A marker weighting approach for enhancing within-family accuracy in genomic prediction.

Genomic selection is revolutionizing plant breeding. However, its practical implementation is still very challenging, since predicted values do not necessarily have high correspondence to the observed phenotypic values. When the goal is to predict within-family, it is not always possible to obtain reasonable accuracies, which is of paramount importance to improve the selection process.

Sequence-based mapping identifies a candidate transcription repressor underlying awn suppression at the B1 locus in wheat.

Awns are stiff, hair-like structures which grow from the lemmas of wheat (Triticum aestivum) and other grasses that contribute to photosynthesis and play a role in seed dispersal. Variation in awn length in domesticated wheat is controlled primarily by three major genes, most commonly the dominant awn suppressor Tipped1 (B1). This study identifies a transcription repressor responsible for awn inhibition at the B1 locus.

Training population selection and use of fixed effects to optimize genomic predictions in a historical USA winter wheat panel.

The optimization of training populations and the use of diagnostic markers as fixed effects increase the predictive ability of genomic prediction models in a cooperative wheat breeding panel. Plant breeding programs often have access to a large amount of historical data that is highly unbalanced, particularly across years. This study examined approaches to utilize these data sets as training populations to integrate genomic selection into existing pipelines.