Variantspark: Cloud-based machine learning for association study of complex phenotype and large-scale genomic data

Arash Bayat*, Piotr Szul, Aidan R. O'Brien, Robert Dunne, Brendan Hosking, Yatish Jain, Cameron Hosking, Oscar J. Luo, Natalie Twine, Denis C. Bauer

*Corresponding author for this work

    Research output: Contribution to journalArticlepeer-review

    7 Citations (Scopus)
    12 Downloads (Pure)


    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.

    Original languageEnglish
    Article numbergiaa077
    Pages (from-to)1-12
    Number of pages12
    Issue number8
    Publication statusPublished - 1 Aug 2020

    Bibliographical note

    Copyright the Author(s) 2020. Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher.


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