Accuracy and Training Population Design for Genomic Selection on Quantitative Traits in Elite North American Oats

Asoro, Franco G.; Newell, Mark A.; Beavis, William D.; Scott, M. Paul; Jannink, Jean‐Luc

doi:10.3835/plantgenome2011.02.0007

Cited by 227 publications

(256 citation statements)

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“…However, the increase was relatively small, even when N H was tripled. This is in contrast to studies on genomic prediction of additive breeding values in plant breeding, where tripling N H could double the accuracy (Asoro et al 2011;. One explanation for this is the already rather high level of prediction accuracy reached.…”

Section: Composition Of Training Setcontrasting

confidence: 46%

Genome Properties and Prospects of Genomic Prediction of Hybrid Performance in a Breeding Program of Maize

et al. 2014

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Maize (Zea mays L.) serves as model plant for heterosis research and is the crop where hybrid breeding was pioneered. We analyzed genomic and phenotypic data of 1254 hybrids of a typical maize hybrid breeding program based on the important Dent 3 Flint heterotic pattern. Our main objectives were to investigate genome properties of the parental lines (e.g., allele frequencies, linkage disequilibrium, and phases) and examine the prospects of genomic prediction of hybrid performance. We found high consistency of linkage phases and large differences in allele frequencies between the Dent and Flint heterotic groups in pericentromeric regions. These results can be explained by the Hill-Robertson effect and support the hypothesis of differential fixation of alleles due to pseudooverdominance in these regions. In pericentromeric regions we also found indications for consistent marker-QTL linkage between heterotic groups. With prediction methods GBLUP and BayesB, the cross-validation prediction accuracy ranged from 0.75 to 0.92 for grain yield and from 0.59 to 0.95 for grain moisture. The prediction accuracy of untested hybrids was highest, if both parents were parents of other hybrids in the training set, and lowest, if none of them were involved in any training set hybrid. Optimizing the composition of the training set in terms of number of lines and hybrids per line could further increase prediction accuracy. We conclude that genomic prediction facilitates a paradigm shift in hybrid breeding by focusing on the performance of experimental hybrids rather than the performance of parental lines in testcrosses.H YBRID breeding was pioneered in maize (Shull 1908) and plays an ever increasing role in other globally important field (Duvick 1999) and vegetable crops (Silva Dias 2010). Maize has also served as a model species for research in heterosis, the phenomenon behind the success of hybrid varieties, for which the genetic mechanisms have been elusive (Duvick 1999;Lippman and Zamir 2006). In recent years, evidence emerged for the importance of (pseudo-)overdominance in the manifestation of heterosis in maize (Lippman and Zamir 2006;Schön et al. 2010) and the particular role of the centromeres in this process (Gore et al. 2009;McMullen et al. 2009). Today, the availability of high-density marker data and whole-genome regression methods developed in the context of genomic prediction (Meuwissen et al. 2001) allows us to revisit this hypothesis by studying key genome properties such as allele frequencies and linkage phases.Consistency of linkage phases between quantitative trait loci (QTL) and markers is a key prerequisite for pooling of diverse breeds and germplams to increase sample size for genetic studies and transferability of their results to different populations (De Roos et al. 2008). Weber et al. (2012) used whole-genome estimates of marker effects of several cattle breeds to investigate across-breed marker-QTL linkage phase consistency. Such a study is still missing for maize and other important crops. For o...

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Section: Composition Of Training Setcontrasting

confidence: 46%

Genome Properties and Prospects of Genomic Prediction of Hybrid Performance in a Breeding Program of Maize

et al. 2014

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“…Most research on genomic selection has focused on accurately predicting the genotypic mean performance over all environments, assuming that all trial locations belong to the same target population of environments (Asoro et al, 2011;Charmet and Storlie, 2012;Storlie and Charmet, 2013;Crossa et al, 2013;Dawson et al, 2013). However, since GEI is widespread, the accuracy of genomic prediction could be lowered especially by the presence of crossover interaction (Burgueño et al, 2012;Dawson et al, 2013;Jarquín et al, 2014).…”

Section: Predictive Abilitymentioning

confidence: 99%

“…Genomic selection was first developed in animal breeding (Meuwissen et al, 2001) and has since been transferred into plant breeding (Heffner et al, 2009). While most of the GS studies have focused on evaluating GS model performance (Bennewitz et al, 2009;Solberg et al, 2009;Heslot et al, 2012;de los Campos et al, 2013) and optimizing the training population in terms of population size (Lorenzana and Bernardo, 2009;Asoro et al, 2011;Lorenz et al, 2012), number of markers (Lorenzana and Bernardo, 2009;Asoro et al, 2011;Heffner et al, 2011), and its structure (Asoro …”

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confidence: 99%

Modeling Genotype × Environment Interaction for Genomic Selection with Unbalanced Data from a Wheat Breeding Program

et al. 2016

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Genomic selection (GS) has successfully been used in plant breeding to improve selection efficiency and reduce breeding time and cost. However, there is not a clear strategy on how to incorporate genotype × environment interaction (GEI) to GS models. Increased prediction accuracy could be achieved using mixed models to exploit GEI by borrowing information from other environments. The objective of this work was to compare strategies to exploit GEI in GS using mixed models. Specifically, we compared strategies to predict new genotypes by borrowing information from other environments modeling the correlation matrix across environments and to design sets of environments aiming for low GEI to predict genomic performance in new environments. We evaluated 1477 advanced wheat (Triticum aestivum L.) lines for yield in 35 location–year combinations genotyped with genotyping‐by‐sequencing (GBS). Mixed models were used to obtain either overall or by‐environment predictions for different sets of environments. Overall accuracy was high (0.5). Borrowing information from relatives evaluated in multiple environments and modeling the correlation matrix across environments was the best strategy to predict new genotypes. On the other hand, the best strategy for predicting the performance of genotypes in new environments was either to predict across locations for single years or to predict within defined mega‐environments (MEs) for any year or location. In summary, higher predictive ability was obtained by characterizing and by modeling GEI in the GS context.

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“…Contudo, ao considerar as populações de baixo LD e com menor variância aditiva, o uso de maiores densidades de SNPs seria necessário, para a obtenção de acurácia de predição satisfatória. No caso de populações biparentais, comuns no melhoramento vegetal, maiores densidades de marcadores serão necessárias, se o LD for baixo (Asoro et al, 2011).…”

Section: Resultsunclassified

Seleção genômica para melhoramento vegetal com diferentes estruturas populacionais

Valente

Viana

Resende

et al. 2016

Pesq. agropec. bras.

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Resumo -O objetivo deste trabalho foi avaliar a eficiência da seleção genômica em diferentes cenários de estrutura populacional em milho-pipoca, com estimação dos efeitos e uso de marcadores na própria população de referência e em populações não relacionadas, e determinar a influência do tamanho efetivo e das relações de parentesco na população de estimação sobre a acurácia da predição. Foram simuladas populações com diferentes desequilíbrios de ligação (LD) e variâncias aditivas, tendo-se considerado diferentes caracteres, densidades de marcadores, herdabilidades e gerações, no total de 144 cenários. Também foram simuladas populações estruturadas em progênies. A acurácia da predição dos valores genéticos aditivos foi obtida por meio da correlação entre os valores paramétricos e os valores estimados por RR-BLUP (ridge regressionbest linear unbiased prediction). Em populações com baixo LD e menor variância aditiva, o uso de maiores densidades de SNP (10 SNP 0,1 cM -1 ) é indicado, e, além disso, o candidato à seleção deve ser relacionado à população de estimação, para que a acurácia de predição seja satisfatória. O uso de população de seleção na mesma geração da população de estimação reduz em pelo menos 8% a acurácia. A estruturação da população em progênies de maior relacionamento e menor tamanho efetivo aumenta a eficiência da seleção genômica.Termos para indexação: Zea mays, acurácia da predição, eficiência da seleção genômica, predição de valor genético, relação de parentesco, tamanho efetivo. Genomic selection for plant breeding with different population structuresAbstract -The objective of this work was to evaluate the efficiency of genomic selection in different population structure scenarios in popcorn, by estimating the effects of and using markers in the same reference population and in populations not related to each other, and to determine the effect of the effective size and kin relationships on the estimation population over the prediction accuracy. Populations were simulated with different linkage disequilibria (LD) and additive variances, taking into account different characters, marker densities, heritabilities, and generations, totaling 144 scenarios. Populations structured in progenies were also simulated. The prediction accuracy of additive breeding values was obtained by correlating true breeding values and breeding values predicted by RR-BLUP (ridge regression-best linear unbiased prediction). In populations with low LD and lower additive variance, the use of higher SNP density (10 SNP 0.1 cM -1 ) is recommended; besides, the selection candidate should be related to the estimation population in order to obtain satisfactory prediction accuracy. The use of a selection population in the same generation of the estimation population reduces accuracy in at least 8%. Populations structured in progenies with stronger relationships and smaller effective size increase the efficiency of genomic selection.

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Accuracy and Training Population Design for Genomic Selection on Quantitative Traits in Elite North American Oats

Cited by 227 publications

References 53 publications

Genome Properties and Prospects of Genomic Prediction of Hybrid Performance in a Breeding Program of Maize

Genome Properties and Prospects of Genomic Prediction of Hybrid Performance in a Breeding Program of Maize

Modeling Genotype × Environment Interaction for Genomic Selection with Unbalanced Data from a Wheat Breeding Program

Seleção genômica para melhoramento vegetal com diferentes estruturas populacionais

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