Genetic Diversity in Mycobacterium tuberculosis Clinical Isolates and Resulting Outcomes of Tuberculosis Infection and Disease.

Tuberculosis claims more human lives than any other bacterial infectious disease and represents a clear and present danger to global health as new tools for vaccination, treatment, and interruption of transmission have been slow to emerge. Additionally, tuberculosis presents with notable clinical heterogeneity, which complicates diagnosis, treatment, and the establishment of nonrelapsing cure. How this heterogeneity is driven by the diversity ofclinical isolates of the causative agent, Mycobacterium tuberculosis, has recently garnered attention. Herein, we review advances in the understanding of how naturally occurring variation in clinical isolates affects transmissibility, pathogenesis, immune modulation, and drug resistance. We also summarize how specific changes in transcriptional responses can modulate infection or disease outcome, together with strain-specific effects on gene essentiality. Further understanding of how this diversity of M. tuberculosis isolates affects disease and treatment outcomes will enable the development of more effective therapeutic options and vaccines for this dreaded disease.

The Content of Volatile Organic Compounds in Calypogeia suecica (Calypogeiaceae, Marchantiophyta) Confirms Genetic Differentiation of This Liverwort Species into Two Groups.

Calypogeia is a genus of liverworts in the family Calypogeiaceae. The subject of this study was Calypogeia suecica. Samples of the liverwort Calypogeia suecica were collected from various places in southern Poland. A total of 25 samples were collected in 2021, and 25 samples were collected in 2022. Volatile organic compounds (VOCs) from liverworts were analyzed by gas chromatography-mass spectrometry (GC-MS). A total of 107 compounds were detected, of which 38 compounds were identified. The identified compounds were dominated by compounds from the sesquiterpene group (up to 34.77%) and sesquiterpenoids (up to 48.24%). The tested samples of Calypogeia suecica also contained compounds belonging the aromatic classification (up to 5.46%), aliphatic hydrocarbons (up to 1.66%), and small amounts of monoterpenes (up to 0.17%) and monoterpenoids (up to 0.30%). Due to the observed differences in the composition of VOCs, the tested plant material was divided into two groups, in accordance with genetic diversity.

Carcass traits and morphometry, typification of the Longissimus dorsi muscle and non-carcass components of hair lambs: can biscuit bran completely replace corn? A machine learning approach.

Biscuit bran (BB) is a co-product with worldwide distribution, with Brazil as the second largest cookie producer in the world with 1,157,051 tons. We evaluate the impact of completely replacing corn with BB on the characteristics and morphometry of carcass of purebred and crossbred Morada Nova lambs using machine learning techniques as an auxiliary method. Twenty male lambs from two genetic groups (GG) were used: purebred red-coated Morada Nova (MNR) and crossbred MNR × white-coated Morada Nova (MNF1). Supervised and unsupervised machine learning techniques were used. No interaction (P > 0.05) was observed between diets (D) and genetic groups (GG) and no simple isolated effect was observed for carcass characteristics, qualitative-quantitative typification of the Longissimus dorsi muscle, weight of non-carcass components, weight and yield of commercial cuts and carcass morphometric measurements. The formation of two horizontal clusters was verified: (i) crossed lambs with corn and BB and (ii) purebred lambs fed corn and BB. Vertically, three clusters were formed based on carcass and meat characteristics of native lambs: (i) thermal insulation, body capacity, true yield, and commercial cuts; (ii) choice, performance, physical carcass traits, and palatability; and (iii) yield cuts and non-carcass components. The heatmap also allowed us to observe that pure MN lambs had a greater body capacity when fed BB, while those fed corn showed superiority in commercial cuts, true yields, and non-carcass components. Crossbred lambs, regardless of diet, showed a greater association of physical characteristics of the carcass, performance, palatability, and less noble cuts. Crossbred lambs, regardless of diet, showed a greater association of physical characteristics of the carcass, performance, palatability, and less noble cuts. BB can be considered an alternative energy source in total replacement of corn. Integrating of machine learning techniques is a useful statistical tool for studies with large numbers of variables, especially when it comes to analyzing complex data with multiple effects in the search for data patterns and insights in decision-making on the farm.

Diversity and Virulence of Streptomyces spp. Causing Potato Common Scab in Prince Edward Island, Canada.

Common scab (CS) is a potato disease that significantly decreases the market value of potato tubers after the development of necrotic lesions on their surface. Streptomyces scabiei is the main causal agent of CS; however, other closely related species, including S. acidiscabies and S. turgidiscabies, have also been shown to cause the disease. In this study, we characterized the genetic and phenotypic diversity of Streptomyces spp. causing CS in Prince Edward Island, the main potato-producing province in Canada. Two hundred and ninety-six pathogenic Streptomyces spp. isolates were retrieved from diseased tubers harvested from six fields located across a longitudinal geographical gradient. Genome fingerprinting analyses using repetitive elements PCR (ERIC- and BOX-PCR) revealed 14 distinct genetic groups. Thirteen groups were taxonomically affiliated with S. scabiei, whereas the fourteenth group was affiliated with S. acidiscabies. Their geographical distribution was characterized and revealed that on average between six and eight different genetic groups were detected per field, with variable abundance. Virulence assays showed strong differences in virulence between the genetic groups, ranging from low to highly virulent. Interestingly, pathogenic Streptomyces spp. populations in each field seem to be dominated by the most virulent genetic groups. The results obtained will contribute to better understanding of the population dynamic of pathogenic Streptomyces spp. causing CS of potato and promoting the development of more efficient detection and intervention tools to manage this important potato disease.

Genetic characterization of Flavobacterium columnare isolates from the Pacific Northwest, USA.

Flavobacterium columnare is the causative agent of columnaris disease. Previous work has demonstrated a high degree of genetic variability among F. columnare isolates, identifying 4 genetic groups (GGs) with some host associations. Herein, a total of 49 F. columnare isolates were characterized, the majority of which were collected from 15 different locations throughout the US Pacific Northwest. Most isolates were collected from 2015-2018 and originated from disease outbreaks in salmonid hatcheries and rearing ponds, sturgeon hatcheries and ornamental fish. Other isolates were part of collections recovered from 1980-2018. Initial identification was confirmed by F. columnare species-specific qPCR. Study isolates were further characterized using a multiplex PCR that differentiates between the 4 currently recognized F. columnare GGs. Multiplex PCR results were supported by repetitive sequence-mediated PCR fingerprinting and gyrB sequence analysis. F. columnare GG1 was the most prevalent (83.7%, n = 41/49), represented by isolates from salmonids (n = 32), white sturgeon (n = 2), channel catfish (n = 1), ornamental goldfish (n = 1), koi (n = 3), wild sunfish (n = 1) and 1 unknown host. Six isolates (12.2%, n = 6/49) were identified as GG3, which were cultured from rainbow trout (n = 3) and steelhead trout (n = 3). Two isolates were identified as GG2 (4.1%, n = 2/49) and were from ornamental fish. No GG4 isolates were cultured in this study. The biological significance of this genetic variability remains unclear, but this variation could have significant implications for fish health management. The results from this study provide baseline data for future work developing strategies to ameliorate columnaris-related losses in the US Pacific Northwest.

Practical implementation of genetic groups in single-step genomic evaluations with Woodbury matrix identity-based genomic relationship inverse.

The growing amount of genomic information in dairy cattle has increased computational and modeling challenges in the single-step evaluations. The computational challenges are due to the dense inverses of genomic (G) and pedigree (A22) relationship matrices of genotyped animals in the single-step mixed model equations. An equivalent mixed model equation is given by single-step genomic BLUP that are based on the T matrix (ssGTBLUP), where these inverses are avoided by expressing G-1 through a product of 2 rectangular matrices, and (A22)-1 through sparse matrix blocks of the inverse of full relationship matrix A-1. A proper way to account genetic groups through unknown parent groups (UPG) after the Quaas-Pollak transformation (QP) is one key factor in a single-step model. When the UPG effects are incompletely accounted, the iterative solving method may have convergence problems. In this study, we investigated computational and predictive performance of ssGTBLUP with residual polygenic (RPG) effect and UPG. The QP transformation used A-1 and, in the complete form, T and (A22)-1 matrices as well. The models were tested with official Nordic Holstein milk production test-day data and model. The results show that UPG can be easily implemented in ssGTBLUP having RPG. The complete QP transformation was computationally feasible when preconditioned conjugate gradient iteration and iteration on data without explicitly setting up G or A22 matrices were used. Furthermore, for good convergence of the preconditioned conjugate gradient method, a complete QP transformation was necessary.

ggroups: an R package for pedigree and genetic groups data.

BACKGROUND: R is a multi-platform statistical software and an object oriented programming language. The package archive network for R provides CRAN repository that features over 15,000 free open source packages, at the time of writing this article (https://cran.r-project.org/web/packages, accessed in October 2019). The package ggroups is introduced in this article. The purpose of this package is providing functions for checking and processing the pedigree, calculation of the additive genetic relationship matrix and its inverse, which are used to study the population structure and predicting the genetic merit of animals. Calculation of the dominance relationship matrix and its inverse are also covered. A concept in animal breeding is genetic groups, which is about the inequality of the average genetic merits for groups of unknown parents. The package provides functions for the calculation of the matrix of genetic group contributions (Q). Calculating Q is computationally demanding, and depending on the size of the pedigree and the number of genetic groups, it might not be feasible using personal computers. Therefore, a computationally optimised function and its parallel processing alternative are provided in the package. RESULTS: Using sample data, outputs from different functions of the package were presented to illustrate a real experience of working with the package. CONCLUSIONS: The presented R package is a free and open source tool mainly for quantitative geneticists and ecologists, who deal with pedigree data. It provides numerous functions for handling pedigree data, and calculating various pedigree-based matrices. Some of the functions are computationally optimised for large-scale data.

Modelling of population structure through contemporary groups in genetic evaluation.

BACKGROUND: Forest trees can occupy extensive geography and environmentally highly variable areas which result in high genetic variability in the direction of pressure from natural selection. At the same time, the majority of conifer species are wind-pollinated from both short and long distances, resulting in wide-spread gene flow, which can lead to maladaptation to local conditions. Quantitative analyses of provenance/progeny tests correct for genetic differences between populations to ensure unbiased genetic parameters are obtained. Commonly, the provenance effect is fitted as a fixed term or can be implemented as a contemporary group in the pedigree. RESULTS: The use of a provenance effect, either as a fixed term or as the same contemporary groups in both maternal and paternal sides of the pedigree, resulted in fairly similar precision of genetic parameters in our case. However, when we developed a phantom contemporary group for the paternal side of the pedigree that considered a different genetic quality of pollen compared with the maternal contribution from trees in the local environment, the model fit and accuracy of breeding values increased. CONCLUSION: Consideration of the mating dynamics and the vector of gene flow are important factors in modelling contemporary genetic groups, particularly when implementing pedigrees within a mixed model framework to obtain unbiased estimates of genetic parameters. This approach is especially important in traits involved in local adaptation.

Single step genomic evaluation for female fertility in Nordic Red dairy cattle.

Joint Nordic (Denmark, Finland, Sweden) genetic evaluation of female fertility is currently based on the multiple trait multilactation animal model (BLUP). Here, single step genomic model (ssGBLUP) was applied for the Nordic Red dairy cattle fertility evaluation. The 11 traits comprised of nonreturn rate and days from first to last insemination in heifers and first three parities, and days from calving to first insemination in the first three parities. Traits had low heritabilities (0.015-0.04), but moderately high genetic correlations between the parities (0.60-0.88). Phenotypic data included 4,226,715 animals with records and pedigree 5,445,392 animals. Unknown parents were assigned into 332 phantom parent groups (PPG). In mixed model equations animals were associated with PPG effects through the pedigree or both the pedigree and genomic information. Genotype information of 46,914 SNPs was available for 33,969 animals in the pedigree. When PPG used pedigree information only, BLUP converged after 2,420 iterations whereas the ssGBLUP evaluation needed over ten thousand iterations. When the PPG effects were solved accounting both the pedigree and the genomic information, the ssGBLUP model converged after 2,406 iterations. Also, with the latter model breeding values by ssGBLUP and BLUP became more consistent and genetic trends followed each other well. Models were validated using forward prediction of the young bulls. Reliabilities and variance inflation of predicted genomic breeding values (values for parent averages in brackets) for the 11 traits ranged 0.22-0.31 (0.10-0.27) and 0.81-0.95 (0.83-1.06), respectively. The ssGBLUP model gave always higher validation reliabilities than BLUP, but largest increases were for the cow fertility traits.

Genetically Different Highly Pathogenic Avian Influenza A(H5N1) Viruses in West Africa, 2015.

To trace the evolution of highly pathogenic influenza A(H5N1) virus in West Africa, we sequenced genomes of 43 viruses collected during 2015 from poultry and wild birds in 5 countries. We found 2 co-circulating genetic groups within clade 2.3.2.1c. Mutations that may increase adaptation to mammals raise concern over possible risk for humans.

Is Pairing with a Relative Heritable? Estimating Female and Male Genetic Contributions to the Degree of Biparental Inbreeding in Song Sparrows (Melospiza melodia).

The degree of inbreeding expressed within populations can profoundly shape evolutionary dynamics. The degree to which individuals inbreed is frequently assumed to evolve in response to selection, for example, resulting from inbreeding depression. Such evolutionary responses require additive genetic variance (VA) in the degree to which individuals inbreed. However, the magnitude of VA in the degree of biparental inbreeding has never been estimated. We devised a quantitative genetic model to estimate sex-specific VA in the degree to which individuals inbreed while accounting for effects of individuals' own coefficients of inbreeding and genetic effects stemming from immigration. We applied this model to the degree of inbreeding expressed through social pairing in free-living song sparrows (Melospiza melodia). Estimates of VA for both sexes appreciably exceeded 0 and the cross-sex genetic covariance was strongly positive, creating substantial total VA in the degree of inbreeding. Our analyses also revealed inbreeding depression in the degree of inbreeding, such that more inbred individuals paired with closer relatives, and immigrant effects, such that individuals with greater genomic contributions from immigrants paired with more distant relatives. We thereby demonstrate that the degree of biparental inbreeding can show substantial VA in nature and might consequently evolve in response to selection.

Genomic selection to resistance to Stenocarpella maydis in maize lines using DArTseq markers.

BACKGROUND: The identification of lines resistant to ear diseases is of great importance in maize breeding because such diseases directly interfere with kernel quality and yield. Among these diseases, ear rot disease is widely relevant due to significant decrease in grain yield. Ear rot may be caused by the fungus Stenocarpella maydi; however, little information about genetic resistance to this pathogen is available in maize, mainly related to candidate genes in genome. In order to exploit this genome information we used 23.154 Dart-seq markers in 238 lines and apply genome-wide selection to select resistance genotypes. We divide the lines into clusters to identify groups related to resistance to Stenocarpella maydi and use Bayesian stochastic search variable approach and rr-BLUP methods to comparate their selection results. RESULTS: Through a principal component analysis (PCA) and hierarchical clustering, it was observed that the three main genetic groups (Stiff Stalk Synthetic, Non-Stiff Stalk Synthetic and Tropical) were clustered in a consistent manner, and information on the resistance sources could be obtained according to the line of origin where populations derived from genetic subgroup Suwan presenting higher levels of resistance. The ridge regression best linear unbiased prediction (rr-BLUP) and Bayesian stochastic search variable (BSSV) models presented equivalent abilities regarding predictive processes. CONCLUSION: Our work showed that is possible to select maize lines presenting a high resistance to Stenocarpella maydis. This claim is based on the acceptable level of predictive accuracy obtained by Genome-wide Selection (GWS) using different models. Furthermore, the lines related to background Suwan present a higher level of resistance than lines related to other groups.

Genetic and Phenotypic Evaluation of European Maize Landraces as a Tool for Conservation and Valorization of Agrobiodiversity.

The ECPGR European Evaluation Network (EVA) for Maize involves genebanks, research institutions, and private breeding companies from nine countries focusing on the valorization of maize genetic resources across Europe. This study describes a diverse collection of 626 local landraces and traditional varieties of maize (Zea mays L.) from nine European genebanks, including criteria for selection of the collection and its genetic and phenotypic diversity. High-throughput pool genotyping grouped the landraces into nine genetic groups with a threshold of 0.6 admixture, while 277 accessions were designated admixed and likely to have resulted from previous breeding activities. The grouping correlated well with the geographic origins of the collection, also reflecting the various pathways of introduction of maize to Europe. Phenotypic evaluations of 588 accessions for flowering time and plant architecture in multilocation trials over three years confirmed the great diversity within the collection, although phenotypic clusters only partially correlated with the genetic grouping. The EVA approach promotes conservation of genetic resources and opens an opportunity to increase genetic variability for developing improved varieties and populations for farmers, with better adaptation to specific environments and greater tolerance to various stresses. As such, the EVA maize collection provides valuable sources of diversity for facing climate change due to the varieties' local adaptation.

Genetic Characterization of African Swine Fever Italian Clusters in the 2022-2023 Epidemic Wave by a Multi-Gene Approach.

The first report of African swine fever virus (ASFV) genotype II in Italy in 2022 marked the beginning of a significant invasion in at least eight Italian regions with different infection clusters. In this study, we used the multi-gene approach to investigate the epidemiological associations between ASFV strains causing cases and outbreaks in wild boar and pigs in Italy from January 2022 to the end of 2023. Our results confirm that all the tested ASFV-positive Italian samples belonged to genotype II and show high homology with genotype II ASFV sequences previously collected in Eurasian countries. Molecular characterization revealed the presence of four genetic groups in Italy. The majority of African swine fever (ASF) samples analyzed in the current study (72%) belonged to genetic group 3, which was the most representative in Europe. The results also provide evidence of the prevalence of genetic group 19 (15.9%). In addition, we identified new putative genetic groups, genetic group 25 (9.1%) and genetic group 26 (3.0%), which have never been described before. This is the first detailed report on the molecular characterization of more than 130 ASFV strains circulating in Italy.

A multi gene-approach genotyping method identifies 24 genetic clusters within the genotype II-European African swine fever viruses circulating from 2007 to 2022.

Introduction: African swine fever (ASF) is a contagious viral disease of pigs and wild boar that poses a major threat to the global swine industry. The genotype II African swine fever virus (ASFV) entered the European Union (EU) in 2014 and since then fourteen countries have been affected, Italy and North Macedonia being the last in 2022. While whole genome sequencing remains the gold standard for the identification of new genetic markers, sequencing of multiple loci with significant variations could be used as a rapid and cost-effective alternative to track outbreaks and study disease evolution in endemic areas. Materials and methods: To further our understanding of the epidemiology and spread of ASFV in Europe, 382 isolates collected during 2007 to 2022 were sequenced. The study was initially performed by sequencing the central variable region (CVR), the intergenic region (IGR) between the I73R and I329L genes and the O174L and K145R genes. For further discrimination, two new PCRs were designed to amplify the IGR between the 9R and 10R genes of the multigene family 505 (MGF505) and the IGR between the I329L and I215L genes. The sequences obtained were compared with genotype II isolates from Europe and Asia. Results: The combination of the results obtained by sequencing these variable regions allowed to differentiate the European II-ASFV genotypes into 24 different groups. In addition, the SNP identified in the IGR I329L-I215L region, not previously described, grouped the viruses from North Macedonia that caused the 2022 outbreaks with viruses from Romania, Bulgaria, Serbia and Greece, differentiating from other genotype II isolates present in Europe and Asia. Furthermore, tandem repeat sequence (TRS) within the 9R-10R genes of the multigene family 505 (MGF505) revealed eight different variants circulating. Discussion: These findings describe a new multi-gene approach sequencing method that can be used in routine genotyping to determine the origin of new introductions in ASF-free areas and track infection dynamics in endemic areas.

The fish pathogen Flavobacterium columnare represents four distinct species: Flavobacterium columnare, Flavobacterium covae sp. nov., Flavobacterium davisii sp. nov. and Flavobacterium oreochromis sp. nov., and emended description of Flavobacterium columnare.

Flavobacterium columnare is the causative agent of columnaris disease in freshwater fish and four discrete genetic groups exist within the species, suggesting that the species designation requires revision. The present study determined the taxonomic status of the four genetic groups of F. columnare using polyphasic and phylogenomic approaches and included five representative isolates from each genetic group (including type strain ATCC 23463T; genetic group 1). 16S rRNA gene sequence analysis revealed genetic group 2 isolate AL-02-36T, genetic group 3 isolate 90-106T, and genetic group 4 isolate Costa Rica 04-02-TNT shared less than <98.8 % sequence identity to F. columnare ATCC 23463T. Phylogenetic analyses of 16S rRNA and gyrB genes using different methodologies demonstrated the four genetic groups formed well-supported and distinct clades within the genus Flavobacterium. The average nucleotide identity (ANI) and digital DNA-DNA hybridization (GGDC) values between F. columnare ATCC 23463T, genetic group 2 isolate AL-02-36T, genetic group 3 isolate 90-106T, and genetic group 4 isolate Costa Rica 04-02-TNT were less than 90.84% and 42.7%, respectively. Biochemical and physiological characteristics were similar among the four genetic groups; however, quantitative differences in fatty acid profiles were detected and MALDI-TOF analyses demonstrated numerous distinguishing peaks unique to each genetic group. Chemotaxonomic, MALDI-TOF characterization and ANI/GGDC calculations afforded differentiation between the genetic groups, indicating each group is a discrete species. Herein, the names F. covae sp. nov. (AL-02-36T), F. davisii sp. nov. (90-106T), and F. oreochromis sp. nov. (Costa Rica 04-02-TNT) are proposed to represent genetic groups 2, 3, and 4, respectively.

Single-step genomic BLUP with many metafounders.

Single-step genomic BLUP (ssGBLUP) model for routine genomic prediction of breeding values is developed intensively for many dairy cattle populations. Compatibility between the genomic (G) and the pedigree (A) relationship matrices remains an important challenge required in ssGBLUP. The compatibility relates to the amount of missing pedigree information. There are two prevailing approaches to account for the incomplete pedigree information: unknown parent groups (UPG) and metafounders (MF). unknown parent groups have been used routinely in pedigree-based evaluations to account for the differences in genetic level between groups of animals with missing parents. The MF approach is an extension of the UPG approach. The MF approach defines MF which are related pseudo-individuals. The MF approach needs a Γ matrix of the size number of MF to describe relationships between MF. The UPG and MF can be the same. However, the challenge in the MF approach is the estimation of Γ having many MF, typically needed in dairy cattle. In our study, we present an approach to fit the same amount of MF as UPG in ssGBLUP with Woodbury matrix identity (ssGTBLUP). We used 305-day milk, protein, and fat yield data from the DFS (Denmark, Finland, Sweden) Red Dairy cattle population. The pedigree had more than 6 million animals of which 207,475 were genotyped. We constructed the preliminary gamma matrix (Γ pre ) with 29 MF which was expanded to 148 MF by a covariance function (Γ 148). The quality of the extrapolation of the Γ pre matrix was studied by comparing average off-diagonal elements between breed groups. On average relationships among MF in Γ 148 were 1.8% higher than in Γ pre . The use of Γ 148 increased the correlation between the G and A matrices by 0.13 and 0.11 for the diagonal and off-diagonal elements, respectively. [G]EBV were predicted using the ssGTBLUP and Pedigree-BLUP models with the MF and UPG. The prediction reliabilities were slightly higher for the ssGTBLUP model using MF than UPG. The ssGBLUP MF model showed less overprediction compared to other models.

Correcting for base-population differences and unknown parent groups in single-step genomic predictions of Norwegian Red cattle.

Bias and inflation in genomic evaluation with the single-step methods have been reported in several studies. Incompatibility between the base-populations of the pedigree-based and the genomic relationship matrix (G) could be a reason for these biases. Inappropriate ways of accounting for missing parents could be another reason for biases in genetic evaluations with or without genomic information. To handle these problems, we fitted and evaluated a fixed covariate (J) that contains ones for genotyped animals and zeros for unrelated non-genotyped animals, or pedigree-based regression coefficients for related non-genotyped animals. We also evaluated alternative ways of fitting the J covariate together with genetic groups on biases and stability of breeding value estimates, and of including it into G as a random effect. In a whole vs. partial data set comparison, four scenarios were investigated for the partial data: genotypes missing, phenotypes missing, both genotypes and phenotypes missing, and pedigree missing. Fitting J either as fixed or random reduced level-bias and inflation and increased stability of genomic predictions as compared to the basic model where neither J nor genetic groups were fitted. In most models, genomic predictions were largely biased for scenarios with missing genotype and phenotype information. The biases were reduced for models which combined group and J effects. Models with these corrected group covariates performed better than the recently published model where genetic groups were encapsulated and fitted as random via the Quaas and Pollak transformation. In our Norwegian Red cattle data, a model which combined group and J regression coefficients was preferred because it showed least bias and highest stability of genomic predictions across the scenarios.

Our study dealt with strategies on how to reduce biases (inflation and level-bias) and improve a parameter related to accuracy (stability) of genomic predictions of breeding values that combine genotyped and non-genotyped animals, which are denoted as single-step genomic predictions. We tried to remedy incompatibilities between the pedigree- and the genomics-based relationships matrices by fitting a covariate (J) that corrects for base-population differences that may occur between both relationship matrices. We also evaluated alternative ways to combine the J covariate and genetic group effects to account for missing parental information, which often occurs in practical breeding schemes. We found that fitting J either as fixed or random reduced level-bias and inflation and increased stability of genomic predictions as compared to the basic model where neither J nor genetic groups were fitted. Level-biases and inflation of breeding value estimates were reduced, and stability of genomic predictions improved for models which combined group and J effects. A model which fits group regression coefficients minus the part that could be explained from pedigree was recommended because it showed least bias and highest stability across the scenarios and has theoretical justification.

Basal Reactivity Evaluated by Infrared Thermography in the "Caballo de Deporte Español" Horse Breed According to Its Coat Color.

Horses have been valued for their diversity of coat color since prehistoric times. In particular, the pleiotropic effect that coat color genes have on behavior determines the way the horse perceives and reacts to its environment. The primary aim of this study was to evaluate the influence of coat color on basal reactivity assessed with infrared thermography as eye temperature at rest (ETR), determine their relation with the results obtained by these horses in Show Jumping competitions and to estimate the genetic parameters for this variable to test its suitability for genetic selection. A General Linear Model (GLM) and Duncan post-hoc analysis indicated differences in ETR due to coat color, sex, age, location, and breed-group factors. A Spearman's rank correlation of 0.11 (p < 0.05) was found with ranking, indicating that less reactive horses were more likely to achieve better rankings. Heritability values ranged from 0.17 to 0.22 and were computed with a model with genetic groups and a model with residual variance heterogeneity. Breeding values were higher with the last genetic model, thus demonstrating the pleiotropic effect of coat color. These results indicate that ETR has a suitable genetic basis to be used in the breeding program to select for basal reactivity due to coat color.

Accounting for population structure in genomic predictions of Eucalyptus globulus.

Genetic groups have been widely adopted in tree breeding to account for provenance effects within pedigree-derived relationship matrices. However, provenances or genetic groups have not yet been incorporated into single-step genomic BLUP ("HBLUP") analyses of tree populations. To quantify the impact of accounting for population structure in Eucalyptus globulus, we used HBLUP to compare breeding value predictions from models excluding base population effects and models including either fixed genetic groups or the marker-derived proxies, also known as metafounders. Full-sib families from 2 separate breeding populations were evaluated across 13 sites in the "Green Triangle" region of Australia. Gamma matrices (Γ) describing similarities among metafounders reflected the geographic distribution of populations and the origins of 2 land races were identified. Diagonal elements of Γ provided population diversity or allelic covariation estimates between 0.24 and 0.56. Genetic group solutions were strongly correlated with metafounder solutions across models and metafounder effects influenced the genetic solutions of base population parents. The accuracy, stability, dispersion, and bias of model solutions were compared using the linear regression method. Addition of genomic information increased accuracy from 0.41 to 0.47 and stability from 0.68 to 0.71, while increasing bias slightly. Dispersion was within 0.10 of the ideal value (1.0) for all models. Although inclusion of metafounders did not strongly affect accuracy or stability and had mixed effects on bias, we nevertheless recommend the incorporation of metafounders in prediction models to represent the hierarchical genetic population structure of recently domesticated populations.