Evaluation and retrospective optimization of genomic selection for yield and disease resistance in spring barley

Tyler Tiede, Kevin P. Smith

Research output: Contribution to journalArticlepeer-review

12 Scopus citations

Abstract

The general applicability of genomic selection (GS) to plant breeding and principles guiding its use have been established by simulation and empirical cross-validation studies. More recently, studies have demonstrated genetic gains over multiple cycles of selection in a variety of crop species. In this study, we provide additional evidence for the effectiveness of GS in an actual breeding program by demonstrating significant gains of 186.1 kg ha−1 and − 1.85 ppm for grain yield and deoxynivalenol, respectively, two unfavorably correlated quantitative traits, across 3 cycles of selection in a spring six-row barley breeding population. With its general effectiveness established, the next step is to increase the accuracy of predictions used in GS and thereby increase genetic gains. For this, we first showed that updating the training population (TP) with phenotyped lines from recent breeding cycles, specifically selected lines, had an overall positive effect on prediction accuracy. Additionally, we investigated four recently proposed algorithms that seek to optimize the composition of a TP. Overall, the optimization algorithms improved prediction accuracy when compared to a randomly selected TP subset of the same size, but which algorithm performed best was dependent on the trait being predicted and other factors discussed within. This retrospective investigation highlights the importance of maintaining and optimizing the TP when using GS in applied breeding to maximize prediction accuracy, thereby maximizing gain from selection and resource utilization efficiency.

Original languageEnglish (US)
Article number55
JournalMolecular Breeding
Volume38
Issue number5
DOIs
StatePublished - May 1 2018

Keywords

  • Applied plant breeding
  • Empirical validation
  • Genomewide selection
  • Genomic selection
  • Training population optimization

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