Limits...
Increased genomic prediction accuracy in wheat breeding through spatial adjustment of field trial data.

Lado B, Matus I, Rodríguez A, Inostroza L, Poland J, Belzile F, del Pozo A, Quincke M, Castro M, von Zitzewitz J - G3 (Bethesda) (2013)

Bottom Line: A mixed-model using moving-means as a covariate was found to best fit the data.The best predictions between environments were obtained when data from different years were used to train the model.Our results confirm that genotyping-by-sequencing is an effective tool to obtain genome-wide information for crops with complex genomes, that these data are efficient for predicting traits, and that correction of spatial variation is a crucial ingredient to increase prediction accuracy in genomic selection models.

View Article: PubMed Central - PubMed

Affiliation: Programa Nacional de Investigación Cultivos de Secano, Instituto Nacional de Investigación Agropecuaria, Est. Exp. La Estanzuela, Colonia 70000, Uruguay.

ABSTRACT
In crop breeding, the interest of predicting the performance of candidate cultivars in the field has increased due to recent advances in molecular breeding technologies. However, the complexity of the wheat genome presents some challenges for applying new technologies in molecular marker identification with next-generation sequencing. We applied genotyping-by-sequencing, a recently developed method to identify single-nucleotide polymorphisms, in the genomes of 384 wheat (Triticum aestivum) genotypes that were field tested under three different water regimes in Mediterranean climatic conditions: rain-fed only, mild water stress, and fully irrigated. We identified 102,324 single-nucleotide polymorphisms in these genotypes, and the phenotypic data were used to train and test genomic selection models intended to predict yield, thousand-kernel weight, number of kernels per spike, and heading date. Phenotypic data showed marked spatial variation. Therefore, different models were tested to correct the trends observed in the field. A mixed-model using moving-means as a covariate was found to best fit the data. When we applied the genomic selection models, the accuracy of predicted traits increased with spatial adjustment. Multiple genomic selection models were tested, and a Gaussian kernel model was determined to give the highest accuracy. The best predictions between environments were obtained when data from different years were used to train the model. Our results confirm that genotyping-by-sequencing is an effective tool to obtain genome-wide information for crops with complex genomes, that these data are efficient for predicting traits, and that correction of spatial variation is a crucial ingredient to increase prediction accuracy in genomic selection models.

Show MeSH

Related in: MedlinePlus

Plot residuals along the field for each model analysis for Santa Rosa nonirrigated trial. The color scale shows the value of residual effects as indicated. (A) Residuals for incomplete blocks, field design; (B) residuals for RC; (C) residuals for RCB_MVNG; (D) residuals for MVNG
© Copyright Policy - open-access
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC3852373&req=5

fig5: Plot residuals along the field for each model analysis for Santa Rosa nonirrigated trial. The color scale shows the value of residual effects as indicated. (A) Residuals for incomplete blocks, field design; (B) residuals for RC; (C) residuals for RCB_MVNG; (D) residuals for MVNG

Mentions: Phenotypic data were collected under different soil water availability in 2011 and 2012. The traits under study (GY, TKW, NKS, DH) were analyzed adjusting for field design and for spatial variation using linear mixed models. The residuals for the adjusted traits in 2011 were heterogeneous due to spatial correlations (Figure 4 and Figure 5). Other models (RC, RCB_MVNG, and MVNG) were considered to reduce correlations between residuals. The RC model was inadequate to correct the residual heterogeneity, because the same spatial correlation was observed (Figure 4 and Figure 5). The other two (RCB_MVNG and MVNG) models adjusted, which include the moving means as a covariable, presented homogeneous residuals along the field (Figure 4 and Figure 5). In addition, the broad sense heritability was calculated for each model. The greatest heritability value was for the MVNG model for all traits (Table 2).


Increased genomic prediction accuracy in wheat breeding through spatial adjustment of field trial data.

Lado B, Matus I, Rodríguez A, Inostroza L, Poland J, Belzile F, del Pozo A, Quincke M, Castro M, von Zitzewitz J - G3 (Bethesda) (2013)

Plot residuals along the field for each model analysis for Santa Rosa nonirrigated trial. The color scale shows the value of residual effects as indicated. (A) Residuals for incomplete blocks, field design; (B) residuals for RC; (C) residuals for RCB_MVNG; (D) residuals for MVNG
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3852373&req=5

fig5: Plot residuals along the field for each model analysis for Santa Rosa nonirrigated trial. The color scale shows the value of residual effects as indicated. (A) Residuals for incomplete blocks, field design; (B) residuals for RC; (C) residuals for RCB_MVNG; (D) residuals for MVNG
Mentions: Phenotypic data were collected under different soil water availability in 2011 and 2012. The traits under study (GY, TKW, NKS, DH) were analyzed adjusting for field design and for spatial variation using linear mixed models. The residuals for the adjusted traits in 2011 were heterogeneous due to spatial correlations (Figure 4 and Figure 5). Other models (RC, RCB_MVNG, and MVNG) were considered to reduce correlations between residuals. The RC model was inadequate to correct the residual heterogeneity, because the same spatial correlation was observed (Figure 4 and Figure 5). The other two (RCB_MVNG and MVNG) models adjusted, which include the moving means as a covariable, presented homogeneous residuals along the field (Figure 4 and Figure 5). In addition, the broad sense heritability was calculated for each model. The greatest heritability value was for the MVNG model for all traits (Table 2).

Bottom Line: A mixed-model using moving-means as a covariate was found to best fit the data.The best predictions between environments were obtained when data from different years were used to train the model.Our results confirm that genotyping-by-sequencing is an effective tool to obtain genome-wide information for crops with complex genomes, that these data are efficient for predicting traits, and that correction of spatial variation is a crucial ingredient to increase prediction accuracy in genomic selection models.

View Article: PubMed Central - PubMed

Affiliation: Programa Nacional de Investigación Cultivos de Secano, Instituto Nacional de Investigación Agropecuaria, Est. Exp. La Estanzuela, Colonia 70000, Uruguay.

ABSTRACT
In crop breeding, the interest of predicting the performance of candidate cultivars in the field has increased due to recent advances in molecular breeding technologies. However, the complexity of the wheat genome presents some challenges for applying new technologies in molecular marker identification with next-generation sequencing. We applied genotyping-by-sequencing, a recently developed method to identify single-nucleotide polymorphisms, in the genomes of 384 wheat (Triticum aestivum) genotypes that were field tested under three different water regimes in Mediterranean climatic conditions: rain-fed only, mild water stress, and fully irrigated. We identified 102,324 single-nucleotide polymorphisms in these genotypes, and the phenotypic data were used to train and test genomic selection models intended to predict yield, thousand-kernel weight, number of kernels per spike, and heading date. Phenotypic data showed marked spatial variation. Therefore, different models were tested to correct the trends observed in the field. A mixed-model using moving-means as a covariate was found to best fit the data. When we applied the genomic selection models, the accuracy of predicted traits increased with spatial adjustment. Multiple genomic selection models were tested, and a Gaussian kernel model was determined to give the highest accuracy. The best predictions between environments were obtained when data from different years were used to train the model. Our results confirm that genotyping-by-sequencing is an effective tool to obtain genome-wide information for crops with complex genomes, that these data are efficient for predicting traits, and that correction of spatial variation is a crucial ingredient to increase prediction accuracy in genomic selection models.

Show MeSH
Related in: MedlinePlus