Content area
Full Text
European Journal of Human Genetics (2015) 23, 13571363& 2015 Macmillan Publishers Limited All rights reserved 1018-4813/15 http://www.nature.com/ejhg
Web End =www.nature.com/ejhg
Claire Dandine-Roulland*,1,2 and Herv Perdry1,2
Many associated single-nucleotide polymorphisms (SNPs) have been identied by association studies for numerous diseases. However, the association between a SNP and a disease can result from a causal variant in linkage disequilibrium (LD) with the considered SNP. Assuming that the true causal variant is among the genotyped SNPs, other authors demonstrated that the power to discriminate between it and other SNPs in LD is low. Here, we propose to take advantage of the information provided by family data to improve the inference on the causal variant: we exploit the linkage information provided by affected sib pairs to discriminate the causal variant from the associated SNPs. The family-based approach improves discrimination power requiringup to ve times less individuals than its casecontrol equivalent. However, the main advantage of family design is the possibility to carry out the procedure one step further: the linkage information allows inference on causal variants, which are not genotyped but in LD with tag-SNPs displaying association, which is impossible with casecontrol design. By means of Bayesian methods, we estimate the LD between the observed SNPs and an unobserved causal variant, as well as the allelic odds ratio at the unobserved causal variant. The proposed procedure is illustrated on a multiple sclerosis (MS) family data set including genotypes of SNPs in IL2RA, conrming the advantage of using a family design to identify causal variants. The results of our method on this data suggest the existence of two distinct causal variants in this gene for the MS.
European Journal of Human Genetics (2015) 23, 13571363; doi:http://dx.doi.org/10.1038/ejhg.2014.284
Web End =10.1038/ejhg.2014.284; published online 14 January 2015
INTRODUCTION
Association studies aim to identify variants associated with a disease, usually focusing on single-nucleotide polymorphisms (SNPs). They are able to detect the variants with modest effect, which are implied in complex diseases, contrarily to linkage analysis.1 In genome-wide association studies (GWAS), the considered variants are tag-SNPs, which capture most common SNPs of the genome through linkage disequilibrium (LD).2 However, the association between a SNP and a disease does not prove the causality link between the two: the association can result from a causal effect of...