Pong-Wong Ricardo, Woolliams John A
Roslin Institute (Edinburgh), Roslin, Midlothian, Scotland, UK.
Genet Sel Evol. 2007 Jan-Feb;39(1):3-25. doi: 10.1186/1297-9686-39-1-3. Epub 2007 Jan 11.
An approach for optimising genetic contributions of candidates to control inbreeding in the offspring generation using semidefinite programming (SDP) was proposed. Formulations were done for maximising genetic gain while restricting inbreeding to a preset value and for minimising inbreeding without regard of gain. Adaptations to account for candidates with fixed contributions were also shown. Using small but traceable numerical examples, the SDP method was compared with an alternative based upon Lagrangian multipliers (RSRO). The SDP method always found the optimum solution that maximises genetic gain at any level of restriction imposed on inbreeding, unlike RSRO which failed to do so in several situations. For these situations, the expected gains from the solution obtained with RSRO were between 1.5-9% lower than those expected from the optimum solution found with SDP with assigned contributions varying widely. In conclusion SDP is a reliable and flexible method for solving contribution problems.
提出了一种使用半定规划(SDP)优化候选个体遗传贡献以控制后代近交的方法。制定了在将近交限制在预设值的同时最大化遗传增益的公式,以及在不考虑增益的情况下最小化近交的公式。还展示了针对具有固定贡献的候选个体的调整方法。通过使用小但可追溯的数值示例,将SDP方法与基于拉格朗日乘数的替代方法(RSRO)进行了比较。与RSRO不同,SDP方法总能找到在对近交施加的任何限制水平下最大化遗传增益的最优解,RSRO在几种情况下未能做到这一点。对于这些情况,RSRO获得的解的预期增益比SDP找到的最优解的预期增益低1.5% - 9%,且指定贡献差异很大。总之,SDP是解决贡献问题的一种可靠且灵活的方法。