Genomic selection.

By Michael E. Goddard, Ben J. Hayes. Journal of animal breeding and genetics = Zeitschrift fur Tierzuchtung und Zuchtungsbiologie, 2007

979 citations recorded by the source corpus.

Subfields: Genetics, Plant Science

View Paper: https://www.semanticscholar.org/paper/ff09115a822d735d742caba2111cf2a6bad1f77c

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

Next-generation phenotyping: requirements and strategies for enhancing our understanding of genotype–phenotype relationships and its relevance to crop improvement (2013, background): “…training populations when working with traits with low heritability and complex inheritance (Calus et al. 2008; Guo et al. 2012; Hayes et al. 2009; Heffner et al. 2010; Jia and Jannink 2012; Kumar et al. 2012; Nakaya and Isobe 2012; Munoz et al. 2011; Resende et al. 2012; Zhao et al. 2011b; Zhong…”
Prospects for genomic selection in conifer breeding: a simulation study of Cryptomeria japonica (2011, background): “This condition might make GS quite efficient, as suggested by Goddard and Hayes (2007).”
Invited review: Genomic selection in dairy cattle: progress and challenges. (2009, methodology): “A second possibility is to infer all marker genotypes for all animals and use these to calculate GEBV (Goddard and Hayes, 2007).”
Extension of the bayesian alphabet for genomic selection (2011, background): “This locus-specific variance has a scaled inverse chi-square prior with few degrees of freedom and a scale parameter, S(2)a, that is often derived from an assumed additive-genetic variance under certain genetic assumptions [11,12].”
Genomic Selection and Prediction in Plant Breeding (2011, background): “Genomic selection (GS) (or genome-wide selection) is an approach for improving quantitative traits (Meuwissen, Hayes, & Goddard 2001; Goddard & Hayes, 2007) that uses all available MM across the genome to estimate genetic values.”
Genomic selection: prediction of accuracy and maximisation of long term response (2009, neutral): “Goddard and Hayes (2007) discuss alternative methods for doing this.”
Predicting Quantitative Traits With Regression Models for Dense Molecular Markers and Pedigree (2009, methodology): “Relationship between LASSO and other regression models used in genomic selection: Standard BR may not be suitable for regressing phenotypes on a large number of markers because shrinkage of regression coefficients is homogeneous across markers (Fernando et al. 2007).”