Using the Animal Model to Accelerate Response to Selection in a Self-Pollinating Crop

Abstract

We used the animal model in S0 (F1) recurrent selection in a self-pollinating crop including, for the first time, phenotypic and relationship records from self progeny, in addition to cross progeny, in the pedigree. We tested the model in Pisum sativum, the autogamous annual species used by Mendel to demonstrate the particulate nature of inheritance. Resistance to ascochyta blight (Didymella pinodes complex) in segregating S0 cross progeny was assessed by best linear unbiased prediction over two cycles of selection. Genotypic concurrence across cycles was provided by pure-line ancestors. From cycle 1, 102/959 S0 plants were selected, and their S1 self progeny were intercrossed and selfed to produce 430 S0 and 575 S2 individuals that were evaluated in cycle 2. The analysis was improved by including all genetic relationships (with crossing and selfing in the pedigree), additive and nonadditive genetic covariances between cycles, fixed effects (cycles and spatial linear trends), and other random effects. Narrow-sense heritability for ascochyta blight resistance was 0.305 and 0.352 in cycles 1 and 2, respectively, calculated from variance components in the full model. The fitted correlation of predicted breeding values across cycles was 0.82. Average accuracy of predicted breeding values was 0.851 for S2 progeny of S1 parent plants and 0.805 for S0 progeny tested in cycle 2, and 0.878 for S1 parent plants for which no records were available. The forecasted response to selection was 11.2% in the next cycle with 20% S0 selection proportion. This is the first application of the animal model to cyclic selection in heterozygous populations of selfing plants. The method can be used in genomic selection, and for traits measured on S0-derived bulks such as grain yield.

Genomic assisted selection for enhancing line breeding: merging genomic and phenotypic selection in winter wheat breeding programs with preliminary yield trials

“…Integrating pedigree or marker data into the estimation of breeding values has been shown to achieve much higher accuracies when selecting already phenotyped lines in several scenarios (Bauer et al 2006; Oakey et al 2007a; Viana et al 2010; Endelman et al 2014; Cowling et al 2015), and was accordingly a very valuable option for enhancing the prediction of line performance across years in this study. The usage of this enhanced phenotypic data from preliminary yield trials for estimating breeding values tackled the problem of predicting tested lines in untested years, while genomic selection usually addresses the more challenging problem of predicting untested lines in untested years.…”

Modeling crop breeding for global food security during climate change

“…Records from S x ‐derived S x +1 plots were used to predict breeding values of S x individuals, following the theory that the self‐family mean provides an improved estimate of the breeding value of the parent for crossing (Walsh & Lynch, ). Records were obtained on both cross progeny and selfs of parent plants, because this increased the accuracy of estimated breeding value (EBV) due to inclusion of self‐relatives in the analysis (Cowling et al., , ). When a genotype was selected for crossing, remnant self‐progeny seeds were used in crossing (Cowling et al., ).…”

“…During the past 30–40 years, index selection based on BLUP breeding values has been adopted widely in animals and perennial tree crops, but not in self‐pollinating grain crops (Bauer & Léon, ). High accuracy of BLUP breeding values was obtained with 2‐year cycles of recurrent selection in a self‐pollinating crop (Cowling et al., ), and long‐term genetic gain in an economic index composed of several low heritability traits was greater with OCS compared to truncation selection (Cowling et al., ).…”

“…We build on this experience to model wheat breeding from 2017 to 2077 to improve an economic index composed of grain yield and three other economic traits (Cowling et al., ), while precisely controlling HST 30 to match prevailing climatic conditions. We have chosen an ABLUP breeding method based on all pedigree relationship information across cycles, including selfing, which results in high accuracy of predicted breeding values (Cowling et al., ) at relatively low cost, and is suitable for implementation in developing countries. Three levels of selection of HST 30 are modeled: (a) no selection, (b) selection for an increase in HST 30 of +2 units by 2077, which matches the “average” climate change model predictions for +2°C increase in land temperatures in 2077 (IPCC, ), and (iii) selection for an increase of HST 30 of +4 units by 2077, which matches the “high” model prediction of +4°C increase in land temperatures in 2077 (IPCC, ).…”

Neglecting legumes has compromised human health and sustainable food production

“…Selection for complex traits was shown to be more efficient when based on genomic relationship information in animals 46 . For grain legumes, many of which are self-pollinating crops, genomic selection offers the prospect of accelerating genetic progress for yield 47 . Advanced phenotyping technologies are available to measure morphological and physiological traits 48 .…”