Fine-mapping of colorectal cancer 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

单核苷酸多态性 生物 表达数量性状基因座 全基因组关联研究 遗传学 1000基因组计划 基因座(遗传学) SNP公司 遗传关联 数量性状位点 连锁不平衡 基因型 计算生物学 基因
作者
Luis G. Carvajal‐Carmona,Jean‐Baptiste Cazier,Angela M. Jones,Kimberley Howarth,Peter Broderick,Alan Pittman,Sara E. Dobbins,Albert Tenesa,Susan M. Farrington,James Prendergast,Evropi Τheodoratou,Rebecca A. Barnetson,David V. Conti,Polly A. Newcomb,John L. Hopper,Mark A. Jenkins,Steven Gallinger,David Duggan,Harry Campbell,David Kerr
出处
期刊:Human Molecular Genetics [Oxford University Press]
卷期号:20 (14): 2879-2888 被引量:63
标识
DOI:10.1093/hmg/ddr190
摘要

We have previously identified several colorectal cancer (CRC)-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 (EIF3H), 16q22.1 (CDH1/CDH3) and 19q13.11 (RHPN2). 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 CRC 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 EIF3H and CDH1 or CDH3, but of UTP23 and ZFP90, respectively. In contrast, none of the top SNPs within these regions was associated with transcript levels at EIF3H, CDH1 or CDH3. 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.
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