Prediction of Genetic Values of Quantitative Traits in Plant Breeding Using Pedigree and Molecular Markers

By Jose Crossa, G. de los Campos, P. Pérez, D. Gianola, Juan Burgueño, Jose Luis Araus, D. Makumbi, R. Singh, Susanne Dreisigacker, Jianbing Yan and 3 more. Genetics, 2010

728 citations recorded by the source corpus.

Subfields: Genetics, Plant Science

View Paper: https://www.semanticscholar.org/paper/c3c8fdcf6568405f01cd6745de8d29456a2c99df

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Genetics

In Plant Science

Citations in the corpus

REVIEW: PART OF A HIGHLIGHT ON BREEDING STRATEGIES FOR FORAGE AND GRASS IMPROVEMENT Will genomic selection be a practical method for plant breeding? (2012, methodology): “GS studies using empirical data from wheat were first reported by Crossa et al. (2010) using 1279 DArT genotypes and 599 wheat lines bred in CIMMYT.”
Genomic Selection in Wheat Breeding using Genotyping‐by‐Sequencing (2012, background): “Genomic selection models have proven to be advantageous for complex traits such as grain yield where many loci of small ef ects control the trait (Burgueño et al., 2012; Crossa et al., 2010; de los Campos et al., 2009; González-Camacho et al., 2012; Jannink et al., 2010).”
Accuracy and Training Population Design for Genomic Selection on Quantitative Traits in Elite North American Oats (2011, background): “…narrow-based biparental populations (Lorenzana and Bernardo, 2009) and in broad-based populations such as multilines of barley ( Hordeum vulgare L.), wheat ( Triticum aestivum L.), and maize ( Zea mays L.) (Zhong et al., 2009; Heff ner et al., 2011; de los Campos et al., 2009; Crossa et al., 2010).”
Genomic Selection in Plant Breeding: Methods, Models, and Perspectives. (2017, methodology): “One of the first assessments of GP in wheat breeding was performed on a collection of 599 wheat lines evaluated in four environments using pedigree and genomic information with two different models [34].”
Genome-Assisted Prediction of Quantitative Traits Using the R Package sommer (2016, methodology): “Although we have shown genomic selection applications using a kinship-based estimation of GEBV, some models depend on knowing marker effects, such as marker × environment interactions [33 – 35], which can be implemented as well using the sommer package.”
Marker-Assisted Plant Breeding: Principles and Practices (2015, background): “Both simulation and empirical studies indicate RKHS to be superior to linear models in prediction of phenotypes of some quantitative traits (Crossa et al. 2010; Gonzalez-Camacho et al. 2012).”
Whole-Genome Regression and Prediction Methods Applied to Plant and Animal Breeding (2013, background): “The mean-squared error (MSE) is the expected value (over possible realizations of the data) of the squared Euclidean distance, MSEðûÞ ¼ E½ûð yÞ2u 2.”
Ridge Regression and Other Kernels for Genomic Selection with R Package rrBLUP (2011, methodology): “Aft er demonstrating features of the soft ware, the accuracy of its prediction methods are compared by cross-validation using structured populations of wheat ( Triticum aestivum L.) (Crossa et al., 2010) and maize ( Zea mays L.) (Yu et al., 2006).”
Relatedness and Genotype × Environment Interaction Affect Prediction Accuracies in Genomic Selection: A Study in Cassava (2013, neutral): “In plants, the use of cross-validation studies on inbred cultivars has been useful (Melchinger et al., 2004; Schön et al., 2004; Crossa et al., 2010; Riedelsheimer et al., 2012; Massman et al., 2012; Windhausen et al., 2012).”
Genomic Selection Accuracy using Multifamily Prediction Models in a Wheat Breeding Program (2011, methodology): “Finally, Crossa et al. (2010) used cross-validation to evaluate GS in 599 historical wheat lines and 284 maize inbreds from the International Maize and Wheat Improvement Center (CIMMYT).”
Genomic Selection and Prediction in Plant Breeding (2011, methodology): “In recent articles, de los Campos et al. (2009a), de los Campos (2010), Pérez et al. (2010), and Crossa et al. (2010) validated GS in plant breeding using genomic regression and showed that models using MM were more accurate in predicting grain yield in wheat and maize than those based on pedigree…”
Genomic Prediction of Breeding Values when Modeling Genotype × Environment Interaction using Pedigree and Dense Molecular Markers (2012, methodology): “Further details about the data set and editing procedures applied are described in Crossa et al. (2010).”