Improving selection decisions with mating information by accounting for Mendelian sampling variances looking two generations ahead

IF 3.6 1区 农林科学 Q1 AGRICULTURE, DAIRY & ANIMAL SCIENCE
Tobias A. M. Niehoff, Jan ten Napel, Piter Bijma, Torsten Pook, Yvonne C. J. Wientjes, Bernadett Hegedűs, Mario P. L. Calus
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引用次数: 0

Abstract

Breeding programs are judged by the genetic level of animals that are used to disseminate genetic progress. These animals are typically the best ones of the population. To maximise the genetic level of very good animals in the next generation, parents that are more likely to produce top performing offspring need to be selected. The ability of individuals to produce high-performing progeny differs because of differences in their breeding values and gametic variances. Differences in gametic variances among individuals are caused by differences in heterozygosity and linkage. The use of the gametic Mendelian sampling variance has been proposed before, for use in the usefulness criterion or Index5, and in this work, we extend existing approaches by not only considering the gametic Mendelian sampling variance of individuals, but also of their potential offspring. Thus, the criteria developed in this study plan one additional generation ahead. For simplicity, we assumed that the true quantitative trait loci (QTL) effects, genetic map and the haplotypes of all animals are known. In this study, we propose a new selection criterion, ExpBVSelGrOff, which describes the genetic level of selected grand-offspring that are produced by selected offspring of a particular mating. We compare our criterion with other published criteria in a stochastic simulation of an ongoing breeding program for 21 generations for proof of concept. ExpBVSelGrOff performed better than all other tested criteria, like the usefulness criterion or Index5 which have been proposed in the literature, without compromising short-term gains. After only five generations, when selection is strong (1%), selection based on ExpBVSelGrOff achieved 5.8% more commercial genetic gain and retained 25% more genetic variance without compromising inbreeding rate compared to selection based only on breeding values. Our proposed selection criterion offers a new tool to accelerate genetic progress for contemporary genomic breeding programs. It retains more genetic variance than previously published criteria that plan less far ahead. Considering future gametic Mendelian sampling variances in the selection process also seems promising for maintaining more genetic variance.
利用交配信息改进选择决策,考虑孟德尔抽样方差,展望未来两代
育种计划的评判标准是用于传播基因进步的动物的基因水平。这些动物通常是种群中最优秀的动物。为了最大限度地提高下一代优秀动物的遗传水平,需要选择更有可能培育出优秀后代的亲本。由于个体的育种值和配子变异性不同,个体产生优秀后代的能力也不同。个体间配子变异的差异是由杂合性和连锁性的差异造成的。配子孟德尔抽样方差曾被提议用于有用性标准或指数5,在这项工作中,我们扩展了现有的方法,不仅考虑了个体的配子孟德尔抽样方差,还考虑了其潜在后代的配子孟德尔抽样方差。因此,本研究制定的标准还能再提前一代。为简单起见,我们假设所有动物的真实数量性状位点(QTL)效应、遗传图谱和单倍型都是已知的。在本研究中,我们提出了一个新的选择标准 ExpBVSelGrOff,它描述了特定交配所产生的被选子代的遗传水平。我们对一个正在进行的育种计划进行了 21 代的随机模拟,将我们的标准与其他已公布的标准进行了比较,以证明我们的概念。ExpBVSelGrOff 的表现优于所有其他测试标准,如有用性标准或文献中提出的 Index5,而且不会影响短期收益。仅经过五代,当选择强度较高时(1%),基于 ExpBVSelGrOff 的选择与仅基于育种值的选择相比,在不影响近交率的情况下,商业遗传增益提高了 5.8%,遗传变异保留率提高了 25%。我们提出的选择标准为当代基因组育种计划提供了一种加快遗传进展的新工具。与之前公布的那些计划不够长远的标准相比,它保留了更多的遗传变异。在选择过程中考虑未来配子孟德尔抽样方差似乎也很有希望保持更多的遗传变异。
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来源期刊
Genetics Selection Evolution
Genetics Selection Evolution 生物-奶制品与动物科学
CiteScore
6.50
自引率
9.80%
发文量
74
审稿时长
1 months
期刊介绍: Genetics Selection Evolution invites basic, applied and methodological content that will aid the current understanding and the utilization of genetic variability in domestic animal species. Although the focus is on domestic animal species, research on other species is invited if it contributes to the understanding of the use of genetic variability in domestic animals. Genetics Selection Evolution publishes results from all levels of study, from the gene to the quantitative trait, from the individual to the population, the breed or the species. Contributions concerning both the biological approach, from molecular genetics to quantitative genetics, as well as the mathematical approach, from population genetics to statistics, are welcome. Specific areas of interest include but are not limited to: gene and QTL identification, mapping and characterization, analysis of new phenotypes, high-throughput SNP data analysis, functional genomics, cytogenetics, genetic diversity of populations and breeds, genetic evaluation, applied and experimental selection, genomic selection, selection efficiency, and statistical methodology for the genetic analysis of phenotypes with quantitative and mixed inheritance.
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