Su Ruilin, Huang Binyang, Tan Junyan, Shen Zhencai, Zhong Ping, Liu Jianfeng
College of Science, China Agricultural University, No. 17, Qinghua East Road, Haidian District, Beijing 100083, China.
College of Animal Science and Technology, China Agricultural University, No. 2, Yuanmingyuan West Road, Haidian District, Beijing 100193, China.
Brief Bioinform. 2025 May 1;26(3). doi: 10.1093/bib/bbaf231.
Genomic prediction is a crucial technique for phenotype estimation, with the genomic best linear unbiased prediction (GBLUP) being the most widely adopted method. Yet, GBLUP falls short in capturing the intricate nonlinear relationships between genomic data and phenotypes. Given its ability to more effectively capture nonlinear genetic effects, machine learning (ML) has become increasingly appealing in genomic prediction. However, almost GBLUP and ML methods utilize all single nucleotide polymorphisms (SNPs) data for prediction, ignoring the fact that only a subset of SNPs are effective. This not only consumes computation time but also has poor prediction accuracy. So, this paper proposed a mutual information stacking method (MISM). Firstly, mutual information was introduced to select the SNPs with effect and remove the redundant SNPs. Then, we constructed a stacking model that can capture both linear and nonlinear relationships between SNPs and phenotypes to improve the prediction accuracy. To assess the effectiveness of MISM, we compared its performance on pig growth traits with GBLUP and other ML methods. The statistical analysis results indicated that MISM outperformed other ML models and GBLUP.
基因组预测是一种用于表型估计的关键技术，其中基因组最佳线性无偏预测（GBLUP）是应用最广泛的方法。然而，GBLUP在捕捉基因组数据与表型之间复杂的非线性关系方面存在不足。鉴于机器学习（ML）能够更有效地捕捉非线性遗传效应，它在基因组预测中越来越具有吸引力。然而，几乎所有的GBLUP和ML方法都利用所有单核苷酸多态性（SNP）数据进行预测，却忽略了只有一部分SNP是有效的这一事实。这不仅消耗计算时间，而且预测准确率也很低。因此，本文提出了一种互信息堆叠方法（MISM）。首先，引入互信息来选择有效SNP并去除冗余SNP。然后，我们构建了一个堆叠模型，该模型可以捕捉SNP与表型之间的线性和非线性关系，以提高预测准确率。为了评估MISM的有效性，我们将其在猪生长性状上的表现与GBLUP和其他ML方法进行了比较。统计分析结果表明，MISM优于其他ML模型和GBLUP。
