代代选择作图分析纯种猪和杂交猪基因组的多基因选择。

IF 3.6 1区 农林科学 Q1 AGRICULTURE, DAIRY & ANIMAL SCIENCE Genetics Selection Evolution Pub Date : 2023-09-14 DOI:10.1186/s12711-023-00836-9
Caleb J Grohmann, Caleb M Shull, Tamar E Crum, Clint Schwab, Timothy J Safranski, Jared E Decker
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引用次数: 0

摘要

背景:在商品家畜育种群体中,利用育种值和选择指标对数量性状进行人工选择,导致成百上千个因果位点及其周围基因组区域的等位基因频率随时间发生变化。在群体遗传学中,这种类型的选择被称为多基因选择。猪育种项目的研究人员和管理者都有动力通过选择作图分析来了解遗传系、品种和种群间表型多样性的遗传基础。在这里,我们应用代代理选择图谱(GPSM),对4个猪群体(每个群体15,457头、15,772头、16,595头和8447头猪)的出生日期单核苷酸多态性(SNP)基因型(38,294-46,458个标记)进行全基因组关联分析,以确定在5到10年的时间内响应人工选择的位点。进行基因跌落模拟分析,为GPSM结果提供背景。在猪育种目标的背景下,比较了每个猪群内部和之间的选定位点。结果:GPSM根据群体组合在15个猪亚群中鉴定出49 ~ 854个基因座处于选择状态(q值小于0.10)。当数据汇集在人群中时,显著关联的数量增加了。此外,在一个以上的人群中发现了几个显著的关联。这些结果表明,在这些猪群中,有共同的选择目标、相似的遗传结构以及响应选择的共同因果变异。在基因下降模拟基因型上测试GPSM时,发现假阳性关联的错误率可以忽略不计(小于或等于0.02%),这表明GPSM在实际猪群中区分了选择和随机遗传漂变。结论:本工作证实了GPSM方法在商品猪群体中检测特定基因座的有效性和可忽略的误差率。我们的研究结果表明猪群之间有共同的选择目标和遗传结构。确定的多基因选择突出了对猪生产重要的位点。
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Analysis of polygenic selection in purebred and crossbred pig genomes using generation proxy selection mapping.

Background: Artificial selection on quantitative traits using breeding values and selection indices in commercial livestock breeding populations causes changes in allele frequency over time at hundreds or thousands of causal loci and the surrounding genomic regions. In population genetics, this type of selection is called polygenic selection. Researchers and managers of pig breeding programs are motivated to understand the genetic basis of phenotypic diversity across genetic lines, breeds, and populations using selection mapping analyses. Here, we applied generation proxy selection mapping (GPSM), a genome-wide association analysis of single nucleotide polymorphism (SNP) genotypes (38,294-46,458 markers) of birth date, in four pig populations (15,457, 15,772, 16,595 and 8447 pigs per population) to identify loci responding to artificial selection over a period of five to ten years. Gene-drop simulation analyses were conducted to provide context for the GPSM results. Selected loci within and across each population of pigs were compared in the context of swine breeding objectives.

Results: The GPSM identified 49 to 854 loci as under selection (Q-values less than 0.10) across 15 subsets of pigs based on combinations of populations. The number of significant associations increased when data were pooled across populations. In addition, several significant associations were identified in more than one population. These results indicate concurrent selection objectives, similar genetic architectures, and shared causal variants responding to selection across these pig populations. Negligible error rates (less than or equal to 0.02%) of false-positive associations were found when testing GPSM on gene-drop simulated genotypes, suggesting that GPSM distinguishes selection from random genetic drift in actual pig populations.

Conclusions: This work confirms the efficacy and the negligible error rates of the GPSM method in detecting selected loci in commercial pig populations. Our results suggest shared selection objectives and genetic architectures across swine populations. The identified polygenic selection highlights loci that are important to swine production.

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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.
期刊最新文献
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