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We performed a genome-wide mapping for the age at first calving ( AFC) with the goal of annotating candidate genes that regulate fertility in Nellore cattle. Phenotypic data from 762 cows and 777k SNP genotypes from 2,992 bulls and cows were used. Single nucleotide polymorphism ( SNP) effects based on the single-step GBLUP methodology were blocked into adjacent windows of 1 Megabase (Mb) to explain the genetic variance. SNP windows explaining more than 0.40% of the AFC genetic variance were identified on chromosomes 2, 8, 9, 14, 16 and 17. From these windows, we identified 123 coding protein genes that were used to build gene networks. From the association study and derived gene networks, putative candidate genes (e.g., PAPPA, PREP, FER1L6, TPR, NMNAT1, ACAD10, PCMTD1, CRH, OPKR1, NPBWR1 and NCOA2) and transcription factors ( TF) ( STAT1, STAT3, RELA, E2F1 and EGR1) were strongly associated with female fertility (e.g., negative regulation of luteinizing hormone secretion, folliculogenesis and establishment of uterine receptivity). Evidence suggests that AFC inheritance is complex and controlled by multiple loci across the genome. As several windows explaining higher proportion of the genetic variance were identified on chromosome 14, further studies investigating the interaction across haplotypes to better understand the molecular architecture behind AFC in Nellore cattle should be undertaken. [ABSTRACT FROM AUTHOR]

The selection of genetically superior individuals is conditional upon accurate breeding value predictions which, in turn, are highly depend on how precisely relationship is represented by pedigree. For that purpose, the numerator relationship matrix is essential as a priori information in mixed model equations. The presence of pedigree errors and/or the lack of relationship information affect the genetic gain because it reduces the correlation between the true and estimated breeding values. Thus, this study aimed to evaluate the effects of correcting the pedigree relationships using single-nucleotide polymorphism (SNP) markers on genetic evaluation accuracies for resistance of beef cattle to ticks. Tick count data from Hereford and Braford cattle breeds were used as phenotype. Genotyping was carried out using a high-density panel (BovineHD - Illumina® bead chip with 777 962 SNPs) for sires and the Illumina BovineSNP50 panel (54 609 SNPs) for their progenies. The relationship between the parents and progenies of genotyped animals was evaluated, and mismatches were based on the Mendelian conflicts counts. Variance components and genetic parameters estimates were obtained using a Bayesian approach via Gibbs sampling, and the breeding values were predicted assuming a repeatability model. A total of 460 corrections in relationship definitions were made (Table 1) corresponding to 1018 (9.5%) tick count records. Among these changes, 97.17% (447) were related to the sire's information, and 2.8% (13) were related to the dam's information. We observed 27.2% (236/868) of Mendelian conflicts for sire-progeny genotyped pairs and 14.3% (13/91) for dam-progeny genotyped pairs. We performed 2174 new definitions of half-siblings according to the correlation coefficient between the coancestry and molecular coancestry matrices. It was observed that higher-quality genetic relationships did not result in significant differences of variance components estimates; however, they resulted in more accurate breeding values predictions. Using SNPs to assess conflicts between parents and progenies increases certainty in relationships and consequently the accuracy of breeding value predictions of candidate animals for selection. Thus, higher genetic gains are expected when compared to the traditional non-corrected relationship matrix. [ABSTRACT FROM AUTHOR]

We studied the effect of including GWAS results on the accuracy of single- and multipopulation genomic predictions. Phenotypes (backfat thickness) and genotypes of animals from two sire lines ( SL1, n = 1146 and SL3, n = 1264) were used in the analyses. First, GWAS were conducted for each line and for a combined data set (both lines together) to estimate the genetic variance explained by each SNP. These estimates were used to build matrices of weights (D), which was incorporated into a GBLUP method. Single population evaluated with traditional GBLUP had accuracies of 0.30 for SL1 and 0.31 for SL3. When weights were employed in GBLUP, the accuracies for both lines increased (0.32 for SL1 and 0.34 for SL3). When a multipopulation reference set was used in GBLUP, the accuracies were higher (0.36 for SL1 and 0.32 for SL3) than in single-population prediction. In addition, putting together the multipopulation reference set and the weights from the combined GWAS provided even higher accuracies (0.37 for SL1, and 0.34 for SL3). The use of multipopulation predictions and weights estimated from a combined GWAS increased the accuracy of genomic predictions. [ABSTRACT FROM AUTHOR]

The objective of this work was to evaluate the efficiency of the supervised independent component regression ( SICR) method for the estimation of genomic values and the SNP marker effects for boar taint and carcass traits in pigs. The methods were evaluated via the agreement between the predicted genetic values and the corrected phenotypes observed by cross-validation. These values were also compared with other methods generally used for the same purposes, such as RR- BLUP, SPCR, SPLS, ICR, PCR and PLS. The SICR method was found to have the most accurate prediction values. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Pig is an important animal for meat production; this is generally associated with characteristics determined prenatally during myogenesis. Expressed sequence tags ( EST) can provide direct information on the transcriptome and indirect information on the relation between the genome and phenotype, giving information about differentially expressed genes ( DEG). In this work, the identification and annotation of DEG from EST libraries of three pig breeds ( Duroc, Large White and Local Breed Piau) were performed followed by real-time PCR analyses during pre- and postnatal stages (21, 40, 70 and 90 days of pregnancy and 107, 121 and 171 days postnatal) from commercial breed animals for analysis of genes expression levels. Therefore, 34 genes differentially expressed were identified, of which 21 grouped in a network related with muscle development. From this, the expression profile of 13 genes was measured, to confirm their relationship with myogenesis like ANKRD2, MYBPC1, NEB and MYL2. These genes showed a prenatal high expression in this study. Besides, novels candidates for muscle development ( TP53 and DCTN1) were listed. These findings can contribute to better explaining gene function mechanism and are helpful in uncovering the pathways that mediate pre- and postnatal skeletal muscle development in vertebrates. [ABSTRACT FROM AUTHOR]