A resampling-based approach to multiple testing with uncertainty in phase.

Characterizing the genetic correlates to complex diseases requires consideration of a large number of potentially informative biological markers. In addition, attention to alignment of alleles within or across chromosomal pairs, commonly referred to as phase, may be essential for uncovering true biological associations. In the context of population based association studies, phase is generally unobservable. Preservation of type-1 error in a setting with multiple testing presents a further analytical challenge. This manuscript combines a likelihood-based approach to handling missing-ness in phase with a resampling method to adjust for multiple testing. Through simulations we demonstrate preservation of the family-wise error rate and reasonable power for detecting associations. The method is applied to a cohort of 626 HIV-1 infected individuals receiving highly active anti-retroviral therapies, to ascertain potential genetic contributions to abnormalities in lipid profiles. The haplotypic effects of 2 genes, hepatic lipase (HL) and endothelial lipase (EL), on high-density lipoprotein cholesterol (HDL-C) are tested.