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

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

SUBMITTER: Bayat A 

PROVIDER: S-EPMC7407261 | biostudies-literature | 2020 Aug

REPOSITORIES: biostudies-literature