The Changes in Genetic Parameters and Genomic Selection of Lambing Rate in Hu Sheep Following Marker‐Assisted Selection

家养绵羊繁殖 遗传力 生物 选择(遗传算法) 单核苷酸多态性 遗传学 数量性状位点 全基因组关联研究 基因座(遗传学) 遗传变异 遗传关联 遗传增益 遗传相关 基因 标记辅助选择 特质 遗传建筑学 候选基因 多基因 进化生物学 等位基因 选择性扫描 基因组选择 遗传标记 连锁不平衡 加性遗传效应 棕色瑞士 遗传模型 基因组学 遗传力缺失问题 遗传分析 计算生物学
作者
Yuan Zhao,XiaoXue Zhang,Fei Li,Huibin Tian,Deyin Zhang,Xiaolong Li,Yukun Zhang,JiangBo Cheng,ZongWu Ma,Changchun Lin,Xiwen Zeng,Liming Zhao,Weimin Wang
出处
期刊:Journal of Animal Breeding and Genetics [Wiley]
卷期号:143 (3): 465-478 被引量:1
标识
DOI:10.1111/jbg.70036
摘要

The identification of quantitative trait locus (QTL) or genes responsible for key agronomic traits has significantly enhanced genetic improvement through marker-assisted selection (MAS). However, the impacts of MAS on genetic parameters and subsequent selection processes have not been thoroughly characterised. Here, through genome-wide selective sweep analysis, we identified a diverse set of genes involved in oocyte meiosis, including PPP3CA, AR, PPP1CB, SPDYA, MAD1L1, and BMPR1B. The genome-wide association study (GWAS) further identified three genes UNC5C, BMPR1B, and PDLIM5 as being associated with lambing rate in Hu sheep. From these analyses, the FecB loci emerged as a potential molecular marker for lambing rate. with an increase of 0.5 lambs per G allele. The heritability of the lambing rate was estimated to be 0.19 (±0.02). Moreover, based on 10-fold cross-validation, the accuracy of genomic selection (GS) was found to be 0.30. Simulated MAS resulted in a reduction of the additive genetic variance components, with estimated heritability dropping to 0.14 (±0.02) and GS accuracy decreasing to 0.18-representing a decline of 26.42% and 34.81%, respectively. To address the reduced GS accuracy, we performed GWAS on the reference set to identify weighted single nucleotide polymorphisms (SNPs). This method has the potential to increase accuracy by 13.8%. Our study found that MAS has a negative impact on GS. To address this issue, we integrated prior information on SNPs from GWAS, which exhibit pleiotropic genetic architecture. This integration enables us to utilise genetic markers for complex traits more effectively, thereby improving the accuracy and efficiency of GS.
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