Haplotype-based association analysis in cohort studies of unrelated individuals.

Exploring the associations between haplotypes and disease phenotypes is an important step toward the discovery of genes that influence complex human diseases. When unrelated subjects are sampled, haplotypes are often ambiguous because of the unknown gametic phase of the measured sites along a chromosome. We consider cohort studies of unrelated subjects which collect data on potentially censored ages of onset of disease along with unphased genotypes and possibly time-varying environmental factors. We formulate the effects of haplotypes and environmental variables on the time to disease occurrence through a semiparametric Cox proportional hazards model, which can accommodate a variety of genetic mechanisms as well as gene-environment interactions. We develop a simple and fast expectation-maximization algorithm to maximize the likelihood for the relative risks and other parameters based on the observable data of unphased genotypes and potentially censored ages of onset. The resultant estimators are consistent, efficient, and asymptotically normal. Simulation studies show that, for practical situations, the parameter estimators are virtually unbiased, the association tests maintain type I errors near nominal levels, the confidence intervals have proper coverage probabilities, and the efficiency loss due to unknown gametic phase is small.