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在内蒙古内蒙古的羊毛特征基因组选择喀什米尔山羊基于GWAS先前标记信息

Huanfeng Yao¹, Na Wang², Yu Li²

¹College of Animal Science, Inner Mongolia Agricultural University, Hohhot 010018, China.

Animals : an open access journal from MDPI

|November 13, 2025

概括

这项研究通过整合全基因组关联研究 (GWAS) 标记,提高了内蒙古喀什米尔山羊 (IMCG) 毛皮特征的基因组预测准确度. 这种改进的选择准确性加速了育种计划中的遗传进步.

科学领域:

动物遗传学和动物繁殖
基因组学就是基因组学.
量化遗传学量化遗传学

背景情况:

内蒙古喀什米尔山羊 (IMCG) 产业依赖羊毛特征 (喀什米尔产量,羊毛长度,喀什米尔长度,喀什米尔直径) 来实现经济可行性.
基因组预测的传统动物模型假定SNP效应分布均,这可能不反映不同特征的遗传复杂性.
准确的基因组预测对于IMCG行业的有效选择和可持续发展至关重要.

研究的目的:

提高IMCGs中关键羊毛特征的基因组预测的准确性.
研究将全基因组关联研究 (GWAS) 衍生先前标记信息纳入基因组评估模型的实用性.
加强IMCGs的遗传选择策略,以加速遗传收益.

主要方法:

在2299个IMCG上进行了全基因组关联研究 (GWAS),使用67,021个归算的SNP.
从GWAS中利用显著的SNP (前5%-20%) 作为先前的生物信息,根据它们对遗传变异的贡献进行加权.
整合加权先前标记与剩余的位点,以构建一个亲属矩阵来估计基因参数和基因组繁殖值,使用修改后的GBLUP方法.

主要成果:

喀什米尔产量 (CY),羊毛长度 (WL),喀什米尔长度 (CL) 和喀什米尔直径 (CD) 的遗传概率估计分别为0.26,0.37,0.09和0.35.
整合前5%的标志物提高了基因组预测准确度,CY为16.67%,CL为19.75%.
整合前10%的标记物提高了WL的精度9.81%,CD的精度10.14%,比传统的GBLUP显著提高.

结论:

整合来自GWAS的先前标记信息显著提高了IMCG羊毛特征的基因组预测准确性.
这种方法可以实现更精确的遗传选择,从而在长期育种计划中加速遗传进步.
精细的基因组评估方法为提高IMCG行业的可持续性和经济生产率提供了有价值的工具.

关键词:

内蒙古喀什米尔山羊喀什米尔山羊羊毛的特征羊毛的特征基因组选择基因组选择预先提供信息.

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Genome-wide Association Studies-GWAS

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