Abstract
Among the various linkage-disequilibrium (LD) fine-mapping methods, two broad classes have received considerable development recently: those based on coalescent theory and those based on haplotype clustering. Using Genetic Analysis Workshop 15 Problem 3 simulated data, the ability of these two classes to localize the causal variation were compared. Our results suggest that a haplotype-clustering-based approach performs favorably, while at the same time requires much less computing than coalescent-based approaches. Further, we observe that 1) when marker density is low, a set of equally spaced single-nucleotide polymorphisms (SNPs) provides better localization than a set of tagging SNPs of equal number; 2) denser sets of SNPs generally lead to better localization, but the benefit diminishes beyond a certain density; 3) larger sample size may do more harm than good when poor selection of markers results in biased LD patterns around the disease locus. These results are explained by how well the set of selected markers jointly approximates the expected LD pattern around a disease locus.
Highlights
Among the various linkage disequilibrium (LD) finemapping methods, two broad classes have received considerable development recently: those based on coalescent theory and those based on haplotype clustering
TreeLD is liberal with correspondingly shorter credible intervals (CI), while WAL was able to maintain the nominal coverage
The empirical coverage of TreeLD intervals increases with the mutation rate when tagging SNPs are used, whereas it is relatively constant, albeit liberal, when equally spaced (ES) SNPs are used
Summary
Among the various linkage disequilibrium (LD) finemapping methods, two broad classes have received considerable development recently: those based on coalescent theory and those based on haplotype clustering. Three particular implementations seem promising: TreeLD by Zollner and Pritchard [1], the software by Molitor et al [2], and that by Waldron et al [3], the first one based on coalescent theory and the latter two (referred to as MOL and WAL) based on haplotype clustering. Coalescent-based LD fine mapping explicitly models the history of current genetic variations. This conceptual advantage poses serious challenges to implementation due to the large number of parameters required to specify evolutionary details and the computational demand. Waldron et al [3] implements a novel similarity measure that takes into account allele frequencies and occasional mismatches from mutations or gene conversions
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