Prediction of total genetic value using genome-wide dense marker maps.

By T. Meuwissen, Ben J. Hayes, Michael E. Goddard. Genetics, 2001

7,420 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/8de0f4d3168750a2509a7ef72131e055aba4c674

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Citations in the corpus

Molecular Markers and Selection for Complex Traits in Plants: Learning from the Last 20 Years (2008, background): “A third approach, genomewide selection (Meuwissen et al., 2001), focuses purely on prediction of performance and avoids QTL mapping altogether (Fig.”
Association Mapping for Enhancing Maize (Zea mays L.) Genetic Improvement (2011, methodology): “Then, in each cycle of GWS, all markers will be used to estimate the breeding value of each line, which will then determine the best lines to be selected for the next cycle (Meuwissen et al., 2001; Bernardo and Yu, 2007).”
Efficient methods to compute genomic predictions. (2008, background): “costs from quadratic with number of markers (Meuwissen et al., 2001) to linear.”
Development and Characterization of a High Density SNP Genotyping Assay for Cattle (2009, methodology): “…Published April 24, 2009 Copyright: 2009 Matukumalli et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.”
Genomic Selection in Wheat Breeding using Genotyping‐by‐Sequencing (2012, background): “G ENOMIC SELECTION (GS) uses genomewide molecular markers to predict complex, quantitative traits in animal and plant breeding (Meuwissen et al., 2001).”
Improving accuracy of genomic predictions within and between dairy cattle breeds with imputed high-density single nucleotide polymorphism panels. (2012, methodology): “To compare BayesR with a well-known Bayesian method, we also ran BayesA.”
REVIEW: PART OF A HIGHLIGHT ON BREEDING STRATEGIES FOR FORAGE AND GRASS IMPROVEMENT Advanced phenotyping offers opportunities for improved breeding of forage and turf species (2012, background): “Spectral reflectance is analysed in the visual (or near-visual) wavelength range without the need to provide artificial illumination to plants in the field.”
Evaluation of Genomic Prediction Methods for Fusarium Head Blight Resistance in Wheat (2012, methodology): “Sites were dei ned as a unique year– location–nursery combination. h e BLUPs were then used as the phenotype-based estimates of breeding values (PEBVs) for the subsequent model training and validation steps.”
Plant Breeding with Genomic Selection: Gain per Unit Time and Cost (2010, methodology): “Accuracy declines as generation number between the last model update and selection candidates increases (Habier et al., 2007; Meuwissen et al., 2001; Muir, 2007), because selection causes changes in variances, allele frequencies, and LD relationships between markers and QTL (Bulmer, 1971; Muir, 2007”
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).”
Genetic Improvement of U.S. Soybean in Maturity Groups II, III, and IV (2014, background): “For example, genomic selection has been proposed to shorten the breeding cycle (Meuwissen et al., 2001; Bernardo, 2010b; Hefner et al., 2009), and DNA and RNA sequencing will increase our understanding of gene function, which can be utilized in the development of future cultivars (Poland and Rife,…”
A review of Bayesian variable selection methods: what, how and which (2009, methodology): “Optimisation of code, use of block-updates (e.g. blocking each Ij and βj together; Meuwissen et al. 2001; Geweke 1996), and local adaptation may also improve performance of the methods.”