Persistency of accuracy of genomic breeding values for different simulated pig breeding programs in developing countries

Genetic improvement of pigs in tropical developing countries has focused on imported exotic populations which have been subjected to intensive selection with attendant high population‐wide linkage disequilibrium (LD). Presently, indigenous pig population with limited selection and low LD are being considered for improvement. Given that the infrastructure for genetic improvement using the conventional BLUP selection methods are lacking, a genome‐wide selection (GS) program was proposed for developing countries. A simulation study was conducted to evaluate the option of using 60 K SNP panel and observed amount of LD in the exotic and indigenous pig populations. Several scenarios were evaluated including different size and structure of training and validation populations, different selection methods and long‐term accuracy of GS in different population/breeding structures and traits. The training set included previously selected exotic population, unselected indigenous population and their crossbreds. Traits studied included number born alive (NBA), average daily gain (ADG) and back fat thickness (BFT). The ridge regression method was used to train the prediction model. The results showed that accuracies of genomic breeding values (GBVs) in the range of 0.30 (NBA) to 0.86 (BFT) in the validation population are expected if high density marker panels are utilized. The GS method improved accuracy of breeding values better than pedigree‐based approach for traits with low heritability and in young animals with no performance data. Crossbred training population performed better than purebreds when validation was in populations with similar or a different structure as in the training set. Genome‐wide selection holds promise for genetic improvement of pigs in the tropics.     

Comparison between haplotype-based and individual snp-based genomic predictions for beef fatty acid profile in Nelore cattle

The aim of this study was to evaluate the genomic predictions using the single-step genomic best linear unbiased predictor (ssGBLUP) method based on SNPs and haplotype markers associated with beef fatty acids (FAs) profile in Nelore cattle. The data set contained records from 963 Nelore bulls finished in feedlot (±90 days) and slaughtered with approximately 24 months of age. Meat samples from the Longissimus dorsi muscle were taken for FAs profile measurement. FAs were quantified by gas chromatography using a SP-2560 capillary column. Animals were genotyped with the high-density SNP panel (BovineHD BeadChip assay) containing 777,962 markers. SNPs with a minor allele frequency and a call rate lower than 0.05 and 0.90, respectively, monomorphic, located on sex chromosomes, and with unknown position were removed from the data set. After genomic quality control, a total of 469,981 SNPs and 892 samples were available for subsequent analyses. Missing genotypes were imputed and phased using the FImpute software. Haplotype blocks were defined based on linkage disequilibrium using the Haploview software. The model to estimate variance components and genetic parameters and to predict the genomic values included the random genetic additive effects, fixed effects of the contemporary group and the age at slaughter as a linear covariate. Accuracies using the haplotype-based approach ranged from 0.07 to 0.31, and those SNP-based ranged from 0.06 to 0.33. Regression coefficients ranged from 0.07 to 0.74 and from 0.08 to 1.45 using the haplotype- and SNP-based approaches, respectively. Despite the low to moderate accuracies for the genomic values, it is possible to obtain genetic progress trough selection using genomic information based either on SNPs or haplotype markers. The SNP-based approach allows less biased genomic evaluations, and it is more feasible when taking into account the computational and operational cost underlying the haplotypes inference.     

Analyses of genetic diversity in five Canadian dairy breeds using pedigree data

The issue of loss of animal genetic diversity, worldwide in general and in Canada in particular, has become noteworthy. The objective of this study was to analyze the trend in within‐breed genetic diversity and identify the major causes of loss of genetic diversity in five Canadian dairy breeds. Pedigrees were analyzed using the software EVA (evolutionary algorithm) and CFC (contribution, inbreeding, coancestry), and a FORTRAN package for pedigree analysis suited for large populations (PEDIG). The average rate of inbreeding in the last generation analyzed (2003 to 2007) was 0.93, 1.07, 1.26, 1.09 and 0.80% for Ayrshire, Brown Swiss, Canadienne, Guernsey and Milking Shorthorn, respectively, and the corresponding estimated effective population sizes were 54, 47, 40, 46 and 66, respectively. Based on coancestry coefficients, the estimated effective population sizes in the last generation were 62, 76, 43, 61 and 76, respectively. The estimated percentage of genetic diversity lost within each breed over the last four decades was 6, 7, 11, 8 and 5%, respectively. The relative proportion of genetic diversity lost due to random genetic drift in the five breeds ranged between 59.3% and 89.7%. The results indicate that each breed has lost genetic diversity over time and that the loss is gaining momentum due to increasing rates of inbreeding and reduced effective population sizes. Therefore, strategies to decrease rate of inbreeding and increase the effective population size are advised.     

Impact of including information from bulls and their daughters in the training population of multiple‐step genomic evaluations in dairy cattle: A simulation study

The objective of this study was to investigate the impact of accounting for parent average (PA) and genotyped daughters’ average (GDA) on the estimation of deregressed estimated breeding values (dEBVs) used as pseudo‐phenotypes in multiple‐step genomic evaluations. Genomic estimated breeding values (GEBVs) were predicted, in eight different simulated scenarios, using dEBVs calculated based on four methods. These methods included PA and GDA in the dEBV (VR) or only GDA (VRpa) and excluded both PA and GDA from the dEBV with either all information or only information from PA and GDA (JA and NEW, respectively). In general, VR and NEW showed the lowest and highest GEBV reliabilities across scenarios, respectively. Among all deregression methods, VRpa and NEW provided the most consistent bias estimates across the majority of scenarios, and they significantly yielded the least biased GEBVs. Our results indicate that removing PA and GDA information from dEBVs used in multiple‐step genomic evaluations can increase the reliability of GEBVs, when both bulls and their daughters are included in the training population.     

Potential insulin secretagogue effects of isovitexin and swertisin isolated from Wilbrandia ebracteata roots in non-diabetic rats

The antihyperglycemic effect and mechanism of action of extracts, fractions and compounds from Wilbrandia ebracteata was studied. The crude extract reduced the glycemia, increased glycogen content and serum insulin in hyperglycemic rats. Also, a significant effect was observed with the n-butanol and metanol subfraction. However, the antihyperglycemic effect of the n-butanol fraction was not observed in diabetic rats. The C-glycosylflavones isovitexin and swertisin showed a strong antihyperglycemic action compared with the extracts and fractions. These results show that the extracts, fractions, and isolated C-glycosylflavones have an antihyperglycemic action that was reinforced by the stimulation on in vivo insulin secretion.     

Association of single nucleotide polymorphisms in the leptin gene with carcass and meat quality traits of beef cattle

Studies with different populations are required to properly characterize the robustness of associations of polymorphisms in candidate genes with economically important traits across beef cattle populations before this sort of genetic information can be used efficiently in breeding and management decisions. The objective of this study was to evaluate the association of previously reported SNP in the bovine leptin gene with carcass and meat quality traits from a large sample of crossbred beef cattle. Five SNP (UASMS1, UASMS2, UASMS3, E2JW, and E2FB) were genotyped on 1,111 crossbred bulls, heifers, and steers. The measured traits included fat, lean, and bone yield (%) by partial rib dissection, grade fat, LM area, HCW, quality grade, LM i.m. fat, and tenderness evaluation of LM and semitendinosus muscle. Only four SNP were analyzed (UASMS1, UASMS2, E2JW, and E2FB), because UASMS1 and UASMS3 were completely linked. A uni-variate mixed-inheritance animal model was used to evaluate the association of either genotypes or haplo-types with the traits. The two leptin exon 2 SNP were associated with fat and lean yield and grade fat (E2JW, P < 0.01; E2FB, P < 0.05), and they interacted in their effect on LM tenderness (P < 0.01). The leptin promoter SNP were either not associated with any of the traits (UASMS2) or with fat yield only (UASMS1). Three haplotypes (TCAC, CCAT, TTAC) were at high frequency in the population (88%) and had similar effects on all the traits. Compared with the common haplotypes, one haplotype (CCTT) showed a significantly different effect on fat and lean yield and grade fat (P < 0.01), and one haplotype (TTTT) had a different effect on LM tenderness (P < 0.03). Therefore, important associations between SNP within the leptin gene with lean yield, fatness (fat yield and subcutaneous fat), and tenderness were detected. Results confirm some of the previously reported associations, but diverge with respect to others, showing that further efforts are required to validate some prospective associations.     

Monitoring für Getreidekrankheiten in Bayern – mehrjährige Auswertungen über die Bedeutung der einzelnen Schaderreger

Since the year 1991, up-to-date data about the actual infestation situations of the most important fungal diseases have been made available to the official advisory service and to the farmers by means of the “Monitoring for cereal diseases”. The classification of infestation is taken according to the scientifically elaborated thresholds of the Wheat Crop System Bayern and the Barley Crop System Bayern, respectively. The extensive data material allowed an evaluation over several years for the various cultivated areas. The least importance in winter wheat had eyespot, brown rust und yellow rust. Most exceedings of the thresholds were caused by Septoria tritici and Drechslera tritici-repentis. On about one third of the examined locations, exceedings of the threshold could already be noted before the growth stage “flag leaf just visible”. According to the infestation situation, in more than half of the cases one treatment would have been appropriate. In the past five years, the net blotch caused by Drechslera teres has turned out to be the most important disease in winter barley. With some difference in importance followed Rhynchosporium leaf blotch, powdery mildew and brown rust. At almost 80% of the locations, threshold exceedings were registered until the growth stage “flag life ligule just visible” (BBCH 39). In the majority of cases, the infestation situation makes a single treatment in winter barley appearing reasonable. In the years 2001–2004 the main pathogen in spring barley had been powdery mildew, followed by Drechslera teres and Rhynchosporium secalis. The exceedings of the thresholds had been spread over a broad range of growth stages. In the past five years the infestation of fungal diseases in triticale has been low. However since 2005 an increase of infestation, especially of powdery mildew, has been registered. The evaluation of the results from the “Monitoring for cereal diseases” over several years allows to estimate the risk of the different fungi in the regions. From that, conclusions about the fungicide intensities required in the average of cases can be drawn.     

Comparison between methods for measuring fecal egg count and estimating genetic parameters for gastrointestinal parasite resistance traits in sheep

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 LMMR and the LTCM 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 FEC 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.     

Linear and Poisson models for genetic evaluation of tick resistance in cross‐bred Hereford x Nellore cattle

Cattle resistance to ticks is measured by the number of ticks infesting the animal. The model used for the genetic analysis of cattle resistance to ticks frequently requires logarithmic transformation of the observations. The objective of this study was to evaluate the predictive ability and goodness of fit of different models for the analysis of this trait in cross‐bred Hereford x Nellore cattle. Three models were tested: a linear model using logarithmic transformation of the observations (MLOG); a linear model without transformation of the observations (MLIN); and a generalized linear Poisson model with residual term (MPOI). All models included the classificatory effects of contemporary group and genetic group and the covariates age of animal at the time of recording and individual heterozygosis, as well as additive genetic effects as random effects. Heritability estimates were 0.08 ± 0.02, 0.10 ± 0.02 and 0.14 ± 0.04 for MLIN, MLOG and MPOI models, respectively. The model fit quality, verified by deviance information criterion (DIC) and residual mean square, indicated fit superiority of MPOI model. The predictive ability of the models was compared by validation test in independent sample. The MPOI model was slightly superior in terms of goodness of fit and predictive ability, whereas the correlations between observed and predicted tick counts were practically the same for all models. A higher rank correlation between breeding values was observed between models MLOG and MPOI. Poisson model can be used for the selection of tick‐resistant animals.