A scalable adaptive quadratic kernel method for interpretable epistasis analysis in complex traits

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Figure 1.
Figure 1.

Overview of QuadKAST. (A) Calibration analysis. We applied QuadKAST to SNPs within 9515 protein-coding genes for four genetic architectures that consist entirely of linear additive effects (N = 50 K individuals, UKB array data). (B) Power analysis. We simulated traits with varying quadratic variance component on a randomly selected subset of 5 K individuals from unrelated white British individuals in UKB. We applied QuadKAST to 1000 randomly selected protein-coding genes and defined power as the ratio of P-values reported by QuadKAST that pass the significance threshold α. In these experiments, Formula is equal to the quadratic heritability Formula. (C) Accuracy. Similar to B, we applied QuadKAST to estimate the quadratic variance component at each gene. (D) Runtime. We evaluated the runtimes of the quadratic kernel option in SKAT (SKAT_QUAD) and QuadKAST by fixing the number of SNPs M = 100 and varying the number of individuals (average runtime across 10 replicates).

This Article

  1. Published in Advance August 29, 2024, doi: 10.1101/gr.279140.124 Genome Res. 34: 1294-1303

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