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

Arash Bayat | Zendy; Piotr Szul | Zendy; Aidan R. O’Brien | Zendy; Robert Dunne | Zendy; Brendan Hosking | Zendy; Yatish Jain | Zendy; Cameron Hosking | Zendy; Oscar Junhong Luo | Zendy; Natalie A. Twine | Zendy; Denis C. Bauer | Zendy

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

Author(s) -

Arash Bayat,

Piotr Szul,

Aidan R. O’Brien,

Robert Dunne,

Brendan Hosking,

Yatish Jain,

Cameron Hosking,

Oscar Junhong Luo,

Natalie A. Twine,

Denis C. Bauer

Publication year - 2020

Publication title -

gigascience

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 2.947

H-Index - 54

ISSN - 2047-217X

DOI - 10.1093/gigascience/giaa077

Subject(s) - epistasis , computational biology , genome wide association study , genetic association , phenotype , scale (ratio) , computer science , biology , population , genomics , genome , gene , genetics , genotype , single nucleotide polymorphism , medicine , physics , environmental health , quantum mechanics

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.

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