Associations between morphometric variables and weight and yields carcass in Pirapitinga Piaractus brachypomus

The objective of this work was to evaluate the association between morphometric variables and carcass characteristics in Pirapitinga. We used a thousand specimens of Pirapitinga with an average weight of 1,200 g, which were stunned, slaughtered, weighed, measured, and processed for morphometric and processing yield analysis, to obtain weights, carcass and fillet yields. Initially, the linearity of the variables was verified. Pearson's simple and partial correlation tests were performed between all metrics. Track analysis was performed considering the weights and yields of carcass and filet as dependent variables and the others as independent variables. Ridge regression models were used to eliminate the effects of multicollinearity among the independent variables. Observations showed that the simple correlations between body weights and yields were superior to the partial ones in terms of magnitude. The length and circumference of the specimens were the measures most linearly associated with weight, carcass weight and fillet with rib weight. As for carcass yield and fillet yield, linear correlation coefficients were low and not significant when associated with body weights. With the path analysis, we could observe significant positive correlations between the morphometric measurements for weight, carcass weight and fillet with rib weight. The standard length, body circumference and body circumference/body height ratio are the most correlated. The latter are the most important variables in the evaluation of body weights in Pirapitinga fish. They can serve as criteria for indirect selection in searching for fish with better carcass characteristics. As for fillet and carcass yields, the morphometric variables have not shown to be efficient for indirect selection.     

Genetic parameters for fertility traits in Nellore bulls

Nellore is the main cattle breed used in Brazil, being the largest commercial herd in the world. Beyond the importance of male reproductive efficiency for farm profit, the use of reproductive techniques, mainly artificial insemination, turns the evaluation of male reproductive traits even more important. Estimation of genetic parameters increases the knowledge on traits variances and allows envisaging the possibility of the inclusion of new traits as selection criterion. Genetic parameters for fifteen traits that can be classified as testicular biometry or physical and morphological semen traits were estimated for a Nellore bull population ranging from 18 to 36 months. Single-trait and bi-trait animal models were used for (co)variance components estimation. The contemporary group was considered as fixed effect and age at measurement as covariable. Scrotal circumference presented heritability of 0.47 ± 0.12. This value is similar to the heritabilities found for all testicular biometry traits (0.34–0.48). Sperm progressive motility, which has a direct effect on bull fertility, presented low heritability (0.07 ± 0.08). Major and total sperm defects presented moderate to high heritabilities (0.49 ± 0.18 and 0.39 ± 0.15, respectively), indicating that great genetic gain can be obtained through selection against sperm defects. High and positive genetic correlations were observed among testicular biometry traits, which also presented favourable genetic correlations with physical and morphological traits of the semen with magnitude ranging from high to low. Scrotal circumference presented moderate to high and favourable genetic correlations with sperm progressive motility, sperm turbulence, major sperm defects and total sperm defects. Thus, the selection for scrotal circumference results in favourable correlated genetic response for semen quality. The results show that the use of scrotal circumference as reference trait for bull fertility is appropriate, since it presents high heritability and favourable genetic correlation with semen quality.     

Is single-step genomic REML with the algorithm for proven and young more computationally efficient when less generations of data are present?

Efficient computing techniques allow the estimation of variance components for virtually any traditional dataset. When genomic information is available, variance components can be estimated using genomic REML (GREML). If only a portion of the animals have genotypes, single-step GREML (ssGREML) is the method of choice. The genomic relationship matrix (G) used in both cases is dense, limiting computations depending on the number of genotyped animals. The algorithm for proven and young (APY) can be used to create a sparse inverse of G (GAPY~-1) with close to linear memory and computing requirements. In ssGREML, the inverse of the realized relationship matrix (H⁻¹) also includes the inverse of the pedigree relationship matrix, which can be dense with a long pedigree, but sparser with short. The main purpose of this study was to investigate whether costs of ssGREML can be reduced using APY with truncated pedigree and phenotypes. We also investigated the impact of truncation on variance components estimation when different numbers of core animals are used in APY. Simulations included 150K animals from 10 generations, with selection. Phenotypes (h² = 0.3) were available for all animals in generations 1–9. A total of 30K animals in generations 8 and 9, and 15K validation animals in generation 10 were genotyped for 52,890 SNP. Average information REML and ssGREML with G⁻¹ and GAPY~-1 using 1K, 5K, 9K, and 14K core animals were compared. Variance components are impacted when the core group in APY represents the number of eigenvalues explaining a small fraction of the total variation in G. The most time-consuming operation was the inversion of G, with more than 50% of the total time. Next, numerical factorization consumed nearly 30% of the total computing time. On average, a 7% decrease in the computing time for ordering was observed by removing each generation of data. APY can be successfully applied to create the inverse of the genomic relationship matrix used in ssGREML for estimating variance components. To ensure reliable variance component estimation, it is important to use a core size that corresponds to the number of largest eigenvalues explaining around 98% of total variation in G. When APY is used, pedigrees can be truncated to increase the sparsity of H and slightly reduce computing time for ordering and symbolic factorization, with no impact on the estimates.     

Genomic evaluation for novel stayability traits in Nellore cattle

Cow stayability plays a major role on the overall profitability of the beef cattle industry, as it is directly related to reproductive efficiency and cow's longevity. Stayability (STAY63) is usually defined as the ability of the cow to calve at least three times until 76 months of age. This is a late-measured and lowly heritable trait, which consequently constrains genetic progress per time unit. Thus, the use of genomic information associated with novel stayability traits measured earlier in life will likely result in higher prediction accuracy and faster genetic progress for cow longevity. In this study, we aimed to compare pedigree-based and single-step GBLUP (ssGBLUP) methods as well as to estimate genetic correlations between the proposed stayability traits: STAY42, STAY53 and STAY64, which are measured at 52, 64 and 76 months of cow's age, considering at least 2, 3 and 4 calving, respectively. ssGBLUP yielded the highest prediction accuracy for all traits. The heritability estimates for STAY42, STAY53, STAY63 and STAY64 were 0.090, 0.151, 0.152 and 0.143, respectively. The genetic correlations between traits ranged from 0.899 (STAY42 and STAY53) to 0.985 (STAY53 and STAY63). The high genetic correlation between STAY42 and STAY53 suggests that besides being related to cow longevity, STAY53 is also associated with the early-stage reproductive efficiency. Thus, STAY53 is recommended as a suitable selection criterion for reproductive efficiency due to its higher heritability, favourable genetic correlation with other traits, and measured earlier in life, compared with the conventional stayability trait, that is STAY63.     

Investigating pig survival in different production phases using genomic models

Pig survival is an economically important trait with relevant social welfare implications, thus standing out as an important selection criterion for the current pig farming system. We aimed to estimate (co)variance components for survival in different production phases in a crossbred pig population as well as to investigate the benefit of including genomic information through single-step genomic best linear unbiased prediction (ssGBLUP) on the prediction accuracy of survival traits compared with results from traditional BLUP. Individual survival records on, at most, 64,894 crossbred piglets were evaluated under two multi-trait threshold models. The first model included farrowing, lactation, and combined postweaning survival, whereas the second model included nursery and finishing survival. Direct and maternal breeding values were estimated using BLUP and ssGBLUP methods. Furthermore, prediction accuracy, bias, and dispersion were accessed using the linear regression validation method. Direct heritability estimates for survival in all studied phases were low (from 0.02 to 0.08). Survival in preweaning phases (farrowing and lactation) was controlled by the dam and piglet additive genetic effects, although the maternal side was more important. Postweaning phases (nursery, finishing, and the combination of both) showed the same or higher direct heritabilities compared with preweaning phases. The genetic correlations between survival traits within preweaning and postweaning phases were favorable and strong, but correlations between preweaning and postweaning phases were moderate. The prediction accuracy of survival traits was low, although it increased by including genomic information through ssGBLUP compared with the prediction accuracy from BLUP. Direct and maternal breeding values were similarly accurate with BLUP, but direct breeding values benefited more from genomic information. Overall, a slight increase in bias was observed when genomic information was included, whereas dispersion of breeding values was greatly reduced. Combined postweaning survival presented higher direct heritability than in the preweaning phases and the highest prediction accuracy among all evaluated production phases, therefore standing out as a candidate trait for improving survival. Survival is a complex trait with low heritability; however, important genetic gains can still be obtained, especially under a genomic prediction framework.     

Autoregressive and random regression test-day models for multiple lactations in genetic evaluation of Brazilian Holstein cattle

Autoregressive (AR) and random regression (RR) models were fitted to test-day records from the first three lactations of Brazilian Holstein cattle with the objective of comparing their efficiency for national genetic evaluations. The data comprised 4,142,740 records of milk yield (MY) and somatic cell score (SCS) from 274,335 cows belonging to 2,322 herds. Although heritabilities were similar between models and traits, additive genetic variance estimates using AR were 7.0 (MY) and 22.2% (SCS) higher than those obtained from RR model. On the other hand, residual variances were lower in both traits when estimated through AR model. The rank correlation between EBV obtained from AR and RR models was 0.96 and 0.94 (MY) and 0.97 and 0.95 (SCS), respectively, for bulls (with 10 or more daughters) and cows. Estimated annual genetic gains for bulls (cows) obtained using AR were 46.11 (49.50) kg for MY and −0.019 (−0.025) score for SCS; whereas using RR these values were 47.70 (55.56) kg and −0.022 (−0.028) score. Akaike information criterion was lower for AR in both traits. Although AR model is more parsimonious, RR model assumes genetic correlations different from the unity within and across lactations. Thus, when these correlations are relatively high, these models tend to yield to similar predictions; otherwise, they will differ more and RR model would be theoretically sounder.     

Linkage fine-mapping and QTLs affecting morpho-agronomic traits of a Mesoamerican × Andean RIL common bean population

This paper proposes the construction of a genetic linkage map with 376 recombinant inbred lines (RILs) derived from a cross between Mesoamerican?×?Andean common bean (Phaseolus vulgaris L) parents based on single nucleotide polymorphism (SNP) markers; and to detect quantitative trait loci (QTLs) associated with seven morpho-agronomic traits: number of days to flowering (DF), number of days to maturity (DM) or crop cycle; plant architecture (ARC); seed yield (YLD); degree of seed flatness (SF); seed shape (SS); and 100-seed weight (SW). A total of 3060 polymorphic SNP markers were used and 2041 segregated at a 1:1 ratio in the RIL population, as expected. These markers were subjected to linkage analysis in each chromosome. The genetic linkage analysis resulted in linkage maps with a total of 1962 markers spanning 1079.21 cM. A total of 29 QTLs associated with seven morpho-agronomic traits were detected on the 11 chromosomes, which explained between 3.83 and 32.92% of the phenotypic variation in DF. A total of 18 candidate genes associated with the detected QTLs were identified and related with biological processes, molecular functions and cellular components.     

Impact of including information from bulls and their daughters in the training population of multiple‐step genomic evaluations in dairy cattle: A simulation study

The objective of this study was to investigate the impact of accounting for parent average (PA) and genotyped daughters’ average (GDA) on the estimation of deregressed estimated breeding values (dEBVs) used as pseudo‐phenotypes in multiple‐step genomic evaluations. Genomic estimated breeding values (GEBVs) were predicted, in eight different simulated scenarios, using dEBVs calculated based on four methods. These methods included PA and GDA in the dEBV (VR) or only GDA (VRpa) and excluded both PA and GDA from the dEBV with either all information or only information from PA and GDA (JA and NEW, respectively). In general, VR and NEW showed the lowest and highest GEBV reliabilities across scenarios, respectively. Among all deregression methods, VRpa and NEW provided the most consistent bias estimates across the majority of scenarios, and they significantly yielded the least biased GEBVs. Our results indicate that removing PA and GDA information from dEBVs used in multiple‐step genomic evaluations can increase the reliability of GEBVs, when both bulls and their daughters are included in the training population.     

Efficiency of low heritability QTL mapping under high SNP density

The efficiency of quantitative trait locus (QTL) mapping methods needs to be investigated assuming high single nucleotide polymorphism (SNP) density and low heritability QTLs. This study assessed the efficiency of the least squares, maximum likelihood, and Bayesian approaches for QTL mapping assuming high SNP density and low heritability QTLs. We simulated 50 samples of 400 F₂ individuals, which were genotyped for 1000 SNPs (average density of one SNP/centiMorgan) and phenotyped for three traits controlled by 12 QTLs and 88 minor genes. The genes were randomly distributed in the regions covered by the SNPs along ten chromosomes. The QTL heritabilities ranged from approximately 1–2% and the sample sizes were 200 and 400. The power of QTL detection ranged from 30 to 60%, the false discovery rate (FDR) ranged from only 0.5–1.2%, and the bias in the QTL position ranged from 4 to 6 cM. The QTL mapping efficiency was not influenced by the degree of dominance. The statistical approaches were comparable regarding the FDR. Regression-based and simple interval mapping methods showed equivalent power of QTL detection and mapping precision. Compared to interval mapping, the inclusive composite interval mapping provided slightly greater QTL detection power and mapping precision only for the intermediate and high heritability QTLs. By maximizing the prior number of QTLs, the Bayesian analysis provided the greatest power of QTL detection. No method proved to be superior.