The potential for enhancing the power of genetic association studies in African Americans through the reuse of existing genotype data

Abstract

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.

Figure 1. Plot of the density function of beta distributions parameterized by mean and heterogeneity factor .

On each subplot the density is shown for two choices of namely  = 0.2 (solid line) and  = 0.25 (dotted lines).

Figure 2. Plot of non-centrality parameter for the Bourgain test for a case-control study using two incompletely admixed populations as sources of cases and controls respectively.

The parameters chosen refer to a test of a variant associated with disease which has 40 percent overall allele frequency and which is associated with a 10 percent difference in frequency between cases and controls (OR = 1.5 per copy). Cases are assumed to have average admixture percentage of 20 percent and controls 25 percent. The within population heterogeneity is specified by a single common heterogeneity parameter as used in Figure 1.

Figure 3. Principal components plots of AABC and selected HapMap samples.

Figure 4. Plot of estimate of proportion of African ancestry from STRUCTURE by participating AABC study.

Figure 5. Plot of variance.

Plot of the variance of according to the number, _, of eigenvectors adjusted for in the Bourgain test of association.

Figure 6. Quantile-quantile plot of p-values from association tests in the hypothesized case-control study in which cases from the CBCS and controls from the MEC are used.

The plotted values indicate adjustment for 0 (uncorrected heavy solid line), 1 (dashed line), 10 (dotted line) and 200 (thin solid line) eigenvectors, by using these components in the calculation of .