TY - JOUR
T1 - Multivariate generalized linear model for genetic pleiotropy
AU - Schaid, Daniel J.
AU - Tong, Xingwei
AU - Batzler, Anthony
AU - Sinnwell, Jason P.
AU - Qing, Jiang
AU - Biernacka, Joanna M.
N1 - Funding Information:
This research was supported by the U.S. Public Health Service, National Institutes of Health, contract grants numbers GM065450, GM28157, and GM61388. We gratefully acknowledge use of the depression data from the Mayo Clinic Pharmacogenomic Research Network Antidepressant Medication Pharma-cogenomic Study. Finally, we would like to thank the patients who participated in the PGRN-AMPS SSRI trial as well as the Mayo Clinic psychiatrists who cared for them. Conflict of Interest: None declared.
Funding Information:
Funding for this work was provided by the U.S. National Institutes of Health, contract grant number GM065450.
Publisher Copyright:
© 2017. Published by Oxford University Press.
PY - 2019/1/1
Y1 - 2019/1/1
N2 - When a single gene influences more than one trait, known as pleiotropy, it is important to detect pleiotropy to improve the biological understanding of a gene. This can lead to improved screening, diagnosis, and treatment of diseases.Yet, most current multivariate methods to evaluate pleiotropy test the null hypothesis that none of the traits are associated with a variant; departures from the null could be driven by just one associated trait. A formal test of pleiotropy should assume a null hypothesis that one or fewer traits are associated with a genetic variant. We recently developed statistical methods to analyze pleiotropy for quantitative traits having a multivariate normal distribution. We now extend this approach to traits that can be modeled by generalized linear models, such as analysis of binary, ordinal, or quantitative traits, or a mixture of these types of traits. Based on methods from estimating equations, we developed a new test for pleiotropy.We then extended the testing framework to a sequential approach to test the null hypothesis that k + 1 traits are associated, given that the null of k associated traits was rejected. This provides a testing framework to determine the number of traits associated with a genetic variant, as well as which traits, while accounting for correlations among the traits. By simulations, we illustrate the Type-I error rate and power of our new methods, describe how they are influenced by sample size, the number of traits, and the trait correlations, and apply the new methods to a genome-wide association study of multivariate traits measuring symptoms of major depression. Our new approach provides a quantitative assessment of pleiotropy, enhancing current analytic practice.
AB - When a single gene influences more than one trait, known as pleiotropy, it is important to detect pleiotropy to improve the biological understanding of a gene. This can lead to improved screening, diagnosis, and treatment of diseases.Yet, most current multivariate methods to evaluate pleiotropy test the null hypothesis that none of the traits are associated with a variant; departures from the null could be driven by just one associated trait. A formal test of pleiotropy should assume a null hypothesis that one or fewer traits are associated with a genetic variant. We recently developed statistical methods to analyze pleiotropy for quantitative traits having a multivariate normal distribution. We now extend this approach to traits that can be modeled by generalized linear models, such as analysis of binary, ordinal, or quantitative traits, or a mixture of these types of traits. Based on methods from estimating equations, we developed a new test for pleiotropy.We then extended the testing framework to a sequential approach to test the null hypothesis that k + 1 traits are associated, given that the null of k associated traits was rejected. This provides a testing framework to determine the number of traits associated with a genetic variant, as well as which traits, while accounting for correlations among the traits. By simulations, we illustrate the Type-I error rate and power of our new methods, describe how they are influenced by sample size, the number of traits, and the trait correlations, and apply the new methods to a genome-wide association study of multivariate traits measuring symptoms of major depression. Our new approach provides a quantitative assessment of pleiotropy, enhancing current analytic practice.
KW - Constrained model
KW - Estimating equations
KW - Multivariate analysis
KW - Sequential testing.
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U2 - 10.1093/biostatistics/kxx067
DO - 10.1093/biostatistics/kxx067
M3 - Article
C2 - 29267957
AN - SCOPUS:85058768562
VL - 20
SP - 111
EP - 128
JO - Biostatistics
JF - Biostatistics
SN - 1465-4644
IS - 1
ER -