Selection of families and parents of sugarcane (Saccharum spp.) through mixed models by joint analysis of two harvests

Neto, Hugo Zeni; Daros, Edelclaiton; Filho, João Carlos Bespalhok; Scapim, Carlos Alberto; Vidigal, María Celeste Gonçalves; Filho, Pedro Soares Vidigal

doi:10.1007/s10681-013-0947-6

Cited by 13 publications

(7 citation statements)

References 19 publications

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“…The GS models fitted in this study only considered additive effects. However, Zeni Neto et al (2013) [55] reported that additive and non-additive genetic effects are equally crucial for the determination of complex traits in sugarcane. Hence, the inclusion of non-additive effects in GS models for sugarcane and other clonally propagated species may improve prediction accuracies [56].…”

Section: Discussionmentioning

confidence: 99%

Near-infrared spectroscopy outperforms genomics for predicting sugarcane feedstock quality traits

Gonçalves

Morota

Costa

et al. 2020

Preprint

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The main objectives of this study were to evaluate the prediction performance of genomic and near-infrared spectroscopy (NIR) data and whether the integration of genomic and NIR predictor variables can increase the prediction accuracy of two feedstock quality traits (fiber and sucrose content) in a sugarcane population (Saccharum spp.). The following three modeling strategies were compared: M1 (genome-based prediction), M2 (NIR-based prediction), and M3 (integration of genomics s and NIR wavenumbers). Data were collected from a commercial population comprised of three hundred and eighty-five individuals, genotyped for single nucleotide polymorphisms (SNPs) and screened using NIR spectroscopy. We compared partial least squares (PLS) and BayesB regression methods to estimate marker and wavenumber effects. In order to assess model performance, we employed random sub-sampling cross-validation to calculate the mean Pearson correlation coefficient between observed and predicted genotypic values. Our results showed that models fitted using BayesB were most predictive than PLS models. We found that NIR (M2) provided the highest prediction accuracy, whereas genomics (M1) presented the lowest predictive ability, regardless of the measured traits and regression methods used. The integration of predictors derived from NIR spectroscopy and genomics into a single model (M3) did not significantly improve the prediction accuracy for the two traits evaluated. These findings suggest that NIR-based prediction can be an effective strategy for predicting the genotypic value of sugarcane clones.

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Section: Discussionmentioning

confidence: 99%

Near-infrared spectroscopy outperforms genomics for predicting sugarcane feedstock quality traits

Gonçalves

Morota

Costa

et al. 2020

Preprint

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“…Selection within families with high genotypic values can increase the probability of finding superior clones in their progenies (Barbosa et al, 2005). Based on this premise, several sugarcane breeding programs have routinely applied family selection before selecting clones Stringer et al, 2011;Barbosa et al, 2012;Zeni Neto et al, 2013).…”

mentioning

confidence: 99%

Simulated Individual Best Linear Unbiased Prediction versus Mass Selection in Sugarcane Families

et al. 2016

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The purpose of this study was to compare the method BLUPIS (best linear unbiased prediction individual simulated) with mass selection in terms of efficiency in identifying the best genotypes in sugarcane (Saccharum officinarum L.) families. Mass selection was performed by two breeders with 25 yr of experience. The BLUPIS procedure selected families with higher means for tons of cane per hectare (TCH) than the overall mean. The number of plants selected per family was calculated by nk=(trueg^k/trueg^i)ni, where ĝ indicates the genotypic value of the kth family; ĝ the genotypic value of the best family; and nj is equal to the number of plants selected in the best family, determined as nj = 45 in this study. Out of 20 best clones forwarded to the third test phase (T3), BLUPIS selected all in the first test phase (T1) and mass selection only two. Therefore, 100% of the clones in the second test phase (T2) had been selected by BLUPIS. The BLUPIS was most efficient in detecting the best genotypes, since all clones that were promoted up to phase T3 were descendants from the best families. The BLUPIS method should be applied in sugarcane breeding programs to ensure the selection of the best genotypes.

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“…Another factor that could explain the low level of prediction accuracies we observed is that the GP models fitted in this study only considered additive effects. Zeni Neto et al (2013) [ 63 ] reported that additive and non-additive genetic effects are equally important for the determination of complex traits in sugarcane. Hence, the inclusion of non-additive effects in GP models for sugarcane and other clonally propagated species may improve prediction accuracies [ 64 ].…”

Section: Discussionmentioning

confidence: 99%

Near-infrared spectroscopy outperforms genomics for predicting sugarcane feedstock quality traits

et al. 2021

View full text Add to dashboard Cite

The main objectives of this study were to evaluate the prediction performance of genomic and near-infrared spectroscopy (NIR) data and whether the integration of genomic and NIR predictor variables can increase the prediction accuracy of two feedstock quality traits (fiber and sucrose content) in a sugarcane population (Saccharum spp.). The following three modeling strategies were compared: M1 (genome-based prediction), M2 (NIR-based prediction), and M3 (integration of genomics and NIR wavenumbers). Data were collected from a commercial population comprised of three hundred and eighty-five individuals, genotyped for single nucleotide polymorphisms and screened using NIR spectroscopy. We compared partial least squares (PLS) and BayesB regression methods to estimate marker and wavenumber effects. In order to assess model performance, we employed random sub-sampling cross-validation to calculate the mean Pearson correlation coefficient between observed and predicted values. Our results showed that models fitted using BayesB were more predictive than PLS models. We found that NIR (M2) provided the highest prediction accuracy, whereas genomics (M1) presented the lowest predictive ability, regardless of the measured traits and regression methods used. The integration of predictors derived from NIR spectroscopy and genomics into a single model (M3) did not significantly improve the prediction accuracy for the two traits evaluated. These findings suggest that NIR-based prediction can be an effective strategy for predicting the genetic merit of sugarcane clones.

show abstract

Selection of families and parents of sugarcane (Saccharum spp.) through mixed models by joint analysis of two harvests

Cited by 13 publications

References 19 publications

Near-infrared spectroscopy outperforms genomics for predicting sugarcane feedstock quality traits

Near-infrared spectroscopy outperforms genomics for predicting sugarcane feedstock quality traits

Simulated Individual Best Linear Unbiased Prediction versus Mass Selection in Sugarcane Families

Near-infrared spectroscopy outperforms genomics for predicting sugarcane feedstock quality traits

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