Genomic Selection for Crop Improvement

By Rajeev K. Varshney, Manish Roorkiwal, M. Sorrells. Cambridge International Law Journal, 2017

1,125 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/06dcd2d89bb4221d0e3d8e8355ad4647202d7663

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In Genetics

Citations in the corpus

Industry-Scale Evaluation of Maize Hybrids Selected for Increased Yield in Drought-Stress Conditions of the US Corn Belt (2015, methodology): “The resulting drought data sets for the target traits were used to construct a sequence of training data sets that enabled genomic prediction for all stages of the breeding programs (Cooper et al., 2014a,b; Desta and Ortiz, 2014; Heffner et al., 2009).”
Revisiting rice breeding methods – evaluating the use of rapid generation advance (RGA) for routine rice breeding (2017, background): “For instance, genomic selection as a breeding strategy in plants has been studied carefully for almost a decade (Heffner et al., 2009), and the primary advantage it offers is not increased accuracy, but rather reduced cycle time (‘L’ from the breeder’s equation indicated above).”
Accelerating Genetic Gain in Sugarcane Breeding Using Genomic Selection (2020, background): “[53] first proposed the i r te population improvement from line dev lopment in a genomics-as isted pl r ra .”
Novel applications of multitask learning and multiple output regression to multiple genetic trait prediction (2016, background): “Given a set of biallelic molecular markers, such as SNPs, with genotype values encoded numerically on a collection of plant, animal or human samples, the goal of genetic trait prediction is to predict the quantitative trait values by simultaneously modeling all marker effects.”
expVIP: a Customizable RNA-seq Data Analysis and Visualization Platform1[OPEN] (2016, background): “The advent of the genomics era represents a great opportunity to accelerate the pace of yield increase in staple crops, for example, by facilitating novel breeding strategies (Heffner et al., 2009) and providing unprecedented numbers of genetic markers (Bevan and Uauy, 2013).”
Perspectives for Genomic Selection Applications and Research in Plants (2015, methodology): “Genomic prediction allows the use of historical phenotypic data to make a selection, thus basing the selection on a broader set of environments than the typical few years of data used by classical phenotypic selection (Heffner et al., 2009).”
A review of breeding objectives, genomic resources, and marker-assisted methods in common bean (Phaseolus vulgaris L.) (2019, background): “GS, another emerging molecular approach, is a form of molecular MAS that enables breeders to increase genetic gain in a short period of time for quantitatively inherited traits (Heffner et al. 2009).”
A Sustainable Agricultural Future Relies on the Transition to Organic Agroecological Pest Management (2018, methodology): “Recently, there have been massive advances in the fields of plant breeding and selection [141], phenotyping [142], metagenomics [143], and chemical ecology [144], which can leveraged for progress in organic agroecological sustainable pest management.”
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”
Genetic and Genomic Resources of Small Millets (2016, methodology): “The approach relies on predicting breeding value of genotypes in a population through association studies of complex phenotype and high-density marker scores (Heffner et al., 2009; Jannink et al., 2010).”