Identification of lung cancer histology-specific variants applying Bayesian framework variant prioritization approaches within the TRICL and ILCCO consortia

Darren R. Brenner; Christopher I. Amos; Yonathan Brhane; Maria N. Timofeeva; Neil Caporaso; Yufei Wang; David C. Christiani; Heike Bickeböller; Ping Yang; Demetrius Albanes; Victoria L. Stevens; Susan Gapstur; James McKay; Paolo Boffetta; David Zaridze; Neonilia Szeszenia-Dabrowska; Jolanta Lissowska; Peter Rudnai; Eleonora Fabianova; Dana Mates; Vladimir Bencko; Lenka Foretova; Vladimir Janout; Hans E. Krokan; Frank Skorpen; Maiken E. Gabrielsen; Lars Vatten; Inger Njølstad; Chu Chen; Gary Goodman; Mark Lathrop; Tõnu Vooder; Kristjan Välk; Mari Nelis; Andres Metspalu; Peter Broderick; Timothy Eisen; Xifeng Wu; Di Zhang; Wei Chen; Margaret R. Spitz; Yongyue Wei; Li Su; Dong Xie; Jun She; Keitaro Matsuo; Fumihiko Matsuda; Hidemi Ito; Angela Risch; Joachim Heinrich; Albert Rosenberger; Thomas Muley; Hendrik Dienemann; John K. Field; Olaide Raji; Ying Chen; John Gosney; Triantafillos Liloglou; Michael P.A. Davies; Michael Marcus; John McLaughlin; Irene Orlow; Younghun Han; Yafang Li; Xuchen Zong; Mattias Johansson; Geoffrey Liu; Shelley S. Tworoger; Loic Le Marchand; Brian E. Henderson; Lynne R. Wilkens; Juncheng Dai; Hongbing Shen; Richard S. Houlston; Maria T. Landi; Paul Brennan; Rayjean J. Hung

doi:10.1093/carcin/bgv128

Identification of lung cancer histology-specific variants applying Bayesian framework variant prioritization approaches within the TRICL and ILCCO consortia

Darren R. Brenner
, Christopher I. Amos
, Yonathan Brhane
, Maria N. Timofeeva
, Neil Caporaso
, Yufei Wang
, David C. Christiani
, Heike Bickeböller
, Ping Yang
, Demetrius Albanes
, Victoria L. Stevens
, Susan Gapstur
, James McKay
, Paolo Boffetta
, David Zaridze
, Neonilia Szeszenia-Dabrowska
, Jolanta Lissowska
, Peter Rudnai
, Eleonora Fabianova
, Dana Mates

Vladimir Bencko, Lenka Foretova, Vladimir Janout, Hans E. Krokan, Frank Skorpen, Maiken E. Gabrielsen, Lars Vatten, Inger Njølstad, Chu Chen, Gary Goodman, Mark Lathrop, Tõnu Vooder, Kristjan Välk, Mari Nelis, Andres Metspalu, Peter Broderick, Timothy Eisen, Xifeng Wu, Di Zhang, Wei Chen, Margaret R. Spitz, Yongyue Wei, Li Su, Dong Xie, Jun She, Keitaro Matsuo, Fumihiko Matsuda, Hidemi Ito, Angela Risch, Joachim Heinrich, Albert Rosenberger, Thomas Muley, Hendrik Dienemann, John K. Field, Olaide Raji, Ying Chen, John Gosney, Triantafillos Liloglou, Michael P.A. Davies, Michael Marcus, John McLaughlin, Irene Orlow, Younghun Han, Yafang Li, Xuchen Zong, Mattias Johansson, Geoffrey Liu, Shelley S. Tworoger, Loic Le Marchand, Brian E. Henderson, Lynne R. Wilkens, Juncheng Dai, Hongbing Shen, Richard S. Houlston, Maria T. Landi, Paul Brennan, Rayjean J. Hung^*

^*Corresponding author for this work

University of Toronto
International Agency for Research on Cancer
Alberta Health Services
Dartmouth College
University of Edinburgh
National Institutes of Health
Institute of Cancer Research
Harvard University
University of Göttingen
Mayo Clinic Rochester, MN
American Cancer Society
Icahn School of Medicine at Mount Sinai
Russian Academy of Medical Sciences - N.N. Blokhin Russian Cancer Research Center
Nofer Institute of Occupational Medicine
Maria Sklodowska-Curie Institute of Oncology
Hungarian National Institute of Environmental Health
Regional Authority of Public Health
National Institute of Public Health
Charles University
Masaryk Memorial Cancer Institute
Palacký University Olomouc
Norwegian University of Science and Technology
University of Tromsø – The Arctic University of Norway
Fred Hutchinson Cancer Research Center
McGill University
University of Tartu
University of Bergen
Institute of Molecular and Cell Biology
Cambridge Biomedical Research Centre
University of Texas MD Anderson Cancer Center
Baylor College of Medicine
Kyushu University
Kyoto University
Aichi Cancer Center Hospital and Research Institute
German Cancer Research Center
German Center for Lung Research (DZL)
Helmholtz Zentrum München - German Research Center for Environmental Health
Translational Research Unit
Heidelberg University
University of Liverpool
Memorial Sloan-Kettering Cancer Center
Princess Margaret Hospital
Brigham and Women’s Hospital
University of Hawai'i at Mānoa
University of Southern California
Nanjing Medical University

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

15 Citations (Scopus)

Abstract

Large-scale genome-wide association studies (GWAS) have likely uncovered all common variants at the GWAS significance level. Additional variants within the suggestive range (0.0001> P> 5× 10^-8) are, however, still of interest for identifying causal associations. This analysis aimed to apply novel variant prioritization approaches to identify additional lung cancer variants that may not reach the GWAS level. Effects were combined across studies with a total of 33 456 controls and 6756 adenocarcinoma (AC; 13 studies), 5061 squamous cell carcinoma (SCC; 12 studies) and 2216 small cell lung cancer cases (9 studies). Based on prior information such as variant physical properties and functional significance, we applied stratified false discovery rates, hierarchical modeling and Bayesian false discovery probabilities for variant prioritization. We conducted a fine mapping analysis as validation of our methods by examining top-ranking novel variants in six independent populations with a total of 3128 cases and 2966 controls. Three novel loci in the suggestive range were identified based on our Bayesian framework analyses: KCNIP4 at 4p15.2 (rs6448050, P= 4.6× 10^-7) and MTMR2 at 11q21 (rs10501831, P= 3.1× 10^-6) with SCC, as well as GAREM at 18q12.1 (rs11662168, P= 3.4× 10^-7) with AC. Use of our prioritization methods validated two of the top three loci associated with SCC (P= 1.05× 10^-4 for KCNIP4, represented by rs9799795) and AC (P= 2.16× 10^-4 for GAREM, represented by rs3786309) in the independent fine mapping populations. This study highlights the utility of using prior functional data for sequence variants in prioritization analyses to search for robust signals in the suggestive range.

Original language	English
Pages (from-to)	1314-1326
Number of pages	13
Journal	Carcinogenesis
Volume	36
Issue number	11
DOIs	https://doi.org/10.1093/carcin/bgv128
Publication status	Published - Nov 2015
Externally published	Yes

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10.1093/carcin/bgv128

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Brenner, D. R., Amos, C. I., Brhane, Y., Timofeeva, M. N., Caporaso, N., Wang, Y., Christiani, D. C., Bickeböller, H., Yang, P., Albanes, D., Stevens, V. L., Gapstur, S., McKay, J., Boffetta, P., Zaridze, D., Szeszenia-Dabrowska, N., Lissowska, J., Rudnai, P., Fabianova, E., ... Hung, R. J. (2015). Identification of lung cancer histology-specific variants applying Bayesian framework variant prioritization approaches within the TRICL and ILCCO consortia. Carcinogenesis, 36(11), 1314-1326. https://doi.org/10.1093/carcin/bgv128

@article{6f1a4df1d1dd4128a4d3803fa1e1a094,

title = "Identification of lung cancer histology-specific variants applying Bayesian framework variant prioritization approaches within the TRICL and ILCCO consortia",

abstract = "Large-scale genome-wide association studies (GWAS) have likely uncovered all common variants at the GWAS significance level. Additional variants within the suggestive range (0.0001> P> 5× 10-8) are, however, still of interest for identifying causal associations. This analysis aimed to apply novel variant prioritization approaches to identify additional lung cancer variants that may not reach the GWAS level. Effects were combined across studies with a total of 33 456 controls and 6756 adenocarcinoma (AC; 13 studies), 5061 squamous cell carcinoma (SCC; 12 studies) and 2216 small cell lung cancer cases (9 studies). Based on prior information such as variant physical properties and functional significance, we applied stratified false discovery rates, hierarchical modeling and Bayesian false discovery probabilities for variant prioritization. We conducted a fine mapping analysis as validation of our methods by examining top-ranking novel variants in six independent populations with a total of 3128 cases and 2966 controls. Three novel loci in the suggestive range were identified based on our Bayesian framework analyses: KCNIP4 at 4p15.2 (rs6448050, P= 4.6× 10-7) and MTMR2 at 11q21 (rs10501831, P= 3.1× 10-6) with SCC, as well as GAREM at 18q12.1 (rs11662168, P= 3.4× 10-7) with AC. Use of our prioritization methods validated two of the top three loci associated with SCC (P= 1.05× 10-4 for KCNIP4, represented by rs9799795) and AC (P= 2.16× 10-4 for GAREM, represented by rs3786309) in the independent fine mapping populations. This study highlights the utility of using prior functional data for sequence variants in prioritization analyses to search for robust signals in the suggestive range.",

author = "Brenner, \{Darren R.\} and Amos, \{Christopher I.\} and Yonathan Brhane and Timofeeva, \{Maria N.\} and Neil Caporaso and Yufei Wang and Christiani, \{David C.\} and Heike Bickeb{\"o}ller and Ping Yang and Demetrius Albanes and Stevens, \{Victoria L.\} and Susan Gapstur and James McKay and Paolo Boffetta and David Zaridze and Neonilia Szeszenia-Dabrowska and Jolanta Lissowska and Peter Rudnai and Eleonora Fabianova and Dana Mates and Vladimir Bencko and Lenka Foretova and Vladimir Janout and Krokan, \{Hans E.\} and Frank Skorpen and Gabrielsen, \{Maiken E.\} and Lars Vatten and Inger Nj{\o}lstad and Chu Chen and Gary Goodman and Mark Lathrop and T{\~o}nu Vooder and Kristjan V{\"a}lk and Mari Nelis and Andres Metspalu and Peter Broderick and Timothy Eisen and Xifeng Wu and Di Zhang and Wei Chen and Spitz, \{Margaret R.\} and Yongyue Wei and Li Su and Dong Xie and Jun She and Keitaro Matsuo and Fumihiko Matsuda and Hidemi Ito and Angela Risch and Joachim Heinrich and Albert Rosenberger and Thomas Muley and Hendrik Dienemann and Field, \{John K.\} and Olaide Raji and Ying Chen and John Gosney and Triantafillos Liloglou and Davies, \{Michael P.A.\} and Michael Marcus and John McLaughlin and Irene Orlow and Younghun Han and Yafang Li and Xuchen Zong and Mattias Johansson and Geoffrey Liu and Tworoger, \{Shelley S.\} and \{Le Marchand\}, Loic and Henderson, \{Brian E.\} and Wilkens, \{Lynne R.\} and Juncheng Dai and Hongbing Shen and Houlston, \{Richard S.\} and Landi, \{Maria T.\} and Paul Brennan and Hung, \{Rayjean J.\}",

year = "2015",

month = nov,

doi = "10.1093/carcin/bgv128",

language = "English",

volume = "36",

pages = "1314--1326",

journal = "Carcinogenesis",

issn = "0143-3334",

number = "11",

}

Brenner, DR, Amos, CI, Brhane, Y, Timofeeva, MN, Caporaso, N, Wang, Y, Christiani, DC, Bickeböller, H, Yang, P, Albanes, D, Stevens, VL, Gapstur, S, McKay, J, Boffetta, P, Zaridze, D, Szeszenia-Dabrowska, N, Lissowska, J, Rudnai, P, Fabianova, E, Mates, D, Bencko, V, Foretova, L, Janout, V, Krokan, HE, Skorpen, F, Gabrielsen, ME, Vatten, L, Njølstad, I, Chen, C, Goodman, G, Lathrop, M, Vooder, T, Välk, K, Nelis, M, Metspalu, A, Broderick, P, Eisen, T, Wu, X, Zhang, D, Chen, W, Spitz, MR, Wei, Y, Su, L, Xie, D, She, J, Matsuo, K, Matsuda, F, Ito, H, Risch, A, Heinrich, J, Rosenberger, A, Muley, T, Dienemann, H, Field, JK, Raji, O, Chen, Y, Gosney, J, Liloglou, T, Davies, MPA, Marcus, M, McLaughlin, J, Orlow, I, Han, Y, Li, Y, Zong, X, Johansson, M, Liu, G, Tworoger, SS, Le Marchand, L, Henderson, BE, Wilkens, LR, Dai, J, Shen, H, Houlston, RS, Landi, MT, Brennan, P & Hung, RJ 2015, 'Identification of lung cancer histology-specific variants applying Bayesian framework variant prioritization approaches within the TRICL and ILCCO consortia', Carcinogenesis, vol. 36, no. 11, pp. 1314-1326. https://doi.org/10.1093/carcin/bgv128

TY - JOUR

T1 - Identification of lung cancer histology-specific variants applying Bayesian framework variant prioritization approaches within the TRICL and ILCCO consortia

AU - Brenner, Darren R.

AU - Amos, Christopher I.

AU - Brhane, Yonathan

AU - Timofeeva, Maria N.

AU - Caporaso, Neil

AU - Wang, Yufei

AU - Christiani, David C.

AU - Bickeböller, Heike

AU - Yang, Ping

AU - Albanes, Demetrius

AU - Stevens, Victoria L.

AU - Gapstur, Susan

AU - McKay, James

AU - Boffetta, Paolo

AU - Zaridze, David

AU - Szeszenia-Dabrowska, Neonilia

AU - Lissowska, Jolanta

AU - Rudnai, Peter

AU - Fabianova, Eleonora

AU - Mates, Dana

AU - Bencko, Vladimir

AU - Foretova, Lenka

AU - Janout, Vladimir

AU - Krokan, Hans E.

AU - Skorpen, Frank

AU - Gabrielsen, Maiken E.

AU - Vatten, Lars

AU - Njølstad, Inger

AU - Chen, Chu

AU - Goodman, Gary

AU - Lathrop, Mark

AU - Vooder, Tõnu

AU - Välk, Kristjan

AU - Nelis, Mari

AU - Metspalu, Andres

AU - Broderick, Peter

AU - Eisen, Timothy

AU - Wu, Xifeng

AU - Zhang, Di

AU - Chen, Wei

AU - Spitz, Margaret R.

AU - Wei, Yongyue

AU - Su, Li

AU - Xie, Dong

AU - She, Jun

AU - Matsuo, Keitaro

AU - Matsuda, Fumihiko

AU - Ito, Hidemi

AU - Risch, Angela

AU - Heinrich, Joachim

AU - Rosenberger, Albert

AU - Muley, Thomas

AU - Dienemann, Hendrik

AU - Field, John K.

AU - Raji, Olaide

AU - Chen, Ying

AU - Gosney, John

AU - Liloglou, Triantafillos

AU - Davies, Michael P.A.

AU - Marcus, Michael

AU - McLaughlin, John

AU - Orlow, Irene

AU - Han, Younghun

AU - Li, Yafang

AU - Zong, Xuchen

AU - Johansson, Mattias

AU - Liu, Geoffrey

AU - Tworoger, Shelley S.

AU - Le Marchand, Loic

AU - Henderson, Brian E.

AU - Wilkens, Lynne R.

AU - Dai, Juncheng

AU - Shen, Hongbing

AU - Houlston, Richard S.

AU - Landi, Maria T.

AU - Brennan, Paul

AU - Hung, Rayjean J.

PY - 2015/11

Y1 - 2015/11

N2 - Large-scale genome-wide association studies (GWAS) have likely uncovered all common variants at the GWAS significance level. Additional variants within the suggestive range (0.0001> P> 5× 10-8) are, however, still of interest for identifying causal associations. This analysis aimed to apply novel variant prioritization approaches to identify additional lung cancer variants that may not reach the GWAS level. Effects were combined across studies with a total of 33 456 controls and 6756 adenocarcinoma (AC; 13 studies), 5061 squamous cell carcinoma (SCC; 12 studies) and 2216 small cell lung cancer cases (9 studies). Based on prior information such as variant physical properties and functional significance, we applied stratified false discovery rates, hierarchical modeling and Bayesian false discovery probabilities for variant prioritization. We conducted a fine mapping analysis as validation of our methods by examining top-ranking novel variants in six independent populations with a total of 3128 cases and 2966 controls. Three novel loci in the suggestive range were identified based on our Bayesian framework analyses: KCNIP4 at 4p15.2 (rs6448050, P= 4.6× 10-7) and MTMR2 at 11q21 (rs10501831, P= 3.1× 10-6) with SCC, as well as GAREM at 18q12.1 (rs11662168, P= 3.4× 10-7) with AC. Use of our prioritization methods validated two of the top three loci associated with SCC (P= 1.05× 10-4 for KCNIP4, represented by rs9799795) and AC (P= 2.16× 10-4 for GAREM, represented by rs3786309) in the independent fine mapping populations. This study highlights the utility of using prior functional data for sequence variants in prioritization analyses to search for robust signals in the suggestive range.

AB - Large-scale genome-wide association studies (GWAS) have likely uncovered all common variants at the GWAS significance level. Additional variants within the suggestive range (0.0001> P> 5× 10-8) are, however, still of interest for identifying causal associations. This analysis aimed to apply novel variant prioritization approaches to identify additional lung cancer variants that may not reach the GWAS level. Effects were combined across studies with a total of 33 456 controls and 6756 adenocarcinoma (AC; 13 studies), 5061 squamous cell carcinoma (SCC; 12 studies) and 2216 small cell lung cancer cases (9 studies). Based on prior information such as variant physical properties and functional significance, we applied stratified false discovery rates, hierarchical modeling and Bayesian false discovery probabilities for variant prioritization. We conducted a fine mapping analysis as validation of our methods by examining top-ranking novel variants in six independent populations with a total of 3128 cases and 2966 controls. Three novel loci in the suggestive range were identified based on our Bayesian framework analyses: KCNIP4 at 4p15.2 (rs6448050, P= 4.6× 10-7) and MTMR2 at 11q21 (rs10501831, P= 3.1× 10-6) with SCC, as well as GAREM at 18q12.1 (rs11662168, P= 3.4× 10-7) with AC. Use of our prioritization methods validated two of the top three loci associated with SCC (P= 1.05× 10-4 for KCNIP4, represented by rs9799795) and AC (P= 2.16× 10-4 for GAREM, represented by rs3786309) in the independent fine mapping populations. This study highlights the utility of using prior functional data for sequence variants in prioritization analyses to search for robust signals in the suggestive range.

UR - https://www.scopus.com/pages/publications/84949569031

U2 - 10.1093/carcin/bgv128

DO - 10.1093/carcin/bgv128

M3 - Article

C2 - 26363033

AN - SCOPUS:84949569031

SN - 0143-3334

VL - 36

SP - 1314

EP - 1326

JO - Carcinogenesis

JF - Carcinogenesis

IS - 11

ER -

Identification of lung cancer histology-specific variants applying Bayesian framework variant prioritization approaches within the TRICL and ILCCO consortia

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