Agnostic Pathway/Gene Set Analysis of Genome-Wide Association Data Identifies Associations for Pancreatic Cancer

Naomi Walsh; Han Zhang; Paula L. Hyland; Qi Yang; Evelina Mocci; Mingfeng Zhang; Erica J. Childs; Irene Collins; Zhaoming Wang; Alan A. Arslan; Laura Beane-Freeman; Paige M. Bracci; Paul Brennan; Federico Canzian; Eric J. Duell; Steven Gallinger; Graham G. Giles; Michael Goggins; Gary E. Goodman; Phyllis J. Goodman; Rayjean J. Hung; Charles Kooperberg; Robert C. Kurtz; Núria Malats; Loic Lemarchand; Rachel E. Neale; Sara H. Olson; Ghislaine Scelo; Xiao O. Shu; Stephen K. Van Den Eeden; Kala Visvanathan; Emily White; Wei Zheng; Demetrius Albanes; Gabriella Andreotti; Ana Babic; William R. Bamlet; Sonja I. Berndt; Ayelet Borgida; Marie Christine Boutron-Ruault; Lauren Brais; Bas Bueno-De-Mesquita; Julie Buring; Kari G. Chaffee; Stephen Chanock; Sean Cleary; Michelle Cotterchio; Lenka Foretova; Charles Fuchs; J. Michael M Gaziano; Edward Giovannucci; Thilo Hackert; Christopher Haiman; Patricia Hartge; Manal Hasan; Kathy J. Helzlsouer; Joseph Herman; Ivana Holcatova; Elizabeth A. Holly; Robert Hoover; Vladimir Janout; Eric A. Klein; Daniel Laheru; I. Min Lee; Lingeng Lu; Satu Mannisto; Roger L. Milne; Ann L. Oberg; Irene Orlow; Alpa V. Patel; Ulrike Peters; Miquel Porta; Francisco X. Real; Nathaniel Rothman; Howard D. Sesso; Gianluca Severi; Debra Silverman; Oliver Strobel; Malin Sund; Mark D. Thornquist; Geoffrey S. Tobias; Jean Wactawski-Wende; Nick Wareham; Elisabete Weiderpass; Nicolas Wentzensen; William Wheeler; Herbert Yu; Anne Zeleniuch-Jacquotte; Peter Kraft; Donghui Li; Eric J. Jacobs; Gloria M. Petersen; Brian M. Wolpin; Harvey A. Risch; Laufey T. Amundadottir; Kai Yu; Alison P. Klein; Rachael Z. Stolzenberg-Solomon

doi:10.1093/jnci/djy155

Agnostic Pathway/Gene Set Analysis of Genome-Wide Association Data Identifies Associations for Pancreatic Cancer

Naomi Walsh, Han Zhang, Paula L. Hyland, Qi Yang, Evelina Mocci, Mingfeng Zhang, Erica J. Childs, Irene Collins, Zhaoming Wang, Alan A. Arslan, Laura Beane-Freeman, Paige M. Bracci, Paul Brennan, Federico Canzian, Eric J. Duell, Steven Gallinger, Graham G. Giles, Michael Goggins, Gary E. Goodman, Phyllis J. GoodmanRayjean J. Hung, Charles Kooperberg, Robert C. Kurtz, Núria Malats, Loic Lemarchand, Rachel E. Neale, Sara H. Olson, Ghislaine Scelo, Xiao O. Shu, Stephen K. Van Den Eeden, Kala Visvanathan, Emily White, Wei Zheng, Demetrius Albanes, Gabriella Andreotti, Ana Babic, William R. Bamlet, Sonja I. Berndt, Ayelet Borgida, Marie Christine Boutron-Ruault, Lauren Brais, Bas Bueno-De-Mesquita, Julie Buring, Kari G. Chaffee, Stephen Chanock, Sean Cleary, Michelle Cotterchio, Lenka Foretova, Charles Fuchs, J. Michael M Gaziano, Edward Giovannucci, Thilo Hackert, Christopher Haiman, Patricia Hartge, Manal Hasan, Kathy J. Helzlsouer, Joseph Herman, Ivana Holcatova, Elizabeth A. Holly, Robert Hoover, Vladimir Janout, Eric A. Klein, Daniel Laheru, I. Min Lee, Lingeng Lu, Satu Mannisto, Roger L. Milne, Ann L. Oberg, Irene Orlow, Alpa V. Patel, Ulrike Peters, Miquel Porta, Francisco X. Real, Nathaniel Rothman, Howard D. Sesso, Gianluca Severi, Debra Silverman, Oliver Strobel, Malin Sund, Mark D. Thornquist, Geoffrey S. Tobias, Jean Wactawski-Wende, Nick Wareham, Elisabete Weiderpass, Nicolas Wentzensen, William Wheeler, Herbert Yu, Anne Zeleniuch-Jacquotte, Peter Kraft, Donghui Li, Eric J. Jacobs, Gloria M. Petersen, Brian M. Wolpin, Harvey A. Risch, Laufey T. Amundadottir, Kai Yu, Alison P. Klein, Rachael Z. Stolzenberg-Solomon

Department of Paediatrics, Obstetrics and Gynaecology, and Preventive Medicine and Public Health

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Abstract

© The Author(s) 2019. Background: Genome-wide association studies (GWAS) identify associations of individual single-nucleotide polymorphisms (SNPs) with cancer risk but usually only explain a fraction of the inherited variability. Pathway analysis of genetic variants is a powerful tool to identify networks of susceptibility genes. Methods: We conducted a large agnostic pathway-based meta-analysis of GWAS data using the summary-based adaptive rank truncated product method to identify gene sets and pathways associated with pancreatic ductal adenocarcinoma (PDAC) in 9040 cases and 12 496 controls. We performed expression quantitative trait loci (eQTL) analysis and functional annotation of the top SNPs in genes contributing to the top associated pathways and gene sets. All statistical tests were two-sided. Results: We identified 14 pathways and gene sets associated with PDAC at a false discovery rate of less than 0.05. After Bonferroni correction (P ≤ 1.3 × 10-5), the strongest associations were detected in five pathways and gene sets, including maturity-onset diabetes of the young, regulation of beta-cell development, role of epidermal growth factor (EGF) receptor transactivation by G protein-coupled receptors in cardiac hypertrophy pathways, and the Nikolsky breast cancer chr17q11-q21 amplicon and Pujana ATM Pearson correlation coefficient (PCC) network gene sets. We identified and validated rs876493 and three correlating SNPs (PGAP3) and rs3124737 (CASP7) from the Pujana ATM PCC gene set as eQTLs in two normal derived pancreas tissue datasets. Conclusion: Our agnostic pathway and gene set analysis integrated with functional annotation and eQTL analysis provides insight into genes and pathways that may be biologically relevant for risk of PDAC, including those not previously identified.

Original language	English
Pages (from-to)	557-567
Journal	Journal of the National Cancer Institute
Volume	111
DOIs	https://doi.org/10.1093/jnci/djy155
Publication status	Published - 1 Jun 2019

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10.1093/jnci/djy155

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Walsh, N., Zhang, H., Hyland, P. L., Yang, Q., Mocci, E., Zhang, M., Childs, E. J., Collins, I., Wang, Z., Arslan, A. A., Beane-Freeman, L., Bracci, P. M., Brennan, P., Canzian, F., Duell, E. J., Gallinger, S., Giles, G. G., Goggins, M., Goodman, G. E., ... Stolzenberg-Solomon, R. Z. (2019). Agnostic Pathway/Gene Set Analysis of Genome-Wide Association Data Identifies Associations for Pancreatic Cancer. Journal of the National Cancer Institute, 111, 557-567. https://doi.org/10.1093/jnci/djy155

@article{f9ea08b746d842669cd208542909771a,

title = "Agnostic Pathway/Gene Set Analysis of Genome-Wide Association Data Identifies Associations for Pancreatic Cancer",

abstract = "{\textcopyright} The Author(s) 2019. Background: Genome-wide association studies (GWAS) identify associations of individual single-nucleotide polymorphisms (SNPs) with cancer risk but usually only explain a fraction of the inherited variability. Pathway analysis of genetic variants is a powerful tool to identify networks of susceptibility genes. Methods: We conducted a large agnostic pathway-based meta-analysis of GWAS data using the summary-based adaptive rank truncated product method to identify gene sets and pathways associated with pancreatic ductal adenocarcinoma (PDAC) in 9040 cases and 12 496 controls. We performed expression quantitative trait loci (eQTL) analysis and functional annotation of the top SNPs in genes contributing to the top associated pathways and gene sets. All statistical tests were two-sided. Results: We identified 14 pathways and gene sets associated with PDAC at a false discovery rate of less than 0.05. After Bonferroni correction (P ≤ 1.3 × 10-5), the strongest associations were detected in five pathways and gene sets, including maturity-onset diabetes of the young, regulation of beta-cell development, role of epidermal growth factor (EGF) receptor transactivation by G protein-coupled receptors in cardiac hypertrophy pathways, and the Nikolsky breast cancer chr17q11-q21 amplicon and Pujana ATM Pearson correlation coefficient (PCC) network gene sets. We identified and validated rs876493 and three correlating SNPs (PGAP3) and rs3124737 (CASP7) from the Pujana ATM PCC gene set as eQTLs in two normal derived pancreas tissue datasets. Conclusion: Our agnostic pathway and gene set analysis integrated with functional annotation and eQTL analysis provides insight into genes and pathways that may be biologically relevant for risk of PDAC, including those not previously identified.",

author = "Naomi Walsh and Han Zhang and Hyland, {Paula L.} and Qi Yang and Evelina Mocci and Mingfeng Zhang and Childs, {Erica J.} and Irene Collins and Zhaoming Wang and Arslan, {Alan A.} and Laura Beane-Freeman and Bracci, {Paige M.} and Paul Brennan and Federico Canzian and Duell, {Eric J.} and Steven Gallinger and Giles, {Graham G.} and Michael Goggins and Goodman, {Gary E.} and Goodman, {Phyllis J.} and Hung, {Rayjean J.} and Charles Kooperberg and Kurtz, {Robert C.} and N{\'u}ria Malats and Loic Lemarchand and Neale, {Rachel E.} and Olson, {Sara H.} and Ghislaine Scelo and Shu, {Xiao O.} and {Van Den Eeden}, {Stephen K.} 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 Ayelet Borgida and Boutron-Ruault, {Marie Christine} and Lauren Brais and Bas Bueno-De-Mesquita and Julie Buring and Chaffee, {Kari G.} and Stephen Chanock and Sean Cleary and Michelle Cotterchio and Lenka Foretova and Charles Fuchs and {M Gaziano}, {J. Michael} and Edward Giovannucci and Thilo Hackert and Christopher Haiman and Patricia Hartge and Manal Hasan and Helzlsouer, {Kathy J.} and Joseph Herman and Ivana Holcatova and Holly, {Elizabeth A.} and Robert Hoover and Vladimir Janout and Klein, {Eric A.} and Daniel Laheru and Lee, {I. Min} and Lingeng Lu and Satu Mannisto and Milne, {Roger L.} and Oberg, {Ann L.} and Irene Orlow and Patel, {Alpa V.} and Ulrike Peters and Miquel Porta and Real, {Francisco X.} and Nathaniel Rothman and Sesso, {Howard D.} and Gianluca Severi and Debra Silverman and Oliver Strobel and Malin Sund and Thornquist, {Mark D.} and Tobias, {Geoffrey S.} and Jean Wactawski-Wende and Nick Wareham and Elisabete Weiderpass and Nicolas Wentzensen and William Wheeler and Herbert Yu and Anne Zeleniuch-Jacquotte and Peter Kraft and Donghui Li and Jacobs, {Eric J.} and Petersen, {Gloria M.} and Wolpin, {Brian M.} and Risch, {Harvey A.} and Amundadottir, {Laufey T.} and Kai Yu and Klein, {Alison P.} and Stolzenberg-Solomon, {Rachael Z.}",

year = "2019",

month = jun,

day = "1",

doi = "10.1093/jnci/djy155",

language = "English",

volume = "111",

pages = "557--567",

}

Walsh, N, Zhang, H, Hyland, PL, Yang, Q, Mocci, E, Zhang, M, Childs, EJ, Collins, I, Wang, Z, Arslan, AA, Beane-Freeman, L, Bracci, PM, Brennan, P, Canzian, F, Duell, EJ, Gallinger, S, Giles, GG, Goggins, M, Goodman, GE, Goodman, PJ, Hung, RJ, Kooperberg, C, Kurtz, RC, Malats, N, Lemarchand, L, Neale, RE, Olson, SH, Scelo, G, Shu, XO, Van Den Eeden, SK, Visvanathan, K, White, E, Zheng, W, Albanes, D, Andreotti, G, Babic, A, Bamlet, WR, Berndt, SI, Borgida, A, Boutron-Ruault, MC, Brais, L, Bueno-De-Mesquita, B, Buring, J, Chaffee, KG, Chanock, S, Cleary, S, Cotterchio, M, Foretova, L, Fuchs, C, M Gaziano, JM, Giovannucci, E, Hackert, T, Haiman, C, Hartge, P, Hasan, M, Helzlsouer, KJ, Herman, J, Holcatova, I, Holly, EA, Hoover, R, Janout, V, Klein, EA, Laheru, D, Lee, IM, Lu, L, Mannisto, S, Milne, RL, Oberg, AL, Orlow, I, Patel, AV, Peters, U, Porta, M, Real, FX, Rothman, N, Sesso, HD, Severi, G, Silverman, D, Strobel, O, Sund, M, Thornquist, MD, Tobias, GS, Wactawski-Wende, J, Wareham, N, Weiderpass, E, Wentzensen, N, Wheeler, W, Yu, H, Zeleniuch-Jacquotte, A, Kraft, P, Li, D, Jacobs, EJ, Petersen, GM, Wolpin, BM, Risch, HA, Amundadottir, LT, Yu, K, Klein, AP & Stolzenberg-Solomon, RZ 2019, 'Agnostic Pathway/Gene Set Analysis of Genome-Wide Association Data Identifies Associations for Pancreatic Cancer', Journal of the National Cancer Institute, vol. 111, pp. 557-567. https://doi.org/10.1093/jnci/djy155

TY - JOUR

T1 - Agnostic Pathway/Gene Set Analysis of Genome-Wide Association Data Identifies Associations for Pancreatic Cancer

AU - Walsh, Naomi

AU - Zhang, Han

AU - Hyland, Paula L.

AU - Yang, Qi

AU - Mocci, Evelina

AU - Zhang, Mingfeng

AU - Childs, Erica J.

AU - Collins, Irene

AU - Wang, Zhaoming

AU - Arslan, Alan A.

AU - Beane-Freeman, Laura

AU - Bracci, Paige M.

AU - Brennan, Paul

AU - Canzian, Federico

AU - Duell, Eric J.

AU - Gallinger, Steven

AU - Giles, Graham G.

AU - Goggins, Michael

AU - Goodman, Gary E.

AU - Goodman, Phyllis J.

AU - Hung, Rayjean J.

AU - Kooperberg, Charles

AU - Kurtz, Robert C.

AU - Malats, Núria

AU - Lemarchand, Loic

AU - Neale, Rachel E.

AU - Olson, Sara H.

AU - Scelo, Ghislaine

AU - Shu, Xiao O.

AU - Van Den Eeden, Stephen K.

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 - Borgida, Ayelet

AU - Boutron-Ruault, Marie Christine

AU - Brais, Lauren

AU - Bueno-De-Mesquita, Bas

AU - Buring, Julie

AU - Chaffee, Kari G.

AU - Chanock, Stephen

AU - Cleary, Sean

AU - Cotterchio, Michelle

AU - Foretova, Lenka

AU - Fuchs, Charles

AU - M Gaziano, J. Michael

AU - Giovannucci, Edward

AU - Hackert, Thilo

AU - Haiman, Christopher

AU - Hartge, Patricia

AU - Hasan, Manal

AU - Helzlsouer, Kathy J.

AU - Herman, Joseph

AU - Holcatova, Ivana

AU - Holly, Elizabeth A.

AU - Hoover, Robert

AU - Janout, Vladimir

AU - Klein, Eric A.

AU - Laheru, Daniel

AU - Lee, I. Min

AU - Lu, Lingeng

AU - Mannisto, Satu

AU - Milne, Roger L.

AU - Oberg, Ann L.

AU - Orlow, Irene

AU - Patel, Alpa V.

AU - Peters, Ulrike

AU - Porta, Miquel

AU - Real, Francisco X.

AU - Rothman, Nathaniel

AU - Sesso, Howard D.

AU - Severi, Gianluca

AU - Silverman, Debra

AU - Strobel, Oliver

AU - Sund, Malin

AU - Thornquist, Mark D.

AU - Tobias, Geoffrey S.

AU - Wactawski-Wende, Jean

AU - Wareham, Nick

AU - Weiderpass, Elisabete

AU - Wentzensen, Nicolas

AU - Wheeler, William

AU - Yu, Herbert

AU - Zeleniuch-Jacquotte, Anne

AU - Kraft, Peter

AU - Li, Donghui

AU - Jacobs, Eric J.

AU - Petersen, Gloria M.

AU - Wolpin, Brian M.

AU - Risch, Harvey A.

AU - Amundadottir, Laufey T.

AU - Yu, Kai

AU - Klein, Alison P.

AU - Stolzenberg-Solomon, Rachael Z.

PY - 2019/6/1

Y1 - 2019/6/1

N2 - © The Author(s) 2019. Background: Genome-wide association studies (GWAS) identify associations of individual single-nucleotide polymorphisms (SNPs) with cancer risk but usually only explain a fraction of the inherited variability. Pathway analysis of genetic variants is a powerful tool to identify networks of susceptibility genes. Methods: We conducted a large agnostic pathway-based meta-analysis of GWAS data using the summary-based adaptive rank truncated product method to identify gene sets and pathways associated with pancreatic ductal adenocarcinoma (PDAC) in 9040 cases and 12 496 controls. We performed expression quantitative trait loci (eQTL) analysis and functional annotation of the top SNPs in genes contributing to the top associated pathways and gene sets. All statistical tests were two-sided. Results: We identified 14 pathways and gene sets associated with PDAC at a false discovery rate of less than 0.05. After Bonferroni correction (P ≤ 1.3 × 10-5), the strongest associations were detected in five pathways and gene sets, including maturity-onset diabetes of the young, regulation of beta-cell development, role of epidermal growth factor (EGF) receptor transactivation by G protein-coupled receptors in cardiac hypertrophy pathways, and the Nikolsky breast cancer chr17q11-q21 amplicon and Pujana ATM Pearson correlation coefficient (PCC) network gene sets. We identified and validated rs876493 and three correlating SNPs (PGAP3) and rs3124737 (CASP7) from the Pujana ATM PCC gene set as eQTLs in two normal derived pancreas tissue datasets. Conclusion: Our agnostic pathway and gene set analysis integrated with functional annotation and eQTL analysis provides insight into genes and pathways that may be biologically relevant for risk of PDAC, including those not previously identified.

AB - © The Author(s) 2019. Background: Genome-wide association studies (GWAS) identify associations of individual single-nucleotide polymorphisms (SNPs) with cancer risk but usually only explain a fraction of the inherited variability. Pathway analysis of genetic variants is a powerful tool to identify networks of susceptibility genes. Methods: We conducted a large agnostic pathway-based meta-analysis of GWAS data using the summary-based adaptive rank truncated product method to identify gene sets and pathways associated with pancreatic ductal adenocarcinoma (PDAC) in 9040 cases and 12 496 controls. We performed expression quantitative trait loci (eQTL) analysis and functional annotation of the top SNPs in genes contributing to the top associated pathways and gene sets. All statistical tests were two-sided. Results: We identified 14 pathways and gene sets associated with PDAC at a false discovery rate of less than 0.05. After Bonferroni correction (P ≤ 1.3 × 10-5), the strongest associations were detected in five pathways and gene sets, including maturity-onset diabetes of the young, regulation of beta-cell development, role of epidermal growth factor (EGF) receptor transactivation by G protein-coupled receptors in cardiac hypertrophy pathways, and the Nikolsky breast cancer chr17q11-q21 amplicon and Pujana ATM Pearson correlation coefficient (PCC) network gene sets. We identified and validated rs876493 and three correlating SNPs (PGAP3) and rs3124737 (CASP7) from the Pujana ATM PCC gene set as eQTLs in two normal derived pancreas tissue datasets. Conclusion: Our agnostic pathway and gene set analysis integrated with functional annotation and eQTL analysis provides insight into genes and pathways that may be biologically relevant for risk of PDAC, including those not previously identified.

UR - http://www.mendeley.com/research/agnostic-pathwaygene-set-analysis-genomewide-association-data-identifies-associations-pancreatic-can

U2 - 10.1093/jnci/djy155

DO - 10.1093/jnci/djy155

M3 - Article

C2 - 30541042

SN - 0027-8874

VL - 111

SP - 557

EP - 567

JO - Journal of the National Cancer Institute

JF - Journal of the National Cancer Institute

ER -

Agnostic Pathway/Gene Set Analysis of Genome-Wide Association Data Identifies Associations for Pancreatic Cancer

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