Technical note: Computing options for genetic evaluation with a large number of genetic markers

¹ Department of Animal and Dairy Science, University of Georgia, Athens 30602

^* To whom correspondence should be addressed. E-mail: shogo{at}uga.edu.

	Abstract

Two simulated and one commercial data sets were used to evaluate computing options for models where effects due to QTL are fit as covariables. The simulated data sets included records on 24,000 animals for 10 traits. Data sets 1 and 2 were simulated with low and high correlations among traits, respectively. The model included an overall mean, 160 covariables as effects due to QTL, the random animal genetic effect, and the random residual effect. A commercial data set included records on about 110,000 animals for 11 growth, reproduction and other traits. The model for included the effects usually fitted for these traits plus 116 covariables as effects due to QTL; models including the number of covariables varied by trait. Initial computing was by the BLUP90IOD program, which applies iteration on data using a preconditioned conjugate gradient algorithm with a diagonal preconditioner. Modifications included adding block preconditioners for effects due to QTL (BQ) and for traits (BT). With the simulated data sets and the original program, one trait analyses without the covariables took 7 s while the 10 trait analyses with the covariables took 15 min for a data set with low correlations and 1 h 40 m for a data set with high correlations. BQ improved the convergence rate but increased the computing time. BT decreased the computing time from 1.5 times (low correlations) to 7 times (high correlation) at a cost of higher memory requirements. For the commercial data and the complete model, computing took 10.3 h with the unmodified program and was reduced to 6 h with BT. Relative changes in computing time and convergence rate with the commercial data set were close to the simulated data set with low correlations among the traits. BQ decreased the number of rounds less than expected. Genetic evaluation with a large number of effects due to QTL fit as covariables is feasible.

Key Words: genetic evaluation, genetic markers, molecular information