Statistical issues in family-based genetic association studies with application to congenital heart defects in Down syndrome

This dissertation is motivated by data generated from a genetic association study of congenital heart defects in Down syndrome (DS). Congenital heart defects are among the most common abnormalities seen at birth. The genetic basis for most congenital heart defects is unknown. One severe form of congenital heart defect, atrioventricular septal defect (AVSD), is highly associated with DS. This makes the DS population a useful tool for discovering of genes that are associated with this specific form of congenital heart defect. Discovering genes that influence risk of AVSD will lead to a better understanding of heart development and of the etiology of these defects. This in turn can lead eventually to improved public health through better screening, prevention, and treatment strategies.;For the genotype-calling problem, we generated two genotype calling methods specifically for disomic family trio data. The first method is an ad-hoc modification of the K-means clustering algorithm that incorporates family information. The second is a likelihood-based method that combines the mixture model approach with a pedigree likelihood. These two methods out-performed existing methods, which ignore the family information, both in simulation studies and a real data analysis. We also extended these two methods to trisomic trio data.;With regard to analysis strategies, we discussed alternative analysis methods for trio designs, particularly for the combination of case trios and control trios that we have in the Down syndrome data. We derived likelihood models that help explain the differences among some published methods. We also proposed an extension of a combined likelihood-based method proposed by Epstein and others for analysis of case trios plus independent controls to our design of case and control trios.;Family trios were collected for the Down syndrome heart study. This dissertation discusses statistical issues raised in genetic association studies using family trio data, including the genotype calling problem (i.e. how to generate genotype data from the raw data produced by high-throughput SNP arrays) and analysis strategies. Although the motivating dataset involves trisomic individuals, we developed statistical methods both for disomic and trisomic data.