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Laville V, Majarian T, de Vries PS, Bentley AR, Feitosa MF, Sung YJ, Rao DC, Manning A, Aschard H. Deriving stratified effects from joint models investigating gene-environment interactions. BMC Bioinformatics 2020; 21:251. [PMID: 32552674 PMCID: PMC7302007 DOI: 10.1186/s12859-020-03569-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2019] [Accepted: 05/28/2020] [Indexed: 11/12/2022] Open
Abstract
Background Models including an interaction term and performing a joint test of SNP and/or interaction effect are often used to discover Gene-Environment (GxE) interactions. When the environmental exposure is a binary variable, analyses from exposure-stratified models which consist of estimating genetic effect in unexposed and exposed individuals separately can be of interest. In large-scale consortia focusing on GxE interactions in which only the joint test has been performed, it may be challenging to get summary statistics from both exposure-stratified and marginal (i.e not accounting for interaction) models. Results In this work, we developed a simple framework to estimate summary statistics in each stratum of a binary exposure and in the marginal model using summary statistics from the “joint” model. We performed simulation studies to assess our estimators’ accuracy and examined potential sources of bias, such as correlation between genotype and exposure and differing phenotypic variances within exposure strata. Results from these simulations highlight the high theoretical accuracy of our estimators and yield insights into the impact of potential sources of bias. We then applied our methods to real data and demonstrate our estimators’ retained accuracy after filtering SNPs by sample size to mitigate potential bias. Conclusions These analyses demonstrated the accuracy of our method in estimating both stratified and marginal summary statistics from a joint model of gene-environment interaction. In addition to facilitating the interpretation of GxE screenings, this work could be used to guide further functional analyses. We provide a user-friendly Python script to apply this strategy to real datasets. The Python script and documentation are available at https://gitlab.pasteur.fr/statistical-genetics/j2s.
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Affiliation(s)
- Vincent Laville
- Department of Computational Biology, USR 3756 CNRS, Institut Pasteur, Paris, France.
| | - Timothy Majarian
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, 02142, USA
| | - Paul S de Vries
- Human Genetics Center, Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, 77030, USA
| | - Amy R Bentley
- Center for Research on Genomics and Global Health, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Mary F Feitosa
- Division of Biostatistics, Department of Genetics, Washington University School of Medecine, St. Louis, MO, 63110, USA
| | - Yun J Sung
- Division of Biostatistics, Department of Genetics, Washington University School of Medecine, St. Louis, MO, 63110, USA
| | - D C Rao
- Division of Biostatistics, Department of Genetics, Washington University School of Medecine, St. Louis, MO, 63110, USA
| | - Alisa Manning
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, 02142, USA.,Center for Human Genetics Research, Massachusetts General Hospital, Boston, MA, 02114, USA
| | - Hugues Aschard
- Department of Computational Biology, USR 3756 CNRS, Institut Pasteur, Paris, France. .,Program in Genetic Epidemiology and Statistical Genetics, Harvard T.H. Chan School of Public Health, Boston, MA, 02115, USA.
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