scholarly journals Corrigendum to: Genomic mating in outbred species: predicting cross usefulness with additive and total genetic covariance matrices

Genetics ◽  
2021 ◽  
Author(s):  
Marnin D Wolfe ◽  
Ariel W Chan ◽  
Peter Kulakow ◽  
Ismail Rabbi ◽  
Jean-Luc Jannink
2004 ◽  
Vol 55 (2) ◽  
pp. 195 ◽  
Author(s):  
Karin Meyer ◽  
David J. Johnston ◽  
Hans-Ulrich Graser

Estimates of covariance components among all 22 traits considered in the current multi-trait genetic evaluation of Australian Hereford cattle were obtained. Traits included 5 weight traits, 8 traits measured through live ultrasound scanning, 3 traits related to reproductive performance, and 6 carcass traits. Estimates were obtained by restricted maximum likelihood, carrying out a series of bivariate analyses. Data for each analysis were selected attempting to maximise the number of animals or animal–parent pairs that had both traits recorded. Estimates were pooled using a weighted 'iterative summing of expanded part matrices' procedure, which ensured positive semi-definite covariance matrices. Models of analyses for individual traits closely resembled those used in genetic evaluation. Results generally agreed with literature results, although estimates of genetic parameters for carcass traits that had few records available tended to fluctuate. Except for 'days to calving', heritability estimates were moderate to high for all traits. Genetic parameters for early growth were different to those for other breeds, with maternal effects for weaning weight being considerably more important and the heritability somewhat lower.


2012 ◽  
Vol 52 (3) ◽  
pp. 126 ◽  
Author(s):  
Andrew A. Swan ◽  
David J. Johnston ◽  
Daniel J. Brown ◽  
Bruce Tier ◽  
Hans-U. Graser

Genomic information has the potential to change the way beef cattle and sheep are selected and to substantially increase genetic gains. Ideally, genomic data will be used in combination with pedigree and phenotypic data to increase the accuracy of estimated breeding values (EBVs) and selection indexes. The first example of this in Australia was the integration of four markers for tenderness into beef cattle breeding values. Subsequently, the availability of high-density single nucleotide polymorphism (SNP) panels has made selection using genomic information possible, while at the same time creating significant challenges for genetic evaluation with regard to both data management and statistical modelling. Reference populations have been established in both the beef cattle and sheep industries, in which an extensive range of phenotypes have been collected and animals genotyped mainly using 50K SNP panels. From this information, genomic predictions of breeding value have been developed, albeit with varying levels of accuracy. These predictions have been incorporated into routine genetic evaluations using three approaches and trial results are now available to breeders. In the first, genomic predictions have been included in genetic evaluation models as additional traits. The challenges with this method have been the construction of consistent genetic covariance matrices, and a significant increase in computing time. The second approach has been to use a selection index procedure to blend genomic predictions with existing EBVs. This method has been shown to produce very similar results, and has the advantage of being simple to implement and fast to operate, although consistent genetic covariance matrices are still required. Third, in sheep a single-step analysis combining a genomic relationship matrix with a standard pedigree-based relationship matrix has been used to estimate breeding values for carcass and eating-quality traits. It is likely that this procedure or one similar will be incorporated into routine evaluations in the near future. While significant progress has been made in implementing methods of integrating genomic information in both beef and sheep evaluations in Australia, the major challenges for the future will be to continue to collect the phenotypes needed to derive accurate genomic predictions, and in managing much larger volumes of genomic data as the number of animals genotyped and the density of markers increase.


Biometrics ◽  
1997 ◽  
Vol 53 (3) ◽  
pp. 1026 ◽  
Author(s):  
Charles J. Goodnight ◽  
James M. Schwartz

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