In:
PLOS Genetics, Public Library of Science (PLoS), Vol. 18, No. 11 ( 2022-11-16), p. e1010464-
Kurzfassung:
The identification and understanding of gene-environment interactions can provide insights into the pathways and mechanisms underlying complex diseases. However, testing for gene-environment interaction remains a challenge since a.) statistical power is often limited and b.) modeling of environmental effects is nontrivial and such model misspecifications can lead to false positive interaction findings. To address the lack of statistical power, recent methods aim to identify interactions on an aggregated level using, for example, polygenic risk scores. While this strategy can increase the power to detect interactions, identifying contributing genes and pathways is difficult based on these relatively global results. Here, we propose RITSS (Robust Interaction Testing using Sample Splitting), a gene-environment interaction testing framework for quantitative traits that is based on sample splitting and robust test statistics. RITSS can incorporate sets of genetic variants and/or multiple environmental factors. Based on the user’s choice of statistical/machine learning approaches, a screening step selects and combines potential interactions into scores with improved interpretability. In the testing step, the application of robust statistics minimizes the susceptibility to main effect misspecifications. Using extensive simulation studies, we demonstrate that RITSS controls the type 1 error rate in a wide range of scenarios, and we show how the screening strategy influences statistical power. In an application to lung function phenotypes and human height in the UK Biobank, RITSS identified highly significant interactions based on subcomponents of genetic risk scores. While the contributing single variant interaction signals are weak, our results indicate interaction patterns that result in strong aggregated effects, providing potential insights into underlying gene-environment interaction mechanisms.
Materialart:
Online-Ressource
ISSN:
1553-7404
DOI:
10.1371/journal.pgen.1010464
DOI:
10.1371/journal.pgen.1010464.g001
DOI:
10.1371/journal.pgen.1010464.g002
DOI:
10.1371/journal.pgen.1010464.g003
DOI:
10.1371/journal.pgen.1010464.g004
DOI:
10.1371/journal.pgen.1010464.g005
DOI:
10.1371/journal.pgen.1010464.t001
DOI:
10.1371/journal.pgen.1010464.t002
DOI:
10.1371/journal.pgen.1010464.t003
DOI:
10.1371/journal.pgen.1010464.t004
DOI:
10.1371/journal.pgen.1010464.s001
DOI:
10.1371/journal.pgen.1010464.s002
DOI:
10.1371/journal.pgen.1010464.s003
Sprache:
Englisch
Verlag:
Public Library of Science (PLoS)
Publikationsdatum:
2022
ZDB Id:
2186725-2
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