Li Mianyan, Hall Thomas, MacHugh David E, Chen Liang, Garrick Dorian, Wang Lixian, Zhao Fuping
State Key Laboratory of Animal Biotech Breeding, Institute of Animal Science, Chinese Academy of Agricultural Sciences, Yuanmingyuan West Road, Beijing, 100193, China.
Animal Genomics Laboratory, UCD School of Agriculture and Food Science, University College Dublin, Belfield, Dublin, D04 V1W8, Ireland.
Brief Bioinform. 2024 Nov 22;26(1). doi: 10.1093/bib/bbae683.
Nonadditive genetic effects pose significant challenges to traditional genomic selection methods for quantitative traits. Machine learning approaches, particularly kernel-based methods, offer promising solutions to overcome these limitations. In this study, we developed a novel machine learning method, KPRR, which integrated a polynomial kernel into ridge regression to effectively capture nonadditive genetic effects. The predictive performance and computational efficiency of KPRR were evaluated using six datasets from various species, encompassing a total of 18 traits. All the traits were known to be influenced by additive, dominance, or epistatic genetic effects. We compared the performance of KPRR against six other genomic prediction methods: SPVR, BayesB, GBLUP, GEBLUP, GDBLUP, and DeepGS. For datasets dominated by additive effects, KPRR achieved superior prediction accuracies in the wheat dataset and comparable performance in the cattle dataset when compared to GBLUP. For datasets influenced by dominance effects, KPRR matched GDBLUP in accuracies in the pig dataset and outperformed GDBLUP in the sheep dataset. For datasets exhibiting epistatic effects, KPRR outperformed other methods in some traits, while BayesB showed superior performance in others. Incorporating nonadditive effects into a GBLUP model led to overall improvements in prediction accuracy. Regarding computational efficiency, KPRR was consistently the fastest, while BayesB was the slowest. Our findings demonstrated that KPRR provided significant advantages over traditional genomic prediction methods in capturing nonadditive effects.
非加性遗传效应对传统的数量性状基因组选择方法提出了重大挑战。机器学习方法，特别是基于核的方法，为克服这些局限性提供了有前景的解决方案。在本研究中，我们开发了一种新颖的机器学习方法KPRR，它将多项式核集成到岭回归中，以有效捕获非加性遗传效应。使用来自不同物种的六个数据集对KPRR的预测性能和计算效率进行了评估，这些数据集总共包含18个性状。已知所有性状都受加性、显性或上位性遗传效应的影响。我们将KPRR的性能与其他六种基因组预测方法进行了比较：SPVR、BayesB、GBLUP、GEBLUP、GDBLUP和DeepGS。对于以加性效应为主的数据集，与GBLUP相比，KPRR在小麦数据集中实现了更高的预测准确性，在牛数据集中的性能与之相当。对于受显性效应影响的数据集，KPRR在猪数据集中的准确性与GDBLUP相当，在绵羊数据集中的表现优于GDBLUP。对于表现出上位性效应的数据集，KPRR在某些性状上优于其他方法，而BayesB在其他性状上表现更优。将非加性效应纳入GBLUP模型可总体提高预测准确性。在计算效率方面，KPRR始终是最快的，而BayesB是最慢的。我们的研究结果表明，在捕获非加性效应方面，KPRR比传统的基因组预测方法具有显著优势。
