A multiparental cross population for mapping <scp>QTL</scp> for agronomic traits in durum wheat (<i><scp>T</scp>riticum turgidum</i> ssp. <i>durum</i>)

Milner, Sara Giulia; Maccaferri, Marco; Huang, Bevan Emma; Mantovani, Paola; Massi, A.; Frascaroli, Elisabetta; Tuberosa, Roberto; Salvi, Silvio

doi:10.1111/pbi.12424

Cited by 72 publications

(70 citation statements)

References 55 publications

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“…The main objectives of this study were (i) to extend and demonstrate the use of the M´E model of López-Cruz et al (2015) but using priors that induce shrinkage (BRR) as well as variable selection (BB) and (ii) to evaluate these GS models using a durum wheat population comprising a balanced, four-parental cross (identified as NCCR) (Milner et al, 2015) evaluated for HD, GVW, GWT, and GY in four environments. One problem we assessed was how to generate breeding-value predictions for additional lines for which phenotypic data were not available (cross-validation problem 1, or CV1); another problem was how to predict breeding values for lines observed in some environments but not in others (crossvalidation problem 2, or CV2).…”

Section: Marker ´ Environment Interaction Regression Modelmentioning

confidence: 99%

“…Details of phenotypic and genotypic data as well as how the multiparental population was developed are given in Milner et al (2015); a brief description is given below.…”

Section: Genotypic and Phenotypic Datamentioning

confidence: 99%

“…These four-way F 1 hybrids were advanced through single-seed descent and bulked in the F 8 generation. The final NCCR population includes 338 recombinant inbred lines (RILs) (Milner et al, 2015). This population is representative of segregating populations commonly used in wheat breeding, where the three-and four-way cross scheme is increasingly adopted to generate wider genotypic variance in segregating populations as compared with the traditional biparental cross.…”

Section: Development Of the Multiparental Nccr Populationmentioning

confidence: 99%

“…This population is representative of segregating populations commonly used in wheat breeding, where the three-and four-way cross scheme is increasingly adopted to generate wider genotypic variance in segregating populations as compared with the traditional biparental cross. Importantly, the balanced NCCR population shows almost no population structure (Milner et al, 2015), making it a much more suitable material for assessing the performance of M´E models than collections of diverse cultivars and breeding lines commonly used in association mapping studies.…”

Section: Development Of the Multiparental Nccr Populationmentioning

confidence: 99%

“…Recent studies in durum wheat included extensive association mapping for agronomic traits for a reference population evaluated under different water regimes (Maccaferri et al, 2011) and a high-density single-nucleotide polymorphism (SNP) map (Maccaferri et al, 2014;Milner et al, 2015). Although in recent years genomic prediction has been applied to bread wheat (de los Campos et al, 2009Crossa et al, 2010;González-Camacho et al, 2012;Heslot et al, 2012;Pérez-Rodríguez et al, 2012;López-Cruz et al, 2015), to our best knowledge, no study…”

Section: Introductionmentioning

confidence: 99%

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Extending the Marker × Environment Interaction Model for Genomic‐Enabled Prediction and Genome‐Wide Association Analysis in Durum Wheat

et al. 2016

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2193RESEARCH G enetic ´ environment interaction (G´E) affects trait heritability and the relative rankings of phenotypes across environments; this introduces challenges when making breeding decisions. The effects of G´E on heritability may be due to scale effects such as changes in the size of quantitative trait loci (QTL) effects across environments or to differential genetic effects on environmental variance. However, G´E can also modulate QTL effects, thus introducing changes in the relative rank of genotypes across environments (Dickerson, 1962;Cockerham, 1963). Falconer (1952) suggested modeling performance in two environments as two correlated traits; this allows modeling both scale effects and rerankings. Extending the Marker ´ Environment Interaction Model for Genomic-Enabled Prediction and Genome-Wide Association Analysis in Durum WheatJosé Crossa,* Gustavo de los Campos, Marco Maccaferri, Roberto Tuberosa, J. Burgueño, and Paulino Pérez-Rodríguez* ABSTRACTThe marker ´ environment interaction (M´E) genomic model can be used to generate predictions for untested individuals and identify genomic regions in which effects are stable across environments and others that show environmental specificity. The objectives of this study were (i) to extend the M´E model using priors that produce shrinkage and variable selection such as Bayesian ridge regression (BRR) and BayesB (BB), respectively, and (ii) to evaluate the genomic prediction accuracy of M´E, single-environment, and across-environment models using a multiparental durum wheat (Triticum turgidum L. spp. duram) population characterized for grain yield (GY), grain volume weight (GVW), 1000-kernel weight (GWT), and heading date (HD) in four environments. Breeding value predictions were generated for two prediction problems: cross-validation problem 1 (CV1) and cross-validation problem 2 (CV2). In general, results showed that the M´E model performed better than the single-environment and acrossenvironment models, in terms of minimizing the model residual variance, for both CV1 and CV2. The improved data-fitting gain over the other models was more evident for GWT and HD (up to twofold differences) than to GY and GVW, which showed more complex genetic bases and smaller single-marker effects. Considering the Bayesian models used, BB showed better overall prediction accuracy than BRR. As proofof-concept for the M´E model, the major controllers of HD-Ppd and FT on chromosomes 2A, 2B, and 7A-showed stable effects across environments as well as environment-specific effects. For GY, besides the regions on chromosomes 2B and 7A, additional chromosome regions with large marker effects were detected in all chromosome groups.

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Section: Marker ´ Environment Interaction Regression Modelmentioning

confidence: 99%

“…Details of phenotypic and genotypic data as well as how the multiparental population was developed are given in Milner et al (2015); a brief description is given below.…”

Section: Genotypic and Phenotypic Datamentioning

confidence: 99%

Section: Development Of the Multiparental Nccr Populationmentioning

confidence: 99%

Section: Development Of the Multiparental Nccr Populationmentioning

confidence: 99%