Robust Tests for Additive Gene-Environment Interaction in Case-Control Studies Using Gene-Environment Independence

Gang Liu; Bhramar Mukherjee; Seunggeun Lee; Alice W Lee; Anna H Wu; Elisa V Bandera; Allan Jensen; Mary Anne Rossing; Kirsten B Moysich; Jenny Chang-Claude; Jennifer A Doherty; Aleksandra Gentry-Maharaj; Lambertus Kiemeney; Simon A Gayther; Francesmary Modugno; Leon Massuger; Ellen L Goode; Brooke L Fridley; Kathryn L Terry; Daniel W Cramer; Susan J Ramus; Hoda Anton-Culver; Argyrios Ziogas; Jonathan P Tyrer; Joellen M Schildkraut; Susanne K Kjaer; Penelope M Webb; Roberta B Ness; Usha Menon; Andrew Berchuck; Paul D Pharoah; Harvey Risch; Celeste Leigh Pearce; Ovarian Cancer Association Consortium

doi:10.1093/aje/kwx243

Robust Tests for Additive Gene-Environment Interaction in Case-Control Studies Using Gene-Environment Independence

Gang Liu, Bhramar Mukherjee, Seunggeun Lee, Alice W Lee, Anna H Wu, Elisa V Bandera, Allan Jensen, Mary Anne Rossing, Kirsten B Moysich, Jenny Chang-Claude, Jennifer A Doherty, Aleksandra Gentry-Maharaj, Lambertus Kiemeney, Simon A Gayther, Francesmary Modugno, Leon Massuger, Ellen L Goode, Brooke L Fridley, Kathryn L Terry, Daniel W CramerSusan J Ramus, Hoda Anton-Culver, Argyrios Ziogas, Jonathan P Tyrer, Joellen M Schildkraut, Susanne K Kjaer, Penelope M Webb, Roberta B Ness, Usha Menon, Andrew Berchuck, Paul D Pharoah, Harvey Risch, Celeste Leigh Pearce, Ovarian Cancer Association Consortium

Department of Clinical Medicine

5 Citations (Scopus)

Abstract

There have been recent proposals advocating the use of additive gene-environment interaction instead of the widely used multiplicative scale, as a more relevant public health measure. Using gene-environment independence enhances statistical power for testing multiplicative interaction in case-control studies. However, under departure from this assumption, substantial bias in the estimates and inflated type I error in the corresponding tests can occur. In this paper, we extend the empirical Bayes (EB) approach previously developed for multiplicative interaction, which trades off between bias and efficiency in a data-adaptive way, to the additive scale. An EB estimator of the relative excess risk due to interaction is derived, and the corresponding Wald test is proposed with a general regression setting under a retrospective likelihood framework. We study the impact of gene-environment association on the resultant test with case-control data. Our simulation studies suggest that the EB approach uses the gene-environment independence assumption in a data-adaptive way and provides a gain in power compared with the standard logistic regression analysis and better control of type I error when compared with the analysis assuming gene-environment independence. We illustrate the methods with data from the Ovarian Cancer Association Consortium.

Original language	English
Journal	American Journal of Epidemiology
Volume	187
Issue number	2
Pages (from-to)	366-377
Number of pages	12
ISSN	0002-9262
DOIs	https://doi.org/10.1093/aje/kwx243
Publication status	Published - 1 Feb 2018

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Liu, G., Mukherjee, B., Lee, S., Lee, A. W., Wu, A. H., Bandera, E. V., Jensen, A., Rossing, M. A., Moysich, K. B., Chang-Claude, J., Doherty, J. A., Gentry-Maharaj, A., Kiemeney, L., Gayther, S. A., Modugno, F., Massuger, L., Goode, E. L., Fridley, B. L., Terry, K. L., ... Ovarian Cancer Association Consortium (2018). Robust Tests for Additive Gene-Environment Interaction in Case-Control Studies Using Gene-Environment Independence. American Journal of Epidemiology, 187(2), 366-377. https://doi.org/10.1093/aje/kwx243

Liu, G, Mukherjee, B, Lee, S, Lee, AW, Wu, AH, Bandera, EV, Jensen, A, Rossing, MA, Moysich, KB, Chang-Claude, J, Doherty, JA, Gentry-Maharaj, A, Kiemeney, L, Gayther, SA, Modugno, F, Massuger, L, Goode, EL, Fridley, BL, Terry, KL, Cramer, DW, Ramus, SJ, Anton-Culver, H, Ziogas, A, Tyrer, JP, Schildkraut, JM, Kjaer, SK, Webb, PM, Ness, RB, Menon, U, Berchuck, A, Pharoah, PD, Risch, H, Pearce, CL & Ovarian Cancer Association Consortium 2018, 'Robust Tests for Additive Gene-Environment Interaction in Case-Control Studies Using Gene-Environment Independence', American Journal of Epidemiology, vol. 187, no. 2, pp. 366-377. https://doi.org/10.1093/aje/kwx243

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title = "Robust Tests for Additive Gene-Environment Interaction in Case-Control Studies Using Gene-Environment Independence",

abstract = "There have been recent proposals advocating the use of additive gene-environment interaction instead of the widely used multiplicative scale, as a more relevant public health measure. Using gene-environment independence enhances statistical power for testing multiplicative interaction in case-control studies. However, under departure from this assumption, substantial bias in the estimates and inflated type I error in the corresponding tests can occur. In this paper, we extend the empirical Bayes (EB) approach previously developed for multiplicative interaction, which trades off between bias and efficiency in a data-adaptive way, to the additive scale. An EB estimator of the relative excess risk due to interaction is derived, and the corresponding Wald test is proposed with a general regression setting under a retrospective likelihood framework. We study the impact of gene-environment association on the resultant test with case-control data. Our simulation studies suggest that the EB approach uses the gene-environment independence assumption in a data-adaptive way and provides a gain in power compared with the standard logistic regression analysis and better control of type I error when compared with the analysis assuming gene-environment independence. We illustrate the methods with data from the Ovarian Cancer Association Consortium.",

author = "Gang Liu and Bhramar Mukherjee and Seunggeun Lee and Lee, {Alice W} and Wu, {Anna H} and Bandera, {Elisa V} and Allan Jensen and Rossing, {Mary Anne} and Moysich, {Kirsten B} and Jenny Chang-Claude and Doherty, {Jennifer A} and Aleksandra Gentry-Maharaj and Lambertus Kiemeney and Gayther, {Simon A} and Francesmary Modugno and Leon Massuger and Goode, {Ellen L} and Fridley, {Brooke L} and Terry, {Kathryn L} and Cramer, {Daniel W} and Ramus, {Susan J} and Hoda Anton-Culver and Argyrios Ziogas and Tyrer, {Jonathan P} and Schildkraut, {Joellen M} and Kjaer, {Susanne K} and Webb, {Penelope M} and Ness, {Roberta B} and Usha Menon and Andrew Berchuck and Pharoah, {Paul D} and Harvey Risch and Pearce, {Celeste Leigh} and {Ovarian Cancer Association Consortium}",

note = "{\textcopyright} The Author(s) 2017. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. All rights reserved. For permissions, please e-mail: [email protected].",

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AU - Liu, Gang

AU - Mukherjee, Bhramar

AU - Lee, Seunggeun

AU - Lee, Alice W

AU - Wu, Anna H

AU - Bandera, Elisa V

AU - Jensen, Allan

AU - Rossing, Mary Anne

AU - Moysich, Kirsten B

AU - Chang-Claude, Jenny

AU - Doherty, Jennifer A

AU - Gentry-Maharaj, Aleksandra

AU - Kiemeney, Lambertus

AU - Gayther, Simon A

AU - Modugno, Francesmary

AU - Massuger, Leon

AU - Goode, Ellen L

AU - Fridley, Brooke L

AU - Terry, Kathryn L

AU - Cramer, Daniel W

AU - Ramus, Susan J

AU - Anton-Culver, Hoda

AU - Ziogas, Argyrios

AU - Tyrer, Jonathan P

AU - Schildkraut, Joellen M

AU - Kjaer, Susanne K

AU - Webb, Penelope M

AU - Ness, Roberta B

AU - Menon, Usha

AU - Berchuck, Andrew

AU - Pharoah, Paul D

AU - Risch, Harvey

AU - Pearce, Celeste Leigh

AU - Ovarian Cancer Association Consortium

N1 - © The Author(s) 2017. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. All rights reserved. For permissions, please e-mail: [email protected].

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N2 - There have been recent proposals advocating the use of additive gene-environment interaction instead of the widely used multiplicative scale, as a more relevant public health measure. Using gene-environment independence enhances statistical power for testing multiplicative interaction in case-control studies. However, under departure from this assumption, substantial bias in the estimates and inflated type I error in the corresponding tests can occur. In this paper, we extend the empirical Bayes (EB) approach previously developed for multiplicative interaction, which trades off between bias and efficiency in a data-adaptive way, to the additive scale. An EB estimator of the relative excess risk due to interaction is derived, and the corresponding Wald test is proposed with a general regression setting under a retrospective likelihood framework. We study the impact of gene-environment association on the resultant test with case-control data. Our simulation studies suggest that the EB approach uses the gene-environment independence assumption in a data-adaptive way and provides a gain in power compared with the standard logistic regression analysis and better control of type I error when compared with the analysis assuming gene-environment independence. We illustrate the methods with data from the Ovarian Cancer Association Consortium.

AB - There have been recent proposals advocating the use of additive gene-environment interaction instead of the widely used multiplicative scale, as a more relevant public health measure. Using gene-environment independence enhances statistical power for testing multiplicative interaction in case-control studies. However, under departure from this assumption, substantial bias in the estimates and inflated type I error in the corresponding tests can occur. In this paper, we extend the empirical Bayes (EB) approach previously developed for multiplicative interaction, which trades off between bias and efficiency in a data-adaptive way, to the additive scale. An EB estimator of the relative excess risk due to interaction is derived, and the corresponding Wald test is proposed with a general regression setting under a retrospective likelihood framework. We study the impact of gene-environment association on the resultant test with case-control data. Our simulation studies suggest that the EB approach uses the gene-environment independence assumption in a data-adaptive way and provides a gain in power compared with the standard logistic regression analysis and better control of type I error when compared with the analysis assuming gene-environment independence. We illustrate the methods with data from the Ovarian Cancer Association Consortium.

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DO - 10.1093/aje/kwx243

M3 - Journal article

C2 - 28633381

SN - 0002-9262

VL - 187

SP - 366

EP - 377

JO - American Journal of Epidemiology

JF - American Journal of Epidemiology

IS - 2

ER -

Robust Tests for Additive Gene-Environment Interaction in Case-Control Studies Using Gene-Environment Independence

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