4.5 Article

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

Journal

HUMAN MOLECULAR GENETICS
Volume 20, Issue 14, Pages 2879-2888

Publisher

OXFORD UNIV PRESS
DOI: 10.1093/hmg/ddr190

Keywords

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Funding

  1. Cancer Research UK
  2. Oxford Comprehensive Biomedical Research Centre
  3. EU
  4. Wellcome Trust Centre for Human Genetics, Oxford [075491/Z/04]
  5. Medical Research Council [G0000657-53203]
  6. Scottish Executive Chief Scientist's Office [K/OPR/2/2/D333, CZB/4/449]
  7. National Cancer Institute
  8. National Institutes of Health [CA-95-011]
  9. Australian Colorectal Cancer Family Registry [UO1 CA097735]
  10. USC Familial Colorectal Neoplasia Collaborative Group [UO1 CA074799]
  11. Mayo Clinic Cooperative Family Registry for Colon Cancer Studies [UO1 CA074800]
  12. Ontario Registry for Studies of Familial Colorectal Cancer [UO1 CA074783]
  13. Seattle Colorectal Cancer Family Registry [UO1 CA074794]
  14. University of Hawaii Colorectal Cancer Family Registry [UO1 CA074806]
  15. MRC [MC_PC_U127527198, MC_U127527198] Funding Source: UKRI
  16. Cancer Research UK [12076] Funding Source: researchfish
  17. Chief Scientist Office [CZB/4/449] Funding Source: researchfish
  18. Medical Research Council [MC_PC_U127527198, MC_U127527198] Funding Source: researchfish

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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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