STATGEN 2024 talk
Identifying GxE through Mendelian Randomization
Xiaofeng Zhu
Statistical power is low and detecting GxE is a challenge.
See:
Aschard H. A perspective on interaction effects in genetic association studies. Genet Epidemiol. 2016 Dec;40(8):678-688. doi: 10.1002/gepi.21989. Epub 2016 Jul 7. PMID: 27390122; PMCID: PMC5132101.
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#Genetics #STATGEN2024 #StatisticalGenetics #MendelianRandomization
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Can test for interaction by testing if the main G effect in a GxE regression (GWIS) differs from the main G effect when the GxE term is not included (GWAS) if there is no mediation through E.
G x E DAG looks very similar to a Mendelian Randomization DAG
Zhu, X., Yang, Y., Lorincz-Comi, N. et al. An approach to identify gene-environment interactions and reveal new biological insight in complex traits. Nat Commun 15, 3385 (2024). https://doi.org/10.1038/s41467-024-47806-3
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This new GxE approach is built upon this approach:
Xiaofeng Zhu, Xiaoyin Li, Rong Xu, Tao Wang, An iterative approach to detect pleiotropy and perform Mendelian Randomization analysis using GWAS summary statistics, Bioinformatics, Volume 37, Issue 10, May 2021, Pages 1390–1400, https://doi.org/10.1093/bioinformatics/btaa985
Tests for the combined effect of GxE and mediation.
Can do a two-step procedure
1. Apply T_MR-GxE to screen whole genome.
2. Apply T_Diff test to those surviving the screen.
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Question: Do you get an effect size estimate?
Answer: We need a mean of E and then we can estimate it.
Question: Why not screen for direct main effects without a GxE effect?
Answer: Doesn't help because the two main effects are independent.
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Daniel E. Weeks