Optimizing Genomic Selection for a Sorghum Breeding Program in Haiti: A Simulation Study
Optimizing Genomic Selection for a Sorghum Breeding Program in Haiti: A Simulation Study
复制标题
DOI：
10.1534/g3.118.200932
复制
发表时间：
2019-02-01
影响因子：
2.6
通讯作者：
Morris, Geoffrey P.
中科院分区：
文献类型：
--
作者：
Muleta, Kebede T.;Pressoir, Gael;Morris, Geoffrey P.
发展中国家的年轻育种计划，如海地的Chibas高粱育种计划，面临着以有限的资源增加遗传增益的挑战。实施基因组选择（GS）可以增加遗传增益，但需要优化GS来应对这些计划的独特挑战和优势。在这里，我们使用模拟来确定条件下，基因组辅助轮回选择（加尔斯）将比表型轮回选择（PRS）在小型新的育种计划更有效。我们比较了遗传增益，每单位增益成本，遗传方差，预测精度的加尔斯（每年两个或三个周期）与PRS（每年一个周期）假设不同的育种群体的大小和性状的遗传结构。对于寡基因结构，加尔斯的最大相对遗传增益优势超过PRS为12- 88%，这仅在前几个周期中观察到。在多基因结构方面，加尔斯提供了26- 165%的最大相对遗传增益优势，并且始终上级优于PRS。平均预测精度在几个选择周期后大幅下降，这表明预测模型应该定期更新。与不更新的情况相比，每年更新预测模型可使遗传增益增加33-39%。对于小群体和寡基因性状，PRS的单位增益成本低于加尔斯。然而，随着群体的扩大和多基因性状的出现，加尔斯的单位增重成本比PRS低67%。总的来说，这些模拟表明，加尔斯可以通过加速育种周期和评估更大的种群来增加小型年轻育种计划的遗传增益。
Young breeding programs in developing countries, like the Chibas sorghum breeding program in Haiti, face the challenge of increasing genetic gain with limited resources. Implementing genomic selection (GS) could increase genetic gain, but optimization of GS is needed to account for these programs' unique challenges and advantages. Here, we used simulations to identify conditions under which genomic-assisted recurrent selection (GARS) would be more effective than phenotypic recurrent selection (PRS) in small new breeding programs. We compared genetic gain, cost per unit gain, genetic variance, and prediction accuracy of GARS (two or three cycles per year) vs. PRS (one cycle per year) assuming various breeding population sizes and trait genetic architectures. For oligogenic architecture, the maximum relative genetic gain advantage of GARS over PRS was 12-88%, which was observed only during the first few cycles. For the polygenic architecture, GARS provided maximum relative genetic gain advantage of 26-165%, and was always superior to PRS. Average prediction accuracy declines substantially after several cycles of selection, suggesting the prediction models should be updated regularly. Updating prediction models every year increased the genetic gain by up to 33-39% compared to no-update scenarios. For small populations and oligogenic traits, cost per unit gain was lower in PRS than GARS. However, with larger populations and polygenic traits cost per unit gain was up to 67% lower in GARS than PRS. Collectively, the simulations suggest that GARS could increase the genetic gain in small young breeding programs by accelerating the breeding cycles and enabling evaluation of larger populations.