TY - JOUR
T1 - Semiparametric analysis of complex polygenic gene-environment interactions in case-control studies
AU - Stalder, Odile
AU - Asher, Alex
AU - Liang, Liang
AU - Carroll, Raymond J.
AU - Ma, Yanyuan
AU - Chatterjee, Nilanjan
N1 - Funding Information:
Stalder and Asher should be considered joint first authors. Carroll is also Distinguished Professor at the University of Technology Sydney. Chatterjee is also Bloomberg Professor of Oncology at the Johns Hopkins University. Stalder was supported by a fellowship from the Fondation Ernest Boninchi. Ma was supported by the U.S. National Science Foundation and National Institute of Neurological Disorders and Stroke. Asher, Liang and Carroll were supported by the National Cancer Institute. Chatterjee’s research was partially funded through a Patient-Centered Outcomes Research Institute Award. The statements and opinions in this article are solely the responsibility of the authors and do not necessarily represent the views of the Patient-Centered Outcomes Research Institute, its Board of Governors or Methodology Committee.
Publisher Copyright:
© 2017 Biometrika Trust.
Copyright:
Copyright 2017 Elsevier B.V., All rights reserved.
PY - 2017/12/1
Y1 - 2017/12/1
N2 - Many methods have recently been proposed for efficient analysis of case-control studies of gene-environment interactions using a retrospective likelihood framework that exploits the natural assumption of gene-environment independence in the underlying population. However, for polygenic modelling of gene-environment interactions, which is a topic of increasing scientific interest, applications of retrospective methods have been limited due to a requirement in the literature for parametric modelling of the distribution of the genetic factors.We propose a general, computationally simple, semiparametric method for analysis of case-control studies that allows exploitation of the assumption of gene-environment independence without any further parametric modelling assumptions about the marginal distributions of any of the two sets of factors. The method relies on the key observation that an underlying efficient profile likelihood depends on the distribution of genetic factors only through certain expectation terms that can be evaluated empirically.We develop asymptotic inferential theory for the estimator and evaluate its numerical performance via simulation studies. An application of the method is presented.
AB - Many methods have recently been proposed for efficient analysis of case-control studies of gene-environment interactions using a retrospective likelihood framework that exploits the natural assumption of gene-environment independence in the underlying population. However, for polygenic modelling of gene-environment interactions, which is a topic of increasing scientific interest, applications of retrospective methods have been limited due to a requirement in the literature for parametric modelling of the distribution of the genetic factors.We propose a general, computationally simple, semiparametric method for analysis of case-control studies that allows exploitation of the assumption of gene-environment independence without any further parametric modelling assumptions about the marginal distributions of any of the two sets of factors. The method relies on the key observation that an underlying efficient profile likelihood depends on the distribution of genetic factors only through certain expectation terms that can be evaluated empirically.We develop asymptotic inferential theory for the estimator and evaluate its numerical performance via simulation studies. An application of the method is presented.
UR - http://www.scopus.com/inward/record.url?scp=85039165947&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85039165947&partnerID=8YFLogxK
U2 - 10.1093/biomet/asx045
DO - 10.1093/biomet/asx045
M3 - Article
C2 - 29430038
AN - SCOPUS:85039165947
VL - 104
SP - 801
EP - 812
JO - Biometrika
JF - Biometrika
SN - 0006-3444
IS - 4
ER -