|Title||Unified Analysis of Secondary Traits in Case-Control Association Studies.|
|Publication Type||Journal Article|
|Year of Publication||2013|
|Authors||Ghosh, Arpita, Fred A. Wright, and Fei Zou|
|Journal||J Am Stat Assoc|
It has been repeatedly shown that in case-control association studies, analysis of a secondary trait which ignores the original sampling scheme can produce highly biased risk estimates. Although a number of approaches have been proposed to properly analyze secondary traits, most approaches fail to reproduce the marginal logistic model assumed for the original case-control trait and/or do not allow for interaction between secondary trait and genotype marker on primary disease risk. In addition, the flexible handling of covariates remains challenging. We present a general retrospective likelihood framework to perform association testing for both binary and continuous secondary traits which respects marginal models and incorporates the interaction term. We provide a computational algorithm, based on a reparameterized approximate profile likelihood, for obtaining the maximum likelihood (ML) estimate and its standard error for the genetic effect on secondary trait, in presence of covariates. For completeness we also present an alternative pseudo-likelihood method for handling covariates. We describe extensive simulations to evaluate the performance of the ML estimator in comparison with the pseudo-likelihood and other competing methods.
|Alternate Journal||J Am Stat Assoc|
|Original Publication||Unified analysis of secondary traits in case-control association studies.|
|PubMed Central ID||PMC3881430|
|Grant List||P01 CA142538 / CA / NCI NIH HHS / United States |
P30 ES010126 / ES / NIEHS NIH HHS / United States
R01 GM074175 / GM / NIGMS NIH HHS / United States
Unified Analysis of Secondary Traits in Case-Control Association Studies.