Ascertainment in two-phase sampling designs for segregation and linkage analysis

The optimization of genetic study designs is critical to detecting and locating genes responsible for complex diseases. It can be affected by many factors. They include the choice of sampling units, sampling procedures, phenotyping, genotyping, and analysis schemes (segregation and linkage analysis, etc).; In this dissertation, first we define two-phase sampling designs based on weighted distributions and then compare a variety of sampling schemes using numerous combinations with different proportions of multiplex (M) and simplex (S) families. Subsequently, we establish two-phase designs for segregation analysis and linkage analysis of general pedigrees by simulations and propose a cost function for linkage analysis in two-phase designs. Our new likelihood proposed for two-phase designs can provide consistent estimates of the parameters and optimal designs. Especially when there is incomplete penetrance, having more simplex families in the samples is necessary to obtain better parameter estimates. Under the fully penetrant dominant model, a sampling design with 9% simplex families provides good estimates of the allele frequency ( q) and recombination fraction (theta). Under the dominant model with penetrance = 0.8, a sampling design with 18% simplex families yields better estimates of q and theta and is more cost effective. Under the dominant model with penetrance = 0.5, samples with 23% to 28% simplex families yield better estimates of q and theta and are more cost effective. Under the recessive model with the full penetrance, samples with 29% to 38% simplex families produce better estimates of q and theta and are more cost effective. Under the recessive models corresponding to a penetrance of 0.8, the effects of simplex families obviously increase; samples with 38% simplex families yield better estimates of q and theta and are most cost effective. Under the recessive models corresponding to a penetrance of 0.5, samples with 41% simplex families have a better capacity for producing consistent estimates of the parameters as well as a higher efficacy regarding cost for testing genetic models.