ArticleGigaScience2020
VariantSpark: Cloud-based machine learning for association study of complex phenotype and large-scale genomic data.
Article in GigaScience, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- AI-powered precision medicine: utilizing genetic risk factor optimization to revolutionize healthcare.NAR genomics and bioinformatics · 2025Review
- Optimizing UK biobank cloud-based research analysis platform to fine-map coronary artery disease loci in whole genome sequencing data.Scientific reports · 2025Article
- Novel Alzheimer's disease genes and epistasis identified using machine learning GWAS platform.Scientific reports · 2023Article
- Thresholding Gini variable importance with a single-trained random forest: An empirical Bayes approach.Computational and structural biotechnology journal · 2023Article
- Data-driven platform for identifying variants of interest in COVID-19 virus.Computational and structural biotechnology journal · 2022Article
- VariantSpark: Cloud-based machine learning for association study of complex phenotype and large-scale genomic data.GigaScience · 2020Article
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Authors and funding
10 authors.
Funding
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Abstract
backgroundMany traits and diseases are thought to be driven by >1 gene (polygenic). Polygenic risk scores (PRS) hence expand on genome-wide association studies by taking multiple genes into account when risk models are built. However, PRS only considers the additive effect of individual genes but not epistatic interactions or the combination of individual and interacting drivers. While evidence of epistatic interactions ais found in small datasets, large datasets have not been processed yet owing to the high computational complexity of the search for epistatic interactions.
findingsWe have developed VariantSpark, a distributed machine learning framework able to perform association analysis for complex phenotypes that are polygenic and potentially involve a large number of epistatic interactions. Efficient multi-layer parallelization allows VariantSpark to scale to the whole genome of population-scale datasets with 100,000,000 genomic variants and 100,000 samples.
conclusionsCompared with traditional monogenic genome-wide association studies, VariantSpark better identifies genomic variants associated with complex phenotypes. VariantSpark is 3.6 times faster than ReForeSt and the only method able to scale to ultra-high-dimensional genomic data in a manageable time.
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