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Qi Z, Bai N, Li Q, Pan S, Gu M. Dietary fishmeal replacement by Clostridium autoethanogenum protein meal influences the nutritional and sensory quality of turbot ( Scophthalmus maximus) via the TOR/AAR/AMPK pathways. ANIMAL NUTRITION (ZHONGGUO XU MU SHOU YI XUE HUI) 2024; 18:84-95. [PMID: 39056058 PMCID: PMC11269857 DOI: 10.1016/j.aninu.2024.04.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/31/2023] [Revised: 01/29/2024] [Accepted: 04/29/2024] [Indexed: 07/28/2024]
Abstract
Clostridium autoethanogenum protein (CAP) is a promising protein source for aquaculture; however, how CAP influences fish quality is worth extensive research. We randomly allocated 630 turbot with initial body weights of about 180 g into 6 groups, with fishmeal-based control diet or diet with CAP replacing 15% (CAP15), 30% (CAP30), 45% (CAP45), 60% (CAP60), or 75% (CAP75) of fishmeal protein. After a 70-d feeding trial, the fillet yield (P = 0.015) and content of protein (P = 0.017), collagen (P < 0.001), hydroxyproline (P < 0.001), C20:5n-3 (P = 0.007), and ∑n-3/∑n-6 polyunsaturated fatty acids ratio (P < 0.001) in turbot muscle was found to decrease linearly with increasing CAP. However, turbot fed CAP15 diet maintained these parameters (P > 0.05). By contrast, the muscle hardness increased linearly with increasing CAP (P = 0.004), accompanied by linear reduction of muscle fiber area (P = 0.003) and expression of myogenesis-related genes, including cathepsin D (ctsd P < 0.001) and muscle ring finger protein 1 (murf 1, P < 0.001). Phosphorylation of protein kinase B (Akt, P < 0.001), target of rapamycin (TOR, P = 0.001), eukaryotic initiation factor 4E-binding protein 1 (4E-BP1, P < 0.001), and ribosomal protein S6 (S6, P < 0.001) decreased linearly; however, phosphorylation of AMP-activated protein kinase (AMPK, P < 0.001), eukaryotic initiation factor 2α (eIF2α, P < 0.001), and the abundance of activating transcription factor 4 (ATF4, P < 0.001) increased with increasing CAP, suggesting that the TOR signaling pathway was inhibited, and the amino acid response (AAR) and AMPK pathways were activated. Additionally, expression of genes related to protein degradation, including myogenic factor 5 (myf 5, P < 0.001), myogenic differentiation (myod, P < 0.001), paired box 7 (pax 7, P < 0.001), and ctsd (P < 0.001), decreased linearly with increasing CAP. In conclusion, CAP could be used to replace up to 15% of fishmeal without negatively impacting turbot quality. However, higher levels of CAP decreased fillet yield, muscle protein content, and muscle fiber diameter while increasing muscle hardness, which could be attributed to the inhibition of the TOR pathway and activation of the AAR and AMPK pathways.
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Affiliation(s)
- Zezheng Qi
- Marine College, Shandong University, Weihai, Shandong, China
- Key Laboratory of Modern Marine Ranching Technology of Weihai, Weihai, Shandong, China
| | - Nan Bai
- Marine College, Shandong University, Weihai, Shandong, China
- Key Laboratory of Modern Marine Ranching Technology of Weihai, Weihai, Shandong, China
| | - Qing Li
- Marine College, Shandong University, Weihai, Shandong, China
- Key Laboratory of Modern Marine Ranching Technology of Weihai, Weihai, Shandong, China
| | - Shihui Pan
- Marine College, Shandong University, Weihai, Shandong, China
- Key Laboratory of Modern Marine Ranching Technology of Weihai, Weihai, Shandong, China
| | - Min Gu
- Marine College, Shandong University, Weihai, Shandong, China
- Key Laboratory of Modern Marine Ranching Technology of Weihai, Weihai, Shandong, China
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Rahbar M, Safari R, Perez-Rostro CI. Defining desired genetic gains for Pacific white shrimp (Litopenaeus vannamei) breeding objective using participatory approaches. J Anim Breed Genet 2024; 141:390-402. [PMID: 38240192 DOI: 10.1111/jbg.12848] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2023] [Revised: 12/31/2023] [Accepted: 01/03/2024] [Indexed: 06/12/2024]
Abstract
The objective of this study was to define desired genetic gains from economically important traits of Pacific white shrimp (Litopenaeus vannamei) using participatory approaches. Two questionnaires were sent out to 100 Pacific white shrimp farmers in all five Iranian shrimp farming provinces. Questionnaire A (Q-A) includes management factors and farming environments. Moreover, in this questionnaire, farmers were asked to rank the fourth most important traits in shrimp among 10 economic traits in the list for genetic improvement. In questionnaire B (Q-B), priorities of the four traits with the highest value were obtained using pairwise comparison. The results showed that the four most important traits were white spot syndrome virus resistance (WSSV), growth rate before 4 months (GR), acute hepatopancreatic necrosis disease resistance (AHPND), and female total weight at ablation (FTW). Medians of the best individual preference values were WSSV (0.222), GR (0.173), AHPND (0.157), and FTW (0.053). Most disagreements were found between the social group preference values in the commercial products and water salinity categories. Desired genetic gains were 1.71%, 1.57%, 0.53% and 0.31% for GR, AHPND, WSSV and FTW, respectively. This study highlighted that despite environmental and management differences, participatory approaches can achieve desired genetic results for Pacific white shrimp breeding programme.
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Affiliation(s)
- Mina Rahbar
- Department of Fisheries, Faculty of Natural Resources, University of Guilan, Sowmeh Sara, Guilan, Iran
| | - Roghieh Safari
- Department of Fisheries and Environmental Sciences, Gorgan University of Agricultural Sciences and Natural Resources, Gorgan, Iran
| | - Carlos I Perez-Rostro
- Genetic Improvement and Aquaculture Production Lab, Instituto Tecnologico de Boca del Rio, Boca del Río, Mexico
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Garcia A, Tsuruta S, Gao G, Palti Y, Lourenco D, Leeds T. 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. Genet Sel Evol 2023; 55:11. [PMID: 36759760 PMCID: PMC9912574 DOI: 10.1186/s12711-023-00782-6] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2022] [Accepted: 01/16/2023] [Indexed: 02/11/2023] Open
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.
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Affiliation(s)
- Andre Garcia
- grid.213876.90000 0004 1936 738XDepartment of Animal and Dairy Science, University of Georgia, Athens, GA 30602 USA
| | - Shogo Tsuruta
- Department of Animal and Dairy Science, University of Georgia, Athens, GA, 30602, USA.
| | - Guangtu Gao
- grid.463419.d0000 0001 0946 3608National Center for Cool and Cold Water Aquaculture, Agricultural Research Service, United States Department of Agriculture, Kearneysville, WV 25430 USA
| | - Yniv Palti
- grid.463419.d0000 0001 0946 3608National Center for Cool and Cold Water Aquaculture, Agricultural Research Service, United States Department of Agriculture, Kearneysville, WV 25430 USA
| | - Daniela Lourenco
- grid.213876.90000 0004 1936 738XDepartment of Animal and Dairy Science, University of Georgia, Athens, GA 30602 USA
| | - Tim Leeds
- grid.463419.d0000 0001 0946 3608National Center for Cool and Cold Water Aquaculture, Agricultural Research Service, United States Department of Agriculture, Kearneysville, WV 25430 USA
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4
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Burns JG, Glenk K, Eory V, Simm G, Wall E. Preferences of European dairy stakeholders in breeding for resilient and efficient cattle: A best-worst scaling approach. J Dairy Sci 2021; 105:1265-1280. [PMID: 34955264 DOI: 10.3168/jds.2021-20316] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2021] [Accepted: 10/16/2021] [Indexed: 12/21/2022]
Abstract
Including resilience in the breeding objective of dairy cattle is gaining increasing attention, primarily as anticipated challenges to production systems, such as climate change, may make some perturbations more difficult to moderate at the farm level. Consequently, the underlying biological mechanisms by which resilience is achieved are likely to become an important part of the system itself, increasing value on the animal's ability to be unperturbed by variable production circumstances, or to quickly return to pre-perturbed levels of productivity and health. However, because the value of improving genetic traits to a system is usually based on known profit functions or bioeconomic models linked to current production conditions, it can be difficult to define longer-term value, especially under uncertain future production circumstances and where nonmonetary values may be progressively more important. We present the novel application of a discrete choice experiment, used to investigate potential antagonisms in the values of genetic improvements for 8 traits to dairy cattle system stakeholders in Europe when the production goal was either efficiency or resilience. A latent class model was used to identify heterogeneous preferences within each production goal, and postestimation was used to identify associations between these preferences and sociodemographic characteristics of respondents. Results suggested 3 distinct latent preference classes for each production goal. For the efficiency goal, yield and feed efficiency traits were generally highly valued, whereas for the resilience goal, health and robustness traits were generally highly valued. In both cases, these traits generally carried a low value in the other production scenario. Overall, in both scenarios, longevity was highly valued; however, the value of this trait in terms of resilience will depend on phenotyping across diverse environments to sufficiently capture performance under various anticipated system challenges. Additionally, results showed significant associations between membership of latent preference classes with education level and profession. In conclusion, as resilience becomes increasingly important, it is likely that a continued reliance on the short-term economic value of traits alone will lead decision makers to misrepresent the importance of some traits, including those with substantial contextual values in terms of resilience.
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Affiliation(s)
- J G Burns
- Global Academy of Agriculture and Food Security, University of Edinburgh, Easter Bush Campus, Edinburgh, EH25 9RG, United Kingdom; Scotland's Rural College (SRUC), Peter Wilson Building, King's Buildings, Edinburgh, EH9 3JG, United Kingdom.
| | - K Glenk
- Scotland's Rural College (SRUC), Peter Wilson Building, King's Buildings, Edinburgh, EH9 3JG, United Kingdom
| | - V Eory
- Scotland's Rural College (SRUC), Peter Wilson Building, King's Buildings, Edinburgh, EH9 3JG, United Kingdom
| | - G Simm
- Global Academy of Agriculture and Food Security, University of Edinburgh, Easter Bush Campus, Edinburgh, EH25 9RG, United Kingdom
| | - E Wall
- Scotland's Rural College (SRUC), Peter Wilson Building, King's Buildings, Edinburgh, EH9 3JG, United Kingdom
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Al-Tobasei R, Ali A, Garcia ALS, Lourenco D, Leeds T, Salem M. Genomic predictions for fillet yield and firmness in rainbow trout using reduced-density SNP panels. BMC Genomics 2021; 22:92. [PMID: 33516179 PMCID: PMC7847601 DOI: 10.1186/s12864-021-07404-9] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2020] [Accepted: 01/22/2021] [Indexed: 12/20/2022] Open
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. Supplementary Information The online version contains supplementary material available at 10.1186/s12864-021-07404-9.
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Affiliation(s)
- Rafet Al-Tobasei
- Computational Science Program, Middle Tennessee State University, Murfreesboro, TN, 37132, USA
| | - Ali Ali
- Department of Animal and Avian Sciences, University of Maryland, College Park, MD, 20742, USA
| | - Andre L S Garcia
- Department of Animal and Dairy Science, University of Georgia, Athens, GA, 30602, USA
| | - Daniela Lourenco
- Department of Animal and Dairy Science, University of Georgia, Athens, GA, 30602, USA
| | - Tim Leeds
- National Center for Cool and Cold Water Aquaculture, Agricultural Research Service, United States Department of Agriculture, Kearneysville, WV, USA
| | - Mohamed Salem
- Department of Animal and Avian Sciences, University of Maryland, College Park, MD, 20742, USA.
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Paakala E, Martín-Collado D, Mäki-Tanila A, Juga J. Farmers’ stated selection preferences differ from revealed AI bull selection in Finnish dairy herds. Livest Sci 2020. [DOI: 10.1016/j.livsci.2020.104117] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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Kariuki CM, van Arendonk JAM, Kahi AK, Komen H. Multiple criteria decision-making process to derive consensus desired genetic gains for a dairy cattle breeding objective for diverse production systems. J Dairy Sci 2017; 100:4671-4682. [PMID: 28390719 DOI: 10.3168/jds.2016-11454] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2016] [Accepted: 02/06/2017] [Indexed: 11/19/2022]
Abstract
Dairy cattle industries contribute to food and nutrition security and are a source of income for numerous households in many developing countries. Selective breeding can enhance efficiency in these industries. Developing dairy industries are characterized by diverse production and marketing systems. In this paper, we use weighted goal aggregating procedure to derive consensus trait preferences for different producer categories and processors. We based the study on the dairy industry in Kenya. The analytic hierarchy process was used to derive individual preferences for milk yield (MY), calving interval (CIN), production lifetime (PLT), mature body weight (MBW), and fat yield (FY). Results show that classical classification of production systems into large-scale and smallholder systems does not capture all differences in trait preferences. These differences became apparent when classification was based on productivity at the individual animal level, with high and low intensity producers and processors as the most important groups. High intensity producers had highest preferences for PLT and MY, whereas low intensity producers had highest preference for CIN and PLT; processors preferred MY and FY the most. The highest disagreements between the groups were observed for FY, PLT, and MY. Individual and group preferences were aggregated into consensus preferences using weighted goal programming. Desired gains were obtained as a product of consensus preferences and percentage genetic gains (G%). These were 2.42, 0.22, 2.51, 0.15, and 0.87 for MY, CIN, PLT, MBW, and FY, respectively. Consensus preferences can be used to derive a single compromise breeding objective for situations where the same genetic resources are used in diverse production and marketing circumstances.
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Affiliation(s)
- C M Kariuki
- Department of Animal Sciences, Chuka University, PO Box 109-60400, Chuka, Kenya; Animal Breeding and Genomics Centre, Wageningen University, PO Box 338, 6700 AH Wageningen, the Netherlands.
| | - J A M van Arendonk
- Hendrix Genetics Research, Technology and Services, PO Box 114, 5830 AC Boxmeer, the Netherlands
| | - A K Kahi
- Animal Breeding and Genomics Group, Department of Animal Sciences, Egerton University, PO Box 536-20115, Egerton, Kenya
| | - H Komen
- Animal Breeding and Genomics Centre, Wageningen University, PO Box 338, 6700 AH Wageningen, the Netherlands
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Sae-Lim P, Kause A, Lillehammer M, Mulder HA. Estimation of breeding values for uniformity of growth in Atlantic salmon (Salmo salar) using pedigree relationships or single-step genomic evaluation. Genet Sel Evol 2017; 49:33. [PMID: 28270100 PMCID: PMC5439168 DOI: 10.1186/s12711-017-0308-3] [Citation(s) in RCA: 39] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2016] [Accepted: 02/28/2017] [Indexed: 01/22/2023] Open
Abstract
Background In farmed Atlantic salmon, heritability for uniformity of body weight is low, indicating that the accuracy of estimated breeding values (EBV) may be low. The use of genomic information could be one way to increase accuracy and, hence, obtain greater response to selection. Genomic information can be merged with pedigree information to construct a combined relationship matrix (\documentclass[12pt]{minimal}
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\begin{document}$${\mathbf{H}}$$\end{document}H matrix) for a single-step genomic evaluation (ssGBLUP), allowing realized relationships of the genotyped animals to be exploited, in addition to numerator pedigree relationships (\documentclass[12pt]{minimal}
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\begin{document}$${\mathbf{A}}$$\end{document}A matrix). We compared the predictive ability of EBV for uniformity of body weight in Atlantic salmon, when implementing either the \documentclass[12pt]{minimal}
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\begin{document}$${\mathbf{H}}$$\end{document}H matrix in the genetic evaluation. We used double hierarchical generalized linear models (DHGLM) based either on a sire-dam (sire-dam DHGLM) or an animal model (animal DHGLM) for both body weight and its uniformity. Results With the animal DHGLM, the use of \documentclass[12pt]{minimal}
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\begin{document}$${\mathbf{A}}$$\end{document}A significantly increased the correlation between the predicted EBV and adjusted phenotypes, which is a measure of predictive ability, for both body weight and its uniformity (41.1 to 78.1%). When log-transformed body weights were used to account for a scale effect, the use of \documentclass[12pt]{minimal}
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\begin{document}$${\mathbf{A}}$$\end{document}A produced a small and non-significant increase (1.3 to 13.9%) in predictive ability. The sire-dam DHGLM had lower predictive ability for uniformity compared to the animal DHGLM. Conclusions Use of the combined numerator and genomic relationship matrix (\documentclass[12pt]{minimal}
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\begin{document}$${\mathbf{H}}$$\end{document}H) significantly increased the predictive ability of EBV for uniformity when using the animal DHGLM for untransformed body weight. The increase was only minor when using log-transformed body weights, which may be due to the lower heritability of scaled uniformity, the lower genetic correlation of transformed body weight with its uniformity compared to the untransformed traits, and the small number of genotyped animals in the reference population. This study shows that ssGBLUP increases the accuracy of EBV for uniformity of body weight and is expected to increase response to selection in uniformity. Electronic supplementary material The online version of this article (doi:10.1186/s12711-017-0308-3) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Panya Sae-Lim
- Nofima Ås, Osloveien 1, P.O. Box 210, 1431, Ås, Norway.
| | - Antti Kause
- Biometrical Genetics, Natural Resources Institute Finland, 31600, Jokioinen, Finland
| | | | - Han A Mulder
- Animal Breeding and Genomics Centre, Wageningen University and Research, P.O. Box 338, 6700 AH, Wageningen, The Netherlands
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Janhunen M, Koskela J, Ninh NH, Vehviläinen H, Koskinen H, Nousiainen A, Thỏa NP. Thermal sensitivity of growth indicates heritable variation in 1-year-old rainbow trout (Oncorhynchus mykiss). Genet Sel Evol 2016; 48:94. [PMID: 27899075 PMCID: PMC5127088 DOI: 10.1186/s12711-016-0272-3] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2016] [Accepted: 11/15/2016] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Rainbow trout is an important aquaculture species, which has a worldwide distribution across various production environments. The diverse locations of trout farms involve remarkable variation in environmental factors such as water temperature, which is of major importance for the performance of fish. Thus, robust fish that could thrive under different and suboptimal thermal conditions is a desirable goal for trout breeding. Using a split-family experimental design (40 full-/half-sib groups) for a rainbow trout population derived from the Finnish national breeding program, we studied how two different rearing temperatures (14 and 20 °C) affect feed intake, growth rate and feed conversion ratio in 1-year-old fish. Furthermore, we quantified the additive genetic (co-)variation for daily growth coefficient (DGC) and its thermal sensitivity (TS), defined as the slope of the growth reaction norm between the two temperatures. RESULTS The fish showed consistently lower feed intake, faster growth and better feed conversion ratio at the lower temperature. Heritability of TS of DGC was moderate ([Formula: see text]). The co-heritability parameter derived from selection index theory, which describes the heritable variance of TS, was negative when the intercept was placed at the lower temperature (-0.28). This resulted in moderate accuracy of selection. At the higher temperature, co-heritability of TS was positive (0.20). The genetic correlation between DGC and its TS was strongly negative (-0.64) when the intercept was at the lower temperature and positive (0.38) but not significantly different from zero at the higher temperature. CONCLUSIONS The considerable amount of genetic variation in TS of growth indicates a potential for selection response and thus for targeted genetic improvement in TS. The negative genetic correlation between DGC and its TS suggests that selection for high growth rate at the lower temperature will result in more temperature-sensitive fish. Instead, the correlated response of TS is less pronounced if the selection for a higher DGC occurred at the higher temperature. It seems possible to control the correlated genetic change of TS while selecting for fast growth across environments, especially if measurements from both environments are available and breeding values for reaction norm slope are directly included in the selection index.
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Affiliation(s)
- Matti Janhunen
- Biometrical Genetics, Natural Resources Institute Finland (Luke), Myllytie 1, 31600, Jokioinen, Finland.
| | - Juha Koskela
- Aquaculture, Natural Resources Institute Finland (Luke), Survontie 9 A, 40500, Jyväskylä, Finland
| | - Nguyễn Hữu Ninh
- Research Institute for Aquaculture No. 3 (RIA-3), Nha Trang, Khanh Hoa, Vietnam
| | - Harri Vehviläinen
- Biometrical Genetics, Natural Resources Institute Finland (Luke), Myllytie 1, 31600, Jokioinen, Finland
| | - Heikki Koskinen
- Tervo Fish Farm, Natural Resources Institute Finland (Luke), Huuhtajantie 160, 72210, Tervo, Finland
| | - Antti Nousiainen
- Tervo Fish Farm, Natural Resources Institute Finland (Luke), Huuhtajantie 160, 72210, Tervo, Finland
| | - Ngô Phú Thỏa
- Research Institute for Aquaculture No. 1 (RIA-1), Dinh Bang, Tu Son, Bac Ninh, Vietnam
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Gonzalez-Pena D, Gao G, Baranski M, Moen T, Cleveland BM, Kenney PB, Vallejo RL, Palti Y, Leeds TD. Genome-Wide Association Study for Identifying Loci that Affect Fillet Yield, Carcass, and Body Weight Traits in Rainbow Trout ( Oncorhynchus mykiss). Front Genet 2016; 7:203. [PMID: 27920797 PMCID: PMC5118429 DOI: 10.3389/fgene.2016.00203] [Citation(s) in RCA: 60] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2016] [Accepted: 11/02/2016] [Indexed: 11/22/2022] Open
Abstract
Fillet yield (FY, %) is an economically-important trait in rainbow trout aquaculture that affects production efficiency. Despite that, FY has received little attention in breeding programs because it is difficult to measure on a large number of fish and cannot be directly measured on breeding candidates. The recent development of a high-density SNP array for rainbow trout has provided the needed tool for studying the underlying genetic architecture of this trait. A genome-wide association study (GWAS) was conducted for FY, body weight at 10 (BW10) and 13 (BW13) months post-hatching, head-off carcass weight (CAR), and fillet weight (FW) in a pedigreed rainbow trout population selectively bred for improved growth performance. The GWAS analysis was performed using the weighted single-step GBLUP method (wssGWAS). Phenotypic records of 1447 fish (1.5 kg at harvest) from 299 full-sib families in three successive generations, of which 875 fish from 196 full-sib families were genotyped, were used in the GWAS analysis. A total of 38,107 polymorphic SNPs were analyzed in a univariate model with hatch year and harvest group as fixed effects, harvest weight as a continuous covariate, and animal and common environment as random effects. A new linkage map was developed to create windows of 20 adjacent SNPs for use in the GWAS. The two windows with largest effect for FY and FW were located on chromosome Omy9 and explained only 1.0-1.5% of genetic variance, thus suggesting a polygenic architecture affected by multiple loci with small effects in this population. One window on Omy5 explained 1.4 and 1.0% of the genetic variance for BW10 and BW13, respectively. Three windows located on Omy27, Omy17, and Omy9 (same window detected for FY) explained 1.7, 1.7, and 1.0%, respectively, of genetic variance for CAR. Among the detected 100 SNPs, 55% were located directly in genes (intron and exons). Nucleotide sequences of intragenic SNPs were blasted to the Mus musculus genome to create a putative gene network. The network suggests that differences in the ability to maintain a proliferative and renewable population of myogenic precursor cells may affect variation in growth and fillet yield in rainbow trout.
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Affiliation(s)
- Dianelys Gonzalez-Pena
- United States Department of Agriculture, National Center for Cool and Cold Water Aquaculture, Agricultural Research ServiceKearneysville, WV, USA
| | - Guangtu Gao
- United States Department of Agriculture, National Center for Cool and Cold Water Aquaculture, Agricultural Research ServiceKearneysville, WV, USA
| | | | | | - Beth M. Cleveland
- United States Department of Agriculture, National Center for Cool and Cold Water Aquaculture, Agricultural Research ServiceKearneysville, WV, USA
| | - P. Brett Kenney
- Division of Animal and Nutritional Sciences, West Virginia UniversityMorgantown, WV, USA
| | - Roger L. Vallejo
- United States Department of Agriculture, National Center for Cool and Cold Water Aquaculture, Agricultural Research ServiceKearneysville, WV, USA
| | - Yniv Palti
- United States Department of Agriculture, National Center for Cool and Cold Water Aquaculture, Agricultural Research ServiceKearneysville, WV, USA
| | - Timothy D. Leeds
- United States Department of Agriculture, National Center for Cool and Cold Water Aquaculture, Agricultural Research ServiceKearneysville, WV, USA
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11
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Alfonso L. Technical note: An approach to derive breeding goals from the preferences of decision makers. J Anim Sci 2016; 94:4498-4506. [DOI: 10.2527/jas.2016-0685] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
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12
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Marjanovic J, Mulder HA, Khaw HL, Bijma P. Genetic parameters for uniformity of harvest weight and body size traits in the GIFT strain of Nile tilapia. Genet Sel Evol 2016; 48:41. [PMID: 27286860 PMCID: PMC4901462 DOI: 10.1186/s12711-016-0218-9] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2015] [Accepted: 05/24/2016] [Indexed: 12/04/2022] Open
Abstract
Background Animal breeding programs have been very successful in improving the mean levels of traits through selection. However, in recent decades, reducing the variability of trait levels between individuals has become a highly desirable objective. Reaching this objective through genetic selection requires that there is genetic variation in the variability of trait levels, a phenomenon known as genetic heterogeneity of environmental (residual) variance. The aim of our study was to investigate the potential for genetic improvement of uniformity of harvest weight and body size traits (length, depth, and width) in the genetically improved farmed tilapia (GIFT) strain. In order to quantify the genetic variation in uniformity of traits and estimate the genetic correlations between level and variance of the traits, double hierarchical generalized linear models were applied to individual trait values. Results Our results showed substantial genetic variation in uniformity of all analyzed traits, with genetic coefficients of variation for residual variance ranging from 39 to 58 %. Genetic correlation between trait level and variance was strongly positive for harvest weight (0.60 ± 0.09), moderate and positive for body depth (0.37 ± 0.13), but not significantly different from 0 for body length and width. Conclusions Our results on the genetic variation in uniformity of harvest weight and body size traits show good prospects for the genetic improvement of uniformity in the GIFT strain. A high and positive genetic correlation was estimated between level and variance of harvest weight, which suggests that selection for heavier fish will also result in more variation in harvest weight. Simultaneous improvement of harvest weight and its uniformity will thus require index selection. Electronic supplementary material The online version of this article (doi:10.1186/s12711-016-0218-9) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Jovana Marjanovic
- Animal Breeding and Genomics Centre, Wageningen University and Research, PO Box 338, 6700 AH, Wageningen, The Netherlands. .,Department of Animal Breeding and Genetics, Swedish University of Agricultural Sciences, Box 7023, 75007, Uppsala, Sweden.
| | - Han A Mulder
- Animal Breeding and Genomics Centre, Wageningen University and Research, PO Box 338, 6700 AH, Wageningen, The Netherlands
| | - Hooi L Khaw
- WorldFish, Jalan Batu Maung, 11960, Bayan Lepas, Penang, Malaysia
| | - Piter Bijma
- Animal Breeding and Genomics Centre, Wageningen University and Research, PO Box 338, 6700 AH, Wageningen, The Netherlands
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Omasaki S, van Arendonk J, Kahi A, Komen H. Defining a breeding objective for Nile tilapia that takes into account the diversity of smallholder production systems. J Anim Breed Genet 2016; 133:404-13. [DOI: 10.1111/jbg.12210] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2015] [Accepted: 02/12/2016] [Indexed: 11/28/2022]
Affiliation(s)
- S.K. Omasaki
- Animal Breeding and Genomics Centre; Wageningen University; Wageningen the Netherlands
- Animal Breeding and Genomics Group; Department of Animal Sciences; Egerton University; Egerton Kenya
| | - J.A.M van Arendonk
- Animal Breeding and Genomics Centre; Wageningen University; Wageningen the Netherlands
| | - A.K. Kahi
- Animal Breeding and Genomics Group; Department of Animal Sciences; Egerton University; Egerton Kenya
| | - H. Komen
- Animal Breeding and Genomics Centre; Wageningen University; Wageningen the Netherlands
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Besson M, Komen H, Aubin J, de Boer IJM, Poelman M, Quillet E, Vancoillie C, Vandeputte M, van Arendonk JAM. Economic values of growth and feed efficiency for fish farming in recirculating aquaculture system with density and nitrogen output limitations: a case study with African catfish (Clarias gariepinus). J Anim Sci 2015; 92:5394-405. [PMID: 25414104 DOI: 10.2527/jas.2014-8266] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
In fish farming, economic values (EV) of breeding goal traits are lacking, even though they are key parameters when defining selection objectives. The aim of this study was to develop a bioeconomic model to estimate EV of 2 traits representing production performances in fish farming: the thermal growth coefficient (TGC) and the feed conversion ratio (FCR). This approach was applied to a farm producing African catfish (Clarias gariepinus) in a recirculating aquaculture system (RAS). In the RAS, 2 factors could limit production level: the nitrogen treatment capacity of the biofilter or the fish density in rearing tanks at harvest. Profit calculation includes revenue from fish sales, cost of juveniles, cost of feed, cost of waste water treatment, and fixed costs. In the reference scenario, profit was modeled to zero. EV were calculated as the difference in profit per kilogram of fish between the current population mean for both traits (µt) and the next generation of selective breeding (µt+Δt) for either TGC or FCR. EV of TGC and FCR were calculated for three generations of hypothetical selection on either TGC or FCR (respectively 6.8% and 7.6% improvement per generation). The results show that changes in TGC and FCR can affect both the number of fish that can be stocked (number of batches per year and number of fish per batch) and the factor limiting production. The EV of TGC and FCR vary and depend on the limiting factors. When dissolved NH3-N is the limiting factor for both µt and µt+Δt, increasing TGC decreases the number of fish that can be stocked but increases the number of batches that can be grown. As a result, profit remains constant and EVTGC is zero. Increasing FCR, however, increases the number of fish stocked and the ratio of fish produced per kilogram of feed consumed ("economic efficiency"). The EVFCR is 0.14 €/kg of fish, and profit per kilogram of fish increases by about 10%. When density is the limiting factor for both µt and µt+Δt, the number of fish stocked per batch is fixed; therefore, extra profit is obtained by increasing either TGC, which increases the annual number of batches, or by decreasing FCR, which decreases annual feed consumption. EVTGC is 0.03 €/kg of fish and EVFCR is 0.05-0.06 €/kg of fish. These results emphasize the importance of calculating economic values in the right context to develop efficient future breeding programs in aquaculture.
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Affiliation(s)
- M Besson
- Animal Breeding and Genomics Centre, Wageningen University, P.O. Box 338, NL-6700 AH Wageningen, the Netherlands AgroParisTech, UMR1313 Génétique animale et biologie intégrative, 16 rue Claude Bernard, F-75231 Paris 05, France INRA, UMR1313 Génétique animale et biologie intégrative, Allée de Vilvert, F-78350 Jouy-en-Josas, France
| | - H Komen
- Animal Breeding and Genomics Centre, Wageningen University, P.O. Box 338, NL-6700 AH Wageningen, the Netherlands
| | - J Aubin
- INRA, Agrocampus Ouest Rennes, UMR1069 Sol Agronomie Spatialisation, 65 rue de Saint Brieuc, F-35042 Rennes, France
| | - I J M de Boer
- Animal Production Systems group, Wageningen University, P.O. Box 338, NL-6700 AH Wageningen, the Netherlands
| | - M Poelman
- IMARES, Wageningen UR, Korringaweg 5, NL-4401 NT Yerseke, the Netherlands
| | - E Quillet
- INRA, UMR1313 Génétique animale et biologie intégrative, Allée de Vilvert, F-78350 Jouy-en-Josas, France
| | - C Vancoillie
- Fishion Breeding, Breedijk 13, NL-5705 CJ Helmond, the Netherlands
| | - M Vandeputte
- INRA, UMR1313 Génétique animale et biologie intégrative, Allée de Vilvert, F-78350 Jouy-en-Josas, France IFREMER, Chemin de Maguelone, F-34250 Palavas-les-Flots, France
| | - J A M van Arendonk
- Animal Breeding and Genomics Centre, Wageningen University, P.O. Box 338, NL-6700 AH Wageningen, the Netherlands
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Sae-Lim P, Kause A, Janhunen M, Vehviläinen H, Koskinen H, Gjerde B, Lillehammer M, Mulder HA. Genetic (co)variance of rainbow trout (Oncorhynchus mykiss) body weight and its uniformity across production environments. Genet Sel Evol 2015; 47:46. [PMID: 25986847 PMCID: PMC4435928 DOI: 10.1186/s12711-015-0122-8] [Citation(s) in RCA: 40] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2014] [Accepted: 04/21/2015] [Indexed: 11/16/2022] Open
Abstract
Background When rainbow trout from a single breeding program are introduced into various production environments, genotype-by-environment (GxE) interaction may occur. Although growth and its uniformity are two of the most important traits for trout producers worldwide, GxE interaction on uniformity of growth has not been studied. Our objectives were to quantify the genetic variance in body weight (BW) and its uniformity and the genetic correlation (rg) between these traits, and to investigate the degree of GxE interaction on uniformity of BW in breeding (BE) and production (PE) environments using double hierarchical generalized linear models. Log-transformed data were also used to investigate whether the genetic variance in uniformity of BW, GxE interaction on uniformity of BW, and rg between BW and its uniformity were influenced by a scale effect. Results Although heritability estimates for uniformity of BW were low and of similar magnitude in BE (0.014) and PE (0.012), the corresponding coefficients of genetic variation reached 19 and 21%, which indicated a high potential for response to selection. The genetic re-ranking for uniformity of BW (rg = 0.56) between BE and PE was moderate but greater after log-transformation, as expressed by the low rg (-0.08) between uniformity in BE and PE, which indicated independent genetic rankings for uniformity in the two environments when the scale effect was accounted for. The rg between BW and its uniformity were 0.30 for BE and 0.79 for PE but with log-transformed BW, these values switched to -0.83 and -0.62, respectively. Conclusions Genetic variance exists for uniformity of BW in both environments but its low heritability implies that a large number of relatives are needed to reach even moderate accuracy of selection. GxE interaction on uniformity is present for both environments and sib-testing in PE is recommended when the aim is to improve uniformity across environments. Positive and negative rg between BW and its uniformity estimated with original and log-transformed BW data, respectively, indicate that increased BW is genetically associated with increased variance in BW but with a decrease in the coefficient of variation. Thus, the scale effect substantially influences the genetic parameters of uniformity, especially the sign and magnitude of its rg.
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Affiliation(s)
- Panya Sae-Lim
- Nofima Ås, Osloveien 1, P.O. Box 210, NO-1431 Ås, Norway. .,Natural Resources Institute Finland (LUKE), Biometrical Genetics, FI-31600, Jokioinen, Finland.
| | - Antti Kause
- Natural Resources Institute Finland (LUKE), Biometrical Genetics, FI-31600, Jokioinen, Finland.
| | - Matti Janhunen
- Natural Resources Institute Finland (LUKE), Biometrical Genetics, FI-31600, Jokioinen, Finland.
| | - Harri Vehviläinen
- Natural Resources Institute Finland (LUKE), Biometrical Genetics, FI-31600, Jokioinen, Finland.
| | - Heikki Koskinen
- Natural Resources Institute Finland (LUKE), Aquaculture Unit, FI-72210, Tervo, Finland.
| | - Bjarne Gjerde
- Nofima Ås, Osloveien 1, P.O. Box 210, NO-1431 Ås, Norway.
| | | | - Han A Mulder
- Animal Breeding and Genomics Centre, Wageningen University, P.O. Box 338, 6700 AH, Wageningen, the Netherlands.
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Sae-Lim P, Kause A, Mulder HA, Martin KE, Barfoot AJ, Parsons JE, Davidson J, Rexroad CE, van Arendonk JAM, Komen H. Genotype-by-environment interaction of growth traits in rainbow trout (Oncorhynchus mykiss): a continental scale study. J Anim Sci 2013; 91:5572-81. [PMID: 24085417 DOI: 10.2527/jas.2012-5949] [Citation(s) in RCA: 46] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Rainbow trout is a globally important fish species for aquaculture. However, fish for most farms worldwide are produced by only a few breeding companies. Selection based solely on fish performance recorded at a nucleus may lead to lower-than-expected genetic gains in other production environments when genotype-by-environment (G × E) interaction exists. The aim was to quantify the magnitude of G × E interaction of growth traits (tagging weight; BWT, harvest weight; BWH, and growth rate; TGC) measured across 4 environments, located in 3 different continents, by estimating genetic correlations between environments. A total of 100 families, of at least 25 in size, were produced from the mating 58 sires and 100 dams. In total, 13,806 offspring were reared at the nucleus (selection environment) in Washington State (NUC) and in 3 other environments: a recirculating aquaculture system in Freshwater Institute (FI), West Virginia; a high-altitude farm in Peru (PE), and a cold-water farm in Germany (GER). To account for selection bias due to selective mortality, a multitrait multienvironment animal mixed model was applied to analyze the performance data in different environments as different traits. Genetic correlation (rg) of a trait measured in different environments and rg of different traits measured in different environments were estimated. The results show that heterogeneity of additive genetic variances was mainly found for BWH measured in FI and PE. Additive genetic coefficient of variation for BWH in NUC, FI, PE, and GER were 7.63, 8.36, 8.64, and 9.75, respectively. Genetic correlations between the same trait in different environments were low, indicating strong reranking (BWT: rg = 0.15 to 0.37, BWH: rg = 0.19 to 0.48, TGC: rg = 0.31 to 0.36) across environments. The rg between BWT in NUC and BWH in both FI (0.31) and GER (0.36) were positive, which was also found between BWT in NUC and TGC in both FI (0.10) and GER (0.20). However, rg were negative between BWT in NUC and both BWH (-0.06) and TGC (-0.20) in PE. Correction for selection bias resulted in higher additive genetic variances. In conclusion, strong G × E interaction was found for BWT, BWH, and TGC. Accounting for G × E interaction in the breeding program, either by using sib information from testing stations or environment-specific breeding programs, would increase genetic gains for environments that differ significantly from NUC.
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Affiliation(s)
- P Sae-Lim
- Animal Breeding and Genomics Centre, Wageningen University, P.O. Box 338, 6700 AH, Wageningen, the Netherlands
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Rexroad CE, Vallejo RL, Liu S, Palti Y, Weber GM. Quantitative trait loci affecting response to crowding stress in an F(2) generation of rainbow trout produced through phenotypic selection. MARINE BIOTECHNOLOGY (NEW YORK, N.Y.) 2013; 15:613-627. [PMID: 23709047 DOI: 10.1007/s10126-013-9512-5] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/12/2013] [Accepted: 05/09/2013] [Indexed: 06/02/2023]
Abstract
Selective breeding programs for salmonids typically aim to improve traits associated with growth and disease resistance. It has been established that stressors common to production environments can adversely affect these and other traits which are important to producers and consumers. Previously, we employed phenotypic selection to create families that exhibit high or low plasma cortisol concentrations in response to crowding stress. Subsequent crosses of high × low phenotypes founded a multigenerational breeding scheme with the aim of dissecting the genetic basis for variation underlying stress response through the identification of quantitative trait loci (QTL). Multiple methods of QTL analyses differing in their assumptions of homozygosity of the causal alleles in the grandparental generation yielded similar results in the F1 generation, and the analysis of two stress response phenotype measurement indexes were highly correlated. In the current study, we conducted a genome scan with microsatellites to detect QTL in the F2 generation of two families created through phenotypic selection and having larger numbers of offspring than families screened in the previous generation. Seven suggestive and three significant QTL were detected, seven of which were not previously detected in the National Center for Cool and Cold Water Aquaculture germplasm, bringing the total number of chromosomes containing significant and suggestive stress response QTL to 4 and 15, respectively. One significant QTL which peaks at 7 cM on chromosome Omy12 spans 12 cM and explains 25 % of the phenotypic variance in family 2008052 particularly warrants further investigation. Five QTL with significant parent-of-origin effects were detected in family 2008052, including two QTL on Omy12. The 95 % confidence intervals for the remaining QTL we detected were broad, requiring validation and fine mapping with other genotyping approaches and mapping strategies. These results will facilitate identification of potential casual alleles that can be employed in strategies aimed at better understanding the genetic and physiological basis of stress responses to crowding in rainbow trout aquaculture production.
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Affiliation(s)
- Caird E Rexroad
- USDA/ARS National Center for Cool and Cold Water Aquaculture, 11861 Leetown Road, Kearneysville, WV 25430, USA.
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Hu G, Gu W, Bai Q, Wang B. Estimation of genetic parameters for growth traits in a breeding program for rainbow trout (Oncorhynchus mykiss) in China. GENETICS AND MOLECULAR RESEARCH 2013; 12:1457-67. [DOI: 10.4238/2013.april.26.7] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
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