基于低深度重测序的多世代联合基因组预测在鸡产蛋性状上的应用效果
刘晓琪
(
华南农业大学动物科学学院;华南农业大学动物科学学院
, 广东 , 广州 510642
)
叶浩强
(
华南农业大学动物科学学院;华南农业大学动物科学学院
, 广东 , 广州 510642
)
李恒丰
(
佛山市南海种禽有限公司，
, 广东 , 佛山 528225
)
蔡柏林
(
华南农业大学动物科学学院;华南农业大学动物科学学院
, 广东 , 广州 510642
)
郑煦灿
(
佛山市南海种禽有限公司，
, 广东 , 佛山 528225
)
聂庆华
(
华南农业大学动物科学学院;华南农业大学动物科学学院
, 广东 , 广州 510642
)
中图分类号:S831.2
文献标志码:A
文章编号:1008-3847(2026)04-0016-08
资助项目：国家重点研发计划重点专项（2021YFD1300100)；广东省农业农村厅种业振兴项目（2024-XPY-07-001）。
摘要:
在鸡的基因组选择中，受限于成本，分型个体少，且单一群体构建的参考群样本量比较有限，影响了基因组预测的准确度。低深度基因组重测序可以捕获全基因组变异，应用于基因组选择时，相较于
高深度测序，能在降低分型成本的同时保持较好的预测性能。合并多个群体进行联合基因组预测可以快速扩大参考群规模以提升预测精度。本研究以佛山市南海种禽有限公司某黄羽肉鸡专门化品系核心育种群为研究对象，利用该群体第 20 世代（G20）和第 21 世代（G21）共 1 910 个个体的低深度全基因组重测序数据与产蛋表型，通过 10 折 10 次重复交叉验证计算预测准确性，系统评估不同参考群与模型组合的基因组选择效果。在单世代参考群中，采用单性状模型（ST-PBLUP、ST-GBLUP 和 ST-ssGBLUP）进行预测 ；在两世代合并参考群中，同时采用单性状模型（ST-PBLUP、ST-GBLUP 和 ST-ssGBLUP）与多性状模型（MT-PBLUP、MT-GBLUP 和 MT-ssGBLUP）开展预测，以探究联合基因组预测在鸡基因组选择中的应用价值。 发现 ：单世代群体和合并世代群体的基因组预测相较常规 PBLUP 方法均具有更高的预测精度，且 ssGBLUP 的预测性能最好 ；联合基因组预测的效果优于单一参考群预测 ；与单一参考群预测相比， ST-ssGBLUP 方法对 5 个产蛋性状的预测准确性提高了 4.57% ~ 25.21%，对于 MT-ssGBLUP 方法，300 日龄产蛋数、正常蛋数 2 个性状在 2 个世代群体间的遗传相关极高（0.995），预测准确性分别提高了14.31% 和 23.41%。结果表明，在鸡的遗传育种上，相较于传统方法，基因组选择可以显著提高预测准确性；多世代联合基因组预测较单一参考群预测能获得更好的预测效果。
关键字:
产蛋性状 低深度全基因组重测序 联合基因组预测 多性状模型
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责任编辑: 王守志
