Abstract
Both haplotype-based and locus-based methods have been proposed as the most powerful methods to employ when fine mapping by association. Although haplotype-based methods utilize more information, they may lose power as a result of overparameterization, given the large number of haplotypes possible over even a few loci. Recently methods have been developed that cluster haplotypes with similar structure in the hope that this reflects shared genealogical ancestry. The aim is to reduce the number of parameters while retaining the genotype information relating to disease susceptibility. We have compared several haplotype-based methods with locus-based methods. We utilized 2 regions (D2 and D4) simulated to be in linkage disequilibrium and to be associated with disease susceptibility, combining 5 replicates at a time to produce 4 datasets that were analyzed. We found little difference in the performance of the haplotype-based methods and the locus-based methods in this dataset.
Highlights
It is widely accepted that for the fine-scale mapping of disease susceptibility loci, association-based approaches are more appropriate than linkage methods
We focused on susceptibility regions D2 and D4, simulated to be in linkage disequilibrium (LD)
We tested a single locus at a time, referred to as method (i). (For region D2 single-nucleotide polymorphisms (SNPs) 1–27 refer to B03T3041 to C04R0282; and for D4 SNPs 1–38 refer to B09T8321 to B09T8360)
Summary
It is widely accepted that for the fine-scale mapping of disease susceptibility loci, association-based approaches are more appropriate than linkage methods. Genome-wide association studies are often forecast, association studies currently focus predominantly on relatively small candidate regions. Such regions are suggested either by strong evidence from linkage studies or from functional arguments and are typically densely genotyped. The parameter space can be reduced while, it is hoped, retaining that phase information relevant to disease susceptibility. Locus-based methods have been refined to incorporate information on the estimated genealogical structure prior to formal testing [4]. The power of such approaches has been examined by Clayton et al [5]
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