Inbreeding and inbreeding depression of female reproductive traits in two populations of Coho salmon selected using BLUP predictors of breeding values

Abstract Levels of inbreeding and inbreeding depression were studied in two populations of Coho salmon (Oncorhynchus kisutch) in Chile. The two populations, termed even year, and odd year were artificially selected by weight at harvest over four generations, using the best linear unbiased prediction (BLUP) of breeding values. Also, general linear models (GLM) were used to analyze the effects of inbreeding on reproductive traits of the females and on survival of the progeny. The selection resulted in 56 -76% of the parents of the base population not contributing with descendents in the fourth generation. The inbreeding rate was greater in the even population (DF=2.45% per generation) than the odd population (DF=1.10% per generation) as a direct consequence of the smaller number of founder individuals in the former population (Ne=61 and 106, respectively). The level of inbreeding in the last generation was 9.5% (S.D.=2. eyed stage. Given the deleterious effects of inbreeding on reproductive traits, salmon selection programs should employ methods which limit the rate of increase of this factor.

Accuracy of genomic predictions using different imputation error rates in aquaculture breeding programs: A simulation study

In breeding programs, genetic evaluations can be performed using phenotypic information from the selection candidates per se or from relatives to obtain breeding values (EBV) by the traditional method known as the Best Linear Unbiased Predictor (BLUP). Using phenotypic information from relatives (e.g. sib-testing) is a common practice particularly in aquaculture, because some economically important traits, including disease resistance and carcass quality, would require the slaughtering of animals before they could become breeders. The ability to better predict genetic merit has made the incorporation of genomic information into genetic evaluation a common practice in livestock and aquaculture species. Genomic selection uses genotypic information from single nucleotide polymorphism (SNP) arrays or genotyping-by-sequencing assays to increase the accuracy of selection by means of exploiting realized within and between family trait information. The cost of genotyping dense SNP panels in the training population and selection candidates limits the practical implementation of genomic selection. Imputation from low- to high-density genotypes represents an alternative which decreases the cost of genotyping while maintaining prediction accuracies. The present study compared EBV accuracies obtained with BLUP and genomic selection (GBLUP) methods using simulation. We simulated five generations of a rainbow trout (Oncorhynchus mykiss) breeding program, using 1662 individuals with real genotypic data from 42,822 SNP as a founder population. The scenarios varied using three heritability levels (h 2 = 0.1; 0.2 and 0.4) and four imputation error rates (10%, 5%, 1% and 0%), mimicking different densities of low-density SNP panels (0.5 K, 3 K, 7 K and 42 K, respectively). The simulations showed: (1) an increase in accuracy ranging from 3% to 25% when comparing GBLUP against BLUP across all scenarios, (2) a non-linear increase in accuracy for both BLUP and GBLUP across generations and heritability levels, and (3) comparable performance between GBLUP0.5 K, GBLUP3K and GBLUP7K models in terms of accuracy. We conclude that low cost genomic selection can be applied in aquaculture breeding programs using a combined approach of low-density SNP panels (e.g. 500 SNPs) and genotype imputation

Bayesian genome wide association analysis for body weight in farmed Atlantic salmon (Salmo salar L.)

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.Fundacao de Amparo a Pesquisa do Estado de Sao Paulo (FAPESP) 2014/20626-4 2015/25232-7 CNPq 308636/2014-

Genomic predictions can accelerate selection for resistance against Piscirickettsia salmonis in Atlantic salmon (Salmo salar)

© 2017 The Author(s). Background: Salmon Rickettsial Syndrome (SRS) caused by Piscirickettsia salmonis is a major disease affecting the Chilean salmon industry. Genomic selection (GS) is a method wherein genome-wide markers and phenotype information of full-sibs are used to predict genomic EBV (GEBV) of selection candidates and is expected to have increased accuracy and response to selection over traditional pedigree based Best Linear Unbiased Prediction (PBLUP). Widely used GS methods such as genomic BLUP (GBLUP), SNPBLUP, Bayes C and Bayesian Lasso may perform differently with respect to accuracy of GEBV prediction. Our aim was to compare the accuracy, in terms of reliability of genome-enabled prediction, from different GS methods with PBLUP for resistance to SRS in an Atlantic salmon breeding program. Number of days to death (DAYS), binary survival status (STATUS) phenotypes, and 50 K SNP array genotypes were obtained from 2601 smolts challenged with P. salmonis. The reliability o

Genome-wide association analysis for body weight identifies candidate genes related to development and metabolism in rainbow trout (Oncorhynchus mykiss)

Growth is one of the most important traits from both a physiological and economic perspective in aquaculture species. Thus, identifying the genomic regions and genes underpinning genetic variation for this trait is of particular interest in several fish species, including rainbow trout. In this work, we perform a genome-wide association study (GWAS) to identify the genomic regions associated with body weight at tagging (BWT) and at 18 months (BW18M) using a dense SNP panel (57 k) and 4596 genotyped rainbow trout from 105 full-sib families belonging to a Chilean breeding population. Analysis was performed by means of single-step GBLUP approach. Genetic variance explained by 20 adjacent SNP windows across the whole genome is reported. To further explore candidate genes, we focused on windows that explained the highest proportion of genetic variance in the top 10 chromosomes for each trait. The main window from the top 10 chromosomes was explored by BLAST using the first and last SNP position of each window to determine the target nucleotide sequence. As expected, the percentage of genetic variance explained by windows was relatively low, due to the polygenic nature of body weight. The most important genomic region for BWT and BW18M were located on chromosomes 15 and 24 and they explained 2.14% and 3.02% of the genetic variance for each trait, respectively. Candidate genes including several growth factors, genes involved in development of skeletal muscle and bone tissue and nutrient metabolism were identified within the associated regions for both traits BWT and BW18M. These results indicate that body weight is polygenic in nature in rainbow trout, with the most important loci explaining as much as 3% of the genetic variance for the trait. The genes identified here represent good candidates for further functional validation to uncover biological mechanisms underlying variation for growth in rainbow trout

Disease resistance in Atlantic salmon (Salmo salar): coinfection of the intracellular bacterial pathogen Piscirickettsia salmonis and the sea louse Caligus rogercresseyi.

BACKGROUND: Naturally occurring coinfections of pathogens have been reported in salmonids, but their consequences on disease resistance are unclear. We hypothesized that 1) coinfection of Caligus rogercresseyi reduces the resistance of Atlantic salmon to Piscirickettsia salmonis; and 2) coinfection resistance is a heritable trait that does not correlate with resistance to a single infection. METHODOLOGY: In total, 1,634 pedigreed Atlantic salmon were exposed to a single infection (SI) of P. salmonis (primary pathogen) or coinfection with C. rogercresseyi (secondary pathogen). Low and high level of coinfection were evaluated (LC = 44 copepodites per fish; HC = 88 copepodites per fish). Survival and quantitative genetic analyses were performed to determine the resistance to the single infection and coinfections. MAIN FINDINGS: C. rogercresseyi significantly increased the mortality in fish infected with P. salmonis (SI mortality = 251/545; LC mortality = 544/544 and HC mortality = 545/545). Heritability estimates for resistance to P. salmonis were similar and of medium magnitude in all treatments (h2SI = 0.23 ± 0.07; h2LC = 0.17 ± 0.08; h2HC = 0.24 ± 0.07). A large and significant genetic correlation with regard to resistance was observed between coinfection treatments (rg LC-HC = 0.99 ± 0.01) but not between the single and coinfection treatments (rg SI-LC = -0.14 ± 0.33; rg SI-HC = 0.32 ± 0.34). CONCLUSIONS/SIGNIFICANCE: C. rogercresseyi, as a secondary pathogen, reduces the resistance of Atlantic salmon to the pathogen P. salmonis. Resistance to coinfection of Piscirickettsia salmonis and Caligus rogercresseyi in Atlantic salmon is a heritable trait. The absence of a genetic correlation between resistance to a single infection and resistance to coinfection indicates that different genes control these processes. Coinfection of different pathogens and resistance to coinfection needs to be considered in future research on salmon farming, selective breeding and conservation