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BACKGROUND: Transmembrane protein 95 (TMEM95) plays a role in male fertility. Previous studies showed that genes with a significant impact on reproductive traits can also affect the growth traits of livestock. Thus, we speculated that the genetic variation of TMEM95 gene may have effects on growth traits of cattle. RESULTS: Two SNPs were genotyped. The rs136174626 and rs41904693 were in the intron 4 and 30 -untranslated region, respectively. The linkage disequilibrium analysis illustrated that these two loci were not linked. The rs136174626 was associated with six growth traits of Nanyang cattle, four traits of Luxi cattle, and three traits of Ji'an cattle. For rs41904693 locus, the GG individuals had greater body height and abdominal girth in Ji' an cattle than TT and TG individuals. In Jinnan cattle, GG and TT individuals had greater body height, height at hip cross, body length, and heart girth than TG individuals. The potential splice site prediction results suggest that the rs136174626 may influence the splicing efficiency of TMEM95, and the miRNA binding site prediction results showed that the rs41904693 may influence the expression of TMEM95 by affecting the binding efficiency of Bta-miR-1584 and TMEM95 30 -UTR. CONCLUSIONS: The findings of the study suggested that the two SNPs in TMEM95 could be a reliable basis for molecular breeding in cattle.

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INTRODUCTION: Biological control holds great promise for environmentally friendly and sustainable management of the phytopathogens. The multi-function features of plant growth-promoting rhizobacteria (PGPR) enable to protect the plants from disease infections by replacing the chemical inputs. The interaction between the plant root exudates and the microbes stimulates the production of secondary metabolism and enzymes and induces systemic resistance in the plants. AIM: The aim was to identify the potential PGPR which would show an antagonistic effect against basmati rice fungal and bacterial diseases. METHODS: In the study, native originating microbes have been isolated, characterized using 16S rRNA sequencing, and used as potential antagonistic microbial isolates against diseases of rice plants. RESULTS: Rhizobacteria isolated from rhizosphere, endo-rhizosphere, and bulk soil samples of Basmati 370 exhibited promising inhibitory activity against rice pathogens. Molecular characterization of bacterial isolates based on 16S rRNA sequencing classified the bacterial isolates into different genera such as Bacillus, Pseudomonas, Streptomyces, Exiguobacterium, Aeromonas, Chryseobacterium, Enterobacter, and Stenotrophomonas. PGPRs exhibited biocontrol activities against various rice diseases like bacterial leaf blight, leaf blast, brown spot, and sheath blight and boost the plant growth traits. CONCLUSION: In the study, the potentially identified PGPRs isolates could be used as efficient bioinoculants as bio-fertilizers and biocontrol agents for sustainable rice crop production.

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We aimed to evaluate the effects of sex and the linear and quadratic components of age of dam at calving, as well as apply a mixed model including maternal effect for the genetic evaluation of weaning (WW) and yearling (YW) weights. The phenotypic database was composed by Charolais, Caracu, Aberdeen Angus, and Canchim purebred and crossbred animals. Single-trait analyses were performed using models that included the maternal effect for WW and YW traits, and a model ignoring this effect on YW (YWNM). The Deviance Information Criterion (DIC), model posterior probabilities (MPP), accuracy of breeding values (ACC), and Spearman's rank correlation were applied to compare the models including and ignoring the maternal effect on YW. Sex and age of dam at calving had significant effects on WW and YW. The direct heritability estimates were 0.21±0.03 and 0.05±0.02, and the maternal heritabilities were 0.11±0.02 and 0.02±0.01 for WW and YW, respectively. The heritabilities estimated for YW may have been influenced by the several genetic groups in the population and by used conventional animal model, which may not have been the better fit model to evaluate YW in this population. The DIC, MPP, and ACC values indicated that YW outperformed the YWNM model, but the rank correlation and percentages of individuals selected in common suggested that the best animals would be selected independently of the model chosen.

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The aim of this study was to evaluate genomic information inclusion in genetic parameter estimation of standardized body weight at birth and at 240, 365, and 450 days of age, and visual scores for body structure, precocity, and body muscularity, measured as yearlings in Nelore cattle. We compared genetic parameters, (co)variance components (estimated from Bayesian inference and Gibbs sampling), breeding value accuracies, genetic trends, and principal component analysis (PCA) obtained through traditional GBLUP and ssGBLUP methods. For all traits analyzed, part of the phenotypic variation was explained by the additive genetic effect, thus indicating the capacity of traits to respond to the selection process. Estimates of genetic correlation, in both methodologies, between body weights and visual scores were, in general, high and positive, showing that the selection for visual scores can lead to heavier animals. Genetic trends showed genetic progress, both when estimated breeding values and genomic estimated breeding values were used. The PCA, genetic trends, and accuracy estimates on breeding values showed that inclusion of single nucleotide polymorphism information contributed towards slightly better estimates of the genetic variability of evaluated traits. Genomic information did not bring greater gains in genetic estimates, due to redundancy of kinship information from the pedigree, which already had complete animal kinship data.

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Abstract IGF-I and IGFALS play a vital stimulator role in skeletal growth, cell differentiation, metabolism, and other physiological processes. A total of 65 (male and female) animals were used in the study. Animals were measured for growth traits at birth weight, weaning weight, and weights at 6 months. The average daily gain (ADG) was calculated from birth to weaning (ADG1) and from birth to 6th month (ADG2). PCR-RFLP analysis was used to detect IGF-I polymorphism at the 5' regulatory region and IGFALS at Exon 1. Three genotypes (AA, AB and BB) were observed for IGF-I/BfoI locus with allele and genotype frequency 0.79(A) and 0.21(B); 0.71(AA), 0.15(AB) and 0.14(BB). Also, three genotypes (AA, AB and BB) were found for IGFALS/HinfI site with allele and genotype frequency as 0.22(A) and 0.78(B); 0.11(AA), 0.23(AB) and 0.66(BB). The genes were in agreement with Hardy-Weinberg equilibrium (p>0.05). Association analysis suggested that IGF-I and IGFALS significantly affected the growth traits (P<0.05). In terms of birth weight, The AA genotypes of IGF-I were higher than AB and BB. The AB genotype in terms of IGF-I had higher ADG2 compared with other genotypes. The AA genotype of the IGFALS gene was higher in terms of birth weight than other genotypes. In addition, the BB genotype was higher ADG1 than AA and BB. It is suggested that polymorphism of the IGF-I and IGFALS genes may be a potential molecular marker for growth traits in Hamdani sheep.

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Estimates of dominance variance for growth traits in beef cattle based on pedigree data vary considerably across studies, and the proportion of genetic variance explained by dominance deviations remains largely unknown. The potential benefits of including nonadditive genetic effects in the genomic model combined with the increasing availability of large genomic data sets have recently renewed the interest in including nonadditive genetic effects in genomic evaluation models. The availability of genomic information enables the computation of covariance matrices of dominant genomic relationships among animals, similar to matrices of additive genomic relationships, and in a more straightforward manner than the pedigree-based dominance relationship matrix. Data from 19,357 genotyped American Angus males were used to estimate additive and dominant variance components for 3 growth traits: birth weight, weaning weight, and postweaning gain, and to evaluate the benefit of including dominance effects in beef cattle genomic evaluations. Variance components were estimated using 2 models: the first one included only additive effects (MG) and the second one included both additive and dominance effects (MGD). The dominance deviation variance ranged from 3% to 8% of the additive variance for all 3 traits. Gibbs sampling and REML estimates showed good concordance. Goodness of fit of the models was assessed by a likelihood ratio test. For all traits, MG fitted the data as well as MGD as assessed either by the likelihood ratio test or by the Akaike information criterion. Predictive ability of both models was assessed by cross-validation and did not improve when including dominance effects in the model. There was little evidence of nonadditive genetic variation for growth traits in the American Angus male population as only a small proportion of genetic variation was explained by nonadditive effects. A genomic model including the dominance effect did not improve the model fit. Consequently, including nonadditive effects in the genomic evaluation model is not beneficial for growth traits in the American Angus male population.

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This study was conducted to investigate the growth hormone (GH; somatotropin-like) gene polymorphisms in 150 water buffalo (Bubalus bubalis) from different regions of Turkey. 404 bp long partial intron 4, exon 5, 3' UTR regions of the GH gene (also called GH/AluI locus) and 347 bp long exon-intron 3 and partial exon 4 regions of the GH gene (also called GH/MspI locus) were amplified, and their PCR products analyzed via DNA sequencing method. Seven genotypes due to twenty single nucleotide polymorphisms (SNP) and one deletion/insertion were identified in a 347 bp long region of the GH/MspI locus. A missense mutation from glycine to glutamate amino acid and four silent mutations in the serine, threonine, and asparagine amino acids were determined in the exon 3 region of the GH gene. Four genotypes due to eight SNP were identified in a 404 bp long region of the GH/AluI locus. A missense mutation from lysine to arginine amino acid and six silent mutations in Leucine, aspartate, histidine, lysine, arginine, and cysteine amino acids were revealed in the exon 5 region of the GH gene. The partial DNA sequence of the GH gene in water buffalos was reported, and these sequences were deposited at the NCBI Genbank database with the accession numbers MN266903-MN266909 and MN530973-MN530976. These SNP may have an effect on economic (such as body composition) and carcass traits, reproduction, and milk yield and content in water buffalo populations and may prove to be useful for water buffalo breeding.(AU)

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The present study aimed to apply artificial neural networks to predict the breeding values of body weight in 6-month age of Kermani sheep. For this purpose, records of 867 lambs including lamb sex, dam age, birth weight, weaning weight, age at 3-month (3 months old), age at 6-month (6 months old) and body weight at 3 months of age were used. Firstly, genetic parameters of the animals were estimated using ASReml software. The data was then pre-processed for using in MATLAB software. After initial experiments on the appropriate neural network architecture for body weight at 6-month age, two networks were examined. A feed-forward back propagation multilayer perceptron (MLP) algorithm was used and 70% of all data used as training data, 15% as testing data and 15% as validating data, to prevent over-fitting of the artificial neural network. Results showed that the both networks capable to predict breeding values for body weight at 6 month-age in Kermani sheep. It can be concluded that artificial neural network has a good ability to predict growth traits in Kermani sheep with an acceptable speed and accuracy. Therefore, this network, instead of commonly-used procedures can be used to estimate the breeding values for productive and reproductive traits in domestic animals.

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The present study aimed to apply artificial neural networks to predict the breeding values of body weight in 6-month age of Kermani sheep. For this purpose, records of 867 lambs including lamb sex, dam age, birth weight, weaning weight, age at 3-month (3 months old), age at 6-month (6 months old) and body weight at 3 months of age were used. Firstly, genetic parameters of the animals were estimated using ASReml software. The data was then pre-processed for using in MATLAB software. After initial experiments on the appropriate neural network architecture for body weight at 6-month age, two networks were examined. A feed-forward back propagation multilayer perceptron (MLP) algorithm was used and 70% of all data used as training data, 15% as testing data and 15% as validating data, to prevent over-fitting of the artificial neural network. Results showed that the both networks capable to predict breeding values for body weight at 6 month-age in Kermani sheep. It can be concluded that artificial neural network has a good ability to predict growth traits in Kermani sheep with an acceptable speed and accuracy. Therefore, this network, instead of commonly-used procedures can be used to estimate the breeding values for productive and reproductive traits in domestic animals.(AU)

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BACKGROUND: In livestock, residual variance has been studied because of the interest to improve uniformity of production. Several studies have provided evidence that residual variance is partially under genetic control; however, few investigations have elucidated genes that control it. The aim of this study was to identify genomic regions associated with within-family residual variance of yearling weight (YW; N = 423) in Nellore bulls with high density SNP data, using different response variables. For this, solutions from double hierarchical generalized linear models (DHGLM) were used to provide the response variables, as follows: a DGHLM assuming non-null genetic correlation between mean and residual variance (rmv ≠ 0) to obtain deregressed EBV for mean (dEBVm) and residual variance (dEBVv); and a DHGLM assuming rmv = 0 to obtain two alternative response variables for residual variance, dEBVv_r0 and log-transformed variance of estimated residuals (ln_[Formula: see text]). RESULTS: The dEBVm and dEBVv were highly correlated, resulting in common regions associated with mean and residual variance of YW. However, higher effects on variance than the mean showed that these regions had effects on the variance beyond scale effects. More independent association results between mean and residual variance were obtained when null rmv was assumed. While 13 and 4 single nucleotide polymorphisms (SNPs) showed a strong association (Bayes Factor > 20) with dEBVv and ln_[Formula: see text], respectively, only suggestive signals were found for dEBVv_r0. All overlapping 1-Mb windows among top 20 between dEBVm and dEBVv were previously associated with growth traits. The potential candidate genes for uniformity are involved in metabolism, stress, inflammatory and immune responses, mineralization, neuronal activity and bone formation. CONCLUSIONS: It is necessary to use a strategy like assuming null rmv to obtain genomic regions associated with uniformity that are not associated with the mean. Genes involved not only in metabolism, but also stress, inflammatory and immune responses, mineralization, neuronal activity and bone formation were the most promising biological candidates for uniformity of YW. Although no clear evidence of using a specific response variable was found, we recommend consider different response variables to study uniformity to increase evidence on candidate regions and biological mechanisms behind it.

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