Skip to main content
ARS Home » Northeast Area » Ithaca, New York » Robert W. Holley Center for Agriculture & Health » Plant, Soil and Nutrition Research » Research » Publications at this Location » Publication #308984

Research Project: Enhancing Breeding of Small Grains through Improved Bioinformatics

Location: Plant, Soil and Nutrition Research

Title: solGS: a web-based tool for genomic selection

Author
item TECLE, ISAAK - Boyce Thompson Institute
item EDWARDS, JEREMY - Boyce Thompson Institute
item MENDA, NAAMA - Boyce Thompson Institute
item EGESI, CHIEDOZIE - National Root Crops Research Institute (NRCRI)
item RABBI, ISMAIL - International Institute Of Tropical Agriculture (IITA)
item KULAKOW, PETER - International Institute Of Tropical Agriculture (IITA)
item KAWUKI, ROBERT - International Crops Research Institute For Semi-Arid Tropics (ICRISAT) - Nigeria
item Jannink, Jean-Luc
item MUELLER, LUKAS - Boyce Thompson Institute

Submitted to: BMC Bioinformatics
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 11/26/2014
Publication Date: 12/14/2014
Publication URL: http://DOI: 10.1186/s12859-014-0398-7
Citation: Tecle, I., Edwards, J., Menda, N., Egesi, C., Rabbi, I.Y., Kulakow, P., Kawuki, R., Jannink, J., Mueller, L.A. 2014. solGS: a web-based tool for genomic selection. BMC Bioinformatics. 15:398.

Interpretive Summary: Genomic selection (GS) promises to improve accuracy in estimating breeding values and to increase genetic gain for quantitative traits compared to traditional breeding methods. Its reliance on high-throughput genome-wide markers and statistical complexity, however, is a serious challenge in data management, analysis, and sharing. A bioinformatics infrastructure for data storage and access, and a user-friendly web-based tool for analyzing and sharing output is needed to make GS more practical for breeders. We developed such a web-based tool and describe it here.

Technical Abstract: Genomic selection (GS) promises to improve accuracy in estimating breeding values and genetic gain for quantitative traits compared to traditional breeding methods. Its reliance on high-throughput genome-wide markers and statistical complexity, however, is a serious challenge in data management, analysis, and sharing. A bioinformatics infrastructure for data storage and access, and user-friendly web-based tool for analysis and sharing output is needed to make GS more practical for breeders. We developed such a web-based tool and describe it here.