Genomic measures of relationship and inbreeding

By Paul M. Vanraden. 2007

154 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/1ba078d85e348a058704e934f0362ef0b734bbc9

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Citations in the corpus

Prediction of Complex Human Traits Using the Genomic Best Linear Unbiased Predictor (2013, unknown): “There are several ways of computing genomic relationships [7,21–24]; here, we have adopted one of the conventions which consist of centering (by subtracting the mean, 2hl ) and standardizing, by dividing by the variance 2hl 1{hl ð Þ, of each marker before computing genomic relationships.”
Genomic Prediction of Breeding Values when Modeling Genotype × Environment Interaction using Pedigree and Dense Molecular Markers (2012, background): “There are multiple ways of mapping markers to estimates of genetic relationships (e.g., Ritland, 1996; Lynch and Ritland, 1999; Van Raden, 2007; Habier et al., 2007; de los Campos et al., 2010), and none is considered to be superior (Makowsky et al., 2011).”
Whole-Genome Regression and Prediction Methods Applied to Plant and Animal Breeding (2013, background): “The most clear case occurs in Holstein dairy cattle where several studies (Hayes et al. 2009a; VanRaden et al. 2009) have confirmed that GS can attain higher prediction accuracy than standard family-based prediction (e.g., parental average).”
Semi-parametric genomic-enabled prediction of genetic values using reproducing kernel Hilbert spaces methods. (2010, unknown): “Another example of an RKHS regression with a model-derived kernel is the case where K is chosen to be a marker-based estimate of a kinship matrix (usually denoted as G , cf, Ritland, 1996; Lynch & Ritland, 1999; Eding & Meuwissen, 2001; Van Raden, 2007; Hayes & Goddard, 2008).”
Invited review: reliability of genomic predictions for North American Holstein bulls. (2009, methodology): “Predictions were computed using linear and nonlinear genomic models (VanRaden, 2007, 2008).”
Hot topic: a unified approach to utilize phenotypic, full pedigree, and genomic information for genetic evaluation of Holstein final score. (2010, methodology): “To facilitate inversion, final analyses used a weighted G as proposed by VanRaden (2008): G = 0.”
Genomic Selection in Plant Breeding: Methods, Models, and Perspectives. (2017, methodology): “GBLUP models [49,50] are widely used in GP, and the extension of GBLUP for incorporating G E interactions has improved the accuracy of predicting unobserved cultivars in environments [23,24,51–56].”
Efficient methods to compute genomic predictions. (2008, methodology): “The other model included a genomic relationship matrix in place of the traditional additive genetic relationship matrix (Garrick, 2007; VanRaden, 2007; Zhang et al., 2007).”
Prediction of Genetic Values of Quantitative Traits in Plant Breeding Using Pedigree and Molecular Markers (2010, methodology): “However, some markers had estimated effects that were large in absolute value; some of those markers are identified by their name in Figure 1, and the estimated effects are given in Table S1.”