nejasný stín na plicích – kazuistika
- Masturbační chování žen v ČR − dotazníková studie
- Úspěšná resuscitativní thorakotomie v přednemocniční neodkladné péči
- Fixní kombinace paracetamol/kodein nabízí synergické analgetické účinky
Najčítanejšie v tomto čísle
- Methylsulfonylmethane increases osteogenesis and regulates the mineralization of the matrix by transglutaminase 2 in SHED cells
- Oregano powder reduces Streptococcus and increases SCFA concentration in a mixed bacterial culture assay
- The characteristic of patulous eustachian tube patients diagnosed by the JOS diagnostic criteria
- Parametric CAD modeling for open source scientific hardware: Comparing OpenSCAD and FreeCAD Python scripts