Genomic-Assisted Prediction of Genetic Value With Semiparametric Procedures

By D. Gianola, Rohan L. Fernando, A. Stella. Genetics, 2006

448 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/21d6720fce33e6212f0b6f0cf71c7f412f465a6c

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Citations in the corpus

Invited review: Genomic selection in dairy cattle: progress and challenges. (2009, background): “Gianola et al. (2006) presented semiparametric procedures for genomic selection, which allowed them to estimate interactions between potentially hundreds of thousands of markers.”
Genomic prediction when some animals are not genotyped (2010, methodology): “[7] bivariate models are suggested, where the two traits are the traits of the genotyped and nongenotyped animals, respectively, and the genetic effect for a genotyped animal is the sum of a polygenic effect and a genomic effect whereas the genetic effect for a non-genotyped animal is just a polygen”
Evaluation of Genomic Prediction Methods for Fusarium Head Blight Resistance in Wheat (2012, methodology): “…models tested were ridge regression (RR) BLUP (Meuwissen et al., 2001; Whittaker et al., 2000), Bayesian LASSO (BL) (de los Campos et al., 2009b; Park and Casella, 2008), reproducing kernel Hilbert spaces (RKHS) (Gianola et al., 2006) regression, and random forest (RF) (Breiman, 2001) regression.”
Genomic Relationship Matrix for Correcting Pedigree Errors in Breeding Populations: Impact on Genetic Parameters and Genomic Selection Accuracy (2014, background): “Recently, a number of analytical approaches (Gianola et al., 2006; de los Campos et al., 2009; Habier et al., 2011) have been developed to study factors that contribute to GS accuracy (Habier et al.”
Hot topic: a unified approach to utilize phenotypic, full pedigree, and genomic information for genetic evaluation of Holstein final score. (2010, background): “For example, estimation of genomic effects has several options (Meuwissen et al., 2001; Gianola et al., 2006; VanRaden, 2008; de los Campos et al., 2009).”
Computing procedures for genetic evaluation including phenotypic, full pedigree, and genomic information. (2009, background): “Differences among methods are mostly on the a priori distribution of marker effects a (Meuwissen et al., 2001; Gianola et al., 2006).”
Predicting Quantitative Traits With Regression Models for Dense Molecular Markers and Pedigree (2009, methodology): “The second type of approach was suggested by Gianola et al. (2006) and Gianola and van Kaam (2008), who proposed using reproducing kernel Hilbert spaces regression (RKHS), with the information set consisting of SNP (single-nucleotide polymorphism) genotypes, possibly supplemented by genealogies.”
Genomic Selection and Prediction in Plant Breeding (2011, methodology): “An alternative to parametric regressions is to use semi-parametric methods, such as reproducing kernel Hilbert spaces (RKHS) regression ( Gianola, Fernando, & Stella 2006 ; Gianola & van Kamm 2008 ; de los Campos et al.”
Genome-Wide Regression and Prediction with the BGLR Statistical Package (2014, unknown): “The 1”
Very early selection for solid wood quality: screening for early winners (2009, background): “This work has been extended by several researchers in animal evaluation (e.g., Calus et al., 2008; Fernando et al., 2007; Gianola et al., 2006), where genomewide selection is becoming mainstream.”
Genomic Selection for Crop Improvement (2017, unknown): “For cases in which estimates of genotypic value are desired in the presence of epistasis, methods are currently being developed and tested (e.g., Gianola et al., 2006; Gianola and van Kaam, 2008; Gonzalez-Recio et al., 2008).”
Genomic selection using regularized linear regression models: ridge regression, lasso, elastic net and their extensions (2012, background): “…type models that allow for interactions between predictors and correlated observations [8,25], nonparameteric or semiparametric regularized regression models [26], or other procedures able to efficiently handle large numbers of interactions, such as random forests, or boosted regression trees [27].”
Genomic Selection in Plant Breeding (2011, methodology): “RKHS was superior to an RR-BLUP model for predicting simulated traits with a large nonadditive component (Gianola et al., 2006).”
A relationship matrix including full pedigree and genomic information. (2009, methodology): “Differences among methods are mostly on the a priori distribution of a (e.g., Meuwissen et al., 2001; Gianola et al., 2006).”
Development and Application of High-density SNP Arrays in Genomic Studies of Domestic Animals (2010, unknown): “In recent years, different statistical approaches for genomic selection have been developed, derived from either nonparametric Bayesian models or parametric methods including genomic best linear unbiased prediction (GBLUP) and mixed regression models (Gianola et al., 2006; Aulchenko et al., 2007; Ve”