Principal component analysis revisited: fast multitrait genetic evaluations with smooth convergence.

IF 2.1 3区 生物学 Q3 GENETICS & HEREDITY G3: Genes|Genomes|Genetics Pub Date : 2024-10-21 DOI:10.1093/g3journal/jkae228
Jon Ahlinder, David Hall, Mari Suontama, Mikko J Sillanpää
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Abstract

A cornerstone in breeding and population genetics is the genetic evaluation procedure, needed to make important decisions on population management. Multivariate mixed model analysis, in which many traits are considered jointly, utilizes genetic and environmental correlations between traits to improve the accuracy. However, the number of parameters in the multitrait model grows exponentially with the number of traits which reduces its scalability. Here, we suggest using principal component analysis to reduce the dimensions of the response variables, and then using the computed principal components as separate responses in the genetic evaluation analysis. As principal components are orthogonal to each other so that phenotypic covariance is abscent between principal components, a full multivariate analysis can be approximated by separate univariate analyses instead which should speed up computations considerably. We compared the approach to both traditional multivariate analysis and factor analytic approach in terms of computational requirement and rank lists according to predicted genetic merit on two forest tree datasets with 22 and 27 measured traits, respectively. Obtained rank lists of the top 50 individuals were in good agreement. Interestingly, the required computational time of the approach only took a few seconds without convergence issues, unlike the traditional approach which required considerably more time to run (7 and 10 h, respectively). The factor analytic approach took approximately 5-10 min. Our approach can easily handle missing data and can be used with all available linear mixed effect model softwares as it does not require any specific implementation. The approach can help to mitigate difficulties with multitrait genetic analysis in both breeding and wild populations.

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主成分分析重温:平稳收敛的快速多特征遗传评估
遗传评估程序是育种和种群遗传学的基石,是对种群管理做出重要决策所必需的。多变量混合模型分析法将许多性状结合起来考虑,利用性状之间的遗传和环境相关性来提高准确性。然而,多性状模型中的参数数量会随着性状数量的增加而呈指数增长,这就降低了模型的可扩展性。在此,我们建议使用主成分分析来减少响应变量的维数,然后在遗传评估分析中使用计算出的主成分作为单独的响应。由于主成分之间是正交的,因此主成分之间不存在表型协方差,因此可以用单独的单变量分析来代替完整的多变量分析,这将大大加快计算速度。我们在两个分别包含 22 和 27 个测量性状的林木数据集上,比较了该方法与传统多元分析方法和因子分析方法在计算要求和根据预测遗传优势排序方面的差异。获得的前 50 个个体的排名表非常一致。有趣的是,该方法所需的计算时间仅为几秒钟,且不会出现收敛问题,而传统方法则需要更长的运行时间(分别为 7 小时和 10 小时)。因子分析方法大约需要 5-10 分钟。我们的方法可以轻松处理缺失数据,并且可以与所有可用的线性混合效应模型软件一起使用,因为它不需要任何特定的实现方法。这种方法有助于减轻育种和野生种群中多特征遗传分析的困难。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
G3: Genes|Genomes|Genetics
G3: Genes|Genomes|Genetics GENETICS & HEREDITY-
CiteScore
5.10
自引率
3.80%
发文量
305
审稿时长
3-8 weeks
期刊介绍: G3: Genes, Genomes, Genetics provides a forum for the publication of high‐quality foundational research, particularly research that generates useful genetic and genomic information such as genome maps, single gene studies, genome‐wide association and QTL studies, as well as genome reports, mutant screens, and advances in methods and technology. The Editorial Board of G3 believes that rapid dissemination of these data is the necessary foundation for analysis that leads to mechanistic insights. G3, published by the Genetics Society of America, meets the critical and growing need of the genetics community for rapid review and publication of important results in all areas of genetics. G3 offers the opportunity to publish the puzzling finding or to present unpublished results that may not have been submitted for review and publication due to a perceived lack of a potential high-impact finding. G3 has earned the DOAJ Seal, which is a mark of certification for open access journals, awarded by DOAJ to journals that achieve a high level of openness, adhere to Best Practice and high publishing standards.
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