Whole-Genome Regression and Prediction Methods Applied to Plant and Animal Breeding

Campos, Gustavo de los; Hickey, John M.; Pong‐Wong, Ricardo; Daetwyler, Hans D.; Calus, M.P.L.

doi:10.1534/genetics.112.143313

Cited by 741 publications

(819 citation statements)

References 126 publications

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“…This is in agreement with previous findings (see e.g. de los Campos et al, 2012a). For example, predictive accuracy using various types of genomic relationship matrices was higher than when using a pedigree-based kinship matrix in pigs .…”

Section: Resultssupporting

confidence: 93%

“…For example, GBLUP and RKHS are direct GS approaches, and BRR and BL are indirect. Several simulation studies have shown that the predictive ability may differ between these two types of approaches, depending on marker coverage of the genome and, perhaps, the genetic architecture of the target trait (de los Campos et al 2012a). However, some models that can be formulated in both manners, such as GBLUP and Ridge regression BLUP should not show differences in prediction ability.…”

Section: Resultsmentioning

confidence: 99%

“…Assume that residuals follow the normal distribution, where s 2 is the residual variance of individual records. Residual correlation in the data was not accounted for in the analysis, whereas heterogeneity of residual variance was partially alleviated by weighting the Tusell, Pé rez, Forni, Wu and Gianola residual variance by r i (de los Campos et al, 2012a). Then from equation (1), the likelihood function stems from the sampling model:…”

Section: Modelsmentioning

confidence: 99%

“…In the Bayesian framework prior assumptions are made about the unknown parameters, and differences in assumptions lead to different Bayesian regression models (de los Campos et al, 2012a). In what follows, the two Bayesian linear regression models employed here are described.…”

Section: Modelsmentioning

confidence: 99%

“…de los Campos et al, 2012a). Differences result from: (1) type of relationship among target traits and explanatory covariates (i.e.…”

Section: Introductionmentioning

confidence: 99%

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Genome-enabled methods for predicting litter size in pigs: a comparison

Tusell

Pérez‐Rodríguez

Forni

et al. 2013

Animal

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Predictive ability of models for litter size in swine on the basis of different sources of genetic information was investigated. Data represented average litter size on 2598, 1604 and 1897 60K genotyped sows from two purebred and one crossbred line, respectively. The average correlation (r) between observed and predicted phenotypes in a 10-fold cross-validation was used to assess predictive ability. Models were: pedigree-based mixed-effects model (PED), Bayesian ridge regression (BRR), Bayesian LASSO (BL), genomic BLUP (GBLUP), reproducing kernel Hilbert spaces regression (RKHS), Bayesian regularized neural networks (BRNN) and radial basis function neural networks (RBFNN). BRR and BL used the marker matrix or its principal component scores matrix (UD) as covariates; RKHS employed a Gaussian kernel with additive codes for markers whereas neural networks employed the additive genomic relationship matrix (G) or UD as inputs. The non-parametric models (RKHS, BRNN, RNFNN) gave similar predictions to the parametric counterparts (average r ranged from 0.15 to 0.23); most of the genome-based models outperformed PED (r 5 0.16). Predictive abilities of linear models and RKHS were similar over lines, but BRNN varied markedly, giving the best prediction (r 5 0.31) when G was used in crossbreds, but the worst (r 5 0.02) when the G matrix was used in one of the purebred lines. The r values for RBFNN ranged from 0.16 to 0.23. Predictive ability was better in crossbreds (0.26) than in purebreds (0.15 to 0.22). This may be related to family structure in the purebred lines.

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Section: Resultssupporting

confidence: 93%

Section: Resultsmentioning

confidence: 99%

Section: Modelsmentioning

confidence: 99%

Section: Modelsmentioning

confidence: 99%

“…de los Campos et al, 2012a). Differences result from: (1) type of relationship among target traits and explanatory covariates (i.e.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Genome-enabled methods for predicting litter size in pigs: a comparison

Tusell

Pérez‐Rodríguez

Forni

et al. 2013

Animal

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Translational Genomics in Crop Breeding for Biotic Stress Resistance: An Introduction

Varshney¹,

Tuberosa²

2013

Translational Genomics for Crop Breeding

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Biotic stresses pose a m ajor threat to crop productivity. Crops are challenged by a plethora of biotic stresses, but only a limited num ber o f key pests and diseases cause the vast majority of economic losses in a particular crop. Plant protection measures such as application of pesticides and deployment of resistant gene(s)/quantitative trait loci (QTLs) into cultivars have so far been quite successful in curtailing the losses; however, these measures have also led to the constant evolution of new biotypes/pathotypes/strains/races o f pest and disease organisms. Hence, there is a continuous need to identify genomic regions that can impart resistance against these variants. The availability of large-scale genomic resources in many crop species has enhanced our understanding on the path to developing host-plant resistance. As a result, numerous race-specific gene(s) and QTLs have now been identified and cloned with the help o f molecular markers. It is quite exciting that these genomic regions are being introgressed into breeding programs of many crops. The objective of this book is to critically review the current availability and utilization of genomic tools for major biotic stresses in important cereals, legumes, vegetables, and tuber and oilseed crop. The book also summarizes the success stories achieved through application of genomics-assisted breeding (GAB), as well as the scope for deployment o f modem breeding methods such as marker-assisted backcrossing (MABC) and genomic selection in the era o f next-generation sequencing (NGS) technologies, which have the potential to advance the genetic gains for enhancing resilience against biotic stress. This chapter summarizes highlights of different chapters included in the book that is expected to be a resource for young researchers, GAB practitioners, and policy makers for employing better strategies toward achieving food security.

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Translational Genomics for Crop Breeding: Abiotic Stress Tolerance, Yield, and Quality, an Introduction

Varshney¹,

Tuberosa²

2013

Translational Genomics for Crop Breeding

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Whole-Genome Regression and Prediction Methods Applied to Plant and Animal Breeding

Cited by 741 publications

References 126 publications

Genome-enabled methods for predicting litter size in pigs: a comparison

Genome-enabled methods for predicting litter size in pigs: a comparison

Translational Genomics in Crop Breeding for Biotic Stress Resistance: An Introduction

Translational Genomics for Crop Breeding: Abiotic Stress Tolerance, Yield, and Quality, an Introduction

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