Genomic selection in plant breeding: from theory to practice.

By Jean-Luc Jannink, A. Lorenz, Hiroyoshi Iwata. Briefings in Functional Genomics, 2010

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Citations in the corpus

High-Throughput SNP Genotyping to Accelerate Crop Improvement (2014, background): “…across globally-important field crops, ranging from 96 SNPs for groundnut to over 1,000 SNPs for ten other crops, including 1,250 for maize, 1,864 for wheat, and 2,015 for rice (www.integratedbreeding.net/ snp-marker-conversion; Khera et al. 2013; He et al. 2014; Pariasca-Tanaka et al. 2014).”
Prediction of Genetic Values of Quantitative Traits in Plant Breeding Using Pedigree and Molecular Markers (2010, background): “…has made possible the use of genomic selection (GS) for prediction of genetic values (Meuwissen et al. 2001) in plants (e.g., Bernardo and Yu 2007; Piepho 2009; Jannink et al. 2010) and animals (Gonzalez-Recio et al. 2008; VanRaden et al. 2008; Hayes et al. 2009; de los Campos et al. 2009a).”
Genomic Selection in Plant Breeding: A Comparison of Models (2012, background): “Several recent reviews are available on GS in plant breeding, in particular Heff ner et al. (2009), Jannink et al. (2010), Xu and Hu (2010), and Lorenz et al. (2011).”
Development of genome-wide SNP assays for rice (2010, background): “…breeders to assess the range of alleles available to them in diverse germplasm resources and to monitor the combinations of alleles that perform well in target environments (Collard and Mackill 2008, Heffner et al. 2009, Jannink et al. 2010, Kim et al. 2010, Moose and Mumm 2008, Xu et al. 2008).”
Genotyping‐by‐Sequencing for Plant Breeding and Genetics (2012, methodology): “While statistical geneticists will always prefer to have as many markers as possible, GS models have diminishing returns on additional markers once the population has reached the point of “marker saturation” (Jannink et al., 2010; Hef ner et al., 2011).”
Ridge Regression and Other Kernels for Genomic Selection with R Package rrBLUP (2011, background): “Th e focus over the last decade has been on genomic selection methods, in which all markers are included in the prediction model (Bernardo and Yu, 2007; Heff ner et al., 2009; Jannink et al., 2010).”
Achievements and Challenges in Improving Temperate Perennial Forage Legumes (2015, background): “Genomic selection (GS) (also known as “whole-genome selection”) (Heffner et al., 2009; Jannink et al., 2010; Lorenz et al., 2011; Hayes et al., 2013) is based on the premise that markers saturating the entire genome can reliably provide an estimate, in aggregate, of the breeding value of an individu”
Molecular Markers and Marker-Assisted Breeding in Plants (2013, background): “Since the application of GS was proposed by Meuwissen et al. (2001) to breeding popula‐ tions, theoretical, simulation and empirical studies have been conducted, mostly in animals (Goddard and Hayes, 2007; Jannink et al., 2010).”
Extension of the bayesian alphabet for genomic selection (2011, neutral): “, [2-6]).”
Breeding Strategies for Adaptation of Pearl Millet and Sorghum to Climate Variability and Change in West Africa. (2012, background): “Genomic selection The enhanced understanding of gene functions in relation to stress tolerance and continual reduction of DNA marker costs makes genomic selection a promising strategy to improve abiotic and biotic stress resistance (Heffner et al. 2009; Jannink et al. 2010).”
Accuracy and Training Population Design for Genomic Selection on Quantitative Traits in Elite North American Oats (2011, methodology): “Regardless of the type of population used, the basic steps for implementation of GS can be summarized in four steps: (i) designing training populations with complete phenotypic and genotypic data, (ii) estimating marker eff ects in the training population, (iii) calculating GEBV of new breeding line”