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Abstract:
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Discoveries and analyses of genetic variants at a gene or exome based on high-throughput sequencing technology are increasingly feasible. Although many association tests have been proposed in literature for testing whether a group of variants in a target region is associated with a disease of interest, however, the analytic challenges remain profound. The power of the well-known tests generally depends on the sample size, numbers of causal and neutral variants, variant frequency, effect size and direction. Further complications arise from missing genotype, population stratification (PS) or misspecification of the working model.We propose a model-free association test based on testing zero proportion of causal variants in the gene region, and show this test to be almost uniformly most powerful among the competing tests under very general simulation conditions with covariates. This test does not require genotype data to be complete and hence difficult imputation can be avoided. We also discuss how to adjust for the effect of population stratification based on principal components, and use a Shanghai breast cancer study to demonstrate application of the new test.
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