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GWASpro: A High-Performance Genome-Wide Association Analysis Server.

ABSTRACT: Summary:We present GWASpro, a high-performance web server for the analyses of large-scale genome-wide association studies (GWAS). GWASpro was developed to provide data analyses for large-scale molecular genetic data, coupled with complex replicated experimental designs such as found in plant science investigations, and to overcome the steep learning curves of existing GWAS software tools. GWASpro supports building complex design matrices, by which complex experimental designs that may include replications, treatments, locations and times, can be accounted for in the linear mixed model (LMM). GWASpro is optimized to handle GWAS data that may consist of up to 10 million markers and 10,000 samples from replicable lines or hybrids. GWASpro provides an interface that significantly reduces the learning curve for new GWAS investigators. Availability and implementation:GWASpro is freely available at https://bioinfo.noble.org/GWASPRO. Supplementary information:Supplementary data are available at Bioinformatics online.

SUBMITTER: Kim B 

PROVIDER: S-EPMC6612817 | biostudies-other | 2018 Dec

REPOSITORIES: biostudies-other

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GWASpro: a high-performance genome-wide association analysis server.

Kim Bongsong B   Dai Xinbin X   Zhang Wenchao W   Zhuang Zhaohong Z   Sanchez Darlene L DL   Lübberstedt Thomas T   Kang Yun Y   Udvardi Michael K MK   Beavis William D WD   Xu Shizhong S   Zhao Patrick X PX  

Bioinformatics (Oxford, England) 20190701 14

<h4>Summary</h4>We present GWASpro, a high-performance web server for the analyses of large-scale genome-wide association studies (GWAS). GWASpro was developed to provide data analyses for large-scale molecular genetic data, coupled with complex replicated experimental designs such as found in plant science investigations and to overcome the steep learning curves of existing GWAS software tools. GWASpro supports building complex design matrices, by which complex experimental designs that may inc  ...[more]