Power and type 1 error for large pedigree analyses of binary traits

Abstract

Studying population isolates with large, complex pedigrees has many advantages for discovering genetic susceptibility loci; however, statistical analyses can be computationally challenging. Allelic association tests need to be corrected for relatedness among study participants, and linkage analyses require subdividing and simplifying the pedigree structures. In this thesis work I simulated SNP (single nucleotide polymorphism) data in complex pedigree structures based on an Amish pedigree. I evaluated type 1 error rates and power when performing two-point and multipoint linkage after dividing the pedigree into subpedigrees. I also ran MQLS (modified likelihood score test) to test for allelic association in the subpedigrees and in the unified pedigree.