Harnessing genomics to fast-track genetic improvement in aquaculture

Abstract

Aquaculture is the fastest-growing farmed food sector and will soon become the primary source of fish and shellfish for human diets. In contrast to crop and livestock production, aquaculture production is derived from numerous, exceptionally diverse species that are typically in the early stages of domestication. Genetic improvement of production traits via well-designed, managed breeding programmes has great potential to help meet the rising seafood demand driven by human population growth. Supported by continuous advances in sequencing and bioinformatics, genomics is increasingly being applied across the broad range of aquaculture species and at all stages of the domestication process to optimize selective breeding. In the future, combining genomic selection with biotechnological innovations, such as genome editing and surrogate broodstock technologies, may further expedite genetic improvement in aquaculture.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

Fig. 1: Summary of global aquaculture diversity and production.
Fig. 2: Genomic selection within an aquaculture breeding programme.
Fig. 3: Discovering functional variants using genomics and genome editing.
Fig. 4: Potential application of surrogate broodstock technology to accelerate genetic gain.

References

  1. 1.

    Anderson, J. L., Asche, F., Garlock, T. & Chu, J. Aquaculture: its role in the future of food. Front. Econ. Glob. 17, 159–173 (2017).

    Article  Google Scholar 

  2. 2.

    Food and Agricultural Organization. FAO Yearbook of Fishery and Aquaculture Statistics 2017. (FAO, 2019).

  3. 3.

    Longo, S. B., Clark, B., York, R. & Jorgenson, A. K. Aquaculture and the displacement of fisheries captures. Conserv. Biol. 33, cobi.13295 (2019).

    Article  Google Scholar 

  4. 4.

    Froehlich, H. E., Runge, C. A., Gentry, R. R., Gaines, S. D. & Halpern, B. S. Comparative terrestrial feed and land use of an aquaculture-dominant world. Proc. Natl Acad. Sci. USA 115, 5295–5300 (2018).

    CAS  Article  PubMed  Google Scholar 

  5. 5.

    Ahmed, N., Thompson, S. & Glaser, M. Global aquaculture productivity, environmental sustainability, and climate change adaptability. Environ. Manage. 63, 159–172 (2019).

    Article  PubMed  Google Scholar 

  6. 6.

    Jennings, S. et al. Aquatic food security: insights into challenges and solutions from an analysis of interactions between fisheries, aquaculture, food safety, human health, fish and human welfare, economy and environment. Fish Fish. 17, 893–938 (2016).

    Article  Google Scholar 

  7. 7.

    Handisyde, N., Telfer, T. C. & Ross, L. G. Vulnerability of aquaculture-related livelihoods to changing climate at the global scale. Fish Fish. 18, 466–488 (2017).

    Article  Google Scholar 

  8. 8.

    Charrier, B., Rolland, E., Gupta, V. & Reddy, C. R. K. Production of genetically and developmentally modified seaweeds: exploiting the potential of artificial selection techniques. Front. Plant. Sci. 6, 127 (2015).

    PubMed  PubMed Central  Google Scholar 

  9. 9.

    Kim, J. K., Yarish, C., Hwang, E. K., Park, M. & Kim, Y. Seaweed aquaculture: cultivation technologies, challenges and its ecosystem services. Algae 32, 1–13 (2017).

    CAS  Article  Google Scholar 

  10. 10.

    Troell, M. et al. Does aquaculture add resilience to the global food system? Proc. Natl Acad. Sci. USA 111, 13257–13263 (2014).

    CAS  Article  PubMed  Google Scholar 

  11. 11.

    Teletchea, F. Animal Domestication: A Brief Overview (IntechOpen, 2019).

  12. 12.

    Georges, M., Charlier, C. & Hayes, B. Harnessing genomic information for livestock improvement. Nat. Rev. Genet. 20, 135–156 (2019).

    CAS  Article  PubMed  Google Scholar 

  13. 13.

    Food and Agricultural Organization. The state of the world’s aquatic genetic resources for food and agriculture (FAO, 2019). This report highlights the value of genetic resources and their potential to enhance the contributions of aquaculture to food security.

  14. 14.

    Gjedrem, T. & Rye, M. Selection response in fish and shellfish: a review. Rev. Aquac. 10, 168–179 (2018).

    Article  Google Scholar 

  15. 15.

    Hill, W. G. Is continued genetic improvement of livestock sustainable? Genetics 202, 877–881 (2016).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  16. 16.

    Abdelrahman, H. et al. Aquaculture genomics, genetics and breeding in the United States: current status, challenges, and priorities for future research. BMC Genomics 18, 191 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  17. 17.

    Mignon-Grasteau, S. et al. Genetics of adaptation and domestication in livestock. Livest. Prod. Sci. 93, 3–14 (2005).

    Article  Google Scholar 

  18. 18.

    Driscoll, C. A., Macdonald, D. W. & O’Brien, S. J. From wild animals to domestic pets, an evolutionary view of domestication. Proc. Natl Acad. Sci. USA 106, 9971–9978 (2009).

    CAS  Article  PubMed  Google Scholar 

  19. 19.

    Harland, J. The origins of aquaculture. Nat. Ecol. Evol. 3, 1378–1379 (2019).

    Article  PubMed  Google Scholar 

  20. 20.

    Nguyen, N. H. Genetic improvement for important farmed aquaculture species with a reference to carp, tilapia and prawns in Asia: achievements, lessons and challenges. Fish Fish. 17, 483–506 (2016).

    Article  Google Scholar 

  21. 21.

    Gjedrem, T., Robinson, N. & Rye, M. The importance of selective breeding in aquaculture to meet future demands for animal protein: a review. Aquaculture 350–353, 117–129 (2012). This review suggests that only 10% of aquaculture production is derived from selective breeding programmes.

    Article  Google Scholar 

  22. 22.

    Janssen, K., Chavanne, H., Berentsen, P. & Komen, H. Impact of selective breeding on European aquaculture. Aquaculture 472, 8–16 (2017).

    Article  Google Scholar 

  23. 23.

    Kumar, G., Engle, C. & Tucker, C. Factors driving aquaculture technology adoption. J. World Aquac. Soc. 49, 447–476 (2018).

    Article  Google Scholar 

  24. 24.

    Janssen, K., Saatkamp, H. & Komen, H. Cost-benefit analysis of aquaculture breeding programs. Genet. Sel. Evol. 50, 2 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  25. 25.

    Teletchea, F. & Fontaine, P. Levels of domestication in fish: implications for the sustainable future of aquaculture. Fish Fish. 15, 181–195 (2014).

    Article  Google Scholar 

  26. 26.

    Lien, S. et al. The Atlantic salmon genome provides insights into rediploidization. Nature 533, 200–205 (2016).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  27. 27.

    Xu, P. et al. Genome sequence and genetic diversity of the common carp, Cyprinus carpio. Nat. Genet. 46, 1212–1219 (2014).

    CAS  Article  PubMed  Google Scholar 

  28. 28.

    Ludwig, A., Belfiore, N. M., Pitra, C., Svirsky, V. & Jenneckens, I. Genome duplication events and functional reduction of ploidy levels in sturgeon (Acipenser, Huso and Scaphirhynchus). Genetics 158, 1203–1215 (2001).

    CAS  PubMed  PubMed Central  Google Scholar 

  29. 29.

    Plough, L. V. Genetic load in marine animals: a review. Curr. Zool. 62, 567–579 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  30. 30.

    Hollenbeck, C. M. & Johnston, I. A. Genomic tools and selective breeding in molluscs. Front. Genet. 9, 253 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  31. 31.

    Zhang, X. et al. Penaeid shrimp genome provides insights into benthic adaptation and frequent molting. Nat. Commun. 10, 356 (2019).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  32. 32.

    Macqueen, D. J. et al. Functional Annotation of All Salmonid Genomes (FAASG): an international initiative supporting future salmonid research, conservation and aquaculture. BMC Genomics 18, 484 (2017). The FAASG white paper, which followed on from the FAANG equivalent (reference 88), describes an initiative to improve annotation of all salmonid genomes, and similar initiatives are likely to follow for other major aquaculture species.

    Article  PubMed  PubMed Central  Google Scholar 

  33. 33.

    van Dijk, E. L., Jaszczyszyn, Y., Naquin, D. & Thermes, C. The third revolution in sequencing technology. Trends Genet. 34, 666–681 (2018).

    Article  CAS  PubMed  Google Scholar 

  34. 34.

    Feron, R. et al. Characterization of a Y-specific duplication/insertion of the anti-Mullerian hormone type II receptor gene based on a chromosome-scale genome assembly of yellow perch, Perca flavescens. Mol. Ecol. Resour. 20, 531–543 (2020).

    CAS  Article  PubMed  Google Scholar 

  35. 35.

    Baird, N. A. et al. Rapid SNP discovery and genetic mapping using sequenced RAD markers. PLoS One 3, e3376 (2008). The discovery of RAD-seq and its applications have been instrumental for application of genomics to genetic improvement of aquaculture species.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. 36.

    Campbell, C. R., Poelstra, J. W. & Yoder, A. D. What is speciation genomics? The roles of ecology, gene flow, and genomic architecture in the formation of species. Biol. J. Linn. Soc. 124, 561–583 (2018).

    Article  Google Scholar 

  37. 37.

    Robledo, D., Palaiokostas, C., Bargelloni, L., Martínez, P. & Houston, R. Applications of genotyping by sequencing in aquaculture breeding and genetics. Rev. Aquac. 10, 670–682 (2018).

    Article  PubMed  Google Scholar 

  38. 38.

    Fernández, J., Toro, M. Á., Sonesson, A. K. & Villanueva, B. Optimizing the creation of base populations for aquaculture breeding programs using phenotypic and genomic data and its consequences on genetic progress. Front. Genet. 5, 414 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. 39.

    You, W. & Hedgecock, D. Boom-and-bust production cycles in animal seafood aquaculture. Rev. Aquacult. 11, 1045–1060 (2018).

    Article  Google Scholar 

  40. 40.

    Doyle, R. W. Inbreeding and disease in tropical shrimp aquaculture: a reappraisal and caution. Aquac. Res. 47, 21–35 (2016).

    Article  Google Scholar 

  41. 41.

    Ashton, D. T., Ritchie, P. A. & Wellenreuther, M. High-density linkage map and QTLs for growth in snapper (Chrysophrys auratus). G3 9, 1027–1035 (2019).

    CAS  PubMed  Google Scholar 

  42. 42.

    Wellenreuther, M., Le Luyer, J., Cook, D., Ritchie, P. A. & Bernatchez, L. Domestication and temperature modulate gene expression signatures and growth in the Australasian snapper Chrysophrys auratus. G3 9, 105–116 (2019).

    CAS  Article  PubMed  Google Scholar 

  43. 43.

    Ashton, D. T., Hilario, E., Jaksons, P., Ritchie, P. A. & Wellenreuther, M. Genetic diversity and heritability of economically important traits in captive Australasian snapper (Chrysophrys auratus). Aquaculture 505, 190–198 (2019).

    Article  Google Scholar 

  44. 44.

    Devlin, R. H. & Nagahama, Y. Sex determination and sex differentiation in fish: an overview of genetic, physiological, and environmental influences. Aquaculture 208, 191–364 (2002).

    CAS  Article  Google Scholar 

  45. 45.

    Kobayashi, Y., Nagahama, Y. & Nakamura, M. Diversity and plasticity of sex determination and differentiation in fishes. Sex. Dev. 7, 115–125 (2013).

    CAS  Article  PubMed  Google Scholar 

  46. 46.

    Martínez, P. et al. Genetic architecture of sex determination in fish: applications to sex ratio control in aquaculture. Front. Genet. 5, 340 (2014).

    PubMed  PubMed Central  Google Scholar 

  47. 47.

    Palaiokostas, C. et al. Mapping and validation of the major sex-determining region in Nile tilapia (Oreochromis niloticus L.) Using RAD sequencing. PLoS One 8, e68389 (2013).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  48. 48.

    Palaiokostas, C. et al. Mapping the sex determination locus in the Atlantic halibut (Hippoglossus hippoglossus) using RAD sequencing. BMC Genomics 14, 566 (2013).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  49. 49.

    Palaiokostas, C. et al. A new SNP-based vision of the genetics of sex determination in European sea bass (Dicentrarchus labrax). Genet. Sel. Evol. 47, 68 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. 50.

    Shi, X. et al. Female-specific SNP markers provide insights into a WZ/ZZ sex determination system for mud crabs Scylla paramamosain, S. tranquebarica and S. serrata with a rapid method for genetic sex identification. BMC Genomics 19, 981 (2018).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  51. 51.

    Vandeputte, M. & Haffray, P. Parentage assignment with genomic markers: a major advance for understanding and exploiting genetic variation of quantitative traits in farmed aquatic animals. Front. Genet. 5, 432 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. 52.

    Khang, P. Van, Phuong, T. H., Dat, N. K., Knibb, W. & Nguyen, N. H. An 8-year breeding program for Asian seabass Lates calcarifer: genetic evaluation, experiences, and challenges. Front. Genet. 9, 191 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  53. 53.

    Reed, D. H. & Frankham, R. How closely correlated are molecular and quantitative measures of genetic variation? A meta-analysis. Evolution 55, 1095–1103 (2001).

    CAS  Article  PubMed  Google Scholar 

  54. 54.

    Bentsen, H. B. et al. Genetic improvement of farmed tilapias: growth performance in a complete diallel cross experiment with eight strains of Oreochromis niloticus. Aquaculture 160, 145–173 (1998).

    Article  Google Scholar 

  55. 55.

    Govindaraju, D. R. An elucidation of over a century old enigma in genetics—heterosis. PLoS Biol. 17, e3000215 (2019).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  56. 56.

    Fuji, K. et al. Marker-assisted breeding of a lymphocystis disease-resistant Japanese flounder (Paralichthys olivaceus). Aquaculture 272, 291–295 (2007).

    Article  Google Scholar 

  57. 57.

    Fuji, K. et al. Identification of a single major genetic locus controlling the resistance to lymphocystis disease in Japanese flounder (Paralichthys olivaceus). Aquaculture 254, 203–210 (2006).

    CAS  Article  Google Scholar 

  58. 58.

    Liu, S. et al. Retrospective evaluation of marker-assisted selection for resistance to bacterial cold water disease in three generations of a commercial rainbow trout breeding population. Front. Genet. 9, 286 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. 59.

    Ayllon, F. et al. The vgll3 locus controls age at maturity in wild and domesticated Atlantic salmon (Salmo salar L.) males. PLoS Genet. 11, e1005628 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. 60.

    Barson, N. J. et al. Sex-dependent dominance at a single locus maintains variation in age at maturity in salmon. Nature 528, 405–408 (2015). Together with reference 59, this study shows the impact of a major effect locus on an important life history and production trait in salmon.

    CAS  Article  PubMed  Google Scholar 

  61. 61.

    Gonen, S. et al. Mapping and validation of a major QTL affecting resistance to pancreas disease (salmonid alphavirus) in Atlantic salmon (Salmo salar). Heredity 115, 405–414 (2015).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  62. 62.

    Boison, S. et al. QTLs associated with resistance to cardiomyopathy syndrome in Atlantic salmon. J. Hered. 110, 727–737 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  63. 63.

    Hillestad, B. & Moghadam, H. K. Genome-wide association study of piscine myocarditis virus (PMCV) resistance in Atlantic salmon (Salmo salar). J. Hered. 110, 720–726 (2019).

    Article  PubMed  Google Scholar 

  64. 64.

    Gu, X. H. et al. Identifying a major QTL associated with salinity tolerance in Nile tilapia using QTL-Seq. Mar. Biotechnol. 20, 98–107 (2018).

    CAS  Article  PubMed  Google Scholar 

  65. 65.

    Houston, R. D. Future directions in breeding for disease resistance in aquaculture species. Rev. Bras. Zootec. 46, 545–551 (2017).

    Article  Google Scholar 

  66. 66.

    Zenger, K. R. et al. Genomic selection in aquaculture: application, limitations and opportunities with special reference to marine shrimp and pearl oysters. Front. Genet. 9, 693 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  67. 67.

    Gjedrem, T. & Baranski, M. Selective Breeding in Aquaculture: an Introduction (Springer, 2010).

  68. 68.

    Meuwissen, T. H., Hayes, B. J. & Goddard, M. E. Prediction of total genetic value using genome-wide dense marker maps. Genetics 157, 1819–1829 (2001). This study highlights the potential of use of genome-wide markers for prediction of breeding values, a technique now widely applied in advanced aquaculture breeding programmes.

    CAS  PubMed  PubMed Central  Google Scholar 

  69. 69.

    Houston, R. D. et al. Development and validation of a high density SNP genotyping array for Atlantic salmon (Salmo salar). BMC Genomics 15, 90 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  70. 70.

    Odegård, J. et al. Genomic prediction in an admixed population of Atlantic salmon (Salmo salar). Front. Genet. 5, 402 (2014). This is the first empirical study of genomic selection in aquaculture, highlighting the benefit compared with pedigree approaches for predicting breeding values.

    PubMed  PubMed Central  Google Scholar 

  71. 71.

    Tsai, H. Y. et al. The genetic architecture of growth and fillet traits in farmed Atlantic salmon (Salmo salar). BMC Genet. 16, 51 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  72. 72.

    Norris, A. Application of genomics in salmon aquaculture breeding programs by Ashie Norris: who knows where the genomic revolution will lead us? Mar. Genomics 36, 13–15 (2017).

    Article  PubMed  Google Scholar 

  73. 73.

    Lillehammer, M., Meuwissen, T. H. E. & Sonesson, A. K. A low-marker density implementation of genomic selection in aquaculture using within-family genomic breeding values. Genet. Sel. Evol. 45, 39 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  74. 74.

    Daetwyler, H. D., Calus, M. P. L., Pong-Wong, R., de los Campos, G. & Hickey, J. M. Genomic prediction in animals and plants: simulation of data, validation, reporting, and benchmarking. Genetics 193, 347–365 (2013).

    Article  Google Scholar 

  75. 75.

    Kriaridou, C., Tsairidou, S., Houston, R. D. & Robledo, D. Genomic prediction using low density marker panels in aquaculture: perform across species, traits, genotyping platforms. Front. Genet. 11, 124 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  76. 76.

    Tsai, H. Y. et al. Construction and annotation of a high density SNP linkage map of the Atlantic salmon (Salmo salar) genome. G3 6, 2173–2179 (2016).

    CAS  Article  PubMed  Google Scholar 

  77. 77.

    Palaiokostas, C. et al. Optimizing genomic prediction of host resistance to koi herpesvirus disease in carp. Front. Genet. 10, 543 (2019).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  78. 78.

    Campbell, N. R., Harmon, S. A. & Narum, S. R. Genotyping-in-thousands by sequencing (GT-seq): a cost effective SNP genotyping method based on custom amplicon sequencing. Mol. Ecol. Resour. 15, 855–867 (2015).

    CAS  Article  PubMed  Google Scholar 

  79. 79.

    Tsai, H.-Y. et al. Genotype imputation to improve the cost-efficiency of genomic selection in farmed Atlantic salmon. G3 7, 1377–1383 (2017). This study highlights a potentially cost-efficient approach to genomic selection in aquaculture that could help democratize the use of the technology to smaller aquaculture sectors.

    CAS  Article  PubMed  Google Scholar 

  80. 80.

    Yoshida, G. M. et al. Accuracy of genotype imputation and genomic predictions in a two-generation farmed Atlantic salmon population using high-density and low-density SNP panels. Aquaculture 491, 147–154 (2018).

    Article  Google Scholar 

  81. 81.

    Tsairidou, S., Hamilton, A., Robledo, D., Bron, J. E. & Houston, R. D. Optimizing low-cost genotyping and imputation strategies for genomic selection in Atlantic salmon. G3 10, 581–590 (2020).

    Article  PubMed  Google Scholar 

  82. 82.

    Ni, G., Cavero, D., Fangmann, A., Erbe, M. & Simianer, H. Whole-genome sequence-based genomic prediction in laying chickens with different genomic relationship matrices to account for genetic architecture. Genet. Sel. Evol. 49, 8 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  83. 83.

    MacLeod, I. M. et al. Exploiting biological priors and sequence variants enhances QTL discovery and genomic prediction of complex traits. BMC Genomics 17, 144 (2016).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  84. 84.

    Hwang, B., Lee, J. H. & Bang, D. Single-cell RNA sequencing technologies and bioinformatics pipelines. Exp. Mol. Med. 50, 96 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  85. 85.

    Gavery, M. R. & Roberts, S. B. Epigenetic considerations in aquaculture. PeerJ 5, e4147 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  86. 86.

    Giuffra, E., Tuggle, C. K. & FAANG Consortium. Functional Annotation of Animal Genomes (FAANG): current achievements and roadmap. Annu. Rev. Anim. Biosci. 7, 65–88 (2019).

    CAS  Article  PubMed  Google Scholar 

  87. 87.

    Emms, D. M. & Kelly, S. OrthoFinder: solving fundamental biases in whole genome comparisons dramatically improves orthogroup inference accuracy. Genome Biol. 16, 157 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  88. 88.

    Andersson, L. et al. Coordinated international action to accelerate genome-to-phenome with FAANG, the functional annotation of animal genomes project. Genome Biol. 16, 57 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  89. 89.

    Saberioon, M., Gholizadeh, A., Cisar, P., Pautsina, A. & Urban, J. Application of machine vision systems in aquaculture with emphasis on fish: state-of-the-art and key issues. Rev. Aquac. 9, 369–387 (2017).

    Article  Google Scholar 

  90. 90.

    Føre, M. et al. Precision fish farming: a new framework to improve production in aquaculture. Biosyst. Eng. 173, 176–193 (2018).

    Article  Google Scholar 

  91. 91.

    Liakos, K. et al. Machine learning in agriculture: a review. Sensors 18, 2674 (2018).

    Article  Google Scholar 

  92. 92.

    Badiola, M., Basurko, O. C., Piedrahita, R., Hundley, P. & Mendiola, D. Energy use in recirculating aquaculture systems (RAS): a review. Aquac. Eng. 81, 57–70 (2018).

    Article  Google Scholar 

  93. 93.

    Sae-Lim, P., Gjerde, B., Nielsen, H. M., Mulder, H. & Kause, A. A review of genotype-by-environment interaction and micro-environmental sensitivity in aquaculture species. Rev. Aquac. 8, 369–393 (2016).

    Article  Google Scholar 

  94. 94.

    Sae-Lim, P. et al. Genetic (co)variance of rainbow trout (Oncorhynchus mykiss) body weight and its uniformity across production environments. Genet. Sel. Evol. 47, 46 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  95. 95.

    Saltz, J. B. et al. Why does the magnitude of genotype-by-environment interaction vary? Ecol. Evol. 8, 6342–6353 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  96. 96.

    Eknath, A. E. & Acosta, B. O. Genetic Improvement of Farmed Tilapias (GIFT) Project: Final Report, March 1988–December 1997 (ICLARM, 1998).

  97. 97.

    Mulder, H. A. Genomic selection improves response to selection in resilience by exploiting genotype by environment interactions. Front. Genet. 7, 178 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  98. 98.

    Luyer, J. Le et al. Parallel epigenetic modifications induced by hatchery rearing in a Pacific salmon. Proc. Natl Acad. Sci. USA 114, 12964–12969 (2017).

    Article  CAS  PubMed  Google Scholar 

  99. 99.

    Jonsson, B. & Jonsson, N. Early environment influences later performance in fishes. J. Fish. Biol. 85, 151–188 (2014).

    CAS  Article  PubMed  Google Scholar 

  100. 100.

    Moghadam, H., Mørkøre, T. & Robinson, N. Epigenetics — potential for programming fish for aquaculture? J. Mar. Sci. Eng. 3, 175–192 (2015).

    Article  Google Scholar 

  101. 101.

    Geurden, I. et al. Early-feeding exposure to a plant-based diet improves its future acceptance and utilization in rainbow trout. Commun. Agric. Appl. Biol. Sci. 78, 157–160 (2013).

    CAS  PubMed  Google Scholar 

  102. 102.

    Uren Webster, T. M., Consuegra, S., Hitchings, M. & Garcia de Leaniz, C. Interpopulation variation in the Atlantic salmon microbiome reflects environmental and genetic diversity. Appl. Environ. Microbiol. 84, e00691–18 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  103. 103.

    Robinson, N. A., Johnsen, H., Moghadam, H., Andersen, Ø. & Tveiten, H. Early developmental stress affects subsequent gene expression response to an acute stress in Atlantic salmon: an approach for creating robust fish for aquaculture? G3 9, 1597–1611 (2019).

    CAS  Article  PubMed  Google Scholar 

  104. 104.

    Zhao, L. et al. Transgenerational acclimation to seawater acidification in the Manila clam Ruditapes philippinarum: Preferential uptake of metabolic carbon. Sci. Total. Environ. 627, 95–103 (2018).

    CAS  Article  PubMed  Google Scholar 

  105. 105.

    Parker, L. M., O’Connor, W. A., Raftos, D. A., Pörtner, H.-O. & Ross, P. M. Persistence of positive carryover effects in the oyster, Saccostrea glomerata, following transgenerational exposure to ocean acidification. PLoS One 10, e0132276 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  106. 106.

    Franěk, R. et al. Isogenic lines in fish — a critical review. Rev. Aquac. https://doi.org/10.1111/raq.12389 (2019)

    Article  Google Scholar 

  107. 107.

    Goddard, M. E. & Whitelaw, E. The use of epigenetic phenomena for the improvement of sheep and cattle. Front. Genet. 5, 247 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  108. 108.

    Brugman, S. et al. A comparative review on microbiota manipulation: lessons from fish, plants, livestock, and human research. Front. Nutr. 5, 80 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  109. 109.

    Derome, N. Microbial Communities in Aquaculture Ecosystems: Improving Productivity and Sustainability (Springer, 2019).

  110. 110.

    Smith, C. C. R., Snowberg, L. K., Gregory Caporaso, J., Knight, R. & Bolnick, D. I. Dietary input of microbes and host genetic variation shape among-population differences in stickleback gut microbiota. ISME J. 9, 2515–2526 (2015).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  111. 111.

    Li, W. et al. Genetic effects on the gut microbiota assemblages of hybrid fish from parents with different feeding habits. Front. Microbiol. 9, 2972 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  112. 112.

    Cornejo-Granados, F. et al. Microbiome of Pacific Whiteleg shrimp reveals differential bacterial community composition between wild, aquacultured and AHPND/EMS outbreak conditions. Sci. Rep. 7, 11783 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  113. 113.

    Llewellyn, M. S., Boutin, S., Hoseinifar, S. H. & Derome, N. Teleost microbiomes: the state of the art in their characterization, manipulation and importance in aquaculture and fisheries. Front. Microbiol. 5, 207 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  114. 114.

    Naylor, R. L. et al. Feeding aquaculture in an era of finite resources. Proc. Natl Acad. Sci. USA 106, 15103–15110 (2009).

    CAS  Article  PubMed  Google Scholar 

  115. 115.

    Wang, G.-D., Xie, H.-B., Peng, M.-S., Irwin, D. & Zhang, Y.-P. Domestication genomics: evidence from animals. Annu. Rev. Anim. Biosci. 2, 65–84 (2014).

    CAS  Article  PubMed  Google Scholar 

  116. 116.

    Lopez Dinamarca, M. E. et al. Multiple selection signatures in farmed Atlantic salmon adapted to different environments across Hemispheres. Front. Genet. 10, 901 (2019).

    Article  CAS  Google Scholar 

  117. 117.

    López, M. E. et al. Comparing genomic signatures of domestication in two Atlantic salmon (Salmo salar L.) populations with different geographical origins. Evol. Appl. 12, 137–156 (2019).

    Article  PubMed  Google Scholar 

  118. 118.

    Glover, K. A. et al. Half a century of genetic interaction between farmed and wild Atlantic salmon: status of knowledge and unanswered questions. Fish Fish. 18, 890–927 (2017). This review highlights the impact of interaction between farmed salmon escapees and wild salmon, showing the need to avoid interbreeding as farmed fish become more domesticated.

    Article  Google Scholar 

  119. 119.

    Roberge, C., Einum, S., Guderley, H. & Bernatchez, L. Rapid parallel evolutionary changes of gene transcription profiles in farmed Atlantic salmon. Mol. Ecol. 15, 9–20 (2005).

    Article  CAS  Google Scholar 

  120. 120.

    Skaala, Ø. et al. An extensive common-garden study with domesticated and wild Atlantic salmon in the wild reveals impact on smolt production and shifts in fitness traits. Evol. Appl. 12, 1001–1016 (2019).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  121. 121.

    Cowen, R. K. & Sponaugle, S. Larval dispersal and marine population connectivity. Ann. Rev. Mar. Sci. 1, 443–466 (2009).

    Article  PubMed  Google Scholar 

  122. 122.

    Varney, R. L., Watts, J. C. & Wilbur, A. E. Genetic impacts of a commercial aquaculture lease on adjacent oyster populations. Aquaculture 491, 310–320 (2018).

    Article  Google Scholar 

  123. 123.

    Hornick, K. M. & Plough, L. V. Tracking genetic diversity in a large-scale oyster restoration program: effects of hatchery propagation and initial characterization of diversity on restored vs. wild reefs. Heredity 123, 92–105 (2019).

    Article  PubMed  Google Scholar 

  124. 124.

    Hindar, K., Fleming, I. A., McGinnity, P. & Diserud, O. Genetic and ecological effects of salmon farming on wild salmon: modelling from experimental results. ICES J. Mar. Sci. 63, 1234–1247 (2006).

    CAS  Article  Google Scholar 

  125. 125.

    Hansen, M. M., Limborg, M. T., Ferchaud, A.-L. & Pujolar, J.-M. The effects of Medieval dams on genetic divergence and demographic history in brown trout populations. BMC Evol. Biol. 14, 122 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  126. 126.

    Horreo, J. L. et al. Long-term effects of stock transfers: synergistic introgression of allochthonous genomes in salmonids. J. Fish. Biol. 85, 292–306 (2014).

    CAS  Article  PubMed  Google Scholar 

  127. 127.

    Heggberget, T. G. et al. Interactions between wild and cultured Atlantic salmon: a review of the Norwegian experience. Fish. Res. 18, 123–146 (1993).

    Article  Google Scholar 

  128. 128.

    Naylor, R. et al. Fugitive salmon: assessing the risks of escaped fish from net-pen aquaculture. Bioscience 55, 427–437 (2005).

    Article  Google Scholar 

  129. 129.

    O’Flynn, F., McGeachy, S. A., Friars, G. W., Benfey, T. J. & Bailey, J. K. Comparisons of cultured triploid and diploid Atlantic salmon (Salmo salar L.). ICES J. Mar. Sci. 54, 1160–1165 (1997).

    Google Scholar 

  130. 130.

    Piferrer, F. et al. Polyploid fish and shellfish: production, biology and applications to aquaculture for performance improvement and genetic containment. Aquaculture 293, 125–156 (2009).

    Article  Google Scholar 

  131. 131.

    Wargelius, A. et al. Dnd knockout ablates germ cells and demonstrates germ cell independent sex differentiation in Atlantic salmon. Sci. Rep. 6, 21284 (2016). This study shows the potential of genome editing using CRISPR to induce sterility in farmed fish.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  132. 132.

    Adams, S. L., Smith, J. F., Taylor, J., McGowan, L. T. & Tervit, H. R. in Cryopreservation and Freeze-Drying Protocols (eds Wolkers, W., & Oldenhof, H.) 329–336 (Springer, 2015).

  133. 133.

    Goswami, M., Mishra, A., Ninawe, A., Trudeau, V. & Lakra, W. Bio-banking: an emerging approach for conservation of fish germplasm. Poultry Fish. Wildl. Sci. 4, (2016).

  134. 134.

    Robles, V. et al. Biology of teleost primordial germ cells (PGCs) and spermatogonia: biotechnological applications. Aquaculture 472, 4–20 (2017).

    CAS  Article  Google Scholar 

  135. 135.

    Food and Agricultural Organization. Genebank Standards for Plant Genetic Resources for Food and Agriculture. (FAO, 2014).

  136. 136.

    Cong, L. et al. Multiplex genome engineering using CRISPR/Cas systems. Science 339, 819–823 (2013).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  137. 137.

    Mali, P. et al. RNA-guided human genome engineering via Cas9. Science 339, 823–826 (2013).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  138. 138.

    Edvardsen, R. B., Leininger, S., Kleppe, L., Skaftnesmo, K. O. & Wargelius, A. Targeted mutagenesis in Atlantic salmon (Salmo salar L.) using the CRISPR/Cas9 system induces complete knockout individuals in the F0 generation. PLoS One 9, e108622 (2014). This study is the first to apply CRISPR genome editing to an aquaculture species, and highlights the possibility of creating double-allele-knockout individuals in the F0 generation.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  139. 139.

    Gratacap, R. L., Wargelius, A., Edvardsen, R. B. & Houston, R. D. Potential of genome editing to improve aquaculture breeding and production. Trends Genet. 35, 672–684 (2019).

    CAS  Article  PubMed  Google Scholar 

  140. 140.

    Sato, M. & Nakamura, S. Possible Production of Genome-Edited Animals Using Gene-Engineered Sperm (eds Chen, Y.C. & Chen, S.J.) (IntechOpen, 2019).

  141. 141.

    Shalem, O. et al. Genome-scale CRISPR-Cas9 knockout screening in human cells. Science 343, 84–87 (2014).

    CAS  Article  PubMed  Google Scholar 

  142. 142.

    Gratacap, R. L. et al. Efficient CRISPR/Cas9 genome editing in a salmonid fish cell line using a lentivirus delivery system. Preprint at bioRxiv https://doi.org/10.1101/734442 (2019).

    Article  Google Scholar 

  143. 143.

    Jenko, J. et al. Potential of promotion of alleles by genome editing to improve quantitative traits in livestock breeding programs. Genet. Sel. Evol. 47, 55 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  144. 144.

    Burkard, C. et al. Pigs lacking the scavenger receptor cysteine-rich domain 5 of CD163 are resistant to porcine reproductive and respiratory syndrome virus 1 infection. J. Virol. 92, JVI.00415-18 (2018).

    Article  Google Scholar 

  145. 145.

    Zohar, Y. Endocrinology and fish farming: aspects in reproduction, growth, and smoltification. Fish Physiol. Biochem. 7, 395–405 (1989).

    CAS  Article  PubMed  Google Scholar 

  146. 146.

    Wong, T.-T. & Zohar, Y. Production of reproductively sterile fish: a mini-review of germ cell elimination technologies. Gen. Comp. Endocrinol. 221, 3–8 (2015).

    CAS  Article  PubMed  Google Scholar 

  147. 147.

    Li, M. et al. Efficient and heritable gene targeting in tilapia by CRISPR/Cas9. Genetics 197, 591–599 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  148. 148.

    Gilbert, L. A. et al. CRISPR-mediated modular RNA-guided regulation of transcription in eukaryotes. Cell 154, 442–451 (2013).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  149. 149.

    Qi, L. S. et al. Repurposing CRISPR as an RNA-guided platform for sequence-specific control of gene expression. Cell 152, 1173–1183 (2013).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  150. 150.

    Sanson, K. R. et al. Optimized libraries for CRISPR-Cas9 genetic screens with multiple modalities. Nat. Commun. 9, 5416 (2018).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  151. 151.

    Tait-Burkard, C. et al. Livestock 2.0 – genome editing for fitter, healthier, and more productive farmed animals. Genome Biol. 19, 204 (2018).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  152. 152.

    Bruce, A. Genome edited animals: learning from GM crops? Transgenic Res. 26, 385–398 (2017).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  153. 153.

    Waltz, E. First genetically engineered salmon sold in Canada. Nature 548, 148 (2017).

    CAS  Article  PubMed  Google Scholar 

  154. 154.

    Yoshizaki, G. & Yazawa, R. Application of surrogate broodstock technology in aquaculture. Fish. Sci. 85, 429–437 (2019).

    CAS  Article  Google Scholar 

  155. 155.

    Okutsu, T., Shikina, S., Kanno, M., Takeuchi, Y. & Yoshizaki, G. Production of trout offspring from triploid salmon parents. Science 317, 1517 (2007).

    CAS  Article  PubMed  Google Scholar 

  156. 156.

    Sid, H. & Schusser, B. Applications of gene editing in chickens: a new era is on the horizon. Front. Genet. 9, 456 (2018).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  157. 157.

    Shelley, C. & Lovatelli, A. Mud crab aquaculture –a practical manual. FAO Fisheries and Aquaculture Technical Paper No. 567 (FAO, 2011).

  158. 158.

    Xu, S., Zhao, L., Xiao, S. & Gao, T. Whole genome resequencing data for three rockfish species of Sebastes. Sci. Data 6, 97 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  159. 159.

    Tsai, H.-Y. et al. Genome wide association and genomic prediction for growth traits in juvenile farmed Atlantic salmon using a high density SNP array. BMC Genomics 16, 969 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  160. 160.

    Tsai, H.-Y. et al. Genomic prediction of host resistance to sea lice in farmed Atlantic salmon populations. Genet. Sel. Evol. 48, 47 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  161. 161.

    Correa, K., Bangera, R., Figueroa, R., Lhorente, J. P. & Yáñez, J. M. The use of genomic information increases the accuracy of breeding value predictions for sea louse (Caligus rogercresseyi) resistance in Atlantic salmon (Salmo salar). Genet. Sel. Evol. 49, 15 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  162. 162.

    Robledo, D., Matika, O., Hamilton, A. & Houston, R. D. Genome-wide association and genomic selection for resistance to amoebic gill disease in Atlantic salmon. G3 8, 1195–1203 (2018).

    CAS  Article  PubMed  Google Scholar 

  163. 163.

    Boison, S. A., Gjerde, B., Hillestad, B., Makvandi-Nejad, S. & Moghadam, H. K. Genomic and transcriptomic analysis of amoebic gill disease resistance in Atlantic salmon (Salmo salar L.). Front. Genet. 10, 68 (2019).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  164. 164.

    Bangera, R., Correa, K., Lhorente, J. P., Figueroa, R. & Yáñez, J. M. Genomic predictions can accelerate selection for resistance against Piscirickettsia salmonis in Atlantic salmon (Salmo salar). BMC Genomics 18, 121 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  165. 165.

    Horn, S. S., Meuwissen, T. H. E., Moghadam, H., Hillestad, B. & Sonesson, A. K. Accuracy of selection for omega-3 fatty acid content in Atlantic salmon fillets. Aquaculture 519, 734767 (2019).

    Article  CAS  Google Scholar 

  166. 166.

    Vallejo, R. L. et al. Accurate genomic predictions for BCWD resistance in rainbow trout are achieved using low-density SNP panels: evidence that long-range LD is a major contributing factor. J. Anim. Breed. Genet. 135, 263–274 (2018).

    CAS  Article  Google Scholar 

  167. 167.

    Vallejo, R. L. et al. Genomic selection models double the accuracy of predicted breeding values for bacterial cold water disease resistance compared to a traditional pedigree-based model in rainbow trout aquaculture. Genet. Sel. Evol. 49, 17 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  168. 168.

    Vallejo, R. L. et al. Evaluation of genome-enabled selection for bacterial cold water disease resistance using progeny performance data in rainbow trout: insights on genotyping methods and genomic prediction models. Front. Genet. 7, 96 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  169. 169.

    Yoshida, G. M., Carvalheiro, R., Rodríguez, F. H., Lhorente, J. P. & Yáñez, J. M. Single-step genomic evaluation improves accuracy of breeding value predictions for resistance to infectious pancreatic necrosis virus in rainbow trout. Genomics 111, 127–132 (2019).

    CAS  Article  PubMed  Google Scholar 

  170. 170.

    Yoshida, G. M. et al. Genomic prediction accuracy for resistance against Piscirickettsia salmonis in farmed rainbow trout. G3 8, 719–726 (2018).

    Article  PubMed  Google Scholar 

  171. 171.

    Vallejo, R. L. et al. Genome-wide association analysis and accuracy of genome-enabled breeding value predictions for resistance to infectious hematopoietic necrosis virus in a commercial rainbow trout breeding population. Genet. Sel. Evol. 51, 47 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  172. 172.

    Silva, R. M. O. et al. Whole-genome mapping of quantitative trait loci and accuracy of genomic predictions for resistance to columnaris disease in two rainbow trout breeding populations. Genet. Sel. Evol. 51, 42 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  173. 173.

    Barría, A. et al. Genomic predictions and genome-wide association study of resistance against Piscirickettsia salmonis in coho salmon (Oncorhynchus kisutch) using ddRAD sequencing. G3 8, 1183–1194 (2018).

    Article  CAS  PubMed  Google Scholar 

  174. 174.

    Palaiokostas, C., Kocour, M., Prchal, M. & Houston, R. D. Accuracy of genomic evaluations of juvenile growth rate in common carp (Cyprinus carpio) using genotyping by sequencing. sing genotyping by sequencing. Front. Genet. 9, 82 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  175. 175.

    Yoshida, G. M. et al. Genome-wide association study and cost-efficient genomic predictions for growth and fillet yield in Nile tilapia (Oreochromis niloticus). G3 9, 2597–2607 (2019).

    CAS  Article  PubMed  Google Scholar 

  176. 176.

    Joshi, R., Skaarud, A., de Vera, M., Alvarez, A. T. & Ødegård, J. Genomic prediction for commercial traits using univariate and multivariate approaches in Nile tilapia (Oreochromis niloticus). Aquaculture 516, 734641 (2020).

    Article  CAS  Google Scholar 

  177. 177.

    Palaiokostas, C. et al. Genome-wide association and genomic prediction of resistance to viral nervous necrosis in European sea bass (Dicentrarchus labrax) using RAD sequencing. Genet. Sel. Evol. 50, 30 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  178. 178.

    Palaiokostas, C., Ferraresso, S., Franch, R., Houston, R. D. & Bargelloni, L. Genomic prediction of resistance to pasteurellosis in gilthead sea bream (Sparus aurata) using 2b-RAD sequencing. G3 6, 3693–3700 (2016).

    CAS  Article  PubMed  Google Scholar 

  179. 179.

    Aslam, M. L. et al. Genetics of resistance to photobacteriosis in gilthead sea bream (Sparus aurata) using 2b-RAD sequencing. BMC Genet. 19, 43 (2018).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  180. 180.

    Saura, M. et al. Disentangling genetic variation for resistance and endurance to scuticociliatosis in turbot using pedigree and genomic information. Front. Genet. 10, 539 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  181. 181.

    Liu, Y. et al. Genomic selection using BayesCπ and GBLUP for resistance against edwardsiella tarda in Japanese flounder (Paralichthys olivaceus). Mar. Biotechnol. 20, 559–565 (2018).

    CAS  Article  PubMed  Google Scholar 

  182. 182.

    Garcia, A. L. S. et al. Development of genomic predictions for harvest and carcass weight in channel catfish. Genet. Sel. Evol. 50, 66 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  183. 183.

    Dong, L., Xiao, S., Wang, Q. & Wang, Z. Comparative analysis of the GBLUP, emBayesB, and GWAS algorithms to predict genetic values in large yellow croaker (Larimichthys crocea). BMC Genomics 17, 460 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  184. 184.

    Nguyen, N. H., Premachandra, H. K. A., Kilian, A. & Knibb, W. Genomic prediction using DArT-Seq technology for yellowtail kingfish Seriola lalandi. BMC Genomics 19, 107 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  185. 185.

    Liu, G. et al. Evaluation of genomic selection for seven economic traits in yellow drum (Nibea albiflora). Mar. Biotechnol. 21, 806–812 (2019).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  186. 186.

    Gutierrez, A. P., Matika, O., Bean, T. P. & Houston, R. D. Genomic selection for growth traits in Pacific Oyster (Crassostrea gigas): potential of low-density marker panels for breeding value prediction. Front. Genet. 9, 391 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  187. 187.

    Gutierrez, A. P. et al. Potential of genomic selection for improvement of resistance to ostreid herpes virus in Pacific oyster (Crassostrea gigas). Anim. Genet. 51, 249–257 (2020).

    CAS  Article  PubMed  Google Scholar 

  188. 188.

    Dou, J. et al. Evaluation of the 2b-RAD method for genomic selection in scallop breeding. Sci. Rep. 6, 19244 (2016).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  189. 189.

    Wang, Y. et al. Predicting growth traits with genomic selection methods in Zhikong scallop (Chlamys farreri). Mar. Biotechnol. 20, 769–779 (2018).

    CAS  Article  PubMed  Google Scholar 

  190. 190.

    Wang, Q. et al. Effects of marker density and population structure on the genomic prediction accuracy for growth trait in Pacific white shrimp Litopenaeus vannamei. BMC Genet. 18, 45 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  191. 191.

    Wang, Q., Yu, Y., Li, F., Zhang, X. & Xiang, J. Predictive ability of genomic selection models for breeding value estimation on growth traits of Pacific white shrimp Litopenaeus vannamei. Chinese J. Oceanol. Limnol. 35, 1221–1229 (2017).

    Article  Google Scholar 

  192. 192.

    Wang, Q. et al. Evaluation on the genomic selection in Litopenaeus vannamei for the resistance against Vibrio parahaemolyticus. Aquaculture 505, 212–216 (2019).

    Article  Google Scholar 

  193. 193.

    Nguyen, N. H., Phuthaworn, C. & Knibb, W. Genomic prediction for disease resistance to Hepatopancreatic parvovirus and growth, carcass and quality traits in Banana shrimp Fenneropenaeus merguiensis. Genomics 112, 2021–2027 (2020).

    CAS  Article  PubMed  Google Scholar 

  194. 194.

    Zhang, X. et al. The sea cucumber genome provides insights into morphological evolution and visceral regeneration. PLoS Biol. 15, e2003790 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  195. 195.

    Hughes, L. C. et al. Comprehensive phylogeny of ray-finned fishes (Actinopterygii) based on transcriptomic and genomic data. Proc. Natl Acad. Sci. USA 115, 6249–6254 (2018).

    CAS  Article  PubMed  Google Scholar 

  196. 196.

    Wanninger, A. & Wollesen, T. The evolution of molluscs. Biol. Rev. Camb. Philos. Soc. 94, 102 (2018).

    Article  PubMed Central  Google Scholar 

  197. 197.

    Wolfe, J. M. et al. A phylogenomic framework, evolutionary timeline and genomic resources for comparative studies of decapod crustaceans. Proc. R. Soc. B Biol. Sci. 286, 20190079 (2019).

    Article  Google Scholar 

  198. 198.

    Dohrmann, M. & Wörheide, G. Dating early animal evolution using phylogenomic data. Sci. Rep. 7, 3599 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  199. 199.

    Plazzi, F. & Passamonti, M. Towards a molecular phylogeny of mollusks: bivalves’ early evolution as revealed by mitochondrial genes. Mol. Phylogenet. Evol. 57, 641–657 (2010).

    CAS  Article  PubMed  Google Scholar 

  200. 200.

    Kumar, S., Stecher, G., Suleski, M. & Hedges, S. B. TimeTree: a resource for timelines, timetrees, and divergence times. Mol. Biol. Evol. 34, 1812–1819 (2017).

    CAS  Article  PubMed  Google Scholar 

  201. 201.

    Diamond, J. Evolution, consequences and future of plant and animal domestication. Nature 418, 700–707 (2002).

    CAS  Article  PubMed  Google Scholar 

  202. 202.

    Bernatchez, L. et al. Harnessing the power of genomics to secure the future of seafood. Trends Ecol. Evol. 32, 665–680 (2017).

    Article  PubMed  Google Scholar 

  203. 203.

    Yue, G. H. Recent advances of genome mapping and marker-assisted selection in aquaculture. Fish Fish. 15, 376–396 (2014).

    Article  Google Scholar 

  204. 204.

    Brooker, A. J. et al. Sustainable production and use of cleaner fish for the biological control of sea lice: recent advances and current challenges. Vet. Rec. 183, 383–383 (2018).

    Article  PubMed  Google Scholar 

  205. 205.

    Sveier, H. & Breck, O. in Cleaner Fish Biology and Aquaculture Applications (5m Publishing, 2018).

  206. 206.

    Treasurer, J. (ed.) Cleaner Fish Biology and Aquaculture Applications (5M Publishing, 2018).

  207. 207.

    Lie, K. K. et al. Loss of stomach, loss of appetite? Sequencing of the ballan wrasse (Labrus bergylta) genome and intestinal transcriptomic profiling illuminate the evolution of loss of stomach function in fish. BMC Genomics 19, 186 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  208. 208.

    Knutsen, T. M. Lumpfish (Cyclopterus lumpus) draft genome assembly. Figshare https://doi.org/10.6084/m9.figshare.7301546.v1 (2018).

    Article  Google Scholar 

  209. 209.

    Lafferty, K. D. et al. Infectious diseases affect marine fisheries and aquaculture economics. Ann. Rev. Mar. Sci. 7, 471–496 (2015).

    Article  PubMed  Google Scholar 

  210. 210.

    Asche, F., Hansen, H., Tveteras, R. & Tveteras, S. The salmon disease crisis in Chile. Mar. Resour. Econ. 24, 405–411 (2009).

    Article  Google Scholar 

  211. 211.

    Verbruggen, B. et al. Molecular mechanisms of white spot syndrome virus infection and perspectives on treatments. Viruses 8, 23 (2016).

    Article  CAS  PubMed Central  Google Scholar 

  212. 212.

    Ødegård, J., Baranski, M., Gjerde, B. & Gjedrem, T. Methodology for genetic evaluation of disease resistance in aquaculture species: challenges and future prospects. Aquac. Res. 42, 103–114 (2011).

    Article  Google Scholar 

  213. 213.

    Yáñez, J. M. et al. Genetic co-variation between resistance against both Caligus rogercresseyi and Piscirickettsia salmonis, and body weight in Atlantic salmon (Salmo salar). Aquaculture 433, 295–298 (2014).

    Article  Google Scholar 

  214. 214.

    Gjedrem, T. Disease resistant fish and shellfish are within reach: a review. J. Mar. Sci. Eng. 3, 146–153 (2015).

    Article  Google Scholar 

  215. 215.

    Bishop, S. C. & Woolliams, J. A. Genomics and disease resistance studies in livestock. Livest. Sci. 166, 190–198 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  216. 216.

    Anacleto, O. et al. Genetic differences in host infectivity affect disease spread and survival in epidemics. Sci. Rep. 9, 4924 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  217. 217.

    Storset, A., Strand, C., Wetten, M., Kjøglum, S. & Ramstad, A. Response to selection for resistance against infectious pancreatic necrosis in Atlantic salmon (Salmo salar L.). Aquaculture 272 (Suppl. 1), S62–S68 (2007).

    Article  Google Scholar 

  218. 218.

    Houston, R. D. et al. Major quantitative trait loci affect resistance to infectious pancreatic necrosis in Atlantic salmon (Salmo salar). Genetics 178, 1109–1115 (2008). Together with reference 219, this study describes a very large effect QTL for disease resistance in aquaculture populations, leading to widespread adoption of marker-assisted selection to help reduce disease incidence.

    Article  PubMed  PubMed Central  Google Scholar 

  219. 219.

    Moen, T., Baranski, M., Sonesson, A. K. & Kjøglum, S. Confirmation and fine-mapping of a major QTL for resistance to infectious pancreatic necrosis in Atlantic salmon (Salmo salar): population-level associations between markers and trait. BMC Genomics 10, 368 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  220. 220.

    Houston, R. D. et al. The susceptibility of Atlantic salmon fry to freshwater infectious pancreatic necrosis is largely explained by a major QTL. Heredity 105, 318–327 (2010).

    CAS  Article  PubMed  Google Scholar 

  221. 221.

    Gheyas, A. A. et al. Segregation of infectious pancreatic necrosis resistance QTL in the early life cycle of Atlantic Salmon (Salmo salar). Anim. Genet. 41, 531–536 (2010).

    CAS  Article  PubMed  Google Scholar 

  222. 222.

    Houston, R. D. et al. Characterisation of QTL-linked and genome-wide restriction site-associated DNA (RAD) markers in farmed Atlantic salmon. BMC Genomics 13, 244 (2012).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  223. 223.

    Moen, T. et al. Epithelial cadherin determines resistance to infectious pancreatic necrosis virus in Atlantic salmon. Genetics 200, 1313–1326 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  224. 224.

    Robledo, D. et al. Gene expression comparison of resistant and susceptible Atlantic salmon fry challenged with infectious pancreatic necrosis virus reveals a marked contrast in immune response. BMC Genomics 17, 279 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

The authors acknowledge funding from the UK Biotechnology and Biological Sciences Research Council (BBSRC), the UK Natural Environment Research Council (NERC) and the Scottish Aquaculture Innovation Centre via the AquaLeap project (reference numbers BB/S004343/1, BB/S004181/1, BB/S004416/1 and BB/S004300/1), and BBSRC Institute Strategic Programme grants (BBS/E/D/20241866, BBS/E/D/20002172 and BBS/E/D/20002174).

Author information

Affiliations

Authors

Contributions

The authors contributed equally to all aspects of the article.

Corresponding author

Correspondence to Ross D. Houston.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Peer review information

Nature Reviews Genetics thanks L. Bernatchez, D. Jerry, N. H. Nguyen and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Related links

AquaLeap project: https://edin.ac/3bFHXs0

Supplementary information

Glossary

Aquaculture

The farming of fish, crustaceans, molluscs, aquatic plants and algae in freshwater or saltwater environments, typically for human food.

Genetic gains

Improvement in average genetic value, and therefore improved phenotypes, in a population due to selection over cycles of selective breeding.

Base populations

Populations of animals used to start a selective breeding programme.

Genomic selection

The selection of breeding individuals for genetic improvement of a trait of interest based on the use of genome-wide genetic markers to estimate genomic breeding values. Genetic marker genotypes and phenotypes are measured in a reference population to predict breeding values of selection candidates that have genotypes only.

Breeding nuclei

The elite broodstock animals that are maintained only for breeding, which is followed by multiplication and dissemination of the genetically improved animals for production.

Surrogate broodstock

Sterile animals used for the production of gametes of another individual, strain or species.

Broodstock

A group of sexually mature individuals used in aquaculture for breeding purposes.

Behavioural plasticity

The ability of an organism to change its behaviour following exposure to stimuli, such as changing environmental conditions.

Genetic bottlenecks

Sharp reductions in genetic diversity, typically due to large reductions in population size caused by environmental events or human activities.

Linked reads

Linking together of short sequence reads to provide long-range orientation, based on the addition of a unique DNA barcode to each read generated from an individual molecule.

Scaffolding

An approach during genome assembly where contigs (that is, continuous assembled sequences) are linked into larger contiguous sequences including gaps of known length.

Genotyping by sequencing

(GBS). A method using high-throughput sequencing to discover and genotype genome-wide single-nucleotide polymorphisms within a population.

Inbreeding depression

The reduced biological fitness in a given population as a result of inbreeding, typically due to deleterious recessive alleles.

Sequential hermaphroditism

Where an individual in a species is born as one sex but can later change to the opposite sex.

Mass spawning

Release of high numbers of eggs and sperm into the water, where fertilization occurs externally. Also known as broadcast spawning.

Soft sweeps

Increases in frequency and/or fixation of a favourable allele at an existing polymorphic locus due to strong positive selection pressure.

Marker-assisted selection

(MAS). The selection of breeding individuals for genetic improvement of a trait of interest based on genetic markers linked to a quantitative trait locus affecting that trait.

Quantitative trait locus

(QTL). A region of the genome that explains a significant component of variation in a trait of interest.

Mendelian sampling

The chance factor in the process of distributing half the genetic material from each parent to the offspring, which is the source of within-family genetic variation.

SNP arrays

A type of DNA microarrays that are used to genotype genome-wide polymorphisms within a population.

Reference population

In genomic selection, the population of animals that have both genotypes and phenotypes. These data are used to estimate genetic marker effects, which are then applied to predict breeding values for genotyped selection candidates.

Accuracy

In the context of genomic selection, accuracy is the correlation between the estimated genomic breeding values and the true breeding values.

Phenotyping

Collection of measurements relating to traits of interest in the goals of a breeding programme.

Genomic best linear unbiased prediction

(GBLUP). A modification of the pedigree-based best linear unbiased prediction method that incorporates SNP information in the form of a genomic relationship matrix and defines the additive genetic covariance among individuals to predict breeding values.

Bayesian models

In the context of genomic selection, the use of multiple-regression methods incorporating prior information on marker effects, which are used widely for genomic prediction of breeding values.

Genotype imputation

The statistical inference of unobserved genotypes based on knowledge of haplotypes in a population, typically used to predict high-density marker genotypes when most individuals are genotyped for low-density marker genotypes.

Causative variants

Polymorphisms within the genome of a population that have a direct effect on a trait of interest, as opposed to simply being a genetic marker associated with the trait.

Genotype–phenotype gap

The gap in knowledge of how variation at the level of the genome causes an effect on a phenotype of interest.

Internet of things

A network of physical objects that use sensors and application program interfaces to connect and exchange data over the Internet.

Genomic relationship matrix

A matrix containing the estimation of the proportion of total genomic DNA shared by any two individuals based on genome-wide genetic marker data.

Introgression

The deliberate movement of a target locus from one species or strain (donor) into another (recipient) by the creation and repeated backcrossing of a hybrid with one of the donor species or strains.

Effective population size

The size of an idealized population that would give rise to the rate of inbreeding and the rate of change in variance of allele frequencies actually observed in the population under consideration. It is approximate to the number of individuals that contribute gametes to the next generation.

Germplasm

In the context of animal breeding, the genetic material of a breeding programme.

Primordial germ cells

The stem cells specified during early development that will differentiate to form male and female gametes, therefore representing the precursors of the germline.

Pleiotropic effects

In the context of genome editing, the unintended impacts on traits other than the target trait due to a specific edit.

Selection intensity

The number of phenotypic standard deviation units that selected parents are superior to the mean of a population.

Ovoviviparous

Producing offspring by means of eggs that are hatched within the body of the parent.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Houston, R.D., Bean, T.P., Macqueen, D.J. et al. Harnessing genomics to fast-track genetic improvement in aquaculture. Nat Rev Genet 21, 389–409 (2020). https://doi.org/10.1038/s41576-020-0227-y

Download citation

Further reading

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing