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Abstract:
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Many methods can detect trait association with risk variants in candidate genomic regions; however, a comparison of their ability to localize risk variants is lacking. We extend a previous study of the detection abilities of these methods to a comparison of their localization abilities. Through coalescent simulation, we compare several association methods. Cases and controls are sampled from a diploid population to mimic human studies. As benchmarks for comparison, we include two methods that cluster phenotypes on the true genealogical trees, a naive Mantel test considered previously in haploid populations and an extension that considers whether case haplotypes carry a risk variant. We first work through a simulated dataset to illustrate the methods. We then perform a simulation study to score the localization and detection properties. In our simulations, the association signal was localized least precisely by the naive Mantel test and most precisely by its extension; the other approaches had intermediate performance. Our results lend support to the potential of genealogy-based approaches to fine-map disease risk variants.
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