Fecal egg count (FEC) is an indicative measurement for parasite infection in sheep. Different FEC methods may show inconsistent results. Not accounting for inconsistencies can be problematic when integrating measurements from different FEC methods for genetic evaluation. The objectives of this study were to evaluate the difference in means and variances between two fecal egg counting methods used in sheep, the Modified McMaster (LMMR) and the Triple Chamber McMaster (LTCM); to estimate variance components for the two FEC methods, treating them as two different traits; and to integrate FEC data from the two different methods and estimate genetic parameters for FEC and other gastrointestinal parasite resistance traits. Fecal samples were collected from a commercial Rideau-Arcott sheep farm in Ontario. Fecal egg counting was performed using both Modified McMaster and the Triple Chamber McMaster methods. Other parasite resistance trait records were collected from the same farm including eye score (FAMACHA ©), body condition score (BCS), and body weight (WT). The two FEC methods were highly genetically (0.94) and phenotypically (0.88) correlated. However, the mean and variance between the two FEC methods were significantly different (P < 0.0001). Therefore, re-scaling is required prior to integrating data from the different methods. For the multiple trait analysis, data from the two fecal egg counting methods were integrated (LFEC) by using records for the LMMR when available and replacing missing records with re-standardized LTCM records converted to the same mean and variance of LMMR. Heritability estimates were 0.12 ± 0.04, 0.07 ± 0.05 , 0.17 ± 0.06, and 0.24 ± 0.07 for LFEC egg count, FAMACHA ©, BCS, and WT, respectively. The estimated genetic correlations between fecal egg count and the other parasite resistance traits were low and not significant (P>0.05) for FAMACHA © (r= 0.24 ± 0.32) and WT (r= 0.22 ± 0.19), and essentially zero for BCS (r= -0.03 ± 0.25), suggesting little to no benefit of using such traits as indicators for LFEC.

Selection based on scrapie genotypes could improve the genetic resistance for scrapie in sheep. However, in practice, few animals are genotyped. The objectives were to define numerical values of scrapie resistance genotypes and adjust for their non-additive genetic effect; evaluate prediction accuracy of ungenotyped animals using linear animal model; and predict and assess selection response based on estimated breeding values (EBV) of ungenotyped animals. The scrapie resistance (SR) was defined by ranking scrapie genotypes from low (0) to high (4) resistance based on genotype risk groups and was also adjusted for non-additive genetic effect of the haplotypes. Genotypes were simulated for 1,671,890 animals from pedigree. The simulated alleles were assigned to scrapie haplotypes in two scenarios of high (SRh) and low (SRl) resistance populations. A sample of 20,000 genotyped animals were used to predict ungenotyped using animal model. Prediction accuracies for ungenotyped animals for SRh and SRl were 0.60 and 0.54, and for allele content were from 0.41 to 0.71, respectively. Response to selection on SRh and SRl increased SR by 0.52 and 0.28, and on allele content from 0.13 to 0.50, respectively. In addition, the selected animals had large proportion of homozygous for the favorable haplotypes. Thus, pre-selection prior to genotyping could reduce genotyping costs for breeding programs. Using a linear animal model to predict SR makes better use of available information for the breeding programs.

The goal of this study was to estimate genetic parameters and predict direct and correlated response to selection for lamb growth traits and ewe reproductive traits, based on single trait selection or combining multiple traits in an optimum index that targets total litter post-weaning weight in the first lambing as the main selection goal. Heritability estimates ranged from 0.04 to 0.19. Genetic correlations between growth and reproductive traits ranged from -0.24 to 0.15. The indirect response to selection for reproductive traits in later lambings, by selecting on first lambing performance, was 11 to 25% greater than direct selection. The response to indirect selection for composite reproductive traits, i.e. total weaning weight or total post-weaning weight, by selecting on individual lamb weaning weight or post-weaning weight was 1 to 69% greater than direct selection, but it was accompanied by a negative response on litter size. However, combining alternate growth and reproductive traits in optimum selection index resulted in correlated response of up to 96% greater than direct selection response for reproductive traits without a negative response on litter size. Therefore, multiple trait selection using an index of component traits was more effective than direct selection for a composite trait.

Fecal egg count (FEC) is an indicative measurement for parasite infection in sheep. Different FEC methods may show inconsistent results. Not accounting for inconsistencies can be problematic when integrating measurements from different FEC methods for genetic evaluation. The objectives of this study were to: evaluate the difference between two FEC methods, the Modified McMaster (LMMR) and the Triple Chamber McMaster (LTCM); estimate the genetic and phenotypic correlations between records from two methods; and estimate genetic parameters for FEC and other gastrointestinal parasite resistance traits. A total of 1,676 fecal samples were collected from a commercial sheep farm between 2012 and 2019. Fecal egg counting was performed using the Modified McMaster (n = 998) and the Triple Chamber McMaster (n = 678) methods. Other parasite resistance trait records were collected from the same farm including eye score (FAMACHA©), body condition score (BCS), and body weight (WT). The mean and variance between the two FEC methods were significantly different (P < 0.0001), but phenotypic and genetic correlations between them were high (0.88 and 0.94, respectively). Therefore, pre-adjustment is required prior to integrating data from the different methods. For multiple trait analysis with other parasite resistance traits, data from the two fecal egg counting methods were integrated (LFEC) by using records for the LMMR when available and replacing missing records with standardized LTCM records for mean and variance of LMMR. Heritability estimates were 0.12, 0.07, 0.17, and 0.24, for LFEC, FAMACHA©, BCS, and the WT, respectively. Estimated genetic correlations between fecal egg count and the other parasite resistance traits were low with FAMACHA© (0.24), BCS (-0.03), and WT (0.22), suggesting little to no benefit of using such traits as indicators for LFEC.

Gastrointestinal nematode infection is one of the major production problems for sheep producers worldwide due its high incidence, morbidity, and mortality in affected flocks. The study of long non-coding RNA (lncRNA) in liver tissue of high (HIR) and low immune responder (LIR) sheep to GINs using RNA-Sequencing technology may provide a better understanding of the gene regulation mechanism associated with the host response to the infection. The aim of this study was to identify differentially expressed (DE) lncRNA between HIR and LIR natural infested sheep and control group. Liver tissue samples from the 13 divergent animals (out of a population of 211) based on their immunoglobulin G levels after vaccination using Hen Egg White (HEW) Lysozyme, and immature abomasum worm counts [HIR (> 4000) (n = 5), LIR (< 1500) (n=5) and control (no parasite challenge) (n=4) groups] were used to perform transcriptomic analysis using RNA-Sequencing. The “Large Gap read mapping “and “Transcript Discovery” tools from CLC Genomics Workbench 20.0.4 (CLC Bio, Aarhus, Denmark), were used to map reads to a reference genome (Oar_rambouillet_v1.0) and transcript discovery, respectively. The FEELnc software was used to identify, from predicted transcript model, potential lncRNAs and classify those transcripts into intro putative lncRNAs and protein coding RNAs. As preliminary results, 8 and 48 DE lncRNAs for HIR and LIR compared to control group were identified, respectively using an adjusted p-value False Discovery Rate (FDR) < 0.05 and Fold change (FC) abs > 2. Functional analyses using the list of DE lncRNAs identified metabolic pathways related to immune function. In depth analysis will help to better understand the physiological mechanisms of resilience of high immune sheep.