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The “dark honeybee” Apis mellifera mellifera, originally the dominating subspecies of the Western honeybee in Central and Northern Europe, is currently threatened by extinction in Austria and its conservation breeding program is carried out by two breeding associations in Salzburg (AMZ – Austrian Mellifera Breeders) and Tyrol (breeding association Kauns-Kaunerberg). In this study, genetic diversity (genomic inbreeding FROH, number of homozygosity segments NROH), the relationship structures and foreign gene proportions in samples from the two Mellifera breeding associations were examined in comparison to A.m.carnica samples using the high-density 70k SNP Illumina® Honey Bee Genotyping Array. The mean Carnica gene-proportion (C-line gene proportion) in the two Mellifera samples was originally 32.4 to 34.4%. By excluding hybridized queens, this gene share was reduced to an average of 12.8–17.7%. In terms of genomic inbreeding, the Austrian Mellifera samples exhibited a mean FROH of 4.20% with maximum values of up to 17.2%. The results of this study clearly illustrated the problems conservation breeding programs in small populations are exposed to. We further demonstrated that the simultaneous consideration of multiple genetic descriptors such as FROH, NROH, ADMIXTURE and the kinship network NETVIEW is effective and practically usable in monitoring breeding programs, which can further be communicated in a way that is understandable to the practical breeders. [ABSTRACT FROM AUTHOR]

The aim of this paper is to present methods for estimating inbreeding and inbreeding effects using genome data and to give an overview of previous results in dairy cattle populations. Arrays with 50,000 single nucleotide polymorphisms (SNPs) evenly distributed across the genome are predominantly used in cattle population studies. The genome-based methods make it possible to localize the clusters and size of the homozygous segments ("runs of homozygosity", ROH) created by inbreeding on the genome, which enables further analyses of the genomic inbreeding coefficient (FROH), the mapping of inbreeding effects, the allele effects in the ROH and the development of the ROH in ancestral generations. Another way to calculate genomic inbreeding coefficients is to use the individual SNPs. This method can lead to an overestimation of genomic inbreeding and inbreeding depression, as homozygous SNP genotypes are not necessarily identical-by-descent. If the ROH clusters and their detrimental effects on the breeding traits in a cattle population are known, then the expected distribution of ROH and their expected effects on performance depression can be predicted for each animal and its progeny. Most studies on genomic inbreeding have been performed in Holstein populations. A 1% increase in FROH was associated with an inbreeding depression in 305-day milk yield of 20 - 61 kg milk, while fat and protein yield showed an inbreeding depression of 1.3 - 1.7 kg and 1.2 - 2.5 kg, respectively. In phenotypic standard deviations of the traits, this corresponds to a decrease of 2.393%, 2.306% and 3.021%. The fertility traits experienced less inbreeding depression with 0.825% of the phenotypic standard deviation in comparison to milk performance. Disease incidences showed inbreeding depression with a 1% increase in FROH by 2.530% of the phenotypic standard deviation. The different lengths of the ROH segments showed very different trends for inbreeding depression depending on the traits and the respective population. The identified ROH regions with significant inbreeding depression were distributed across different chromosomal clusters and the effects varied greatly in size. A concentration on a few chromosomes or clusters was not found for any trait. Only a small number of loci in ROH exhibited pleiotropic effects on inbreeding depression. With the introduction of genomic selection, a faster fixation of loci and higher inbreeding increase occurs. Therefore, efficient inbreeding management is necessary for cattle populations. This can be improved by denser SNP sets. [ABSTRACT FROM AUTHOR]

Matrix metalloproteinases (MMPs) are proteinases released by gingival cells, macrophages and neutrophils, induced by potentially pathogenic periodontal bacteria of the subgingival plaque, which play a critical role in the pathogenesis of periodontal disease. The expression of MMPs is controlled by chromosome 11. Single nucleotide polymorphisms (SNPs) are linked with variations in the secretion of MMPs, resulting in periodontal disease progression. Genetic studies aim to find the markers for early diagnosis and prevention of the related diseases. This systematic review focuses on finding the association between the MMPs and periodontitis among Indians. A literature review was performed, including studies published between January 1 2012 and May 2024 were incorporated. This systematic review included 1,046 participants in seven Indian studies, and substantial evidence was found for an association between MMP-9 (–1562C/T) and periodontitis in Indian population.

The backbone of the genomic selection program is a large reference population with many genotyped and progenytested sires or cows with own performance records. The reference population is needed for the estimation of marker effects, by genomic best linear unbiased prediction, which in turn are used to predict the direct genomic values of genotyped selection candidates. Genomic selection nearly doubled the genetic progress in the German Holsteins. This article reviews recent and future developments in genomic dairy cattle breeding. The following subjects are considered: The choice of novel SNPs to be used in routine applications, novel candidate traits for an extended breeding goal, genomic crossbreeding methods and the development of inbreeding with genomic tools to control it. [ABSTRACT FROM AUTHOR]

The article focuses on the application of MALDI-TOF Mass Spectrometry in molecular biology diagnostics, particularly for the sensitive detection of single nucleotide variations (SNPs) in a multiplex approach. Topics include the principle of MALDI-TOF, its versatility in detecting genomic DNA or cDNA mutations, its role in thrombophilia diagnostics, testing before fluorouracil-based therapy, and its use in SARS-CoV-2 variant typing.

Sequence data can be used in genomic selection (GS), genome-wide association studies (GWAS) and the analysis of recessive lethal defects instead of, or complementary to, SNP array genotypes and increase the power of those applications in various ways. Because sequencing large study cohorts is still expensive, genotype imputation - the prediction of missing genotypes - is a cost-effective alternative to generate sequence-level genotypes for a large number of animals. These genotypes can then be used in subsequent studies and, for example, facilitate the identification of causative mutations. In this context, it is important to consider the accuracy of imputation and to know the factors affecting it. As the availability of sequence data from (warmblood) horses is limited, for this species, imputation is a good possibility to generate sequence level genotypes, which can boost the power of GWAS and the identification of recessive lethal defects. [ABSTRACT FROM AUTHOR]

The accuracy of genomic breeding value estimation depends strongly on the size and genetic composition of the reference population. For the German Warmblood horse, a sufficiently large reference population could be established through the collaboration of five German breeding association. The present study deals with the influence of the structure of the joint reference population on the accuracy of genomic prediction for the trait withers height. For the evaluation, 2,895 mares with genotype information for 60,801 single nucleotide polymorphisms (SNPs) were available. A two-step approach was used and three different (cross-validation) scenarios were tested and compared. For the first cross-validation approach, a classical 5-fold cross-validation with ten replicates was performed using the entire reference population. Since a previous analysis of genomic relatedness revealed substructures reflecting the different breeding foci of the participating breeding associations, in the second cross-validation approach, the reference population was divided by breeding orientation ('jumping' and 'dressage') and the prediction accuracy within the two subsets was investigated. In the third approach, prediction accuracy was estimated when horses of one breeding orientation were predicted using SNP effects estimated using horses of the other breeding orientation. Correlations between conventional breeding values and direct genomic breeding values ranged from 0.63 to 0.71 for the first cross-validation approach, from 0.60 to 0.69 for the second cross-validation approach, and from 0.12 to 0.36 for the third approach. The results show that the use of a reference population across breeds is currently the best strategy for the German warmblood horse. Furthermore, the findings indicate that sufficiently large kinship relations between calibration and validation are of elementary importance and that relatively small sample sizes can be effectively compensated for through ensuring as close genetic relationships between these two groups as possible. [ABSTRACT FROM AUTHOR]

In German warmblood breeding, efforts are being made to move forward from conventional breeding value estimation towards genomic breeding value estimation (gEBV). An additional application of the genomic information generated in the process of gEBV is the move from microsatellite typing (MS) for pedigree control towards single nucleotide polymorphism (SNP) technology. For the transition phase between the two methods, imputation of MS alleles based on SNP data offers an efficient, cost-saving approach. A ten-fold cross-validation was carried out to test whether imputation is more suitable across horse breeds, within breeds or within groups of genetic similarity (clusters). For the final study, 2,607 mares were available, which had been genotyped for up to 17 MS and over 70,000 SNPs and which belonged to the Holstein, Westphalian, Oldenburg, Oldenburg International and Trakehner breeds. Average imputation accuracies of over 89% were achieved in all three approaches, with accuracies of over 96% achieved for those MS with a genotyping rate over 90%. Besides parentage control and gEBV, the availability of genomic information offers further applications, such as population monitoring, diversity management and control of genetically caused diseases as well as lethal variants. [ABSTRACT FROM AUTHOR]

The potential of genomics to sustainably strengthen the breeding programs of horses has been realized and implies high motivation of the equine breeding sector to implement genomically enhanced applications in routine. This means considerable challenges for the infrastructure which must be able to cope with new and increased requirements. Laboratory analyses of genome-wide markers, so-called Single Nucleotide Polymorphisms (SNPs), and related logistics play key roles in the development. For optimization of performance and reliability, the different use profiles need to be considered. Since 2017, collaborative efforts of practice and science have succeeded in increasing knowledge and providing prototypes for genomically enhanced applications. However, further steps needed to be taken before practical implementation. It was crucial to ensure that the provided closed system of digital support, starting with the order of some analysis and ending with use of the results, is in place and fully functional before the first SNP-based routine for German horse breeding was introduced in 2021. The equine genomic database allows storage and administration of SNP genotypic data generated with different platforms and chip types, such that SNP data can be efficiently used for joint analyses. Genomic services denote the new set of tools for studbooks and individual breeders that allow ordering laboratory and SNP data analyses. This area is supposed to develop dynamically to convey practical benefit of using genomic tools for horses. [ABSTRACT FROM AUTHOR]

Breeds can be easily identified using molecular tools based on genomic architecture of breed within a species. Moreover, in developing countries like Pakistan where the danger of genetic erosion is a serious threat due to uncontrolled crossbreeding, the application of such tools is promising for breed identification and conservation. Thus, the current research was planned to prepare AFLP based genetic markers for distinction of Red Sindhi cattle from crossbred population. In current study, 50 and 48 unrelated (males as well as females) were sampled for Red Sindhi and crossbred cattle populations, respectively. AFLP method was used to fetch prospective molecular markers present only in Red Sindhi however, lacking in crossbred and vice versa. The analysis generated eleven markers that were translated to single nucleotide polymorphism markers for genotyping. These markers were used for allelic variation analysis in both sampled populations. Moreover, single selected markers generated an identification probability of 0.800 while a probability for misjudgment of 0.313 for Red Sindhi cattle. However, the combination of three markers yielded a probability for judgment of 0.941 while a probability of misjudgment was 0.413. The study yielded panel of AFLP markers for identification of Red Sindhi that can be used as breed purity test to discriminate between Red Sindhi and crossbred population. [ABSTRACT FROM AUTHOR]

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Abstract An F2 chicken population was established from a crossbreeding between a Xinghua line and a White Recessive Rock line. A total of 502 F2 chickens in 17 full-sib families from six hatches was obtained, and phenotypic data of 488 individuals were available for analysis. A total of 46 SNP on GGA1 was initially selected based on the average physical distance using the dbSNP database of NCBI. After the polymorphism levels in all F0 individuals (26 individuals) and part of the F1 individuals (22 individuals) were verified, 30 informative SNP were potentially available to genotype all F2 individuals. The linkage map was constructed using Cri-Map. Interval mapping QTL analyses were carried out. QTL for body weight (BW) of 35 d and 42 d, 49 d and 70 d were identified on GGA1 at 351–353 cM and 360 cM, respectively. QTL for abdominal fat weight was on GGA1 at 205 cM, and for abdominal fat rate at 221 cM. Two novel QTL for fat thickness under skin and fat width were detected at 265 cM and 72 cM, respectively.

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Copyright of Archiv für Kriminologie is the property of Schmidt-Roemhild Verlag and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

With the deciphering of whole genomes, a tremendous amount of data has been made available for research. In order to analyze all this information within a reasonable time frame and at reasonable cost, new technologies in DNA analysis as well as enzyme assays have been developed in the last few years in a symbiosis of biological, automation and miniaturization technologies. Examples are chip technologies for DNA analysis and microplate enzyme assays for high-throughput screening. Applications in biomedical research and in microbial diagnostics were presented.

A genetic influence on blood pressure was demonstrated more than 100 years ago and a simple Mendelian inheritance was initially presumed. Platt and Pickering conducted a lively debate on this topic. Platt favored the idea that a single gene or only a few genes were responsible for high blood pressure. Pickering presented research results, which supported the assumption that many genes exerted an influence on blood pressure. This was all in a period when it was not even known what genes were. Genome-wide association studies (GWAS) according to the Pickering model have identified > 500 blood pressure relevant gene loci, which are distributed over the whole genome. Each individual gene exerts only a small effect on blood pressure. The dark horses of hypertension research are the secondary causes. In pheochromocytoma, primary aldosteronism, Cushing's syndrome and even fibromuscular dysplasia (renovascular hypertension) the results indicate that a genetic cause regularly underlies secondary hypertension. This would therefore also partially confirm Platt's theory. In the meantime, a multitude of forms of hypertension have been described with a genetic inheritance according to Mendel. Each of these genetic variants exerts a considerable influence on blood pressure. A multitude of novel physiological mechanisms were explained by this. These findings will become therapeutically important. Therefore, it is incumbent upon clinicians to be optimally informed about these research results.