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

Author:

Kim Bongsong1,Dai Xinbin1,Zhang Wenchao1,Zhuang Zhaohong1,Sanchez Darlene L2,Lübberstedt Thomas3,Kang Yun1,Udvardi Michael K1,Beavis William D3,Xu Shizhong4,Zhao Patrick X1ORCID

Affiliation:

1. Noble Research Institute, Ardmore, OK, USA

2. AgriLife Research Center, Texas A&M University, Beaumont, TX, USA

3. Department of Agronomy, Iowa State University, Ames, IA, USA

4. Department of Botany and Plant Sciences, University of California, Riverside, CA, USA

Abstract

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. 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.

Funder

National Science Foundation

Noble Research Institute

North Central Soybean Research Program

Baker Center for Plant Breeding

USDA-NIFA project

GF Sprague Endowment of the Agronomy Department at Iowa State University

Publisher

Oxford University Press (OUP)

Subject

Computational Mathematics,Computational Theory and Mathematics,Computer Science Applications,Molecular Biology,Biochemistry,Statistics and Probability

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