By C. R. Henderson (auth.), Prof. Dr. Daniel Gianola, Dr. Keith Hammond (eds.)
Developments in records and computing in addition to their software to genetic development of cattle won momentum over the past twenty years. this article reports and consolidates the statistical foundations of animal breeding. this article will turn out worthwhile as a reference resource to animal breeders, quantitative geneticists and statisticians operating in those components. it's going to additionally function a textual content in graduate classes in animal breeding technique with prerequisite classes in linear versions, statistical inference and quantitative genetics.
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Extra resources for Advances in Statistical Methods for Genetic Improvement of Livestock
1985). The family of power transformations can be represented as (A. =O) 19 for y>O. --0. Also, it can accommodate several commonly used transformations. =-1 yields the reciprocal transformation. 3) holds such that the distribution of the vector of residuals is N(O,Icr~). 3), ~ and u are vectors of "fixed" and "random" effects, respectively. ,y)1 = II I~Yi /~Yi I = II Yi is the Jacobian of the transformation. 5) where from now on x and y[A] will be used interchangeably. 1 Prior Distributions The unknown parameters are ~,u,cr;,1..
The first summation in this expression is over the parental and F1 lines; the last two are over the backcross progeny to the two parental lines, respectively. There is a total of four parameters: the two parental means (~1 and ~3)' the F 1 mean ~~ and the common environmental variance (02). These are reduced to three parameters in the cases of additive genes [~=(~1+~3)/2], parent 1 dominant (~1 =~) or parent 3 dominant ~=~). 2 Polygenic Inheritance If the trait difference between the two parental lines is due to a large number of additive loci with equal effects, then, in the limit as the number of loci tends to infinity, the log likelihood is where ~loo=(~l+~)/2 and ~oo=(~3+~)/2.
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