BACKGROUND:The development of effective disease management strategies is crucial for the assurance of welfare and sustainability of the aquaculture industries. Pancreas disease (PD) is a major challenge faced by Atlantic salmon aquaculture with viral outbreaks resulting in substantial production losses and raising significant welfare concerns for farmed salmon populations. Previous research has identified several quantitative trait loci (QTL) associated with PD resistance accounting for a substantial additive genetic component. However, pinpointing the underlying causal variation remains challenging, partly due to the location of the QTL within duplicated regions of the Atlantic salmon genome that share high sequence similarity. The present study leverages the latest advancements in Atlantic salmon genomics in order to uncover the genetic landscape underlying PD resistance and identify genomic variation with putative functional impact on disease response. RESULTS:Association mapping and haplotype analysis of fish challenged with salmonid alphavirus (SAV3), either through peritoneal injection or infectious cohabitation, confirmed the presence of a major QTL region on chromosome Ssa03. Additionally, another QTL on Ssa07 was detected, linked to infection-specific response. Transcriptomics analysis of the genes overlapping the Ssa03 QTL region revealed significant expression differences among three tandemly duplicated gig1-like genes, whereas allele-specific expression analysis detected several SNPs with putative functional impact on the particular genes. Use of long-read sequencing and construction of disease-associated haplotypes identified more complex variation in the region, offering a detailed exploration of the genetic architecture underlying PD resistance. Finally, integration of the regulatory landscape of Atlantic salmon during response to viral infection improved genomic resolution, providing novel insight into the potential causal variation underlying pancreas disease in Atlantic salmon. CONCLUSIONS:This study provides a detailed investigation of the genetic architecture underlying PD resistance in farmed Atlantic salmon. Using advanced genomic resources, three copies of the gig1-like gene were identified as likely causal candidates for a major QTL associated with PD resistance. Additionally, genomic variations with potential functional impact on gig1-like expression were uncovered. These findings hold promise for application in developing effective disease management strategies in Atlantic salmon aquaculture.

The facultative intracellular bacterium Piscirickettsia salmonis causes Salmon Rickettsial Syndrome (SRS) in Coho salmon, Atlantic salmon, and other salmonids. SRS causes large mortalities in Chilean aquaculture and leads to heavy usage of antibiotics. In Atlantic salmon SRS resistance is a polygenic trait with moderate to large heritability. In coho salmon a large QTL for SRS-resistance was earlier found on chromosome 21. In the present study we have further characterized genetic resistance to SRS in coho salmon and searched for putative candidate genes underlying the QTL. The mean heritability of survival was 0.31 and 0.58 on the observed and liability scale, respectively. The QTL on chromosome 21 explained from 26% to 97% of genetic variation within 12 different datasets. Two SNPs were substantially more significant compared to other SNPs and in very strong linkage disequilibrium with each other. The resistance allele was found to be dominant over the susceptibility allele at these SNPs. One of the two SNPs was located within the first exon of two genes which are transcribed in opposite directions: a histidine triad nucleotide-binding protein 3 ( hint3 ) gene and a gene (LOC109866666) encoding a long non-coding RNA (lncRNA). Genotypes at the SNP were correlated with expression levels at both hint3 and the lncRNA gene, and the differential expression was manifested in both SRS-challenged and non-challenged fish. The exonic SNP is located 3 base pairs upstream of the start codon of the hint3 gene, at a position which is crucial for effective translation according to the rules of Kozak. The resistance allele at the SNP correlates to increased expression levels and increased translation levels at hint3 , although the latter remains to be experimentally proven. Thus, it seems plausible that the QTL is due to the action of the hint3 gene and/or the gene lncRNA gene encoded by LOC109866666. The hint3 gene on chromosome 21 is different from homologs in Atlantic salmon, and no Atlantic salmon homologs of LOC109866666 were found. Thus, it might be possible to increase SRS-resistance of Atlantic salmon by inserting the coho gene(s) through gene editing.

Sea lice (Caligus rogercresseyi) are ectoparasites that cause significant production losses in the salmon aquaculture industry. Atlantic salmon (Salmo salar) is an important salmonid species for the aquaculture industry and highly susceptible to sea lice infestation. Sea lice load has been shown to be a polygenic trait, controlled by several quantitative trait loci (QTL) with small to medium effects. To improve the detection of QTL associated with sea lice load, we imputed single nucleotide polymorphism (SNP) data from medium- to high-density and conducted genome-wide association studies (GWAS). The imputation from 50 K to 600 K SNP genotypes was performed on 6144 fish from four different populations, which were challenged against sea lice. A metaGWAS was carried out for lice count, lice density, and log-transformed lice density. We identified two genomic regions highly associated with sea lice load on chromosomes (ssa) 3 and 12. Important functional candidate genes are mucin-16-like (ssa3), filamentous-growth-regulator 23-like (ssa3) and fibroblast growth factor receptor-like 1 (ssa12). Several other genes found here suggest that tissue repair, cytoskeletal modification and immune response may play an important role in the genetic variation of sea lice load. Our results confirm the highly polygenic architecture of sea lice load and provide novel insights into the genomic regions and candidate genes underlying the genetic variation for this trait in Atlantic salmon.

Sea lice are copepod ectoparasites of major importance in salmonid aquaculture. Under controlled challenge testing, individual lice count has moderate heritability and has been suggested as a trait in genetic selection for parasite control, under the assumption that selection for reduced individual parasite burden provides group-level protection against the parasite. Recent studies indicate that genetic variation of lice count in Atlantic salmon is mostly explained by variation in initial infestation, rather than ability to limit parasite burden after infestation. Results from a selection experiment are presented, with two Atlantic salmon (Salmo salar) lines divergently selected for low/high parasite burden, showing substantial between-strain difference under common-garden testing. An experiment was performed where the final generation of the divergently selected salmon lines were challenge tested separately to assess potential for group-level protection against sea lice. The results showed that, despite the two groups being clearly different under common garden testing, the line difference was not significant when tested separately, i.e., no evidence for group-level protection. However, in a follow-up experiment, using a more realistic lice challenge model under water-flow, expected to be less favorable for the parasite, significant group-level differences were found, albeit smaller than under common garden testing. The results show that the potential for group-level protection is lower than suggested by within-group genetic variation in lice count, and more so for environments giving the parasites easy access to hosts. These results cast some doubt about the efficacy of selective breeding for reduced lice count as a tool for group-level parasite control in densely populated fish farm environments.

Salmonid rickettsial syndrome (SRS) remains as one of the most important syndromes affecting salmon farming. Genetic improvement has proven to be a viable alternative to reduce mortality in breeding stock. Understanding the genetic architecture of resistance has been a matter of ongoing research aimed at establishing the most appropriate method by which genomic information can be incorporated into breeding programs. However, the genetic architecture of complex traits such as SRS resistance may vary due to genetic and environmental background. In this work, we used the genotypes of a total of 5839 Atlantic salmon from 4 different experimental challenges against Piscirickttsia salmonis, which were imputed sequentially from 65 K to 200 K and then from 200 K to 930 K SNP to perform within-population genomic-association analyses, followed by a meta-analysis of resistance to SRS defined as binary survival and day of death. We found a clear meta-QTL on Ssa02, Ssa17 and Ssa22 for both traits while gene-based meta-analysis revealed 16 genes in common for both traits. Genetic variance explained by 75 significant SNP from meta-analysis for BS ranged from 19.44% to 51.99% while the 177 SNP found for TD ranged from 27.05% to 66.64% in the individual populations. Our results suggest a polygenic genetic architecture, provide novel insights into the candidate genes underpinning resistance to P. salmonis in Salmo salar and also lays foundations for future works on prioritizing markers for improving accuracy and the cost-effectiveness of genomic tools for genetic improvement of this species.

Sea lice infestation is one of the major fish health problems that occurs during the grow-out phase in Atlantic salmon (Salmo salar) aquaculture. In this study, we integrated different genomic approaches, including wholegenome sequencing (WGS), genotype imputation and meta-analysis of genome-wide association studies (GWAS), to identify single-nucleotide polymorphisms (SNPs) associated with sea lice count in Atlantic salmon. Different sets of trait-associated SNPs were prioritized and compared against randomly chosen markers, based on the accuracy of genomic predictions for the trait. Lice count phenotypes and dense genotypes of five breeding populations challenged with sea lice were used. Genotype imputation was applied to increase SNP density of challenged animals to WGS level. The summary statistics from GWAS of each population were then combined in a meta-analysis to increase the sample size and improve the statistical power of associations. Eight different genotyping scenarios were considered for genomic prediction: 70K_array: 70 K standard genotyping panel; 70K_priori: 70 K SNPs with the highest p-values identified in the meta-analysis; 30K_priori: 30 K SNPs with the highest p-values identified in the meta-analysis; WGS: SNPs imputed to whole-genome sequencing level. The remaining four scenarios were the same SNP sets with a linkage disequilibrium (LD) pruning filter: 70K_array_LD; 70K_priori_LD; 30K_priori_LD and WGS_LD. Genomic prediction accuracy was evaluated using a five-fold crossvalidation scheme in two different populations which were excluded from the meta-analysis to remove possible validation-reference bias. Results showed significant genetic variation for the number of sea lice in Atlantic salmon across populations, with heritabilities ranging from 0.06 to 0.24. The meta-analysis identified several SNPs associated with sea lice resistance, mainly in Ssa03 and Ssa09 chromosomes. Genomic prediction using the GWAS-based prioritized SNPs showed higher accuracy compared to the standard SNP array in most of scenarios; achieving up to 57% increase in accuracy. Accuracy of prioritized scenarios was higher for the 70K_priori than the 30K_priori. The use of WGS data in genomic prediction presented marginal or negative accuracy gain compared to the standard SNP array. The LD-pruning filter presented no benefits, reducing accuracy in most scenarios. Overall, our study demonstrated the potential of prioritized imputed sequence variants from multipopulation GWAS meta-analysis to improve prediction accuracy for sea lice counts in Atlantic salmon. The findings suggest that incorporating WGS data and prioritized SNPs from GWAS meta-analysis can accelerate the genetic progress of selection for polygenic traits in salmon aquaculture.

Recent advancements in genomic technologies have led to the discovery and application of DNA-markers [e.g. single nucleotide polymorphisms (SNPs)] for the genetic improvement of several aquaculture species. The identification of specific genomic regions associated with economically important traits, using, for example, genome-wide association studies (GWAS), has allowed the discovery and incorporation of markers linked to quantitative trait loci (QTL) into aquaculture breeding programs through marker-assisted selection (MAS). However, most of the traits of economic relevance are expected to be controlled by many QTLs, each one explaining only a small proportion of the genetic variation. For traits under polygenic control, prediction of the genetic merit of animals based on the sum of effects at positions across the entire genome (i.e. genomic estimated breeding values, GEBV, which are used for what has become known as genomic selection), has been demonstrated to speed the rate of genetic gain for several traits in aquaculture breeding. The aim of this review was to provide an overview of the development and application of genomic technologies in uncovering the genetic basis of complex traits and accelerating the genetic progress in aquaculture species, as well as providing future perspectives about the deployment of novel molecular technologies for selective breeding in coming years.

Flavobacterium psychrophilum is the causative agent of bacterial cold-water disease (CWBD) and rainbow trout fry syndrome (RTFS), which affect salmonids. To better understand this pathogen and its interaction with the host during infection, including to support the development of resistant breeds and new vaccines and treatments, there is a pressing need for reliable and reproducible immersion challenge models that more closely mimic natural routes of infection. The aim of this present study was to evaluate a challenge model developed previously for rainbow trout for use in Atlantic salmon. First, preliminary challenges were conducted in Atlantic salmon (n = 120) and rainbow trout (n = 80) fry using two F. psychrophilum isolates collected from each fish species, respectively; fish had been pretreated with 200 mg/L hydrogen peroxide for 1 h. Thereafter, the main challenge was performed for just one F. psychrophilum isolate for each species (at 2 × 107  CFU/mL) but using larger cohorts (Atlantic salmon: n = 1187; rainbow trout: n = 2701). Survival in the main challenge was 81.2% in Atlantic salmon (21 days post-challenge) and 45.3% in rainbow trout (31 days post-challenge). Mortalities progressed similarly during the preliminary and main challenges for both species, demonstrating the reproducibility of this model. This is the first immersion challenge model of F. psychrophilum to be developed successfully for Atlantic salmon.

Nile tilapia has become the most important species within freshwater aquaculture worldwide, the global tilapia market being expected to reach US$ 25 billion before the end of 2028. Since on an average 50% of the biomass is being lost during outbreaks of Francisellosis in tilapia farms, this study was undertaken to estimate the genetic parameters for resistance to this economically important disease of Nile tilapia, and calculate the genetic correlation with body weights at different ages. A further aim was to characterize the potential of, and to select the best genomic model for, the implementation of genomic selection for resistance to Francisellosis in the commercial Nile tilapia breeding program. The study revealed moderate to high heritabilities (0.37 ? 0.05 to 0.51 ? 0.06) for resistance to Francisellosis using both pedigree and genomic-based estimates. The genetic correlation between resistance to Francisellosis and five different growth traits at different ages ranged from-0.09 to-0.29. Compared to pedigree-based models, GBLUP was found to increase the predictive ability for resistance to Francisellosis by 57%, and the use of Bayesian models would increase this predictive ability by up to 217% in the practical breeding scenario. Thus, the study shows the potential to increase resistance to Francisellosis by genetic selection in commercial Nile tilapia breeding programs. The genetic correlation between resistance to Francisellosis and growth traits showed negative trends, although they were significantly not different from zero. A very large increase in predictive ability for resistance to Francisellosis can be achieved by using Bayesian models for genomic selection.

Background Streptococcosis is a major bacterial disease in Nile tilapia that is caused by Streptococcus agalactiae infection, and development of resistant strains of Nile tilapia represents a sustainable approach towards combating this disease. In this study, we performed a controlled disease trial on 120 full-sib families to (i) quantify and characterize the potential of genomic selection for survival to S. agalactiae infection in Nile tilapia, and (ii) identify the best genomic model and the optimal density of single nucleotide polymorphisms (SNPs) for this trait. Methods In total, 40 fish per family (15 fish intraperitoneally injected and 25 fish as cohabitants) were used in the challenge test. Mortalities were recorded every 3 h for 35 days. After quality control, genotypes (50,690 SNPs) and phenotypes (0 for dead and 1 for alive) for 2472 cohabitant fish were available. Genetic parameters were obtained using various genomic selection models (genomic best linear unbiased prediction (GBLUP), BayesB, BayesC, BayesR and BayesS) and a traditional pedigree-based model (PBLUP). The pedigree-based analysis used a deep 17-generation pedigree. Prediction accuracy and bias were evaluated using five replicates of tenfold cross-validation. The genomic models were further analyzed using 10 subsets of SNPs at different densities to explore the effect of pruning and SNP density on predictive accuracy. Results Moderate estimates of heritabilities ranging from 0.15 ± 0.03 to 0.26 ± 0.05 were obtained with the different models. Compared to a pedigree-based model, GBLUP (using all the SNPs) increased prediction accuracy by 15.4%. Furthermore, use of the most appropriate Bayesian genomic selection model and SNP density increased the prediction accuracy up to 71%. The 40 to 50 SNPs with non-zero effects were consistent for all BayesB, BayesC and BayesS models with respect to marker id and/or marker locations. Conclusions These results demonstrate the potential of genomic selection for survival to S. agalactiae infection in Nile tilapia. Compared to the PBLUP and GBLUP models, Bayesian genomic models were found to boost the prediction accuracy significantly.

Background One objective of this study was to identify putative quantitative trait loci (QTL) that affect indicator phenotypes for growth, nitrogen, and carbon metabolism in muscle, liver, and adipose tissue, and for feed efficiency. Another objective was to perform an RNAseq analysis (184 fish from all families), to identify genes that are associated with carbon and nitrogen metabolism in the liver. The material consisted of a family experiment that was performed in freshwater and included 2281 individuals from 23 full-sib families. During the 12-day feed conversion test, families were randomly allocated to family tanks (50 fish per tank and 2 tanks per family) and fed a fishmeal-based diet labeled with the stable isotopes 15 N and 13 C at inclusion levels of 2 and 1%, respectively. Results Using a linear mixed-model algorithm, a QTL for pre-smolt growth was identified on chromosome 9 and a QTL for carbon metabolism in the liver was identified on chromosome 12 that was closely related to feed conversion ratio on a tank level. For the indicators of feed efficiency traits that were derived from the stable isotope ratios ( 15 N and 13 C) of muscle tissue and growth, no convincing QTL was detected, which suggests that these traits are polygenic. The transcriptomic analysis showed that high carbon and nitrogen metabolism was associated with individuals that convert protein from the feed more efficiently, primarily due to higher expression of the proteasome, lipid, and carbon metabolic pathways in liver. In addition, we identified seven transcription factors that were associated with carbon and nitrogen metabolism and located in the identified QTL regions. Conclusions Analyses revealed one QTL associated with pre-smolt growth and one QTL for carbon metabolism in the liver. Both of these traits are associated with feed efficiency. However, more accurate mapping of the putative QTL will require a more diverse family material. In this experiment, fish that have a high carbon and nitrogen metabolism in the liver converted protein from the feed more efficiently, potentially because of a higher expression of the proteasome, lipid, and carbon metabolic pathways in liver. Within the QTL regions, we detected seven transcription factors that were associated with carbon and nitrogen metabolism.

Using an appropriate Bayesian genomic selection model and optimising it for SNP density increased prediction accuracy up to ∼71%, compared to a pedigree-based model. This result is encouraging for practical implementation of genomic selection for S. agalactiae resistance in Nile tilapia breeding programs.

Background Understanding genetic architecture is essential for determining how traits will change in response to evolutionary processes such as selection, genetic drift and/or gene flow. In Atlantic salmon, age at maturity is an important life history trait that affects factors such as survival, reproductive success, and growth. Furthermore, age at maturity can seriously impact aquaculture production. Therefore, characterizing the genetic architecture that underlies variation in age at maturity is of key interest. Results Here, we refine our understanding of the genetic architecture for age at maturity of male Atlantic salmon using a genome-wide association study of 11,166 males from a single aquaculture strain, using imputed genotypes at 512,397 single nucleotide polymorphisms (SNPs). All individuals were genotyped with a 50K SNP array and imputed to higher density using parents genotyped with a 930K SNP array and pedigree information. We found significant association signals on 28 of 29 chromosomes ( P -values: 8.7 × 10 −133 –9.8 × 10 −8 ), including two very strong signals spanning the six6 and vgll3 gene regions on chromosomes 9 and 25, respectively. Furthermore, we identified 116 independent signals that tagged 120 candidate genes with varying effect sizes. Five of the candidate genes found here were previously associated with age at maturity in other vertebrates, including humans. Discussion These results reveal a mixed architecture of large-effect loci and a polygenic component that consists of multiple smaller-effect loci, suggesting a more complex genetic architecture of Atlantic salmon age at maturity than previously thought. This more complex architecture will have implications for selection on this key trait in aquaculture and for management of wild salmon populations.

Red coloration of muscle tissue (flesh) is a unique trait in several salmonid genera, including Atlantic salmon. The color results from dietary carotenoids deposited in the flesh, whereas the color intensity is affected both by diet and genetic components. Herein we report on a genome-wide association study (GWAS) to identify genetic variation underlying this trait. Two SNPs on ssa26 showed strong associations to the flesh color in salmon. Two genes known to be involved in carotenoid metabolism were located in this QTL- region: beta-carotene oxygenase 1 (bco1) and beta-carotene oxygenase 1 like (bco1l). To determine whether flesh color variation is caused by one, or both, of these genes, functional studies were carried out including mRNA and protein expression in fish with red and pale flesh color. The catalytic abilities of these two genes were also tested with different carotenoids. Our results suggest bco1l to be the most likely gene to explain the flesh color variation observed in this population.

Males and females often differ in their fitness optima for shared traits that have a shared genetic basis, leading to sexual conflict. Morphologically differentiated sex chromosomes can resolve this conflict and protect sexually antagonistic variation, but they accumulate deleterious mutations. However, how sexual conflict is resolved in species that lack differentiated sex chromosomes is largely unknown. Here we present a chromosome-anchored genome assembly for rainbow trout (Oncorhynchus mykiss) and characterize a 55-Mb double-inversion supergene that mediates sex-specific migratory tendency through sex-dependent dominance reversal, an alternative mechanism for resolving sexual conflict. The double inversion contains key photosensory, circadian rhythm, adiposity and sex-related genes and displays a latitudinal frequency cline, indicating environmentally dependent selection. Our results show sex-dependent dominance reversal across a large autosomal supergene, a mechanism for sexual conflict resolution capable of protecting sexually antagonistic variation while avoiding the homozygous lethality and deleterious mutations associated with typical heteromorphic sex chromosomes.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Single-nucleotide polymorphisms (SNPs) are highly abundant markers, which are broadly distributed in animal genomes. For rainbow trout (Oncorhynchus mykiss), SNP discovery has been previously done through sequencing of restriction-site associated DNA (RAD) libraries, reduced representation libraries (RRL) and RNA sequencing. Recently we have performed high coverage whole genome resequencing with 61 unrelated samples, representing a wide range of rainbow trout and steelhead populations, with 49 new samples added to 12 aquaculture samples from AquaGen (Norway) that we previously used for SNP discovery. Of the 49 new samples, 11 were double-haploid lines from Washington State University (WSU) and 38 represented wild and hatchery populations from a wide range of geographic distribution and with divergent migratory phenotypes. We then mapped the sequences to the new rainbow trout reference genome assembly (GCA_002163495.1) which is based on the Swanson YY doubled haploid line. Variant calling was conducted with FreeBayes and SAMtools mpileup, followed by filtering of SNPs based on quality score, sequence complexity, read depth on the locus, and number of genotyped samples. Results from the two variant calling programs were compared and genotypes of the double haploid samples were used for detecting and filtering putative paralogous sequence variants (PSVs) and multi-sequence variants (MSVs). Overall, 30,302,087 SNPs were identified on the rainbow trout genome 29 chromosomes and 1,139,018 on unplaced scaffolds, with 4,042,723 SNPs having high minor allele frequency (MAF > 0.25). The average SNP density on the chromosomes was one SNP per 64 bp, or 15.6 SNPs per 1 kb. Results from the phylogenetic analysis that we conducted indicate that the SNP markers contain enough population-specific polymorphisms for recovering population relationships despite the small sample size used. Intra-Population polymorphism assessment revealed high level of polymorphism and heterozygosity within each population. We also provide functional annotation based on the genome position of each SNP and evaluate the use of clonal lines for filtering of PSVs and MSVs. These SNPs form a new database, which provides an important resource for a new high density SNP array design and for other SNP genotyping platforms used for genetic and genomics studies of this iconic salmonid fish species.

Heart and skeletal muscle inflammation (HSMI) is a disease that causes substantial economic loss and animal welfare problems in farming of salmonids. In Atlantic salmon, outbreaks of the disease can cause up to 20 % mortality at affected sites, and morbidity is frequently close to 100 %, resulting in under-sized fish and poor product quality. Aiming at the identification of DNA markers to be used in marker-assisted selection (MAS), we performed a genomewide association study (GWAS) on resistance to HSMI. Histopathology scores and CT-values for an infection-correlated gene were used as traits, and genotypes were obtained using a custom Affymetrix 50k SNP-chip for Atlantic salmon. The scan revealed that the trait is largely under the control of two major QTL, located on two chromosomes. The two QTL were responsible for more than 25 % of the phenotypic variation in histopathology scores, and almost 10 % of the phenotypic variation in CT-values, indicating a substantial potential for genetic improvement by means of MAS.

Domesticated salmon species have the potential to provide key insights on the role of genetic variation arising from in adaptive processes. Many existing aquaculture strains are descended from few sources and share similar breeding goals, but have been introduced to novel environments. Therefore, comparisons between strains and their source populations serve as a key resource for examining modes of evolution; the detection of signatures of selection across the genome can help to identify genes underlying quantitative traits that have played a role within and between populations. Many domesticated strains of Coho salmon are descended from a few source populations in the Pacific Northwest in the USA, and have been either developed for culture locally, or introduced into different environments in Chile. Most strains have been in culture for more than 20 generations and have experienced intense natural and artificial selection. Still, relevant genomic information to asses to what extent this population processes had shaped Coho salmon populations is not available for this species. In this paper, we develop a genome reference using synteny between species of the genera Onchorhynchus that was used for SNP discovery and to assess genetic variability at the whole genome level. The results show that important differences exist between commercial populations, from the 6 populations 4 Chilean and 2 from north America. The FST values appear to be more related to genetic drift, but still the functional analysis of the SNPs shows that transcription factors (such as AUTS) and immune related genes (such as NFKBIA) explained the genomic differences at specific sites of the genome. These regions appear to explain parallel effects of both domestication and artificial selection. Overall, the results show that important differences exist between commercial populations, which are likely explained by domestication at the production level and artificial selection for specific traits, primarily growth rate.