General Information
| Description | Genome-wide estimation of additive effects (alpha) based on trait quantile distribution from pool-sequencing experiments |
| Homepage | https://github.com/aflevel/GWAlpha |
Publications
GWAlpha: genome-wide estimation of additive effects (alpha) based on trait quantile distribution from pool-sequencing experiments
Motivation:
Sequencing pools of individuals (Pool-Seq) is a cost-effective way to gain insight into the genetics of complex traits, but as yet no parametric method has been developed to both test for genetic effects and estimate their magnitude. Here, we propose GWAlpha, a flexible method to obtain parametric estimates of genetic effects genome-wide from Pool-Seq experiments.
Results:
We showed that GWAlpha powerfully replicates the results of Genome-Wide Association Studies (GWAS) from model organisms. We perform simulation studies that illustrate the effect on power of sample size and number of pools and test the method on different experimental data.
Availability and Implementation:
GWAlpha is implemented in python, designed to run on Linux operating system and tested on Mac OS. It is freely available at https://github.com/aflevel/GWAlpha.
Contact:
afournier@unimelb.edu.au
Supplementary information:
Supplementary data are available at Bioinformatics online.
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