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Life Sciences Commons

Open Access. Powered by Scholars. Published by Universities.®

2019

Soybean

Horticulture

Department of Agronomy and Horticulture: Faculty Publications

Articles 1 - 2 of 2

Full-Text Articles in Life Sciences

Generating High Density, Low Cost Genotype Data In Soybean [Glycine Max (L.) Merr.], Mary M. Happ, Haichuan Wang, George L. Graef, David L. Hyten Jan 2019

Generating High Density, Low Cost Genotype Data In Soybean [Glycine Max (L.) Merr.], Mary M. Happ, Haichuan Wang, George L. Graef, David L. Hyten

Department of Agronomy and Horticulture: Faculty Publications

Obtaining genome-wide genotype information for millions of SNPs in soybean [Glycine max (L.) Merr.] often involves completely resequencing a line at 5X or greater coverage. Currently, hundreds of soybean lines have been resequenced at high depth levels with their data deposited in the NCBI Short Read Archive. This publicly available dataset may be leveraged as an imputation reference panel in combination with skim (low coverage) sequencing of new soybean genotypes to economically obtain high-density SNP information. Ninety-nine soybean lines resequenced at an average of 17.1X were used to generate a reference panel, with over 10 million SNPs called using …


Response Surface Analysis Of Genomic Prediction Accuracy Values Using Quality Control Covariates In Soybean, Diego Jarquin, Reka Howard, George L. Graef, Aaron Lorenz Jan 2019

Response Surface Analysis Of Genomic Prediction Accuracy Values Using Quality Control Covariates In Soybean, Diego Jarquin, Reka Howard, George L. Graef, Aaron Lorenz

Department of Agronomy and Horticulture: Faculty Publications

An important and broadly used tool for selection purposes and to increase yield and genetic gain in plant breeding programs is genomic prediction (GP). Genomic prediction is a technique where molecular marker information and phenotypic data are used to predict the phenotype (eg, yield) of individuals for which only marker data are available. Higher prediction accuracy can be achieved not only by using efficient models but also by using quality molecular marker and phenotypic data. The steps of a typical quality control (QC) of marker data include the elimination of markers with certain level of minor allele frequency (MAF) and …