Abstract
Evaluating multiple binary outcomes is common in genetic studies of complex diseases. These outcomes are often correlated because they are collected from the same individual and they may share common marker effects. In this paper, we propose a procedure to test for effect of a single nucleotide polymorphism-set on multiple, possibly correlated, binary responses. We develop a score-based test using a non-parametric modeling framework that jointly models the global effect of the marker set. We account for the non-linear effects and potentially complicated interaction between markers using reproducing kernels. Our testing procedure only requires estimation under the null hypothesis and we use multivariate generalized estimating equations to estimate the model components to account for the correlation among the outcomes. We evaluate finite sample performance of our test via simulation study and demonstrate our methods using the Clinical Antipsychotic Trials of Intervention Effectiveness antibody study data and the CoLaus study data.
| Original language | English |
|---|---|
| Pages (from-to) | 117-138 |
| Number of pages | 22 |
| Journal | Statistics in Biosciences |
| Volume | 10 |
| Issue number | 1 |
| DOIs | |
| Publication status | Published - 2018 Apr 1 |
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All Science Journal Classification (ASJC) codes
- Statistics and Probability
- Biochemistry, Genetics and Molecular Biology (miscellaneous)
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A Powerful Test for SNP Effects on Multivariate Binary Outcomes Using Kernel Machine Regression. / Davenport, Clemontina A.; Maity, Arnab; Sullivan, Patrick F.; Tzeng, Jung-Ying.
In: Statistics in Biosciences, Vol. 10, No. 1, 01.04.2018, p. 117-138.Research output: Contribution to journal › Article
TY - JOUR
T1 - A Powerful Test for SNP Effects on Multivariate Binary Outcomes Using Kernel Machine Regression
AU - Davenport, Clemontina A.
AU - Maity, Arnab
AU - Sullivan, Patrick F.
AU - Tzeng, Jung-Ying
PY - 2018/4/1
Y1 - 2018/4/1
N2 - Evaluating multiple binary outcomes is common in genetic studies of complex diseases. These outcomes are often correlated because they are collected from the same individual and they may share common marker effects. In this paper, we propose a procedure to test for effect of a single nucleotide polymorphism-set on multiple, possibly correlated, binary responses. We develop a score-based test using a non-parametric modeling framework that jointly models the global effect of the marker set. We account for the non-linear effects and potentially complicated interaction between markers using reproducing kernels. Our testing procedure only requires estimation under the null hypothesis and we use multivariate generalized estimating equations to estimate the model components to account for the correlation among the outcomes. We evaluate finite sample performance of our test via simulation study and demonstrate our methods using the Clinical Antipsychotic Trials of Intervention Effectiveness antibody study data and the CoLaus study data.
AB - Evaluating multiple binary outcomes is common in genetic studies of complex diseases. These outcomes are often correlated because they are collected from the same individual and they may share common marker effects. In this paper, we propose a procedure to test for effect of a single nucleotide polymorphism-set on multiple, possibly correlated, binary responses. We develop a score-based test using a non-parametric modeling framework that jointly models the global effect of the marker set. We account for the non-linear effects and potentially complicated interaction between markers using reproducing kernels. Our testing procedure only requires estimation under the null hypothesis and we use multivariate generalized estimating equations to estimate the model components to account for the correlation among the outcomes. We evaluate finite sample performance of our test via simulation study and demonstrate our methods using the Clinical Antipsychotic Trials of Intervention Effectiveness antibody study data and the CoLaus study data.
UR - http://www.scopus.com/inward/record.url?scp=85015985735&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85015985735&partnerID=8YFLogxK
U2 - 10.1007/s12561-017-9189-9
DO - 10.1007/s12561-017-9189-9
M3 - Article
AN - SCOPUS:85015985735
VL - 10
SP - 117
EP - 138
JO - Statistics in Biosciences
JF - Statistics in Biosciences
SN - 1867-1764
IS - 1
ER -