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 statistic for cases and controls (the HWD trend). The main characteristics of the WA statistic are that it improves on the weaknesses, and maintains the strengths, of both the CA trend test and the HWD trend test. Data from three different populations in the Genetic Analysis Workshop 14 (GAW14) simulated dataset (Aipotu, Karangar, and Danacaa) were first subjected to model-free linkage analysis to find regions exhibiting linkage. Then, for fine-scale mapping, 140 SNPs within the significant linkage regions were analyzed with the WA test statistic on replicates of the three populations, both separately and combined. The regions that were significant in the multipoint linkage analysis were also significant in this fine-scale mapping. The most significant regions that were obtained using the WA statistic were regions in chromosome 3 (B03T3056–B03T3058, p-value < 1 × 10-10 ) and chromosome 9 (B09T8332–B09T8334, p-value 1 × 10-6 ). Based on the results of the simulated GAW14 data, the WA test statistic showed good performance and could narrow down the region containing the susceptibility locus. However, the strength of the signal depends on both the strength of the linkage disequilibrium and the heterozygosity of the linked marker.
Highlights
It has been shown that fine-scale mapping of a susceptibility locus for a complex disease can be accomplished by evaluating the deviation from Hardy-Weinberg equilibrium (HWE)
Genome scan using model-free linkage analysis In our analysis, we observed 8 microsatellite markers with a significance level of p ≤ 0.005 Evidence of linkage was found on chromosome 1
We compared the performance of the CA, weighted average (WA), and HardyWeinberg disequilibrium (HWD) tests in this dataset and found that the HWD trend test always had low power
Summary
It has been shown that fine-scale mapping of a susceptibility locus for a complex disease can be accomplished by evaluating the deviation from Hardy-Weinberg equilibrium (HWE). Feder et al [1], Nielsen et al [2] and Jiang et al [3] have discussed using the HardyWeinberg disequilibrium (HWD) test on affected individuals alone. From their results, this HWD test tends to perform well for a recessive disease model and could be more precise in gene localization, but has no power at all for a multiplicative disease model. Devlin and Roeder [5] proposed genomic control to allow for population heterogeneity when using the CA trend test. They showed by simulation that the (page number not for citation purposes)
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