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 - 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.
UR - http://www.scopus.com/inward/record.url?scp=85058768562&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85058768562&partnerID=8YFLogxK
U2 - 10.1093/biostatistics/kxx067
DO - 10.1093/biostatistics/kxx067
M3 - Article
C2 - 29267957
AN - SCOPUS:85058768562
SN - 1465-4644
VL - 20
SP - 111
EP - 128
JO - Biostatistics
JF - Biostatistics
IS - 1
ER -