University of Missouri, Columbia, MO, 65211, USA.
The Maschhoff's, LLC, Carlyle, IL, 62231, USA.
Genet Sel Evol. 2023 Sep 14;55(1):62. doi: 10.1186/s12711-023-00836-9.
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.
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.
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.
在商业牲畜养殖群体中，使用育种值和选择指数对数量性状进行人工选择，导致数百个或数千个因果基因座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，这种类型的选择称为多基因选择。猪育种计划的研究人员和管理人员有动机使用选择图谱分析来了解遗传谱系、品种和群体之间表型多样性的遗传基础。在这里，我们应用了代际代理选择图谱（GPSM），这是一种针对出生日期单核苷酸多态性（SNP）基因型（38294-46458 个标记）的全基因组关联分析，对四个猪群体（每个群体 15457、15772、16595 和 8447 头猪）进行分析，以确定在五到十年的时间内对人工选择做出响应的基因座。进行基因剔除模拟分析为 GPSM 结果提供了背景。在猪的每个群体中，在所选择的基因座内和跨群体进行了比较，以了解猪的选育目标。
GPSM 在基于种群组合的 15 个猪子集内识别出 49 到 854 个基因座（Q 值小于 0.10）作为选择对象。当将数据汇总到种群中时，显著关联的数量增加。此外，在多个群体中也发现了一些显著关联。这些结果表明，这些猪群体之间存在共同的选择目标、相似的遗传结构和共同的因果变异，这些变异对选择作出响应。在对基因剔除模拟基因型进行 GPSM 测试时，发现假阳性关联的错误率（小于或等于 0.02%）可忽略不计，这表明 GPSM 能够在实际猪群体中区分选择和随机遗传漂变。
这项工作证实了 GPSM 方法在检测商业猪群体中选择基因座的有效性和可忽略不计的错误率。我们的结果表明，猪的遗传结构和选择目标在不同群体中是相似的。所鉴定的多基因选择突出了对猪生产重要的基因座。
