Fine-mapping of XXXX susceptibility loci at 8q23.3, 16q22.1 and 19q13.11: refinement of association signals and use of in silico analysis to suggest functional variation and unexpected candidate target genes.

@entity72713 @entity19936 @entity5962 @entity72714 @entity53119 @entity14 @entity36199
We have previously identified several @entity14 ( @entity14 )-associated polymorphisms using genome-wide association (GWA) analysis. We sought to fine-map the location of the functional variants for three of these regions at 8q23.3 ( @entity36199 ), 16q22.1 ( @entity5962 / @entity19936 ) and 19q13.11 ( @entity72713 ). We genotyped two case-control sets at high density in the selected regions and used existing data from four other case-control sets, comprising a total of 9328 @entity14 cases and 10 480 controls. To improve marker density, we imputed genotypes from the 1000 Genomes Project and Hapmap3 data sets. All three regions contained smaller areas in which a cluster of single nucleotide polymorphisms (SNPs) showed clearly stronger association signals than surrounding SNPs, allowing us to assign those areas as the most likely location of the disease-associated functional variant. Further fine-mapping within those areas was generally unhelpful in identifying the functional variation based on strengths of association. However, functional annotation suggested a relatively small number of functional SNPs, including some with potential regulatory function at 8q23.3 and 16q22.1 and a non-synonymous SNP in RPHN2. Interestingly, the expression quantitative trait locus browser showed a number of highly associated SNP alleles correlated with mRNA expression levels not of @entity36199 and @entity5962 or @entity19936 , but of @entity72714 and @entity53119 , respectively. In contrast, none of the top SNPs within these regions was associated with transcript levels at @entity36199 , @entity5962 or @entity19936 . Our post-GWA study highlights benefits of fine-mapping of common disease variants in combination with publicly available data sets. In addition, caution should be exercised when assigning functionality to candidate genes in regions discovered through GWA analysis.
