Low Heritability Traits Research Articles

Comparative Study of Single-Trait and Multi-Trait Genomic Prediction Models.

Conventional genomic selection models trait individually, neglecting complex trait interactions. Multi-trait models address this by considering genetic correlations, thus improving breeding value accuracy. Despite their theoretical benefits, quantifying these models' breeding advantages across genetic backgrounds is essential. This study evaluates the benefits of multi-trait models under varying population sizes and three levels of genetic correlations (low, medium, high) using simulations based on 50 K chip data from 5000 individuals. In equal heritability scenarios, the multi-trait GBLUP model consistently outperforms single-trait models, with breeding advantages increasing with heritability. For example, with a reference population of 4500, improvements range from 0.3% to 4.1%. Notably, trait combinations with low heritability are insensitive to changes in genetic correlation, with gains remaining ≤ 0.1% across different genetic correlations under low heritability conditions. In differing heritability scenarios, the multi-trait model's benefits vary, particularly enhancing low-heritability traits when paired with high-heritability ones. Additionally, modeling time increases as genetic correlation decreases. The results of this study indicate that multi-trait models improve breeding accuracy but require more modeling time and place higher demands on algorithms and software. We recommend breeding strategies tailored to different phenotypes and genetic backgrounds to balance efficiency and accuracy.

Genomic prediction and validation strategies for reproductive traits in Holstein cattle across different Chinese regions and climatic conditions

Accurate genomic predictions of breeding values for traits included in the selection indexes are paramount for optimizing genetic progress in populations under selection. The size of the reference populations is a major factor influencing the accuracy of genomic predictions, which is even more important for lowly-heritable traits such as fertility and reproduction indicators. Combining data from different geographical regions or countries can be beneficial for genomic prediction of these lowly heritable traits. Therefore, the objectives of this study were to: 1) evaluate the benefits of performing across-regional genomic evaluations for reproduction traits in Chinese Holstein cattle; and, 2) assess the feasibility of validating genomic predictions across environments based on reaction norm models (RNM) and the Linear Regression (LR) method after accounting for genotype-by-environment interactions. Phenotypic records from 194,574 cows collected across 47 farms located in 2 regions of China were used for this study. The reference and validation populations were defined based on birth year for applying the LR validation method. The traits evaluated included: interval from first to last insemination (IFL), conception rate at the first insemination (CR_f), and number of inseminations (NS) recorded in heifers and first-parity cows. The results indicated that combining data from different regions resulted in greater genomic prediction accuracies compared with using data from single regions, with increases ranging from 2.74% to 93.81%. This improvement was particularly notable for the region with the least amount of available data, where the increases ranged from 26.49% to 93.81%. Furthermore, the predictive abilities could be validated for all studied traits based on the LR method across different environments when fitting RNM. The prediction accuracies and bias of genomic breeding values based on RNM were better than regular single-trait animal models in extreme climatic conditions for IFL and NS, whereas limited increases in predictive abilities were observed for CR_f. Across-regional genomic prediction by RNM can account for genotype-by-environment interactions, potentially increase the accuracy of genomic prediction, and predict the performances of individuals in the environments with limited phenotypic data available.

313 Many farmers want a prediction of future performance, not necessarily breeding potential

Abstract High heritability is a characteristic of many performance traits in cattle translating to a relatively strong correlation between an (accurate) estimated breeding value for an animal and its (future) production potential. However, the broad sense heritability (i.e., numerator in the formula includes both additive and non-additive genetic variance) is often larger than the narrow sense heritability (i.e., numerator includes only the additive genetic variance) especially for low heritability traits like reproductive performance. Moreover, the repeatability of a trait can be greater again, the difference between the heritability and repeatability is the inclusion of a permanent environmental variance in the numerator. Accurate and pertinent omic-based animal-level features can contribute to an increase in narrow sense heritability owing to a possible reduction in the residual variance; such data can also provide insights into both non-additive genetic variance and the permanent environmental variance. Even at the level of the contemporary group, omic-level data from the likes of bulk blood/saliva/milk sample, can improve how systematic environmental effects are modeled in the mixed model equations thus also reducing the residual variance. The greater heritability will contribute to a strengthening of the correlation between future phenotypic performance potential and estimated additive genetic merit. Importantly, omic measures for individual animals can help predict the permanent environmental effect of that animal capturing the yet untapped contributor to phenotypic variance. Irrespective, predictions need to be replaced by prescriptions; end users are inundated with data yet crave actionable information. Animal breeders have a lush (pardon the pun!) history of distilling estimates of genetic merit for a plethora of traits into a single value per animal (i.e., breeding indexes); this is the first step in transitioning from predictions to prescriptions. Given the familiarity of end users with breeding indexes, the potential exists to develop production indexes to rank commercial animals on future expected performance taking cognizance of genetic and non-genetic effects. Such production indexes align well with the associated breeding index but where the estimates of genetic merit for the traits in question are replaced by estimates of production merit; these estimated production values are the sum of the additive genetic, non-additive genetic and permanent environmental effects. The weighting factors in the production indexes are more short-term using current costs and prices as opposed to breeding indexes which are longer term. In comparison with the breeding indexes, the production indexes can include ancillary information like gender, age, and month of calving/birth. Like breeding indexes, they are updated in real time. Examples of the application of genome-enabled production indexes are guides for valuing animals for purchase or culling.

Genetic improvement approaches of indigenous cattle breeds for adaptation, conservation and sustainable utilization to changing climate in Ethiopia

This paper attempts to review the cattle genetic improvement approaches for sustainable utilization, adaptation, and conservation in the face of changing climatic conditions. Livestock production is affected by climate change, which poses a greater threat to populations that rely on them for their overall food security. Climate change negatively affects cattle production directly through its impact on animal physiology, behavior, and health and indirectly through its effect on feed and water availability, quality and quantity of pasture, forage crops, and rangeland due to increased temperature droughts. Improvement of cattle genetic resources that are efficient and well adapted to extreme temperatures, low-quality diets, and disease challenges is critical to effectively cope with climate change. Designing suitable breeding strategies will facilitate improving the performance of cattle breeds and enhance their tolerance to the dynamics of climate change. Replacement of local cattle breeds with exotic ones and unplanned crossbreeding with them without enough consideration of environmental conditions are among the major factors contributing to the loss of locally adapted breeds that possess certain adaptive traits. Maintenance of indigenous cattle’s genetic diversity, which underpins resistance to environmental stresses is a viable strategy to mitigate the possible effects of future climatic challenges. In contrast to the traditional selection, genomic selection increases the accuracy of selection with the largest genetic gain, for the low heritability traits such as adaptability and longevity. In conclusion, any breeding strategies should be relevant in terms of breed suitability, performance, and adaptability in the given production environment to sustain cattle production.

62 Implications of factor analyses and Bayesian network learning to develop composite traits of size attributes in developing beef heifers

Abstract Advancements in phenotypic data collection methods provide opportunities to capture complex biological traits more efficiently. Even so, interpreting those phenotypes and using them in selection decisions is challenging in beef cattle production. Objectives of this study were to 1) develop composite phenotype(s) of economic importance in beef heifers given attributes of their body conformation and ultrasound-based measures of reproductive and carcass traits, and 2) identify associated genomic regions under selection using the structure equation modeling (SEM) approach. In this study, we hypothesized that the interplay of factor analysis and Bayesian Network Learning (BNL) approaches would better define the complex composite traits for size and carcass-related traits in beef heifers. Phenotypic data of admixed beef heifers (n = 336) for different reproductive, body conformational, and carcass-associated phenotypic traits (n = 16) were collected. Using exploratory and confirmatory factor analyses, two latent variables explaining 14 phenotypic traits were identified, herein called Body Size (BS; n = 7 traits represented) and Body Composition (BC; n = 7 traits represented). To efficiently model them in genome-wide association studies (GWAS), fixed effects of data collection year, dam age, and primary breed group as well as causal network structure identified through BNL were used within SEM model of GWAS. Body Size was directly or indirectly contributing to BC based on BNL structure. Given the data-driven approach and novelty of the data, BayesCπ model was applied with the sliding window of 1 Mb to avoid noise and linkage disequilibrium implications. Regions capturing large amounts of genetic variance (Posterior Probability of Association, PPA ≥ 0.8) were mapped to their closest gene for enrichment analysis. Twenty-four different genes were identified associated with both composite phenotypes. Functional enrichment analysis of significantly involved pathways (FDR < 0.05) indicated that genes from the HERC family (HERC3, HERC6, and HERC5) were found involved in cellular growth, and those from desmosomes (DC2, and DC3) were involved in morphogenesis like BS. Genes related to energy metabolism like IMPAD1, and PIGY, along with genes associated with traits like muscular development, and other carcass traits (i.e., FAM184B, NCAPG, and LCORL), were also found associated with BS and BC. Heritability ranged from 0.551 ± 0.048 to 0.897 ± 0.145 for BS and BC, respectively. The combined heritability of reproductive and carcass traits (BC) was much greater than the heritability of any related individual trait reported to date and was comparable for individual BS-related traits. Given this, we conclude that the implications of factor analyses with BNL can provide better biological insights for improved genomic selection in beef cattle, even for traits of low heritability.

Genetic variation in bovine LAP3 and SIRT1 genes associated with fertility traits in dairy cattle.

The genetic progress of fertility and reproduction traits in dairy cattle has been constrained by the low heritability of these traits. Identifying candidate genes and variants associated with fertility and reproduction could enhance the accuracy of genetic selection and expedite breeding process of dairy cattle with low-heritability traits. While the bovine LAP3 and SIRT1 genes exhibit well-documented associations with milk production traits in dairy cattle, their effect on cow fertility have not yet been explored. Eleven single nucleotide polymorphisms (SNPs), comprising five in the promoter (rs717156555: C > G, rs720373055: T > C, rs516876447: A > G, rs461857269: C > T and rs720349928: G > A), two in 5'UTR (rs722359733: C > T and rs462932574: T > G), two in intron 12 (rs110932626: A > G and rs43702363: C > T), and one in 3'UTR of exon 13 (rs41255599: C > T) in LAP3 and one in SIRT1 (rs718329990:T > C) genes, have previously been reported to be associated with various traits of milk production and clinical mastitis in Sahiwal and Karan Fries dairy cattle. In this study, the analysis primarily aimed to assess the impact of SNPs within LAP3 and SIRT1 genes on fertility traits in Sahiwal and Karan Fries cattle. Association studies were conducted using mixed linear models, involving 125 Sahiwal and 138 Karan Fries animals in each breed. The analysis utilized a designated PCR-RFLP panel. In the promoter region of the LAP3 gene, all variants demonstrated significant (P < 0.05) associations with AFC, except for rs722359733: C > T. However, specific variants with the LAP3 gene's promoter region, namely rs722359733: C > T, rs110932626: A > G, rs43702363: C > T, and rs41255599: C > T, showed significant associations with CI and DO in Sahiwal and Karan Fries cows, respectively. The SNP rs718329990: T > C in the promoter region of SIRT1 gene exhibited a significant association with CI and DO in Sahiwal cattle. Haplotype-based association analysis revealed significant associations between haplotype combinations and AFC, CI and DO in the studied dairy cattle population. Animals with H2H3 and H2H4 haplotype combination exhibited higher AFC, CI and DO than other combinations. These results affirm the involvement of the LAP3 and SIRT1 genes in female fertility traits, indicating that polymorphisms within these genes are linked to the studied traits. Overall, the significant SNPs and haplotypes identified in this study could have the potential to enhance herd profitability and ensure long-term sustainability on dairy farms by enabling the selection of animals with early age first calving and enhance reproductive performance in the dairy cattle breeding program.

Maximizing efficiency in sunflower breeding through historical data optimization

Genomic selection (GS) has become an increasingly popular tool in plant breeding programs, propelled by declining genotyping costs, an increase in computational power, and rediscovery of the best linear unbiased prediction methodology over the past two decades. This development has led to an accumulation of extensive historical datasets with genotypic and phenotypic information, triggering the question of how to best utilize these datasets. Here, we investigate whether all available data or a subset should be used to calibrate GS models for across-year predictions in a 7-year dataset of a commercial hybrid sunflower breeding program. We employed a multi-objective optimization approach to determine the ideal years to include in the training set (TRS). Next, for a given combination of TRS years, we further optimized the TRS size and its genetic composition. We developed the Min_GRM size optimization method which consistently found the optimal TRS size, reducing dimensionality by 20% with an approximately 1% loss in predictive ability. Additionally, the Tails_GEGVs algorithm displayed potential, outperforming the use of all data by using just 60% of it for grain yield, a high-complexity, low-heritability trait. Moreover, maximizing the genetic diversity of the TRS resulted in a consistent predictive ability across the entire range of genotypic values in the test set. Interestingly, the Tails_GEGVs algorithm, due to its ability to leverage heterogeneity, enhanced predictive performance for key hybrids with extreme genotypic values. Our study provides new insights into the optimal utilization of historical data in plant breeding programs, resulting in improved GS model predictive ability.

Genomic prediction for agronomic traits in a diverse Flax (Linum usitatissimum L.) germplasm collection

Breeding programs require exhaustive phenotyping of germplasms, which is time-demanding and expensive. Genomic prediction helps breeders harness the diversity of any collection to bypass phenotyping. Here, we examined the genomic prediction’s potential for seed yield and nine agronomic traits using 26,171 single nucleotide polymorphism (SNP) markers in a set of 337 flax (Linum usitatissimum L.) germplasm, phenotyped in five environments. We evaluated 14 prediction models and several factors affecting predictive ability based on cross-validation schemes. Models yielded significant variation among predictive ability values across traits for the whole marker set. The ridge regression (RR) model covering additive gene action yielded better predictive ability for most of the traits, whereas it was higher for low heritable traits by models capturing epistatic gene action. Marker subsets based on linkage disequilibrium decay distance gave significantly higher predictive abilities to the whole marker set, but for randomly selected markers, it reached a plateau above 3000 markers. Markers having significant association with traits improved predictive abilities compared to the whole marker set when marker selection was made on the whole population instead of the training set indicating a clear overfitting. The correction for population structure did not increase predictive abilities compared to the whole collection. However, stratified sampling by picking representative genotypes from each cluster improved predictive abilities. The indirect predictive ability for a trait was proportionate to its correlation with other traits. These results will help breeders to select the best models, optimum marker set, and suitable genotype set to perform an indirect selection for quantitative traits in this diverse flax germplasm collection.

Simulations of multiple breeding strategy scenarios in common bean for assessing genomic selection accuracy and model updating.

The aim of this study was to evaluate the accuracy of the ridge regression best linear unbiased prediction model across different traits, parent population sizes, and breeding strategies when estimating breeding values in common bean (Phaseolus vulgaris). Genomic selection was implemented to make selections within a breeding cycle and compared across five different breeding strategies (single seed descent, mass selection, pedigree method, modified pedigree method, and bulk breeding) following 10 breeding cycles. The model was trained on a simulated population of recombinant inbreds genotyped for 1010 single nucleotide polymorphism markers including 38 known quantitative trait loci identified in the literature. These QTL included 11 for seed yield, eight for white mold disease incidence, and 19 for days to flowering. Simulation results revealed that realized accuracies fluctuate depending on the factors investigated: trait genetic architecture, breeding strategy, and the number of initial parents used to begin the first breeding cycle. Trait architecture and breeding strategy appeared to have a larger impact on accuracy than the initial number of parents. Generally, maximum accuracies (in terms of the correlation between true and estimated breeding value) were consistently achieved under a mass selection strategy, pedigree method, and single seed descent method depending on the simulation parameters being tested. This study also investigated model updating, which involves retraining the prediction model with a new set of genotypes and phenotypes that have a closer relation to the population being tested. While it has been repeatedly shown that model updating generally improves prediction accuracy, it benefited some breeding strategies more than others. For low heritability traits (e.g., yield), conventional phenotype-based selection methods showed consistent rates of genetic gain, but genetic gain under genomic selection reached a plateau after fewer cycles. This plateauing is likely a cause of faster fixation of alleles and a diminishing of genetic variance when selections are made based on estimated breeding value as opposed to phenotype.

Estimation of genetic parameters of Holstein calf survival.

Calf survival is not only an animal welfare issue but also helps to avoid huge losses in economic and genetic material due to calf mortality. Therefore, improving calf survival is essential in dairy breeding. The objective of this study was to explore the factors affecting the survival of Holstein calves in the Ningxia Region and to estimate the genetic parameters of calves using linear models and threshold models. Descriptive statistics were made for 43,847 Holstein calves born from 2018 to 2022 in Ningxia. The number of calves that died at 2-30 d was the highest, the survival rate was the lowest at 451-750 d, followed by 61-180 d and 2-30 d. Studies on the survival rates of calves born in different months have found that calves born in April have the lowest survival rates and calves born in October and December have higher survival rates. Calves born in autumn, third parity, and singleton calves are more likely to survive. The heritability of calf survival traits ranged from 0.002 ~ 0.136. Thus survival is a low heritability trait. Genetic correlation between different survival stages ranged from 0.3991 (2-30 d to 451-750 d) to 0.9985 (361-450 d to 451-750 d), the phenotypic correlation ranged from 0.1476 (2-30 d to 451-750 d) to 0.9582 (361-450 d to 451-750 d). The low genetic correlation between early and late survival suggests that survival in early and late stages may be influenced by different genetic factors. This study is helpful to understand the survival status of Holstein calves and provide a theoretical basis for improving the survival rate of calves.

100 Pedigree Effective Population Size Affects Genomic Prediction Accuracy in Katahdin Sheep

Abstract Katahdin sheep are a composite hair breed of medium-size, relatively prolific, with potential to exhibit resistance to gastrointestinal parasites. Recently, genomic predictions of breeding values were introduced in Katahdins. Prediction accuracy depends on the genetic variation present, and selection using genomically-enhanced breeding values may reduce diversity. We aimed to characterize the current genetic variability in Katahdins utilizing their extensive pedigree. The National Sheep Improvement Program provided the pedigree of 92,030 sheep born from 1984-2019. Two reference populations were defined for animals born between 2017-2019: all Katahdins (n = 23,494), and Katahdins with at least three generations of known Katahdin ancestry (n = 9,327). Using ENDOG and PopReport, effective population sizes (Ne) were estimated. We then considered the effect of Ne on the size of the genomic reference population needed to achieve certain levels of prediction accuracy. Weaning weight, a lowly heritable trait for this breed (h2 = 0.13), was evaluated. Based on individual increase in inbreeding, inbreeding coefficient of animal adjusted for equivalent complete generations, the estimated Ne for the full pedigree, reference 1, and reference 2 were 92, 104, and 87, respectively. When inbreeding coefficients were regressed or log-regressed on birth date and adjusted for generation interval, the Ne estimates were 53 and 49 for reference 1, and 76 and 67 for reference 2, respectively. For our average Ne = 75 with 3,960 effective chromosomal segments, the accuracy of prediction for weaning weight was estimated for genomic reference population sizes (N) of 10k, 15k, 20k, and 25k. Starting at N = 10k, the prediction accuracy was 0.58, and as N increased by 5k increments the accuracy increased to 0.65, 0.70, and 0.74. Conversely, with our smallest Ne = 49 with 2,548 effective chromosomal segments, the prediction accuracy was 0.64, 0.71, 0.75, and 0.78 for N = 10k, 15k, 20k, and 25k, respectively. Estimated Ne varied depending on the method utilized, and tended to be larger for reference 1 than 2. However, this was not the case for Ne based on regression. Inbreeding coefficients were greater in reference 2 than 1, and the generation interval was shorter in reference 2 (2.84 years) than in 1 (2.97 years). These combined differences led to larger Ne estimates for reference 2 than 1, reflecting the impact of different criteria used to obtain this statistic. The generally small, estimated Ne benefitted genomic prediction due to a greater shared number of effective chromosomal segments. Investing in larger genomic reference populations will only marginally increase prediction accuracy even for lowly heritable traits. Selection using genomically-enhanced breeding values will accelerate genetic gains, potentially reducing the Ne of Katahdins and thereby increasing prediction accuracy. Yet, long-term gains may be at risk due to the loss of genetic diversity.

Including genomic information in the genetic evaluation of production and reproduction traits in South African Merino sheep.

Genomic selection (GS) has become common in sheep breeding programmes in Australia, New Zealand, France and Ireland but requires validation in South Africa (SA). This study aimed to compare the predictive ability, bias and dispersion of pedigree BLUP (ABLUP) and single-step genomic BLUP (ssGBLUP) for production and reproduction traits in South African Merinos. Animals in this study originated from five research and five commercial Merino flocks and included between 54,072 and 79,100 production records for weaning weight (WW), yearling weight (YW), fibre diameter (FD), clean fleece weight (CFW) and staple length (SL). For reproduction traits, the dataset included 58,744 repeated records from 17,268 ewes for the number of lambs born (NLB), number of lambs weaned (NLW) and the total weight weaned (TWW). The single-step relationship matrix, H, was calculated using PreGS90 software combining 2811 animals with medium density (~50 K) genotypes and the pedigree of 88,600 animals. Assessment was based on single-trait analysis using ASREML V4.2 software. The accuracy of prediction was evaluated according to the 'LR-method' by a cross-validation design. Validation candidates were assigned according to Scenario I: born after a certain time point; and Scenario II: born in a particular flock. In Scenario I, the genotyping rate for the reference population between 2006 and the 2013 cut-off point approached 7% of animals with phenotypes and 20% of their sires. For reproduction traits, about 20% of ewes born between 2006 and 2011 cut-off were genotyped, along with 15% of their sires. Genotyping rates in the validation set of Scenario I were 3.7% (production) and 11.4% (reproduction) for candidates and 35% of their sires. Sires were allowed to have progeny in both the reference and validation set. In Scenario I, ssGBLUP increased the accuracy of prediction for all traits except NLB, ranging between +8% (0.62-0.67) for FD and +44% (0.36-0.52) for WW. This showed a promising gain in accuracy despite a modestly sized reference population. In the 'across flock validation' (Scenario II), overall accuracy was lower, but with greater differences between ABLUP and ssGBLUP ranging between +17% (0.12-0.14) for TWW and +117% (0.18-0.39) for WW. There was little indication of severe bias, but some traits were prone to over dispersion and the use of genomic information did not improve this. These results were the first to validate the benefit of genomic information in South African Merinos. However, because production traits are moderately heritable and easy to measure at an early age, future research should be aimed at best exploiting GS methods towards genetic prediction of sex-limited and/or lowly heritable traits such as NLW. GS methods should be combined with dedicated efforts to increase genetic links between sectors and improved phenotyping by commercial flocks.

Comparison of the statistical methods for genome-wide association studies on simulated quantitative traits of domesticated goats (Capra hircus L.)

Genome-wide association studies (GWAS) are one of the essential methods used to find associations between phenotypes and genes. The main problem in GWAS methods is false positives that occur from family relationships and population structure. In this study, the performances and powers of traditional and state-of-the-art statistical methods used in genome-wide association studies were compared on nine simulated quantitative traits using genotypic data of the domesticated goats (Capra hircus L.). The traits were simulated with varying degrees of heritability from 0.05 to 0.8. The findings revealed that Blink and FarmCPU are the outperforming methods to control false positives more efficiently for the medium and high heritability traits. At the same time, Blink promises to identify true positive signals more efficiently for the low heritability traits. The other multi-loci statistical methods SUPER and MLMM perform worse than Blink and FarmCPU but better than all of the single-locus methods GLM, MLM, CMLM and ECLMM. Additionally, Bonferroni or Holm's adjustment procedures were better in multiple testing of associations at the 1 % significance level to get more accurate results for the high heritability traits.

Including genotypic information in genetic evaluations increases the accuracy of sheep breeding values.

The impact of inclusion of genome-wide genotypes into breeding value predictions for UK Texel sheep is addressed in this article. The main aim was to investigate the level of change in the accuracy values for EBVs when information from animal genotypes is incorporated into the genetic evaluations. New genetic parameters for a range of lamb growth, carcass composition and health traits are described and applied in the estimation of conventional breeding values (EBVs) for almost 822,000 animals as well as genomic breeding values (gEBVs) after adding 10,143 genotypes. Principal component analyses showed that there are no major distinct groups; hence, the population is mainly homogenous and genetically well-linked. Results suggested that the highest change in accuracy was observed for the animals that are not phenotyped but have good links to the reference population. This was seen especially for the lowly heritable health traits thereby proving that the use of genotypes in breeding values estimation may accelerate the genetic gain by producing more accurate values especially for young, un-phenotyped animals.

Genomic prediction in pigs using data from a commercial crossbred population: insights from the Duroc x (Landrace x Yorkshire) three-way crossbreeding system

BackgroundGenomic selection is widely applied for genetic improvement in livestock crossbreeding systems to select excellent nucleus purebred (PB) animals and to improve the performance of commercial crossbred (CB) animals. Most current predictions are based solely on PB performance. Our objective was to explore the potential application of genomic selection of PB animals using genotypes of CB animals with extreme phenotypes in a three-way crossbreeding system as the reference population. Using real genotyped PB as ancestors, we simulated the production of 100,000 pigs for a Duroc x (Landrace x Yorkshire) DLY crossbreeding system. The predictive performance of breeding values of PB animals for CB performance using genotypes and phenotypes of (1) PB animals, (2) DLY animals with extreme phenotypes, and (3) random DLY animals for traits of different heritabilities ({h}^{2} = 0.1, 0.3, and 0.5) was compared across different reference population sizes (500 to 6500) and prediction models (genomic best linear unbiased prediction (GBLUP) and Bayesian sparse linear mixed model (BSLMM)).ResultsUsing a reference population consisting of CB animals with extreme phenotypes showed a definite predictive advantage for medium- and low-heritability traits and, in combination with the BSLMM model, significantly improved selection response for CB performance. For high-heritability traits, the predictive performance of a reference population of extreme CB phenotypes was comparable to that of PB phenotypes when the effect of the genetic correlation between PB and CB performance ({r}_{pc}) on the accuracy obtained with a PB reference population was considered, and the former could exceed the latter if the reference size was large enough. For the selection of the first and terminal sires in a three-way crossbreeding system, prediction using extreme CB phenotypes outperformed the use of PB phenotypes, while the optimal design of the reference group for the first dam depended on the percentage of individuals from the corresponding breed that the PB reference data comprised and on the heritability of the target trait.ConclusionsA commercial crossbred population is promising for the design of the reference population for genomic prediction, and selective genotyping of CB animals with extreme phenotypes has the potential for maximizing genetic improvement for CB performance in the pig industry.

Accuracy of Selection in Early Generations of Field Pea Breeding Increases by Exploiting the Information Contained in Correlated Traits.

Accuracy of predicted breeding values (PBV) for low heritability traits may be increased in early generations by exploiting the information available in correlated traits. We compared the accuracy of PBV for 10 correlated traits with low to medium narrow-sense heritability (h2) in a genetically diverse field pea (Pisum sativum L.) population after univariate or multivariate linear mixed model (MLMM) analysis with pedigree information. In the contra-season, we crossed and selfed S1 parent plants, and in the main season we evaluated spaced plants of S0 cross progeny and S2+ (S2 or higher) self progeny of parent plants for the 10 traits. Stem strength traits included stem buckling (SB) (h2 = 0.05), compressed stem thickness (CST) (h2 = 0.12), internode length (IL) (h2 = 0.61) and angle of the main stem above horizontal at first flower (EAngle) (h2 = 0.46). Significant genetic correlations of the additive effects occurred between SB and CST (0.61), IL and EAngle (-0.90) and IL and CST (-0.36). The average accuracy of PBVs in S0 progeny increased from 0.799 to 0.841 and in S2+ progeny increased from 0.835 to 0.875 in univariate vs MLMM, respectively. An optimized mating design was constructed with optimal contribution selection based on an index of PBV for the 10 traits, and predicted genetic gain in the next cycle ranged from 1.4% (SB), 5.0% (CST), 10.5% (EAngle) and -10.5% (IL), with low achieved parental coancestry of 0.12. MLMM improved the potential genetic gain in annual cycles of early generation selection in field pea by increasing the accuracy of PBV.

Correlações canônicas entre caracteres de trigo de alta e baixa herdabilidade via modelos mistos

ABSTRACT: Canonical correlation analysis based on genotypic correlations allows determining the associations between groups of traits and carrying out the direct or indirect selection of superior genotypes. This study investigated the existence of linear and multivariate relationships between high and low heritability traits via canonical correlation analysis based on genotypic correlations. The experiment was conducted at the Professor Diogo Alves de Melo Experimental Field at the Universidade Federal de Viçosa, in Viçosa, MG. 90 wheat cultivars were evaluated under a 9 × 10 alpha-lattice design, with three replications and plots consisting of four rows of three meters spaced at 0.20 meters. Canonical groups were established between spike height and plant height, days for heading, number of spikelets per spike, and number of grains per spike (Group I) and, spike weight, spike grain mass, 100-grain mass, hectoliter weight, and grain yield (Group II). There was dependence between the established groups, which allowed the investigation of the relationships between traits based on their genotypic values. The traits cycle and plant height can be used for indirect selection of genotypes superior in hectoliter weight and grain yield, which are important factors for industries and farmers.

Effect of minor allele frequency and density of single nucleotide polymorphism marker arrays on imputation performance and prediction ability using the single-step genomic Best Linear Unbiased Prediction in a simulated beef cattle population

Context In beef cattle populations, there is little evidence regarding the minimum number of genetic markers needed to obtain reliable genomic prediction and imputed genotypes. Aims This study aimed to evaluate the impact of single nucleotide polymorphism (SNP) marker density and minor allele frequency (MAF), on genomic predictions and imputation performance for high and low heritability traits using the single-step genomic Best Linear Unbiased Prediction methodology (ssGBLUP) in a simulated beef cattle population. Methods The simulated genomic and phenotypic data were obtained through QMsim software. 735 293 SNPs markers and 7000 quantitative trait loci (QTL) were randomly simulated. The mutation rate (10−5), QTL effects distribution (gamma distribution with shape parameter = 0.4) and minor allele frequency (MAF ≥ 0.02) of markers were used for quality control. A total of 335k SNPs (high density, HD) and 1000 QTLs were finally considered. Densities of 33 500 (35k), 16 750 (16k), 4186 (4k) and 2093 (2k) SNPs were customised through windows of 10, 20, 80 and 160 SNPs by chromosome, respectively. Three marker selection criteria were used within windows: (1) informative markers with MAF values close to 0.5 (HI); (2) less informative markers with the lowest MAF values (LI); (3) markers evenly distributed (ED). We evaluated the prediction of the high-density array and of 12 scenarios of customised SNP arrays, further the imputation performance of them. The genomic predictions and imputed genotypes were obtained with Blupf90 and FImpute software, respectively, and statistics parameters were applied to evaluate the accuracy of genotypes imputed. The Pearson’s correlation, the coefficient of regression, and the difference between genomic predictions and true breeding values were used to evaluate the prediction ability (PA), inflation (b), and bias (d), respectively. Key results Densities above 16k SNPs using HI and ED criteria displayed lower b, higher PA and higher imputation accuracy. Consequently, similar values of PA, b and d were observed with the use of imputed genotypes. The LI criterion with densities higher than 35k SNPs, showed higher PA and similar predictions using imputed genotypes, however lower b and quality of imputed genotypes were observed. Conclusion The results obtained showed that at least 5% of HI or ED SNPs available in the HD array are necessary to obtain reliable genomic predictions and imputed genotypes. Implications The development of low-density customised arrays based on criteria of MAF and even distribution of SNPs, might be a cost-effective and feasible approach to implement genomic selection in beef cattle.

The genetic and phenotypic associations between lamb survival outcomes and other traits recorded at lambing

Context Australian sheep breeding values (ASBVs) for lambing ease (LE) are estimated by Sheep Genetics, by using a threshold animal model at the lamb level, in a tri-variate analysis that includes data on birth weight, gestation length and lambing ease score. The implications of these traits for lamb survival, or the use of other indirect traits to improve accuracy of ASBVs for LE, are not currently being considered. Ultimately, it is desirable to extend the analysis to outcomes for individual lamb survival. Aim The present study investigated implications of LE for lamb survival outcomes, accounting for litter size, and examined associations with other traits recorded at or shortly after lambing in maternal sheep breeds. Methods Equivalent linear models were used to compare lamb- and ewe-level models with various combinations of additional random effects. In particular, lambing ease was treated as a different trait for single-born and twin-born lambs, to identify changes in genetic correlations associated with litter size between LE and other traits. Other traits included lambs recorded dead at birth, survival to weaning, lamb birth weight, gestation length and maternal behaviour score. Key results Individual lamb survival outcomes inferred from field data and dead at birth lambs, are lowly heritable traits influenced by both direct and maternal effects. Lamb survival is positively correlated with birth weight, but negatively correlated with gestation length, lambing ease score (increasing lambing difficulty) and dead at birth lambs. Genetic and phenotypic correlations demonstrated that birth weight and lambing ease are antagonistic traits, more so for single-born lambs. Genetic correlations were moderate between dead at birth lambs and LE (0.40–0.45 singles; 0.15–0.36 including data from twins) or lamb survival (−0.63 to −0.81 singles; −0.00 to −0.23 including data from twins) and can add to the accuracy of genetic evaluation for these traits. In contrast, maternal behaviour score was predominantly an ewe trait, and will therefore add to accuracy of evaluation only for maternal effects. Lamb-level models appeared to underestimate heritability, sometimes compensated for by larger variance, and over-estimate genetic correlations for some traits relative to ewe-level models. Conclusions Expanding the current lambing ease analysis to include dead at birth records and lamb survival outcomes would provide more detailed options for breeders to develop breeding goals to improve outcomes for both ewes and lambs. Further work is required to expand analyses to include threshold and continuous traits and understand genetic contributions to ewe survival traits. Implications Relative selection emphasis on LE and birth weight must be considered in light of the expected litter size in which lambs will be born, to ensure favourable outcomes for lamb survival overall. Accuracy of genetic evaluation for LE can be improved using data on dead at birth. Equivalent ewe model analyses are possible. Completeness of pedigree, availability of informative lamb level data and integration with other traits are also factors to consider for the choice between operational lamb- versus ewe-level models for genetic evaluation systems.

Review on trends of Selection superior Dairy cattle through marker assisted selection methods

Marker-Assisted Selection is selection that used for indirect selection of superior breeding animals that depend on identifying association between genetic marker and linked quantitative traits loci. Since the association between marker and quantitative traits loci depends on distance between marker and target traits. As soon as markers linked to quantitative traits loci have been identified, they can be used in selection programme of dairy cattle that is beneficial when the traits are difficult and expensive to measure and low heritability and recessive traits in dairy cattle. Therefore, Marker-Assisted Selection facilitate the exploitation of existing genetic diversity in breeding populations and can be used to improve desirable traits in dairy cattle. Indeed, currently Marker-Assisted Selection is the most widely used application of marker systems in selection of dairy breeding. Quantitative traits loci affecting milk yield has been identified on 20 of the 29 bovine chromosomes. quantitative traits loci for milk yield on chromosome 1, 3, 9 and 20 are with evidence of quantitative traits loci at lower reported frequency on other chromosomes (5, 7, 10, 12, 14 17, 18, 21, 23, 27 and 29). However, the main limitation of marker-assisted selection is increased cost involved in sample collection for genotyping and complete genotype information. Hence, this paper was aimed to review trends on selection of dairy cattle based on marker assisted selection approach.