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© Research
Publication : BMC bioinformatics

Deriving stratified effects from joint models investigating gene-environment interactions.

Scientific Fields
Diseases
Organisms
Applications
Technique

Published in BMC bioinformatics - 18 Jun 2020

Laville V, Majarian T, de Vries PS, Bentley AR, Feitosa MF, Sung YJ, Rao DC, Manning A, Aschard H, ,

Link to Pubmed [PMID] – 32552674

Link to DOI – 10.1186/s12859-020-03569-4

BMC Bioinformatics 2020 Jun; 21(1): 251

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.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.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.

https://pubmed.ncbi.nlm.nih.gov/32552674