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Amoebic gill disease (AGD) is one of the largest threats to salmon aquaculture, causing serious economic and animal welfare burden. Treatments can be expensive and environmentally damaging, hence the need for alternative strategies. Breeding for disease resistance can contribute to prevention and control of AGD, providing long-term cumulative benefits in selected stocks. The use of genomic selection can expedite selection for disease resistance due to improved accuracy compared to pedigree-based approaches. The aim of this work was to quantify and characterize genetic variation in AGD resistance in salmon, the genetic architecture of the trait, and the potential of genomic selection to contribute to disease control. An AGD challenge was performed in ∼1,500 Atlantic salmon, using gill damage and amoebic load as indicator traits for host resistance. Both traits are heritable (h2 ∼0.25-0.30) and show high positive correlation, indicating they may be good measurements of host resistance to AGD. While the genetic architecture of resistance appeared to be largely polygenic in nature, two regions on chromosome 18 showed suggestive association with both AGD resistance traits. Using a cross-validation approach, genomic prediction accuracy was up to 18% higher than that obtained using pedigree, and a reduction in marker density to ∼2,000 SNPs was sufficient to obtain accuracies similar to those obtained using the whole dataset. This study indicates that resistance to AGD is a suitable trait for genomic selection, and the addition of this trait to Atlantic salmon breeding programs can lead to more resistant stocks.

Background The genetic architecture of complex traits in farmed animal populations is of interest from a scientific and practical perspective. The use of genetic markers to predict the genetic merit (breeding values) of individuals is commonplace in modern farm animal breeding schemes. Recently, high density SNP arrays have become available for Atlantic salmon, which facilitates genomic prediction and association studies using genome-wide markers and economically important traits. The aims of this study were (i) to use a high density SNP array to investigate the genetic architecture of weight and length in juvenile Atlantic salmon; (ii) to assess the utility of genomic prediction for these traits, including testing different marker densities; (iii) to identify potential candidate genes underpinning variation in early growth. Results A pedigreed population of farmed Atlantic salmon ( n  = 622) were measured for weight and length traits at one year of age, and genotyped for 111,908 segregating SNP markers using a high density SNP array. The heritability of both traits was estimated using pedigree and genomic relationship matrices, and was comparable at around 0.5 and 0.6 respectively. The results of the GWA analysis pointed to a polygenic genetic architecture, with no SNPs surpassing the genome-wide significance threshold, and one SNP associated with length at the chromosome-wide level. SNPs surpassing an arbitrary threshold of significance ( P  < 0.005, ~ top 0.5 % of markers) were aligned to an Atlantic salmon reference transcriptome, identifying 109 SNPs in transcribed regions that were annotated by alignment to human, mouse and zebrafish protein databases. Prediction of breeding values was more accurate when applying genomic (GBLUP) than pedigree (PBLUP) relationship matrices (accuracy ~ 0.7 and 0.58 respectively) and 5,000 SNPs were sufficient for obtaining this accuracy increase over PBLUP in this specific population. Conclusions The high density SNP array can effectively capture the additive genetic variation in complex traits. However, the traits of weight and length both appear to be very polygenic with only one SNP surpassing the chromosome-wide threshold. Genomic prediction using the array is effective, leading to an improvement in accuracy compared to pedigree methods, and this improvement can be achieved with only a small subset of the markers in this population. The results have practical relevance for genomic selection in salmon and may also provide insight into variation in the identified genes underpinning body growth and development in salmonid species.

Background The study explored the accuracy and biases of genomic selection in two commercial Pekin duck lines, focusing on their performance under real-world breeding practices. A dataset of 26 K duck records with phenotype and imputed genotype information (60 K chip) was analysed for growth, welfare, and primary feather length traits. Mixed linear models with relationship matrices from pedigree (BLUP) or markers (GBLUP) were used to estimate variance components and breeding values. We then assessed selection accuracies and biases to determine the most appropriate models. Results Results showed high imputation accuracies of 0.93 for line A and 0.92 for line D. Heritability estimates from pedigree were generally higher than those from genomic markers. For example, juvenile weight (JW) heritability ranged from 0.22 in line A and 0.25 in line D using markers, to 0.39 and 0.50, respectively, using the pedigree. Slaughter body weight (BW) had similar trends. Gait heritability was low (0.07) using markers in both lines, while breast muscle depth (BD) also had lower estimates (0.15–0.16). For line A, genomic prediction accuracies were higher with the G-matrix, especially for BW (r 2 =0.68-0.70) and JW with r 2 of 0.49. Estimates for gait and foot pad dermatitis (FPD) improved using the G-matrix at 0.58 vs. 0.24 and 0.68 vs. 0.44, respectively, compared to pedigree information. Similar improvements were found for line D, with BD estimates improving from 0.50 to 0.71 using the G-matrix. Line A showed minimal bias (0.01-0.19) with the G-matrix compared to 0.02-0.41 with the A-matrix; the highest bias was for JW. Line D had lower biases with the G-matrix (0.02-0.17) than in line A with markers, while higher biases were observed using pedigree (0.01-0.37). Conclusions These findings indicate that all traits were heritable with higher prediction accuracies and lower biases using GBLUP compared to BLUP. This study demonstrates the effectiveness of GBLUP in improving prediction accuracy and reducing bias in selection traits of Pekin ducks, particularly for traits with low heritability.

Background Sea lice have significant negative economic and welfare impacts on marine Atlantic salmon farming. Since host resistance to sea lice has a substantial genetic component, selective breeding can contribute to control of lice. Genomic selection uses genome-wide marker information to predict breeding values, and can achieve markedly higher accuracy than pedigree-based methods. Our aim was to assess the genetic architecture of host resistance to sea lice, and test the utility of genomic prediction of breeding values. Individual lice counts were measured in challenge experiments using two large Atlantic salmon post-smolt populations from a commercial breeding programme, which had genotypes for ~33 K single nucleotide polymorphisms (SNPs). The specific objectives were to: (i) estimate the heritability of host resistance; (ii) assess its genetic architecture by performing a genome-wide association study (GWAS); (iii) assess the accuracy of predicted breeding values using varying SNP densities (0.5 to 33 K) and compare it to that of pedigree-based prediction; and (iv) evaluate the accuracy of prediction in closely and distantly related animals. Results Heritability of host resistance was significant (0.22 to 0.33) in both populations using either pedigree or genomic relationship matrices. The GWAS suggested that lice resistance is a polygenic trait, and no genome-wide significant quantitative trait loci were identified. Based on cross-validation analysis, genomic predictions were more accurate than pedigree-based predictions for both populations. Although prediction accuracies were highest when closely-related animals were used in the training and validation sets, the benefit of having genomic-versus pedigree-based predictions within a population increased as the relationships between training and validation sets decreased. Prediction accuracy reached an asymptote with a SNP density of ~5 K within populations, although higher SNP density was advantageous for cross-population prediction. Conclusions Host resistance to sea lice in farmed Atlantic salmon has a significant genetic component. Phenotypes relating to host resistance can be predicted with moderate to high accuracy within populations, with a major advantage of genomic over pedigree-based methods, even at relatively sparse SNP densities. Prediction accuracies across populations were low, but improved with higher marker densities. Genomic selection can contribute to lice control in salmon farming.

Average daily gain (ADG), body weight (BW), primary feather length (PRF) and breast depth (BD) are economically important traits in duck production, and understanding the genetic architecture of these traits remains limited. Genome-wide association studies (GWAS) can provide insight into the genetic mechanism underlying these traits. Increasing the power of GWAS by applying approaches such as genotype imputation can improve the ability to detect quantitative trait loci associations with polygenic traits. To increase the power of detecting marker-trait associations in this study, we also exploited imputed data and on a larger sample size. The objective of this study was to investigate marker-trait associations for ADG, BW, PRF and BD in ducks. First, we conducted univariate GWA analyses using chip data (hence medium density data) using 45 K autosomal SNPs and 1445 ducks from single breeding line. Second, we exploited imputed data with a larger sample size (13020) from the same line and performed univariate analyses. Comparison of SNP signals between the medium density and imputed data identified 63 common SNPs that were co-localized on chromosome 4. Several functional candidate genes such as PPARGC1A , LDB2 and LCORL were found within or close to the identified region. Indeed, the LCORL-NCAPG region has been reported in many mammalian species to be related to growth. Considering results from both analyses, current findings propose novel putative pleiotropic candidate quantitative trait loci (QTL) with the associated genes for the traits we analysed while identifying the most promising QTL region on chromosome 4.

East Coast fever, a tick-borne cattle disease caused by the Theileria parva parasite, is among the biggest natural killers of cattle in East Africa, leading to over 1 million deaths annually. Here we report on the genetic analysis of a cohort of Bos indicus (Boran) cattle demonstrating heritable tolerance to infection with T . parva ( h 2 = 0.65, s.e. 0.57). Through a linkage analysis we identify a 6 Mb genomic region on bovine chromosome 15 that is significantly associated with survival outcome following T . parva exposure. Testing this locus in an independent cohort of animals replicates this association with survival following T . parva infection. A stop gained variant in a paralogue of the FAF1 gene in this region was found to be highly associated with survival across both related and unrelated animals, with only one of the 20 homozygote carriers (T/T) of this change succumbing to the disease in contrast to 44 out of 97 animals homozygote for the reference allele (C/C). Consequently, we present a genetic locus linked to tolerance of one of Africa’s most important cattle diseases, raising the promise of marker-assisted selection for cattle that are less susceptible to infection by T . parva .

Balancing selection provides a plausible explanation for the maintenance of deleterious alleles at moderate frequency in livestock, including lethal recessives exhibiting heterozygous advantage in carriers. In the current study, a leg weakness syndrome causing mortality of piglets in a commercial line showed monogenic recessive inheritance, and a region on chromosome 15 associated with the syndrome was identified by homozygosity mapping. Whole genome resequencing of cases and controls identified a mutation causing a premature stop codon within exon 3 of the porcine Myostatin (MSTN) gene, similar to those causing a double-muscling phenotype observed in several mammalian species. The MSTN mutation was in Hardy-Weinberg equilibrium in the population at birth, but significantly distorted amongst animals still in the herd at 110 kg, due to an absence of homozygous mutant genotypes. In heterozygous form, the MSTN mutation was associated with a major increase in muscle depth and decrease in fat depth, suggesting that the deleterious allele was maintained at moderate frequency due to heterozygous advantage (allele frequency, q = 0.22). Knockout of the porcine MSTN by gene editing has previously been linked to problems of low piglet survival and lameness. This MSTN mutation is an example of putative balancing selection in livestock, providing a plausible explanation for the lack of disrupting MSTN mutations in pigs despite many generations of selection for lean growth.

Genomic selection uses genome-wide marker information to predict breeding values for traits of economic interest, and is more accurate than pedigree-based methods. The development of high density SNP arrays for Atlantic salmon has enabled genomic selection in selective breeding programs, alongside high-resolution association mapping of the genetic basis of complex traits. However, in sibling testing schemes typical of salmon breeding programs, trait records are available on many thousands of fish with close relationships to the selection candidates. Therefore, routine high density SNP genotyping may be prohibitively expensive. One means to reducing genotyping cost is the use of genotype imputation, where selected key animals (e.g., breeding program parents) are genotyped at high density, and the majority of individuals (e.g., performance tested fish and selection candidates) are genotyped at much lower density, followed by imputation to high density. The main objectives of the current study were to assess the feasibility and accuracy of genotype imputation in the context of a salmon breeding program. The specific aims were: (i) to measure the accuracy of genotype imputation using medium (25 K) and high (78 K) density mapped SNP panels, by masking varying proportions of the genotypes and assessing the correlation between the imputed genotypes and the true genotypes; and (ii) to assess the efficacy of imputed genotype data in genomic prediction of key performance traits (sea lice resistance and body weight). Imputation accuracies of up to 0.90 were observed using the simple two-generation pedigree dataset, and moderately high accuracy (0.83) was possible even with very low density SNP data (∼250 SNPs). The performance of genomic prediction using imputed genotype data was comparable to using true genotype data, and both were superior to pedigree-based prediction. These results demonstrate that the genotype imputation approach used in this study can provide a cost-effective method for generating robust genome-wide SNP data for genomic prediction in Atlantic salmon. Genotype imputation approaches are likely to form a critical component of cost-efficient genomic selection programs to improve economically important traits in aquaculture.

Ticks cause substantial production losses for beef and dairy cattle. Cattle resistance to ticks is one of the most important factors affecting tick control, but largely neglected due to the challenge of phenotyping. In this study, we evaluate the pooling of tick resistance phenotyped reference populations from multi-country beef cattle breeds to assess the possibility of improving host resistance through multi-trait genomic selection. Data consisted of tick counts or scores assessing the number of female ticks at least 4.5 mm length and derived from seven populations, with breed, country, number of records and genotyped/phenotyped animals being respectively: Angus (AN), Brazil, 2,263, 921/1,156, Hereford (HH), Brazil, 6,615, 1,910/2,802, Brangus (BN), Brazil, 2,441, 851/851, Braford (BO), Brazil, 9,523, 3,062/4,095, Tropical Composite (TC), Australia, 229, 229/229, Brahman (BR), Australia, 675, 675/675, and Nguni (NG), South Africa, 490, 490/490. All populations were genotyped using medium density Illumina SNP BeadChips and imputed to a common high-density panel of 332,468 markers. The mean linkage disequilibrium (LD) between adjacent SNPs varied from 0.24 to 0.37 across populations and so was sufficient to allow genomic breeding values (GEBV) prediction. Correlations of LD phase between breeds were higher between composites and their founder breeds (0.81 to 0.95) and lower between NG and the other breeds (0.27 and 0.35). There was wide range of estimated heritability (0.05 and 0.42) and genetic correlation (-0.01 and 0.87) for tick resistance across the studied populations, with the largest genetic correlation observed between BN and BO. Predictive ability was improved under the old-young validation for three of the seven populations using a multi-trait approach compared to a single trait within-population prediction, while whole and partial data GEBV correlations increased in all cases, with relative improvements ranging from 3% for BO to 64% for TC. Moreover, the multi-trait analysis was useful to correct typical over-dispersion of the GEBV. Results from this study indicate that a joint genomic evaluation of AN, HH, BN, BO and BR can be readily implemented to improve tick resistance of these populations using selection on GEBV. For NG and TC additional phenotyping will be required to obtain accurate GEBV.

The increasing prevalence of bovine tuberculosis (bTB) in the UK and the limitations of the currently available diagnostic and control methods require the development of complementary approaches to assist in the sustainable control of the disease. One potential approach is the identification of animals that are genetically more resistant to bTB, to enable breeding of animals with enhanced resistance. This paper focuses on prediction of resistance to bTB. We explore estimation of direct genomic estimated breeding values (DGVs) for bTB resistance in UK dairy cattle, using dense SNP chip data, and test these genomic predictions for situations when disease phenotypes are not available on selection candidates. We estimated DGVs using genomic best linear unbiased prediction methodology, and assessed their predictive accuracies with a cross validation procedure and receiver operator characteristic (ROC) curves. Furthermore, these results were compared with theoretical expectations for prediction accuracy and area-under-the-ROC-curve (AUC). The dataset comprised 1151 Holstein-Friesian cows (bTB cases or controls). All individuals (592 cases and 559 controls) were genotyped for 727,252 loci (Illumina Bead Chip). The estimated observed heritability of bTB resistance was 0.23±0.06 (0.34 on the liability scale) and five-fold cross validation, replicated six times, provided a prediction accuracy of 0.33 (95% C.I.: 0.26, 0.40). ROC curves, and the resulting AUC, gave a probability of 0.58, averaged across six replicates, of correctly classifying cows as diseased or as healthy based on SNP chip genotype alone using these data. These results provide a first step in the investigation of the potential feasibility of genomic selection for bTB resistance using SNP data. Specifically, they demonstrate that genomic selection is possible, even in populations with no pedigree data and on animals lacking bTB phenotypes. However, a larger training population will be required to improve prediction accuracies.