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1
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Graff RE, Cavazos TB, Thai KK, Kachuri L, Rashkin SR, Hoffman JD, Alexeeff SE, Blatchins M, Meyers TJ, Leong L, Tai CG, Emami NC, Corley DA, Kushi LH, Ziv E, Van Den Eeden SK, Jorgenson E, Hoffmann TJ, Habel LA, Witte JS, Sakoda LC. Cross-cancer evaluation of polygenic risk scores for 16 cancer types in two large cohorts. Nat Commun 2021; 12:970. [PMID: 33579919 PMCID: PMC7880989 DOI: 10.1038/s41467-021-21288-z] [Citation(s) in RCA: 48] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2020] [Accepted: 01/19/2021] [Indexed: 02/07/2023] Open
Abstract
Even distinct cancer types share biological hallmarks. Here, we investigate polygenic risk score (PRS)-specific pleiotropy across 16 cancers in European ancestry individuals from the Genetic Epidemiology Research on Adult Health and Aging cohort (16,012 cases, 50,552 controls) and UK Biobank (48,969 cases, 359,802 controls). Within cohorts, each PRS is evaluated in multivariable logistic regression models against all other cancer types. Results are then meta-analyzed across cohorts. Ten positive and one inverse cross-cancer associations are found after multiple testing correction. Two pairs show bidirectional associations; the melanoma PRS is positively associated with oral cavity/pharyngeal cancer and vice versa, whereas the lung cancer PRS is positively associated with oral cavity/pharyngeal cancer, and the oral cavity/pharyngeal cancer PRS is inversely associated with lung cancer. Overall, we validate known, and uncover previously unreported, patterns of pleiotropy that have the potential to inform investigations of risk prediction, shared etiology, and precision cancer prevention strategies.
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Affiliation(s)
- Rebecca E Graff
- Department of Epidemiology and Biostatistics, University of California San Francisco, San Francisco, CA, USA.,Division of Research, Kaiser Permanente Northern California, Oakland, CA, USA.,Helen Diller Family Comprehensive Cancer Center, University of California San Francisco, San Francisco, CA, USA
| | - Taylor B Cavazos
- Program in Biological and Medical Informatics, University of California San Francisco, San Francisco, CA, USA
| | - Khanh K Thai
- Division of Research, Kaiser Permanente Northern California, Oakland, CA, USA
| | - Linda Kachuri
- Department of Epidemiology and Biostatistics, University of California San Francisco, San Francisco, CA, USA
| | - Sara R Rashkin
- Department of Epidemiology and Biostatistics, University of California San Francisco, San Francisco, CA, USA
| | - Joshua D Hoffman
- Department of Epidemiology and Biostatistics, University of California San Francisco, San Francisco, CA, USA
| | - Stacey E Alexeeff
- Division of Research, Kaiser Permanente Northern California, Oakland, CA, USA
| | - Maruta Blatchins
- Division of Research, Kaiser Permanente Northern California, Oakland, CA, USA
| | - Travis J Meyers
- Department of Epidemiology and Biostatistics, University of California San Francisco, San Francisco, CA, USA
| | - Lancelote Leong
- Department of Epidemiology and Biostatistics, University of California San Francisco, San Francisco, CA, USA
| | - Caroline G Tai
- Department of Epidemiology and Biostatistics, University of California San Francisco, San Francisco, CA, USA
| | - Nima C Emami
- Department of Epidemiology and Biostatistics, University of California San Francisco, San Francisco, CA, USA.,Program in Biological and Medical Informatics, University of California San Francisco, San Francisco, CA, USA
| | - Douglas A Corley
- Division of Research, Kaiser Permanente Northern California, Oakland, CA, USA
| | - Lawrence H Kushi
- Division of Research, Kaiser Permanente Northern California, Oakland, CA, USA
| | - Elad Ziv
- Helen Diller Family Comprehensive Cancer Center, University of California San Francisco, San Francisco, CA, USA.,Institute for Human Genetics, University of California San Francisco, San Francisco, CA, USA.,Department of Medicine, University of California San Francisco, San Francisco, CA, USA
| | - Stephen K Van Den Eeden
- Division of Research, Kaiser Permanente Northern California, Oakland, CA, USA.,Department of Urology, University of California San Francisco, San Francisco, CA, USA
| | - Eric Jorgenson
- Division of Research, Kaiser Permanente Northern California, Oakland, CA, USA
| | - Thomas J Hoffmann
- Department of Epidemiology and Biostatistics, University of California San Francisco, San Francisco, CA, USA.,Division of Research, Kaiser Permanente Northern California, Oakland, CA, USA.,Institute for Human Genetics, University of California San Francisco, San Francisco, CA, USA
| | - Laurel A Habel
- Division of Research, Kaiser Permanente Northern California, Oakland, CA, USA
| | - John S Witte
- Department of Epidemiology and Biostatistics, University of California San Francisco, San Francisco, CA, USA. .,Helen Diller Family Comprehensive Cancer Center, University of California San Francisco, San Francisco, CA, USA. .,Institute for Human Genetics, University of California San Francisco, San Francisco, CA, USA. .,Department of Urology, University of California San Francisco, San Francisco, CA, USA.
| | - Lori C Sakoda
- Division of Research, Kaiser Permanente Northern California, Oakland, CA, USA. .,Department of Health System Science, Kaiser Permanente Bernard J. Tyson School of Medicine, Pasadena, CA, USA.
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2
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Rashkin SR, Graff RE, Kachuri L, Thai KK, Alexeeff SE, Blatchins MA, Cavazos TB, Corley DA, Emami NC, Hoffman JD, Jorgenson E, Kushi LH, Meyers TJ, Van Den Eeden SK, Ziv E, Habel LA, Hoffmann TJ, Sakoda LC, Witte JS. Pan-cancer study detects genetic risk variants and shared genetic basis in two large cohorts. Nat Commun 2020; 11:4423. [PMID: 32887889 PMCID: PMC7473862 DOI: 10.1038/s41467-020-18246-6] [Citation(s) in RCA: 147] [Impact Index Per Article: 29.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2020] [Accepted: 08/13/2020] [Indexed: 12/14/2022] Open
Abstract
Deciphering the shared genetic basis of distinct cancers has the potential to elucidate carcinogenic mechanisms and inform broadly applicable risk assessment efforts. Here, we undertake genome-wide association studies (GWAS) and comprehensive evaluations of heritability and pleiotropy across 18 cancer types in two large, population-based cohorts: the UK Biobank (408,786 European ancestry individuals; 48,961 cancer cases) and the Kaiser Permanente Genetic Epidemiology Research on Adult Health and Aging cohorts (66,526 European ancestry individuals; 16,001 cancer cases). The GWAS detect 21 genome-wide significant associations independent of previously reported results. Investigations of pleiotropy identify 12 cancer pairs exhibiting either positive or negative genetic correlations; 25 pleiotropic loci; and 100 independent pleiotropic variants, many of which are regulatory elements and/or influence cross-tissue gene expression. Our findings demonstrate widespread pleiotropy and offer further insight into the complex genetic architecture of cross-cancer susceptibility.
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Affiliation(s)
- Sara R Rashkin
- Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA, USA
| | - Rebecca E Graff
- Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA, USA.,Division of Research, Kaiser Permanente Northern California, Oakland, CA, USA
| | - Linda Kachuri
- Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA, USA
| | - Khanh K Thai
- Division of Research, Kaiser Permanente Northern California, Oakland, CA, USA
| | - Stacey E Alexeeff
- Division of Research, Kaiser Permanente Northern California, Oakland, CA, USA
| | - Maruta A Blatchins
- Division of Research, Kaiser Permanente Northern California, Oakland, CA, USA
| | - Taylor B Cavazos
- Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA, USA.,Program in Biological and Medical Informatics, University of California, San Francisco, San Francisco, CA, USA
| | - Douglas A Corley
- Division of Research, Kaiser Permanente Northern California, Oakland, CA, USA
| | - Nima C Emami
- Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA, USA.,Program in Biological and Medical Informatics, University of California, San Francisco, San Francisco, CA, USA
| | - Joshua D Hoffman
- Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA, USA
| | - Eric Jorgenson
- Division of Research, Kaiser Permanente Northern California, Oakland, CA, USA
| | - Lawrence H Kushi
- Division of Research, Kaiser Permanente Northern California, Oakland, CA, USA
| | - Travis J Meyers
- Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA, USA
| | - Stephen K Van Den Eeden
- Division of Research, Kaiser Permanente Northern California, Oakland, CA, USA.,Department of Urology, University of California, San Francisco, San Francisco, CA, USA
| | - Elad Ziv
- Institute for Human Genetics, University of California, San Francisco, San Francisco, CA, USA.,Department of Medicine, University of California, San Francisco, San Francisco, CA, USA.,Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA, USA
| | - Laurel A Habel
- Division of Research, Kaiser Permanente Northern California, Oakland, CA, USA
| | - Thomas J Hoffmann
- Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA, USA.,Division of Research, Kaiser Permanente Northern California, Oakland, CA, USA.,Institute for Human Genetics, University of California, San Francisco, San Francisco, CA, USA
| | - Lori C Sakoda
- Division of Research, Kaiser Permanente Northern California, Oakland, CA, USA.
| | - John S Witte
- Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA, USA. .,Department of Urology, University of California, San Francisco, San Francisco, CA, USA. .,Institute for Human Genetics, University of California, San Francisco, San Francisco, CA, USA. .,Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA, USA.
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3
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Abstract
Nordic twin studies have played a critical role in understanding cancer etiology and elucidating the nature of familial effects on site-specific cancers. The NorTwinCan consortium is a collaborative effort that capitalizes on unique research advantages made possible through the Nordic system of registries. It was constructed by linking the population-based twin registries of Denmark, Finland, Norway and Sweden to their country-specific national cancer and cause-of-death registries. These linkages enable the twins to be followed many decades for cancer incidence and mortality. To date, two major linkages have been conducted: NorTwinCan I in 2011-2012 and NorTwinCan II in 2018. Overall, there are 315,413 eligible twins, 57,236 incident cancer cases and 58 years of follow-up, on average. In the initial phases of our work, NorTwinCan established the world's most comprehensive twin database for studying cancer, developed novel analytical approaches tailored to address specific research considerations within the context of the Nordic data and leveraged these models and data in research publications that provide the most accurate estimates of heritability and familial risk of cancers reported in the literature to date. Our findings indicate an excess familial risk for nearly all cancers and demonstrate that the incidence of cancer among twins mirrors the rate in the general population. They also revealed that twin concordance for cancer most often manifests across, rather than within, cancer sites, and we are currently focusing on the analysis of these cross-cancer associations.
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Wu YH, Graff RE, Passarelli MN, Hoffman JD, Ziv E, Hoffmann TJ, Witte JS. Identification of Pleiotropic Cancer Susceptibility Variants from Genome-Wide Association Studies Reveals Functional Characteristics. Cancer Epidemiol Biomarkers Prev 2018; 27:75-85. [PMID: 29150481 PMCID: PMC5760292 DOI: 10.1158/1055-9965.epi-17-0516] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2017] [Revised: 09/05/2017] [Accepted: 10/17/2017] [Indexed: 12/17/2022] Open
Abstract
Background: There exists compelling evidence that some genetic variants are associated with the risk of multiple cancer sites (i.e., pleiotropy). However, the biological mechanisms through which the pleiotropic variants operate are unclear.Methods: We obtained all cancer risk associations from the National Human Genome Research Institute-European Bioinformatics Institute GWAS Catalog, and correlated cancer risk variants were clustered into groups. Pleiotropic variant groups and genes were functionally annotated. Associations of pleiotropic cancer risk variants with noncancer traits were also obtained.Results: We identified 1,431 associations between variants and cancer risk, comprised of 989 unique variants associated with 27 unique cancer sites. We found 20 pleiotropic variant groups (2.1%) composed of 33 variants (3.3%), including novel pleiotropic variants rs3777204 and rs56219066 located in the ELL2 gene. Relative to single-cancer risk variants, pleiotropic variants were more likely to be in genes (89.0% vs. 65.3%, P = 2.2 × 10-16), and to have somewhat larger risk allele frequencies (median RAF = 0.49 versus 0.39, P = 0.046). The 27 genes to which the pleiotropic variants mapped were suggestive for enrichment in response to radiation and hypoxia, alpha-linolenic acid metabolism, cell cycle, and extension of telomeres. In addition, we observed that 8 of 33 pleiotropic cancer risk variants were associated with 16 traits other than cancer.Conclusions: This study identified and functionally characterized genetic variants showing pleiotropy for cancer risk.Impact: Our findings suggest biological pathways common to different cancers and other diseases, and provide a basis for the study of genetic testing for multiple cancers and repurposing cancer treatments. Cancer Epidemiol Biomarkers Prev; 27(1); 75-85. ©2017 AACR.
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Affiliation(s)
- Yi-Hsuan Wu
- Department of Epidemiology and Biostatistics, University of California San Francisco, San Francisco, California
| | - Rebecca E Graff
- Department of Epidemiology and Biostatistics, University of California San Francisco, San Francisco, California
| | - Michael N Passarelli
- Department of Epidemiology, Geisel School of Medicine, Dartmouth College, Hanover, New Hampshire
| | - Joshua D Hoffman
- Department of Epidemiology and Biostatistics, University of California San Francisco, San Francisco, California
| | - Elad Ziv
- Institute for Human Genetics, University of California San Francisco, San Francisco, California
- Division of General Internal Medicine, Department of Medicine, University of California San Francisco, San Francisco, California
- Helen Diller Family Comprehensive Cancer Center, University of California San Francisco, San Francisco, California
| | - Thomas J Hoffmann
- Department of Epidemiology and Biostatistics, University of California San Francisco, San Francisco, California
- Institute for Human Genetics, University of California San Francisco, San Francisco, California
| | - John S Witte
- Department of Epidemiology and Biostatistics, University of California San Francisco, San Francisco, California.
- Institute for Human Genetics, University of California San Francisco, San Francisco, California
- Helen Diller Family Comprehensive Cancer Center, University of California San Francisco, San Francisco, California
- Department of Urology, University of California San Francisco, San Francisco, California
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5
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Nickerson ML, Das S, Im KM, Turan S, Berndt SI, Li H, Lou H, Brodie SA, Billaud JN, Zhang T, Bouk AJ, Butcher D, Wang Z, Sun L, Misner K, Tan W, Esnakula A, Esposito D, Huang WY, Hoover RN, Tucker MA, Keller JR, Boland J, Brown K, Anderson SK, Moore LE, Isaacs WB, Chanock SJ, Yeager M, Dean M, Andresson T. TET2 binds the androgen receptor and loss is associated with prostate cancer. Oncogene 2017; 36:2172-2183. [PMID: 27819678 PMCID: PMC5391277 DOI: 10.1038/onc.2016.376] [Citation(s) in RCA: 56] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2015] [Revised: 08/15/2016] [Accepted: 08/29/2016] [Indexed: 12/11/2022]
Abstract
Genetic alterations associated with prostate cancer (PCa) may be identified by sequencing metastatic tumour genomes to identify molecular markers at this lethal stage of disease. Previously, we characterized somatic alterations in metastatic tumours in the methylcytosine dioxygenase ten-eleven translocation 2 (TET2), which is altered in 5-15% of myeloid, kidney, colon and PCas. Genome-wide association studies previously identified non-coding risk variants associated with PCa and melanoma. We perform fine-mapping of PCa risk across TET2 using genotypes from the PEGASUS case-control cohort and identify six new risk variants in introns 1 and 2. Oligonucleotides containing two risk variants are bound by the transcription factor octamer-binding protein 1 (Oct1/POU2F1) and TET2 and Oct1 expression are positively correlated in prostate tumours. TET2 is expressed in normal prostate tissue and reduced in a subset of tumours from the Cancer Genome Atlas (TCGA). Small interfering RNA-mediated TET2 knockdown (KD) increases LNCaP cell proliferation, migration and wound healing, verifying loss drives a cancer phenotype. Endogenous TET2 bound the androgen receptor (AR) and AR-coactivator proteins in LNCaP cell extracts, and TET2 KD increases prostate-specific antigen (KLK3/PSA) expression. Published data reveal TET2 binding sites and hydroxymethylcytosine proximal to KLK3. A gene co-expression network identified using TCGA prostate tumour RNA-sequencing identifies co-regulated cancer genes associated with 2-oxoglutarate (2-OG) and succinate metabolism, including TET2, lysine demethylase (KDM) KDM6A, BRCA1-associated BAP1, and citric acid cycle enzymes IDH1/2, SDHA/B, and FH. The co-expression signature is conserved across 31 TCGA cancers suggesting a putative role for TET2 as an energy sensor (of 2-OG) that modifies aspects of androgen-AR signalling. Decreased TET2 mRNA expression in TCGA PCa tumours is strongly associated with reduced patient survival, indicating reduced expression in tumours may be an informative biomarker of disease progression and perhaps metastatic disease.
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Affiliation(s)
- M L Nickerson
- Cancer and Inflammation Program, National Cancer Institute, National Institutes of Health, Frederick, MD, USA
| | - S Das
- Protein Characterization Laboratory, Cancer Research Technology Program, Leidos Biomedical Research, Inc., Frederick National Laboratory for Cancer Research, Frederick, MD, USA
| | - K M Im
- Data Science for Genomics, Ellicott City, MD, USA
| | - S Turan
- Cancer and Inflammation Program, National Cancer Institute, National Institutes of Health, Frederick, MD, USA
| | - S I Berndt
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - H Li
- Cancer and Inflammation Program, National Cancer Institute, National Institutes of Health, Frederick, MD, USA
- Basic Research Program, Leidos Biomedical Research, Inc., Frederick National Laboratory for Cancer Research, Frederick, MD, USA
| | - H Lou
- Cancer and Inflammation Program, National Cancer Institute, National Institutes of Health, Frederick, MD, USA
- Basic Research Program, Leidos Biomedical Research, Inc., Frederick National Laboratory for Cancer Research, Frederick, MD, USA
| | - S A Brodie
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
- Cancer Genomics Research Laboratory, Leidos Biomedical Research, Inc., Frederick National Laboratory for Cancer Research, Frederick, MD, USA
| | - J N Billaud
- Ingenuity Systems, Inc., Redwood City, CA, USA
| | - T Zhang
- Laboratory of Translational Genomics, National Cancer Institute, Bethesda, MD, USA
| | - A J Bouk
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
- Cancer Genomics Research Laboratory, Leidos Biomedical Research, Inc., Frederick National Laboratory for Cancer Research, Frederick, MD, USA
| | - D Butcher
- Pathology and Histotechnology Laboratory, Leidos Biomedical Research, Inc., Frederick National Laboratory for Cancer Research, Frederick, MD, USA
| | - Z Wang
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - L Sun
- Mouse Cancer Genetics Program, National Cancer Institute, National Institutes of Health, Frederick, MD, USA
| | - K Misner
- Cancer and Inflammation Program, National Cancer Institute, National Institutes of Health, Frederick, MD, USA
| | - W Tan
- Cancer and Inflammation Program, National Cancer Institute, National Institutes of Health, Frederick, MD, USA
- Basic Research Program, Leidos Biomedical Research, Inc., Frederick National Laboratory for Cancer Research, Frederick, MD, USA
| | - A Esnakula
- Department of Pathology, Howard University College of Medicine, Howard University Hospital, NW, Washington, DC, USA
| | - D Esposito
- Protein Expression Laboratory, Leidos Biomedical Research, Inc., Frederick National Laboratory for Cancer Research, Frederick, MD, USA
| | - W Y Huang
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - R N Hoover
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - M A Tucker
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - J R Keller
- Mouse Cancer Genetics Program, National Cancer Institute, National Institutes of Health, Frederick, MD, USA
| | - J Boland
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
- Cancer Genomics Research Laboratory, Leidos Biomedical Research, Inc., Frederick National Laboratory for Cancer Research, Frederick, MD, USA
| | - K Brown
- Laboratory of Translational Genomics, National Cancer Institute, Bethesda, MD, USA
| | - S K Anderson
- Cancer and Inflammation Program, National Cancer Institute, National Institutes of Health, Frederick, MD, USA
| | - L E Moore
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - W B Isaacs
- School of Medicine, Johns Hopkins University, Baltimore, MD, USA
| | - S J Chanock
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - M Yeager
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
- Cancer Genomics Research Laboratory, Leidos Biomedical Research, Inc., Frederick National Laboratory for Cancer Research, Frederick, MD, USA
| | - M Dean
- Cancer and Inflammation Program, National Cancer Institute, National Institutes of Health, Frederick, MD, USA
| | - T Andresson
- Protein Characterization Laboratory, Cancer Research Technology Program, Leidos Biomedical Research, Inc., Frederick National Laboratory for Cancer Research, Frederick, MD, USA
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6
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Frank C, Sundquist J, Yu H, Hemminki A, Hemminki K. Concordant and discordant familial cancer: Familial risks, proportions and population impact. Int J Cancer 2017; 140:1510-1516. [PMID: 28006863 DOI: 10.1002/ijc.30583] [Citation(s) in RCA: 53] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2016] [Revised: 11/14/2016] [Accepted: 12/05/2016] [Indexed: 01/09/2023]
Abstract
Relatives of cancer patients are at an increased risk of the same (concordant) cancer but whether they are at a risk for different (discordant) cancers is largely unknown - beyond well characterized hereditary cancer syndromes - but would be of major scientific and clinical interest. We therefore decided to resolve the issue by analyzing familial risks when family members were diagnosed with any discordant cancers. We compared the population impact of concordant to discordant familial cancer. The Swedish Family-Cancer Database (FCD) was used to calculate familial relative risks (RRs) for family members of cancer patients, for the 27 most common cancers. Population attributable fractions (PAFs) were estimated for concordant and discordant family histories. Discordant cancers in the family were detected as significant risk factors for the majority of cancers, although the corresponding RRs were modest compared to RRs for concordant cancers. Risks increased with the number of affected family members with the highest RRs for pancreatic (2.31), lung (1.69), kidney (1.98), nervous system (1.79) and thyroid cancers (3.28), when 5 or more family members were diagnosed with discordant cancers. For most cancers, the PAF for discordant family history exceeded that for concordant family history. Our findings suggest that there is an unspecific genetic predisposition to cancer with clinical consequences. We consider it unlikely that shared environmental risk factors could essentially contribute to the risks for diverse discordant cancers, which are likely driven by genetic predisposition. The identification of genes that moderately increase the risk for many cancers will be a challenge.
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Affiliation(s)
- Christoph Frank
- Division of Molecular Genetic Epidemiology, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 580, Heidelberg, D-69120, Germany
| | - Jan Sundquist
- Center for Primary Health Care Research, Lund University, Malmö, 205 02, Sweden.,Stanford Prevention Research Center, Stanford University School of Medicine, 94305-5705, Stanford, California, USA
| | - Hongyao Yu
- Division of Molecular Genetic Epidemiology, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 580, Heidelberg, D-69120, Germany
| | - Akseli Hemminki
- Cancer Gene Therapy Group, Faculty of Medicine, University of Helsinki, Finland.,Helsinki University Hospital Comprehensive Cancer Center, Helsinki, Finland
| | - Kari Hemminki
- Division of Molecular Genetic Epidemiology, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 580, Heidelberg, D-69120, Germany.,Center for Primary Health Care Research, Lund University, Malmö, 205 02, Sweden
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7
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Hemminki K, Försti A, Sundquist K, Sundquist J, Li X. Familial associations of lymphoma and myeloma with autoimmune diseases. Blood Cancer J 2017; 7:e515. [PMID: 28157190 PMCID: PMC5301032 DOI: 10.1038/bcj.2016.123] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2016] [Revised: 11/08/2016] [Accepted: 11/28/2016] [Indexed: 11/09/2022] Open
Abstract
Many B-cell neoplasms are associated with autoimmune diseases (AIDs) but most evidence is based on a personal rather than a family history of AIDs. Here we calculated risks for non-Hodgkin lymphoma (NHL), Hodgkin lymphoma (HL) and multiple myeloma (MM) when family members were diagnosed with any of 44 different AIDs, or, independently, risk for AIDs when family members were diagnosed with a neoplasm. A total of 64 418 neoplasms and 531 155 AIDs were identified from Swedish nationwide health care records. NHL was associated with a family history of five AIDs, all increasing the risk, HL was associated with one AID increasing and three AIDs decreasing the risk while MM had no association. A family history of NHL was associated with eight, HL with seven and MM with seven different AIDs, nine increasing and 13 decreasing the risk. The present family data on B-cell neoplasms and AIDs show an approximately equal number of associations for risk increase and risk decrease, suggesting that inherited genes or gene-environment interactions may increase the risk or be protective. These results differed from published data on personal history of AID, which only report increased risks, often vastly higher and for different AIDs compared with the present data.
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Affiliation(s)
- K Hemminki
- Division of Molecular Genetic Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany.,Center for Primary Health Care Research, Lund University, Malmö, Sweden
| | - A Försti
- Division of Molecular Genetic Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany.,Center for Primary Health Care Research, Lund University, Malmö, Sweden
| | - K Sundquist
- Center for Primary Health Care Research, Lund University, Malmö, Sweden.,Stanford Prevention Research Center, Stanford University School of Medicine, Stanford, CA, USA
| | - J Sundquist
- Center for Primary Health Care Research, Lund University, Malmö, Sweden.,Stanford Prevention Research Center, Stanford University School of Medicine, Stanford, CA, USA
| | - X Li
- Center for Primary Health Care Research, Lund University, Malmö, Sweden
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8
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Wang QL, Tan WL, Zhao YJ, Shao MM, Chu JH, Huang XD, Li J, Luo YY, Peng LN, Cui QH, Feng T, Yang J, Han YL. Data analysis in the post-genome-wide association study era. Chronic Dis Transl Med 2016; 2:231-234. [PMID: 29063047 PMCID: PMC5643765 DOI: 10.1016/j.cdtm.2016.11.009] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2016] [Indexed: 02/01/2023] Open
Abstract
Since the first report of a genome-wide association study (GWAS) on human age-related macular degeneration, GWAS has successfully been used to discover genetic variants for a variety of complex human diseases and/or traits, and thousands of associated loci have been identified. However, the underlying mechanisms for these loci remain largely unknown. To make these GWAS findings more useful, it is necessary to perform in-depth data mining. The data analysis in the post-GWAS era will include the following aspects: fine-mapping of susceptibility regions to identify susceptibility genes for elucidating the biological mechanism of action; joint analysis of susceptibility genes in different diseases; integration of GWAS, transcriptome, and epigenetic data to analyze expression and methylation quantitative trait loci at the whole-genome level, and find single-nucleotide polymorphisms that influence gene expression and DNA methylation; genome-wide association analysis of disease-related DNA copy number variations. Applying these strategies and methods will serve to strengthen GWAS data to enhance the utility and significance of GWAS in improving understanding of the genetics of complex diseases or traits and translate these findings for clinical applications.
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Affiliation(s)
- Qiao-Ling Wang
- Department of Etiology and Carcinogenesis, National Cancer Center/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - Wen-Le Tan
- Department of Etiology and Carcinogenesis, National Cancer Center/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - Yan-Jie Zhao
- Department of Etiology and Carcinogenesis, National Cancer Center/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - Ming-Ming Shao
- Department of Etiology and Carcinogenesis, National Cancer Center/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - Jia-Hui Chu
- Department of Etiology and Carcinogenesis, National Cancer Center/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - Xu-Dong Huang
- Department of Etiology and Carcinogenesis, National Cancer Center/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - Jun Li
- Department of Etiology and Carcinogenesis, National Cancer Center/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - Ying-Ying Luo
- Department of Etiology and Carcinogenesis, National Cancer Center/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - Lin-Na Peng
- Department of Etiology and Carcinogenesis, National Cancer Center/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - Qiong-Hua Cui
- Department of Etiology and Carcinogenesis, National Cancer Center/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - Ting Feng
- Department of Etiology and Carcinogenesis, National Cancer Center/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - Jie Yang
- Department of Etiology and Carcinogenesis, National Cancer Center/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - Ya-Ling Han
- Department of Etiology and Carcinogenesis, National Cancer Center/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
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Rosa R, D'Amato V, De Placido S, Bianco R. Approaches for targeting cancer stem cells drug resistance. Expert Opin Drug Discov 2016; 11:1201-1212. [PMID: 27700193 DOI: 10.1080/17460441.2016.1243525] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
Abstract
INTRODUCTION Several reports have suggested that a population of undifferentiated cells known as cancer stem cells (CSCs), is responsible for cancer formation and maintenance. In the last decade, the presence of CSCs in solid cancers have been reported. Areas covered: This review summarizes the main approaches for targeting CSCs drug resistance. It is indeed known that CSCs may contribute to resistance to conventional chemotherapy, radiotherapy and targeted agents. Among the mechanisms by which CSCs escape anticancer therapies, removal of therapeutic agents by drug efflux pumps, enhanced DNA damage repair, activation of mitogenic/anti-apoptotic pathways; the main features of CSCs, stemness and EMT, are involved, as well as the capability to evade immune response. Expert opinion: Different approaches are suitable to target CSCs mediated drug resistance. Some of them are currently under clinical evaluation in different cancer types. A better understanding of CSC biology, as well as more accurate study design, may maximize the therapeutic effects of these agents. In this respect, it is important to establish: (i) which molecules should be targeted; (ii) what drug combinations may be suitable; (iii) which patient settings will CSC targeting offer the highest clinical benefit; and (iv) how to integrate therapeutic approaches targeting CSCs with standard cancer therapy.
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Affiliation(s)
- Roberta Rosa
- a Dipartimento di Medicina Clinica e Chirurgia , Università di Napoli Federico II , Napoli , Italy
| | - Valentina D'Amato
- a Dipartimento di Medicina Clinica e Chirurgia , Università di Napoli Federico II , Napoli , Italy
| | - Sabino De Placido
- a Dipartimento di Medicina Clinica e Chirurgia , Università di Napoli Federico II , Napoli , Italy
| | - Roberto Bianco
- a Dipartimento di Medicina Clinica e Chirurgia , Università di Napoli Federico II , Napoli , Italy
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10
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Unravelling the human genome-phenome relationship using phenome-wide association studies. Nat Rev Genet 2016; 17:129-45. [PMID: 26875678 DOI: 10.1038/nrg.2015.36] [Citation(s) in RCA: 182] [Impact Index Per Article: 20.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
Advances in genotyping technology have, over the past decade, enabled the focused search for common genetic variation associated with human diseases and traits. With the recently increased availability of detailed phenotypic data from electronic health records and epidemiological studies, the impact of one or more genetic variants on the phenome is starting to be characterized both in clinical and population-based settings using phenome-wide association studies (PheWAS). These studies reveal a number of challenges that will need to be overcome to unlock the full potential of PheWAS for the characterization of the complex human genome-phenome relationship.
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11
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Lee E, Stram DO, Ek WE, Onstad LE, MacGregor S, Gharahkhani P, Ye W, Lagergren J, Shaheen NJ, Murray LJ, Hardie LJ, Gammon MD, Chow WH, Risch HA, Corley DA, Levine DM, Whiteman DC, Bernstein L, Bird NC, Vaughan TL, Wu AH. Pleiotropic analysis of cancer risk loci on esophageal adenocarcinoma risk. Cancer Epidemiol Biomarkers Prev 2015; 24:1801-3. [PMID: 26364162 DOI: 10.1158/1055-9965.epi-15-0596] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2015] [Accepted: 08/20/2015] [Indexed: 11/16/2022] Open
Abstract
BACKGROUND Several cancer-associated loci identified from genome-wide association studies (GWAS) have been associated with risks of multiple cancer sites, suggesting pleiotropic effects. We investigated whether GWAS-identified risk variants for other common cancers are associated with risk of esophageal adenocarcinoma (EA) or its precursor, Barrett's esophagus. METHODS We examined the associations between risks of EA and Barrett's esophagus and 387 SNPs that have been associated with risks of other cancers, by using genotype imputation data on 2,163 control participants and 3,885 (1,501 EA and 2,384 Barrett's esophagus) case patients from the Barrett's and Esophageal Adenocarcinoma Genetic Susceptibility Study, and investigated effect modification by smoking history, body mass index (BMI), and reflux/heartburn. RESULTS After correcting for multiple testing, none of the tested 387 SNPs were statistically significantly associated with risk of EA or Barrett's esophagus. No evidence of effect modification by smoking, BMI, or reflux/heartburn was observed. CONCLUSIONS Genetic risk variants for common cancers identified from GWAS appear not to be associated with risks of EA or Barrett's esophagus. IMPACT To our knowledge, this is the first investigation of pleiotropic genetic associations with risks of EA and Barrett's esophagus.
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Affiliation(s)
- Eunjung Lee
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, California.
| | - Daniel O Stram
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, California
| | - Weronica E Ek
- Statistical Genetics, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
| | - Lynn E Onstad
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, Washington
| | - Stuart MacGregor
- Statistical Genetics, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
| | - Puya Gharahkhani
- Statistical Genetics, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
| | - Weimin Ye
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Jesper Lagergren
- Department of Molecular Medicine and Surgery, Karolinska Institutet, Stockholm, Sweden. Division of Cancer Studies, King's College London, London, United Kingdom
| | - Nicholas J Shaheen
- Division of Gastroenterology and Hepatology, University of North Carolina School of Medicine, University of North Carolina, Chapel Hill, North Carolina
| | - Liam J Murray
- Centre for Public Health, Queen's University Belfast, United Kingdom
| | - Laura J Hardie
- Division of Epidemiology, University of Leeds, Leeds, United Kingdom
| | - Marilie D Gammon
- Department of Epidemiology, University of North Carolina, Chapel Hill, North Carolina
| | - Wong-Ho Chow
- Department of Epidemiology, MD Anderson Cancer Center, Houston, Texas
| | - Harvey A Risch
- Yale School of Public Health, Department of Chronic Disease Epidemiology, New Haven, Connecticut
| | - Douglas A Corley
- Division of Research, Kaiser Permanente Northern California, Oakland, California. San Francisco Medical Center, Kaiser Permanente Northern California, San Francisco, California
| | - David M Levine
- Department of Biostatistics, University of Washington School of Public Health, Seattle, Washington
| | - David C Whiteman
- Cancer Control, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
| | - Leslie Bernstein
- Department of Population Sciences, Beckman Research Institute and City of Hope Comprehensive Cancer Center, Duarte, California
| | - Nigel C Bird
- Department of Oncology, University of Sheffield Medical School, Sheffield, United Kingdom
| | - Thomas L Vaughan
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, Washington
| | - Anna H Wu
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, California
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12
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Tyler AL, Crawford DC, Pendergrass SA. The detection and characterization of pleiotropy: discovery, progress, and promise. Brief Bioinform 2015. [PMID: 26223525 DOI: 10.1093/bib/bbv050] [Citation(s) in RCA: 28] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023] Open
Abstract
The impact of a single genetic locus on multiple phenotypes, or pleiotropy, is an important area of research. Biological systems are dynamic complex networks, and these networks exist within and between cells. In humans, the consideration of multiple phenotypes such as physiological traits, clinical outcomes and drug response, in the context of genetic variation, can provide ways of developing a more complete understanding of the complex relationships between genetic architecture and how biological systems function in health and disease. In this article, we describe recent studies exploring the relationships between genetic loci and more than one phenotype. We also cover methodological developments incorporating pleiotropy applied to model organisms as well as humans, and discuss how stepping beyond the analysis of a single phenotype leads to a deeper understanding of complex genetic architecture.
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13
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Crawford DC, Goodloe R, Farber-Eger E, Boston J, Pendergrass SA, Haines JL, Ritchie MD, Bush WS. Leveraging Epidemiologic and Clinical Collections for Genomic Studies of Complex Traits. Hum Hered 2015. [PMID: 26201699 DOI: 10.1159/000381805] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022] Open
Abstract
BACKGROUND/AIMS Present-day limited resources demand DNA and phenotyping alternatives to the traditional prospective population-based epidemiologic collections. METHODS To accelerate genomic discovery with an emphasis on diverse populations, we--as part of the Epidemiologic Architecture for Genes Linked to Environment (EAGLE) study--accessed all non-European American samples (n = 15,863) available in BioVU, the Vanderbilt University biorepository linked to de-identified electronic medical records, for genomic studies as part of the larger Population Architecture using Genomics and Epidemiology (PAGE) I study. Given previous studies have cautioned against the secondary use of clinically collected data compared with epidemiologically collected data, we present here a characterization of EAGLE BioVU, including the billing and diagnostic (ICD-9) code distributions for adult and pediatric patients as well as comparisons made for select health metrics (body mass index, glucose, HbA1c, HDL-C, LDL-C, and triglycerides) with the population-based National Health and Nutrition Examination Surveys (NHANES) linked to DNA samples (NHANES III, n = 7,159; NHANES 1999-2002, n = 7,839). RESULTS Overall, the distributions of billing and diagnostic codes suggest this clinical sample is a mixture of healthy and sick patients like that expected for a contemporary American population. CONCLUSION Little bias is observed among health metrics, suggesting this clinical collection is suitable for genomic studies along with traditional epidemiologic cohorts.
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Affiliation(s)
- Dana C Crawford
- Department of Epidemiology and Biostatistics, Institute for Computational Biology, Case Western Reserve University, Cleveland, Ohio, USA
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Leveraging Multi-ethnic Evidence for Mapping Complex Traits in Minority Populations: An Empirical Bayes Approach. Am J Hum Genet 2015; 96:740-52. [PMID: 25892113 DOI: 10.1016/j.ajhg.2015.03.008] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2014] [Accepted: 03/10/2015] [Indexed: 01/21/2023] Open
Abstract
Elucidating the genetic basis of complex traits and diseases in non-European populations is particularly challenging because US minority populations have been under-represented in genetic association studies. We developed an empirical Bayes approach named XPEB (cross-population empirical Bayes), designed to improve the power for mapping complex-trait-associated loci in a minority population by exploiting information from genome-wide association studies (GWASs) from another ethnic population. Taking as input summary statistics from two GWASs-a target GWAS from an ethnic minority population of primary interest and an auxiliary base GWAS (such as a larger GWAS in Europeans)-our XPEB approach reprioritizes SNPs in the target population to compute local false-discovery rates. We demonstrated, through simulations, that whenever the base GWAS harbors relevant information, XPEB gains efficiency. Moreover, XPEB has the ability to discard irrelevant auxiliary information, providing a safeguard against inflated false-discovery rates due to genetic heterogeneity between populations. Applied to a blood-lipids study in African Americans, XPEB more than quadrupled the discoveries from the conventional approach, which used a target GWAS alone, bringing the number of significant loci from 14 to 65. Thus, XPEB offers a flexible framework for mapping complex traits in minority populations.
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15
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Panagiotou OA, Travis RC, Campa D, Berndt SI, Lindstrom S, Kraft P, Schumacher FR, Siddiq A, Papatheodorou SI, Stanford JL, Albanes D, Virtamo J, Weinstein SJ, Diver WR, Gapstur SM, Stevens VL, Boeing H, Bueno-de-Mesquita HB, Barricarte Gurrea A, Kaaks R, Khaw KT, Krogh V, Overvad K, Riboli E, Trichopoulos D, Giovannucci E, Stampfer M, Haiman C, Henderson B, Le Marchand L, Gaziano JM, Hunter DJ, Koutros S, Yeager M, Hoover RN, Chanock SJ, Wacholder S, Key TJ, Tsilidis KK. A genome-wide pleiotropy scan for prostate cancer risk. Eur Urol 2015; 67:649-57. [PMID: 25277271 PMCID: PMC4359641 DOI: 10.1016/j.eururo.2014.09.020] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2014] [Accepted: 09/13/2014] [Indexed: 01/17/2023]
Abstract
BACKGROUND No single-nucleotide polymorphisms (SNPs) specific for aggressive prostate cancer have been identified in genome-wide association studies (GWAS). OBJECTIVE To test if SNPs associated with other traits may also affect the risk of aggressive prostate cancer. DESIGN, SETTING, AND PARTICIPANTS SNPs implicated in any phenotype other than prostate cancer (p≤10(-7)) were identified through the catalog of published GWAS and tested in 2891 aggressive prostate cancer cases and 4592 controls from the Breast and Prostate Cancer Cohort Consortium (BPC3). The 40 most significant SNPs were followed up in 4872 aggressive prostate cancer cases and 24,534 controls from the Prostate Cancer Association Group to Investigate Cancer Associated Alterations in the Genome (PRACTICAL) consortium. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS Odds ratios (ORs) and 95% confidence intervals (CIs) for aggressive prostate cancer were estimated. RESULTS AND LIMITATIONS A total of 4666 SNPs were evaluated by the BPC3. Two signals were seen in regions already reported for prostate cancer risk. rs7014346 at 8q24.21 was marginally associated with aggressive prostate cancer in the BPC3 trial (p=1.6×10(-6)), whereas after meta-analysis by PRACTICAL the summary OR was 1.21 (95% CI 1.16-1.27; p=3.22×10(-18)). rs9900242 at 17q24.3 was also marginally associated with aggressive disease in the meta-analysis (OR 0.90, 95% CI 0.86-0.94; p=2.5×10(-6)). Neither of these SNPs remained statistically significant when conditioning on correlated known prostate cancer SNPs. The meta-analysis by BPC3 and PRACTICAL identified a third promising signal, marked by rs16844874 at 2q34, independent of known prostate cancer loci (OR 1.12, 95% CI 1.06-1.19; p=4.67×10(-5)); it has been shown that SNPs correlated with this signal affect glycine concentrations. The main limitation is the heterogeneity in the definition of aggressive prostate cancer between BPC3 and PRACTICAL. CONCLUSIONS We did not identify new SNPs for aggressive prostate cancer. However, rs16844874 may provide preliminary genetic evidence on the role of the glycine pathway in prostate cancer etiology. PATIENT SUMMARY We evaluated whether genetic variants associated with several traits are linked to the risk of aggressive prostate cancer. No new such variants were identified.
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Affiliation(s)
- Orestis A Panagiotou
- Division of Cancer Epidemiology & Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Ruth C Travis
- Cancer Epidemiology Unit, Nuffield Department of Clinical Medicine, University of Oxford, Oxford, UK
| | - Daniele Campa
- Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Sonja I Berndt
- Division of Cancer Epidemiology & Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Sara Lindstrom
- Department of Epidemiology, Harvard School of Public Health, Harvard University, Boston, MA, USA
| | - Peter Kraft
- Department of Epidemiology, Harvard School of Public Health, Harvard University, Boston, MA, USA
| | - Fredrick R Schumacher
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Afshan Siddiq
- Department of Genomics of Common Disease, School of Public Health, Imperial College London, London, UK
| | - Stefania I Papatheodorou
- Cyprus International Institute for Environmental and Public Health, Cyprus University of Technology, Limassol, Cyprus
| | - Janet L Stanford
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Demetrius Albanes
- Division of Cancer Epidemiology & Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Jarmo Virtamo
- Department of Chronic Disease Prevention, National Institute for Health and Welfare, Helsinki, Finland
| | - Stephanie J Weinstein
- Division of Cancer Epidemiology & Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - W Ryan Diver
- Epidemiology Research Program, American Cancer Society, Atlanta, GA, USA
| | - Susan M Gapstur
- Epidemiology Research Program, American Cancer Society, Atlanta, GA, USA
| | - Victoria L Stevens
- Epidemiology Research Program, American Cancer Society, Atlanta, GA, USA
| | - Heiner Boeing
- Department of Epidemiology, German Institute of Human Nutrition Potsdam-Rehbruecke, Nuthetal, Germany
| | - H Bas Bueno-de-Mesquita
- Department of Epidemiology and Biostatistics, Imperial College School of Public Health, London, UK; Department for Determinants of Chronic Diseases (DCD), National Institute for Public Health and the Environment (RIVM), Bilthoven, Netherlands; Department of Gastroenterology and Hepatology, University Medical Centre, Utrecht, Netherlands; Department of Social and Preventive Medicine, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia
| | - Aurelio Barricarte Gurrea
- Navarre Public Health Institute, Pamplona, Spain; Consortium for Biomedical Research in Epidemiology and Public Health, Madrid, Spain
| | - Rudolf Kaaks
- Division of Cancer Epidemiology, German Cancer Research Center, Heidelberg, Germany
| | - Kay-Tee Khaw
- Clinical Gerontology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Vittorio Krogh
- Epidemiology and Prevention Unit, Department of Preventive & Predictive Medicine, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy
| | - Kim Overvad
- Department of Public Health, Section for Epidemiology, Aarhus University, Aarhus, Denmark
| | - Elio Riboli
- Department of Epidemiology and Biostatistics, Imperial College School of Public Health, London, UK
| | - Dimitrios Trichopoulos
- Department of Epidemiology, Harvard School of Public Health, Harvard University, Boston, MA, USA; Hellenic Health Foundation, Athens, Greece; Bureau of Epidemiologic Research, Academy of Athens, Athens, Greece; Department of Nutrition, Harvard School of Public Health, Boston, MA, USA
| | - Edward Giovannucci
- Department of Epidemiology, Harvard School of Public Health, Harvard University, Boston, MA, USA; Department of Nutrition, Harvard School of Public Health, Boston, MA, USA; Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Meir Stampfer
- Department of Epidemiology, Harvard School of Public Health, Harvard University, Boston, MA, USA
| | - Christopher Haiman
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Brian Henderson
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | | | - J Michael Gaziano
- Department of Epidemiology, Harvard School of Public Health, Harvard University, Boston, MA, USA; Department of Medicine, Harvard Medical School, Boston, MA, USA; Division of Aging, Brigham and Women's Hospital, Boston, MA, USA
| | - David J Hunter
- Department of Epidemiology, Harvard School of Public Health, Harvard University, Boston, MA, USA; Department of Nutrition, Harvard School of Public Health, Boston, MA, USA
| | - Stella Koutros
- Division of Cancer Epidemiology & Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Meredith Yeager
- Core Genotyping Facility Frederick National Laboratory for Cancer Research, Gaithersburg, MD, USA
| | - Robert N Hoover
- Division of Cancer Epidemiology & Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Stephen J Chanock
- Division of Cancer Epidemiology & Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA; Core Genotyping Facility Frederick National Laboratory for Cancer Research, Gaithersburg, MD, USA
| | - Sholom Wacholder
- Division of Cancer Epidemiology & Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Timothy J Key
- Cancer Epidemiology Unit, Nuffield Department of Clinical Medicine, University of Oxford, Oxford, UK
| | - Konstantinos K Tsilidis
- Cancer Epidemiology Unit, Nuffield Department of Clinical Medicine, University of Oxford, Oxford, UK; Department of Hygiene and Epidemiology, University of Ioannina, School of Medicine, Ioannina, Greece.
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