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Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills
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Efficient gene-environment interaction testing through bootstrap aggregating.

Michael Lau1,2, Sara Kress3, Tamara Schikowski3

  • 1Mathematical Institute, Heinrich Heine University, Düsseldorf, Germany. michael.lau@hhu.de.

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|January 17, 2023
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Summary
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This study introduces novel statistical methods using bagging and random forests to enhance the detection of gene-environment (GxE) interactions for complex diseases. These approaches improve statistical power while using the full dataset for robust GxE interaction analysis.

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Area of Science:

  • Genetics and Genomics
  • Environmental Health
  • Statistical Bioinformatics

Background:

  • Gene-environment (GxE) interactions are crucial for complex phenotypes but are challenging to detect using traditional methods.
  • Univariate tests lack power and do not account for genetic locus interplay; existing genetic risk score (GRS) methods reduce sample size for interaction testing.

Purpose of the Study:

  • To develop and evaluate novel statistical tests for detecting GxE interactions with increased statistical power.
  • To overcome limitations of existing GRS approaches by utilizing the full dataset for both GRS construction and interaction testing.

Main Methods:

  • Proposed a statistical test employing bagging (bootstrap aggregating) for GRS construction and its out-of-bag prediction mechanism.
  • Investigated the use of random forests as a GRS construction method to incorporate interactions between genetic loci.
  • Evaluated performance through a simulation study and applied the methods to a German cohort dataset.

Main Results:

  • Both novel procedures demonstrated higher statistical power for detecting GxE interactions compared to existing methods.
  • The methods successfully controlled the type I error rate.
  • The random-forests-based test generally outperformed the bagging-based elastic net test in simulation scenarios.

Conclusions:

  • Bagging and random forest-based GRS construction offer powerful new tools for GxE interaction analysis.
  • These methods effectively utilize the entire dataset, enhancing statistical efficiency.
  • Preliminary analysis suggests a potential GxE interaction between air pollution and rheumatoid arthritis in a German cohort.