Miller Webb, Wright Stephen J, Zhang Yu, Schuster Stephan C, Hayes Vanessa M
Center for Comparative Genomics and Bioinformatics, Penn State, University Park, PA 16802, USA.
Pac Symp Biocomput. 2010:43-53. doi: 10.1142/9789814295291_0006.
Methods from genetics and genomics can be employed to help save endangered species. One potential use is to provide a rational strategy for selecting a population of founders for a captive breeding program. The hope is to capture most of the available genetic diversity that remains in the wild population, to provide a safe haven where representatives of the species can be bred, and eventually to release the progeny back into the wild. However, the founders are often selected based on a random-sampling strategy whose validity is based on unrealistic assumptions. Here we outline an approach that starts by using cutting-edge genome sequencing and genotyping technologies to objectively assess the available genetic diversity. We show how combinatorial optimization methods can be applied to these data to guide the selection of the founder population. In particular, we develop a mixed-integer linear programming technique that identifies a set of animals whose genetic profile is as close as possible to specified abundances of alleles (i.e., genetic variants), subject to constraints on the number of founders and their genders and ages.
遗传学和基因组学方法可用于帮助拯救濒危物种。一种潜在用途是为圈养繁殖计划选择一群奠基者提供合理策略。希望捕获野生种群中剩余的大部分可用遗传多样性，提供一个安全的避难所用于繁殖该物种的代表，最终将后代放归野外。然而，奠基者通常基于随机抽样策略来选择，其有效性基于不现实的假设。在此，我们概述一种方法，该方法首先使用前沿的基因组测序和基因分型技术客观评估可用的遗传多样性。我们展示如何将组合优化方法应用于这些数据以指导奠基者群体的选择。特别是，我们开发了一种混合整数线性规划技术，该技术在奠基者数量及其性别和年龄的限制条件下，识别出一组动物，其基因概况尽可能接近指定的等位基因（即遗传变异）丰度。
