دورية أكاديمية
Genomic selection models substantially improve the accuracy of genetic merit predictions for fillet yield and body weight in rainbow trout using a multi-trait model and multi-generation progeny testing
| العنوان: | Genomic selection models substantially improve the accuracy of genetic merit predictions for fillet yield and body weight in rainbow trout using a multi-trait model and multi-generation progeny testing |
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| المؤلفون: | Andre Garcia, Shogo Tsuruta, Guangtu Gao, Yniv Palti, Daniela Lourenco, Tim Leeds |
| المصدر: | Genetics Selection Evolution, Vol 55, Iss 1, Pp 1-12 (2023) |
| بيانات النشر: | BMC, 2023. |
| سنة النشر: | 2023 |
| المجموعة: | LCC:Animal culture LCC:Genetics |
| مصطلحات موضوعية: | Animal culture, SF1-1100, Genetics, QH426-470 |
| الوصف: | Abstract Background In aquaculture, the proportion of edible meat (FY = fillet yield) is of major economic importance, and breeding animals of superior genetic merit for this trait can improve efficiency and profitability. Achieving genetic gains for fillet yield is possible using a pedigree-based best linear unbiased prediction (PBLUP) model with direct and indirect selection. To investigate the feasibility of using genomic selection (GS) to improve FY and body weight (BW) in rainbow trout, the prediction accuracy of GS models was compared to that of PBLUP. In addition, a genome-wide association study (GWAS) was conducted to identify quantitative trait loci (QTL) for the traits. All analyses were performed using a two-trait model with FY and BW, and variance components, heritability, and genetic correlations were estimated without genomic information. The data used included 14,165 fish in the pedigree, of which 2742 and 12,890 had FY and BW phenotypic records, respectively, and 2484 had genotypes from the 57K single nucleotide polymorphism (SNP) array. Results The heritabilities were moderate, at 0.41 and 0.33 for FY and BW, respectively. Both traits were lowly but positively correlated (genetic correlation; r = 0.24), which suggests potential favourable correlated genetic gains. GS models increased prediction accuracy compared to PBLUP by up to 50% for FY and 44% for BW. Evaluations were found to be biased when validation was performed on future performances but not when it was performed on future genomic estimated breeding values. Conclusions The low but positive genetic correlation between fillet yield and body weight indicates that some improvement in fillet yield may be achieved through indirect selection for body weight. Genomic information increases the prediction accuracy of breeding values and is an important tool to accelerate genetic progress for fillet yield and growth in the current rainbow trout population. No significant QTL were found for either trait, indicating that both traits are polygenic, and that marker-assisted selection will not be helpful to improve these traits in this population. |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | German English French |
| تدمد: | 1297-9686 |
| Relation: | https://doaj.org/toc/1297-9686 |
| DOI: | 10.1186/s12711-023-00782-6 |
| URL الوصول: | https://doaj.org/article/01edec849b724c43bc54e8adb5a9b97f |
| رقم الأكسشن: | edsdoj.01edec849b724c43bc54e8adb5a9b97f |
| قاعدة البيانات: | Directory of Open Access Journals |
Find this article in full text from Springer Verlag. 
Full Text Finder | تدمد: | 12979686 |
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| DOI: | 10.1186/s12711-023-00782-6 |