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Evaluation of association of HNF1B variants with diverse cancers: Collaborative analysis of data from 19 genome-wide association studies

  • Australian Melanoma Family Study Investigators
  • , The PanScan Consortium
  • , Katherine S. Elliott
  • , Eleftheria Zeggini
  • , Mark I. McCarthy
  • , Julius Gudmundsson
  • , Patrick Sulem
  • , Simon N. Stacey
  • , Steinunn Thorlacius
  • , Laufey Amundadottir
  • , Henrik Grönberg
  • , Jianfeng Xu
  • , Valerie Gaborieau
  • , Rosalind A. Eeles
  • , David E. Neal
  • , Jenny L. Donovan
  • , Freddie C. Hamdy
  • , Kenneth Muir
  • , Shih Jen Hwang
  • , Margaret R. Spitz
  • Brent Zanke, Luis Carvajal-Carmona, Kevin M. Brown, Nicholas K. Hayward, Stuart Macgregor, Ian P.M. Tomlinson, Mathieu Lemire, Christopher I. Amos, Joanne M. Murabito, William B. Isaacs, Douglas F. Easton, Paul Brennan, Rosa B. Barkardottir, Daniel F. Gudbjartsson, Thorunn Rafnar, David J. Hunter, Stephen J. Chanock, Kari Stefansson, John P.A. Ioannidis, Graham J. Mann, John L. Hopper, Joanne F. Aitken, Richard F. Kefford, Graham G. Giles, Bruce K. Armstrong, Gloria M. Petersen, Charles S. Fuchs, Peter Kraft, Rachael Z. Stolzenberg-Solomon, Kevin B. Jacobs, Alan A. Arslan, Shannon M. Lynch
  • University of Oxford
  • University of Cambridge
  • deCODE Genetics
  • National Institutes of Health
  • Karolinska Institutet
  • Wake Forest University
  • International Agency for Research on Cancer
  • Royal Marsden Hospital
  • University of Bristol
  • University of Warwick
  • University of Texas MD Anderson Cancer Center
  • Ontario Institute for Cancer Research
  • University of Ottawa
  • Translational Genomics Research Institute
  • The Royal Brisbane and Women’s Hospital
  • Boston University
  • Johns Hopkins University
  • Landspitali University Hospital
  • University of Iceland
  • Harvard University
  • University of Ioannina
  • Tufts University
  • University of Sydney
  • University of Melbourne
  • Cancer Council Queensland
  • Cancer Council Victoria
  • Mayo Clinic
  • Dana-Farber Cancer Institute
  • SAIC
  • Bioinformed Consulting Services
  • New York University

Research output: Contribution to journalArticlepeer-review

27 Scopus citations

Abstract

Background: Genome-wide association studies have found type 2 diabetes-associated variants in the HNF1B gene to exhibit reciprocal associations with prostate cancer risk. We aimed to identify whether these variants may have an effect on cancer risk in general versus a specific effect on prostate cancer only. Methodology/Principal Findings: In a collaborative analysis, we collected data from GWAS of cancer phenotypes for the frequently reported variants of HNF1B, rs4430796 and rs7501939, which are in linkage disequilibrium (r2 = 0.76, HapMap CEU). Overall, the analysis included 16 datasets on rs4430796 with 19,640 cancer cases and 21,929 controls; and 21 datasets on rs7501939 with 26,923 cases and 49,085 controls. Malignancies other than prostate cancer included colorectal, breast, lung and pancreatic cancers, and melanoma. Meta-analysis showed large between-dataset heterogeneity that was driven by different effects in prostate cancer and other cancers. The per-T2D-risk-allele odds ratios (95% confidence intervals) for rs4430796 were 0.79 (0.76, 0.83)] per G allele for prostate cancer (p<10-15 for both); and 1.03 (0.99, 1.07) for all other cancers. Similarly for rs7501939 the per-T2D-risk-allele odds ratios (95% confidence intervals) were 0.80 (0.77, 0.83) per T allele for prostate cancer (p<10-15 for both); and 1.00 (0.97, 1.04) for all other cancers. No malignancy other than prostate cancer had a nominally statistically significant association. Conclusions/Significance: The examined HNF1B variants have a highly specific effect on prostate cancer risk with no apparent association with any of the other studied cancer types.

Original languageEnglish
Article numbere10858
Pages (from-to)e10858
JournalPLoS ONE
Volume5
Issue number5
DOIs
StatePublished - May 28 2010

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 3 - Good Health and Well-being
    SDG 3 Good Health and Well-being

Keywords

  • Cooperative Behavior
  • Databases, Genetic
  • Genetic Predisposition to Disease
  • Genome-Wide Association Study/methods
  • Hepatocyte Nuclear Factor 1-beta/genetics
  • Humans
  • Neoplasms/genetics
  • Polymorphism, Single Nucleotide/genetics

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