Development of genomic predictions for Angus cattle in Brazil incorporating genotypes from related American sires

Genomic prediction has become the new standard for genetic improvement programs, and currently, there is a desire to implement this technology for the evaluation of Angus cattle in Brazil. Thus, the main objective of this study was to assess the feasibility of evaluating young Brazilian Angus (BA) bulls and heifers for 12 routinely recorded traits using single-step genomic BLUP (ssGBLUP) with and without genotypes from American Angus (AA) sires. The second objective was to obtain estimates of effective population size (Ne) and linkage disequilibrium (LD) in the Brazilian Angus population. The dataset contained phenotypic information for up to 277,661 animals belonging to the Promebo® breeding program, pedigree for 362,900, of which 1,386 were genotyped for 50k, 77k, and 150k SNP panels. After imputation and quality control, 61,666 SNP were available for the analyses. In addition, genotypes from 332 American Angus (AA) sires widely used in Brazil were retrieved from the AA Association database to be used for genomic predictions. Bivariate animal models were used to estimate variance components, traditional EBV, and genomic EBV (GEBV). Validation was carried out with the linear regression method (LR) using young-genotyped animals born between 2013 and 2015 without phenotypes in the reduced dataset and with records in the complete dataset. Validation animals were further split into progeny of BA and AA sires to evaluate if their progenies would benefit by including genotypes from AA sires. The Ne was 254 based on pedigree and 197 based on LD, and the average LD (±SD) and distance between adjacent SNPs across all chromosomes was 0.27 (±0.27) and 40743.68 bp, respectively. Prediction accuracies with ssGBLUP outperformed BLUP for all traits, improving accuracies by, on average, 16% for BA young bulls and heifers. The GEBV prediction accuracies ranged from 0.37 (total maternal for weaning weight and tick count) to 0.54 (yearling precocity) across all traits, and dispersion (LR coefficients) fluctuated between 0.92 and 1.06. Inclusion of genotyped sires from the AA improved GEBV accuracies by 2%, on average, compared to using only the BA reference population. Our study indicated that genomic information could help to improve GEBV accuracies and hence genetic progress in the Brazilian Angus population. The inclusion of genotypes from American Angus sires heavily used in Brazil just marginally increased the GEBV accuracies for selection candidates.

Mega-Environment Analysis To Assess Adaptability, Stability, And Genomic Predictions In Grain Sorghum Hybrids

Multi-environment trials (MET) are fundamental for assessing genotype-by-environment interaction (GxE) effects, adaptability and stability of genotypes and provide valuable information about target regions. As such, a MET involving grain sorghum hybrid combinations derived from elite inbred lines adapted to diverse sorghum production regions was developed to assess agronomic performance, stability, and genomic-enabled prediction accuracies within mega-environments (ME). Ten females and ten males from the Texas A&M and Kansas State sorghum breeding programs were crossed following a factorial mating scheme to generate 100 hybrids. Grain yield, plant height, and days to anthesis were assessed in a MET consisting of ten environments across Texas and Kansas over two years. Genotype plus Genotype-by-block-of-environment biplot (GGB) assessed ME, while the "mean-vs-stability" view of the biplot and the Bayesian Finlay-Wilkinson regression evaluated hybrid adaptability and stability. A genomic prediction model including the GxE effect was applied within ME to assess prediction accuracy. Results suggest that grain sorghum hybrid combinations involving lines adapted to different target regions can produce superior hybrids. GGB confirmed distinct regions of sorghum adaption in the U.S. Further, genomic predictions within ME reported inconsistent results, suggesting that additional effects rather than the correlations between environments are influencing genomic prediction of grain sorghum hybrids.

A Comprehensive Comparison of Haplotype-Based Single-Step Genomic Predictions in Livestock Populations With Different Genetic Diversity Levels: A Simulation Study

The level of genetic diversity in a population is inversely proportional to the linkage disequilibrium (LD) between individual single nucleotide polymorphisms (SNPs) and quantitative trait loci (QTLs), leading to lower predictive ability of genomic breeding values (GEBVs) in high genetically diverse populations. Haplotype-based predictions could outperform individual SNP predictions by better capturing the LD between SNP and QTL. Therefore, we aimed to evaluate the accuracy and bias of individual-SNP- and haplotype-based genomic predictions under the single-step-genomic best linear unbiased prediction (ssGBLUP) approach in genetically diverse populations. We simulated purebred and composite sheep populations using literature parameters for moderate and low heritability traits. The haplotypes were created based on LD thresholds of 0.1, 0.3, and 0.6. Pseudo-SNPs from unique haplotype alleles were used to create the genomic relationship matrix (G) in the ssGBLUP analyses. Alternative scenarios were compared in which the pseudo-SNPs were combined with non-LD clustered SNPs, only pseudo-SNPs, or haplotypes fitted in a second G (two relationship matrices). The GEBV accuracies for the moderate heritability-trait scenarios fitting individual SNPs ranged from 0.41 to 0.55 and with haplotypes from 0.17 to 0.54 in the most (Ne ≅ 450) and less (Ne < 200) genetically diverse populations, respectively, and the bias fitting individual SNPs or haplotypes ranged between −0.14 and −0.08 and from −0.62 to −0.08, respectively. For the low heritability-trait scenarios, the GEBV accuracies fitting individual SNPs ranged from 0.24 to 0.32, and for fitting haplotypes, it ranged from 0.11 to 0.32 in the more (Ne ≅ 250) and less (Ne ≅ 100) genetically diverse populations, respectively, and the bias ranged between −0.36 and −0.32 and from −0.78 to −0.33 fitting individual SNPs or haplotypes, respectively. The lowest accuracies and largest biases were observed fitting only pseudo-SNPs from blocks constructed with an LD threshold of 0.3 (p < 0.05), whereas the best results were obtained using only SNPs or the combination of independent SNPs and pseudo-SNPs in one or two G matrices, in both heritability levels and all populations regardless of the level of genetic diversity. In summary, haplotype-based models did not improve the performance of genomic predictions in genetically diverse populations.

Temporal phenomic predictions from unoccupied aerial systems can outperform genomic predictions

A major challenge of genetic improvement and selection is to accurately predict individuals with the highest fitness in a population without direct measurement. Over the last decade genomic predictions (GP) based on genome-wide markers have become reliable and routine. Now phenotyping technologies, including unoccupied aerial systems (UAS also known as drones), can characterize individuals with a data depth comparable to genomics when used throughout growth. This study, for the first time, demonstrated that the prediction power of temporal UAS phenomic data can achieve or exceed that of genomic data. UAS data containing red-green-blue (RGB) bands over fifteen growth time points and multispectral (RGB, red-edge and near infrared) bands over twelve time points were compared across 280 unique maize hybrids. Through cross validation of untested genotypes in tested environments (CV2), temporal phenomic prediction (TPP) outperformed GP (0.80 vs 0.71); TPP and GP performed similarly in three other cross validation scenarios. Genome wide association mapping using area under temporal curves of vegetation indices (VIs) revealed 24.5 percent of a total of 241 discovered loci (59 loci) had associations with multiple VIs, explaining up to 51 percent of grain yield variation, less than GP and TPP predicted. This suggests TPP, like GP, integrates small effect loci well improving plant fitness predictions. More importantly, temporal phenomic prediction appeared to work successfully on unrelated individuals unlike genomic prediction.

36 Effect of Blending and Tuning Relationship Matrices in Single-step Genomic BLUP

Single-step GBLUP (ssGBLUP) relies on the combination of genomic (G) and pedigree relationships for all (A) and genotyped animals (A22). The procedure implemented in the BLUPF90 software suite first involves combining a small percentage of A22 into G (blending) to avoid singularity problems, then an adjustment to account for the fact the genetic base in G and A22 is different (tuning). However, blending before tuning may not reflect the actual difference between pedigree and genomic base because the blended matrix already contains a portion of A22. The objective of this study was to evaluate the impact of tuning before blending on predictivity, bias, and inflation of GEBV, indirect predictions (IP), and SNP effects from ssGBLUP using American Angus and US Holstein data. We used four different scenarios to obtain genomic predictions: BlendFirst_TunedG2, TuneFirst_TunedG2, BlendFirst_TunedG4, and TuneFirst_TunedG4. TunedG2 adjusts mean diagonals and off-diagonals of G to be similar to the ones in A22, whereas TunedG4 adjusts based on the fixation index. Over 6 million growth records were available for Angus and 5.9 million udder depth records for Holsteins. Genomic information was available on 51,478 Angus and 105,116 Holstein animals. Predictivity and reliability were obtained for 19,056 and 1,711 validation Angus and Holsteins, respectively. We observed the same predictivity and reliability for GEBV or IP in all four scenarios, ranging from 0.47 to 0.60 for Angus and was 0.67 for Holsteins. Slightly less bias was observed when tuning was done before blending. Correlation of SNP effects between scenarios was > 0.99. Refined tuning before blending had no impact on GEBV and marginally reduced the bias. This option will be implemented in the BLUPF90 software suite.

41 Using Sequence Data to Increase Accuracy of Genomic Predictions in Livestock: Are We There Yet?

As sequence data is becoming available for many livestock species, there is a question on whether this information can help to boost the accuracy of genomic predictions beyond what has already been achieved with SNP chips. Several studies have been conducted by our group using simulated and real livestock populations that included from 1,000 to 100,000 animals with full or imputed sequence information. For the real datasets, the potential causative variants were identified based on genome-wide association (GWA) and were added to the current SNP chips. Additional scenarios included the use of only causative variants and the use of all sequence SNP. Genomic predictions were obtained based on single-step GBLUP (ssGBLUP), and in some cases, Bayesian regressions. Overall, in real datasets, we observed no significant increase in accuracy by using all sequence SNP, causative variants alone, or combined with SNP currently used for genomic prediction. However, an increase in accuracy of almost 100% was observed in simulated datasets when the causative variants were added to a 60k SNP panel and their simulated variances were accounted for by the prediction model. Our results show that if true causative variants are identified, together with their position and the variance explained, a boost in accuracy can be observed. This raises a question on the effectiveness of the methods and size of the datasets used to select causative variants in real livestock populations. We observed distinct GWA methods work differently depending on the data structure, and the number of genotyped animals with phenotypes. The combination of large-scale sequence and other layers of omics data (e.g., functional data) can help to identify some of the true causative variants. This could possibly promote an increase in the accuracy of genomic predictions in real populations.

44 Accuracy of Genomic Predictions over Time in Broilers

The objectives of this research were to investigate trends for accuracy of genomic predictions over time in a broiler population accumulating data, and to test if data from distant generations are useful in maintaining the accuracy of genomic predictions in selection candidates. The data contained 820k phenotypes for a growth trait (GROW), 200k for two feed efficiency traits (FE1 and FE2), and 42k for a dissection trait (DT). The pedigree included 1.2M animals across 7 years, over 100k from the last 4 years were genotyped. Accuracy was calculated by the linear regression method. Before genotypes became available for training populations, accuracy was nearly stable despite the accumulation of phenotypes and pedigrees. When the first year of genomic data was included in the training population, accuracy increased 56, 77, 39, and 111% for GROW, FE1, FE2, and DT, respectively. With genomic information, the accuracies increased every year except the last one, when they declined for GROW and FE2. The decay of accuracy over time was evaluated in progeny, grand-progeny, and great-grand-progeny of training populations. Without genotypes, the average decline in accuracy across traits was 41% from progeny to grand-progeny, and 19% from grand-progeny to great-grand-progeny. Whit genotypes, the average decline across traits was 14% from progeny to grand-progeny, and 2% from grand-progeny to great-grand-progeny. The accuracies in the last 3 generations were the same when the training population included 5 or 2 years of data, and a marginal decrease was observed when the training population included only 1 year of data. Training sets including genomic information provided an increased accuracy and persistence of genomic predictions compared to training sets without genomic data. The two most recent years of data were enough to maintain the accuracy of predictions in selection candidates.