Crescimento de codornas de diferentes grupos genéticos por meio de modelos não lineares

Age at first calving (AFC) plays an important role in the economic efficiency of beef cattle production. This trait can be affected by a combination of genetic and environmental factors, leading to physiological changes in response to heifers' adaptation to a wide range of environments. Genomewide association studies through the reaction norm model were carried out to identify genomic regions associated with AFC in Nellore heifers, raised under different environmental conditions (EC). The SNP effects for AFC were estimated in three EC levels (Low, Medium, and High, corresponding to average contemporary group effects on yearling body weight equal to 159.40, 228.6 and 297.6 kg, respectively), which unraveled shared and unique genomic regions for AFC in Low, Medium, and High EC levels, that varied according to the genetic correlation between AFC in different EC levels. The significant genomic regions harbored key genes that might play an important biological role in controlling hormone signaling and metabolism. Shared genomic regions among EC levels were identified on BTA 2 and 14, harboring candidate genes associated with energy metabolism (IGFBP2, IGFBP5, SHOX, SMARCAL1, LYN, RPS20, MOS, PLAG1, CHCD7, and SDR16C6). Gene set enrichment analyses identified important biological functions related to growth, hormone levels affecting female fertility, physiological processes involved in female pregnancy, gamete generation, ovulation cycle, and age at puberty. the genomic regions highlighted differences in the physiological processes linked to AFC in different EC levels and metabolic processes that support complex interactions between the gonadotropic axes and sexual precocity in nellore heifers. open Scientific RepoRtS | (2020) 10:6481 | https://doi.org/10.1038/s41598-020-63516-4 www.nature.com/scientificreports www.nature.com/scientificreports/ pathway and gene network analyses from these results can be performed to uncover mechanisms whereby the environment can potentially affect the sexual precocity in cattle. Such knowledge regarding genomic regions and biological pathways involved with GxE interactions in Nellore heifers' sexual precocity is important to identify molecular mechanisms underlying the phenotypic responses to different environments. Hence, this study was carried out to evaluate the changes in the SNP effect estimates, as well as the biological processes associated with age at first calving in three environmental conditions, combining RN models and GWAS. Materials and Methodsethics approval. The animal procedures in this study were approved by Animal Care of the São Paulo State University (UNESP), School of Agricultural and Veterinary Science Ethical Committee (protocol number 18.340/16). All the data sampling was performed in accordance with CEUA/ FCAV-UNESP guidelines and regulations.phenotypic and genotypic data. Age at first calving (AFC) records were obtained from 185,356Nellore heifers belonging to three commercial breeding programs (DeltaGen, Paint -CRV Lagoa and Cia de Melhoramento), which are p...

Multitrait meta-analyses are a strategy to produce more accurate genome-wide association studies, especially for complex phenotypes. We carried out a meta-analysis study for traits related to sexual precocity in tropical beef cattle (Nellore and Brahman) aiming to identify important genomic regions affecting these traits. The traits included in the analyses were age at first calving (AFC), early pregnancy (EP), age at first corpus luteum (AGECL), first postpartum anoestrus interval (PPAI), and scrotal circumference (SC). The traits AFC, EP, and SCN were measured in Nellore cattle, while AGECL, PPAI, and SCB were measured in Brahman cattle. Meta-analysis resulted in 108 significant single-nucleotide polymorphisms (SNPs), at an empirical threshold P-value of 1.39 × 10-5 (false discovery rate [FDR] < 0.05). Within 0.5 Mb of the significant SNP, candidate genes were annotated and analyzed for functional enrichment. Most of the closest genes to the SNP with higher significance in each chromosome have been associated with important roles in reproductive function. They are TSC22D2, KLF7, ARHGAP29, 7SK, MAP3K5, TLE3, WDR5, TAF3, TMEM68, PPP1R15B, NR2F2, GALR1, SUFU, and KCNU1. We did not observe any significant SNP in BTA5, BTA12, BTA17, BTA18, BTA19, BTA20, BTA22, BTA23, BTA25, and BTA28. Although the majority of significant SNPs are in BTA14, it was identified significant associations in multiple chromosomes (19 out of 29 autosomes), which is consistent with the postulation that reproductive traits are complex polygenic phenotypes. Five proposed association regions harbor the majority of the significant SNP (76%) and were distributed over four chromosomes (P < 1.39 × 10-5, FDR < 0.05): BTA2 (5.55%) from 95 to 96 Mb, BTA4 (5.55%) from 94.1 to 94.8 Mb, BTA14 (59.26%) from 24 to 25 Mb and 29 to 30 Mb, and BTA21 (5.55%) from 6.7 Mb to 11.4 Mb. These regions harbored key genes related to reproductive function. Moreover, these genes were enriched for functional groups associated with immune response, maternal-fetal tolerance, pregnancy maintenance, embryo development, fertility, and response to stress. Further studies including other breeds and precocity traits could confirm the importance of these regions and identify new candidate regions for sexual precocity in beef cattle.

ABSTRACT. Several human health problems have been related to the allergenic constitution of bovine milk due to the body's immune reaction to milk proteins. It is necessary find solutions to minimize the occurrence of such reactions, given the importance of milk as a source of animal protein. The aim of this study was to evaluate the allelic frequency of the CSN2 gene and to evaluate differences in the characteristics of Gir and Guzerá bovine milk. One hundred and fifty-six cows were used (68 Gir and 88 Guzerá) from the Felipe Camarão Experimental Station herd of the Agricultural Research Corporation of RN (EMPARN). DNA extractions were carried out from hair follicles of the animals; the gene was then amplified and sequenced in an ABI 3100 automatic sequencer. The obtained sequences were submitted to analysis using the Geneious 5.6.5 ® program. Data were submitted to analysis of variance and to the Tukey- Kramer test at 5% probability and cluster analyses by main components were performed. Allele frequencies were 98 and 97% for the A2 allele and 0.96 and 0.93% for the genotype A2A2 for Gir and Guzerá, respectively. Gir and Guzerá animals showed differences in protein, lactose, and nonfat dry extract levels. Although correlations between milk yield and the production and some milk components are moderate, increases in milk yield are always greater than the increase in constituent yield. In addition, even though Guzerá animals have a higher percentage of protein, lactose, and non-fat dry extract, milk from Zebu breeds is an alternative for individuals sensitive to β-casein protein.

BackgroundLeptin has a strong relation to important traits in animal production, such as carcass composition, feed intake, and reproduction. It is mainly produced by adipose cells and acts predominantly in the hypothalamus. In this study, circulating leptin and its gene expression in muscle were evaluated in two groups of young Nellore bulls with divergent feed efficiency. Individual dry matter intake (DMI) and average daily gain (ADG) of 98 Nellore bulls were evaluated in feedlot for 70 d to determinate the residual feed intake (RFI) and select 20 animals for the high feed efficient (LRFI) and 20 for the low feed efficient (HRFI) groups. Blood samples were collected on d 56 and at slaughter (80 d) to determine circulating plasma leptin. Samples of Longissimus dorsi were taken at slaughter for leptin gene expression levels.ResultsDMI and RFI were different between groups and LRFI animals showed less back fat and rump fat thickness, as well as less pelvic and kidney fat weight. Circulating leptin increased over time in all animals. Plasma leptin was greater in LRFI on 56 d and at slaughter (P = 0.0049). Gene expression of leptin were greater in LRFI animals (P = 0.0022) in accordance with the plasma levels. The animals of the LRFI group were leaner, ate less, and had more circulating leptin and its gene expression.ConclusionThese findings demonstrated that leptin plays its physiological role in young Nellore bulls, probably controlling food intake because feed efficient animals have more leptin and lower residual feed intake.

Summary Brazilian beef cattle are raised predominantly on pasture in a wide range of environments. In this scenario, genotype by environment (G×E) interaction is an important source of phenotypic variation in the reproductive traits. Hence, the evaluation of G×E interactions for heifer’s early pregnancy (HP) and scrotal circumference (SC) traits in Nellore cattle, belonging to three breeding programs, was carried out to determine the animal’s sensitivity to the environmental conditions (EC). The dataset consisted of 85 874 records for HP and 151 553 records for SC, from which 1800 heifers and 3343 young bulls were genotyped with the BovineHD BeadChip. Genotypic information for 826 sires was also used in the analyses. EC levels were based on the contemporary group solutions for yearling body weight. Linear reaction norm models (RNM), using pedigree information (RNM_A) or pedigree and genomic information (RNM_H), were used to infer G×E interactions. Two validation schemes were used to assess the predictive ability, with the following training populations: (a) forward scheme—dataset was split based on year of birth from 2008 for HP and from 2011 for SC; and (b) environment‐specific scheme—low EC (−3.0 and −1.5) and high EC (1.5 and 3.0). The inclusion of the H matrix in RNM increased the genetic variance of the intercept and slope by 18.55 and 23.00% on average respectively, and provided genetic parameter estimates that were more accurate than those considering pedigree only. The same trend was observed for heritability estimates, which were 0.28–0.56 for SC and 0.26–0.49 for HP, using RNM_H, and 0.26–0.52 for SC and 0.22–0.45 for HP, using RNM_A. The lowest correlation observed between unfavorable (−3.0) and favorable (3.0) EC levels were 0.30 for HP and −0.12 for SC, indicating the presence of G×E interaction. The G×E interaction effect implied differences in animals’ genetic merit and re‐ranking of animals on different environmental conditions. SNP marker–environment interaction was detected for Nellore sexual precocity indicator traits with changes in effect and variance across EC levels. The RNM_H captured G×E interaction effects better than RNM_A and improved the predictive ability by around 14.04% for SC and 21.31% for HP. Using the forward scheme increased the overall predictive ability for SC (20.55%) and HP (11.06%) compared with the environment‐specific scheme. The results suggest that the inclusion of genomic information combined with the pedigree to assess the G×E interaction leads to more accurate variance components and genetic parameter estimates.

Background Over the past decade, Fourier transform infrared (FTIR) spectroscopy has been used to predict novel milk protein phenotypes. Genomic data might help predict these phenotypes when integrated with milk FTIR spectra. The objective of this study was to investigate prediction accuracy for milk protein phenotypes when heterogeneous on-farm, genomic, and pedigree data were integrated with the spectra. To this end, we used the records of 966 Italian Brown Swiss cows with milk FTIR spectra, on-farm information, medium-density genetic markers, and pedigree data. True and total whey protein, and five casein, and two whey protein traits were analyzed. Multiple kernel learning constructed from spectral and genomic (pedigree) relationship matrices and multilayer BayesB assigning separate priors for FTIR and markers were benchmarked against a baseline partial least squares (PLS) regression. Seven combinations of covariates were considered, and their predictive abilities were evaluated by repeated random sub-sampling and herd cross-validations (CV). Results Addition of the on-farm effects such as herd, days in milk, and parity to spectral data improved predictions as compared to those obtained using the spectra alone. Integrating genomics and/or the top three markers with a large effect further enhanced the predictions. Pedigree data also improved prediction, but to a lesser extent than genomic data. Multiple kernel learning and multilayer BayesB increased predictive performance, whereas PLS did not. Overall, multilayer BayesB provided better predictions than multiple kernel learning, and lower prediction performance was observed in herd CV compared to repeated random sub-sampling CV. Conclusions Integration of genomic information with milk FTIR spectral can enhance milk protein trait predictions by 25% and 7% on average for repeated random sub-sampling and herd CV, respectively. Multiple kernel learning and multilayer BayesB outperformed PLS when used to integrate heterogeneous data for phenotypic predictions.

Milk proteins genetic variants have always attracted a lot of interest as they are associated with important issues relating to milk composition and technological properties. An important debate has recently opened at an international level on the role of β-casein (β-CN) A1 and A2 polymorphisms, toward human health. For this reason, a lot of efforts has been put into the promotion of A2 milk by companies producing and selling A1-free milk, leading the farmers and breeders to switch toward A2 milk production without paying attention on the potential effect of the processability of milk into cheese. The aim of the present work was to evaluate the effects of β-CN, specifically the A1 and A2 allelic variants, on the detailed milk protein profile and cheese-making traits in individual milk samples of 1,133 Holstein Friesian cows. The protein fractions were measured with reversed-phase (RP)-HPLC (expressed in g/L and % N), and the cheese-making traits, namely milk coagulation properties, cheese yield, and curd nutrient recoveries assessed at the individual level, with a nano-scale cheese-making procedure. The β-CN (CSN2), κ-CN (CSN3), and β-lactoglobulin (LGB) genetic variants were first identified through RP-HPLC and then confirmed through genotyping. Estimates of the effects of protein genotypes were obtained using a mixed inheritance model that considered, besides the standard nuisance variables (i.e., days in milk, parity, and herd-date), the milk protein genes located on chromosome 6 (CSN2, CSN3) and on chromosome 11 (LGB), and the polygenic background of the animals. Milk protein genes (CSN2, CSN3, and LGB) explained an important part of the additive genetic variance in the traits evaluated. The β-CN A1A1 was associated with a significantly lower production of whey proteins, particularly of β-lactoglobulin (−8.2 and −6.8% for g/L and % N, respectively) and α-lactalbumin (−4.7 and −4.4% for g/L and % N, respectively), and a higher production of β-CN (6.8 and 6.1% for g/L and % N, respectively) with respect to the A2A2 genotype. Regarding milk cheese-making ability, the A2A2 genotype showed the worst performance compared with the other genotypes, particularly with respect to the BA1, with a higher rennet coagulation time (7.1 and 28.6% compared with A1A1 and BA1, respectively) and a lower curd firmness at 30 min. Changes in milk protein composition through an increase in the frequency of the A2 allele in the production process could lead to a worsening of the coagulation and curd firming traits.