SimGWAS: a fast method for simulation of large scale case-control GWAS summary statistics.

ABSTRACT:

Motivation

Methods for analysis of GWAS summary statistics have encouraged data sharing and democratized the analysis of different diseases. Ideal validation for such methods is application to simulated data, where some 'truth' is known. As GWAS increase in size, so does the computational complexity of such evaluations; standard practice repeatedly simulates and analyses genotype data for all individuals in an example study.

Results

We have developed a novel method based on an alternative approach, directly simulating GWAS summary data, without individual data as an intermediate step. We mathematically derive the expected statistics for any set of causal variants and their effect sizes, conditional upon control haplotype frequencies (available from public reference datasets)

SUBMITTER: Fortune MD 

PROVIDER: S-EPMC6546134 | biostudies-literature | 2019 Jun

REPOSITORIES: biostudies-literature