Improving multi-population genomic prediction accuracy using multi-trait GBLUP models which incorporate global or local genetic correlation information

• Published in: Briefings in bioinformatics Vol. 25; no. 4
• Main Authors: Teng, Jun, Zhai, Tingting, Zhang, Xinyi, Zhao, Changheng, Wang, Wenwen, Tang, Hui, Wang, Dan, Shang, Yingli, Ning, Chao, Zhang, Qin
• Format: Journal Article
• Language: English
• Published: England Oxford University Press 23.05.2024; Oxford Publishing Limited (England)
• Subjects: Accuracy; Algorithms; Correlation; Datasets; Genetics, Population - methods; Genomics; Genomics - methods; Genotype; Genotype-environment interactions; Genotypes; Humans; Models, Genetic; Population genetics; Population studies; Populations; Predictions; Problem Solving Protocol; Quantitative Trait Loci; MTGBLUP; genomic prediction; global genetic correlation; multi-population; local genetic correlation
• ISSN: 1467-5463; 1477-4054; 1477-4054
• DOI: 10.1093/bib/bbae276

Abstract In the application of genomic prediction, a situation often faced is that there are multiple populations in which genomic prediction (GP) need to be conducted. A common way to handle the multi-population GP is simply to combine the multiple populations into a single population. However, since these populations may be subject to different environments, there may exist genotype-environment interactions which may affect the accuracy of genomic prediction. In this study, we demonstrated that multi-trait genomic best linear unbiased prediction (MTGBLUP) can be used for multi-population genomic prediction, whereby the performances of a trait in different populations are regarded as different traits, and thus multi-population prediction is regarded as multi-trait prediction by employing the between-population genetic correlation. Using real datasets, we proved that MTGBLUP outperformed the conventional multi-population model that simply combines different populations together. We further proposed that MTGBLUP can be improved by partitioning the global between-population genetic correlation into local genetic correlations (LGC). We suggested two LGC models, LGC-model-1 and LGC-model-2, which partition the genome into regions with and without significant LGC (LGC-model-1) or regions with and without strong LGC (LGC-model-2). In analysis of real datasets, we demonstrated that the LGC models could increase universally the prediction accuracy and the relative improvement over MTGBLUP reached up to 163.86% (25.64% on average).