A computational approach to animal breeding

Abstract

Abstract. We propose a computational model of mating strategies for controlled animal breeding programs. To the best of our knowledge, this is the first computational model of this problem. We focus on algorithms for two extremes of the possible goals of breeding programs: 1) breeding for maximum genetic diversity and 2) breeding for a target genotype. These two goals are representative of conservation biology and agricultural livestock management respectively. Our main results consist of upper and lower bounds for the average number of matings to achieve these goals in our computational model. For example, we give a breeding algorithm which creates a population of 2 n genetically distinct individuals using no more than about 2.3 · 2 n matings on average. We also give a breeding algorithm which creates a target individual with n desirable traits using no more than 2n log 2 n matings on average.

PreZon: Prediction by Zone and Its Application to Egg Productivity in Chickens

“…Several selection approaches, including phenotype, selection index, and best linear unbiased prediction (BLUP), have been used to estimate breeding values [11, 12]. One computational model of mating strategy in a controlled breeding program provides a novel viable and robust approach to designing [13]. Thus far, these selection programs have been restricted to inbreeding or to a closed line.…”

Towards a Classification of Hamiltonian Cycles in the 6-Cube

“…Hamiltonian cycles and paths are of interest in many hypercube-like structures: in star graphs [22], in crossed hypercubes [39], in hypercubes with prescribed edges [17], and many more. In general, binary hypercubes are important in many areas of science, e.g., supercomputing [26], algorithmic biology [7], network protocols [6], and cryptography.…”