Genomic analysis and prediction of genomic values for distichiasis in Staffordshire bull terriers.

BACKGROUND: Distichiasis is a condition characterized by aberrant hairs along the eyelid margins. The symptoms are usually mild but can lead to ulcerations and lesions of the cornea in severe cases. It is the most frequently noted ocular disorder in Norwegian Staffordshire bull terriers (SBT), with a prevalence above 18% in the adult population. A complex inheritance is assumed, but there is sparse knowledge about the genetic background of distichiasis in dogs. We have performed a genome-wide association study of distichiasis in SBT and used genomic data in an attempt to predict genomic values for the disorder. RESULTS: We identified four genetic regions on CFA1, CFA18, CFA32 and CFA34 using a mixed linear model association analysis and a Bayesian mixed model analysis. Genomic values were predicted using GBLUP and a Bayesian approach, BayesR. The genomic prediction showed that the 1/4 of dogs with predicted values most likely to acquire distichiasis had a 3.9 -4.0 times higher risk of developing distichiasis compared to the quarter (1/4) of dogs least likely to acquire the disease. There was no significant difference between the two methods used. CONCLUSION: Four genomic regions associated with distichiasis were discovered in the association analysis, suggesting that distichiasis in SBT is a complex trait involving numerous loci. The four associated regions need to be confirmed in an independent sample. We also used all 95 K SNPs for genomic prediction and showed that genomic prediction can be a helpful tool in selective breeding schemes at breed level aiming at reducing the prevalence of distichiasis in SBTs in the future, even if the predictive value of single dogs may be low.

Distichiasis is a condition where abnormal hairs grow along the margin of the eyelids. It's common in Staffordshire bull terriers and can cause eye problems of variable severity. The abnormal eye hairs can be found during an eye inspection performed by a veterinarian.We performed a genome-wide association analysis and identified four genomic areas associated with the condition. But more genes may be involved in causing the disease.We have used genomic data to predict genomic values. Genomic values can be used to predict the total load of disease-associated alleles. Genomic prediction would therefore be helpful at the breed level, similar to pedigree-based breeding values, to reduce the prevalence of dogs with distichiasis, even if the low accuracy to predict phenotypes in individual dogs may be a challenge. More research is needed to confirm these findings and see if genomic prediction could be a helpful tool within dog breeding in the future.

Genetic analyses of nutrient digestibility measured by fecal near-infrared spectroscopy in pigs.

Improved nutrient digestibility is an important trait in genetic improvement in pigs due to global resource scarcity, increased human population and greenhouse gas emissions from pork production. Further, poor nutrient digestibility represents a direct nutrient loss, which affects the profit of the farmer. The aim of this study was to estimate genetic parameters for apparent total tract digestibility of nitrogen (ATTDn), crude fat (ATTDCfat), dry matter (ATTDdm), and organic matter (ATTDom) and to investigate their genetic relationship to other relevant production traits in pigs. Near-infrared spectroscopy was used for prediction of total nitrogen content and crude fat content in feces. The predicted content was used to estimate apparent total tract digestibility of the different nutrients by using an indicator method, where acid insoluble ash was used as an indigestible marker. Average ATTDdm, ATTDom, ATTDn, and ATTDCfat ranged from 61% to 75.3%. Moderate heritabilities was found for all digestibility traits and ranged from 0.15 to 0.22. The genetic correlations among the digestibility traits were high (>0.8), except for ATTDCfat, which had no significant genetic correlation to the other digestibility traits. Significant genetic correlations were found between ATTDn and feed consumption between 40 and 120 kg live weight (F40120) (-0.54 ± 0.11) and ATTDdm and F40120 (-0.35 ± 0.12) and ATTDom and F40120 (-0.28 ± 0.13). No significant genetic correlations were found between digestibility traits and loin depth at 100 kg, nor backfat thickness at 100 kg (BF), except between BF and ATTDn (-0.31 ± 0.14). These results suggested that selection for improved feed efficiency through reduced feed intake within a weight interval, also has led to improved ATTDdm, ATTDom, and ATTDn. Further, the digestibility traits are heritable, but mainly related to feed intake and general function of the intestines, as opposed to allocation of feed resources to different tissues in the body.

Improved nutrient digestibility is an important trait in genetic improvement of pigs due to global resource scarcity, increased human population and greenhouse gas emissions from pork production. The main aim of this study was to investigate whether nutrient digestibility traits in pigs are heritable, and if they are genetically linked to other production traits. The results showed that digestibility of dry matter, organic matter, nitrogen, and crude fat are heritable, and can be selected for in a pig breeding program. The traits are genetically linked to other relevant production traits, such as feed intake, but not to carcass traits, such as loin depth. The results suggest that nutrient digestibility are traits that can be selected for, and that the traits are under indirect selection through other traits in the pig breeding program. The results also indicate that the nutrient digestibility traits express how well the animal utilizes consumed feed, rather than allocating feed to different tissue deposition.

Genetic architecture of fusarium head blight disease resistance and associated traits in Nordic spring wheat.

KEY MESSAGE: This study identified a significant number of QTL that are associated with FHB disease resistance in NMBU spring wheat panel by conducting genome-wide association study. Fusarium head blight (FHB) is a widely known devastating disease of wheat caused by Fusarium graminearum and other Fusarium species. FHB resistance is quantitative, highly complex and divided into several resistance types. Quantitative trait loci (QTL) that are effective against several of the resistance types give valuable contributions to resistance breeding. A spring wheat panel of 300 cultivars and breeding lines of Nordic and exotic origins was tested in artificially inoculated field trials and subjected to visual FHB assessment in the years 2013-2015, 2019 and 2020. Deoxynivalenol (DON) content was measured on harvested grain samples, and anther extrusion (AE) was assessed in separate trials. Principal component analysis based on 35 and 25 K SNP arrays revealed the existence of two subgroups, dividing the panel into European and exotic lines. We employed a genome-wide association study to detect QTL associated with FHB traits and identify marker-trait associations that consistently influenced FHB resistance. A total of thirteen QTL were identified showing consistent effects across FHB resistance traits and environments. Haplotype analysis revealed a highly significant QTL on 7A, Qfhb.nmbu.7A.2, which was further validated on an independent set of breeding lines. Breeder-friendly KASP markers were developed for this QTL that can be used in marker-assisted selection. The lines in the wheat panel harbored from zero to five resistance alleles, and allele stacking showed that resistance can be significantly increased by combining several of these resistance alleles. This information enhances breeders´ possibilities for genomic prediction and to breed cultivars with improved FHB resistance.

Predicting survival and longevity of sows using purebred and crossbred data.

Survival and longevity are very important traits in pig breeding. From an economic standpoint, it is favorable to keep the sows for another parity instead of replacing them and, from the animal's perspective, better welfare is achieved if they do not experience health problems. It is challenging to record longevity in purebred (PB) nucleus herds because animals are more likely to be replaced based on breeding value and high replacement rates rather than inability to produce. Crossbred (CB) sows are, however, submitted to lower replacement rates and are more likely to be kept in the farm longer if they can produce large and robust litters. Therefore, the objective of this study was to investigate whether the use of CB phenotypes could improve prediction accuracy of longevity for PBs. In addition, a new definition of survival was investigated. The analyzed data included phenotypes from two PB dam lines and their F1 cross. Three traits were evaluated: 1) whether or not the sow got inseminated for a second litter within 85 d of first farrowing (Longevity 1-2), 2) how many litters the sow can produce within 570 d of first farrowing [Longevity 1-5 (LGY15)], and 3) a repeatability trait that indicates whether or not the sow survived until the next parity (Survival). Traits were evaluated both as the same across breeds and as different between breeds. Results indicated that longevity is not the same trait in PB and CB animals (low genetic correlation). In addition, there were differences between the two PB lines in terms of which trait definition gave the greatest prediction accuracy. The repeatability trait (Survival) gave the greatest prediction accuracy for breed B, but LGY15 gave the greatest prediction accuracy for breed A. Prediction accuracy for CBs was generally poor. The Survival trait is recorded earlier in life than LGY15 and seemed to give a greater prediction accuracy for young animals than LGY15 (until own phenotype was available). Thus, for selection of young animals for breeding, Survival would be the preferred trait definition. In addition, results indicated that lots of data were needed to get accurate estimates of breeding values and that, if CB performance is the breeding goal, CB phenotypes should be used in the genetic evaluation.

Genetic Connectedness Between Norwegian White Sheep and New Zealand Composite Sheep Populations With Similar Development History.

The Norwegian White sheep (NWS) and New Zealand Terminal Sire Composite (NZC) sheep breeds have been developed based on crossing of multiple breeds, mainly of Northern European origin. A close genetic relationship between these populations could enable across-country genomic evaluations. The main objectives of this study were to assess the genetic connectedness between Norwegian and New Zealand sheep populations and estimate numerous genetic diversity metrics for these two populations. A total of 792 NWS and 16,912 NZC animals were genotyped using a high-density Illumina SNP chip panel (∼606K SNPs). The NZC animals were grouped based on their breed composition as: Finn, Lamb Supreme, Primera, Texel, "Other Dual Purpose", and "Other Terminal Sire". The average level of linkage disequilibrium ranged from 0.156 (for Primera) to 0.231 (for Finn). The lowest consistency of gametic phase was estimated between NWS and Finn (0.397), and between NWS and Texel (0.443), respectively. Similar consistency of gametic phase was estimated between NWS and the other NZC populations (∼ 0.52). For all composite sheep populations analyzed in this study, the majority of runs of homozygosity (ROH) segments identified had short length (<2,500 kb), indicating ancient (instead of recent) inbreeding. The variation in the number of ROH segments observed in the NWS was similar to the variation observed in Primera and Lamb Supreme. There was no clear discrimination between NWS and NZC based on the first few principal components. In addition, based on admixture analyses, there seems to be a significant overlap of the ancestral populations that contributed to the development of both NWS and NZC. There were no evident signatures of selection in these populations, which might be due to recent crossbreeding. In conclusion, the NWS composite breed was shown to be moderately related to NZC populations, especially Primera and Lamb Supreme. The findings reported here indicate a promising opportunity for collaborative genomic analyses involving NWS and NZC sheep populations.

Genetic Variation, GWAS and Accuracy of Prediction for Host Resistance to Sparicotyle chrysophrii in Farmed Gilthead Sea Bream (Sparus aurata).

Gilthead sea bream (Sparus aurata) belongs to a group of teleost which has high importance in Mediterranean aquaculture industry. However, industrial production is increasingly compromised by an elevated outbreak of diseases in sea cages, especially a disease caused by monogeneans parasite Sparicotyle chrysophrii. This parasite mainly colonizes gill tissues of host and causes considerable economical losses with mortality and reduction in growth. The aim of current study was to explore the genetics of host resistance against S. chrysophrii and investigate the potential for genomic selection to possibly accelerate genetic progress. To achieve the desired goals, a test population derived from the breeding nucleus of Andromeda Group was produced. This experimental population was established by crossing of parents mated in partial factorial crosses of ∼8 × 8 using 58 sires and 62 dams. The progeny obtained from this mating design was challenged with S. chrysophrii using a controllable cohabitation infection model. At the end of the challenge, fish were recorded for parasite count, and all the recorded fish were tissue sampled for genotyping by sequencing using 2b-RAD methodology. The initial (before challenge test) and the final body weight (after challenge test) of the fish were also recorded. The results obtained through the analysis of phenotypic records (n = 615) and the genotypic data (n = 841, 724 offspring and 117 parents) revealed that the resistance against this parasite is lowly heritable (h 2 = 0.147 with pedigree and 0.137 with genomic information). We observed moderately favorable genetic correlation (R g = -0.549 to -0.807) between production traits (i.e., body weight and specific growth rate) and parasite count, which signals a possibility of indirect selection. A locus at linkage group 17 was identified that surpassed chromosome-wide Bonferroni threshold which explained 22.68% of the total genetic variance, and might be playing role in producing genetic variation. The accuracy of prediction was improved by 8% with genomic information compared to pedigree.

Assessment of fecal near-infrared spectroscopy to predict feces chemical composition and apparent total-tract digestibility of nutrients in pigs.

Apparent total-tract digestibility (ATTD) of nutrients could be an alternative measure of feed efficiency (FE) when breeding for robust animals that are fed fiber-rich diets. Apparent total-tract digestibility of nutrients requires measuring individual feed intake of a large number of animals which is expensive and complex. Alternatively, ATTD of nutrients and feces chemical composition can be predicted using fecal near-infrared reflectance spectroscopy (FNIRS). The objective of this study was to assess if the feces chemical composition and ATTD of nutrients can be predicted using FNIRS that originate from various pig-experimental datasets. Fecal samples together with detailed information on the feces chemical composition and ATTD of nutrients were obtained from four different pig experiments. Feces near-infrared spectroscopy was analyzed from fecal samples of a complete dataset. The model was calibrated using the FNIRS and reference samples of feces chemical composition and ATTD of nutrients. The robustness and predictability of the model were evaluated by the r2 and the closeness between SE of calibration (SEC) and SE of cross-validation (SECV). Prediction of the feces chemical components and ATTD of nutrients were successful as SEC and SECV were equivalent. Calibration model was developed to estimate the ATTD of nutrients and fecal chemical composition from the FNIRS and worked well for OM (r2 = 0.94; SEC = 48.5; SECV = 56.6), CP (r2 = 0.89; SEC = 18.1; SECV = 18.8), GE (r2 = 0.92; SEC = 1.2; SECV = 1.4), NDF (r2 = 0.94; SEC = 55; SECV = 60.2), OM digestibility (r2 = 0.94; SEC = 5.5; SECV = 6.7), GE digestibility (r2 = 0.88; SEC = 2.3; SECV = 2.6), and fat digestibility (r2 = 0.79; SEC = 6, SECV = 6.8). However, the SE of prediction was slightly higher than what has been reported in another study. The prediction of feces chemical composition for fat (r2 = 0.69; SEC = 11.7, SECV = 12.3), CP digestibility (r2 = 0.63; SEC = 2.3; SECV = 2.7), and NDF digestibility (r2 = 0.64, SEC = 7.7, SECV = 8.8) was moderate. We conclude that the FNIRS accurately predicts the chemical composition of feces and ATTD of nutrients for OM, CP, and GE. The approach of FNIRS is a cost-effective method for measuring digestibility and FE in a large-scale pig-breeding programs.

Short communication: Genomic prediction using imputed whole-genome sequence variants in Brown Swiss Cattle.

The accuracy of genomic prediction determines response to selection. It has been hypothesized that accuracy of genomic breeding values can be increased by a higher density of variants. We used imputed whole-genome sequence data and various single nucleotide polymorphism (SNP) selection criteria to estimate genomic breeding values in Brown Swiss cattle. The extreme scenarios were 50K SNP chip data and whole-genome sequence data with intermediate scenarios using linkage disequilibrium-pruned whole-genome sequence variants, only variants predicted to be missense, or the top 50K variants from genome-wide association studies. We estimated genomic breeding values for 3 traits (somatic cell score, nonreturn rate in heifers, and stature) and found differences in accuracy levels between traits. However, among different SNP sets, accuracy was very similar. In our analyses, sequence data led to a marginal increase in accuracy for 1 trait and was lower than 50K for the other traits. We concluded that the inclusion of imputed whole-genome sequence data does not lead to increased accuracy of genomic prediction with the methods.

Combined detection and introgression of QTL in outbred populations.

BACKGROUND: Detecting a QTL is only the first step in genetic improvement programs. When a QTL with desirable characteristics is found, e.g. in a wild or unimproved population, it may be interesting to introgress the detected QTL into the commercial population. One approach to shorten the time needed for introgression is to combine both QTL identification and introgression, into a single step. This combines the strengths of fine mapping and backcrossing and paves the way for introgression of desirable but unknown QTL into recipient animal and plant lines. METHODS: The method consisting in combining QTL mapping and gene introgression has been extended from inbred to outbred populations in which QTL allele frequencies vary both in recipient and donor lines in different scenarios and for which polygenic effects are included in order to model background genes. The effectiveness of the combined QTL detection and introgression procedure was evaluated by simulation through four backcross generations. RESULTS: The allele substitution effect is underestimated when the favourable QTL allele is not fixed in the donor line. This underestimation is proportional to the frequency differences of the favourable QTL allele between the lines. In most scenarios, the estimates of the QTL location are unbiased and accurate. The retained donor chromosome segment and linkage drag are similar to expected values from other published studies. CONCLUSIONS: In general, our results show that it is possible to combine QTL detection and introgression even in outbred species. Separating QTL mapping and introgression processes is often thought to be longer and more costly. However, using a combined process saves at least one generation. With respect to the linkage drag and obligatory drag, the results of the combined detection and introgression scheme are very similar to those of traditional introgression schemes.