Invited review: reliability of genomic predictions for North American Holstein bulls.

By Paul M. Vanraden, Curtis P. Van Tassell, G. Wiggans, Tad Stewart Sonstegard, Robert D. Schnabel, Jeremy F. Taylor, Flavio S. Schenkel. Journal of Dairy Science, 2009

1,279 citations recorded by the source corpus.

Subfields: Genetics, Plant Science

View Paper: https://www.semanticscholar.org/paper/eb6ae3e2d2854cad64025adcb905457d9df5c042

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

Genomic selection in plant breeding: from theory to practice. (2010, background): “While it is well known that GEBV accuracy increases as the size of the training population increases [21], to our knowledge no research has been conducted on training population design to develop accurate GEBV models while minimizing resources spent on phenotyping.”
Genomic Selection Accuracy using Multifamily Prediction Models in a Wheat Breeding Program (2011, background): “Th is similarity in model performance is consistent with other empirical GS studies (Verbyla et al., 2010; VanRaden et al., 2009; Hayes et al., 2009a, b; Moser et al., 2009; Luan et al., 2009; Lorenzana and Bernardo, 2009; Su et al., 2010; Heff ner et al., unpublished data, 2010).”
Hot topic: a unified approach to utilize phenotypic, full pedigree, and genomic information for genetic evaluation of Holstein final score. (2010, methodology): “However, little (if any) loss of accuracy occurred for most dairy cattle traits by assuming equal rather than different variance for each SNP marker (Cole et al., 2009; VanRaden et al., 2009b).”
Accuracy of estimated genomic breeding values for wool and meat traits in a multi-breed sheep population (2010, positive): “…groups of methods have similar accuracies (correlations between genomic breeding values and traditional breeding values) for most traits (Hayes et al. 2009a; VanRaden et al. 2009) and it has been shown by simulation that this equivalency occurs when many QTL affect a trait (Daetwyler et al. 2010).”
Genomic prediction when some animals are not genotyped (2010, methodology): “The second method (two-step) is a two-step procedure similar to methods used in practical genomic selection [14,15] and is based on genotyped animals only using the model”
Improving accuracy of genomic predictions within and between dairy cattle breeds with imputed high-density single nucleotide polymorphism panels. (2012, methodology): “To accurately predict genomic breeding values for selection candidates with no phenotype of their own, a very large reference population of genotyped and phenotyped individuals is required to derive the prediction equation (Goddard, 2009; VanRaden et al., 2009; Brøndum et al., 2011).”
The QTN Program and the Alleles That Matter for Evolution: All That’s Gold Does Not Glitter (2011, positive): “Even the humble microsatellite, a classic neutral marker, is no such thing: short-motif tandem repeat polymorphisms are widely implicated in transcriptional regulation (Rockman and Wray 2002; Vinces et al. 2009; Hannan 2010).”
Prediction of Genetic Values of Quantitative Traits in Plant Breeding Using Pedigree and Molecular Markers (2010, background): “These findings are in agreement with simulation-based studies such as those mentioned above and with empirical evidence reported in animal breeding (e.g., GonzalezRecio et al. 2008; VanRaden et al. 2008; Hayes et al. 2009; Weigel et al. 2009).”
Applied Genetics and Genomics in Alfalfa Breeding (2012, background): “While successful application of genomic selection has been reported in cattle [115,116], few experiments have been reported in plants [117].”
Genetic management of small populations: A review (2009, background): “The latest thread for small dairy cattle breeds is the introduction of genomic selection (Meuwissen et al, 2001 ), which requires thousands of progeny tested bulls for the accurate estimation of the effects of >50,000 single nucleotide polymorphism (SNP) markers (VanRaden et al, 2009 ).”
The Impact of Genetic Architecture on Genome-Wide Evaluation Methods (2010, methodology): “There are of course exceptions to the rule and, for example, in dairy cattle BayesB performed better than GBLUP in milk fat content (Vanraden et al. 2009).”
Development and Application of High-density SNP Arrays in Genomic Studies of Domestic Animals (2010, positive): “Genomic selection has been adopted for genetic evaluation of dairy cattle in the United States of America (VanRaden et al., 2009) and is being considered in the International Cattle Genetic Evaluation Project (http://www-interbull.slu.se).”
Prediction of Complex Human Traits Using the Genomic Best Linear Unbiased Predictor (2013, positive): “For instance, for prediction of estimated breeding values [27] reported an average (over 20 traits) gain in predictive correlation of 20%.”
A relationship matrix including full pedigree and genomic information. (2009, methodology): “Although the assumption is arguable, positing a more complicated prior distribution resulted in little gain in practice (VanRaden et al., 2009).”