Accuracy of genomic breeding values and predictive ability for postweaning liveweight and age at first calving in a Nellore cattle population with missing sire information.

The multiple sire system (MSS) is a common mating scheme in extensive beef production systems. However, MSS does not allow paternity identification and lead to inaccurate genetic predictions. The objective of this study was to investigate the implementation of single-step genomic BLUP (ssGBLUP) in different scenarios of uncertain paternity in the evaluation for 450-day adjusted liveweight (W450) and age at first calving (AFC) in a Nellore cattle population. To estimate the variance components using BLUP and ssGBLUP, the relationship matrix (A) with different proportions of animals with missing sires (MS) (scenarios 0, 25, 50, 75, and 100% of MS) was created. The genotyped animals with MS were randomly chosen, and ten replicates were performed for each scenario and trait. Five groups of animals were evaluated in each scenario: PHE, all animals with phenotypic records in the population; SIR, proven sires; GEN, genotyped animals; YNG, young animals without phenotypes and progeny; and YNGEN, young genotyped animals. The additive genetic variance decreased for both traits as the proportion of MS increased in the population when using the regular REML. When using the ssGBLUP, accuracies ranged from 0.13 to 0.47 for W450 and from 0.10 to 0.25 for AFC. For both traits, the prediction ability of the direct genomic value (DGV) decreased as the percentage of MS increased. These results emphasize that indirect prediction via DGV of young animals is more accurate when the SNP effects are derived from ssGBLUP with a reference population with known sires. The ssGBLUP could be applied in situations of uncertain paternity, especially when selecting young animals. This methodology is shown to be accurate, mainly in scenarios with a high percentage of MS.

Genomic prediction ability for beef fatty acid profile in Nelore cattle using different pseudo-phenotypes.

The aim of the present study was to compare the predictive ability of SNP-BLUP model using different pseudo-phenotypes such as phenotype adjusted for fixed effects, estimated breeding value, and genomic estimated breeding value, using simulated and real data for beef FA profile of Nelore cattle finished in feedlot. A pedigree with phenotypes and genotypes of 10,000 animals were simulated, considering 50% of multiple sires in the pedigree. Regarding to phenotypes, two traits were simulated, one with high heritability (0.58), another with low heritability (0.13). Ten replicates were performed for each trait and results were averaged among replicates. A historical population was created from generation zero to 2020, with a constant size of 2000 animals (from generation zero to 1000) to produce different levels of linkage disequilibrium (LD). Therefore, there was a gradual reduction in the number of animals (from 2000 to 600), producing a "bottleneck effect" and consequently, genetic drift and LD starting in the generation 1001 to 2020. A total of 335,000 markers (with MAF greater or equal to 0.02) and 1000 QTL were randomly selected from the last generation of the historical population to generate genotypic data for the test population. The phenotypes were computed as the sum of the QTL effects and an error term sampled from a normal distribution with zero mean and variance equal to 0.88. For simulated data, 4000 animals of the generations 7, 8, and 9 (with genotype and phenotype) were used as training population, and 1000 animals of the last generation (10) were used as validation population. A total of 937 Nelore bulls with phenotype for fatty acid profiles (Sum of saturated, monounsaturated, omega 3, omega 6, ratio of polyunsaturated and saturated and polyunsaturated fatty acid profile) were genotyped using the Illumina BovineHD BeadChip (Illumina, San Diego, CA) with 777,962 SNP. To compare the accuracy and bias of direct genomic value (DGV) for different pseudo-phenotypes, the correlation between true breeding value (TBV) or DGV with pseudo-phenotypes and linear regression coefficient of the pseudo-phenotypes on TBV for simulated data or DGV for real data, respectively. For simulated data, the correlations between DGV and TBV for high heritability traits were higher than obtained with low heritability traits. For simulated and real data, the prediction ability was higher for GEBV than for Yc and EBV. For simulated data, the regression coefficient estimates (b(Yc,DGV)), were on average lower than 1 for high and low heritability traits, being inflated. The results were more biased for Yc and EBV than for GEBV. For real data, the GEBV displayed less biased results compared to Yc and EBV for SFA, MUFA, n-3, n-6, and PUFA/SFA. Despite the less biased results for PUFA using the EBV as pseudo-phenotype, the b(Yi,DGV estimates obtained for the different pseudo-phenotypes (Yc, EBV and GEBV) were very close. Genomic information can assist in improving beef fatty acid profile in Zebu cattle, since the use of genomic information yielded genomic values for fatty acid profile with accuracies ranging from low to moderate. Considering both simulated and real data, the ssGBLUP model is an appropriate alternative to obtain more reliable and less biased GEBVs as pseudo-phenotype in situations of missing pedigree, due to high proportion of multiple sires, being more adequate than EBV and Yc to predict direct genomic value for beef fatty acid profile.

Genomic regions and pathways associated with gastrointestinal parasites resistance in Santa Inês breed adapted to tropical climate.

BACKGROUND: The aim of this study was to estimate variance components and to identify genomic regions and pathways associated with resistance to gastrointestinal parasites, particularly Haemonchus contortus, in a breed of sheep adapted to tropical climate. Phenotypes evaluations were performed to verify resistance to gastrointestinal parasites, and were divided into two categories: i) farm phenotypes, assessing body condition score (BCS), degree of anemia assessed by the famacha chart (FAM), fur score (FS) and feces consistency (FC); and ii) lab phenotypes, comprising blood analyses for hematocrit (HCT), white blood cell count (WBC), red blood cell count (RBC), hemoglobin (HGB), platelets (PLT) and transformed (log10) egg per gram of feces (EPGlog). A total of 576 animals were genotyped with the Ovine SNP12k BeadChip (Illumina, Inc.), that contains 12,785 bialleleic SNP markers. The variance components were estimated using a single trait model by single step genomic BLUP procedure. RESULTS: The overall linkage disequilibrium (LD) mean between pairs of markers measured by r2 was 0.23. The overall LD mean between markers considering windows up to 10 Mb was 0.07. The mean LD between adjacent SNPs across autosomes ranged from 0.02 to 0.10. Heritability estimates were low for EPGlog (0.11), moderate for RBC (0.18), PLT (0.17) HCT (0.20), HGB (0.16) and WBC (0.22), and high for FAM (0.35). A total of 22, 21, 23, 20, 26, 25 and 23 windows for EPGlog for FAM, WBC, RBC, PLT, HCT and HGB traits were identified, respectively. Among the associated windows, 10 were shown to be common to HCT and HGB traits on OAR1, OAR2, OAR3, OAR5, OAR8 and OAR15. CONCLUSION: The traits indicating gastrointestinal parasites resistance presented an adequate genetic variability to respond to selection in Santa Inês breed, and it is expected a higher genetic gain for FAM trait when compared to the others. The level of LD estimated for markers separated by less than 1 Mb indicated that the Ovine SNP12k BeadChip might be a suitable tool for identifying genomic regions associated with traits related to gastrointestinal parasite resistance. Several candidate genes related to immune system development and activation, inflammatory response, regulation of lymphocytes and leukocytes proliferation were found. These genes may help in the selection of animals with higher resistance to parasites.

Correction: Genomic Regions Associated with Feed Efficiency Indicator Traits in an Experimental Nellore Cattle Population.

[This corrects the article DOI: 10.1371/journal.pone.0164390.].

Genetic correlation estimates between beef fatty acid profile with meat and carcass traits in Nellore cattle finished in feedlot.

The objective of this study was to estimate the genetic-quantitative relationships between the beef fatty acid profile with the carcass and meat traits of Nellore cattle. A total of 1826 bulls finished in feedlot conditions and slaughtered at 24 months of age on average were used. The following carcass and meat traits were analysed: subcutaneous fat thickness (BF), shear force (SF) and total intramuscular fat (IMF). The fatty acid (FA) profile of the Longissimus thoracis samples was determined. Twenty-five FAs (18 individuals and seven groups of FAs) were selected due to their importance for human health. The animals were genotyped with the BovineHD BeadChip and, after quality control for single nucleotide polymorphisms (SNPs), only 470,007 SNPs from 1556 samples remained. The model included the random genetic additive direct effect, the fixed effect of the contemporary group and the animal's slaughter age as a covariable. The (co)variances and genetic parameters were estimated using the REML method, considering an animal model (single-step GBLUP). A total of 25 multi-trait analyses, with four traits, were performed considering SF, BF and IMF plus each individual FA. The heritability estimates for individual saturated fatty acids (SFA) varied from 0.06 to 0.65, for monounsaturated fatty acids (MUFA) it varied from 0.02 to 0.14 and for polyunsaturated fatty acids (PUFA) it ranged from 0.05 to 0.68. The heritability estimates for Omega 3, Omega 6, SFA, MUFA and PUFA sum were low to moderate, varying from 0.09 to 0.20. The carcass and meat traits, SF (0.06) and IMF (0.07), had low heritability estimates, while BF (0.17) was moderate. The genetic correlation estimates between SFA sum, MUFA sum and PUFA sum with BF were 0.04, 0.64 and -0.41, respectively. The genetic correlation estimates between SFA sum, MUFA sum and PUFA sum with SF were 0.29, -0.06 and -0.04, respectively. The genetic correlation estimates between SFA sum, MUFA sum and PUFA sum with IMF were 0.24, 0.90 and -0.67, respectively. The selection to improve meat tenderness in Nellore cattle should not change the fatty acid composition in beef, so it is possible to improve this attribute without affecting the nutritional beef quality in zebu breeds. However, selection for increased deposition of subcutaneous fat thickness and especially the percentage of intramuscular fat should lead to changes in the fat composition, highlighting a genetic antagonism between meat nutritional value and acceptability by the consumer.