Genome-wide gene⇓diabetes and gene⇓obesity interaction scan in 8,255 cases and 11,900 controls from panscan and PanC4 consortia

Hongwei Tang; Lai Jiang; Rachael Z. Stolzenberg-Solomon; Alan A. Arslan; Laura E. Beane Freeman; Paige M. Bracci; Paul Brennan; Federico Canzian; Mengmeng Du; Steven Gallinger; Graham G. Giles; Phyllis J. Goodman; Charles Kooperberg; Loc Le Marchand; Rachel E. Neale; Xiao Ou Shu; Kala Visvanathan; Emily White; Wei Zheng; Demetrius Albanes; Gabriella Andreotti; Ana Babic; William R. Bamlet; Sonja I. Berndt; Amanda Blackford; Bas Bueno-De-Mesquita; Julie E. Buring; Daniele Campa; Stephen J. Chanock; Erica Childs; Eric J. Duell; Charles Fuchs; J. Michael Gaziano; Michael Goggins; Patricia Hartge; Manal H. Hassam; Elizabeth A. Holly; Robert N. Hoover; Rayjean J. Hung; Robert C. Kurtz; I. Min Lee; Nuria Malats; Roger L. Milne; Kimmie Ng; Ann L. Oberg; Irene Orlow; Ulrike Peters; Miquel Porta; Kari G. Rabe; Nathaniel Rothman; Ghislaine Scelo; Howard D. Sesso; Debra T. Silverman; Ian M. Thompson; Anne Tjønneland; Antonia Trichopoulou; Jean Wactawski-Wende; Nicolas Wentzensen; Lynne R. Wilkens; Herbert Yu; Anne Zeleniuch-Jacquotte; Laufey T. Amundadottir; Eric J. Jacobs; Gloria M. Petersen; Brian M. Wolpin; Harvey A. Risch; Nilanjan Chatterjee; Alison P. Klein; Donghui Li; Peter Kraft; Peng Wei

doi:10.1158/1055-9965.EPI-20-0275

Genome-wide gene⇓diabetes and gene⇓obesity interaction scan in 8,255 cases and 11,900 controls from panscan and PanC4 consortia

Hongwei Tang, Lai Jiang, Rachael Z. Stolzenberg-Solomon, Alan A. Arslan, Laura E. Beane Freeman, Paige M. Bracci, Paul Brennan, Federico Canzian, Mengmeng Du, Steven Gallinger, Graham G. Giles, Phyllis J. Goodman, Charles Kooperberg, Loc Le Marchand, Rachel E. Neale, Xiao Ou Shu, Kala Visvanathan, Emily White, Wei Zheng, Demetrius AlbanesGabriella Andreotti, Ana Babic, William R. Bamlet, Sonja I. Berndt, Amanda Blackford, Bas Bueno-De-Mesquita, Julie E. Buring, Daniele Campa, Stephen J. Chanock, Erica Childs, Eric J. Duell, Charles Fuchs, J. Michael Gaziano, Michael Goggins, Patricia Hartge, Manal H. Hassam, Elizabeth A. Holly, Robert N. Hoover, Rayjean J. Hung, Robert C. Kurtz, I. Min Lee, Nuria Malats, Roger L. Milne, Kimmie Ng, Ann L. Oberg, Irene Orlow, Ulrike Peters, Miquel Porta, Kari G. Rabe, Nathaniel Rothman, Ghislaine Scelo, Howard D. Sesso, Debra T. Silverman, Ian M. Thompson, Anne Tjønneland, Antonia Trichopoulou, Jean Wactawski-Wende, Nicolas Wentzensen, Lynne R. Wilkens, Herbert Yu, Anne Zeleniuch-Jacquotte, Laufey T. Amundadottir, Eric J. Jacobs, Gloria M. Petersen, Brian M. Wolpin, Harvey A. Risch, Nilanjan Chatterjee, Alison P. Klein, Donghui Li, Peter Kraft, Peng Wei

Quantitative Health Sciences

Research output: Contribution to journal › Article › peer-review

2 Scopus citations

Abstract

Background: Obesity and diabetes are major modifiable risk factors for pancreatic cancer. Interactions between genetic variants and diabetes/obesity have not previously been comprehensively investigated in pancreatic cancer at the genome-wide level. Methods: We conducted a gene–environment interaction (GxE) analysis including 8,255 cases and 11,900 controls from four pancreatic cancer genome-wide association study (GWAS) datasets (Pancreatic Cancer Cohort Consortium I–III and Pancreatic Cancer Case Control Consortium). Obesity (body mass index ≥30 kg/m²) and diabetes (duration ≥3 years) were the environmental variables of interest. Approximately 870,000 SNPs (minor allele frequency ≥0.005, genotyped in at least one dataset) were analyzed. Case–control (CC), case-only (CO), and joint-effect test methods were used for SNP-level GxE analysis. As a complementary approach, gene-based GxE analysis was also performed. Age, sex, study site, and principal components accounting for population substructure were included as covariates. Meta-analysis was applied to combine individual GWAS summary statistics. Results: No genome-wide significant interactions (departures from a log-additive odds model) with diabetes or obesity were detected at the SNP level by the CC or CO approaches. The joint-effect test detected numerous genome-wide significant GxE signals in the GWAS main effects top hit regions, but the significance diminished after adjusting for the GWAS top hits. In the gene-based analysis, a significant interaction of diabetes with variants in the FAM63A (family with sequence similarity 63 member A) gene (significance threshold P < 1.25 10⁶) was observed in the meta-analysis (P_GxE ¼ 1.2 10⁶, P_Joint ¼ 4.2 10⁷). Conclusions: This analysis did not find significant GxE interactions at the SNP level but found one significant interaction with diabetes at the gene level. A larger sample size might unveil additional genetic factors via GxE scans. Impact: This study may contribute to discovering the mechanism of diabetes-associated pancreatic cancer.

Original language	English (US)
Pages (from-to)	1784-1791
Number of pages	8
Journal	Cancer Epidemiology Biomarkers and Prevention
Volume	29
Issue number	9
DOIs	https://doi.org/10.1158/1055-9965.EPI-20-0275
State	Published - Sep 2020

ASJC Scopus subject areas

General Medicine

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10.1158/1055-9965.EPI-20-0275

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Tang, H., Jiang, L., Stolzenberg-Solomon, R. Z., Arslan, A. A., Beane Freeman, L. E., Bracci, P. M., Brennan, P., Canzian, F., Du, M., Gallinger, S., Giles, G. G., Goodman, P. J., Kooperberg, C., Le Marchand, L., Neale, R. E., Shu, X. O., Visvanathan, K., White, E., Zheng, W., ... Wei, P. (2020). Genome-wide gene⇓diabetes and gene⇓obesity interaction scan in 8,255 cases and 11,900 controls from panscan and PanC4 consortia. Cancer Epidemiology Biomarkers and Prevention, 29(9), 1784-1791. https://doi.org/10.1158/1055-9965.EPI-20-0275

Tang, H, Jiang, L, Stolzenberg-Solomon, RZ, Arslan, AA, Beane Freeman, LE, Bracci, PM, Brennan, P, Canzian, F, Du, M, Gallinger, S, Giles, GG, Goodman, PJ, Kooperberg, C, Le Marchand, L, Neale, RE, Shu, XO, Visvanathan, K, White, E, Zheng, W, Albanes, D, Andreotti, G, Babic, A, Bamlet, WR, Berndt, SI, Blackford, A, Bueno-De-Mesquita, B, Buring, JE, Campa, D, Chanock, SJ, Childs, E, Duell, EJ, Fuchs, C, Michael Gaziano, J, Goggins, M, Hartge, P, Hassam, MH, Holly, EA, Hoover, RN, Hung, RJ, Kurtz, RC, Lee, IM, Malats, N, Milne, RL, Ng, K, Oberg, AL, Orlow, I, Peters, U, Porta, M, Rabe, KG, Rothman, N, Scelo, G, Sesso, HD, Silverman, DT, Thompson, IM, Tjønneland, A, Trichopoulou, A, Wactawski-Wende, J, Wentzensen, N, Wilkens, LR, Yu, H, Zeleniuch-Jacquotte, A, Amundadottir, LT, Jacobs, EJ, Petersen, GM, Wolpin, BM, Risch, HA, Chatterjee, N, Klein, AP, Li, D, Kraft, P & Wei, P 2020, 'Genome-wide gene⇓diabetes and gene⇓obesity interaction scan in 8,255 cases and 11,900 controls from panscan and PanC4 consortia', Cancer Epidemiology Biomarkers and Prevention, vol. 29, no. 9, pp. 1784-1791. https://doi.org/10.1158/1055-9965.EPI-20-0275

@article{728353a89f8e41769f2b5f24b31e03f5,

title = "Genome-wide gene⇓diabetes and gene⇓obesity interaction scan in 8,255 cases and 11,900 controls from panscan and PanC4 consortia",

abstract = "Background: Obesity and diabetes are major modifiable risk factors for pancreatic cancer. Interactions between genetic variants and diabetes/obesity have not previously been comprehensively investigated in pancreatic cancer at the genome-wide level. Methods: We conducted a gene–environment interaction (GxE) analysis including 8,255 cases and 11,900 controls from four pancreatic cancer genome-wide association study (GWAS) datasets (Pancreatic Cancer Cohort Consortium I–III and Pancreatic Cancer Case Control Consortium). Obesity (body mass index ≥30 kg/m2) and diabetes (duration ≥3 years) were the environmental variables of interest. Approximately 870,000 SNPs (minor allele frequency ≥0.005, genotyped in at least one dataset) were analyzed. Case–control (CC), case-only (CO), and joint-effect test methods were used for SNP-level GxE analysis. As a complementary approach, gene-based GxE analysis was also performed. Age, sex, study site, and principal components accounting for population substructure were included as covariates. Meta-analysis was applied to combine individual GWAS summary statistics. Results: No genome-wide significant interactions (departures from a log-additive odds model) with diabetes or obesity were detected at the SNP level by the CC or CO approaches. The joint-effect test detected numerous genome-wide significant GxE signals in the GWAS main effects top hit regions, but the significance diminished after adjusting for the GWAS top hits. In the gene-based analysis, a significant interaction of diabetes with variants in the FAM63A (family with sequence similarity 63 member A) gene (significance threshold P < 1.25 106) was observed in the meta-analysis (PGxE ¼ 1.2 106, PJoint ¼ 4.2 107). Conclusions: This analysis did not find significant GxE interactions at the SNP level but found one significant interaction with diabetes at the gene level. A larger sample size might unveil additional genetic factors via GxE scans. Impact: This study may contribute to discovering the mechanism of diabetes-associated pancreatic cancer.",

author = "Hongwei Tang and Lai Jiang and Stolzenberg-Solomon, {Rachael Z.} and Arslan, {Alan A.} and {Beane Freeman}, {Laura E.} and Bracci, {Paige M.} and Paul Brennan and Federico Canzian and Mengmeng Du and Steven Gallinger and Giles, {Graham G.} and Goodman, {Phyllis J.} and Charles Kooperberg and {Le Marchand}, Loc and Neale, {Rachel E.} and Shu, {Xiao Ou} and Kala Visvanathan and Emily White and Wei Zheng and Demetrius Albanes and Gabriella Andreotti and Ana Babic and Bamlet, {William R.} and Berndt, {Sonja I.} and Amanda Blackford and Bas Bueno-De-Mesquita and Buring, {Julie E.} and Daniele Campa and Chanock, {Stephen J.} and Erica Childs and Duell, {Eric J.} and Charles Fuchs and {Michael Gaziano}, J. and Michael Goggins and Patricia Hartge and Hassam, {Manal H.} and Holly, {Elizabeth A.} and Hoover, {Robert N.} and Hung, {Rayjean J.} and Kurtz, {Robert C.} and Lee, {I. Min} and Nuria Malats and Milne, {Roger L.} and Kimmie Ng and Oberg, {Ann L.} and Irene Orlow and Ulrike Peters and Miquel Porta and Rabe, {Kari G.} and Nathaniel Rothman and Ghislaine Scelo and Sesso, {Howard D.} and Silverman, {Debra T.} and Thompson, {Ian M.} and Anne Tj{\o}nneland and Antonia Trichopoulou and Jean Wactawski-Wende and Nicolas Wentzensen and Wilkens, {Lynne R.} and Herbert Yu and Anne Zeleniuch-Jacquotte and Amundadottir, {Laufey T.} and Jacobs, {Eric J.} and Petersen, {Gloria M.} and Wolpin, {Brian M.} and Risch, {Harvey A.} and Nilanjan Chatterjee and Klein, {Alison P.} and Donghui Li and Peter Kraft and Peng Wei",

note = "Publisher Copyright: {\textcopyright} 2020 American Association for Cancer Research.",

year = "2020",

month = sep,

doi = "10.1158/1055-9965.EPI-20-0275",

language = "English (US)",

volume = "29",

pages = "1784--1791",

journal = "Cancer Epidemiology Biomarkers and Prevention",

issn = "1055-9965",

publisher = "American Association for Cancer Research Inc.",

number = "9",

}

TY - JOUR

T1 - Genome-wide gene⇓diabetes and gene⇓obesity interaction scan in 8,255 cases and 11,900 controls from panscan and PanC4 consortia

AU - Tang, Hongwei

AU - Jiang, Lai

AU - Stolzenberg-Solomon, Rachael Z.

AU - Arslan, Alan A.

AU - Beane Freeman, Laura E.

AU - Bracci, Paige M.

AU - Brennan, Paul

AU - Canzian, Federico

AU - Du, Mengmeng

AU - Gallinger, Steven

AU - Giles, Graham G.

AU - Goodman, Phyllis J.

AU - Kooperberg, Charles

AU - Le Marchand, Loc

AU - Neale, Rachel E.

AU - Shu, Xiao Ou

AU - Visvanathan, Kala

AU - White, Emily

AU - Zheng, Wei

AU - Albanes, Demetrius

AU - Andreotti, Gabriella

AU - Babic, Ana

AU - Bamlet, William R.

AU - Berndt, Sonja I.

AU - Blackford, Amanda

AU - Bueno-De-Mesquita, Bas

AU - Buring, Julie E.

AU - Campa, Daniele

AU - Chanock, Stephen J.

AU - Childs, Erica

AU - Duell, Eric J.

AU - Fuchs, Charles

AU - Michael Gaziano, J.

AU - Goggins, Michael

AU - Hartge, Patricia

AU - Hassam, Manal H.

AU - Holly, Elizabeth A.

AU - Hoover, Robert N.

AU - Hung, Rayjean J.

AU - Kurtz, Robert C.

AU - Lee, I. Min

AU - Malats, Nuria

AU - Milne, Roger L.

AU - Ng, Kimmie

AU - Oberg, Ann L.

AU - Orlow, Irene

AU - Peters, Ulrike

AU - Porta, Miquel

AU - Rabe, Kari G.

AU - Rothman, Nathaniel

AU - Scelo, Ghislaine

AU - Sesso, Howard D.

AU - Silverman, Debra T.

AU - Thompson, Ian M.

AU - Tjønneland, Anne

AU - Trichopoulou, Antonia

AU - Wactawski-Wende, Jean

AU - Wentzensen, Nicolas

AU - Wilkens, Lynne R.

AU - Yu, Herbert

AU - Zeleniuch-Jacquotte, Anne

AU - Amundadottir, Laufey T.

AU - Jacobs, Eric J.

AU - Petersen, Gloria M.

AU - Wolpin, Brian M.

AU - Risch, Harvey A.

AU - Chatterjee, Nilanjan

AU - Klein, Alison P.

AU - Li, Donghui

AU - Kraft, Peter

AU - Wei, Peng

PY - 2020/9

Y1 - 2020/9

N2 - Background: Obesity and diabetes are major modifiable risk factors for pancreatic cancer. Interactions between genetic variants and diabetes/obesity have not previously been comprehensively investigated in pancreatic cancer at the genome-wide level. Methods: We conducted a gene–environment interaction (GxE) analysis including 8,255 cases and 11,900 controls from four pancreatic cancer genome-wide association study (GWAS) datasets (Pancreatic Cancer Cohort Consortium I–III and Pancreatic Cancer Case Control Consortium). Obesity (body mass index ≥30 kg/m2) and diabetes (duration ≥3 years) were the environmental variables of interest. Approximately 870,000 SNPs (minor allele frequency ≥0.005, genotyped in at least one dataset) were analyzed. Case–control (CC), case-only (CO), and joint-effect test methods were used for SNP-level GxE analysis. As a complementary approach, gene-based GxE analysis was also performed. Age, sex, study site, and principal components accounting for population substructure were included as covariates. Meta-analysis was applied to combine individual GWAS summary statistics. Results: No genome-wide significant interactions (departures from a log-additive odds model) with diabetes or obesity were detected at the SNP level by the CC or CO approaches. The joint-effect test detected numerous genome-wide significant GxE signals in the GWAS main effects top hit regions, but the significance diminished after adjusting for the GWAS top hits. In the gene-based analysis, a significant interaction of diabetes with variants in the FAM63A (family with sequence similarity 63 member A) gene (significance threshold P < 1.25 106) was observed in the meta-analysis (PGxE ¼ 1.2 106, PJoint ¼ 4.2 107). Conclusions: This analysis did not find significant GxE interactions at the SNP level but found one significant interaction with diabetes at the gene level. A larger sample size might unveil additional genetic factors via GxE scans. Impact: This study may contribute to discovering the mechanism of diabetes-associated pancreatic cancer.

AB - Background: Obesity and diabetes are major modifiable risk factors for pancreatic cancer. Interactions between genetic variants and diabetes/obesity have not previously been comprehensively investigated in pancreatic cancer at the genome-wide level. Methods: We conducted a gene–environment interaction (GxE) analysis including 8,255 cases and 11,900 controls from four pancreatic cancer genome-wide association study (GWAS) datasets (Pancreatic Cancer Cohort Consortium I–III and Pancreatic Cancer Case Control Consortium). Obesity (body mass index ≥30 kg/m2) and diabetes (duration ≥3 years) were the environmental variables of interest. Approximately 870,000 SNPs (minor allele frequency ≥0.005, genotyped in at least one dataset) were analyzed. Case–control (CC), case-only (CO), and joint-effect test methods were used for SNP-level GxE analysis. As a complementary approach, gene-based GxE analysis was also performed. Age, sex, study site, and principal components accounting for population substructure were included as covariates. Meta-analysis was applied to combine individual GWAS summary statistics. Results: No genome-wide significant interactions (departures from a log-additive odds model) with diabetes or obesity were detected at the SNP level by the CC or CO approaches. The joint-effect test detected numerous genome-wide significant GxE signals in the GWAS main effects top hit regions, but the significance diminished after adjusting for the GWAS top hits. In the gene-based analysis, a significant interaction of diabetes with variants in the FAM63A (family with sequence similarity 63 member A) gene (significance threshold P < 1.25 106) was observed in the meta-analysis (PGxE ¼ 1.2 106, PJoint ¼ 4.2 107). Conclusions: This analysis did not find significant GxE interactions at the SNP level but found one significant interaction with diabetes at the gene level. A larger sample size might unveil additional genetic factors via GxE scans. Impact: This study may contribute to discovering the mechanism of diabetes-associated pancreatic cancer.

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JO - Cancer Epidemiology Biomarkers and Prevention

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ER -

Genome-wide gene⇓diabetes and gene⇓obesity interaction scan in 8,255 cases and 11,900 controls from panscan and PanC4 consortia

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