Genomic Selection for Grain Yield and Quality Traits in Oat (Avena sativa L)
Presentation Type
Poster
Student
Yes
Track
Precision Ag/Biological Sciences Application
Abstract
Genomic selection (GS) is the process by which the genetic improvement of plant is accomplished using marker based genomic prediction (GP) of value of an individual as a genetic parent. Traditionally, it has required a series of selection for 10 years or more to release improved seeds. GS has potential of reducing the years of breeding through prediction of progenies performance and saves resources. We used a separate panel of 222 oat (Avena sativa L) lines genotyped with 38,000 SNP markers for three generations. Genomic selection (GS) was applied to over seven phenotypic traits in the oat breeding program of South Dakota State University at four locations for three years. GS prediction accuracy, correlation between observed and model prediction, from cross-validation approach were compared using six GS models such as RRBLUP, GAUSS, PLSR, ELNET, RF, and AVEWe found that the AVE method was giving better prediction with average accuracy of 0.25, 050, 0.56, 0.66, 59, and 0.48 for yield, protein content, plump groat, groat oil content, plant height, and groat beta glucan content, respectively. Overall, all six GS models appear to be applicable for predicting quality traits, but we recommend AVG method for quantitative traits like yield.
Key words: genomics, selection, markers, Oat, model, predictions
Start Date
2-5-2019 12:00 PM
End Date
2-5-2019 1:00 PM
Genomic Selection for Grain Yield and Quality Traits in Oat (Avena sativa L)
Volstorff A
Genomic selection (GS) is the process by which the genetic improvement of plant is accomplished using marker based genomic prediction (GP) of value of an individual as a genetic parent. Traditionally, it has required a series of selection for 10 years or more to release improved seeds. GS has potential of reducing the years of breeding through prediction of progenies performance and saves resources. We used a separate panel of 222 oat (Avena sativa L) lines genotyped with 38,000 SNP markers for three generations. Genomic selection (GS) was applied to over seven phenotypic traits in the oat breeding program of South Dakota State University at four locations for three years. GS prediction accuracy, correlation between observed and model prediction, from cross-validation approach were compared using six GS models such as RRBLUP, GAUSS, PLSR, ELNET, RF, and AVEWe found that the AVE method was giving better prediction with average accuracy of 0.25, 050, 0.56, 0.66, 59, and 0.48 for yield, protein content, plump groat, groat oil content, plant height, and groat beta glucan content, respectively. Overall, all six GS models appear to be applicable for predicting quality traits, but we recommend AVG method for quantitative traits like yield.
Key words: genomics, selection, markers, Oat, model, predictions