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ABSTRACT: A method that predicts the genetic composition and inbreeding (F) of the future dairy cow population using information on
the current cow population, semen use and progeny test bulls is described. This is combined with information on genetic merit
of bulls to compare bull selection methods that minimise F and maximise breeding value for profit (called APR in Australia).
The genetic composition of the future cow population of Australian Holstein-Friesian (HF) and Jersey up to 6 years into the
future was predicted. F in Australian HF and Jersey breeds is likely to increase by about 0.002 and 0.003 per year between
2002 and 2008, respectively. A comparison of bull selection methods showed that a method that selects the best bull from all
available bulls for each current or future cow, based on its calf's APR minus F depression, is better than bull selection
methods based on APR alone, APR adjusted for mean F of prospective progeny after random mating and mean APR adjusted for the
relationship between the selected bulls. This method reduced F of prospective progeny by about a third to a half compared
to the other methods when bulls are mated to current and future cows that will be available 5 to 6 years from now. The method
also reduced the relationship between the bulls selected to nearly the same extent as the method that is aimed at maximising
genetic gain adjusted for the relationship between bulls. The method achieves this because cows with different pedigree exist
in the population and the method selects relatively unrelated bulls to mate to these different cows. Selecting the best bull
for each current or future cow so that the calf's genetic merit minus F depression is maximised can slow the rate of increase
in F in the population.
inbreeding-dairy cattle-bull selection methods-genetic merit
Genetics Selection Evolution 04/2012; 39(4):1-21. · 2.88 Impact Factor
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ABSTRACT: Prediction of genetic merit using dense SNP genotypes can be used for estimation of breeding values for selection of livestock, crops, and forage species; for prediction of disease risk; and for forensics. The accuracy of these genomic predictions depends in part on the genetic architecture of the trait, in particular number of loci affecting the trait and distribution of their effects. Here we investigate the difference among three traits in distribution of effects and the consequences for the accuracy of genomic predictions. Proportion of black coat colour in Holstein cattle was used as one model complex trait. Three loci, KIT, MITF, and a locus on chromosome 8, together explain 24% of the variation of proportion of black. However, a surprisingly large number of loci of small effect are necessary to capture the remaining variation. A second trait, fat concentration in milk, had one locus of large effect and a host of loci with very small effects. Both these distributions of effects were in contrast to that for a third trait, an index of scores for a number of aspects of cow confirmation ("overall type"), which had only loci of small effect. The differences in distribution of effects among the three traits were quantified by estimating the distribution of variance explained by chromosome segments containing 50 SNPs. This approach was taken to account for the imperfect linkage disequilibrium between the SNPs and the QTL affecting the traits. We also show that the accuracy of predicting genetic values is higher for traits with a proportion of large effects (proportion black and fat percentage) than for a trait with no loci of large effect (overall type), provided the method of analysis takes advantage of the distribution of loci effects.
PLoS Genetics 01/2010; 6(9):e1001139. · 8.69 Impact Factor
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ABSTRACT: Continued production of food in areas predicted to be most affected by climate change, such as dairy farming regions of Australia, will be a major challenge in coming decades. Along with rising temperatures and water shortages, scarcity of inputs such as high energy feeds is predicted. With the motivation of selecting cattle adapted to these changing environments, we conducted a genome wide association study to detect DNA markers (single nucleotide polymorphisms) associated with the sensitivity of milk production to environmental conditions. To do this we combined historical milk production and weather records with dense marker genotypes on dairy sires with many daughters milking across a wide range of production environments in Australia. Markers associated with sensitivity of milk production to feeding level and sensitivity of milk production to temperature humidity index on chromosome nine and twenty nine respectively were validated in two independent populations, one a different breed of cattle. As the extent of linkage disequilibrium across cattle breeds is limited, the underlying causative mutations have been mapped to a small genomic interval containing two promising candidate genes. The validated marker panels we have reported here will aid selection for high milk production under anticipated climate change scenarios, for example selection of sires whose daughters will be most productive at low levels of feeding.
PLoS ONE 02/2009; 4(8):e6676. · 4.09 Impact Factor
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ABSTRACT: A method that predicts the genetic composition and inbreeding (F) of the future dairy cow population using information on the current cow population, semen use and progeny test bulls is described. This is combined with information on genetic merit of bulls to compare bull selection methods that minimise F and maximise breeding value for profit (called APR in Australia). The genetic composition of the future cow population of Australian Holstein-Friesian (HF) and Jersey up to 6 years into the future was predicted. F in Australian HF and Jersey breeds is likely to increase by about 0.002 and 0.003 per year between 2002 and 2008, respectively. A comparison of bull selection methods showed that a method that selects the best bull from all available bulls for each current or future cow, based on its calf's APR minus F depression, is better than bull selection methods based on APR alone, APR adjusted for mean F of prospective progeny after random mating and mean APR adjusted for the relationship between the selected bulls. This method reduced F of prospective progeny by about a third to a half compared to the other methods when bulls are mated to current and future cows that will be available 5 to 6 years from now. The method also reduced the relationship between the bulls selected to nearly the same extent as the method that is aimed at maximising genetic gain adjusted for the relationship between bulls. The method achieves this because cows with different pedigree exist in the population and the method selects relatively unrelated bulls to mate to these different cows. Selecting the best bull for each current or future cow so that the calf's genetic merit minus F depression is maximised can slow the rate of increase in F in the population.
Genetics Selection Evolution 39(4):369-89. · 2.88 Impact Factor
