Major Advances in Genetic Evaluation Techniques

Powell, R.L.; Norman, H.D.

doi:10.3168/jds.s0022-0302(06)72201-9

Cited by 26 publications

(23 citation statements)

References 68 publications

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“…These values suggest that the RP model assumption, that each parity is genetically the same trait, is not correct. This is consistent with the literature which generally reports the three parities as separate traits (Weller, 1986;Schaeffer et al, 2000;Powell and Norman, 2006). Furthermore, the ANIM model would be more suitable than the SIRE model as the genetic parameter estimates are closer to the correct values and much more precise as shown by the lower standard deviations.…”

Section: Estimation Of Genetic Parameterssupporting

confidence: 80%

Selecting an appropriate genetic evaluation model for selection in a developing dairy sector

McGill

Mulder

Thomson

et al. 2014

Animal

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This study aimed to identify genetic evaluation models (GEM) to accurately select cattle for milk production when only limited data are available. It is based on a data set from the Pakistani Sahiwal progeny testing programme which includes records from five government herds, each consisting of 100 to 350 animals, with lactation records dating back to 1968. Different types of GEM were compared, namely: (1) multivariate v. repeatability model when using the first three lactations, (2) an animal v. a sire model, (3) different fixed effects models to account for effects such as herd, year and season; and (4) fitting a model with genetic parameters fixed v. estimating the genetic parameters as part of the model fitting process. Two methods were used for the comparison of models. The first method used simulated data based on the Pakistani progeny testing system and compared estimated breeding values with true breeding values. The second method used cross-validation to determine the best model in subsets of actual Australian herd-recorded data. Subsets were chosen to reflect the Pakistani data in terms of herd size and number of herds. Based on the simulation and the cross-validation method, the multivariate animal model using fixed genetic parameters was generally the superior GEM, but problems arise in determining suitable values for fixing the parameters. Using mean square error of prediction, the best fixed effects structure could not be conclusively determined. The simulation method indicated the simplest fixed effects structure to be superior whereas in contrast, the cross-validation method on actual data concluded that the most complex one was the best. In conclusion it is difficult to propose a universally best GEM that can be used in any data set of this size. However, some general recommendations are that it is more appropriate to estimate the genetic parameters when evaluating for selection purposes, the animal model was superior to the sire model and that in the Pakistani situation the repeatability model is more suitable than a multivariate.

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Section: Estimation Of Genetic Parameterssupporting

confidence: 80%

Selecting an appropriate genetic evaluation model for selection in a developing dairy sector

McGill

Mulder

Thomson

et al. 2014

Animal

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“…Without the inclusion of the inbreeding coefficient in the model, these animals would have their breeding values underestimated because of inbreeding depression. Other authors also agreed with this approach, using or defending the use of the inbreeding coefficients in the model (Kennedy et al, 1988;Ferraz, 1993;Mourão et al, 2005;Powell and Norman, 2006). The analysis of AFC considered a model that contemplated: the contemporary groups, composed by year and season of birth, as a fixed effect, the linear component of the inbreeding coefficient as a covariate; the animal additive genetic random effect, and the residual random effect.…”

Section: Productive and Reproductive Data Analysismentioning

confidence: 99%

Bos indicus or Bos taurus mitochondrial DNA - comparison of productive and reproductive breeding values in a Guzerat dairy herd

Paneto¹,

Ferraz²,

Balieiro³

et al. 2008

Genet. Mol. Res.

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ABSTRACT. The observation of bovine mitochondrial DNA (mtDNA) polymorphisms allows the separation of American zebu cattle, according to its maternal lineage ancestry, into two groups: one with Bos indicus mtDNA and other with Bos taurus mtDNA. The aim of the present study was to determine the productive and reproductive differences between these two groups, in a Guzerat dairy herd. The genotyping of a sample of 56 animals allowed the categorization of most of the 3835 animals in the pedigree file. The production file included 3528 calving and 3198 lactation records from 729 cows, born during the years 1947 to 2007. The traits considered were: lactation milk yield (LMY); days in milk (DIM); age at first calving (AFC), and calving interval (CI). Heritabilities and breeding values were estimated using an animal model. The regression of the average breeding values per year of birth indicated the genetic trends of the herd. The heritability coefficients estimated for LMY, DIM, AFC, and CI were 0.42, 0.43, 0.20, and 0.10, Bos indicus or Bos taurus mitochondrial DNA -Guzerat respectively. The genetic trends were similar for both groups, pointing to an improvement in the productive and a worsening in the reproductive traits. The two groups differed significantly regarding the average estimated breeding values for LMY, DIM and AFC, in the starting period, until 1970, but no differences were observed in the more recent years, after 1970. The segregation between the groups existed in the starting period, probably because the Bos taurus contributions to the herd had occurred more recently at that moment. The conclusion is that mtDNA has no significant effect on these traits.

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“…Knowledge of the genomic variants causing phenotypic variation has not been needed to improve performance by selection. Advances in statistical methodology accompanied by accumulated performance and pedigree records have enabled national and international genetic evaluation systems to predict EBV for entire populations (Powell and Norman, 2006;Golden et al, 2009). Promising young candidates can be selected using EBV predicted from their own performance and the performance of their relatives.…”

Section: Introductionmentioning

confidence: 99%

BREEDING AND GENETICS SYMPOSIUM: Networks and pathways to guide genomic selection1–3

Snelling

Cushman

Keele

et al. 2013

Journal of Animal Science

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Many traits affecting profi tability and sustainability of meat, milk, and fi ber production are polygenic, with no single gene having an overwhelming infl uence on observed variation. No knowledge of the specifi c genes controlling these traits has been needed to make substantial improvement through selection. Signifi cant gains have been made through phenotypic selection enhanced by pedigree relationships and continually improving statistical methodology. Genomic selection, recently enabled by assays for dense SNP located throughout the genome, promises to increase selection accuracy and accelerate genetic improvement by emphasizing the SNP most strongly correlated to phenotype although the genes and sequence variants affecting phenotype remain largely unknown. These genomic predictions theoretically rely on linkage disequilibrium (LD) between genotyped SNP and unknown functional variants, but familial linkage may increase effectiveness when predicting individuals related to those in the training data. Genomic selection with functional SNP genotypes should be less reliant on LD patterns shared by training and target populations, possibly allowing robust prediction across unrelated populations.

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Major Advances in Genetic Evaluation Techniques

Cited by 26 publications

References 68 publications

Selecting an appropriate genetic evaluation model for selection in a developing dairy sector

Selecting an appropriate genetic evaluation model for selection in a developing dairy sector

Bos indicus or Bos taurus mitochondrial DNA - comparison of productive and reproductive breeding values in a Guzerat dairy herd

BREEDING AND GENETICS SYMPOSIUM: Networks and pathways to guide genomic selection1–3

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