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PLoS One. 2016 Jul 5;11(7):e0157521. doi: 10.1371/journal.pone.0157521. eCollection 2016.

Fine-Mapping of Common Genetic Variants Associated with Colorectal Tumor Risk Identified Potential Functional Variants.

Author information

1
Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, United States of America.
2
Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, United States of America.
3
School of Public Health, University of Washington, Seattle, WA, United States of America.
4
Division of Gastroenterology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States of America.
5
Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI, United States of America.
6
Service de Génétique Médicale, CHU Nantes, Nantes, France.
7
Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany.
8
German Cancer Consortium (DKTK), Heidelberg, Germany.
9
Division of Research, Kaiser Permanente Medical Care Program of Northern California, Oakland, CA, United States of America.
10
Keck School of Medicine, University of Southern California, Los Angeles, CA, United States of America.
11
Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany.
12
Translational Genomics Research Institute, Phoenix, Arizona, United States of America.
13
Department of Surgery, Mount Sinai Hospital, Toronto, ON, Canada.
14
Division of Epidemiology, Department of Population Health, New York University School of Medicine, New York, NY, United States of America.
15
Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Victoria, Australia.
16
Ontario Institute for Cancer Research, Toronto, ON, Canada.
17
Departments of Medical Biophysics and Molecular Genetics, University of Toronto, Toronto, ON, Canada.
18
Epidemiology Program, University of Hawaii Cancer Center, Honolulu, HI, United States of America.
19
Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, United States of America.
20
Genome Sciences, University of Washington, Seattle, WA, United States of America.
21
Centre for Public Health Research, Massey University, Wellington, New Zealand.
22
Department of Medicine and Epidemiology, University of Pittsburgh Medical Center, Pittsburgh, PA, United States of America.
23
Division of Cancer Control and Population Sciences, National Cancer Institute, Bethesda, MD, United States of America.
24
Department of Internal Medicine, University of Utah Health Sciences Center, Salt Lake City, UT, United States of America.
25
Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, United States of America.
26
Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, United States of America.

Abstract

Genome-wide association studies (GWAS) have identified many common single nucleotide polymorphisms (SNPs) associated with colorectal cancer risk. These SNPs may tag correlated variants with biological importance. Fine-mapping around GWAS loci can facilitate detection of functional candidates and additional independent risk variants. We analyzed 11,900 cases and 14,311 controls in the Genetics and Epidemiology of Colorectal Cancer Consortium and the Colon Cancer Family Registry. To fine-map genomic regions containing all known common risk variants, we imputed high-density genetic data from the 1000 Genomes Project. We tested single-variant associations with colorectal tumor risk for all variants spanning genomic regions 250-kb upstream or downstream of 31 GWAS-identified SNPs (index SNPs). We queried the University of California, Santa Cruz Genome Browser to examine evidence for biological function. Index SNPs did not show the strongest association signals with colorectal tumor risk in their respective genomic regions. Bioinformatics analysis of SNPs showing smaller P-values in each region revealed 21 functional candidates in 12 loci (5q31.1, 8q24, 11q13.4, 11q23, 12p13.32, 12q24.21, 14q22.2, 15q13, 18q21, 19q13.1, 20p12.3, and 20q13.33). We did not observe evidence of additional independent association signals in GWAS-identified regions. Our results support the utility of integrating data from comprehensive fine-mapping with expanding publicly available genomic databases to help clarify GWAS associations and identify functional candidates that warrant more onerous laboratory follow-up. Such efforts may aid the eventual discovery of disease-causing variant(s).

PMID:
27379672
PMCID:
PMC4933364
DOI:
10.1371/journal.pone.0157521
[Indexed for MEDLINE]
Free PMC Article

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