Aquaculture Europe 2021

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Add To Calendar 06/10/2021 15:30:0006/10/2021 15:50:00Europe/LisbonAquaculture Europe 2021A GENOME WIDE ASSOCIATION (GWAS) ANALYSIS FOR PARASITE RESISTANCE IN EUROPEAN SEA BASS Dicentrarhus labraxFunchal-HotelThe European Aquaculture Societywebmaster@aquaeas.orgfalseDD/MM/YYYYaaVZHLXMfzTRLzDrHmAi181982

A GENOME WIDE ASSOCIATION (GWAS) ANALYSIS FOR PARASITE RESISTANCE IN EUROPEAN SEA BASS Dicentrarhus labrax

S. Oikonomou1,3*, M. Papapetrou1, Z. Kazlari1, K. Papanna2, L. Papaharisis2, T. Manousaki3, D. Loukovitis1,4, L. Kottaras2, A. Dimitroglou2, E. Gourzioti2, C. Pagonis2, A. Kostandis2, C. S. Tsigenopoulos3, D. Chatziplis1

 

1Laboratory of Agrobiotechnology and Inspection of Agricultural Products, Department of Agriculture, International Hellenic University, 57400 Sindos, Thessaloniki, Greece

2Nireus Aquaculture SA, Greece

3Institute of Marine Biology, Biotechnology and Aquaculture (IMBBC), Hellenic Centre for Marine Research (HCMR) Crete, Greece

4Research Institute of Animal Science, ELGO Demeter, 58100 Paralimni, Giannitsa, Greece

Email: valiaekonomou@hotmail.com

 



Introduction

One of the most important traits in aquaculture, following growth performance, is disease resistance. Diseases can be caused by parasites, viruses, bacteria etc. A common ectoparasite which significantly affects the European seabass aquaculture production is Lernanthropus kroyeri (Copepoda: Lernanthropidae) Van Beneden, 1851). Generally, L. kroyeri parasitizes the European seabass and affects both production and welfare by increased mortality after the infestation (Sobhana, 2009; Tokşen 2010; Chavanne et al., 2016).

Materials and Methods

In total, 2,425 European sea bass (D. labrax) juveniles originating from the Nireus breeding program were individually pit-tagged, fin-clipped and challenged to the copepod L. kroyeri through natural cohabitation in an environment heavily infested with the parasite (Sagiada site, GR 32 FISH 012). After 4 months in the sea cages, all fish were scarified. The parasite resistance trait was defined according to the counts of parasite found on each individual fish. Experienced personnel counted the number of parasites on each gill arch, on both sides of the fish, with the use of stereoscopes. The heritability of the parasite count was estimated using an animal model (BLUP) and selective genotyping was applied in order to genotype a sample of 1,078 fish with the 30K Affymetrix MedFISH SNP-array. Quality control was performed using plink software (SNP call rate 90%, MAF 0.05, HWE 10-6) (Purcell et al., 2007). GWAS analysis was performed using GEMMA (Zhou and Stephens, 2012, 2014), a univariate animal model using as depended variable the parasite count, the genomic relationship matrix among candidates as a random effect while no fixed effect was fitted in the model. Bonferroni correction was also used in the analysis (Bonferroni, 1936). Finally, the linkage disequilibrium was estimated using the pairwise correlation between all pairs of SNPs using plink software.

Results and Discussion

Parasites were counted in all fish and their average number was 25 ±13.26 while the range was from 1 to 84 parasites. Heritability for parasite count (0.29, BLUP) revealed the existence of substantial additive genetic variation. Since both within and between family variation was evident, from the pedigree-based analysis (animal model), selective genotyping was applied based both on the within family variance of the parasite count and the discordant EBVs for parasite count from the animal model. This method of selective genotyping was utilized to capture both within and between family genetic variations in our genotyped sample (1,078 fish). The dataset which passed the quality control successfully was consisted of 1,076 offspring and 26,821 SNPs. SNP distribution is illustrated in Fig 1, while SNPs which were unstructured were collected in chromosome 25.

To our knowledge, this is one of the first applications of the newly designed 30K Medfish SNP-array in a genomic association analysis and our results indicate an average Linkage disequilibrium (LD = 0.07) in all chromosomes. The GWAS analysis performed revealed two SNPs (p-value: 0.0000023034, 0.0000060397, Fig 2) close to the Bonferroni correction limit in chr 8 (a=0.05, 0.0000018642). The phenotypic variation explained by the two SNPs was estimated to be 2% each. Nevertheless, a higher LD would improve the power of the GWAS analysis. Moreover, if a larger sample was available as well as a higher SNP density on chromosome 8 stronger associations could be revealed. Further analysis on genomic heritability and alternative modes of inheritance for the GWAS on parasite count is being conducted.

Reference

Bonferroni, C. E. (1936) ‘Teoriastatisticadelleclassi e calcolodelleprobabilità’, Pubblicazioni del Royal IstitutoSuperiore di ScienzeEconomiche e Commerciali di Firenze, 8, pp. 3–62.

Chavanne, H. et al. (2016) ‘A comprehensive survey on selective breeding programs and seed market in the European aquaculture fish industry’, Aquaculture International, 24(5), pp. 1287–1307. doi: 10.1007/s10499-016-9985-0.

Purcell, S. et al. (2007) ‘PLINK: A tool set for whole-genome association and population-based linkage analyses’, American Journal of Human Genetics, 81(3), pp. 559–575. doi: 10.1086/519795.

R Core Team (2020) ‘R: A language and environment for statistical computing. R Core Team (2020).’ Vienna, Austria: R Foundation for Statistical Computing, p. 2020. Available at: https://www.r-project.org/.

Sobhana, K.S., 2009. Diseases of seabass in cage culture and control measures, in: National Training on Cage Culture of Seabass. pp. 87–93.

Tokşen, E., 2010. Treatment Trials Of Parasites Of Sea Bass ( Dicentrarchus labrax ) and Sea Bream ( Sparus Aurata ) in Turkey. 2nd Int. Symp. Sustain. Dev.

Zhou, X. and Stephens, M. (2012) ‘Genome-wide efficient mixed-model analysis for association studies’, Nature Genetics, 44(7), pp. 821–824. doi: 10.1038/ng.2310.

Zhou, X. and Stephens, M. (2014) ‘Efficient multivariate linear mixed model algorithms for genome-wide association studies’, Nature Methods, 11(4), pp. 407–409. doi: 10.1038/nmeth.2848.