Accuracies of genomic breeding values in American Angus beef cattle using K-means clustering for cross-validation

Abstract

BackgroundGenomic selection is a recently developed technology that is beginning to revolutionize animal breeding. The objective of this study was to estimate marker effects to derive prediction equations for direct genomic values for 16 routinely recorded traits of American Angus beef cattle and quantify corresponding accuracies of prediction.MethodsDeregressed estimated breeding values were used as observations in a weighted analysis to derive direct genomic values for 3570 sires genotyped using the Illumina BovineSNP50 BeadChip. These bulls were clustered into five groups using K-means clustering on pedigree estimates of additive genetic relationships between animals, with the aim of increasing within-group and decreasing between-group relationships. All five combinations of four groups were used for model training, with cross-validation performed in the group not used in training. Bivariate animal models were used for each trait to estimate the genetic correlation between deregressed estimated breeding values and direct genomic values.ResultsAccuracies of direct genomic values ranged from 0.22 to 0.69 for the studied traits, with an average of 0.44. Predictions were more accurate when animals within the validation group were more closely related to animals in the training set. When training and validation sets were formed by random allocation, the accuracies of direct genomic values ranged from 0.38 to 0.85, with an average of 0.65, reflecting the greater relationship between animals in training and validation. The accuracies of direct genomic values obtained from training on older animals and validating in younger animals were intermediate to the accuracies obtained from K-means clustering and random clustering for most traits. The genetic correlation between deregressed estimated breeding values and direct genomic values ranged from 0.15 to 0.80 for the traits studied.ConclusionsThese results suggest that genomic estimates of genetic merit can be produced in beef cattle at a young age but the recurrent inclusion of genotyped sires in retraining analyses will be necessary to routinely produce for the industry the direct genomic values with the highest accuracy.

Accelerating genetic gains in legumes for the development of prosperous smallholder agriculture: integrating genomics, phenotyping, systems modelling and agronomy

“…The legume community has been successful in developing several molecular breeding products despite the late arrival of genomic resources and trait-associated markers (Varshney et al, 2013a,b;Pandey et al, 2016;Varshney, 2016). Some key examples include resistance to Fusarium wilt and ascochyta blight (Varshney et al, 2013b) and improved drought tolerance (Varshney et al, 2013a) in chickpea; resistance to nematode and high oleic acid (Chu et al, 2011), resistance to leaf rust , and resistance to high oleic acid (Janila et al, 2016) in groundnut; resistance to rust disease (Khanh et al, 2013), soybean mosaic virus (Saghai-Maroof et al, 2008;Shi et al, 2009;Parhe et al, 2017), and low phytate (Landau-Ellis and Pantalone, 2009) in soybean; Striga resistance and seed size in cowpea (Lucas et al, 2015; see Boukar et al, 2016); pyramid genes for resistance to ascochyta blight and anthracnose in lentil (Taran et al, 2003); powdery mildew resistance (Ghafoor and McPhee 2012), lodging resistance (Zhang et al, 2006), frost tolerance (see Tayeh et al, 2015b), and Aphanomyces root rot resistance (Lavaud et al, 2015) in pea; and resistance to common bacterial blight disease (Miklas et al, 2000(Miklas et al, , 2006Mutlu et al, 2005;O'Boyle and Kelly, 2007), rust and viruses (Stavely, 2000), rust, anthracnose, and angular leaf spot (Oliveira et al, 2008), rust (Feleiro et al, 2001), and anthracnose (Alzate-Marin et al, 1999) in common bean. Several of these improved lines have either been released or are in the release pipeline in different countries.…”

“…In addition, the efficiency and accuracy with which superior lines can be predicted also depend on the size of the reference population (Jannink et al, 2010;Lorenz et al, 2011). The genetic relatedness or population structure (Saatchi et al, 2011;Riedelsheimer et al, 2013;Wray et al, 2013) may result in overestimating the heritability of the traits (Price et al, 2010;Visscher et al, 2012;Wray et al, 2013). The population structure of the training population can be determined with greater accuracy using genome wide SNPs compared with the simple sequence repeats and SNP arrays (Isidro et al, 2015).…”

Accuracy of genomic prediction within and across populations for nematode resistance and body weight traits in sheep

“…At present, the accuracy of GEBV has been evaluated in experiments involving several livestock species, such as dairy (Harris et al, 2009;Hayes et al, 2009b) and beef (Saatchi et al, 2011) cattle populations, chicken (González-Recio et al, 2009) and sheep (Daetwyler et al, 2010a(Daetwyler et al, , 2012a(Daetwyler et al, , 2012bDuchemin et al, 2012). Apart from the study by Kemper et al (2011), the use of high-density genomic information to select for nematode resistance in sheep has received less attention.…”

Genomic Prediction in Animals and Plants: Simulation of Data, Validation, Reporting, and Benchmarking

“…Being cognizant of the impact of relationships on the accuracy of genomic estimated breeding values allows cross-validation procedures to be modified so that the accuracy can be calculated within and across groups of individuals such as families, generations, genetic groups, strains, lines, and breeds. Saatchi et al (2011) proposed an approach for designing cross-validation schemes that uses k-means clustering based on genomic relationships to partition the data into the various folds to minimize the relationships between training populations and testing populations.The independence of data sets used for calculating the predictand and genomic breeding values is an additional important factor. Prediction accuracies may be biased upward when the phenotypes used to estimate the genomic breeding values are also included in calculation of adjusted progeny means or when estimated breeding values for training and testing that are obtained from the same evaluation (e.g., Amer and Banos 2010).…”