Functional informed genome-wide interaction analysis of body mass index, diabetes and colorectal cancer risk.,Cancer Medicine

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Functional informed genome-wide interaction analysis of body mass index, diabetes and colorectal cancer risk.
Cancer Medicine ( IF 2.9 ) Pub Date : 2020-03-24 , DOI: 10.1002/cam4.2971
Zhiyu Xia ₁ , Yu-Ru Su ₂ , Paneen Petersen ₂ , Lihong Qi ₃ , Andre E Kim ₄ , Jane C Figueiredo _{4,

5} , Yi Lin ₂ , Hongmei Nan ₆ , Lori C Sakoda _{2,

7} , Demetrius Albanes ₈ , Sonja I Berndt ₈ , Stéphane Bézieau ₉ , Stephanie Bien ₂ , Daniel D Buchanan _{10,

11,

12} , Graham Casey ₁₃ , Andrew T Chan _{14,

15,

16,

17,

18,

19} , David V Conti ₄ , David A Drew _{14,

16} , Steven J Gallinger ₂₀ , W James Gauderman ₄ , Graham G Giles _{21,

22} , Stephen B Gruber ₄ , Marc J Gunter ₂₃ , Michael Hoffmeister ₂₄ , Mark A Jenkins ₂₂ , Amit D Joshi _{16,

18} , Loic Le Marchand ₂₅ , Juan P Lewinger ₄ , Li Li ₂₆ , Noralane M Lindor ₂₇ , Victor Moreno _{28,

29,

30,

31} , Neil Murphy ₂₃ , Rami Nassir ₃₂ , Polly A Newcomb _{1,

2} , Shuji Ogino _{18,

33,

34} , Gad Rennert _{35,

36,

37} , Mingyang Song _{14,

16,

38} , Xiaoliang Wang ₂ , Alicja Wolk ₃₉ , Michael O Woods ₄₀ , Hermann Brenner _{24,

41} , Emily White _{1,

2} , Martha L Slattery ₄₂ , Edward L Giovannucci _{15,

18,

38} , Jenny Chang-Claude _{43,

44} , Paul D P Pharoah ₄₅ , Li Hsu _{2,

46} , Peter T Campbell ₄₇ , Ulrike Peters _{1,

2}

Affiliation

Department of Epidemiology, University of Washington, Seattle, WA, USA.
Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, USA.
Department of Public Health Sciences, University of California Davis, Davis, CA, USA.
Department of Preventive Medicine &, USC Norris Comprehensive Cancer Center, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.
Department of Medicine, Samuel Oschin Comprehensive Cancer Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
Department of Epidemiology, Richard M. Fairbanks School of Public Health, Indiana University, Indianapolis, IN, USA.
Division of Research, Kaiser Permanente Northern California, Oakland, CA, USA.
Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA.
Service de Génétique Médicale, Centre Hospitalier Universitaire (CHU) Nantes, Nantes, France.
Colorectal Oncogenomics Group, Department of Clinical Pathology, The University of Melbourne, Parkville, Victoria, Australia.
University of Melbourne Centre for Cancer Research, Victorian Comprehensive Cancer Centre, Parkville, Victoria, Australia.
Genetic Medicine and Family Cancer Clinic, The Royal Melbourne Hospital, Parkville, Victoria, Australia.
Center for Public Health Genomics, University of Virginia, Charlottesville, VA, USA.
Division of Gastroenterology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
Channing Division of Network Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
Clinical and Translational Epidemiology Unit, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
Broad Institute of Harvard and MIT, Cambridge, MA, USA.
Department of Epidemiology, Harvard T.H. Chan School of Public Health, Harvard University, Boston, MA, USA.
Department of Immunology and Infectious Diseases, Harvard T.H. Chan School of Public Health, Harvard University, Boston, MA, USA.
Lunenfeld Tanenbaum Research Institute, Mount Sinai Hospital, University of Toronto, Toronto, Ontario, Canada.
Cancer Epidemiology Division, Melbourne, Victoria, Australia.
Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Victoria, Australia.
Section of Nutrition and Metabolism, International Agency for Research on Cancer, Lyon, France.
Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany.
University of Hawaii Cancer Center, Honolulu, HI, USA.
Department of Family Medicine, University of Virginia, Charlottesville, VA, USA.
Department of Health Science Research, Mayo Clinic, Scottsdale, AZ, USA.
Oncology Data Analytics Program, Catalan Institute of Oncology-IDIBELL, L'Hospitalet de Llobregat, Barcelona, Spain.
CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain.
Department of Clinical Sciences, Faculty of Medicine, University of Barcelona, Barcelona, Spain.
ONCOBEL Program, Bellvitge Biomedical Research Institute (IDIBELL), L'Hospitalet de Llobregat, Barcelona, Spain.
Department of Pathology, School of Medicine, Umm Al-Qura University, Makkah, Saudi Arabia.
Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
Department of Oncologic Pathology, Dana-Farber Cancer Institute, Boston, MA, USA.
Department of Community Medicine and Epidemiology, Lady Davis Carmel Medical Center, Haifa, Israel.
Ruth and Bruce Rappaport Faculty of Medicine, Technion-Israel Institute of Technology, Haifa, Israel.
Clalit National Cancer Control Center, Haifa, Israel.
Department of Nutrition, Harvard T.H. Chan School of Public Health, Harvard University, Boston, MA, USA.
Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden.
Memorial University of Newfoundland, Discipline of Genetics, St. John's, Canada.
Division of Preventive Oncology, German Cancer Research Center (DKFZ), National Center for Tumor Diseases (NCT), Heidelberg, Germany.
Department of Internal Medicine, University of Utah, Salt Lake City, UT, USA.
Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany.
University Medical Centre Hamburg-Eppendorf, University Cancer Centre Hamburg (UCCH), Hamburg, Germany.
Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK.
Department of Biostatistics, University of Washington, Seattle, WA, USA.
Behavioral and Epidemiology Research Group, American Cancer Society, Atlanta, GA, USA.

BACKGROUND Body mass index (BMI) and diabetes are established risk factors for colorectal cancer (CRC), likely through perturbations in metabolic traits (e.g. insulin resistance and glucose homeostasis). Identification of interactions between variation in genes and these metabolic risk factors may identify novel biologic insights into CRC etiology. METHODS To improve statistical power and interpretation for gene-environment interaction (G × E) testing, we tested genetic variants that regulate expression of a gene together for interaction with BMI (kg/m2 ) and diabetes on CRC risk among 26 017 cases and 20 692 controls. Each variant was weighted based on PrediXcan analysis of gene expression data from colon tissue generated in the Genotype-Tissue Expression Project for all genes with heritability ≥1%. We used a mixed-effects model to jointly measure the G × E interaction in a gene by partitioning the interactions into the predicted gene expression levels (fixed effects), and residual G × E effects (random effects). G × BMI analyses were stratified by sex as BMI-CRC associations differ by sex. We used false discovery rates to account for multiple comparisons and reported all results with FDR <0.2. RESULTS Among 4839 genes tested, genetically predicted expressions of FOXA1 (P = 3.15 × 10-5 ), PSMC5 (P = 4.51 × 10-4 ) and CD33 (P = 2.71 × 10-4 ) modified the association of BMI on CRC risk for men; KIAA0753 (P = 2.29 × 10-5 ) and SCN1B (P = 2.76 × 10-4 ) modified the association of BMI on CRC risk for women; and PTPN2 modified the association between diabetes and CRC risk in both sexes (P = 2.31 × 10-5 ). CONCLUSIONS Aggregating G × E interactions and incorporating functional information, we discovered novel genes that may interact with BMI and diabetes on CRC risk.

中文翻译：

对体重指数、糖尿病和结直肠癌风险进行功能知情的全基因组相互作用分析。

背景技术体重指数（BMI）和糖尿病是结直肠癌（CRC）的既定危险因素，可能是通过代谢特征（例如胰岛素抵抗和葡萄糖稳态）的扰动实现的。识别基因变异与这些代谢危险因素之间的相互作用可能会为结直肠癌病因学提供新的生物学见解。方法为了提高基因-环境相互作用 (G × E) 测试的统计功效和解释，我们在 26 017 例病例和 20 例患者中测试了调节基因表达与 BMI (kg/m2) 和糖尿病相互作用的基因变异。第692章控制每个变体的权重基于 PrediXcan 对基因型组织表达项目中产生的结肠组织基因表达数据的分析，其中所有基因的遗传力≥1%。我们使用混合效应模型通过将相互作用划分为预测的基因表达水平（固定效应）和残余 G × E 效应（随机效应）来联合测量基因中的 G × E 相互作用。 G × BMI 分析按性别分层，因为 BMI-CRC 关联因性别而异。我们使用错误发现率来解释多重比较，并报告 FDR <0.2 的所有结果。结果在测试的 4839 个基因中，FOXA1 (P = 3.15 × 10-5 )、PSMC5 (P = 4.51 × 10-4 ) 和 CD33 (P = 2.71 × 10-4 ) 的基因预测表达改变了 BMI 与 CRC 风险之间的关联男士用； KIAA0753 (P = 2.29 × 10-5 ) 和 SCN1B (P = 2.76 × 10-4 ) 改变了 BMI 与女性 CRC 风险之间的关联； PTPN2 改变了两性糖尿病和 CRC 风险之间的关联 (P = 2.31 × 10-5 )。结论通过汇总 G × E 相互作用并结合功能信息，我们发现了可能与 BMI 和糖尿病对 CRC 风险产生相互作用的新基因。

更新日期：2020-03-24

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