ArticleNature communications2023
Fast kernel-based association testing of non-linear genetic effects for biobank-scale data.
Article in Nature communications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
7 citing papers in PubMed, 13 citations in OpenAlex.
- iRUNNER: A Baseline Mutation Burden Regression for Identifying Gene Interaction Between Rare Variants for Diseases.Genomics, proteomics & bioinformatics · 2025Article
- HyperdCas12a-based multiplexed genetic regulation in Candida albicans.Nucleic acids research · 2025Article
- Powerful one-dimensional scan to detect heterotic quantitative trait loci.Nature communications · 2025Article
- Decoding ancestry-specific genetic risk: interpretable deep feature selection reveals prostate cancer SNP disparities in diverse populations.BioData mining · 2025Article
- Resolving discrepancies between chimeric and multiplicative measures of higher-order epistasis.Nature communications · 2025Article
- A scalable adaptive quadratic kernel method for interpretable epistasis analysis in complex traits.Genome research · 2024Article
- Identifying latent genetic interactions in genome-wide association studies using multiple traits.Genome medicine · 2024Article
Corrections and comments
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Authors and funding
6 authors at 3 institutions in 2 countries.
Funding
Abstract
Our knowledge of non-linear genetic effects on complex traits remains limited, in part, due to the modest power to detect such effects. While kernel-based tests offer a versatile approach to test for non-linear relationships between sets of genetic variants and traits, current approaches cannot be applied to Biobank-scale datasets containing hundreds of thousands of individuals. We propose, FastKAST, a kernel-based approach that can test for non-linear effects of a set of variants on a quantitative trait. FastKAST provides calibrated hypothesis tests while enabling analysis of Biobank-scale datasets with hundreds of thousands of unrelated individuals from a homogeneous population. We apply FastKAST to 53 quantitative traits measured across ≈ 300 K unrelated white British individuals in the UK Biobank to detect sets of variants with non-linear effects at genome-wide significance.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.