A General Framework for Association Tests with Multivariate Traits in Large-scale Genomics Studies

Overview

Journal Genet Epidemiol

Specialties Genetics
Public Health

Date 2013 Nov 15

PMID 24227293

Citations 32

Authors

Qianchuan He

Christy L Avery

Dan-Yu Lin

Affiliations

Soon will be listed here.

Abstract

Genetic association studies often collect data on multiple traits that are correlated. Discovery of genetic variants influencing multiple traits can lead to better understanding of the etiology of complex human diseases. Conventional univariate association tests may miss variants that have weak or moderate effects on individual traits. We propose several multivariate test statistics to complement univariate tests. Our framework covers both studies of unrelated individuals and family studies and allows any type/mixture of traits. We relate the marginal distributions of multivariate traits to genetic variants and covariates through generalized linear models without modeling the dependence among the traits or family members. We construct score-type statistics, which are computationally fast and numerically stable even in the presence of covariates and which can be combined efficiently across studies with different designs and arbitrary patterns of missing data. We compare the power of the test statistics both theoretically and empirically. We provide a strategy to determine genome-wide significance that properly accounts for the linkage disequilibrium (LD) of genetic variants. The application of the new methods to the meta-analysis of five major cardiovascular cohort studies identifies a new locus (HSCB) that is pleiotropic for the four traits analyzed.

Citing Articles

An adaptive and robust method for multi-trait analysis of genome-wide association studies using summary statistics.

Deng Q, Song C, Lin S Eur J Hum Genet. 2023; 32(6):681-690.

PMID: 37237036 PMC: 11153499. DOI: 10.1038/s41431-023-01389-7.

Statistical Inference for High-Dimensional Pathway Analysis with Multiple Responses.

Liu Y, Sun W, Hsu L, He Q Comput Stat Data Anal. 2022; 169.

PMID: 35125572 PMC: 8813039. DOI: 10.1016/j.csda.2021.107418.

Association of Melanoma-Risk Variants with Primary Melanoma Tumor Prognostic Characteristics and Melanoma-Specific Survival in the GEM Study.

Davari D, Orlow I, Kanetsky P, Luo L, Busam K, Sharma A Curr Oncol. 2021; 28(6):4756-4771.

PMID: 34898573 PMC: 8628692. DOI: 10.3390/curroncol28060401.

Disease-Associated Risk Variants in Are Associated with Tumor-Infiltrating Lymphocyte Presence in Primary Melanomas in the Population-Based GEM Study.

Davari D, Orlow I, Kanetsky P, Luo L, Edmiston S, Conway K Cancer Epidemiol Biomarkers Prev. 2021; 30(12):2309-2316.

PMID: 34607836 PMC: 8643342. DOI: 10.1158/1055-9965.EPI-21-0686.

Comparison of adaptive multiple phenotype association tests using summary statistics in genome-wide association studies.

Sitlani C, Baldassari A, Highland H, Hodonsky C, McKnight B, Avery C Hum Mol Genet. 2021; 30(15):1371-1383.

PMID: 33949650 PMC: 8283209. DOI: 10.1093/hmg/ddab126.

References

Watanabe R, Ghosh S, Langefeld C, Valle T, Hauser E, Magnuson V . The Finland-United States investigation of non-insulin-dependent diabetes mellitus genetics (FUSION) study. II. An autosomal genome scan for diabetes-related quantitative-trait loci. Am J Hum Genet. 2000; 67(5):1186-200. PMC: 1288561. View

Yang Q, Wu H, Guo C, Fox C . Analyze multivariate phenotypes in genetic association studies by combining univariate association tests. Genet Epidemiol. 2010; 34(5):444-54. PMC: 3090041. DOI: 10.1002/gepi.20497. View

van der Sluis S, Posthuma D, Dolan C . TATES: efficient multivariate genotype-phenotype analysis for genome-wide association studies. PLoS Genet. 2013; 9(1):e1003235. PMC: 3554627. DOI: 10.1371/journal.pgen.1003235. View

Lawson H, Cady J, Partridge C, Wolf J, Semenkovich C, Cheverud J . Genetic effects at pleiotropic loci are context-dependent with consequences for the maintenance of genetic variation in populations. PLoS Genet. 2011; 7(9):e1002256. PMC: 3169520. DOI: 10.1371/journal.pgen.1002256. View

Amos C, Laing A . A comparison of univariate and multivariate tests for genetic linkage. Genet Epidemiol. 1993; 10(6):671-6. DOI: 10.1002/gepi.1370100657. View

Lin D, Tang Z . A general framework for detecting disease associations with rare variants in sequencing studies. Am J Hum Genet. 2011; 89(3):354-67. PMC: 3169821. DOI: 10.1016/j.ajhg.2011.07.015. View

Rouault T, Tong W . Iron-sulfur cluster biogenesis and human disease. Trends Genet. 2008; 24(8):398-407. PMC: 2574672. DOI: 10.1016/j.tig.2008.05.008. View

Ferreira M, Purcell S . A multivariate test of association. Bioinformatics. 2008; 25(1):132-3. DOI: 10.1093/bioinformatics/btn563. View

Sivakumaran S, Agakov F, Theodoratou E, Prendergast J, Zgaga L, Manolio T . Abundant pleiotropy in human complex diseases and traits. Am J Hum Genet. 2011; 89(5):607-18. PMC: 3213397. DOI: 10.1016/j.ajhg.2011.10.004. View

10.

Lawn R, Wade D, Garvin M, Wang X, Schwartz K, Porter J . The Tangier disease gene product ABC1 controls the cellular apolipoprotein-mediated lipid removal pathway. J Clin Invest. 1999; 104(8):R25-31. PMC: 481052. DOI: 10.1172/JCI8119. View

11.

Sun G, Gargus J, Ta D, Vickery L . Identification of a novel candidate gene in the iron-sulfur pathway implicated in ataxia-susceptibility: human gene encoding HscB, a J-type co-chaperone. J Hum Genet. 2003; 48(8):415-419. DOI: 10.1007/s10038-003-0048-9. View

12.

Lin D . An efficient Monte Carlo approach to assessing statistical significance in genomic studies. Bioinformatics. 2004; 21(6):781-7. DOI: 10.1093/bioinformatics/bti053. View

13.

Avery C, He Q, North K, Ambite J, Boerwinkle E, Fornage M . A phenomics-based strategy identifies loci on APOC1, BRAP, and PLCG1 associated with metabolic syndrome phenotype domains. PLoS Genet. 2011; 7(10):e1002322. PMC: 3192835. DOI: 10.1371/journal.pgen.1002322. View

14.

Liu J, Pei Y, Papasian C, Deng H . Bivariate association analyses for the mixture of continuous and binary traits with the use of extended generalized estimating equations. Genet Epidemiol. 2008; 33(3):217-27. PMC: 2745071. DOI: 10.1002/gepi.20372. View

15.

Grundy S . Pre-diabetes, metabolic syndrome, and cardiovascular risk. J Am Coll Cardiol. 2012; 59(7):635-43. DOI: 10.1016/j.jacc.2011.08.080. View

16.

Paaby A, Rockman M . The many faces of pleiotropy. Trends Genet. 2012; 29(2):66-73. PMC: 3558540. DOI: 10.1016/j.tig.2012.10.010. View

17.

OBrien P . Procedures for comparing samples with multiple endpoints. Biometrics. 1984; 40(4):1079-87. View

18.

Conneely K, Boehnke M . Meta-analysis of genetic association studies and adjustment for multiple testing of correlated SNPs and traits. Genet Epidemiol. 2010; 34(7):739-46. PMC: 3070606. DOI: 10.1002/gepi.20538. View

19.

Maity A, Sullivan P, Tzeng J . Multivariate phenotype association analysis by marker-set kernel machine regression. Genet Epidemiol. 2012; 36(7):686-95. PMC: 3703860. DOI: 10.1002/gepi.21663. View

20.

Jiang C, Zeng Z . Multiple trait analysis of genetic mapping for quantitative trait loci. Genetics. 1995; 140(3):1111-27. PMC: 1206666. DOI: 10.1093/genetics/140.3.1111. View