Search

Phenethylamine (PEA) is a naturally occurring trace amine that acts as a modulator in the central nervous system. It is widely sold as a dietary supplement and advertised for its mood enhancing effects and should support weight loss. It is prohibited in sports and itemized as a stimulant on the Prohibited List issued by the World Anti‐Doping Agency (WADA). After oral administration of PEA, its urinary concentration is found only slightly elevated while metabolites of PEA show a significant increase. Besides 2‐(2‐hydroxyphenyl)acetamide sulfate, especially phenylacetylglutamine (PAG) was found at significantly elevated urinary concentrations after the administration. Due to large inter‐ and intra‐individual variations in urinary concentrations of all metabolites, establishing a concentration or concentration ratio‐based threshold remained complicated to unambiguously identify post‐administration samples. In accordance with the approach employed in detecting testosterone misuse, the applicability of isotope ratio mass spectrometry to differentiate between endogenously elevated concentrations and PEA administrations was investigated. A method encompassing solid‐phase extraction combined with acetylation and high‐performance liquid chromatography (HPLC)‐based clean‐up was developed and validated for PEA. The more abundant metabolite PAG was purified by a direct injection approach on the HPLC and could be analyzed without the need for derivatization. Both methods were validated considering applicable WADA regulations. A reference population encompassing n = 57 samples was investigated to establish population‐based thresholds considering the carbon isotope ratios (CIRs) found at natural abundance for PAG. The derived threshold was tested for its applicability by re‐analysis of numerous post‐administration samples encompassing single‐ and multi‐dose trials. [ABSTRACT FROM AUTHOR]

When solving path-planning problems, a traditional genetic algorithm has some drawbacks such as being prone to falling into premature convergence, a relatively slow convergence rate, and generating multiple invalid paths during crossover and mutation operations. It also depends heavily on the initial population and empirical core parameters. In this paper, a robot path-planning method based on an improved genetic algorithm is proposed. The crossover and variation probabilities of the genetic algorithm are given by an adaptive function during the population iterations, and deletion and optimisation operators are proposed to improve the performance of the algorithm. The reference population is introduced for those inferior individuals eliminated by the main population, and the high-quality gene fragments among them are extracted and added to the main population to speed up the search procedure and to avoid missing the optimal solution. The simulation results show that the adaptive function speeds up the convergence of the algorithm and ensures the searching ability. The addition of the deletion and optimisation operators shortens the length of the optimal path. The reference population significantly accelerates the convergence speed of the algorithm and ensures the stability of the population throughout the process. [ABSTRACT FROM AUTHOR]

Brazilian livestock breeding programmes strive to enhance the genetics of beef cattle, with a strong emphasis on the Nellore breed, which has an extensive database and has achieved significant genetic progress in the last years. There are other indicine breeds that are economically important in Brazil; however, these breeds have more modest sets of phenotypes, pedigree and genotypes, slowing down their genetic progress as their predictions are less accurate. Combining several breeds in a multi‐breed evaluation could help enhance predictions for those breeds with less information available. This study aimed to evaluate the feasibility of multi‐breed, single‐step genomic best linear unbiased predictor genomic evaluations for Nellore, Brahman, Guzerat and Tabapua. Multi‐breed evaluations were contrasted to the single‐breed ones. Data were sourced from the National Association of Breeders and Researchers of Brazil and included pedigree (4,207,516), phenotypic (328,748), and genomic (63,492) information across all breeds. Phenotypes were available for adjusted weight at 210 and 450 days of age, and scrotal circumference at 365 days of age. Various scenarios were evaluated to ensure pedigree and genomic information compatibility when combining different breeds, including metafounders (MF) or building the genomic relationship matrix with breed‐specific allele frequencies. Scenarios were compared using the linear regression method for bias, dispersion and accuracy. The results showed that using multi‐breed evaluations significantly improved accuracy, especially for smaller breeds like Guzerat and Tabapua. The validation statistics indicated that the MF approach provided accurate predictions, albeit with some bias. While single‐breed evaluations tended to have lower accuracy, merging all breeds in multi‐breed evaluations increased accuracy and reduced dispersion. This study demonstrates that multi‐breed genomic evaluations are proper for indicine beef cattle breeds. The MF approach may be particularly beneficial for less‐represented breeds, addressing limitations related to small reference populations and incompatibilities between G and A22. By leveraging genomic information across breeds, breeders and producers can make more informed selection decisions, ultimately improving genetic gain in these cattle populations. [ABSTRACT FROM AUTHOR]

This study aimed to evaluate the distribution of plasma troponin I concentration and establish the 99th percentile reference for hs-cTnI in a hospitalized population without a cardiovascular discharge diagnosis from the Shandong area.

Background: Few studies have been conducted on haematological reference intervals (RIs) in Icelandic horses. Reference intervals have been published for Icelandic horses in Austria and a preliminary study in Iceland compared haematological values in riding horses to published RIs for other breeds as well as Icelandic horses abroad. Haematological parameters can vary greatly due to factors such as breed, gender, age, reproductive status, and training, as well as feeding, prior exercise and management method. Icelandic broodmares are kept on pasture under supervision throughout the year, with haylage provided during the winter, and it is therefore of interest to establish haematological reference intervals for pregnant broodmares in Iceland. The purpose of this study was to establish haematological RIs specific to Icelandic broodmares in the first months of pregnancy, kept on pasture. Blood samples from 183 mares, stabilised in EDTA were analysed using IDEXX ProCyte Dx and total protein was analysed in serum samples from 157 of the 183 mares, using IDEXX Catalyst One analyser. The RIs were established using the guidelines of the American Society for Veterinary Clinical Pathology. Results: The RIs for red blood cell count, haematocrit and haemoglobin were higher in pasture-kept Icelandic mares in early pregnancy, most of which were lactating, than in pregnant mares of other breeds. This was also true for white blood cell count, as well as numbers of monocytes, eosinophils, and basophils, which in some instances might illustrate problems in the automated categorisation of some leukocytes. Conclusions: As no RIs have been published for other pasture-kept Icelandic horses, future investigations should include other groups of pasture-kept Icelandic horses. Such an analysis might elucidate the effect of breed, management, and pregnancy on haematological values in pasture-kept Icelandic horses. [ABSTRACT FROM AUTHOR]

ABSTRACT: Genomic selection was deployed in Lacaune dairy breed in 2015. Lacaune population split in 1972 into 2 breeding companies with associated flocks, and there have been very few exchanges of animals between the subpopulations, leading to divergence of the 2 subpopulations. In spite of that, there is a joint genomic prediction. The objective of this study is to understand how this structuring affects prediction accuracy. We analyzed all the data available from Lacaune breeding program for milk yield: around 6 million phenotypes, 2 million animals in the pedigree and more than 29,000 genotyped animals, including 3,434 and 2,868 AI rams for each company. To consider missing pedigree, we set up genetic groups using the theory of metafounders. First, we studied the pedigree and genomic structures of the 2 subpopulations calculating Fst, evolution of average pedigree relationships across time and principal components analysis of genomic relationships. In a second part, we compared the reliability between different scenarios: an evaluation with a single reference population (Alone), an evaluation with a joint reference population (Together) and an evaluation of one subpopulation based on the reference population of the other group (Indirect). The low Fst value (0.02) reveals that the 2 subpopulations are still genetically close. Nevertheless, a low and constant average relationship between the animals of the 2 subpopulations confirms the absence of recent connections between them. We can see with principal component analysis results that even if they are close, they diverge over time. Finally, we observe small gains in accuracy of Together versus Alone, in spite of whereas doubling the reference population size in Together. These gains vary across years and subpopulations: less than 0.08 (0.46 to 0.54; ratio of accuracy for the partial and whole evaluations—corresponding to the greatest change in this ratio for breeding company 1, observed for the cohort 2016) for one subpopulation and between 0.03 (0.55 to 0.58) and 0.17 (0.48 to 0.65) for the other. To conclude, the 2 subpopulations remain close enough genetically so that their combined evaluation is advantageous, even if only slightly.

Genomic selection was deployed in Lacaune dairy breed in 2015. Lacaune population split in 1972 into 2 breeding companies with associated flocks, and there have been very few exchanges of animals between the subpopulations, leading to divergence of the 2 subpopulations. In spite of that, there is a joint genomic prediction. The objective of this study is to understand how this structuring affects prediction accuracy. We analyzed all the data available from Lacaune breeding program for milk yield: around 6 million phenotypes, 2 million animals in the pedigree and more than 29,000 genotyped animals, including 3,434 and 2,868 AI rams for each company. To consider missing pedigree, we set up genetic groups using the theory of metafounders. First, we studied the pedigree and genomic structures of the 2 subpopulations calculating F st , evolution of average pedigree relationships across time and principal components analysis of genomic relationships. In a second part, we compared the reliability between different scenarios: an evaluation with a single reference population (Alone), an evaluation with a joint reference population (Together) and an evaluation of one subpopulation based on the reference population of the other group (Indirect). The low F st value (0.02) reveals that the 2 subpopulations are still genetically close. Nevertheless, a low and constant average relationship between the animals of the 2 subpopulations confirms the absence of recent connections between them. We can see with principal component analysis results that even if they are close, they diverge over time. Finally, we observe small gains in accuracy of Together versus Alone, in spite of whereas doubling the reference population size in Together. These gains vary across years and subpopulations: less than 0.08 (0.46 to 0.54; ratio of accuracy for the partial and whole evaluations—corresponding to the greatest change in this ratio for breeding company 1, observed for the cohort 2016) for one subpopulation and between 0.03 (0.55 to 0.58) and 0.17 (0.48 to 0.65) for the other. To conclude, the 2 subpopulations remain close enough genetically so that their combined evaluation is advantageous, even if only slightly. [Display omitted] [ABSTRACT FROM AUTHOR]

Currently, enhancing the collaboration between related breeds is of main importance to increase the competitivity and the sustainability of local breeds. One type of collaboration is the development of an across-breed reference population that will allow a better management of local breeds. For this purpose, the genomic relatedness between the local target breed and possible breeds to be included in the reference population should be estimated. In Europe, there are several local red-pied cattle breeds that would benefit from this kind of collaboration. However, how different red-pied cattle breeds from the Benelux are related to each other and can collaborate is still unclear. The objectives of this study were therefore: (1) to estimate the level of inbreeding of the East Belgian Red and White (EBRW), the Red-Pied of the Ösling (RPO) and Dutch red-pied cattle breeds; (2) to determine the genomic relatedness of several red-pied cattle breeds, with a special focus on two endangered breeds: the EBRW and the RPO, and (3) based on the second objective, to detect animals from other breeds that were genomically close enough to be considered as advantageous in the creation of an across-breed reference population of EBRW or RPO. The estimated inbreeding levels based on runs of homozygosity were relatively low for almost all the studied breeds and especially for the EBRW and RPO. This would imply that inbreeding is currently not an issue in these two endangered breeds and that their sustainability is not threatened by their level of inbreeding. The results from the principal component analysis, the phylogenetic tree and the clustering all highlighted that the EBRW and RPO breeds were included in the genomic continuum of the studied red-pied cattle breeds and can be therefore considered as genomically close to Dutch red-pied cattle breeds, highlighting the possibility of a collaboration between these breeds. Especially, EBRW animals were closely related to Deep Red and Improved Red animals while, to a lesser extent, the RPO animals were closely related to the Meuse-Rhine-Yssel breed. Based on these results, we could use distance measures, based either on the principal component analysis or clustering, to detect animals from Dutch breeds that were genomically closest to the EBRW or RPO breeds. This will finally allow the building of an across-breed reference population for EBRW or RPO for further genomic evaluations, considering these genomically closest animals from other breeds.

Simple Summary: The effectiveness of selecting individuals based on pedigree information (MCA) and genomic information (MCG) as a reference population for genotype imputation was assessed using a Japanese Black cow population. Pedigree depth to construct the pedigree-based additive genetic relationship matrix (A matrix) affected the results of MCA, and the method of calculating the genomic relationship matrix (G matrix) affected the results of MCG. For the studied population, MCG appeared to be a better choice than MCA to select cows as a reference population for higher-density genotype imputation to perform genomic prediction and a genome-wide association study. As optimization methods to identify the best animals for dense genotyping to construct a reference population for genotype imputation, the MCA and MCG methods, which use the pedigree-based additive genetic relationship matrix (A matrix) and the genomic relationship matrix (G matrix), respectively, have been proposed. We assessed the performance of MCA and MCG methods using 575 Japanese Black cows. Pedigree data were provided to trace back up to five generations to construct the A matrix with changing the pedigree depth from 1 to 5 (five MCA methods). Genotype information on 36,426 single-nucleotide polymorphisms was used to calculate the G matrix based on VanRaden's methods 1 and 2 (two MCG methods). The MCG always selected one cow per iteration, while MCA sometimes selected multiple cows. The number of commonly selected cows between the MCA and MCG methods was generally lower than that between different MCA methods or between different MCG methods. For the studied population, MCG appeared to be more reasonable than MCA in selecting cows as a reference population for higher-density genotype imputation to perform genomic prediction and a genome-wide association study. [ABSTRACT FROM AUTHOR]

Ecological restoration is addressing the challenge of biodiversity conservation in landscapes where native vegetation has been extensively cleared. Reestablishing ecological interactions that support self‐sustainable populations plays an essential role in restoration efforts. For animal‐pollinated plant species, comparing mating system dynamics within restoration populations with natural remnant populations can inform the progress of restoration activities. We assessed mating system parameters, seed weight, invertebrate floral visitors, and genetic diversity for two restoration populations and two native reference remnant populations of the animal‐pollinated, woody shrub/tree, Hakea laurina (Proteaceae) in the Southwest Australian Floristic Region. There was no difference in outcrossing rates between the two sites (restoration and reference populations combined) or treatments (sites combined). However, one restoration population showed significantly lower outcrossing that was correlated with reduced seed weight, greater inbreeding, and significantly lower invertebrate richness and abundance compared to its nearby remnant population. In contrast, in the other restoration population, all measures were comparable to the reference remnant population. Local genetic diversity available in remnant populations was captured in both restoration populations. Limitations to pollinator services may be affecting mating patterns and potentially population fitness, in a younger restoration population with reduced proximity to remnant vegetation, which has a tall coplanted overstorey species and spatially aggregated individuals. We highlight these aspects of restoration populations as important areas of ongoing research that will impact the reestablishment of ecological interactions, and the assessment of mating system dynamics as a valuable tool to inform the current progress of restoration activities. [ABSTRACT FROM AUTHOR]

Purpose: High-sensitivity cardiac troponin I (hs-cTnI) and T (hs-cTnT) have been demonstrated to have lower sex-specific 99th percentiles in healthy females. However, these sex-specific thresholds are not widely adopted in clinical practice which could lead to underdiagnosis of acute myocardial infarction in females. We conducted a systematic review to explore sex-specific 99th percentiles for hs-cTnI and hs-cTnT from healthy reference populations., Methods: The principles of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were used to complete this systematic review. We used PubMed and OVID EMBASE to search for original studies published between November 2017 and November 2021 that included reference populations used to establish the 99th percentiles of hs-cTnI and hs-cTnT with the following inclusion criteria: adults; English language; samples taken as part of a healthy, reference population; studies using high-sensitivity troponin assay; and sample size > 300. Studies were excluded if the reference population sample size was < 300, if a conventional troponin assay was used, or if they did not include independently derived, sex-specific 99th percentiles. Data was extracted from the studies through Covidence to perform a qualitative data synthesis. Female-specific, male-specific, and overall 99th percentiles for hs-cTn were compared., Findings: We reviewed 131 articles of which 19 met inclusion criteria. These 19 studies derived sex-specific 99th percentiles for 11 different hs-cTnI assays and 9 different hs-cTnT assays. More than 90% (13 of 14 studies) of hs-cTnI assays found lower female 99th percentiles compared to male and to overall 99th percentiles. One study included nine different hs-cTnI assays, of which only one assay resulted in a higher female 99th percentile compared to male and to overall 99th percentiles. Eight of nine hs-cTnT studies (88.9%) found lower female 99th percentiles compared to male and to overall 99th percentiles., Implications: The data shows significantly lower 99th percentiles in females compared to 99th percentiles in males and overall. Incorporating these sex-specific 99th percentile cut-offs into clinical practice could lead to increased diagnosis and potentially better outcomes for females presenting with acute myocardial infarction., Competing Interests: Declaration of competing interest None declared., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Abstract Background Growth references are traditionally constructed relative to chronological age, despite inter-individual variations in pubertal timing. A new type of height reference was recently developed allowing growth to be aligned based on onset of pubertal height growth. We here aim to develop a corresponding reference for pubertal weight. Methods To model QEPS-weight, 3595 subjects (1779 girls) from GrowUp1974Gothenburg and GrowUp1990Gothenburg were used. The QEPS-height-model was transformed to a corresponding QEPS-weight-model; thereafter, QEPS-weight was modified by an individual, constitutional weight-height-factor. Longitudinal weight and length/height measurements from 1418 individuals (698 girls) from GrowUp1990Gothenburg were then used to create weight references aligned for height at pubertal onset (the age at 5% of P-function growth, AgeP5). GrowUp1974Gothenburg subgroups based on pubertal timing, stature at pubertal onset, and childhood body composition were assessed using the references. Results References (median, SDS) for total weight (QEPS-functions), weight specific to puberty (P-function), and weight gain in the absence of specific pubertal growth (basic weight, QES-functions), allowing alignment of individual growth based on age at pubertal onset. For both sexes, basic weight was greater than average for late maturing, tall and high-BMI subgroups. The P-function-related weight was greater than average in short and lower than average in tall children, in those with high BMI, and in girls but not boys with low BMI. Conclusions New pubertal weight references allow individual variations in pubertal timing to be taken into consideration when evaluating growth. When used together with the comparable pubertal height reference, this will improve growth monitoring in clinical practice for identifying abnormal growth and serve as a valuable research tool providing insight into human growth.

Genomic selection has a great potential in aquaculture breeding since many important traits are not directly measured on the candidates themselves. However, its implementation has been hindered by staggering genotyping costs because of many individual genotypes. In this study, we explored the potential of DNA pooling for creating a reference population as a tool for genomic selection of a binary trait. Two datasets from the SalmoBreed population challenged with salmonid alphavirus, which causes pancreas disease, were used. Dataset-1, that includes 855 individuals (478 survivors and 377 dead), was used to develop four DNA pool samples (i.e., 2 pools each for dead and survival). Dataset-2 includes 914 individuals (435 survivors and 479 dead) belonging to 65 full-sibling families and was used to develop insilico DNA pools. SNP effects from the pool data were calculated based on allele frequencies estimated from the pools and used to calculate genomic breeding values (GEBVs). The correlation between SNP effects estimated based on individual genotypes and pooled data increased from 0.3 to 0.912 when the number of pools increased from 1 to 200. A similar trend was also observed for the correlation between GEBVs, which increased from 0.84 to 0.976, as the number of pools per phenotype increased from 1 to 200. For dataset-1, the accuracy of prediction was 0.71 and 0.70 when the DNA pools were sequenced in 40× and 20×, respectively, compared to an accuracy of 0.73 for the SNP chip genotypes. For dataset-2, the accuracy of prediction increased from 0.574 to 0.691 when the number of in-silico DNA pools increased from 1 to 200. For this dataset, the accuracy of prediction using individual genotypes was 0.712. A limited effect of sequencing depth on the correlation of GEBVs and prediction accuracy was observed. Results showed that a large number of pools are required to achieve as good prediction as individual genotypes; however, alternative effective pooling strategies should be studied to reduce the number of pools without reducing the prediction power. Nevertheless, it is demonstrated that pooling of a reference population can be used as a tool to optimize between cost and accuracy of selection. [ABSTRACT FROM AUTHOR]

Reference population databases are an essential tool in variant and gene interpretation. Their use guides the identification of pathogenic variants amidst the sea of benign variation present in every human genome, and supports the discovery of new disease–gene relationships. The Genome Aggregation Database (gnomAD) is currently the largest and most widely used publicly available collection of population variation from harmonized sequencing data. The data is available through the online gnomAD browser (https://gnomad.broadinstitute.org/) that enables rapid and intuitive variant analysis. This review provides guidance on the content of the gnomAD browser, and its usage for variant and gene interpretation. We introduce key features including allele frequency, per‐base expression levels, constraint scores, and variant co‐occurrence, alongside guidance on how to use these in analysis, with a focus on the interpretation of candidate variants and novel genes in rare disease. [ABSTRACT FROM AUTHOR]

Introduction: Age- and sex-specific reference intervals (RIs) for high-sensitivity cardiac troponin I (hs-cTnI) are not available in China. The objective of the present research was to determine the assay-specific 99th percentile upper reference limits (URLs) for hs-cTnI in healthy Chinese adults. Methods: Apparently healthy individuals were first screened with a questionnaire. The reference population was selected according to the International Federation of Clinical Chemistry (IFCC) criteria using surrogate biomarker for diabetes, myocardial dysfunction, and renal dysfunction. The serum concentration of hs-cTnI was measured using the automatized VITROS 5600 Immunodiagnostic system (Ortho Clinical Diagnostics, Raritan, NJ, USA). Results: A total of 2,183 healthy individuals (1,051 men and 1,132 women aged from 20 to 95 years) were enrolled in the study. The serum overall 99th percentile URLs of hs-cTnI were 11.1 ng/L (12.5 ng/L in men, 9.6 ng/L in women). In both men and women, the 99th percentile URLs were significantly higher in individuals ≥55 years old than those in the <55 years old, especially in women. Moreover, 78.8% of males and 70.1% of females presented with hs-cTnI concentrations above the limit of detection of 0.43 ng/L. Conclusion: The hs-cTnI-VITROS assay coincided with the performance standard of the IFCC for high-sensitivity cTnI assays. The 99th percentile URLs for hs-cTnI in healthy Chinese adults were different from the manufacturer declared and appeared heterogeneous, potentially susceptible to factors such as age and sex. [ABSTRACT FROM AUTHOR]

Optimizing the construction and update strategies for reference and candidate populations is the basis of the application of genomic selection (GS). In this study, we first simulated1200-purebred-pigs population that have been popular in China for 20 generations to study the effects of different population sizes and the relationship between individuals of the reference and candidate populations. The results showed that the accuracy was positively correlated with the size of the reference population within the same generation (r = 0.9366, p < 0.05), while was negatively correlated with the number of generation intervals between the reference and candidate populations (r = −0.9267, p < 0.01). When the reference population accumulated more than seven generations, the accuracy began to decline. We then simulated the population structure of 1200 purebred pigs for five generations and studied the effects of different heritabilities (0.1, 0.3, and 0.5), genotyping proportions (20, 30, and 50%), and sex ratios on the accuracy of the genomic estimate breeding value (GEBV) and genetic progress. The results showed that if the proportion of genotyping individuals accounts for 20% of the candidate population, the traits with different heritabilities can be genotyped according to the sex ratio of 1:1male to female. If the proportion is 30% and the traits are of low heritability (0.1), the sex ratio of 1:1 male to female is the best. If the traits are of medium or high heritability, the male-to-female ratio is 1:1, 1:2, or 2:1, which may achieve higher genetic progress. If the genotyping proportion is up to 50%, for low heritability traits (0.1), the proportion of sows from all genotyping individuals should not be less than 25%, and for the medium and high heritability traits, the optimal choice for the male-to-female ratio is 1:1, which may obtain the greatest genetic progress. This study provides a reference for determining a construction and update plan for the reference population of breeding pigs. [ABSTRACT FROM AUTHOR]

Genomic selection has a great potential in aquaculture breeding since many important traits are not directly measured on the candidates themselves. However, its implementation has been hindered by staggering genotyping costs because of many individual genotypes. In this study, we explored the potential of DNA pooling for creating a reference population as a tool for genomic selection of a binary trait. Two datasets from the SalmoBreed population challenged with salmonid alphavirus, which causes pancreas disease, were used. Dataset-1, that includes 855 individuals (478 survivors and 377 dead), was used to develop four DNA pool samples (i.e., 2 pools each for dead and survival). Dataset-2 includes 914 individuals (435 survivors and 479 dead) belonging to 65 full-sibling families and was used to develop in-silico DNA pools. SNP effects from the pool data were calculated based on allele frequencies estimated from the pools and used to calculate genomic breeding values (GEBVs). The correlation between SNP effects estimated based on individual genotypes and pooled data increased from 0.3 to 0.912 when the number of pools increased from 1 to 200. A similar trend was also observed for the correlation between GEBVs, which increased from 0.84 to 0.976, as the number of pools per phenotype increased from 1 to 200. For dataset-1, the accuracy of prediction was 0.71 and 0.70 when the DNA pools were sequenced in 40× and 20×, respectively, compared to an accuracy of 0.73 for the SNP chip genotypes. For dataset-2, the accuracy of prediction increased from 0.574 to 0.691 when the number of in-silico DNA pools increased from 1 to 200. For this dataset, the accuracy of prediction using individual genotypes was 0.712. A limited effect of sequencing depth on the correlation of GEBVs and prediction accuracy was observed. Results showed that a large number of pools are required to achieve as good prediction as individual genotypes; however, alternative effective pooling strategies should be studied to reduce the number of pools without reducing the prediction power. Nevertheless, it is demonstrated that pooling of a reference population can be used as a tool to optimize between cost and accuracy of selection.

Optimizing the construction and update strategies for reference and candidate populations is the basis of the application of genomic selection (GS). In this study, we first simulated1200-purebred-pigs population that have been popular in China for 20 generations to study the effects of different population sizes and the relationship between individuals of the reference and candidate populations. The results showed that the accuracy was positively correlated with the size of the reference population within the same generation (r = 0.9366, p < 0.05), while was negatively correlated with the number of generation intervals between the reference and candidate populations (r = −0.9267, p < 0.01). When the reference population accumulated more than seven generations, the accuracy began to decline. We then simulated the population structure of 1200 purebred pigs for five generations and studied the effects of different heritabilities (0.1, 0.3, and 0.5), genotyping proportions (20, 30, and 50%), and sex ratios on the accuracy of the genomic estimate breeding value (GEBV) and genetic progress. The results showed that if the proportion of genotyping individuals accounts for 20% of the candidate population, the traits with different heritabilities can be genotyped according to the sex ratio of 1:1male to female. If the proportion is 30% and the traits are of low heritability (0.1), the sex ratio of 1:1 male to female is the best. If the traits are of medium or high heritability, the male-to-female ratio is 1:1, 1:2, or 2:1, which may achieve higher genetic progress. If the genotyping proportion is up to 50%, for low heritability traits (0.1), the proportion of sows from all genotyping individuals should not be less than 25%, and for the medium and high heritability traits, the optimal choice for the male-to-female ratio is 1:1, which may obtain the greatest genetic progress. This study provides a reference for determining a construction and update plan for the reference population of breeding pigs.

[Display omitted] • We introduce a novel indirect method for optimizing reference populations. • Consideration of correlated parameters reduces inter-individual variability. • We developed a model provided a tailored reference population for platelet counts. • This approach unveils a new thrombocytosis threshold. • This method could be used to determine new reference intervals from hospital data. In laboratory medicine, test results are generally interpreted with 95% reference intervals but correlations between laboratory tests are usually ignored. We aimed to use hospital big data to optimize and personalize laboratory data interpretation, focusing on platelet count. Laboratory tests were extracted from the hospital database and exploited by an algorithmic stepwise procedure. For any given laboratory test Y, an "optimized and personalized reference population" was defined by keeping only patients whose laboratory values for all Y-correlated tests fell within their own usual reference intervals, and by partitioning groups by individual-specific variables like sex and age category. The method was applied to platelet count. Laboratory data were recorded for 28,082 individuals. At the end of the algorithmic process, seven correlated laboratory tests were chosen, resulting in a reference sample of 159 platelet counts. A new 95 % reference interval was constructed [152–334 × 109/L], notably reduced (27.2 %) compared to conventional reference values [150–400 × 109/L]. The reference interval was validated on a sample of 2,129 patients from another downtown laboratory, emphasizing the potential transference of the hospital-derived reference limits. This method offers new perspectives in laboratory data interpretation, especially in patient screening and longitudinal follow-up. [ABSTRACT FROM AUTHOR]

Imputation may be used to rescue genomic data from animals that would otherwise be eliminated due to a lower than desired call rate. The aim of this study was to compare the accuracy of genotype imputation for Afrikaner, Brahman, and Brangus cattle of South Africa using within- and multiple-breed reference populations. A total of 373, 309, and 101 Afrikaner, Brahman, and Brangus cattle, respectively, were genotyped using the GeneSeek Genomic Profiler 150 K panel that contained 141,746 markers. Markers with MAF ≤ 0.02 and call rates ≤ 0.95 or that deviated from Hardy Weinberg Equilibrium frequency with a probability of ≤ 0.0001 were excluded from the data as were animals with a call rate ≤ 0.90. The remaining data included 99,086 SNPs and 360 Afrikaner, 75,291 SNPs and 288 animals Brahman, and 97,897 SNPs and 99 Brangus animals. A total of 7986, 7002, and 7000 SNP from 50 Afrikaner and Brahman and 30 Brangus cattle, respectively, were masked and then imputed using BEAGLE v3 and FImpute v2. The within-breed imputation yielded accuracies ranging from 89.9 to 96.6% for the three breeds. The multiple-breed imputation yielded corresponding accuracies from 69.21 to 88.35%. The results showed that population homogeneity and numerical representation for within and across breed strategies, respectively, are crucial components for improving imputation accuracies. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to determine the reference ranges for hematological, biochemical and endocrinological parameters in the blood of the commercial F1 Landrace x Yorkshire and Yorkshire x Landrace piglets aged 1 and 21 days. A total of 72 animals were enrolled in this study. The following reference ranges were determined for the blood parameters considered (day 1 vs. day 21): WBC 10.0-12.4 vs. 11.5-13.7 × 109/L; RBC 3.72-4.06 vs. 5.25-5.73 × 1012/L; HGB 71.4-79.0 vs. 100-110 g/L; HCT 24.4-26.5 vs. 35.7-39.1 %; MCV 64.4-66.0 vs. 72.7-74.8 fL; MCH 20.9-21.7 vs. 24.7-25.6 pg; MCHC 321-329 vs. 367-378 g/L; AST 78.5-98.9 vs. 51.8-66 U/L; ALT 72.5-84.5 vs. 65.9-76.5 U/L; CHOL 2.41-2.71 vs. 3.81-4.27 mmol/L; TGC 1.55-1.93 vs. 1.85-2.25 mmol/L; GLU 5.58-6.24 vs. 5.97-6.67 mmol/L; TPROT 55.2-59.4 vs. 65-69.9 g/L; ALB 13.4-15.1 vs. 27.8-30.9 g/L, GLOB 41.3-44.8 vs. 35.3-40.9 g/L; UREA mmol/L; TBIL 36.6-44.5 vs. 29.4-40.8 μmol/L; ALFAAMYLASE 897-998 vs. 851-949 U/L; LIPASE 74.1-79.7 vs. 88.3-98.1 U/L; CORT 140-196 vs. 41.4-63.8 ng/mL; INSUL 4.04-5.68 vs. 1.71-2.41 μU/L. The RBC, HGB, HCT, MCV, MCH, and MCHC values recorded were found to be higher in the piglets aged 21 days than those in the piglets aged 1 day. The total leukocyte count did not differ between the experimental groups. Lower AST, ALT, globulin, cortisol and insulin values were detected in the piglets aged 21 days than those in the piglets aged 1 day. However, the values of triglycerides, cholesterol, total protein, albumin and lipase were found to be higher in the piglets aged 21 days than those in the piglets aged 1 day. The glucose, alpha amylase and total bilirubin (TBIL) values did not differ significantly between the experimental groups. The Kolmogorov-Smirnov test conducted shows that there are significant differences in the frequency distribution of most of the blood parameters tested in the pigs aged 1 and 21 days, which confirms that a separate reference interval must be determined and applied to the respective piglet ages considered. The establishment of reference values for different age categories can facilitate the assessment of health status and productive performance of farm piglets.

Genomic selection in plants and animals has become a standard tool for breeding because of the advantages of high accuracy and short generation intervals. Implementation of this technology is hindered by the high cost of genotyping and other factors. The aim of this study was to determine an optional marker density panel and reference population size for using genomic selection of goats, with speculation on the number of QTLs that affect the important economic traits of goats. In addition, the effect of buck population size in the reference population on the accuracy of genomic estimated breeding value (GEBV) was discussed. Based on the previous genetic evaluation results of Inner Mongolia White Cashmere Goats, live body weight (LBW, h2 = 0.11) and fiber diameter (FD, h2 = 0.34) were chosen to perform genomic selection in this study. Reasonable genome parameters and generation transmission processes were set, and phenotypic and genotype data of the two traits were simulated. Then, different sizes of the reference population and validation population were selected from progeny. The GEBVs were obtained by six methods, including GBLUP (Genomic Best Linear Unbiased Prediction), ssGBLUP (Single Step Genomic Best Linear Unbiased Prediction), BayesA, BayesB, Bayesian ridge regression, and Bayesian LASSO. The correlation coefficient between the predicted and realized phenotypes from simulation was calculated and used as a measure of the accuracy of GEBV in each trait. The results showed that the medium marker density Panel (45 K) could be used for genomic selection in goats, which can ensure the accuracy of the GEBV. The reference population size of 1,500 can achieve greater genetic progress in genomic selection for fiber diameter and live body weight in goats by comparing with the population size below this level. The accuracy of the GEBV for live body weight and fiber diameter was better when the number of QTLs was 100 and 50, respectively. Additionally, the accuracy of GEBV was discovered to be good when the buck population size was up to 200. Meanwhile, the accuracy of the GEBV for medium heritability traits (FDs) was found to be higher than the accuracy of the GEBV for low heritability traits (LBWs). These findings will provide theoretical guidance for genomic selection in goats by using real data.

Le dosage des IGF-I est aujourd'hui au carrefour de la pathologie endocrinienne, indispensable aussi bien pour le diagnostic que pour la prise en charge thérapeutique. Pourtant, de part la complexité et l'intrication des facteurs de régulation et l'existence de difficultés techniques, l'interprétation des résultats est loin d'être une procédure banale. Dans ce travail, nous avons rappelé les principales sources d'interférences, les situations cliniques susceptibles de compromettre les résultats ainsi que les recommandations concernant chaque cas litigieux car il est utile pour tout praticien d'en être averti afin de minimiser les risques d'erreurs. The IGF-I assay is now at the crossroads of endocrine pathology, which is essential both for diagnosis and for therapeutic management. However, due to the complexity and the intricacies of the regulatory factors and the existence of technical difficulties, results' interpretation is far from a mundane procedure. In this work, we recalled the main sources of interference, the particular clinical situations that could compromise the results and the recommendations for each disputed case because we think it is useful for any practitioner to be warned in order to better manage them and to minimize errors 'risk. [ABSTRACT FROM AUTHOR]

Different dual-energy x-ray absorptiometry (DXA) hardware can affect bone mineral density (BMD) measurements and different reference populations can affect t-scores. Long-term analyses describing differences in the relationship between BMD and t-scores are lacking. BMD-values were plotted against t-scores for 241 Lunar DXA scans on females obtained over 18 years from several centers in Sweden and Norway. The result of the plot was compared to hardware/software versions, reference populations and different software analysis settings (Basic vs Enhanced analysis for total body and Single Photon Absorptiometry (SPA) vs Lunar calibration for forearm). For the forearm compartments, we found different BMD-t-score relationships depending on the use of SPA or Lunar calibration (p <0.001). With Lunar calibration, BMD-values were 24% higher, but there was no effect on t-scores. Total body measurements with iDXA scanners and Enhanced analysis for Prodigy scanners (software version 14.10) resulted in a different BMD-t-score relationship compared to the other hardware/software versions (p <0.001), with the largest discrepancy for lower BMD-values. Switching from Basic to Enhanced analysis generally decreased BMD-values and often changed t-scores (both increased and decreased). For the femoral neck, there were two different BMD-t-score relationships caused by different reference populations (p <0.001). In contrast to total body, the difference for femoral neck was more pronounced for higher values, with little impact in the clinical decision-making area. Hardware, software, reference populations and software analysis settings can affect the BMD-t-score relationship, but do so differently for different compartments. The BMD-t-score-plot is a simple and effective tool to discover systematic differences. Longitudinal analyses of DXA scans should be performed based on raw data analyzed in "one run" with the same software version and settings, in order to avoid systematic differences. [ABSTRACT FROM AUTHOR]

As optimization methods to identify the best animals for dense genotyping to construct a reference population for genotype imputation, the MCA and MCG methods, which use the pedigree-based additive genetic relationship matrix (A matrix) and the genomic relationship matrix (G matrix), respectively, have been proposed. We assessed the performance of MCA and MCG methods using 575 Japanese Black cows. Pedigree data were provided to trace back up to five generations to construct the A matrix with changing the pedigree depth from 1 to 5 (five MCA methods). Genotype information on 36,426 single-nucleotide polymorphisms was used to calculate the G matrix based on VanRaden’s methods 1 and 2 (two MCG methods). The MCG always selected one cow per iteration, while MCA sometimes selected multiple cows. The number of commonly selected cows between the MCA and MCG methods was generally lower than that between different MCA methods or between different MCG methods. For the studied population, MCG appeared to be more reasonable than MCA in selecting cows as a reference population for higher-density genotype imputation to perform genomic prediction and a genome-wide association study.

The use of serial measurement of cardiac troponin (cTn) is recommended by international guidelines for the diagnosis of myocardial infarction (MI) since 2000. This article focuses on factors influencing temporal changes in high-sensitive cTn (hs)-cTn and the impact of these factors on the diagnosis of non-ST-segment elevation MI (NSTEMI). The recommendations proposed by three different international guidelines published in 2020–2021 for the management of acute coronary syndromes (ACS) in patients presenting without persistent ST-segment elevation (NSTE) show some discrepancies. In particular, there is no agreement among these guidelines about cut-off or absolute change values to be used for the rule-in, especially regarding the use of sex-specific cut-off values. Furthermore, there are no sufficient evidences on the diagnostic accuracy and cost effectiveness related to cut-off values suggested for algorithms to be used by some hs-cTnI methods. [ABSTRACT FROM AUTHOR]

Biological information regarding markers and gene association may be used to attribute different weights for single nucleotide polymorphism (SNP) in genome‐wide selection. Therefore, we aimed to evaluate the predictive ability and the bias of genomic prediction using models that allow SNP weighting in the genomic relationship matrix (G) building, with and without incorporating biological information to obtain the weights. Firstly, we performed a genome‐wide association studies (GWAS) in data set containing single‐ (SL) or a multi‐line (ML) pig population for androstenone, skatole and indole levels. Secondly, 1%, 2%, 5%, 10%, 30% and 50% of the markers explaining the highest proportions of the genetic variance for each trait were selected to build gene networks through the association weight matrix (AWM) approach. The number of edges in the network was computed and used to derive weights for G (AWM‐WssGBLUP). The single‐step GBLUP (ssGBLUP) and weighted ssGBLUP (WssGBLUP) were used as standard scenarios. All scenarios presented predictive abilities different from zero; however, the great overlap in their confidences interval suggests no differences among scenarios. Most of scenarios of based on AWM provide overestimations for skatole in both SL and ML populations. On the other hand, the skatole and indole prediction were no biased in the ssGBLUP (S1) in both SL and ML populations. Most of scenarios based on AWM provide no biased predictions for indole in both SL and ML populations. In summary, using biological information through AWM matrix and gene networks to derive weights for genomic prediction resulted in no increase in predictive ability for boar taint compounds. In addition, this approach increased the number of analyses steps. Thus, we can conclude that ssGBLUP is most appropriate for the analysis of boar taint compounds in comparison with the weighted strategies used in the present work. [ABSTRACT FROM AUTHOR]

Keywords: acute coronary syndrome; cardiac troponins; guidelines; high-sensitivity immunoassay; myocardial infarction; POCT methods; quality specification; reference population EN acute coronary syndrome cardiac troponins guidelines high-sensitivity immunoassay myocardial infarction POCT methods quality specification reference population 1133 1135 3 06/21/22 20220701 NES 220701 A very common aphorism among experts in laboratory medicine is that "good, fast and cheap" laboratory testing is a mission impossible [[1]]. Overall, these studies [[8]], [[9]], [[10]] confirm that some hs-cTnI POCT methods meet the quality specifications recommended for hs-cTnI assay according to international guidelines [[13], [14]]. Although hs-cTnI POCT methods significantly reduce the TAT because test results can be obtained at patient bed, they still require a blood draw. Acute coronary syndrome, guidelines, myocardial infarction, quality specification, cardiac troponins, high-sensitivity immunoassay, POCT methods, reference population. [Extracted from the article]

Objective Models for genomic selection assume that the reference population is an unselected population. However, in practice, genotyped individuals, such as progeny-tested bulls, are highly selected, and the reference population is created after preselection. In dairy cattle, the intensity of selection is higher in males than in females, suggesting that cows can be added to the reference population with less bias and loss of accuracy. The objective is to develop formulas applied to any genomic prediction studies or practice with preselected animals as reference population. Methods We developed formulas for calculating the reliability and bias of genomically enhanced breeding values (GEBV) in the reference population where individuals are preselected on estimated breeding values. Based on the formulas presented, deterministic simulation was conducted by varying heritability, preselection percentage, and the reference population size. Results The number of bulls equal to a cow regarding the reliability of GEBV was expressed through a simple formula for the reference population consisting of preselected animals. The bull population was vastly superior to the cow population regarding the reliability of GEBV for low-heritability traits. However, the superiority of reliability from the bull reference population over the cow population decreased as heritability increased. Bias was greater for bulls than cows. Bias and reduction in reliability of GEBV due to preselection was alleviated by expanding reference population. Conclusion Cows are easier in expanding reference population size compared with bulls and alleviate bias and reduction in reliability of GEBV of bulls which are highly preselected than cows by expanding the cow reference population.

The use of different criteria for the definition of the reference population, combined with measures of accuracy of nutritional diagnoses, can enable the definition of the best set of Integrated Diagnosis and Recommendation System (DRIS) standards for sugarcane. In this context, the objective was to use the accuracy measures proposed by Wadt and Lemos to define the best criterion for defining the reference population for the DRIS method in sugarcane, based on the nutritional status for phosphorus. The database was created based on productivity and leaf contents of N, P, K, Ca, Mg, S, B, Cu, Fe, Mn and Zn for sugar cane, considered in two agricultural years (2013 and 2014), making a total of 1,224 samples, composed of five reference populations that showed productivity above 172; 193; 214; 235 and 256 t ha−1. The Average Nutritional Balance Index (NBIm), DRIS index and productivity were studied; degree of agreement between standards and measures of accuracy of nutritional diagnoses for phosphorus. The measures of global accuracy, excess and nutritional deficiency, showed diagnostic performance below 50% in the 2013 and 201 harvests. The accuracy for balance, sufficiency and nutritional toxicity showed high performance of the nutritional diagnoses regardless of the harvest. The analysis of the accuracy of the nutritional diagnoses for phosphorus identified low performance of all sets of DRIS standards, however, with better performance of the standards derived from reference populations of average productivity + 1.0 standard deviation in the 2013 and 2014 harvests. [ABSTRACT FROM AUTHOR]

Copyright of Kriminologisches Journal is the property of Julius Beltz GmbH & Co. KG Beltz Juventa 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.)

Aim: To update the Swedish references for weight, weight‐for‐height and body mass index (BMI) considering the secular trend for height but not including that for weight. Methods: Longitudinal measures of height and weight were obtained (0‐18 years) from 1418 (698 girls) healthy children from the GrowUp 1990 Gothenburg cohort born at term to non‐smoking mothers and Nordic parents. A total of 145 individuals with extreme BMI value vs GrowUp 1974 BMI SDS reference were excluded (0‐2 years: ±4SDS, 2 < years: −3SDS, +2.3SDS). References were constructed using the LMS method. Results: The updated weight reference became similar to the GrowUp 1974 Gothenburg reference: BMI increased rapidly up to lower levels in the 1990 cohort during infancy/early childhood, similar in both groups in late childhood/adolescence, despite lower values at +2SDS. Compared with the WHO weight standard, median and −2SDS weight values were higher for the 1990 cohort, whereas +2SDS values were lower, resulting in narrower normal range. Median values were greater and ±2SDS narrower for the 1990 vs the WHO weight‐for‐height reference. International Obesity Task force (IOTF) BMI lines for definitions for over‐ and underweight were added. Conclusion: We present updated references for weight, weight‐for‐height and BMI, providing a healthy goal for weight development when monitoring growth within healthcare settings. [ABSTRACT FROM AUTHOR]

Accurate genomic evaluation depend on large reference population with reliable performance information such as predicted breeding value (PBVs). The aim of this study was to identify the most appropriate reference population to predict the genomic breeding value for Iran Holstein dairy breeding programs. Phenotypes and genotypes were simulated based on the dairy cattle Iran population program (open breeding nucleus with gene flows between the nucleus and the commercial population). Medium (0.3) and low (0.05) heritability levels were considered independently. All simulations were performed with 10 replications and the results were evaluated. In the first study, female cows were selected for genotyping in four scenarios: random selection, individuals with upper and lower extremities of phenotypic value, highest phenotypic value and highest breeding value with maximum accuracy; and these females are added to the reference population. Single Step BLUP (SSBLUP) was used to predict the genomic breeding value for individuals in the population. The accuracy and unbiased coefficient of predicted breeding value were investigated. The results showed that when female animals with the highest and lowest phenotypic values were selected (the second scenario of determining females), the highest accuracy of prediction of breeding value was observed compared to other scenarios. Determination of substances with high phenotypic value (third scenario of female selection) showed the least bias. The use of imported males with genotype and their use alone as a reference population showed the least accuracy and the most bias. The combination of males and females showed an increase in accuracy and a decrease in bias compared to the scenarios for males or females alone. However, in relation to the size of the population similar to females, no improvement in the prediction of the breeding value was observed. Therefore, in terms of economic conditions (genotyping costs), the use of only female cows in the reference population (2000 females genotyped), according to the second scenario of female selection, is the best strategy to form a reference population and genomic evaluation at the lowest cost, in Iran. [ABSTRACT FROM AUTHOR]

Imputation can be used to obtain a large number of high-density genotypes at the cost of procuring low-density panels. Accurate imputation requires a well-formed reference population of high-density genotypes to enable statistical inference. Five methods were compared using commercial Wagyu genotype data to identify individuals to produce a “well-formed” reference population. Two methods utilised a relationship matrix (MCG and MCA), two of which utilised a haplotype block library (AHAP2 and IWS), and the last selected high influential sires with greater than 10 progeny (PROG). The efficacy of the methods was assessed based on the total proportion of genetic variance accounted for and the number of haplotypes captured, as well as practical considerations in implementing these methods. Concordance was high between the MCG and MCA and between AHAP2 and IWS but was low between these groupings. PROG-selected animals were most similar to MCA. MCG accounted for the greatest proportion of genetic variance in the population (35%, while the other methods accounted for approximately 30%) and the greatest number of unique haplotypes when a frequency threshold was applied. MCG was also relatively simple to implement, although modifications need to be made to account for DNA availability when running over a whole population. Of the methods compared, MCG is the recommended starting point for an ongoing sequencing project.

Genomic selection (GS) is commonly used in livestock and increasingly in plant breeding. Relying on phenotypes and genotypes of a reference population, GS allows performance prediction for young individuals having only genotypes. This is expected to achieve fast high genetic gain but with a potential loss of genetic diversity. Existing methods to conserve genetic diversity depend mostly on the choice of the breeding individuals. In this study, we propose a modification of the reference population composition to mitigate diversity loss. Since the high cost of phenotyping is the limiting factor for GS, our findings are of major economic interest. This study aims to answer the following questions: how would decisions on the reference population affect the breeding population, and how to best select individuals to update the reference population and balance maximizing genetic gain and minimizing loss of genetic diversity? We investigated three updating strategies for the reference population: random, truncation, and optimal contribution (OC) strategies. OC maximizes genetic merit for a fixed loss of genetic diversity. A French Montbéliarde dairy cattle population with 50K SNP chip genotypes and simulations over 10 generations were used to compare these different strategies using milk production as the trait of interest. Candidates were selected to update the reference population. Prediction bias and both genetic merit and diversity were measured. Changes in the reference population composition slightly affected the breeding population. Optimal contribution strategy appeared to be an acceptable compromise to maintain both genetic gain and diversity in the reference and the breeding populations.

The one-sample log-rank test allows to compare the survival of a single sample with a prefixed reference survival curve. It naturally applies in single-arm phase IIa trials with time-to-event endpoint. Several authors have described that the original one-sample log-rank test is conservative when sample size is small and have proposed strategies to correct the conservativeness. Here, we propose an alternative approach to improve the one-sample log-rank test. Our new one-sample log-rank statistic is based on the unique transformation of the underlying counting process martingale such that the moments of the limiting normal distribution have no shared parameters. Simulation results show that the new one-sample log-rank test gives type I error rate and power close to the nominal levels also when sample size is small, while relevantly reducing the required sample size to achieve the desired power as compared to current approaches to design studies to compare the survival outcome of a sample with a reference. [ABSTRACT FROM AUTHOR]

Agriculture is an essential component of the evolution of mankind. Environmental changes, human population growth, and increasing global demand for animal and vegetable food products have led to an urgent need for optimization of food production. Genomic selection is one of the most promising and safest methods for improving the genetic qualities of farm animals and plants. Genomic selection is based on the principle of using information from a large number of genetic markers distributed throughout the genome and serves to identify such diversity in this genome that is sufficient enough to predict breeding values without knowing exactly where specific genes are located. However, the effectiveness of genomic selection has currently reached a certain limit. In addition, it can be applied only within industrial breeds of animals and plant lines. The review discusses the ways to increase the effectiveness and fields of application for genomic selection, in particular, by implementing technologies from related sciences. [ABSTRACT FROM AUTHOR]

Summary: In causal inference, and specifically in the causes‐of‐effects problem, one is interested in how to use statistical evidence to understand causation in an individual case, and in particular how to assess the so‐called probability of causation. The answer involves the use of potential responses, which describe what would have happened to the outcome if we had observed a different value for the exposure. However, even given the best possible statistical evidence for the association between exposure and outcome, we can typically only provide bounds for the probability of causation. Dawid and his colleagues highlighted some fundamental conditions, namely exogeneity, comparability and sufficiency, that are required to obtain such bounds from experimental data. The aim of the present paper is to provide methods to find, in specific cases, the best subsample of the reference data set to satisfy these requirements. For this, we introduce a new variable, expressing the preference whether or not to be exposed, and we set the question up as a model selection problem. The best model is selected by using the marginal probability of the responses and a suitable prior over the model space. An application in the educational field is presented. [ABSTRACT FROM AUTHOR]

The aim of this work was to validate the gas chromatography/combustion/isotope ratio mass spectrometry method in Havana Antidoping Laboratory and verify its implementation with a study of the Cuban population. The method was precise and accurate inside the linear working range; the limit of quantification and the uncertainty were compliant with TD2019IRMS. The study of the Cuban population showed no differences in δ13C values between females and males. Only three values of Δδ13C showed significant differences between sexes (PD‐T, OHA‐T, and 11‐keto‐Et‐T). The values of δ13C between −17.8‰ and −21.2‰ (upper and lower limits based on normal distribution) were consistent with other populations where C4 plant derivatives prevail in the diet. [ABSTRACT FROM AUTHOR]

There is a forecast that global demand for foods of animal and plant origin will increase by 74% by 2050 (Food and Agriculture Organization of the United Nations). Satisfying this demand without a destructive effect on the environment is only possible while maintaining the principles of organic farming, as well as introducing new technologies in animal husbandry and crop production. Genomic selection as one of the most promising and safest methods for improving the genetic qualities of farm animals and plants can play a key role in this process. This review summarizes information on genomic selection, indicates possible growth points of this direction, demonstrates how a genomic estimation of the breeding value is constructed and what are the key conditions required for its implementation, and discusses the advantages and limitations of genomic and marker selection. [ABSTRACT FROM AUTHOR]

In recent years, the formulation of some immunoassays with high-sensitivity analytical performance allowed the accurate measurement of cardiac troponin I (cTnI) and T (cTnT) levels in reference subjects. Several studies have demonstrated the association between the risk of major cardiovascular events and cardiac troponin concentrations even for biomarker values within the reference intervals. High-sensitivity cTnI and cTnT methods (hs-cTn) enable to monitor myocardial renewal and remodelling, and to promptly identify patients at highest risk ofheart failure. An early and effective treatment of individuals at higher cardiovascular risk may revert the initial myocardial remodelling and slow down heart failure progression. Specific clinical trials should be carried out to demonstrate the efficacy and efficiency of the general population screening by means of cost-benefit analysis, in order to better identify individuals at higher risk for heart failure (HF) progression with hs-cTn methods. [ABSTRACT FROM AUTHOR]

The reliability of genomic prediction is influenced by several factors, including the size of the reference population, which makes genomic prediction for breeds with a relatively small population size challenging, such as Australian Red dairy cattle. Including other breeds in the reference population may help to increase the size of the reference population, but the reliability of genomic prediction is also influenced by the relatedness between the reference and validation population. Our objective was to optimize the reference population for genomic prediction of Australian Red dairy cattle. A reference population comprising up to 3,248 Holstein bulls, 48,386 Holstein cows, 807 Jersey bulls, 8,734 Jersey cows, and 3,041 Australian Red cows and a validation population with between 208 and 224 Australian Red Bulls were used, with records for milk, fat, and protein yield, somatic cell count, fertility, and survival. Three different analyses were implemented: single-trait genomic best linear unbiased predictor (GBLUP), multi-trait GBLUP, and single-trait Bayes R, using 2 different medium-density SNP panels: the standard 50K chip and a custom array of variants that were expected to be enriched for causative mutations. Various reference populations were constructed containing the Australian Red cows and all Holstein and Jersey bulls and cows, all Holstein and Jersey bulls, all Holstein bulls and cows, all Holstein bulls, and a subset of the Holstein individuals varying the relatedness between Holsteins and Australian Reds and the number of Holsteins. Varying the relatedness between reference and validation populations only led to small changes in reliability. Whereas adding a limited number of closely related Holsteins increased reliabilities compared with within-breed prediction, increasing the number of Holsteins decreased the reliability. The multi-trait GBLUP, which considered the same trait in different breeds as correlated traits, yielded higher reliabilities than the single-trait GBLUP. Bayes R yielded lower reliabilities than multi-trait GBLUP and outperformed single-trait GBLUP for larger reference populations. Our results show that increasing the size of a multi-breed reference population may result in a reference population dominated by one breed and reduce the reliability to predict in other breeds. [ABSTRACT FROM AUTHOR]

Bovine respiratory disease (BRD) is a leading cause of morbidity and mortality in dairy calves, with detrimental long-term effects that include stunted growth, increased age at first calving, and decreased milk production in first lactation. The objectives of this study were to establish a protocol for objective and efficient assessment of BRD phenotypes in preweaned dairy calves, develop a genomic reference population with well-defined clinical and subclinical phenotypes, identify chromosomal regions associated with BRD in a genome-wide association study, estimate genetic parameters of BRD, and predict genomic breeding values of dairy calves. A total of 1,107 Holstein calves from 6 dairy farms in southern Wisconsin were examined using clinical respiratory scoring and lung ultrasound at 3 and 6 wk of age. The clinical respiratory score was based on visual appraisal of eyes, nose, ears, cough, and temperature. Lung ultrasound scores were assigned based on the amount of consolidation present. Calves were genotyped with a commercially available SNP array and after quality control and imputation to higher density, 690,291 SNP markers and 1,014 individuals remained. Single-step genome-wide association study and single-step genomic best linear unbiased prediction were applied to binary 3- and 6-wk phenotypes considered as overall respiratory healthy versus affected (RESP3, RESP6) or as presence or absence of lung consolidation (CON3, CON6). Lung ultrasound combined with a clinical scoring system allowed for efficient and objective assessment for the prevalence of BRD. Proportions of variance attributed to 1-Mb non-overlapping windows suggested genomic regions that may contain putative candidate genes, most notably regions on Bos taurus autosomes 1, 6, 7, 10, 11, 12, 15, 17, 18, 27, and 28 that explained 0.70 to 1.45% of the genetic variance. Heritability estimates were higher at 3 wk (0.214 and 0.241 for CON3 and RESP3, respectively) than 6 wk (0.084 and 0.111 for CON6 and RESP6, respectively), and mean reliabilities of genomic estimated breeding vales for calves with genotypes and phenotypes ranged from 0.12 for CON6 to 0.30 for RESP3. [ABSTRACT FROM AUTHOR]

Empirical tests of comparability between restored and natural plant populations can be powerful tools for measuring progress towards restoration objectives, though they are rarely applied to critical metrics like genetic diversity, mating systems, and reproductive output via seed production. Here we compared these ecological and genetic measures for restored populations and nearby remnant reference populations of Melaleuca acuminata , a common understory shrub species of the Southwest Australian Floristic Region. Levels of diversity in 12 nuclear microsatellite markers developed for the species were moderate to high. Estimates of genetic diversity, mating system parameters and reproductive output (capsule width, capsule height, capsule volume [capsule width by capsule height], number of seeds per capsule or seed viability) were similar between restored and remnant populations. There was no evidence of genetic bottleneck effects in restored populations and allelic differentiation between restored and remnant populations at each site was low (DST ≤ 0.055). Results indicate that restored populations are comprised of genetically diverse local provenance material, and that pollinators are effectively delivering pollination-related ecosystem services equivalent to those at the reference populations. [ABSTRACT FROM AUTHOR]