Genetic variant predictors of gene expression provide new insight into risk of colorectal cancer

Stephanie A. Bien; Yu-Ru Su; David V. Conti; Tabitha A. Harrison; Conghui Qu; Xingyi Guo; Yingchang Lu; Demetrius Albanes; Paul L. Auer; Barbara L. Banbury; Sonja I. Berndt; Stéphane Bézieau; Hermann Brenner; Daniel D. Buchanan; Bette J. Caan; Peter T. Campbell; Christopher S. Carlson; Andrew T. Chan; Jenny Chang-Claude; Sai Chen; Charles M. Connolly; Douglas F. Easton; Edith J.M. Feskens; Steven Gallinger; Graham G. Giles; Marc J. Gunter; Jochen Hampe; Jeroen R. Huyghe; Michael Hoffmeister; Thomas J. Hudson; Eric J. Jacobs; Mark A. Jenkins; Ellen Kampman; Hyun Min Kang; Tilman Kühn; Sébastien Küry; Flavio Lejbkowicz; Loic Le Marchand; Roger L. Milne; Christopher I. Li; Annika Lindblom; Noralane M. Lindor; Vicente Martín; Caroline E. McNeil; Marilena Melas; Victor Moreno; Polly A. Newcomb; Kenneth Offit; Paul D.P. Pharaoh; John D. Potter; Chenxu Qu; Elio Riboli; Gad Rennert; Núria Sala; Clemens Schafmayer; Peter C. Scacheri; Stephanie L. Schmit; Gianluca Severi; Martha L. Slattery; Joshua D. Smith; Antonia Trichopoulou; Rosario Tumino; Cornelia M. Ulrich; Fränzel J.B. van Duijnhoven; Bethany Van Guelpen; Stephanie J. Weinstein; Emily White; Alicja Wolk; Michael O. Woods; Anna H. Wu; Goncalo R. Abecasis; Graham Casey; Deborah A. Nickerson; Stephen B. Gruber; Li Hsu; Wei Zheng; Ulrike Peters

doi:10.1007/s00439-019-01989-8

Genetic variant predictors of gene expression provide new insight into risk of colorectal cancer

Stephanie A. Bien^*, Yu-Ru Su, David V. Conti, Tabitha A. Harrison, Conghui Qu, Xingyi Guo, Yingchang Lu, Demetrius Albanes, Paul L. Auer, Barbara L. Banbury, Sonja I. Berndt, Stéphane Bézieau, Hermann Brenner, Daniel D. Buchanan, Bette J. Caan, Peter T. Campbell, Christopher S. Carlson, Andrew T. Chan, Jenny Chang-Claude, Sai ChenCharles M. Connolly, Douglas F. Easton, Edith J.M. Feskens, Steven Gallinger, Graham G. Giles, Marc J. Gunter, Jochen Hampe, Jeroen R. Huyghe, Michael Hoffmeister, Thomas J. Hudson, Eric J. Jacobs, Mark A. Jenkins, Ellen Kampman, Hyun Min Kang, Tilman Kühn, Sébastien Küry, Flavio Lejbkowicz, Loic Le Marchand, Roger L. Milne, Christopher I. Li, Annika Lindblom, Noralane M. Lindor, Vicente Martín, Caroline E. McNeil, Marilena Melas, Victor Moreno, Polly A. Newcomb, Kenneth Offit, Paul D.P. Pharaoh, John D. Potter, Chenxu Qu, Elio Riboli, Gad Rennert, Núria Sala, Clemens Schafmayer, Peter C. Scacheri, Stephanie L. Schmit, Gianluca Severi, Martha L. Slattery, Joshua D. Smith, Antonia Trichopoulou, Rosario Tumino, Cornelia M. Ulrich, Fränzel J.B. van Duijnhoven, Bethany Van Guelpen, Stephanie J. Weinstein, Emily White, Alicja Wolk, Michael O. Woods, Anna H. Wu, Goncalo R. Abecasis, Graham Casey, Deborah A. Nickerson, Stephen B. Gruber, Li Hsu, Wei Zheng, Ulrike Peters

^*Corresponding author for this work

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

39 Citations (Scopus)

Abstract

Genome-wide association studies have reported 56 independently associated colorectal cancer (CRC) risk variants, most of which are non-coding and believed to exert their effects by modulating gene expression. The computational method PrediXcan uses cis-regulatory variant predictors to impute expression and perform gene-level association tests in GWAS without directly measured transcriptomes. In this study, we used reference datasets from colon (n = 169) and whole blood (n = 922) transcriptomes to test CRC association with genetically determined expression levels in a genome-wide analysis of 12,186 cases and 14,718 controls. Three novel associations were discovered from colon transverse models at FDR ≤ 0.2 and further evaluated in an independent replication including 32,825 cases and 39,933 controls. After adjusting for multiple comparisons, we found statistically significant associations using colon transcriptome models with TRIM4 (discovery P = 2.2 × 10− 4, replication P = 0.01), and PYGL (discovery P = 2.3 × 10− 4, replication P = 6.7 × 10− 4). Interestingly, both genes encode proteins that influence redox homeostasis and are related to cellular metabolic reprogramming in tumors, implicating a novel CRC pathway linked to cell growth and proliferation. Defining CRC risk regions as one megabase up- and downstream of one of the 56 independent risk variants, we defined 44 non-overlapping CRC-risk regions. Among these risk regions, we identified genes associated with CRC (P < 0.05) in 34/44 CRC-risk regions. Importantly, CRC association was found for two genes in the previously reported 2q25 locus, CXCR1 and CXCR2, which are potential cancer therapeutic targets. These findings provide strong candidate genes to prioritize for subsequent laboratory follow-up of GWAS loci. This study is the first to implement PrediXcan in a large colorectal cancer study and findings highlight the utility of integrating transcriptome data in GWAS for discovery of, and biological insight into, risk loci.

Original language	English
Pages (from-to)	307-326
Number of pages	20
Journal	Human Genetics
Volume	138
Issue number	4
Early online date	28 Feb 2019
DOIs	https://doi.org/10.1007/s00439-019-01989-8
Publication status	Published - Apr 2019

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39 Citations
1 Comment/Letter to the editor

Correction to: Genetic variant predictors of gene expression provide new insight into risk of colorectal cancer
Bien, S. A., Su, Y. R., Conti, D. V., Harrison, T. A., Qu, C., Guo, X., Lu, Y., Albanes, D., Auer, P. L., Banbury, B. L., Berndt, S. I., Bézieau, S., Brenner, H., Buchanan, D. D., Caan, B. J., Campbell, P. T., Carlson, C. S., Chan, A. T., Chang-Claude, J. & Chen, S. & 58 others, Connolly, C. M., Easton, D. F., Feskens, E. J. M., Gallinger, S., Giles, G. G., Gunter, M. J., Hampe, J., Huyghe, J. R., Hoffmeister, M., Hudson, T. J., Jacobs, E. J., Jenkins, M. A., Kampman, E., Kang, H. M., Kühn, T., Küry, S., Lejbkowicz, F., Le Marchand, L., Milne, R. L., Li, L., Li, C. I., Lindblom, A., Lindor, N. M., Martín, V., McNeil, C. E., Melas, M., Moreno, V., Newcomb, P. A., Offit, K., Pharaoh, P. D. P., Potter, J. D., Qu, C., Riboli, E., Rennert, G., Sala, N., Schafmayer, C., Scacheri, P. C., Schmit, S. L., Severi, G., Slattery, M. L., Smith, J. D., Trichopoulou, A., Tumino, R., Ulrich, C. M., van Duijnhoven, F. J. B., Van Guelpen, B., Weinstein, S. J., White, E., Wolk, A., Woods, M. O., Wu, A. H., Abeçasis, G. R., Casey, G., Nickerson, D. A., Gruber, S. B., Hsu, L., Zheng, W. & Peters, U., 1 Jul 2019, In: Human Genetics. 138, 7, p. 789-791
Research output: Contribution to journal › Comment/Letter to the editor › Academic
2 Citations (Scopus)

1 Finished

PREVIEW: PREVention of diabetes through lifestyle Intervention and population studies in Europe and around the World
1/01/13 → 31/12/18
Project: EU research project

Cite this

Bien, S. A., Su, Y.-R., Conti, D. V., Harrison, T. A., Qu, C., Guo, X., Lu, Y., Albanes, D., Auer, P. L., Banbury, B. L., Berndt, S. I., Bézieau, S., Brenner, H., Buchanan, D. D., Caan, B. J., Campbell, P. T., Carlson, C. S., Chan, A. T., Chang-Claude, J., ... Peters, U. (2019). Genetic variant predictors of gene expression provide new insight into risk of colorectal cancer. Human Genetics, 138(4), 307-326. https://doi.org/10.1007/s00439-019-01989-8

@article{a508aa513acf4be3a75644f8fc19037b,

title = "Genetic variant predictors of gene expression provide new insight into risk of colorectal cancer",

abstract = "Genome-wide association studies have reported 56 independently associated colorectal cancer (CRC) risk variants, most of which are non-coding and believed to exert their effects by modulating gene expression. The computational method PrediXcan uses cis-regulatory variant predictors to impute expression and perform gene-level association tests in GWAS without directly measured transcriptomes. In this study, we used reference datasets from colon (n = 169) and whole blood (n = 922) transcriptomes to test CRC association with genetically determined expression levels in a genome-wide analysis of 12,186 cases and 14,718 controls. Three novel associations were discovered from colon transverse models at FDR ≤ 0.2 and further evaluated in an independent replication including 32,825 cases and 39,933 controls. After adjusting for multiple comparisons, we found statistically significant associations using colon transcriptome models with TRIM4 (discovery P = 2.2 × 10− 4, replication P = 0.01), and PYGL (discovery P = 2.3 × 10− 4, replication P = 6.7 × 10− 4). Interestingly, both genes encode proteins that influence redox homeostasis and are related to cellular metabolic reprogramming in tumors, implicating a novel CRC pathway linked to cell growth and proliferation. Defining CRC risk regions as one megabase up- and downstream of one of the 56 independent risk variants, we defined 44 non-overlapping CRC-risk regions. Among these risk regions, we identified genes associated with CRC (P < 0.05) in 34/44 CRC-risk regions. Importantly, CRC association was found for two genes in the previously reported 2q25 locus, CXCR1 and CXCR2, which are potential cancer therapeutic targets. These findings provide strong candidate genes to prioritize for subsequent laboratory follow-up of GWAS loci. This study is the first to implement PrediXcan in a large colorectal cancer study and findings highlight the utility of integrating transcriptome data in GWAS for discovery of, and biological insight into, risk loci.",

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issn = "0340-6717",

publisher = "Springer",

number = "4",

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Bien, SA, Su, Y-R, Conti, DV, Harrison, TA, Qu, C, Guo, X, Lu, Y, Albanes, D, Auer, PL, Banbury, BL, Berndt, SI, Bézieau, S, Brenner, H, Buchanan, DD, Caan, BJ, Campbell, PT, Carlson, CS, Chan, AT, Chang-Claude, J, Chen, S, Connolly, CM, Easton, DF, Feskens, EJM, Gallinger, S, Giles, GG, Gunter, MJ, Hampe, J, Huyghe, JR, Hoffmeister, M, Hudson, TJ, Jacobs, EJ, Jenkins, MA, Kampman, E, Kang, HM, Kühn, T, Küry, S, Lejbkowicz, F, Le Marchand, L, Milne, RL, Li, CI, Lindblom, A, Lindor, NM, Martín, V, McNeil, CE, Melas, M, Moreno, V, Newcomb, PA, Offit, K, Pharaoh, PDP, Potter, JD, Qu, C, Riboli, E, Rennert, G, Sala, N, Schafmayer, C, Scacheri, PC, Schmit, SL, Severi, G, Slattery, ML, Smith, JD, Trichopoulou, A, Tumino, R, Ulrich, CM, van Duijnhoven, FJB, Van Guelpen, B, Weinstein, SJ, White, E, Wolk, A, Woods, MO, Wu, AH, Abecasis, GR, Casey, G, Nickerson, DA, Gruber, SB, Hsu, L, Zheng, W & Peters, U 2019, 'Genetic variant predictors of gene expression provide new insight into risk of colorectal cancer', Human Genetics, vol. 138, no. 4, pp. 307-326. https://doi.org/10.1007/s00439-019-01989-8

TY - JOUR

T1 - Genetic variant predictors of gene expression provide new insight into risk of colorectal cancer

AU - Bien, Stephanie A.

AU - Su, Yu-Ru

AU - Conti, David V.

AU - Harrison, Tabitha A.

AU - Qu, Conghui

AU - Guo, Xingyi

AU - Lu, Yingchang

AU - Albanes, Demetrius

AU - Auer, Paul L.

AU - Banbury, Barbara L.

AU - Berndt, Sonja I.

AU - Bézieau, Stéphane

AU - Brenner, Hermann

AU - Buchanan, Daniel D.

AU - Caan, Bette J.

AU - Campbell, Peter T.

AU - Carlson, Christopher S.

AU - Chan, Andrew T.

AU - Chang-Claude, Jenny

AU - Chen, Sai

AU - Connolly, Charles M.

AU - Easton, Douglas F.

AU - Feskens, Edith J.M.

AU - Gallinger, Steven

AU - Giles, Graham G.

AU - Gunter, Marc J.

AU - Hampe, Jochen

AU - Huyghe, Jeroen R.

AU - Hoffmeister, Michael

AU - Hudson, Thomas J.

AU - Jacobs, Eric J.

AU - Jenkins, Mark A.

AU - Kampman, Ellen

AU - Kang, Hyun Min

AU - Kühn, Tilman

AU - Küry, Sébastien

AU - Lejbkowicz, Flavio

AU - Le Marchand, Loic

AU - Milne, Roger L.

AU - Li, Christopher I.

AU - Lindblom, Annika

AU - Lindor, Noralane M.

AU - Martín, Vicente

AU - McNeil, Caroline E.

AU - Melas, Marilena

AU - Moreno, Victor

AU - Newcomb, Polly A.

AU - Offit, Kenneth

AU - Pharaoh, Paul D.P.

AU - Potter, John D.

AU - Qu, Chenxu

AU - Riboli, Elio

AU - Rennert, Gad

AU - Sala, Núria

AU - Schafmayer, Clemens

AU - Scacheri, Peter C.

AU - Schmit, Stephanie L.

AU - Severi, Gianluca

AU - Slattery, Martha L.

AU - Smith, Joshua D.

AU - Trichopoulou, Antonia

AU - Tumino, Rosario

AU - Ulrich, Cornelia M.

AU - van Duijnhoven, Fränzel J.B.

AU - Van Guelpen, Bethany

AU - Weinstein, Stephanie J.

AU - White, Emily

AU - Wolk, Alicja

AU - Woods, Michael O.

AU - Wu, Anna H.

AU - Abecasis, Goncalo R.

AU - Casey, Graham

AU - Nickerson, Deborah A.

AU - Gruber, Stephen B.

AU - Hsu, Li

AU - Zheng, Wei

AU - Peters, Ulrike

PY - 2019/4

Y1 - 2019/4

N2 - Genome-wide association studies have reported 56 independently associated colorectal cancer (CRC) risk variants, most of which are non-coding and believed to exert their effects by modulating gene expression. The computational method PrediXcan uses cis-regulatory variant predictors to impute expression and perform gene-level association tests in GWAS without directly measured transcriptomes. In this study, we used reference datasets from colon (n = 169) and whole blood (n = 922) transcriptomes to test CRC association with genetically determined expression levels in a genome-wide analysis of 12,186 cases and 14,718 controls. Three novel associations were discovered from colon transverse models at FDR ≤ 0.2 and further evaluated in an independent replication including 32,825 cases and 39,933 controls. After adjusting for multiple comparisons, we found statistically significant associations using colon transcriptome models with TRIM4 (discovery P = 2.2 × 10− 4, replication P = 0.01), and PYGL (discovery P = 2.3 × 10− 4, replication P = 6.7 × 10− 4). Interestingly, both genes encode proteins that influence redox homeostasis and are related to cellular metabolic reprogramming in tumors, implicating a novel CRC pathway linked to cell growth and proliferation. Defining CRC risk regions as one megabase up- and downstream of one of the 56 independent risk variants, we defined 44 non-overlapping CRC-risk regions. Among these risk regions, we identified genes associated with CRC (P < 0.05) in 34/44 CRC-risk regions. Importantly, CRC association was found for two genes in the previously reported 2q25 locus, CXCR1 and CXCR2, which are potential cancer therapeutic targets. These findings provide strong candidate genes to prioritize for subsequent laboratory follow-up of GWAS loci. This study is the first to implement PrediXcan in a large colorectal cancer study and findings highlight the utility of integrating transcriptome data in GWAS for discovery of, and biological insight into, risk loci.

AB - Genome-wide association studies have reported 56 independently associated colorectal cancer (CRC) risk variants, most of which are non-coding and believed to exert their effects by modulating gene expression. The computational method PrediXcan uses cis-regulatory variant predictors to impute expression and perform gene-level association tests in GWAS without directly measured transcriptomes. In this study, we used reference datasets from colon (n = 169) and whole blood (n = 922) transcriptomes to test CRC association with genetically determined expression levels in a genome-wide analysis of 12,186 cases and 14,718 controls. Three novel associations were discovered from colon transverse models at FDR ≤ 0.2 and further evaluated in an independent replication including 32,825 cases and 39,933 controls. After adjusting for multiple comparisons, we found statistically significant associations using colon transcriptome models with TRIM4 (discovery P = 2.2 × 10− 4, replication P = 0.01), and PYGL (discovery P = 2.3 × 10− 4, replication P = 6.7 × 10− 4). Interestingly, both genes encode proteins that influence redox homeostasis and are related to cellular metabolic reprogramming in tumors, implicating a novel CRC pathway linked to cell growth and proliferation. Defining CRC risk regions as one megabase up- and downstream of one of the 56 independent risk variants, we defined 44 non-overlapping CRC-risk regions. Among these risk regions, we identified genes associated with CRC (P < 0.05) in 34/44 CRC-risk regions. Importantly, CRC association was found for two genes in the previously reported 2q25 locus, CXCR1 and CXCR2, which are potential cancer therapeutic targets. These findings provide strong candidate genes to prioritize for subsequent laboratory follow-up of GWAS loci. This study is the first to implement PrediXcan in a large colorectal cancer study and findings highlight the utility of integrating transcriptome data in GWAS for discovery of, and biological insight into, risk loci.

U2 - 10.1007/s00439-019-01989-8

DO - 10.1007/s00439-019-01989-8

M3 - Article

SN - 0340-6717

VL - 138

SP - 307

EP - 326

JO - Human Genetics

JF - Human Genetics

IS - 4

ER -

Genetic variant predictors of gene expression provide new insight into risk of colorectal cancer

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Correction to: Genetic variant predictors of gene expression provide new insight into risk of colorectal cancer

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