Gengler, N  
Tijani, A  
Wiggans, George 
Submitted to: Interbull Annual Meeting Proceedings
Publication Type: Proceedings Publication Acceptance Date: 3/18/1999 Publication Date: N/A Citation: Interpretive Summary: The random regression models that have been proposed for statistical analysis of testday yields of dairy animals are computationally demanding, and few computing algorithms have existed that can simplify the calculations. An alternative algorithm for solving random regression test day models was developed to allow use of those models for extremely large data sets such as the U.S. database for dairy records. The algorithm also facilitates the integration of 305day lactation records when no testday records are available and simplifies the development of an index for lactation performance that includes genetic differences in lactation curve (persistency) and genetic effects of parity (maturity rate). In addition to the relative simplicity of the method developed, it allows several other statistical techniques to be applied: 1) a simplification of computations by making use of recent advances in solving algorithms that allow missing values; 2) a transformation to limit the number of regressions and create variables with biological meanings such as total yield, persistency, and maturity rate; 3) more complicated parameter structures than those usually considered in random regression models (for example, additional random effects such as interaction of herd and sire); and 4) accommodation of additional traits such as lactation yields for cows without testday records. The use of this computing algorithm will allow the development of genetic evaluations for dairy animals that are more accurate because they are based on yields recorded on test day and, therefore, better accounting of environmental effects can be made. Technical Abstract: An alternative algorithm for solving random regression testday models was developed to allow use of those models for extremely large data sets such as the U.S. database for dairy records. The algorithm also facilitates integration of data from 305day records when no testday records are available and simplifies development of an index for lactation performance that includes genetic differences in lactation curve (persistency) and genetic effects of parity (maturity rate). Equations are solved in two iterative steps: 1) estimation or update of regression coefficients based on testday yields for a given lactation and 2) estimation of fixed and random effects on those coefficients. Solutions were shown to be theoretically equivalent to regular solutions for this class of random regression model. In a test computation with 57,034 firstlactation test day milk yields from 7173 Holstein cows, correlations between solutions from the two solution methods were all >0.98 after only two iterations on the two steps. In addition to the relative simplicity of the proposed method, it allows several other techniques to be applied in the second step: 1) a canonical transformation to simplify computations (uncorrelated regressions) by making use of recent advances in solving algorithms that allow missing values; 2) a transformation to limit the number of regressions and create variates with biological meanings such as total yield, persistency, and maturity rate; 3) more complicated (co)variance structures than those usually considered in random regression models (e.g., additional random effects such as interaction of herd and sire); and 4) accommodation of additional traits for cows without testday records.
