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Additional file 1. Estimates of Ne (logarithmic scale) across the last 100 generations for each population analysed. Straight lines represent estimates obtained using data from parents and dashed lines represent estimates obtained using data from offspring.

Additional file 2. Estimates of Ne (logarithmic scale) obtained with the LD method of Hayes et al. [5] across the last 100 generations for each population analysed.

Additional file 3. Estimates of Ne (logarithmic scale) obtained by computer simulations with the LD methods of Hayes et al. [5] and Santiago et al. [6], using the software SLiM3 [48].

This deliverable describes a major task in WP1 “Selective Breeding for Robust Fish” to design, implement and validate a SNP-array that will serve as a basic tool for selective breeding in both species. SNP-chips have already been developed and applied to improve livestock in traditionally domesticated terrestrial animals such as cow (see Nikolazzi et al. 2014 for an overview), chicken (Groenen et al. 2011; Kranis et al. 2013), pig (Ramos et al. 2009) and goat (Tosser-Klopp et al.2014). Regarding fish, previous experience on SNP-chip development comes among others from the Atlantic salmon (Houston et al. 2014; Yanez et al. 2014), common carp (Xu et al. 2014) and rainbow trout (Palti et al. 2015). Especially for the Atlantic salmon, the chip application has led to GWAS analysis (Correa et al. 2015; Tsai et al. 2015) illustrating the advantages of such a tool. Till now, the chosen strategies from different studies include either sequencing a part of the genome (e.g. RNASeq, GBS) of many individuals or whole genome re-sequencing of fewer individuals. The former provides higher coverage of the sequenced moiety of the genome, while the latter identifies many more markers evenly distributed across the genome with lower coverage. In this deliverable, farmed as well as wild populations’ samples of both European sea bass (BSS) and gilthead sea bream (SBG) were collected under the responsibility of HCMR and UNIPD, respectively. Samples covered the full range from the Atlantic Ocean to the Eastern Mediterranean; some additional domesticated populations were provided through the MedAID consortium. The samples were deeply sequenced using the technique PoolSeq (Hivert et al. 2018) which resulted in the discovery of SNPs genome-wide. Following multiple bioinformatic filters, a total of 29,888 and 29,807 SNP markers for BSS and SBG, respectively, have been included in the Med_Fish SNP chip. Both sequencing and bioinformatic analysis effort in means of costs and human resources were jointly undertaken by the PerformFISH and MedAID consortia towards having a common SNP-arrays for both species. Furthermore, a number of SNPs was shared by the French-funded project GèneSEA to allow for comparable results between the two platforms. A first validation of the designed SNP revealed a highly successful tool with 90% high resolution markers for BSS and 85% for SBG.

This deliverable describes a major task in WP1 “Selective Breeding for Robust Fish” to design, implement and validate a SNP-array that will serve as a basic tool for selective breeding in both species. SNP-chips have already been developed and applied to improve livestock in traditionally domesticated terrestrial animals such as cow (see Nikolazzi et al. 2014 for an overview), chicken (Groenen et al. 2011; Kranis et al. 2013), pig (Ramos et al. 2009) and goat (Tosser-Klopp et al.2014). Regarding fish, previous experience on SNP-chip development comes among others from the Atlantic salmon (Houston et al. 2014; Yanez et al. 2014), common carp (Xu et al. 2014) and rainbow trout (Palti et al. 2015). Especially for the Atlantic salmon, the chip application has led to GWAS analysis (Correa et al. 2015; Tsai et al. 2015) illustrating the advantages of such a tool. Till now, the chosen strategies from different studies include either sequencing a part of the genome (e.g. RNASeq, GBS) of many individuals or whole genome re-sequencing of fewer individuals. The former provides higher coverage of the sequenced moiety of the genome, while the latter identifies many more markers evenly distributed across the genome with lower coverage. In this deliverable, farmed as well as wild populations’ samples of both European sea bass (BSS) and gilthead sea bream (SBG) were collected under the responsibility of HCMR and UNIPD, respectively. Samples covered the full range from the Atlantic Ocean to the Eastern Mediterranean; some additional domesticated populations were provided through the MedAID consortium. The samples were deeply sequenced using the technique PoolSeq (Hivert et al. 2018) which resulted in the discovery of SNPs genome-wide. Following multiple bioinformatic filters, a total of 29,888 and 29,807 SNP markers for BSS and SBG, respectively, have been included in the Med_Fish SNP chip. Both sequencing and bioinformatic analysis effort in means of costs and human resources were jointly undertaken by the PerformFISH and MedAID consortia towards having a common SNP-arrays for both species. Furthermore, a number of SNPs was shared by the French-funded project GèneSEA to allow for comparable results between the two platforms. A first validation of the designed SNP revealed a highly successful tool with 90% high resolution markers for BSS and 85% for SBG.

8 Pág. Departamento de ​Mejora Genética Animal (INIA), This work was supported by the European Union’s Seventh Framework Programme (KBBE.2013.1.2-659 10 under Grant Agreement No. 613611 FISHBOOST project), the European Commission Horizon 2020 (H2020) Framework Programme through grant agreement no 727315 MedAID project (Mediterranean Aquaculture Integrated Development), by Ministerio de Ciencia e Innovación (CGL2016-75904-C2), MCIN/AEI/https://doi.org/10.13039/501100011033 (PID2020-114426GB-C22 and PID2020-114426GB-C2), Xunta de Galicia (GRC, ED431C 2020-05) and Centro singular de investigación de Galicia accreditation 2019–2022, and the European Union (European Regional Development Fund—ERDF), Fondos Feder “Unha maneira de facer Europa”. MK and MP were also supported by Ministry of Education, Youth and Sports of the Czech Republic—project Biodiverzity (CZ.02.1.01/0.0/0.0/16_025/0007370). The Roslin Institute was partly funded by Biotechnology and Biological Sciences Research Council Institute Strategic Programme grants (BBS/E/D/20241866, BBS/E/D/20002172 and BBS/E/D/20002174).