结合GO/KEGG功能注释提高了不同肉牛群体基因组预测的准确性和稳定性
Incorporating GO/KEGG Functional Annotations Improves the Accuracy and Stability of Genomic Prediction Across Diverse Beef Cattle Populations.
摘要(中文译文)
肉牛基因组选择（GS）在跨群体预测中面临挑战。参考群体的盲目扩大会因遗传噪声而降低准确性，而GBLUP等传统模型忽略了SNP的功能异质性。本研究利用三个具有不同遗传关系的模拟肉牛群体和一个对应出生体重（遗传力0.42）的单一性状，评估功能注释对跨群体GS的影响。我们比较了使用全部SNP或经GO和KEGG注释的SNP集的GBLUP、ssGBLUP和wGBLUP。采用基于多维标度（MDS）和固定指数（FST）的筛选构建混合参考群体。与通用SNP相比，功能注释SNP提高了跨群体预测的准确性和稳定性。在遗传关系较近的群体（PopB）中，GO-wGBLUP在10%参考比例下达到0.55~0.60的预测准确性，高于GBLUP（约0.50）和ssGBLUP（约0.52）。在遗传分化程度高的群体（PopA）中，当参考比例从10%增加到20%时，KEGG-wGBLUP的准确性损失较小（下降6.7%，从0.45降至0.42），低于GO-wGBLUP（下降10%，从0.50降至0.45）和GBLUP（下降20%，从0.35降至0.28）。在各情景中，功能SNP集将参考群体扩大导致的准确性损失从GBLUP的20.0%降低至GO-wGBLUP的12.5%。这些结果表明，wGBLUP结合GO或KEGG注释可提高肉牛出生体重性状跨群体GS的准确性和稳健性。基于GO的模型更适合遗传关系较近的群体，而基于KEGG的模型更适合高度分化的群体。所提出的框架为肉牛育种中的多群体GS设计提供了实用参考。
Abstract
GS in beef cattle faces challenges in cross-population prediction. Blind expansion of reference populations can reduce accuracy because of genetic noise, and traditional models such as GBLUP ignore functional heterogeneity of SNPs. In this study, we used three simulated beef cattle populations with different genetic relationships and a single trait corresponding to birth weight (heritability 0.42) to evaluate the effect of functional annotations on cross-population GS. We compared GBLUP, ssGBLUP and wGBLUP using either all SNPs or SNP sets annotated by GO and KEGG. Mixed reference populations were constructed with multidimensional scaling (MDS)- and fixation index (FST)-based screening. Functionally annotated SNPs increased cross-population prediction accuracy and stability compared with generic SNPs. In the population with close genetic relatedness (PopB), GO-wGBLUP achieved a prediction accuracy of 0.55-0.60 at a 10% reference proportion, higher than GBLUP (about 0.50) and ssGBLUP (about 0.52). In the population with high genetic differentiation (PopA), KEGG-wGBLUP showed a smaller loss of accuracy when the reference proportion increased from 10% to 20% (6.7% decline, from 0.45 to 0.42) than GO-wGBLUP (10% decline, from 0.50 to 0.45) and GBLUP (20% decline, from 0.35 to 0.28). Across scenarios, functional SNP sets reduced the loss of accuracy due to reference population expansion from 20.0% in GBLUP to 12.5% in GO-wGBLUP. These results indicate that wGBLUP combined with GO or KEGG annotations can improve the accuracy and robustness of cross-population GS for beef cattle birth weight traits. GO-based models are more suitable for closely related populations, whereas KEGG-based models are more suitable for highly differentiated populations. The proposed framework provides a practical reference for multi-population GS design in beef cattle breeding.
如何引用
AMA
Le Zhou, Lin Zhu, Fengying Ma, Mingjuan Gu, Risu Na, Wenguang Zhang. Incorporating GO/KEGG Functional Annotations Improves the Accuracy and Stability of Genomic Prediction Across Diverse Beef Cattle Populations.. Animals : an open access journal from MDPI. 2026; doi:10.3390/ani16121776.
APA
Le Zhou, Lin Zhu, Fengying Ma, Mingjuan Gu, Risu Na, Wenguang Zhang (2026). Incorporating GO/KEGG Functional Annotations Improves the Accuracy and Stability of Genomic Prediction Across Diverse Beef Cattle Populations.. Animals : an open access journal from MDPI. https://doi.org/10.3390/ani16121776
GB/T 7714
Le Zhou, Lin Zhu, Fengying Ma, Mingjuan Gu, Risu Na, Wenguang Zhang. Incorporating GO/KEGG Functional Annotations Improves the Accuracy and Stability of Genomic Prediction Across Diverse Beef Cattle Populations.[J]. Animals : an open access journal from MDPI, 2026 doi:10.3390/ani16121776.
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