Explore the vast range of titles available.

Background Runs of homozygosity are long, uninterrupted stretches of homozygous genotypes that enable reliable estimation of levels of inbreeding (i.e., autozygosity) based on high-throughput, chip-based single nucleotide polymorphism (SNP) genotypes. While the theoretical definition of runs of homozygosity is straightforward, their empirical identification depends on the type of SNP chip used to obtain the data and on a number of factors, including the number of heterozygous calls allowed to account for genotyping errors. We analyzed how SNP chip density and genotyping errors affect estimates of autozygosity based on runs of homozygosity in three cattle populations, using genotype data from an SNP chip with 777 972 SNPs and a 50 k chip. Results Data from the 50 k chip led to overestimation of the number of runs of homozygosity that are shorter than 4 Mb, since the analysis could not identify heterozygous SNPs that were present on the denser chip. Conversely, data from the denser chip led to underestimation of the number of runs of homozygosity that were longer than 8 Mb, unless the presence of a small number of heterozygous SNP genotypes was allowed within a run of homozygosity. Conclusions We have shown that SNP chip density and genotyping errors introduce patterns of bias in the estimation of autozygosity based on runs of homozygosity. SNP chips with 50 000 to 60 000 markers are frequently available for livestock species and their information leads to a conservative prediction of autozygosity from runs of homozygosity longer than 4 Mb. Not allowing heterozygous SNP genotypes to be present in a homozygosity run, as has been advocated for human populations, is not adequate for livestock populations because they have much higher levels of autozygosity and therefore longer runs of homozygosity. When allowing a small number of heterozygous calls, current software does not differentiate between situations where these calls are adjacent and therefore indicative of an actual break of the run versus those where they are scattered across the length of the homozygous segment. Simple graphical tests that are used in this paper are a current, yet tedious solution.

Intramuscular fat (IMF) plays an important role in determining meat quality traits such as flavor, tenderness, and juiciness. While numerous studies have investigated the genetic basis of IMF in commercial pig breeds, data on local breeds remain rather limited. In this study, we used RNA-sequencing to characterize the transcriptomic differences between high-IMF and low-IMF Black Slavonian pigs, a native Croatian breed known for superior meat quality. Muscle samples ( Longissimus thoracis et lumborum ) from 14 pigs with divergent IMF levels were collected shortly after slaughter, preserved in liquid nitrogen, and stored at − 80 °C until RNA extraction. Intramuscular fat content was determined from the same muscle 24 h post mortem using the Soxhlet extraction method (ISO 1443:1973). These samples were then analyzed to identify differentially expressed genes (DEGs) and enriched pathways. A total of 519 genes were differentially expressed ( p  ≤ 0.05), with 457 remaining significant after false discovery rate correction. The high-IMF group exhibited upregulation of genes associated with lipid metabolism (e.g., SCD, ADIPOQ, CIDEC, FABP4), PPAR signaling, and adipogenesis, while genes linked to muscle structure and oxidative metabolism were downregulated. Functional enrichment and gene set enrichment analyses highlighted coordinated regulation of pathways related to fatty acid biosynthesis, extracellular matrix (ECM) remodelling, angiogenesis, and Notch signaling. Notably, several ECM-related genes (LAMA1, TIMP4) and angiogenic factors (FGF2, NRP1) were significantly upregulated, suggesting that adipocyte expansion in muscle requires parallel vascular and structural adaptations. Importantly, several of the top 30 DEGs, including EHD2, NRG4, UTRN, FLNA, and HMCN1, represent novel candidate genes not previously linked to IMF in pigs, pointing to potential breed-specific mechanisms. These genes are associated with membrane trafficking, paracrine signalling, cytoskeletal re-modelling, and ECM dynamics. Our findings contribute new molecular insights into IMF regulation in local pig breeds and provide a foundation for developing targeted breeding strategies to improve pork quality through intramuscular fat enhancement.

While mitogenome mutations leading to pathological manifestations are rare, more than 200 such mutations have been described in humans. In contrast, pathogenic mitogenome mutations are rare in domestic animals and have not been described at all in cattle. In the small local Slovenian cattle breed Cika, we identified (next-generation sequencing) two cows with the T10432C mitogenome mutation in the ND4L gene, which corresponds to the human T10663C mutation known to cause Leber’s hereditary optic neuropathy (LHON). Pedigree analysis revealed that the cows in which the mutation was identified belong to two different maternal lineages with 217 individual cows born between 1997 and 2020. The identified mutation and its maternal inheritance were confirmed by Sanger sequencing across multiple generations, whereas no single analysis revealed evidence of heteroplasmy. A closer clinical examination of one cow with the T10432C mutation revealed exophthalmos, whereas histopathological examination revealed retinal ablations, subretinal oedema, and haemorrhage. The results of these analyses confirm the presence of mitochondrial mutation T10432C with homoplasmic maternal inheritance as well as clinical and histopathological signs similar to LHON in humans. Live animals with the mutation could be used as a suitable animal model that can improve our understanding of the pathogenesis of LHON and other mitochondriopathies.

Background The importance of sheep breeding in the Mediterranean part of the eastern Adriatic has a long tradition since its arrival during the Neolithic migrations. Sheep production system is extensive and generally carried out in traditional systems without intensive systematic breeding programmes for high uniform trait production (carcass, wool and milk yield). Therefore, eight indigenous Croatian sheep breeds from eastern Adriatic treated here as metapopulation (EAS), are generally considered as multipurpose breeds (milk, meat and wool), not specialised for a particular type of production, but known for their robustness and resistance to certain environmental conditions. Our objective was to identify genomic regions and genes that exhibit patterns of positive selection signatures, decipher their biological and productive functionality, and provide a \"genomic\" characterization of EAS adaptation and determine its production type. Results We identified positive selection signatures in EAS using several methods based on reduced local variation, linkage disequilibrium and site frequency spectrum (eROHi, iHS, nSL and CLR). Our analyses identified numerous genomic regions and genes (e.g., desmosomal cadherin and desmoglein gene families) associated with environmental adaptation and economically important traits. Most candidate genes were related to meat/production and health/immune response traits, while some of the candidate genes discovered were important for domestication and evolutionary processes (e.g., HOXa gene family and FSIP2 ). These results were also confirmed by GO and QTL enrichment analysis. Conclusions Our results contribute to a better understanding of the unique adaptive genetic architecture of EAS and define its productive type, ultimately providing a new opportunity for future breeding programmes. At the same time, the numerous genes identified will improve our understanding of ruminant (sheep) robustness and resistance in the harsh and specific Mediterranean environment.

Background Genome-wide prediction has become the method of choice in animal and plant breeding. Prediction of breeding values and phenotypes are routinely performed using large genomic data sets with number of markers on the order of several thousands to millions. The number of evaluated individuals is usually smaller which results in problems where model sparsity is of major concern. The LASSO technique has proven to be very well-suited for sparse problems often providing excellent prediction accuracy. Several computationally efficient LASSO algorithms have been developed, but optimization of hyper-parameters can be demanding. Results We have developed a novel automatic adaptive LASSO (AUTALASSO) based on the alternating direction method of multipliers (ADMM) optimization algorithm. The two major hyper-parameters of ADMM are the learning rate and the regularization factor. The learning rate is automatically tuned with line search and the regularization factor optimized using Golden section search. Results show that AUTALASSO provides superior prediction accuracy when evaluated on simulated and real bull data compared to the adaptive LASSO, LASSO and ridge regression implemented in the popular glmnet software. Conclusions The AUTALASSO provides a very flexible and computationally efficient approach to GWP, especially when it is important to obtain high prediction accuracy and genetic gain. The AUTALASSO also has the capability to perform GWAS of both additive and dominance effects with smaller prediction error than the ordinary LASSO.

The primary objective of any conservation breeding program is to preserve the genetic diversity of populations. This objective is a persistent challenge, especially in small populations which are prone to loss of heterozygosity. In this study, we proposed a novel parent-selection strategy aimed at the long-term maintenance of high levels of genetic diversity. Our approach is based on estimating the Probability of Offspring Heterozygosity (POH)—the likelihood that a mating will produce heterozygous offspring—using SNP genotype data. This strategy was evaluated through computer simulations, where parental pairs with the highest POH values were preferentially selected to produce the next generation. Simulations explored the effects of varying the number of breeding pairs, and the number of unlinked SNP markers. Selection based on POH resulted in observed heterozygosity (HOBS) consistently exceeding expected heterozygosity (HEXP), a trend that was sustained for up to 1000 generations. While further evaluation is needed within more complex population genetic frameworks—accounting for linkage disequilibrium, recombination, optimal contribution, and phenotypic selection—our findings highlight the potential of POH as a valuable tool for enhancing genetic diversity in conservation breeding programs.

Background Research into the genetic diversity of honey bee ( Apis mellifera L. ) populations has become increasingly significant in recent decades, primarily due to population declines attributed to human activities and climate change. As a species of great importance, breeding programs that leverage understanding of genomic diversity could offer solutions to mitigate these challenges. The objective of this study was to examine the genomic diversity and population structure of Carniolan honey bees ( Apis mellifera carnica ) using the Illumina SNP chip on a large honey bee sample collected from Central and South-Eastern European countries. The study also aims to offer recommendations for future breeding programs. Results Our analysis involved Discriminant Analysis of Principal Components (DAPC), heterozygosity, admixture analysis, fixation indices (F ST ), Neighbour-Joining tree, gene flow and Isolation-by-distance analysis. DAPC indicated distinct separation between the Carniolan and Italian honey bee ( Apis mellifera ligustica ) populations, whereas the admixture analysis revealed varying levels of gene flow and genetic admixture within the Carniolan honey bee populations, demonstrating closer relationships between specific geographic regions (confirmed by Isolation-by-distance analysis). Furthermore, the research of heterozygosity, genomic inbreeding, pairwise F ST values, and Neighbour-Joining tree provided insights into the patterns of genetic differentiation and similarity among the populations of Carniolan honey bee within its natural habitat. We have observed genetic homogeneity of the Carniolan honey bee population when considered in a broader genetic/geographical context. However, the Carniolan honey bee has sufficient genetic diversity in its geographical home range that needs to be carefully monitored and maintained. Conclusions This study provides important insights into the genetic composition, differentiation, and relationships among Carniolan honey bee populations in Central and South-Eastern European countries. The findings are crucial for conservation efforts, breeding programs, and sustainable beekeeping practices. They emphasise the importance of considering genetic factors and population structure in the breeding and management of honey bees. By understanding these genetic relationships, we can develop strategies to preserve genetic diversity, improve breeding outcomes, and ensure the resilience of honey bee populations in the face of environmental changes and challenges. This knowledge can also inform policy makers and stakeholders on best practices to maintain healthy bee populations, which are vital for ecosystem services and agricultural productivity.

Background Tibetan Terrier is a popular medium-sized companion dog breed. According to the history of the breed, the western population of Tibetan Terriers includes two lineages, Lamleh and Luneville. These two lineages derive from a small number of founder animals from the native Tibetan Terrier population, which were brought to Europe in the 1920s. For almost a century, the western population of Tibetan Terriers and the native population in Tibet were reproductively isolated. In this study, we analysed the structure of the western population of Tibetan Terriers, the original native population from Tibet and of different crosses between these two populations. We also examined the genetic relationships of Tibetan Terriers with other dog breeds, especially terriers and some Asian breeds, and the within-breed structure of both Tibetan Terrier populations. Results Our analyses were based on high-density single nucleotide polymorphism (SNP) array (Illumina HD Canine 170 K) and microsatellite (18 loci) genotypes of 64 Tibetan Terriers belonging to different populations and lineages. For the comparative analysis, we used 348 publicly available SNP array genotypes of dogs from other breeds. We found that the western population of Tibetan Terriers and the native Tibetan Terriers clustered together with other Asian dog breeds, whereas all other terrier breeds were grouped into a separate group. We were also able to differentiate the western Tibetan Terrier lineages (Lamleh and Luneville) from the native Tibetan Terrier population. Conclusions Our results reveal the relationships between the western and native populations of Tibetan Terriers and support the hypothesis that Tibetan Terrier belongs to the group of ancient dog breeds of Asian origin, which are close to the ancestors of the modern dog that were involved in the early domestication process. Thus, we were able to reject the initial hypothesis that Tibetan Terriers belong to the group of terrier breeds. The existence of this native population of Tibetan Terriers at its original location represents an exceptional and valuable genetic resource.

To test for the presence of purging in populations, the classical pedigree-based inbreeding coefficient (F) can be decomposed into Kalinowski’s ancestral (FANC) and new (FNEW) inbreeding coefficients. The FANC and FNEW can be calculated by a stochastic approach known as gene dropping. However, the only publicly available algorithm for the calculation of FANC and FNEW, implemented in GRain v 2.1 (and also incorporated in the PEDIG software package), has produced biased estimates. The FANC was systematically underestimated and consequently, FNEW was overestimated. To illustrate this bias, we calculated FANC and FNEW by hand for simple example pedigrees. We revised the GRain program so that it now provides unbiased estimates. Correlations between the biased and unbiased estimates of FANC and FNEW, obtained for example data sets of Hungarian Pannon White rabbits (22,781 individuals) and Dutch Holstein Friesian cattle (37,061 individuals), were high, i.e., >0.96. Although the magnitude of bias appeared to be small, results from studies based on biased estimates should be interpreted with caution. The revised GRain program (v 2.2) is now available online and can be used to calculate unbiased estimates of FANC and FNEW.

The dominant phenotype of greying with age in horses, caused by a 4.6-kb duplication in intron 6 of STX17, is associated with a high incidence of melanoma and vitiligo-like skin depigmentation. However, the progressive greying and the incidence of melanoma, vitiligo-like depigmentation, and amount of speckling in these horses do not follow a simple inheritance pattern. To understand their inheritance, we analysed the melanoma grade, grey level, vitiligo grade, and speckling grade of 1,119 Grey horses (7,146 measurements) measured in six countries over a 9-year period. We estimated narrow sense heritability (h(2)), and we decomposed this parameter into polygenic heritability (h(2) (POLY)), heritability due to the Grey (STX17) mutation (h(2) (STX17)), and heritability due to agouti (ASIP) locus (h(2) (ASIP)). A high heritability was found for greying (h(2) = 0.79), vitiligo (h(2) = 0.63), and speckling (h(2) = 0.66), while a moderate heritability was estimated for melanoma (h(2) = 0.37). The additive component of ASIP was significantly different from zero only for melanoma (h(2) (ASIP) = 0.02). STX17 controlled large proportions of phenotypic variance (h(2) (STX17) = 0.18-0.55) and overall heritability (h(2) (STX17)/h(2) = 0.28-0.83) for all traits. Genetic correlations among traits were estimated as moderate to high, primarily due to the effects of the STX17 locus. Nevertheless, the correlation between progressive greying and vitiligo-like depigmentation remained large even after taking into account the effects of STX17. We presented a model where four traits with complex inheritance patterns are strongly influenced by a single mutation. This is in line with evidence of recent studies in domestic animals indicating that some complex traits are, in addition to the large number of genes with small additive effects, influenced by genes of moderate-to-large effect. Furthermore, we demonstrated that the STX17 mutation explains to a large extent the moderate to high genetic correlations among traits, providing an example of strong pleiotropic effects caused by a single gene.