A unified stepwise regression procedure for evaluating the relative effects of polymorphisms within a gene using case/control or family data: Application to HLA in type 1 diabetes

A stepwise logistic-regression procedure is proposed for evaluation of the relative importance of variants at different sites within a small genetic r egion. By fitting statistical models with main effects, rather than modelin g the full haplotype effects, we generate tests, with few degrees of freedo m, that are likely to be powerful for detecting primary etiological determi nants. The approach is applicable to either case/control or nuclear-family data, with case/control data modeled via unconditional and family data via conditional logistic regression. Four different conditioning strategies are proposed for evaluation of effects at multiple, closely linked loci when f amily data are used. The first strategy results in a likelihood that is equ ivalent to analysis of a matched case/control study with each affected offs pring matched to three pseudocontrols, whereas the second strategy is equiv alent to matching each affected offspring with between one and three pseudo controls. Both of these strategies require parental phase (i.e., those hapl otypes present in the parents) to be inferable. Families in which phase can not be determined must be discarded, which can considerably reduce the effe ctive size of a data set, particularly when large numbers of loci that are not very polymorphic are being considered. Therefore, a third strategy is p roposed in which knowledge of parental phase is not required, which allows those families with ambiguous phase to be included in the analysis. The fou rth and final strategy is to use conditioning method 2 when parental phase can be inferred and to use conditioning method 3 otherwise. The methods are illustrated using nuclear-family data to evaluate the contribution of loci in the HLA region to the development of type 1 diabetes.