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Although Mendel chose seven unrelated traits in peas to study gene segregation, most traits involve multiple gene interactions that create a spectrum of phenotypes. When the interaction of various genes or alleles at different locations influences a phenotype, this is called epistasis. Epistasis often involves one gene masking or interfering with the expression of another (antagonistic epistasis). Epistasis often occurs when different genes are part of the same biochemical pathway. The...
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A scalable adaptive quadratic kernel method for interpretable epistasis analysis in complex traits.

Boyang Fu1, Prateek Anand2, Aakarsh Anand2

  • 1Department of Computer Science, University of California, Los Angeles, Los Angeles, California 90095, USA; boyang1995@ucla.edu sriram@cs.ucla.edu.

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Summary
This summary is machine-generated.

We developed QuadKAST, a scalable algorithm to detect genetic interactions (epistasis) in large datasets. This method accurately identifies quadratic genetic effects, offering new insights into complex human traits.

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Area of Science:

  • Genetics
  • Statistical genetics
  • Bioinformatics

Background:

  • Understanding genetic interactions (epistasis) is crucial for complex human traits, but current algorithms lack scalability and interpretability.
  • Existing set-based association tests improve epistasis detection but struggle with large Biobank datasets.

Purpose of the Study:

  • To introduce QuadKAST, a scalable algorithm for detecting pairwise genetic interaction (quadratic) effects within genetic variant sets.
  • To provide quantified interpretation of detected epistasis effects.

Main Methods:

  • Developed QuadKAST, a scalable algorithm for testing pairwise interaction effects (quadratic effects) in small to medium-sized sets of genetic variants (window size ≤100).
  • Validated QuadKAST through comprehensive simulations for calibration, sensitivity, and accuracy in estimating quadratic effects.
  • Applied QuadKAST to UK Biobank data (≈300,000 individuals) to test quadratic effects within 9515 protein-coding genes for 52 quantitative phenotypes.

Main Results:

  • QuadKAST demonstrated good calibration, high sensitivity for epistatic signals, and accurate estimation of quadratic effects in simulations.
  • Identified 32 significant trait-gene pairs across 17 traits and 29 genes exhibiting quadratic effects in the UK Biobank data.
  • The variance explained by quadratic effects was comparable to additive effects, with some pairs showing a higher ratio.

Conclusions:

  • QuadKAST enables large-scale, detailed investigation of epistasis in human complex traits.
  • The findings highlight the significant role and importance of epistasis in trait variation.
  • This method offers new insights into the genetic architecture of complex human phenotypes.