An Iterative Algorithm for Optimum Contribution Selection in Large Scale Breeding Programs

Abstract Text: A novel iterative algorithm, Gencont2, for calculating optimum genetic contributions was developed. It was validated by comparing it with a previous program, Gencont, on three datasets obtained from practical breeding programs of three species (cattle, pig and sheep). The numbers of selection candidates were 2,929, 3,907 and 6,875 for the pig, cattle and sheep datasets respectively. In most cases, both algorithms select the same candidates and gave very similar results in genetic gain.  In cases, when there were differences in number of animals to select, the extra selected candidates had contributions within the range of 0.006–0.08%. The correlations between assigned contributions were very close to 1; however, Gencont2 considerably decreased the computation time by 90% to 95% (13 to 22 times faster) compared to Gencont. This fast iterative algorithm makes the practical implementation of OC selection feasible in large scale breeding programs.

Keywords:

Inbreeding

optimum genetic contributions

genetic gain