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This study aimed to investigate the prediction ability for growth and maternal traits using different low-density customized SNP arrays selected by informativeness and distribution of markers across the genome employing single-step genomic BLUP (ssGBLUP). Phenotypic records for adjusted weight at 210 and 450 days of age were utilized. A total of 945 animals were genotyped with high-density chip, and 267 individuals born after 2008 were selected as validation population. We evaluated 11 scenarios using five customized density arrays (40 k, 20 k, 10 k, 5 k and 2 k) and the HD array was used as desirable scenario. The GEBV predictions and BIF (Beef Improvement Federation) accuracy were obtained with BLUPF90 family programs. Linear regression was used to evaluate the prediction ability, inflation, and bias of GEBV of each customized array. An overestimation of partial GEBVs in contrast with complete GEBVs and increase of BIF accuracy with the density arrays diminished were observed. For all traits, the prediction ability was higher as the array density increased and it was similar with customized arrays higher than 10 k SNPs. Level of inflation was lower as the density array increased of and was higher for MW210 effect. The bias was susceptible to overestimation of GEBVs when the density customized arrays decreased. These results revealed that the BIF accuracy is sensible to overestimation using low-density customized arrays while the prediction ability with least 10,000 informative SNPs obtained from the Illumina BovineHD BeadChip shows accurate and less biased predictions. Low-density customized arrays under ssGBLUP method could be feasible and cost-effective in genomic selection., (© 2022. The Author(s), under exclusive licence to Institute of Plant Genetics Polish Academy of Sciences.)

This study focused on the identification of QTL regions, candidate genes, and network related genes based on the first 3 lactations (LAC3) of milk, fat, and protein yields, and somatic cell score (SCS) in Portuguese Holstein cattle. Additionally, the results were compared with those from only first lactation (LAC1) data. The analyses were performed using the weighted single-step GWAS under an autoregressive test-day (TD) multiple lactations model. A total of 11,434,294 and 4,725,673 TD records from LAC3 and LAC1, respectively, including 38,323 autosomal SNPs and 1338 genotyped animals were used in GWAS analyses. A total of 51 (milk), 5 (fat), 24 (protein), and 4 (SCS) genes were associated to previously annotated relevant QTL regions for LAC3. The CACNA2D1 at BTA4 explained the highest proportion of genetic variance respectively for milk, fat, and protein yields. For SCS, the TRNAG-CCC at BTA14, MAPK10, and PTPN3 genes, both at BTA6 were considered important candidate genes. The accessed network refined the importance of the reported genes. CACNA2D1 regulates calcium density and activation/inactivation kinetics of calcium transport in the mammary gland; whereas TRNAG-CCC, MAPK10, and PTPN3 are directly involved with inflammatory processes widely derived from mastitis. In conclusion, potential candidate genes (TRNAG-CCC, MAPK10, and PTPN3) associated with somatic cell were highlighted, which further validation studies are needed to clarify its mechanism action in response to mastitis. Moreover, most of the candidate genes identified were present in both (LAC3 and LAC1) for milk, fat and protein yields, except for SCS, in which no candidate genes were shared between LAC3 and LAC1. The larger phenotypic information provided by LAC3 dataset was more effective to identify relevant genes, providing a better understanding of the genetic architecture of these traits over all lactations simultaneously. [ABSTRACT FROM AUTHOR]

The objective of this study was to evaluate the genetic parameters of two generations of zebrafish breeding program. The base population was formed by crossing individuals of six commercial stocks of zebrafish, resulting in a nucleus with 60 families. Two generations were evaluated, with a total of 780 and 781 individuals for the first and second generation, respectively. The selection was made based on the mean genetic value of each family, followed by mass selection of the breeders. Mathematical models that considered the fixed (age, density in the larval stage, sex, and generation) and random (animal additive genetics, common to full-sibs, and residual) effects were evaluated using BLUPF90 program family. Weight and total length were used as response variables. Total length was the best selection criterion because it had a higher heritability (0.30) than weight (0.22). There was a high common to full-sib effect, especially in the first generation of animals. For second-generation data, the heritability was 0.26 for total length, as well as a lower common to full-sib effect for length. The best model obtained for this evaluation was considering all effects, being age and density as first and second polynomial, respectively. The genetic and phenotypic correlations for weight and length were 0.87 and 0.75, respectively. These results indicate that genetic breeding using total length as the selection criterion may produce a larger and heavier zebrafish strain. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]