VariantSpark: Cloud-based machine learning for association study of complex phenotype and large-scale genomic data. Issue 8 (6th August 2020)

• Record Type: Journal Article
• Authors: Bayat, Arash; Szul, Piotr; O'Brien, Aidan R; Dunne, Robert; Hosking, Brendan; Jain, Yatish; Hosking, Cameron; Luo, Oscar J; Twine, Natalie; Bauer, Denis C
• Is Part Of: GigaScience. Volume 9:Issue 8(2020)
• Publication Date: 2020-08-06
• Subjects: Information storage and retrieval systems -- Research -- Periodicals; Biology -- Research -- Periodicals; Medical sciences -- Research -- Periodicals; Database management -- Periodicals; 570.285
• DOI: 10.1093/gigascience/giaa077
• Languages: English
• ISSNs: 2047-217X

Abstract

Abstract: Background: Many 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. Findings: We 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. Conclusions: Compared 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.