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.     

Differentially expressed genes identified through RNA‐seq with extreme values of principal components for beef fatty acid in Nelore cattle

The aim of this study was to identify differentially expressed genes (DEG) in the Longissimus thoracis muscle of Nelore cattle related to fatty acid (FA) profile through RNA sequencing and principal component analysis (PCA). Two groups of 10 animals each were selected containing PC1 and PC2 extreme DEG values (HIGH × LOW) for each FA group. The intramuscular fat (IMF) was compared between cluster groups by ANOVA, and only the sum of monounsaturated FA (MUFA) and ω3 showed significant differences (p < .05). Interestingly, the highest percentage (95%) of phenotypic variation explained by the sum of the first two PC was observed for ω3, which also displayed the lowest number of DEG (n = 1). The lowest percentage (59%) was observed for MUFA, which also revealed the largest number of DEG (n = 66). Since only MUFA and ω3 exhibited significant differences between cluster groups, we can conclude that the differences observed for the remaining groups are not due to the percentage of IMF. Several genes that have been previously associated with meat quality and FA traits were identified as DEG in this study. The functional analysis revealed one KEGG pathway and eight GO terms as significant (p < .05), in which we highlighted the purine metabolism, glycolytic process, adenosine triphosphate binding and bone development. These results strongly contribute to the knowledge of the biological mechanisms involved in meat FA profile of Nelore cattle.     

Risk factors associated with sero-positivity to Toxoplasma gondii in captive neotropical felids from Brazil

From September 1995 to February 2001, blood samples were collected from 865 neotropical felids belonging to 8 different species. These animals were housed in 86 institutions located in 78 cities of 20 Brazilian states. Our goal was to identify the risk factors associated with sero-positivity to Toxoplasma gondii in captive neotropical felids from Brazil. All serum samples were tested by the modified agglutination test (MAT), using formalin-fixed whole tachyzoites and mercaptoethanol. For each animal an individual questionnaire was filled with questions about tattoo number, felid species, age, sex, origin, number of animals in the group, introduction of new animals in the group, time in the institution, eating meat previously frozen for a period <7 days in the last 6 months, eating meat of run-over or euthanized animals in the last 6 months, predation of rodents or birds in the last 6 months and presence of domestic cats near the enclosures in the last 6 months. The total sero-prevalence was 55% (95% CI: 52%, 57%). We estimated a prevalence of 46% (95% CI: 40%, 54%) for jaguarundi (Puma yagouaroundi); 58% (95% CI: 53%, 63%) for ocelot (Leopardus pardalis); 50% (95% CI: 45%, 56%) for oncilla (L. tigrinus); 54% (95% CI: 46%, 62%) for margay (L. wiedii); 12% (95% CI: 4%, 31%) for Pampas-cat (L. colocolo); 83% (95% CI: 65%, 93%) for Geoffroy's-cat (L. geoffroyi); 64% (95% CI: 50%, 68%) for jaguar (Panthera onca) and 48% (95% CI: 42%, 54%) for puma (Puma concolor). Multiple logistic regression was used to evaluate the association between the variables in the questionnaire and sero-positivity to T. gondii. We concluded that the independent risk factors for toxoplasmosis were: age >3 years (OR = 4.75 [2.75; 8.2]), eating meat previously frozen for a period <7 days (OR = 2.23 [1.24; 4.01]), and consumption of animals that were run-over or euthanized (OR = 1.64; [1.14; 2.37]).     

Detection of Ecballium elaterium in hedgerow olive orchards using a low-cost uncrewed aerial vehicle and open-source algorithms

BACKGROUND

Ecballium elaterium (common name: squirting cucumber) is an emerging weed problem in hedgerow or superintensive olive groves under no tillage. It colonizes the inter-row area infesting the natural or sown cover crops, and is considered a hard-to-control weed. Research in other woody crops has shown E. elaterium has a patchy distribution, which makes this weed susceptible to design a site-specific control strategy only addressed to E. elaterium patches. Therefore, the aim of this work was to develop a methodology based on the analysis of imagery acquired with an uncrewed aerial vehicle (UAV) to detect and map E. elaterium infestations in hedgerow olive orchards.

RESULTS

The study was conducted in two superintensive olive orchards, and the images were taken using a UAV equipped with an RGB sensor. Flights were conducted on two dates: in May, when there were various weeds infesting the orchard, and in September, when E. elaterium was the only infesting weed. UAV-orthomosaics in the first scenario were classified using random forest models, and the orthomosaics from September with E. elaterium as the only weed, were analyzed using an unsupervised algorithm. In both cases, the overall accuracies were over 0.85, and the producer's accuracies for E. elaterium ranged between 0.74 and 1.00.

CONCLUSION

These results allow the design of a site-specific and efficient herbicide control protocol which would represent a step forward in sustainable weed management. The development of these algorithms in free and open-source software fosters their application in small and medium farms. © 2022 The Authors. Pest Management Science published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.     

Genomic prediction for meat and carcass traits in Nellore cattle using a Markov blanket algorithm

This study was carried out to evaluate the advantage of preselecting SNP markers using Markov blanket algorithm regarding the accuracy of genomic prediction for carcass and meat quality traits in Nellore cattle. This study considered 3675, 3680, 3660 and 524 records of rib eye area (REA), back fat thickness (BF), rump fat (RF), and Warner–Bratzler shear force (WBSF), respectively, from the Nellore Brazil Breeding Program. The animals have been genotyped using low-density SNP panel (30 k), and subsequently imputed for arrays with 777 k SNPs. Four Bayesian specifications of genomic regression models, namely Bayes A, Bayes B, Bayes Cπ and Bayesian Ridge Regression methods were compared in terms of prediction accuracy using a five folds cross-validation. Prediction accuracy for REA, BF and RF was all similar using the Bayesian Alphabet models, ranging from 0.75 to 0.95. For WBSF, the predictive ability was higher using Bayes B (0.47) than other methods (0.39 to 0.42). Although the prediction accuracies using Markov blanket of SNP markers were lower than those using all SNPs, for WBSF the relative gain was lower than 13%. With a subset of informative SNPs markers, identified using Markov blanket, probably, is possible to capture a large proportion of the genetic variance for WBSF. The development of low-density and customized arrays using Markov blanket might be cost-effective to perform a genomic selection for this trait, increasing the number of evaluated animals, improving the management decisions based on genomic information and applying genomic selection on a large scale.     

Contrasting edge and pasture matrix effects on ant diversity from fragmented landscapes across multiple spatial scales

Landscape Ecology - Harmful effects of habitat loss and fragmentation can be detected across multiple spatial scales, yet most studies that aim to characterize these effects take place at a single...     

Exploring the common ground of landscape ecology and landscape archaeology through a case study from eastern Anatolia,Turkey

Landscape Ecology - Landscape archaeology has a lot to offer to landscape ecology, being an interdisciplinary approach that emphasizes the study of long-term human–environment dynamics. We...