Genomic Selection Accuracy using Multifamily Prediction Models in a Wheat Breeding Program

By E. Heffner, Jean-Luc Jannink, M. Sorrells. 2011

411 citations recorded by the source corpus.

Genomic selection (GS) uses genome‐wide molecular marker data to predict the genetic value of selection candidates in breeding programs. In plant breeding, the ability to produce large numbers of progeny per cross allows GS to be conducted within each family. However, this approach requires phenotypes of lines from each cross before conducting GS. This will prolong the selection cycle and may result in lower gains per year than approaches that estimate marker‐effects with multiple families from previous selection cycles. In this study, phenotypic selection (PS), conventional marker‐assisted selection (MAS), and GS prediction accuracy were compared for 13 agronomic traits in a population of 374 winter wheat (Triticum aestivum L.) advanced‐cycle breeding lines. A cross‐validation approach that trained and validated prediction accuracy across years was used to evaluate effects of model selection, training population size, and marker density in the presence of genotype × environment interactions (G×E). The average prediction accuracies using GS were 28% greater than with MAS and were 95% as accurate as PS. For net merit, the average accuracy across six selection indices for GS was 14% greater than for PS. These results provide empirical evidence that multifamily GS could increase genetic gain per unit time and cost in plant breeding.

Subfields: Genetics, Plant Science

View Paper: https://www.semanticscholar.org/paper/82dce4283a48ac4c99e09a2344a8733b85856314

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Citations in the corpus

Next-generation phenotyping: requirements and strategies for enhancing our understanding of genotype–phenotype relationships and its relevance to crop improvement (2013, background): “…training populations when working with traits with low heritability and complex inheritance (Calus et al. 2008; Guo et al. 2012; Hayes et al. 2009; Heffner et al. 2010; Jia and Jannink 2012; Kumar et al. 2012; Nakaya and Isobe 2012; Munoz et al. 2011; Resende et al. 2012; Zhao et al. 2011b; Zhong…”
Genomewide Selection versus Marker‐assisted Recurrent Selection to Improve Grain Yield and Stover‐quality Traits for Cellulosic Ethanol in Maize (2013, positive): “In empirical cross-validation studies in maize (Lorenzana and Bernardo, 2009; Guo et al., 2012), barley (Hordeum vulgare L.; Lorenzana and Bernardo, 2009), oat (Avena sativa L.; Asoro et al., 2011), and wheat (Triticum aestivum L.; Heffner et al., 2011), GWS predictions of observed performance were ”
Adding Genetically Distant Individuals to Training Populations Reduces Genomic Prediction Accuracy in Barley (2015, background): “ReseaRch M any factors affect genomic prediction accuracy, including model, marker density, TP composition, trait complexity , and precision of phenotyping (Combs and Bernardo, 2013; Lorenz et al., 2012; Lorenz, 2013; Heffner et al., 2011).”
Genotyping‐by‐Sequencing for Plant Breeding and Genetics (2012, background): “While statistical geneticists will always prefer to have as many markers as possible, GS models have diminishing returns on additional markers once the population has reached the point of “marker saturation” (Jannink et al., 2010; Hef ner et al., 2011).”
Evaluation of Genomic Prediction Methods for Fusarium Head Blight Resistance in Wheat (2012, background): “, 2011a), and sets of breeding lines representative of a single breeding program (Crossa et al., 2010; Heffner et al., 2011b).”
Accelerating Genetic Gain in Sugarcane Breeding Using Genomic Selection (2020, background): “To maintain a high prediction accuracy in GS-based breeding programs, the TP must be frequently updated with new phenotyped and genotyped accessions [91,92].”
Comparing genomic selection and marker-assisted selection for Fusarium head blight resistance in wheat (Triticum aestivum L.) (2016, background): “However, for polygenic traits, this increase in accuracy relative to RR-BLUP is usually small (VanRaden 2008; Moser et al. 2009; Heffner et al. 2011; Resende et al. 2012).”
Accuracy and Training Population Design for Genomic Selection on Quantitative Traits in Elite North American Oats (2011, background): “…narrow-based biparental populations (Lorenzana and Bernardo, 2009) and in broad-based populations such as multilines of barley ( Hordeum vulgare L.), wheat ( Triticum aestivum L.), and maize ( Zea mays L.) (Zhong et al., 2009; Heff ner et al., 2011; de los Campos et al., 2009; Crossa et al., 2010).”
Genomic Selection in Plant Breeding: A Comparison of Models (2012, methodology): “Those results clearly showed the similarities in terms of GEBVs between the linear models represented Average MSE 0.67 0.67 0.69 0.68 0.68 0.76 0.64 1.36 0.72 10.54 †Barley 1, Limagrain Europe, Riom, France; Barley CAP (Barley Coordinated Agricultural Project, 2011); Bay Sha (Loudet et al. 2002); Pa”
Genomic Prediction of Hybrid Wheat Performance (2013, result): “Despite the context dependency of genomic selection, the observed prediction accuracy was comparable to the accuracy reached for predicting grain yield per se performance in wheat (e.g., Crossa et al., 2010; Heffner et al., 2011; Heslot et al., 2012).”