A powerful method for pleiotropic analysis under composite null hypothesis identifies novel shared loci between Type 2 Diabetes and Prostate Cancer
Autoři:
Debashree Ray aff001; Nilanjan Chatterjee aff002
Působiště autorů:
Department of Epidemiology, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, Maryland, United States of America
aff001; Department of Biostatistics, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, Maryland, United States of America
aff002; Department of Oncology, School of Medicine, Johns Hopkins University, Baltimore, Maryland, United States of America
aff003
Vyšlo v časopise:
A powerful method for pleiotropic analysis under composite null hypothesis identifies novel shared loci between Type 2 Diabetes and Prostate Cancer. PLoS Genet 16(12): e1009218. doi:10.1371/journal.pgen.1009218
Kategorie:
Research Article
doi:
https://doi.org/10.1371/journal.pgen.1009218
Souhrn
There is increasing evidence that pleiotropy, the association of multiple traits with the same genetic variants/loci, is a very common phenomenon. Cross-phenotype association tests are often used to jointly analyze multiple traits from a genome-wide association study (GWAS). The underlying methods, however, are often designed to test the global null hypothesis that there is no association of a genetic variant with any of the traits, the rejection of which does not implicate pleiotropy. In this article, we propose a new statistical approach, PLACO, for specifically detecting pleiotropic loci between two traits by considering an underlying composite null hypothesis that a variant is associated with none or only one of the traits. We propose testing the null hypothesis based on the product of the Z-statistics of the genetic variants across two studies and derive a null distribution of the test statistic in the form of a mixture distribution that allows for fractions of variants to be associated with none or only one of the traits. We borrow approaches from the statistical literature on mediation analysis that allow asymptotic approximation of the null distribution avoiding estimation of nuisance parameters related to mixture proportions and variance components. Simulation studies demonstrate that the proposed method can maintain type I error and can achieve major power gain over alternative simpler methods that are typically used for testing pleiotropy. PLACO allows correlation in summary statistics between studies that may arise due to sharing of controls between disease traits. Application of PLACO to publicly available summary data from two large case-control GWAS of Type 2 Diabetes and of Prostate Cancer implicated a number of novel shared genetic regions: 3q23 (ZBTB38), 6q25.3 (RGS17), 9p22.1 (HAUS6), 9p13.3 (UBAP2), 11p11.2 (RAPSN), 14q12 (AKAP6), 15q15 (KNL1) and 18q23 (ZNF236).
Klíčová slova:
Case-control studies – Genetic loci – Genetics – Genetics of disease – Genome-wide association studies – Quantitative trait loci – Research errors – Single nucleotide polymorphisms
Zdroje
1. Solovieff N, Cotsapas C, Lee PH, Purcell SM, Smoller JW. Pleiotropy in complex traits: challenges and strategies. Nat Rev Genet. 2013;14(7):483–495. doi: 10.1038/nrg3461 23752797
2. 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):607–618. doi: 10.1016/j.ajhg.2011.10.004 22077970
3. Wu YH, Graff RE, Passarelli MN, Hoffman JD, Ziv E, Hoffmann TJ, et al. Identification of pleiotropic cancer susceptibility variants from genome-wide association studies reveals functional characteristics. Cancer Epidemiol Biomarkers Prev. 2018;27(1):75–85. doi: 10.1158/1055-9965.EPI-17-0516 29150481
4. Cotsapas C, Voight BF, Rossin E, Lage K, Neale BM, Wallace C, et al. Pervasive sharing of genetic effects in autoimmune disease. PLoS Genet. 2011;7(8):e1002254. doi: 10.1371/journal.pgen.1002254 21852963
5. Cross-Disorder Group of the Psychiatric Genomics Consortium. Identification of risk loci with shared effects on five major psychiatric disorders: a genome-wide analysis. Lancet. 2013;381(9875):1371–1379. doi: 10.1016/S0140-6736(12)62129-1 23453885
6. Amare AT, Vaez A, Hsu YH, Direk N, Kamali Z, Howard DM, et al. Bivariate genome-wide association analyses of the broad depression phenotype combined with major depressive disorder, bipolar disorder or schizophrenia reveal eight novel genetic loci for depression. Mol Psychiatry. 2019; p. 1. doi: 10.1038/s41380-018-0336-6 30626913
7. Li R, Brockschmidt FF, Kiefer AK, Stefansson H, Nyholt DR, Song K, et al. Six novel susceptibility Loci for early-onset androgenetic alopecia and their unexpected association with common diseases. PLoS Genet. 2012;8(5):e1002746. doi: 10.1371/journal.pgen.1002746 22693459
8. Hui KY, Fernandez-Hernandez H, Hu J, Schaffner A, Pankratz N, Hsu NY, et al. Functional variants in the LRRK2 gene confer shared effects on risk for Crohn’s disease and Parkinson’s disease. Sci Transl Med. 2018;10(423):eaai7795. doi: 10.1126/scitranslmed.aai7795 29321258
9. Pickrell JK, Berisa T, Liu JZ, Ségurel L, Tung JY, Hinds DA. Detection and interpretation of shared genetic influences on 42 human traits. Nat Genet. 2016;48(7):709–717. doi: 10.1038/ng.3570 27182965
10. Yang C, Li C, Wang Q, Chung D, Zhao H. Implications of pleiotropy: challenges and opportunities for mining Big Data in biomedicine. Front Genet. 2015;6:229. doi: 10.3389/fgene.2015.00229 26175753
11. Gratten J, Visscher PM. Genetic pleiotropy in complex traits and diseases: implications for genomic medicine. Genome Med. 2016;8(1):78. doi: 10.1186/s13073-016-0332-x 27435222
12. Hackinger S, Zeggini E. Statistical methods to detect pleiotropy in human complex traits. Open Biol. 2017;7(11):170125. doi: 10.1098/rsob.170125 29093210
13. Ray D, Chatterjee N. Effect of non-normality and low count variants on cross-phenotype association tests in GWAS. Eur J Hum Genet. 2020;28:300–312. doi: 10.1038/s41431-019-0514-2 31582815
14. Baker AR, Goodloe RJ, Larkin EK, Baechle DJ, Song YE, Phillips LS, et al. Multivariate association analysis of the components of metabolic syndrome from the Framingham Heart Study. In: BMC Proc. vol. 3. BioMed Central; 2009. p. S42.
15. Inouye M, Ripatti S, Kettunen J, Lyytikäinen LP, Oksala N, Laurila PP, et al. Novel Loci for metabolic networks and multi-tissue expression studies reveal genes for atherosclerosis. PLoS Genet. 2012;8(8):e1002907. doi: 10.1371/journal.pgen.1002907 22916037
16. Medina-Gomez C, Kemp JP, Dimou NL, Kreiner E, Chesi A, Zemel BS, et al. Bivariate genome-wide association meta-analysis of pediatric musculoskeletal traits reveals pleiotropic effects at the SREBF1/TOM1L2 locus. Nat Commun. 2017;8(1):121. doi: 10.1038/s41467-017-00108-3 28743860
17. Heid IM, Winkler TW. A multitrait GWAS sheds light on insulin resistance. Nat Genet. 2017;49(1):7. doi: 10.1038/ng.3758
18. Shen X, Klarić L, Sharapov S, Mangino M, Ning Z, Wu D, et al. Multivariate discovery and replication of five novel loci associated with immunoglobulin GN-glycosylation. Nat Commun. 2017;8(1):447. doi: 10.1038/s41467-017-00453-3 28878392
19. Zhao W, Rasheed A, Tikkanen E, Lee JJ, Butterworth AS, Howson JM, et al. Identification of new susceptibility loci for type 2 diabetes and shared etiological pathways with coronary heart disease. Nat Genet. 2017;49(10):1450–1457. doi: 10.1038/ng.3943 28869590
20. Baselmans BM, Jansen R, Ip HF, van Dongen J, Abdellaoui A, van de Weijer MP, et al. Multivariate genome-wide analyses of the well-being spectrum. Nat Genet. 2019;51(3):445–451. doi: 10.1038/s41588-018-0320-8 30643256
21. Nath AP, Ritchie SC, Grinberg NF, Tang HH, Huang QQ, Teo SM, et al. Multivariate genome-wide association analysis of a cytokine network reveals variants with widespread immune, haematological, and cardiometabolic pleiotropy. Am J Hum Genet. 2019;105(6):1076–1090. doi: 10.1016/j.ajhg.2019.10.001 31679650
22. Andreassen OA, Thompson WK, Schork AJ, Ripke S, Mattingsdal M, Kelsoe JR, et al. Improved detection of common variants associated with schizophrenia and bipolar disorder using pleiotropy-informed conditional false discovery rate. PLoS Genet. 2013;9(4):e1003455. doi: 10.1371/journal.pgen.1003455 23637625
23. Chung D, Yang C, Li C, Gelernter J, Zhao H. GPA: a statistical approach to prioritizing GWAS results by integrating pleiotropy and annotation. PLoS Genet. 2014;10(11):e1004787. doi: 10.1371/journal.pgen.1004787 25393678
24. Liley J, Wallace C. A pleiotropy-informed Bayesian false discovery rate adapted to a shared control design finds new disease associations from GWAS summary statistics. PLoS Genet. 2015;11(2):e1004926. doi: 10.1371/journal.pgen.1004926 25658688
25. Ming J, Wang T, Yang C. LPM: a latent probit model to characterize the relationship among complex traits using summary statistics from multiple GWASs and functional annotations. Bioinformatics. 2020;36(8):2506–2514. doi: 10.1093/bioinformatics/btz947 31860024
26. Zhang Q, Feitosa M, Borecki IB. Estimating and testing pleiotropy of single genetic variant for two quantitative traits. Genet Epidemiol. 2014;38(6):523–530. doi: 10.1002/gepi.21837 25044106
27. Schaid DJ, Tong X, Larrabee B, Kennedy RB, Poland GA, Sinnwell JP. Statistical methods for testing genetic pleiotropy. Genetics. 2016;204(2):483–497. doi: 10.1534/genetics.116.189308 27527515
28. Lutz SM, Fingerlin TE, Hokanson JE, Lange C. A general approach to testing for pleiotropy with rare and common variants. Genet Epidemiol. 2017;41(2):163–170. doi: 10.1002/gepi.22011 27900789
29. Schaid DJ, Tong X, Batzler A, Sinnwell JP, Qing J, Biernacka JM. Multivariate generalized linear model for genetic pleiotropy. Biostatistics. 2017;20(1):111–128.
30. 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):904–909. doi: 10.1038/ng1847 16862161
31. Kang HM, Sul JH, Service SK, Zaitlen NA, Kong Sy, Freimer NB, et al. Variance component model to account for sample structure in genome-wide association studies. Nat Genet. 2010;42(4):348–354. doi: 10.1038/ng.548 20208533
32. Broadaway KA, Cutler DJ, Duncan R, Moore JL, Ware EB, Jhun MA, et al. A statistical approach for testing cross-phenotype effects of rare variants. Am J Hum Genet. 2016;98(3):525–540. doi: 10.1016/j.ajhg.2016.01.017 26942286
33. Berger RL. In: Panchapakesan S, Balakrishnan N, editors. Likelihood ratio tests and intersection-union tests. Boston, MA: Birkhäuser Boston; 1997. p. 225–237.
34. MacKinnon DP, Lockwood CM, Hoffman JM, West SG, Sheets V. A comparison of methods to test mediation and other intervening variable effects. Psychol Methods. 2002;7(1):83–104. doi: 10.1037/1082-989X.7.1.83 11928892
35. Sobel ME. Asymptotic confidence intervals for indirect effects in structural equation models. Sociol Methodol. 1982;13:290–312. doi: 10.2307/270723
36. Huang YT. Genome-wide analyses of sparse mediation effects under composite null hypotheses. Ann Appl Stat. 2019;13(1):60–84. doi: 10.1214/18-AOAS1181
37. Craig CC. On the frequency function of xy. Ann Math Statist. 1936;7(1):1–15.
38. R Core Team. R: A language and environment for statistical computing; 2018. Available from: https://www.R-project.org/.
39. MacKinnon DP, Warsi G, Dwyer JH. A simulation study of mediated effect measures. Multivariate Behav Res. 1995;30(1):41–62. doi: 10.1207/s15327906mbr3001_3 20157641
40. Wellcome Trust Case Control Consortium, et al. Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls. Nature. 2007;447(7145):661–678. doi: 10.1038/nature05911 17554300
41. Mitchell BD, Fornage M, McArdle PF, Cheng YC, Pulit S, Wong Q, et al. Using previously genotyped controls in genome-wide association studies (GWAS): application to the Stroke Genetics Network (SiGN). Front Genet. 2014;5:95. doi: 10.3389/fgene.2014.00095 24808905
42. Lin DY, Sullivan PF. Meta-analysis of genome-wide association studies with overlapping subjects. Am J Hum Genet. 2009;85(6):862–872. doi: 10.1016/j.ajhg.2009.11.001 20004761
43. Ray D, Boehnke M. Methods for meta-analysis of multiple traits using GWAS summary statistics. Genet Epidemiol. 2018;42(2):134–145. doi: 10.1002/gepi.22105 29226385
44. Bulik-Sullivan B, Finucane HK, Anttila V, Gusev A, Day FR, Loh PR, et al. An atlas of genetic correlations across human diseases and traits. Nat Genet. 2015;47(11):1236–1241. doi: 10.1038/ng.3406 26414676
45. Wang T, Elston RC. Improved power by use of a weighted score test for linkage disequilibrium mapping. Am J Hum Genet. 2007;80(2):353–360. doi: 10.1086/511312 17236140
46. Basu S, Pan W. Comparison of statistical tests for disease association with rare variants. Genet Epidemiol. 2011;35(7):606–619. doi: 10.1002/gepi.20609 21769936
47. Ray D, Li X, Pan W, Pankow JS, Basu S. A Bayesian partitioning model for the detection of multilocus effects in case-control studies. Hum Hered. 2015;79(2):69–79. doi: 10.1159/000369858 26044550
48. Giovannucci E, Rimm EB, Stampfer MJ, Colditz GA, Willett WC. Diabetes mellitus and risk of prostate cancer (United States). Cancer Causes Control. 1998;9(1):3–9. doi: 10.1023/A:1008822917449 9486458
49. Kasper JS, Giovannucci E. A meta-analysis of diabetes mellitus and the risk of prostate cancer. Cancer Epidemiol Biomarkers Prev. 2006;15(11):2056–2062. doi: 10.1158/1055-9965.EPI-06-0410 17119028
50. Waters KM, Henderson BE, Stram DO, Wan P, Kolonel LN, Haiman CA. Association of diabetes with prostate cancer risk in the multiethnic cohort. Am J Epidemiol. 2009;169(8):937–945. doi: 10.1093/aje/kwp003 19240222
51. Machiela MJ, Lindström S, Allen NE, Haiman CA, Albanes D, Barricarte A, et al. Association of type 2 diabetes susceptibility variants with advanced prostate cancer risk in the Breast and Prostate Cancer Cohort Consortium. Am J Epidemiol. 2012;176(12):1121–1129. doi: 10.1093/aje/kws191 23193118
52. Gallagher EJ, LeRoith D. Epidemiology and molecular mechanisms tying obesity, diabetes, and the metabolic syndrome with cancer. Diabetes Care. 2013;36(Supplement 2):S233–S239. doi: 10.2337/dcS13-2001 23882051
53. Frayling T, Colhoun H, Florez J. A genetic link between type 2 diabetes and prostate cancer. Diabetologia. 2008;51(10):1757–1760. doi: 10.1007/s00125-008-1114-9 18696045
54. Pierce BL, Ahsan H. Genetic susceptibility to type 2 diabetes is associated with reduced prostate cancer risk. Hum Hered. 2010;69(3):193–201. doi: 10.1159/000289594 20203524
55. Meyer TE, Boerwinkle E, Morrison AC, Volcik KA, Sanderson M, Coker AL, et al. Diabetes genes and prostate cancer in the Atherosclerosis Risk in Communities study. Cancer Epidemiol Biomarkers Prev. 2010;19(2):558–565. doi: 10.1158/1055-9965.EPI-09-0902 20142250
56. Yu OHY, Foulkes WD, Dastani Z, Martin RM, Eeles R, Richards JB, et al. An assessment of the shared allelic architecture between type II diabetes and prostate cancer. Cancer Epidemiol Biomarkers Prev. 2013;22(8):1473–1475. doi: 10.1158/1055-9965.EPI-13-0476 23704474
57. Xue A, Wu Y, Zhu Z, Zhang F, Kemper KE, Zheng Z, et al. Genome-wide association analyses identify 143 risk variants and putative regulatory mechanisms for type 2 diabetes. Nat Commun. 2018;9(1):2941. doi: 10.1038/s41467-018-04951-w 30054458
58. Morris AP, Voight BF, Teslovich TM, Ferreira T, Segre AV, Steinthorsdottir V, et al. Large-scale association analysis provides insights into the genetic architecture and pathophysiology of type 2 diabetes. Nat Genet. 2012;44(9):981–990. doi: 10.1038/ng.2383 22885922
59. Banda Y, Kvale MN, Hoffmann TJ, Hesselson SE, Ranatunga D, Tang H, et al. Characterizing race/ethnicity and genetic ancestry for 100,000 subjects in the Genetic Epidemiology Research on Adult Health and Aging (GERA) cohort. Genetics. 2015;200(4):1285–1295. doi: 10.1534/genetics.115.178616 26092716
60. Bycroft C, Freeman C, Petkova D, Band G, Elliott LT, Sharp K, et al. The UK Biobank resource with deep phenotyping and genomic data. Nature. 2018;562(7726):203–209. doi: 10.1038/s41586-018-0579-z 30305743
61. Schumacher FR, Al Olama AA, Berndt SI, Benlloch S, Ahmed M, Saunders EJ, et al. Association analyses of more than 140,000 men identify 63 new prostate cancer susceptibility loci. Nat Genet. 2018;50(7):928. doi: 10.1038/s41588-018-0142-8 29892016
62. Bulik-Sullivan BK, Loh PR, Finucane HK, Ripke S, Yang J, Patterson N, et al. LD Score regression distinguishes confounding from polygenicity in genome-wide association studies. Nat Genet. 2015;47(3):291–295. doi: 10.1038/ng.3211 25642630
63. Zheng J, Erzurumluoglu AM, Elsworth BL, Kemp JP, Howe L, Haycock PC, et al. LD Hub: a centralized database and web interface to perform LD score regression that maximizes the potential of summary level GWAS data for SNP heritability and genetic correlation analysis. Bioinformatics. 2016;33(2):272–279. doi: 10.1093/bioinformatics/btw613 27663502
64. Watanabe K, Taskesen E, Van Bochoven A, Posthuma D. Functional mapping and annotation of genetic associations with FUMA. Nat Commun. 2017;8(1):1826. doi: 10.1038/s41467-017-01261-5 29184056
65. Giambartolomei C, Vukcevic D, Schadt EE, Franke L, Hingorani AD, Wallace C, et al. Bayesian test for colocalisation between pairs of genetic association studies using summary statistics. PLoS Genet. 2014;10(5):e1004383. doi: 10.1371/journal.pgen.1004383 24830394
66. Guo H, Fortune MD, Burren OS, Schofield E, Todd JA, Wallace C. Integration of disease association and eQTL data using a Bayesian colocalisation approach highlights six candidate causal genes in immune-mediated diseases. Hum Mol Genet. 2015;24(12):3305–3313. doi: 10.1093/hmg/ddv077 25743184
67. Watanabe K, Stringer S, Frei O, Mirkov MU, de Leeuw C, Polderman TJ, et al. A global overview of pleiotropy and genetic architecture in complex traits. Nat Genet. 2019;51(9):1339–1348. doi: 10.1038/s41588-019-0481-0 31427789
68. Koscielny G, An P, Carvalho-Silva D, Cham JA, Fumis L, Gasparyan R, et al. Open Targets: a platform for therapeutic target identification and validation. Nucleic Acids Res. 2016;45(D1):D985–D994. doi: 10.1093/nar/gkw1055 27899665
69. Võsa U, Claringbould A, Westra HJ, Bonder MJ, Deelen P, Zeng B, et al. Unraveling the polygenic architecture of complex traits using blood eQTL metaanalysis. bioRxiv. 2018
70. Mahajan A, Taliun D, Thurner M, Robertson NR, Torres JM, Rayner NW, et al. Fine-mapping type 2 diabetes loci to single-variant resolution using high-density imputation and islet-specific epigenome maps. Nat Genet. 2018;50:1505–1513. doi: 10.1038/s41588-018-0241-6 30297969
71. GTEx Consortium, et al. Genetic effects on gene expression across human tissues. Nature. 2017;550(7675):204–213. doi: 10.1038/nature24277 29022597
72. Benjamini Y, Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Statist Soc B. 1995;57(1):289–300.
73. Bonovas S, Filioussi K, Tsantes A. Diabetes mellitus and risk of prostate cancer: a meta-analysis. Diabetologia. 2004;47(6):1071–1078. doi: 10.1007/s00125-004-1415-6 15164171
74. Tande AJ, Platz EA, Folsom AR. The metabolic syndrome is associated with reduced risk of prostate cancer. Am J Epidemiol. 2006;164(11):1094–1102. doi: 10.1093/aje/kwj320 16968859
75. Buniello A, MacArthur JAL, Cerezo M, Harris LW, Hayhurst J, Malangone C, et al. The NHGRI-EBI GWAS Catalog of published genome-wide association studies, targeted arrays and summary statistics 2019. Nucleic Acids Res. 2019;47(D1):D1005–D1012. doi: 10.1093/nar/gky1120 30445434
76. Bioconductor Package Maintainer. liftOver: Changing genomic coordinate systems with rtracklayer::liftOver.; 2019. Available from: https://www.bioconductor.org/help/workflows/liftOver/.
77. Gudmundsson J, Sulem P, Steinthorsdottir V, Bergthorsson JT, Thorleifsson G, Manolescu A, et al. Two variants on chromosome 17 confer prostate cancer risk, and the one in TCF2 protects against type 2 diabetes. Nat Genet. 2007;39(8):977–983. doi: 10.1038/ng2062 17603485
78. Liberzon A, Subramanian A, Pinchback R, Thorvaldsdóttir H, Tamayo P, Mesirov JP. Molecular signatures database (MSigDB) 3.0. Bioinformatics. 2011;27(12):1739–1740. doi: 10.1093/bioinformatics/btr260 21546393
79. Kutmon M, Riutta A, Nunes N, Hanspers K, Willighagen EL, Bohler A, et al. WikiPathways: capturing the full diversity of pathway knowledge. Nucleic Acids Res. 2015;44(D1):D488–D494. doi: 10.1093/nar/gkv1024 26481357
80. Lindström S, Finucane H, Bulik-Sullivan B, Schumacher FR, Amos CI, Hung RJ, et al. Quantifying the genetic correlation between multiple cancer types. Cancer Epidemiol Biomarkers Prev. 2017;26(9):1427–1435. doi: 10.1158/1055-9965.EPI-17-0211 28637796
Článek vyšel v časopise
PLOS Genetics
2020 Číslo 12
- 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
- Exploiting codon usage identifies intensity-specific modifiers of Ras/MAPK signaling in vivo
- Common maternal and fetal genetic variants show expected polygenic effects on risk of small- or large-for-gestational-age (SGA or LGA), except in the smallest 3% of babies
- PEA15 loss of function and defective cerebral development in the domestic cat
- Precision medicine in cats—The right biomedical model may not be the mouse!
