The objective of this study was to identify genomic regions that are associated with meat quality traits in the Nellore breed. Nellore steers were finished in feedlots and slaughtered at a commercial slaughterhouse. This analysis included 1,822 phenotypic records of tenderness and 1,873 marbling records. After quality control, 1,630 animals genotyped for tenderness, 1,633 animals genotyped for marbling, and 369,722 SNPs remained. The results are reported as the proportion of variance explained by windows of 150 adjacent SNPs. Only windows with largest effects were considered. The genomic regions were located on chromosomes 5, 15, 16 and 25 for marbling and on chromosomes 5, 7, 10, 14 and 21 for tenderness. These windows explained 3,89% and 3,80% of the additive genetic variance for marbling and tenderness, respectively. The genes associated with the traits are related to growth, muscle development and lipid metabolism. The study of these genes in Nellore cattle is the first step in the identification of causal mutations that will contribute to the genetic evaluation of the breed.

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.

The aim of this study was to determine the extent (r) of linkage disequilibrium (LD) in the genome of Nellore cattle, and to examine associations between single nucleotide polymorphisms (SNP) and age at first calving (AFC) and early pregnancy (EP) using a panel of high-density SNPs and data from 1182 Nellore females. A total of 13 contemporary groups (CG) were used consisting of farm, season, and year of birth. For genome-wide association analysis, SNPs with a minor allele frequency (MAF)<0.05 and animals with a call rate<0.90 were excluded, totaling 431,885 SNPs. For statistical analysis, a linear model was used for AFC and a threshold model for EP. To estimate the significance of the associations for the two traits, the model included the categorical fixed effects of CG, SNPs, and sire. In addition, the polygenic effect was included in the analysis. The additive effects and dominance deviations of Bonferroni-adjusted significant SNPs for AFC and EP were estimated using orthogonal contrasts. The average estimate of r for all autosomes was 0.18 at a distance of 4.8kb and the mean MAF was 0.25±0.13. The LD decreased as the distance between markers increased: 0.35 (1kb) to 0.12 (100kb). Eleven significant associations were detected in seven different chromosomes. Seven SNPs were associated with AFC and four were associated with EP. Three SNPs were significant for both traits. The identification of SNPs associated with AFC and EP may contribute for selecting sexually precocious animals.

An efficient strategy to improve QTL detection power is performing across-breed validation studies. Variants segregating across breeds are expected to be in high linkage disequilibrium (LD) with causal mutations affecting economically important traits. The aim of this study was to validate, in a Tropical Composite cattle (TC) population, QTL associations identified for sexual precocity traits in a Nellore and Brahman meta-analysis genome-wide association study. In total, 2,816 TC, 8,001 Nellore, and 2,210 Brahman animals were available for the analysis. For that, genomic regions significantly associated with puberty traits in the meta-analysis study were validated for the following sexual precocity traits in TC: age at first corpus luteum (AGECL), first postpartum anestrus interval (PPAI), and scrotal circumference at 18 months of age (SC). We considered validated QTL those underpinned by significant markers from the Nellore and Brahman meta-analysis (P ≤ 10–4) that were also significant for a TC trait, i.e., presenting a P-value of ≤10–3 for AGECL, PPAI, or SC. We also considered as validated QTL those regions where significant markers in the reference population were at ±250 kb from significant markers in the validation population. Using this criteria, 49 SNP were validated for AGECL, 4 for PPAI, and 14 for SC, from which 5 were in common with AGECL, totaling 62 validated SNP for these traits and 30 candidate genes surrounding them. Considering just candidate genes closest to the top SNP of each chromosome, for AGECL 8 candidate genes were identified: COL8A1, PENK, ENSBTAG00000047425, BPNT1, ADAMTS17, CCHCR1, SUFU, and ENSBTAG00000046374. For PPAI, 3 genes emerged as candidates (PCBP3, KCNK10, and MRPS5), and for SC 8 candidate genes were identified (SNORA70, TRAC, ASS1, BPNT1, LRRK1, PKHD1, PTPRM, and ENSBTAG00000045690). Several candidate regions presented here were previously associated with puberty traits in cattle. The majority of emerging candidate genes are related to biological processes involved in reproductive events, such as maintenance of gestation, and some are known to be expressed in reproductive tissues. Our results suggested that some QTL controlling early puberty seem to be segregating across cattle breeds adapted to tropical conditions.

This study was designed to test the impact of quality control, density and allele frequency of single nucleotide polymorphisms (SNP) markers on the accuracy of genomic predictions, using three traits with different heritabilities and two methods of prediction in a Nellore cattle population genotyped with the Illumina Bovine HD Assay. A total of 1756; 3150 and 3119 records of age at first calving (AFC); weaning weight (WW) and yearling weight (YW), respectively, were used. Three scenarios with different exclusion thresholds for minor allele frequency (MAF), deviation from Hardy–Weinberg equilibrium (HWE) and correlation between SNP pairs (r2) were constructed for all traits: (1) high rigor (S1): call rate &lt;0.98, MAF &lt;0.05, HWE with P &lt;10−5, and r2 &gt;0.999; (2) Moderate rigor (S2): call rate &lt;0.85 and MAF &lt;0.01; (3) Low rigor (S3): only non-autosomal SNP and those mapped on the same position were excluded. Additionally, to assess the prediction accuracy from different markers density, six panels (10K, 50K, 100K, 300K, 500K and 700K) were customised using the high-density genotyping assay as reference. Finally, from the markers available in high-density genotyping assay, six groups (G) with different minor allele frequency bins were defined to estimate the accuracy of genomic prediction. The range of MAF bins was approximately equal for the traits studied: G1 (0.000–0.009), G2 (0.010–0.064), G3 (0.065–0.174), G4 (0.175–0.325), G5 (0.326–0.500) and G6 (0.000–0.500). The Genomic Best Linear Unbiased Predictor and BayesCπ methods were used to estimate the SNP marker effects. Five-fold cross-validation was used to measure the accuracy of genomic prediction for all scenarios. There were no effects of genotypes quality control criteria on the accuracies of genomic predictions. For all traits, the higher density panel did not provide greater prediction accuracies than the low density one (10K panel). The groups of SNP with low MAF (MAF ≤0.007 for AFC, MAF ≤0.009 for WW and MAF ≤0.008 for YW) provided lower prediction accuracies than the groups with higher allele frequencies.