Improving Genomic Prediction for Pre‐Harvest Sprouting Tolerance in Wheat by Weighting Large‐Effect Quantitative Trait Loci

Abstract

Preharvest sprouting (PHS) is a major problem in wheat (Triticum aestivum L.) that occurs when grains in a mature spike germinate before harvest, resulting in reduced yield, quality, and grain sale price. Improving PHS tolerance is a challenge to wheat breeders because it is quantitatively inherited and tedious to score. Genomic selection (GS) is particularly useful for predicting phenotypes that are costly and time consuming to assess. In our study, single nucleotide polymorphism (SNP) markers obtained by genotyping‐by‐sequencing were used to identify significant marker trait associations and develop predictive models for PHS tolerance. A panel of 1118 breeding lines and cultivars (genotypes) representative of U.S. Great Plains hard winter wheat germplasm was scored for PHS tolerance over multiple years. A genomewide association approach was used to identify quantitative trait loci (QTL) among the individuals. Two primary factors were examined for their influence on model accuracy: the effect of including identified QTL and kernel color as fixed effects in the model and increasing marker number. Model accuracy did not improve with kernel color information, but weighting QTL increased predictive performance. Thus, the combination of marker‐assisted and genomic selection outperformed all other methods. Optimum marker number was reached at 4000 SNPs. Overall, model accuracies were promising (0.49 to 0.62) and confirm effectiveness of GS for predicting PHS tolerance in wheat.

Improving the baking quality of bread wheat by genomic selection in early generations

“…Nevertheless, these issues suggest a prudent interpretation of interesting markers identified in GWAS, and marker-trait associations should be validated with data that was left-out for mapping. We suggest thus favouring known major QTL like Fr - 2 for frost tolerance (Erath et al 2017 ; Würschum et al 2017 ) or TaPHS1 for pre-harvest sprouting (Moore et al 2017 ) when predicting complex traits with W-BLUP models in bread wheat and other species. Important genes associated with dough rheological parameters like the Glu - 1 loci could nevertheless be readily identified by GWAS (Zheng et al 2009 ); however, it often fails to detect rare variants like the wbm gene (Furtado et al 2015 ; Bernardo 2016 ; Guzmán et al 2016b ).…”

“…Extending this idea, Spindel et al ( 2016 ) suggested to integrate de novo mapped marker-trait associations into genomic prediction models. Liu et al ( 2016 ) could though not find any advantage of this method in the analysis of a large hybrid wheat population for quality traits, while other studies reported a significant increase in prediction accuracy of this method (Boeven et al 2016 ; Moore et al 2017 ). The increase in prediction accuracy using the same population for marker-trait associations discovery and subsequent prediction model validation has been termed the inside trading effect by Arruda et al ( 2016 ), and is the result of selecting predictors before leaving observations that are supposed to be unobserved out, leading consequently to an overfit of the respective prediction models to the training data.…”

A connected half‐sib family training population for genomic prediction in barley

“…In theory, accounting for large effect markers as fixed effects in genomic prediction should improve PA and relative efficiency of selection when heritability is high and the marker explains over 10% of the genetic variance (Bernardo, 2014; Rutkoski et al., 2012). Empirical studies support incorporation of fixed marker effects from diagnostic markers (Daetwyler, Bansal, Bariana, Hayden, & Hayes, 2014) or de novo GWAS (Bian & Holland, 2017; Herter et al., 2019; Moore et al., 2017; Spindel et al., 2016) in the TP to improve PA. In contrast, Rice and Lipka (2018) reported minimal to negative gains in PA from combined genomic prediction and de novo GWAS across the majority of 216 genetic architecture simulations, possibly due to differences in marker effect across TP and VP.…”

Genomic Prediction and Indirect Selection for Grain Yield in US Pacific Northwest Winter Wheat Using Spectral Reflectance Indices from High-Throughput Phenotyping

“…It should be noted that adding secondary traits as fixed effects in the model is not always advantageous for increasing prediction accuracy. In the case of Moore et al [51], for example, using kernel color as fixed effects did not improve accuracy for pre-harvest sprouting (PHS) in hard winter wheat, indicating that the trait per se was not a reliable predictor of tolerance to PHS. In the current study, low or negative prediction accuracies were observed particularly when performing predictions across different environments (i.e., LND predicting PUL and vice versa) even when SRI was included in the model for the independent validations.…”