Multivariate Genomic Selection and Potential of Rapid Indirect Selection with Speed Breeding in Spring Wheat

Abstract

Genomic selection (GS) can be effective in breeding for quantitative traits, such as yield, by reducing the selection cycle duration. Speed breeding (SB) uses extended photoperiod and temperature control to enable rapid generation advancement. Together, GS and SB can synergistically reduce the breeding cycle by quickly producing recombinant inbred lines (RILs) and enabling indirect phenotypic selection to improve for key traits, such as height and flowering time, prior to field trials. In addition, traits measured under SB (SB traits) correlated with field‐based yield could improve yield prediction in multivariate GS. A 193‐line spring wheat (Triticum aestivum L.) training population (TP), tested for grain yield in the field in multiple environments, was used to predict grain yield of a 350‐line selection candidate (SC) population, across multiple environments. Four SB traits measured on the TP and SC populations were used to derive principal components, which were incorporated into multivariate GS models. Predictive ability was significantly increased by multivariate GS, in some cases being twice as high as univariate GS. Based on these results, an efficient breeding strategy is proposed combining SB and multivariate GS using yield‐correlated SB traits for yield prediction. The potential for early indirect SB phenotypic selection for targeted population improvement prior to trials was also investigated. Plant height and flowering time showed strong relative predicted efficiency to indirect selection, in some cases as high as direct field selection. The higher selection intensity and rate of generation turnover under SB may enable a greater rate of genetic gain than direct field phenotyping.

Genome-based trait prediction in multi- environment breeding trials in groundnut

“…This approach has been utilized extensively in livestock breeding (Hays and Goddard 2010;van der Werf 2013;Hays et al 2013;Meuwissen et al 2016) and is still evolving in plant breeding. If integrated with rapid generation advancement technology such as speed breeding, the GS can make remarkable achievement and positive impact on breeding programs (Watson et al 2019) including groundnut (Pandey et al 2020).…”

A hybrid optimal contribution approach to drive short-term gains while maintaining long-term sustainability in a modern plant breeding program

“…The largest theoretical gains come from reduced generation intervals, or rapid-cycling, where superior individuals are used as parents at earlier stages than is typically possible through traditional phenotypic selection (Schaeffer 2006; Hickey et al 2017a). By pushing generation turnover rates to their biological (Christopher et al 2015; Hickey et al 2017b; Watson et al 2019) and logistical limits (Cobb et al 2019), genetic gain can be drastically accelerated beyond traditional breeding methods (Schaeffer 2006). Accurate prediction of breeding values is required for rapid-cycling to be effective, and is achieved through large, highly related training populations.…”

Genomic interventions for sustainable agriculture

“…Importantly, the improvement in genetic gain was higher in the case of low‐heritability traits and with higher number of SB cycles. Recent empirical evidence in wheat demonstrates the potential of SpeedGS for rapid population improvement where phenotyping of SB traits in combination with multivariate GS could guide the selection of lines for field trials or next breeding cycle (Watson et al ., 2019). These recent studies highlight the immense scope for ‘customizing the breeding pipelines’ (Voss‐Fels et al ., 2019) in order to accommodate SB and GS to achieve higher rate of genetic gains in crop breeding programmes.…”