- This post explains the maths behind the formula 3n × 2m (the number of unique zygotes) mentioned in last week’s post.
- Mating two superior animals together does improve your chance of producing superior offspring, but because of the completely random nature of gene assortment there is no way to predict which particular combination of genes will end up in which gamete. There is no way to control which egg is fertilised by which sperm, and ultimately chance and even luck still factor in the genome you are dealt. Some gamete combinations may be so detrimental that the embryo dies very early on. Two animals who together produced a superb animal may never do so again, but at the same time a disappointing mating outcome doesn’t mean future ones will be. So yes, there absolutely is a lot of randomness in genetic inheritance — far, far more than you may have thought. It is important to be aware of this randomness, and how, because of it, you do not have as much control over your breeding as you may think. But this is a blog about better breeding, and just knowing what you can’t control is a big step on the way to being a better breeder. We will be going deeper and deeper in our understanding, covering genes, statistics and selection strategies along the way.
- Genetic parameters for a range of sheep production traits have been reviewed from estimates published over the last decade. Weighted means and standard errors of estimates of direct and maternal heritability, common environmental effects and the correlation between direct and maternal effects are presented for various growth, carcass and meat, wool, reproduction, disease resistance and feed intake traits. Weighted means and confidence intervals for the genetic and phenotypic correlations between these traits are also presented. A random effects model that incorporated between and within study variance components was used to obtain the weighted means and variances. The weighted mean heritability estimates for the major wool traits (clean fleece weight, fibre diameter and staple length) and all the growth traits were based on more than 20 independent estimates, with the other wool traits based on more than 10 independent estimates. The mean heritability estimates for the carcass and meat traits were based on very few estimates except for fat (27) and muscle depth (11) in live animals. There were more than 10 independent estimates of heritability for most reproduction traits and for worm resistance, but few estimates for other sheep disease traits or feed intake. The mean genetic and phenotypic correlations were based on considerably smaller numbers of independent estimates. There were a reasonable number of estimates of genetic correlations among most of the wool and growth traits, although there were few estimates for the wool quality traits and among the reproduction traits. Estimates of genetic correlations between the groups of different production traits were very sparse. The mean genetic correlations generally had wide confidence intervals reflecting the large variation between estimates and relatively small data sets (number of sires) used. More accurate estimates of genetic parameters and in particular correlations between economically important traits are required for accurate genetic evaluation and development of breeding objectives.
- Data from seven research resource flocks across Australia were combined to provide accurate estimates of genetic correlations among production traits in Merino sheep. The flocks represented contemporary Australian Merino fine, medium and broad wool strains over the past 30 years. Over 110,000 records were available for analysis for each of the major wool traits, and 50,000 records for reproduction and growth traits with over 2700 sires and 25,000 dams. Individual models developed from the single trait analyses were extended to the various combinations of two-trait models to obtain genetic correlations among six wool traits [clean fleece weight (CFW), greasy fleece weight, fibre diameter (FD), yield, coefficient of variation of fibre diameter and standard deviation of fibre diameter], four growth traits [birth weight, weaning weight, yearling weight (YWT), and hogget weight] and four reproduction traits [fertility, litter size, lambs born per ewe joined, lambs weaned per ewe joined (LW/EJ)]. This study has provided for the first time a comprehensive matrix of genetic correlations among these 14 wool, growth and reproduction traits. The large size of the data set has also provided estimates with very low standard errors. A moderate positive genetic correlation was observed between CFW and FD (0.29 +/- 0.02). YWT was positively correlated with CFW (0.23 +/- 0.04), FD (0.17 +/- 0.04) and LWEJ (0.58 +/- 0.06), while LW/EJ was negatively correlated with CFW (-0.26 +/- 0.05) and positively correlated with FD (0.06 +/- 0.04) and LS (0.68 +/- 0.04). These genetic correlations, together with the estimates of heritability and other parameters provide the basis for more accurate prediction of outcomes in complex sheep-breeding programmes designed to improve several traits.
- Pacomarca is an experimental ranch founded by the INCA group to act as a selection nucleus from which basic genetic improvement of alpaca fibre can spread throughout the rural communities in the Peruvian Altiplano. State-of-art techniques in animal science, such as performance recording or assisted reproduction including embryo transfer, are applied to demonstrate their usefulness in the Altiplano conditions. Pacomarca has developed useful software (Paco Pro) to carry out the integral processing of production and reproduction data. Mating is carried out individually, and gestation is diagnosed via ultrasound. Breeding values estimated from a modern genetic evaluation are used for selection, and embryo transfer is applied to increase the selection intensity. However, the objective of Pacomarca goes beyond, extending its advances to the small rural communities. Training courses for farmers are organised while searching for new ways of improving the performance of alpacas both technically and scientifically.