PRS-on-Spark (PRSoS): a novel, efficient and flexible approach for generating polygenic risk scores.

ABSTRACT:

Background

Polygenic risk scores (PRS) describe the genomic contribution to complex phenotypes and consistently account for a larger proportion of variance in outcome than single nucleotide polymorphisms (SNPs) alone. However, there is little consensus on the optimal data input for generating PRS, and existing approaches largely preclude the use of imputed posterior probabilities and strand-ambiguous SNPs i.e., A/T or C/G polymorphisms. Our ability to predict complex traits that arise from the additive effects of a large number of SNPs would likely benefit from a more inclusive approach.

Results

We developed PRS-on-Spark (PRSoS), a software implemented in Apache Spark and Python that accommodates different data inputs and strand-ambiguous SNPs to calculate PRS. We compared p

SUBMITTER: Chen LM 

PROVIDER: S-EPMC6083617 | biostudies-literature | 2018 Aug

REPOSITORIES: biostudies-literature