Salmonid rickettsial syndrome (SRS), caused by the intracellular bacterium Piscirickettsia salmonis, is one of the main diseases affecting rainbow trout (Oncorhynchus mykiss) farming. To accelerate genetic progress, genomic selection methods can be used as an effective approach to control the disease. The aims of this study were: (i) to compare the accuracy of estimated breeding values using pedigree-based best linear unbiased prediction (PBLUP) with genomic BLUP (GBLUP), single-step GBLUP (ssGBLUP), Bayes C, and Bayesian Lasso (LASSO); and (ii) to test the accuracy of genomic prediction and PBLUP using different marker densities (0.5, 3, 10, 20, and 27 K) for resistance against P. salmonis in rainbow trout. Phenotypes were recorded as number of days to death (DD) and binary survival (BS) from 2416 fish challenged with P. salmonis. A total of 1934 fish were genotyped using a 57 K single-nucleotide polymorphism (SNP) array. All genomic prediction methods achieved higher accuracies than PBLUP. The relative increase in accuracy for different genomic models ranged from 28 to 41% for both DD and BS at 27 K SNP. Between different genomic models, the highest relative increase in accuracy was obtained with Bayes C (∼40%), where 3 K SNP was enough to achieve a similar accuracy to that of the 27 K SNP for both traits. For resistance against P. salmonis in rainbow trout, we showed that genomic predictions using GBLUP, ssGBLUP, Bayes C, and LASSO can increase accuracy compared with PBLUP. Moreover, it is possible to use relatively low-density SNP panels for genomic prediction without compromising accuracy predictions for resistance against P. salmonis in rainbow trout.

Piscirickettsia salmonis is one of the main infectious diseases affecting coho salmon (Oncorhynchus kisutch) farming, and current treatments have been ineffective for the control of this disease. Genetic improvement for P. salmonis resistance has been proposed as a feasible alternative for the control of this infectious disease in farmed fish. Genotyping by sequencing (GBS) strategies allow genotyping of hundreds of individuals with thousands of single nucleotide polymorphisms (SNPs), which can be used to perform genome wide association studies (GWAS) and predict genetic values using genome-wide information. We used double-digest restriction-site associated DNA (ddRAD) sequencing to dissect the genetic architecture of resistance against P. salmonis in a farmed coho salmon population and to identify molecular markers associated with the trait. We also evaluated genomic selection (GS) models in order to determine the potential to accelerate the genetic improvement of this trait by means of using genome-wide molecular information. A total of 764 individuals from 33 full-sib families (17 highly resistant and 16 highly susceptible) were experimentally challenged against P. salmonis and their genotypes were assayed using ddRAD sequencing. A total of 9,389 SNPs markers were identified in the population. These markers were used to test genomic selection models and compare different GWAS methodologies for resistance measured as day of death (DD) and binary survival (BIN). Genomic selection models showed higher accuracies than the traditional pedigree-based best linear unbiased prediction (PBLUP) method, for both DD and BIN. The models showed an improvement of up to 95% and 155% respectively over PBLUP. One SNP related with B-cell development was identified as a potential functional candidate associated with resistance to P. salmonis defined as DD.

Fillet yield (FY) and harvest weight (HW) are economically important traits in Nile tilapia production. Genetic improvement of these traits, especially for FY, are lacking, due to the absence of efficient methods to measure the traits without sacrificing fish and the use of information from relatives to selection. However, genomic information could be used by genomic selection to improve traits that are difficult to measure directly in selection candidates, as in the case of FY. The objectives of this study were: (i) to perform genome-wide association studies (GWAS) to dissect the genetic architecture of FY and HW, (ii) to evaluate the accuracy of genotype imputation and (iii) to assess the accuracy of genomic selection using true and imputed low-density (LD) single nucleotide polymorphism (SNP) panels to determine a cost-effective strategy for practical implementation of genomic information in tilapia breeding programs. The data set consisted of 5,866 phenotyped animals and 1,238 genotyped animals (108 parents and 1,130 offspring) using a 50K SNP panel. The GWAS were performed using all genotyped and phenotyped animals. The genotyped imputation was performed from LD panels (LD0.5K, LD1K and LD3K) to high-density panel (HD), using information from parents and 20% of offspring in the reference set and the remaining 80% in the validation set. In addition, we tested the accuracy of genomic selection using true and imputed genotypes comparing the accuracy obtained from pedigree-based best linear unbiased prediction (PBLUP) and genomic predictions. The results from GWAS supports evidence of the polygenic nature of FY and HW. The accuracy of imputation ranged from 0.90 to 0.98 for LD0.5K and LD3K, respectively. The accuracy of genomic prediction outperformed the estimated breeding value from PBLUP. The use of imputation for genomic selection resulted in an increased relative accuracy independent of the trait and LD panel analyzed. The present results suggest that genotype imputation could be a cost-effective strategy for genomic selection in Nile tilapia breeding programs.

The aim of this study was to compare the accuracy of breeding values (EBVs) predicted using the traditional pedigree based Best Linear Unbiased Prediction (PBLUP) and the single-step genomic Best Linear Unbiased Prediction (ssGBLUP) for resistance against infectious pancreatic necrosis virus (IPNV) in rainbow trout. A total of 2278 animals were challenged against IPNV and 768 individuals were genotyped using a 57 K single nucleotide polymorphism array for rainbow trout. Accuracies for both methods were assessed using five-fold cross-validation. The heritabilities were higher for PBLUP compared to ssGBLUP. The ssGBLUP accuracies outperformed PBLUP in 7 and 11% for days to death and binary survival, respectively. The ssGBLUP could be an alternative approach to improve the accuracy of breeding values for resistance against infectious pancreatic necrosis virus in rainbow trout, using information from genotyped and non-genotyped animals.

The objectives of this study were: (i) to assess genotype imputation accuracy in different scenarios using genomewide single nucleotide polymorphisms (SNP) data from a population comprising two generations of farmed Atlantic salmon and (ii) to assess the accuracy of genomic predictions for a quantitative trait (body weight) using the imputed genotypes. The pedigree consisted of 53 parents and 1069 offspring genotyped using a high-density SNP panel (50 K). Two groups were created: Group A: 90% of the offspring were included into training and 10% into validation sets; Group B: 10% of the offspring were included into training and 90% into validation sets. Different scenarios of available genotypic information from relatives were tested for the two groups previously described. Imputation was performed using three in silico low-density panels (0.5, 3 and 6 K) with all markers except the markers present on the low-density panel masked in the validation sets. The accuracy of genomic selection was tested using the scenarios that resulted in the best and the worst imputation accuracy for the three low density panels and were compared to accuracy obtained from pedigree-based best linear unbiased prediction (PBLUP) and genomic predictions using the 50 K SNP panel. In general, imputation accuracy ranged from 0.74 to 0.98 depending on scenario. For the best scenario with the highest number of animals in reference population (Group A), the accuracy of imputation ranged from 0.95 to 0.98 depending on the low-density panel used. For the best scenario with the lowest number of animals in reference population (Group B), the accuracy of imputation ranged from 0.94 to 0.98 depending on the low-density panel used. In general, the number of SNPs in the low-density panels had a greater influence on the accuracy of imputation than the size of the reference set. The accuracies of genomic predictions using imputed genotypes, ranging from 0.71 to 0.73, outperformed PBLUP (0.66) and were identical or very similar to the use of all true genotype data (0.73). The high imputation and genomic prediction accuracy suggest that the imputation of genotypes from low density (0.5 to 3 K) to high density (50 K) could be a cost-effective strategy for the feasibility of the practical implementation of genomic selection in Atlantic salmon.

Nile tilapia belongs to the second most cultivated group of fish in the world, mainly because of its favorable characteristics for production. Genetic improvement programs and domestication process of Nile tilapia may have modified the genome through selective pressure, leaving signals that can be detected at the molecular level. In this work, signatures of selection were identified using genomewide SNP data, by two haplotype-based (iHS and Rsb) and one F ST based method. Whole-genome re-sequencing of 326 individuals from three strains (A, B and C) of farmed tilapia maintained in Brazil and Costa Rica was carried out using Illumina HiSeq 2500 technology. After applying conventional SNP-calling and quality-control filters, ~ 1.3 M high-quality SNPs were inferred and used as input for the iHS, Rsb and F ST based methods. We detected several candidate genes putatively subjected to selection in each strain. A considerable number of these genes are associated with growth (e.g. NCAPG, KLF3, TBC1D1, TTN), early development (e.g. FGFR3, PFKFB3), and immunity traits (e.g. NLRC3, PIGR, MAP1S). These candidate genes represent putative genomic landmarks that could be associated to traits of biological and commercial interest in farmed nile tilapia. Nile tilapia (Oreochromis niloticus) is a teleost fish of the Cichlidae family native to Africa and the Middle East. The geographic range of the species extends from 8°N to 32°N 1. The first record of domestication is dated around 3,500 years ago as evidenced in paintings at the Theban tombs in Egypt 2. Nowadays, this species is the second most cultivated group of fish in the world 3. Favorable characteristics for production include rapid growth, adaptability to different culture conditions, tolerance to high densities, disease resistance, easy reproduction, and tolerance to low concentrations of oxygen 4. Genetic improvement programs (GIPs) for Nile tilapia began in 1988 as an approach to counteract the production decrease generated by introgressions with Mozambique tilapia (Oreochromis mossambicus) 5,6. Since then, nearly twenty GIPs have been established for Nile tilapia around the world 7,8. GIPs aim to improve traits of commercial interest, such as growth rate, disease resistance, cold and salinity tolerance 7. The GIFT (Genetic Improvement of Farmed Tilapia) 9 Nile tilapia strain was developed by the ICLARM (International Centre for Living Aquatic Resources Management, now the WorldFish Center), in collaboration with the Norwegian Institute of Aquaculture Research (AKVAFORSK, now NOFIMA Marin) 1. The implementation of GIPs for the GIFT population has been successful, because growth rate in Nile tilapia has doubled in five generations, showing that this species had a positive response to selection 1 .

We performed a genome-wide association study to detect markers associated with growth traits in Atlantic salmon. The analyzed traits included body weight at tagging (BWT) and body weight at 25 months (BW25M). Genotypes of 4662 animals were imputed from the 50K SNP chip to the 200K SNP chip using fimpute software. The markers were simultaneously modeled using Bayes C to identify genomic regions associated with the traits. We identified windows explaining a maximum of 3.71% and 3.61% of the genetic variance for BWT and BW25M respectively. We found potential candidate genes located within the top ten 1-Mb windows for BWT and BW25M. For instance, the vitronectin (VTN) gene, which has been previously reported to be associated with cell growth, was found within one of the top ten 1-Mb windows for BWT. In addition, the WNT1-inducible-signaling pathway protein 3, melanocortin 2 receptor accessory protein 2, myosin light chain kinase, transforming growth factor beta receptor type 3 and myosin light chain 1 genes, which have been reported to be associated with skeletal growth in humans, growth stimulation during the larval stage in zebrafish, body weight in pigs, feed conversion in chickens and growth rate of sheep skeletal muscle respectively, were found within some of the top ten 1-Mb windows for BW25M. These results indicate that growth traits are most likely controlled by many variants with relatively small effects in Atlantic salmon. The genomic regions associated with the traits studied here may provide further insight into the functional regions underlying growth traits in this species.

The estimation of linkage disequilibrium between molecular markers within a population is critical when establishing the minimum number of markers required for association studies, genomic selection, and inferring historical events influencing different populations. This work aimed to evaluate the extent and decay of linkage disequilibrium in a coho salmon breeding population using a high-density SNP array. Linkage disequilibrium was estimated between a total of 93,502 SNPs found in 64 individuals (33 dams and 31 sires) from the breeding population. The markers encompass all 30 coho salmon chromosomes and comprise 1,684.62 Mb of the genome. The average density of markers per chromosome ranged from 48.31 to 66 per 1 Mb. The minor allele frequency averaged 0.26 (with a range from 0.22 to 0.27). The overall average linkage disequilibrium among SNPs pairs measured as r 2 was 0.10. The Average r 2 value decreased with increasing physical distance, with values ranging from 0.21 to 0.07 at a distance lower than 1 kb and up to 10 Mb, respectively. An r 2 threshold of 0.2 was reached at distance of approximately 40 Kb. Chromosomes Okis05, Okis15 and Okis28 showed high levels of linkage disequilibrium (>0.20 at distances lower than 1 Mb). Average r 2 values were lower than 0.15 for all chromosomes at distances greater than 4 Mb. An effective population size of 43 was estimated for the population 10 generations ago, and 325, for 139 generations ago. Based on the effective number of chromosome segments, we suggest that at least 74,000 SNPs would be necessary for an association mapping study and genomic predictions. Therefore, the SNP panel used allowed us to capture high-resolution information in the farmed coho salmon population. Furthermore, based on the contemporary N e , a new mate allocation strategy is suggested to increase the effective population size.