Zhang Hongzhi, Pang Zhixu, Wang Wannian, Qiao Liying, Liu Wenzhong
Department of Animal Genetics, Breeding and Reproduction, College of Animal Science, Shanxi Agricultural University, Taigu 030801, China.
Key Laboratory of Farm Animal Genetic Resources Exploration and Precision Breeding of Shanxi Province, Taigu 030801, China.
Animals (Basel). 2025 May 10;15(10):1383. doi: 10.3390/ani15101383.
Natural or artificial selection could shape genetic architecture, e.g., the relationship between minor allele frequency (MAF) and the effect sizes of causal variants (CVs). This study aimed to investigate the impact of the MAF-effect size relationship (as a selection signature, ) on genomic prediction and heritability estimation in livestock, using both simulated data (Holstein) and real datasets (Holstein and pigs). We evaluated the performance of two models: (1) selection-adjusted genomic best linear unbiased prediction (GBLUP-S), and (2) MAF-stratified selection-adjusted genomic best linear unbiased prediction (GBLUP-SMS). Simulation results demonstrated that for traits under strong negative selection ( < -1), both GBLUP-S and GBLUP-SMS outperformed classic GBLUP. The prediction accuracy of GBLUP-S improved by 0.011-0.031, while GBLUP-SMS achieved a gain of 0.005-0.025. Furthermore, GBLUP-SMS exhibited lower sensitivity to variations in values, whereas GBLUP-S heavily relied on accurate specification. When the true was matched, GBLUP-SMS generated more unbiased (or comparable) heritability estimates and higher prediction accuracy relative to GBLUP-S. Critically, mismatched in GBLUP-S led to increased bias in heritability estimates and reduced prediction accuracy. Cross-validation with real phenotypic data from Holsteins and pigs demonstrated that implementing selection-adjust methods improved prediction accuracy by 0.015 for FP in Holsteins and 0.01 for T1 in pigs, while enhancing the unbiasedness of heritability estimates across all traits. Negative selection signatures were identified for cattle ( = -0.5) and pig T1, T2, and T3 ( = -1.5, -1, and -2, respectively). These findings advance the theoretical framework of GBLUP-based genomic prediction and heritability estimation.
自然选择或人工选择能够塑造遗传结构，例如，次要等位基因频率（MAF）与因果变异（CVs）效应大小之间的关系。本研究旨在利用模拟数据（荷斯坦奶牛）和真实数据集（荷斯坦奶牛和猪），探究MAF-效应大小关系（作为一种选择特征）对家畜基因组预测和遗传力估计的影响。我们评估了两种模型的性能：（1）选择调整后的基因组最佳线性无偏预测（GBLUP-S），以及（2）MAF分层选择调整后的基因组最佳线性无偏预测（GBLUP-SMS）。模拟结果表明，对于受到强负选择（< -1）的性状，GBLUP-S和GBLUP-SMS均优于经典GBLUP。GBLUP-S的预测准确性提高了0.011 - 0.031，而GBLUP-SMS提高了0.005 - 0.025。此外，GBLUP-SMS对值变化的敏感性较低，而GBLUP-S严重依赖于准确的设定。当真实值匹配时，相对于GBLUP-S，GBLUP-SMS产生的遗传力估计更无偏（或相当）且预测准确性更高。至关重要的是，GBLUP-S中不匹配的值会导致遗传力估计偏差增加和预测准确性降低。使用来自荷斯坦奶牛和猪的真实表型数据进行交叉验证表明，实施选择调整方法可使荷斯坦奶牛的FP预测准确性提高0.015，猪的T1预测准确性提高0.01，同时增强所有性状遗传力估计的无偏性。在牛（ = -0.5）和猪的T1、T2和T3（分别为 = -1.5、-1和 -2）中鉴定出了负选择特征。这些发现推进了基于GBLUP的基因组预测和遗传力估计的理论框架。
