The Use of Haplotypes in the Identification of Interaction between SNPs

Although haplotypes can provide great insight into the complex relationships between functional polymorphisms at a locus, their use in modern association studies has been limited. This is due to our inability to directly observe haplotypes in studies of unrelated individuals, but also to the extra complexity involved in their analysis and the difficulty in identifying which is the truly informative haplotype. Using a series of simulations, we tested a number of different models of a haplotype carrying two functional single nucleotide polymorphisms (SNPs) to assess the ability of haplotypic analysis to identify functional interactions between SNPs at the same locus. We found that, when phase is known, analysis of the haplotype is more powerful than analysis of the individual SNPs. The difference between the two approaches becomes less either as an increasing number of non-informative SNPs are included, or when the haplotypic phase is unknown, while in both cases the SNP association becomes progressively better at identifying the association. Our results suggest that when novel genotyping and bioinformatics methods are available to reconstruct haplotypic phase, this will permit the emergence of a new wave of haplotypic analysis able to consider interactions between SNPs with increased statistical power.</p

The Potential for Enhancing the Power of Genetic Association Studies in African Americans through the Reuse of Existing Genotype Data

We consider the feasibility of reusing existing control data obtained in genetic association studies in order to reduce costs for new studies. We discuss controlling for the population differences between cases and controls that are implicit in studies utilizing external control data. We give theoretical calculations of the statistical power of a test due to Bourgain et al (Am J Human Genet 2003), applied to the problem of dealing with case-control differences in genetic ancestry related to population isolation or population admixture. Theoretical results show that there may exist bounds for the non-centrality parameter for a test of association that places limits on study power even if sample sizes can grow arbitrarily large. We apply this method to data from a multi-center, geographically-diverse, genome-wide association study of breast cancer in African-American women. Our analysis of these data shows that admixture proportions differ by center with the average fraction of European admixture ranging from approximately 20% for participants from study sites in the Eastern United States to 25% for participants from West Coast sites. However, these differences in average admixture fraction between sites are largely counterbalanced by considerable diversity in individual admixture proportion within each study site. Our results suggest that statistical correction for admixture differences is feasible for future studies of African-Americans, utilizing the existing controls from the African-American Breast Cancer study, even if case ascertainment for the future studies is not balanced over the same centers or regions that supplied the controls for the current study

An omnibus test for family-based association studies with multiple SNPs and multiple phenotypes

We propose an omnibus family-based association test (MFBAT) that can be applied to multiple markers and multiple phenotypes and that has only one degree of freedom. The proposed test statistic extends current FBAT methodology to incorporate multiple markers as well as multiple phenotypes. Using simulation studies, power estimates for the proposed methodology are compared with the standard methodologies. On the basis of these simulations, we find that MFBAT substantially outperforms other methods, including haplotypic approaches and doing multiple tests with single single-nucleotide polymorphisms (SNPs) and single phenotypes. The practical relevance of the approach is illustrated by an application to asthma in which SNP/phenotype combinations are identified and reach overall significance that would not have been identified using other approaches. This methodology is directly applicable to cases in which there are multiple SNPs, such as candidate gene studies, cases in which there are multiple phenotypes, such as expression data, and cases in which there are multiple phenotypes and genotypes, such as genome-wide association studies that incorporate expression profiles as phenotypes. This program is available in the PBAT analysis package