GWAPP: A Web Application for Genome-wide Association Mapping in A. thaliana

TL;DR

GWAPP is an interactive web application that enables rapid, user-friendly genome-wide association studies in Arabidopsis thaliana, facilitating genetic analysis of natural variation with efficient computation and visualization tools.

Contribution

It introduces GWAPP, a web-based platform that simplifies and accelerates GWAS in Arabidopsis thaliana using an efficient Python implementation and interactive visualization features.

Findings

GWAPP can perform GWAS on 1386 ecotypes within minutes.

The platform provides interactive Manhattan and LD plots for data exploration.

It supports inclusion of candidate SNPs as cofactors in models.

Abstract

Arabidopsis thaliana is an important model organism for understanding the genetics and molecular biology of plants. Its highly selfing nature, together with other important features, such as small size, short generation time, small genome size, and wide geographic distribution, make it an ideal model organism for understanding natural variation. Genome-wide association studies (GWAS) have proven a useful technique for identifying genetic loci responsible for natural variation in A. thaliana. Previously genotyped accessions (natural inbred lines) can be grown in replicate under different conditions, and phenotyped for different traits. These important features greatly simplify association mapping of traits and allow for systematic dissection of the genetics of natural variation by the entire Arabidopsis community. To facilitate this, we present GWAPP, an interactive web-based application for conducting GWAS in A. thaliana. Using an efficient Python implementation of a linear mixed model, traits measured for a subset of 1386 publicly available ecotypes can be uploaded and mapped with an efficient mixed model and other methods in just a couple of minutes. GWAPP features an extensive, interactive, and a user-friendly interface that includes interactive manhattan plots and interactive local and genome-wide LD plots. It facilitates exploratory data analysis by implementing features such as the inclusion of candidate SNPs in the model as cofactors.