A powerful method of combining measures of association and Hardy-Weinberg disequilibrium for fine-mapping in case-control studies

Abstract

We present a new method for fine-mapping a disease susceptibility locus using a case-control design. The new method, termed the 'weighted average (WA) statistic', averages the Cochran-Armitage (CA) trend test statistic and the difference between the Hardy Weinberg disequilibrium test statistics (the HWD trend) for cases and controls. The main features of the WA statistic are that it mitigates against the weaknesses, and maintains the strong points, of both the CA trend test and the HWD trend test. To allow for the extra variance induced by population structure and cryptic relatedness, the WA statistic can be adjusted for variance inflation. Based on the results of a simulation study, when there is no population structure the WA test statistic shows good performance under a variety of genetic disease models. When there is population structure, the adjusted WA statistic maintains the correct probability of type I error. Under all genetic disease models investigated, the adjusted WA statistic has better power than the adjusted CA trend test, the HWD trend test or the product of the adjusted CA trend test and the HWD trend test statistics.