Vu Sang V, Knibb Wayne, Gondro Cedric, Subramanian Sankar, Nguyen Ngoc T H, Alam Mobashwer, Dove Michael, Gilmour Arthur R, Vu In Van, Bhyan Salma, Tearle Rick, Khuong Le Duy, Le Tuan Son, O'Connor Wayne
GeneCology Research Centre, University of the Sunshine Coast, Sippy Downs, QLD, Australia.
School of Science, Technology and Engineering, University of the Sunshine Coast, Sippy Downs, QLD, Australia.
Front Genet. 2021 Jul 8;12:661276. doi: 10.3389/fgene.2021.661276. eCollection 2021.
Genetic improvement for quality traits, especially color and meat yield, has been limited in aquaculture because the assessment of these traits requires that the animals be slaughtered first. Genotyping technologies do, however, provide an opportunity to improve the selection efficiency for these traits. The main purpose of this study is to assess the potential for using genomic information to improve meat yield (soft tissue weight and condition index), body shape (cup and fan ratios), color (shell and mantle), and whole weight traits at harvest in the Portuguese oyster, . The study consisted of 647 oysters: 188 oysters from 57 full-sib families from the first generation and 459 oysters from 33 full-sib families from the second generation. The number per family ranged from two to eight oysters for the first and 12-15 oysters for the second generation. After quality control, a set of 13,048 markers were analyzed to estimate the genetic parameters (heritability and genetic correlation) and predictive accuracy of the genomic selection for these traits. The multi-locus mixed model analysis indicated high estimates of heritability for meat yield traits: 0.43 for soft tissue weight and 0.77 for condition index. The estimated genomic heritabilities were 0.45 for whole weight, 0.24 for cup ratio, and 0.33 for fan ratio and ranged from 0.14 to 0.54 for color traits. The genetic correlations among whole weight, meat yield, and body shape traits were favorably positive, suggesting that the selection for whole weight would have beneficial effects on meat yield and body shape traits. Of paramount importance is the fact that the genomic prediction showed moderate to high accuracy for the traits studied (0.38-0.92). Therefore, there are good prospects to improve whole weight, meat yield, body shape, and color traits using genomic information. A multi-trait selection program using the genomic information can boost the genetic gain and minimize inbreeding in the long-term for this population.
在水产养殖中,品质性状(尤其是颜色和肉产量)的遗传改良一直受到限制,因为对这些性状的评估需要先宰杀动物。然而,基因分型技术确实为提高这些性状的选择效率提供了机会。本研究的主要目的是评估利用基因组信息提高葡萄牙牡蛎收获时的肉产量(软组织重量和肥满度指数)、体型(杯状和扇状比例)、颜色(贝壳和外套膜)以及全重性状的潜力。该研究包括647只牡蛎:第一代来自57个全同胞家系的188只牡蛎和第二代来自33个全同胞家系的459只牡蛎。第一代每个家系的牡蛎数量为2至8只,第二代则为12至15只。经过质量控制后,分析了一组13048个标记,以估计这些性状的遗传参数(遗传力和遗传相关性)以及基因组选择的预测准确性。多位点混合模型分析表明,肉产量性状的遗传力估计值较高:软组织重量为0.43,肥满度指数为0.77。全重的估计基因组遗传力为0.45,杯状比例为0.24,扇状比例为0.33,颜色性状的遗传力在0.14至0.54之间。全重、肉产量和体型性状之间的遗传相关性呈有利的正相关,这表明选择全重对肉产量和体型性状有有益影响。至关重要的是,基因组预测对所研究的性状显示出中等至高的准确性(0.38 - 0.92)。因此,利用基因组信息改善全重、肉产量、体型和颜色性状有良好的前景。使用基因组信息的多性状选择计划可以在长期内提高该群体的遗传增益并最小化近亲繁殖。