Rafet Al-Tobasei1, Ali Ali2, Andre L S Garcia3, Daniela Lourenco3, Tim Leeds4, Mohamed Salem5. 1. Computational Science Program, Middle Tennessee State University, Murfreesboro, TN, 37132, USA. 2. Department of Animal and Avian Sciences, University of Maryland, College Park, MD, 20742, USA. 3. Department of Animal and Dairy Science, University of Georgia, Athens, GA, 30602, USA. 4. National Center for Cool and Cold Water Aquaculture, Agricultural Research Service, United States Department of Agriculture, Kearneysville, WV, USA. 5. Department of Animal and Avian Sciences, University of Maryland, College Park, MD, 20742, USA. mosalem@umd.edu.
Abstract
BACKGROUND: One of the most important goals for the rainbow trout aquaculture industry is to improve fillet yield and fillet quality. Previously, we showed that a 50 K transcribed-SNP chip can be used to detect quantitative trait loci (QTL) associated with fillet yield and fillet firmness. In this study, data from 1568 fish genotyped for the 50 K transcribed-SNP chip and ~ 774 fish phenotyped for fillet yield and fillet firmness were used in a single-step genomic BLUP (ssGBLUP) model to compute the genomic estimated breeding values (GEBV). In addition, pedigree-based best linear unbiased prediction (PBLUP) was used to calculate traditional, family-based estimated breeding values (EBV). RESULTS: The genomic predictions outperformed the traditional EBV by 35% for fillet yield and 42% for fillet firmness. The predictive ability for fillet yield and fillet firmness was 0.19-0.20 with PBLUP, and 0.27 with ssGBLUP. Additionally, reducing SNP panel densities indicated that using 500-800 SNPs in genomic predictions still provides predictive abilities higher than PBLUP. CONCLUSION: These results suggest that genomic evaluation is a feasible strategy to identify and select fish with superior genetic merit within rainbow trout families, even with low-density SNP panels.
BACKGROUND: One of the most important goals for the rainbow trout aquaculture industry is to improve fillet yield and fillet quality. Previously, we showed that a 50 K transcribed-SNP chip can be used to detect quantitative trait loci (QTL) associated with fillet yield and fillet firmness. In this study, data from 1568 fish genotyped for the 50 K transcribed-SNP chip and ~ 774 fish phenotyped for fillet yield and fillet firmness were used in a single-step genomic BLUP (ssGBLUP) model to compute the genomic estimated breeding values (GEBV). In addition, pedigree-based best linear unbiased prediction (PBLUP) was used to calculate traditional, family-based estimated breeding values (EBV). RESULTS: The genomic predictions outperformed the traditional EBV by 35% for fillet yield and 42% for fillet firmness. The predictive ability for fillet yield and fillet firmness was 0.19-0.20 with PBLUP, and 0.27 with ssGBLUP. Additionally, reducing SNP panel densities indicated that using 500-800 SNPs in genomic predictions still provides predictive abilities higher than PBLUP. CONCLUSION: These results suggest that genomic evaluation is a feasible strategy to identify and select fish with superior genetic merit within rainbow trout families, even with low-density SNP panels.
Authors: Roger L Vallejo; Timothy D Leeds; Guangtu Gao; James E Parsons; Kyle E Martin; Jason P Evenhuis; Breno O Fragomeni; Gregory D Wiens; Yniv Palti Journal: Genet Sel Evol Date: 2017-02-01 Impact factor: 4.297
Authors: Hsin-Yuan Tsai; Oswald Matika; Stefan McKinnon Edwards; Roberto Antolín-Sánchez; Alastair Hamilton; Derrick R Guy; Alan E Tinch; Karim Gharbi; Michael J Stear; John B Taggart; James E Bron; John M Hickey; Ross D Houston Journal: G3 (Bethesda) Date: 2017-04-03 Impact factor: 3.154
Authors: Carolina Peñaloza; Agustin Barria; Athina Papadopoulou; Chantelle Hooper; Joanne Preston; Matthew Green; Luke Helmer; Jacob Kean-Hammerson; Jennifer C Nascimento-Schulze; Diana Minardi; Manu Kumar Gundappa; Daniel J Macqueen; John Hamilton; Ross D Houston; Tim P Bean Journal: Front Genet Date: 2022-07-15 Impact factor: 4.772