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 MatesVladimir 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

Quantitative Health Sciences

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

8 Scopus citations

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 (US)
Pages (from-to)	1314-1326
Number of pages	13
Journal	Carcinogenesis
Volume	36
Issue number	11
DOIs	https://doi.org/10.1093/carcin/bgv128
State	Published - Nov 2015

ASJC Scopus subject areas

Cancer Research

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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

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

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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 (US)",

volume = "36",

pages = "1314--1326",

journal = "Carcinogenesis",

issn = "0143-3334",

publisher = "Oxford University Press",

number = "11",

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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.

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ER -

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

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