Genetic Values Research Articles

GENETIC CHARACTERIZATION OF RELICT ENDEMIC LIQUIDAMBAR ORIENTALIS (ALTINGIACEAE) POPULATIONS

Liquidambar orientalis, a relict endemic species, is distributed in the south-west of Turkey. The natural spread areas of the species have been decreasing since the early 1900s. This poses a great threat to the future and genetic diversity of the species. In this study, the genetic diversity of a total of 190 individuals in ten populations was examined with ten polymorphic ISSR primers. Data analysis was performed using POPGENE ver. 1.32 and GenAlEx 6.503 softwares. According to the results of the analysis, a total of 271 loci were identified. For the ten populations, the rate of polymorphic loci ranged from 21.03% to 34.69%. The mean number of observed alleles (Na) and mean number of effective alleles (Ne) were found as 1.98 ± 0.13 and 1.47 ± 24 0.37, respectively. Nei's genetic diversity values (h) ranged from 0.07 ± 0.15 to 0.12 ± 0.18. According to the results of Molecular Analysis of Variance (AMOVA) and POPGENE ver. 1.32 analysis, it was determined that the main contribution to the genetic variance originated from among the populations. In addition, the gene flow level (NM) was found to be low with a value of 0.27 per generation.

Genetic characterization of Zeyheria tuberculosa progenies and evaluation for formation of a seed orchard

Zeyheria tuberculosa, a native species of Brazil known for its significant potential in silviculture and genetic improvement, holds prominence among various species. In this study, our objective was to assess the diversity, genetic structure, and feasibility of establishing a seedling seed orchard (SSO) for this species. A total of 71 progenies were collected from different locations and were used in our experiment in Ijaci - MG. We genotyped 92 individuals (nine families with eight individuals, two families with seven individuals, and one family with six individuals), specifically selecting those with the highest predicted genetic values, using ten ISSR primers. The molecular markers employed effectively detected polymorphism (PIC = 0.44). The population exhibited moderate to high genetic diversity, as evidenced by observed (AO = 2.00) and effective alleles (AE = 1.61), Nei's diversity index (H* = 0.35), and Shannon's diversity index (I* = 0.52). Molecular variance analysis indicated significant genetic differentiation between the progenies (Φst = 0.19), yet the majority of the variation was observed within them (80.1%). Employing a Bayesian approach, we identified the formation of two distinct genetic groups, further confirming the non-genetic structure of the population. These findings affirm the potential of the Z. tuberculosa progenies to contribute to the establishment of a seedling seed orchard, supporting genetic improvement strategies and the conservation of the species' genetic diversity.

Архивы клонов плюсовых деревьев сосны обыкновенной в Республике Карелия

Archives of clones (ACs) of plus trees (PTs) are laid to preserve their genotypes. The genotype of each plus tree is unique, and its loss is irreplaceable. In addition, scientific observations are carried out in the archives, in particular, the selection of clones for forest seed orchards (FCOs) of increased genetic value (FSO-I,5). The aim of this research has been to study the dynamics of creating and preserving the archives of clones, as well as their state, in Russia in general and in the Republic of Karelia in particular. As of 2019 there were 31,514 plus trees in the Russian Federation, and the archives of clones covered an area of 573.6 ha, or 17 % less than in 1976. In the Republic of Karelia, despite discarding 40 % of them, as of 01.01.2023, there were 1,486 plus trees, including 1,100 trees of the most valuable forest species – Scots pine. Only 12.8 % of the progeny of this species grow in the archives of clones (8.4 % of a total of 1,695 selected Scots pine plus trees). For a full-fledged archive of clones (1,695 pcs. of the progeny of Scots pine plus trees), it is required to additionally plant 38.9 ha (in two repetitions – 81.3 ha) of trees. First of all, it is necessary to create the archives of clones of Scots pine from the plus trees listed in the register of the Republic of Karelia as of 2023 (1,110 pcs.). In this case, the area of additional planting will be 52 ha. To date, the selection inventory of forests in Russia as a whole and its regions, including the Republic of Karelia, has been partially completed, i.e. the selection of plus trees, considering the huge size of forested area, has also been carried out to a limited extent. Meanwhile, it is the plus plantations, plus trees and forest seed orchards created by their clone progeny that are a sample of the best phenotypic gene pool of the main forest-forming species, both in the regions of the RF and in the country as a whole. But over time these objects are aging, dying and being cut down. The best specimens may not always be preserved even in forest seed orchards and matrix plantations (MPs). Constant deforestation, a decrease in the age of felling, and the natural aging of certified plus tress necessitate the resumption of systematic selection of plus trees in the best ripening plantations of the Republic, especially its northern and central parts, and the cultivation of the best trees in the archives of clones in the form of clone progeny. This article indicates the importance of clone archives for preserving the valuable gene pool of forest woody plants and their use even in the event of the disappearance of plus trees in the places of selection and registration.

Genetic Diversity of Flying Fish (Exocoetidae) in Southeast Sulawesi, Indonesia

Flying fish (Exocoetidae) is a pelagic fishery commodity that holds economic value among the people of eastern Indonesia. Flying fish are advantageous as they are abundant in Indonesian sea waters, making them an affordable source of omega-3, which is beneficial for human health. However, the high demand for flying fish has resulted in overfishing, which poses a risk of reducing their natural population. Proper management of this commodity is necessary to minimize these risks. Effective management requires adequate information, including genetic diversity data. This study aims to determine the genetic diversity of flying fish in Southeast Sulawesi, specifically in the Banda Sea and Bone Bay populations. Tissue samples were collected from flying fish in the two populations during the study in March 2022. The results show that the genetic diversity values of flying fish in Bone Bay and the Banda Sea were 0.984 and 1.00, respectively, while the nucleotide diversity values were 0.021 and 0.018. High genetic diversity values and nucleotide diversity values were found between the two populations, with values of 0.990 and 0.020, respectively. Based on phylogenetic analysis, the two populations in Southeast Sulawesi are genetically similar.

Phylogenetic Relationship of Diadema: Emphasis on The Two Distinct Clades of D. Setosum With The Inclusion of Long Spine Black Sea Urchin From Malaysian Borneo

Diadema urchins (family Diadematidae) are ecologically important bioindicators of coral reef ecosystems and seagrass beds. Diadema urchins which are widely distributed and broadcast spawners, have been frequently utilized as model invertebrate species for zoogeography research of the Indo-West Pacific region. So far, Malaysian Borneo, located at the geographic center of Maritime Southeast Asia, has been under-sampled. This study aims to fill this sampling gap and provide the first record of Diadema setosum from Malaysian Borneo using genetic diagnostics to conclusively establish the clade-level identity of the species. According to Cytochrome Oxidase I gene analysis, Diadema is monophyletic. Seven species of Diadema namely Diadema palmeri, Diadema clarki, Diadema mexicanum, Diadema antillarum, Diadema paucispinum, Diadema africanum, and Diadema savignyi, formed their subclades with strong bootstrap values, demonstrating interspecific variation. The findings of this study provide further evidence for the presence of two distinct monophyletic clades, with all D. setosum individuals forming a monophyletic clade that later split into two distinct subclades, dividing Red Sea population (D. setosum-b) and Indo-West Pacific populations (D. setosum-a), supported by a significant genetic divergence value ranging from 6.3% to 9.1%. This study also revealed notable levels of nucleotide and population subdivision between the D. setosum from the Indo-West Pacific and the Red Sea populations (Nst = 0.891; Fst = 0.886) with a low number of migrants per generation (Nm = 0.065). This may suggest geographic isolation due to ecological factors preventing each other from surviving in the territory of the other, or that the two clades of D. setosum were a separate species. Additional morphological and molecular analysis is required in the future to ascertain the level of divergence and further resolve the taxonomic confusion within the genus Diadema.

Determination of target genes for classified molecular subtypes of triple-negative breast cancer form microarray gene expression profiling: An integrative in silico approach.

Highly heterogeneous triple-negative breast cancer (TNBC) has tough clinical features, which were gradually solving and improving in diagnosis by the molecular subtyping of TNBC. Presently, this study was focused on analyzing the genetic makeup of TNBC subtypes. This study explored the MicroArray expression profiling of differentially expressed genes in molecular subtypes BL1, BL2, IM, luminal androgen receptor, M, and mesenchymal stem-like of TNBC by analyzing the Gene Expression Omnibus dataset GSE167213. Various gene ontologies-based protein-protein interaction (PPI) networks were subtyped TNBC genes. The effect of genetic alteration on TNBC cases was also interpreted. The MicroArray gene expression profiling was done through R programming and subjected to functional annotation through the database for annotation, visualization, and integrated discovery. The PPI networking of functionally associated genes was interpreted by STRING. The survival analysis was done through cBioPortal. The t -test was used through R programming to generate the P values for a test of the significance of expressed genes. A total of 54,613 significant probes were analyzed in the TNBC MicroArray dataset. The functional PPI networks of BL1, BL2, and IM upregulated genes showed significant associations. The survival analysis of differentially expressed genes showed the significant prognostic effect of 32 upregulated genes of different subtypes on TNBC cases with genetic alterations, whereas the remaining genes showed no significant effects. The output of the present study provided significant target gene panels for different TNBC subtypes, which would add an informative genetic value to TNBC diagnosis.

Assessing the Genetic Variation of Swarna rice (Oryza sativa L.) Cultivars using SSR marker

Swarna rice cultivars (Indian cultivar) are widely grown in the northern part of Bangladesh because of their higher yield, drought tolerance, higher quality of grain and good plant appearance. Therefore, a study was conducted to observe genetic diversity among eight Swarna cultivars grown in Bangladesh. The genetic diversity of the cultivars was assessed by one hundred SSR (simple sequence repeat) markers. Among these, forty-four primers were polymorphic. In total, 188 alleles from eight rice genotypes with an average of 4.27 alleles per locus were identified. Allele numbers varied from 2 to 11. The average polymorphism information content (PIC) was 0.533, with a range of 0.195 to 0.889. The most suitable markers found were RM149, RM232, and RM3421, which provided the highest PIC value of 0.889. Genetic dissimilarity coefficient values between pairs indicated that the Guti Swarna and Nepali Swarna cultivars along with the Guti Swarna and Deshi Guti Swarna cultivars were the most genetically distant (0.818) cultivars whereas the Nepali Swarna and Nironjon Swarna cultivars were the closest (0.091). A dendrogram constructed based on polymorphism indicated five major groups. It was observed that the Guti Swarna and Sumon Swarna cultivars formed two different solitary groups, I and II whereas the Nepali Swarna and Nironjon Swarna formed group III, the Lal Swarna and Swarna5 constructed group IV, and the Deshi Guti Swarna and Tiger Indian Swarna were in group V. Our findings may help in designing appropriate cross combinations for further improvement of Swarna cultivars.

Ensemble learning for integrative prediction of genetic values with genomic variants

BackgroundWhole genome variants offer sufficient information for genetic prediction of human disease risk, and prediction of animal and plant breeding values. Many sophisticated statistical methods have been developed for enhancing the predictive ability. However, each method has its own advantages and disadvantages, so far, no one method can beat others.ResultsWe herein propose an Ensemble Learning method for Prediction of Genetic Values (ELPGV), which assembles predictions from several basic methods such as GBLUP, BayesA, BayesB and BayesCπ, to produce more accurate predictions. We validated ELPGV with a variety of well-known datasets and a serious of simulated datasets. All revealed that ELPGV was able to significantly enhance the predictive ability than any basic methods, for instance, the comparison p-value of ELPGV over basic methods were varied from 4.853E−118 to 9.640E−20 for WTCCC dataset.ConclusionsELPGV is able to integrate the merit of each method together to produce significantly higher predictive ability than any basic methods and it is simple to implement, fast to run, without using genotype data. is promising for wide application in genetic predictions.

A novel 193-plex MPS panel integrating STRs and SNPs highlights the application value of forensic genetics in individual identification and paternity testing.

Massively parallel sequencing (MPS) has emerged as a promising technology for targeting multiple genetic loci simultaneously in forensic genetics. Here, a novel 193-plex panel was designed to target 28 A-STRs, 41 Y-STRs, 21 X-STRs, 3 sex-identified loci, and 100 A-SNPs by employing a single-end 400bp sequencing strategy on the MGISEQ-2000™ platform. In the present study, a series of validations and sequencing of 1642 population samples were performed to evaluate the overall performance of the MPS-based panel and its practicality in forensic application according to the SWGDAM guidelines. In general, the 193-plex markers in our panel showed good performance in terms of species specificity, stability, and repeatability. Compared to commercial kits, this panel achieved 100% concordance for standard gDNA and 99.87% concordance for 14,560 population genotypes. Moreover, this panel detected 100% of the loci from 0.5ng of DNA template and all unique alleles at a 1:4 DNA mixture ratio (0.2ng minor contributor), and the applicability of the proposed approach for tracing and degrading DNA was further supported by case samples. In addition, several forensic parameters of STRs and SNPs were calculated in a population study. High CPE and CPD values greater than 0.9999999 were clearly demonstrated and these results could be useful references for the application of this panel in individual identification and paternity testing. Overall, this 193-plex MPS panel has been shown to be a reliable, repeatable, robust, inexpensive, and powerful tool sufficient for forensic practice.

Developing breeding values for shedding using industry data from New Zealand sheep

ABSTRACT Changes in wool prices, labour costs and animal welfare have sparked interest in several new sheep traits. Shedding, the ability of moulting or seasonal wool loss in sheep, is one of them. Here, we aimed to develop breeding values for shedding using existing data from New Zealand flocks. We used breeder-collected shedding scores, 0 = ‘no shedding on any part of the body’ to 10 = ‘wool shed fully from all the body’, for Wiltshire and Wiltshire-composite lambs born between 2003 and 2022. After QC, 20,653 phenotypic records from 25,066 animals with pedigree information (393 sires and 7439 dams from 12 flocks) were included in the analyses. Genetic parameters and breeding values were obtained using a genetic additive model with pedigree data (BLUP model), assuming shedding scores were quantitative. The genetic model incorporated the following variables: date of birth, sex, birth and rearing rank, age of dam and contemporary group. We found a high heritability for lamb shedding, 0.54 (0.02), and estimated a low-to-moderate prediction accuracy of breeding values (ranging from 0.09 to 0.36) using forward-validation. Our results confirmed that shedding can be subject to selection and that pedigree-based breeding values for this trait can be a useful tool for breeders.

Diversity and Clonality in Populations of Phytophthora citrophthora and P. syringae Causing Brown Rot of Citrus in California.

Phytophthora citrophthora and P. syringae are currently the primary causal organisms of brown rot of citrus fruits in California. To possibly find an explanation for the prevalence of the previously minor species P. syringae, we determined the population structures of both pathogens in California using next-generation sequencing and population genomics analyses. Whole-genome sequencing and aligning with newly assembled reference genomes identified 972,266 variants in 132 isolates of P. citrophthora and 422,208 variants in 154 isolates (including 24 from noncitrus tree crops) of P. syringae originating from three major growing regions. The resulting data sets were visualized using principal component analysis, discriminant analysis of principal components, unweighted pair-group method with arithmetic mean dendrograms, fastStructure, and minimum spanning networks, and we obtained the index of association, diversity summary statistics, and genetic distance statistics values GST, G''ST, and Jost's D. Subpopulations of both species were mostly defined by their geographic origin indicating restricted dispersal of inoculum. Except for five isolates, the population structure of P. citrophthora (that is heterothallic and unlikely to reproduce sexually) was clonal to semi-clonal, with very little genetic diversity within and among subgroups. In contrast, the population structure of P. syringae was also clonal to semi-clonal, but isolates were placed into four main clusters of much higher diversity. Clonality in both species can be explained by a high level of asexual reproduction. The higher diversity in the homothallic P. syringae is likely due to commonly occurring sexual reproduction. One distinct cluster of P. syringae consisted solely of isolates from noncitrus hosts; therefore, the origin of P. syringae in citrus could not be resolved.

Genomic dissection of brown planthopper, Nilaparvata lugens (Hemiptera: Delphacidae) resistance in Indica rice genotypes

AbstractThe population growth and the regular breakout of Nilaparvata lugens pose a significant risk to rice cultivation. Four different N. lugens biotypes have been identified worldwide, with biotype 4 being the most destructive and prevalent throughout Asia, particularly in India. Therefore, a rice variety with multiple resistance genes/alleles is required for effective management of N. lugens. Hence, 191 rice genotypes collected from various parts of India were evaluated for resistance to N. lugens. Further, SSR markers representing 23 different N. lugens resistant (R) genes were assayed to identify genomic regions associated with resistance. The results of the genetic analysis showed that the average genetic diversity value of all markers was 0.165 and polymorphic information content of 0.145 for all the markers used. The population structure and cluster analysis divided the studied genotypes into three distinct groups, with resistant genotypes grouped separately. These findings were confirmed by the principal coordinate analysis, which categorized resistant genotypes, moderately resistant genotypes, and susceptible genotypes into distinct components. Additionally, 90% of the genetic variation was between individuals of populations and 10% between the populations. Marker‐trait association study through mixed linear model and generalized linear model identified six SSR markers such as RM6732 (Bph15), RM314 (Bph6), RM16999 (Bph6), RM7 (QBph3), RM401 (bph4), and RM7102 (Bph1), which were significantly associated with various phenotypic parameters, such as feeding mark, honeydew excretion, percent damage and nymphal survival. The resistant genes identified in these genotypes could help in the marker‐assisted rice variety development with durable resistance against N. lugens.

How do stochastic processes and genetic threshold effects explain incomplete penetrance and inform causal disease mechanisms?

Incomplete penetrance is the rule rather than the exception in Mendelian disease. In syndromic monogenic disorders, phenotypic variability can be viewed as the combination of incomplete penetrance for each of multiple independent clinical features. Within genetically identical individuals, such as isogenic model organisms, stochastic variation at molecular and cellular levels is the primary cause of incomplete penetrance according to a genetic threshold model. By defining specific probability distributions of causal biological readouts and genetic liability values, stochasticity and incomplete penetrance provide information about threshold values in biological systems. Ascertainment of threshold values has been achieved by simultaneous scoring of relatively simple phenotypes and quantitation of molecular readouts at the level of single cells. However, this is much more challenging for complex morphological phenotypes using experimental and reductionist approaches alone, where cause and effect are separated temporally and across multiple biological modes and scales. Here I consider how causal inference, which integrates observational data with high confidence causal models, might be used to quantify the relative contribution of different sources of stochastic variation to phenotypic diversity. Collectively, these approaches could inform disease mechanisms, improve predictions of clinical outcomes and prioritize gene therapy targets across modes and scales of gene function. This article is part of a discussion meeting issue 'Causes and consequences of stochastic processes in development and disease'.

GWABLUP: genome-wide association assisted best linear unbiased prediction of genetic values

BackgroundSince the very beginning of genomic selection, researchers investigated methods that improved upon SNP-BLUP (single nucleotide polymorphism best linear unbiased prediction). SNP-BLUP gives equal weight to all SNPs, whereas it is expected that many SNPs are not near causal variants and thus do not have substantial effects. A recent approach to remedy this is to use genome-wide association study (GWAS) findings and increase the weights of GWAS-top-SNPs in genomic predictions. Here, we employ a genome-wide approach to integrate GWAS results into genomic prediction, called GWABLUP.ResultsGWABLUP consists of the following steps: (1) performing a GWAS in the training data which results in likelihood ratios; (2) smoothing the likelihood ratios over the SNPs; (3) combining the smoothed likelihood ratio with the prior probability of SNPs having non-zero effects, which yields the posterior probability of the SNPs; (4) calculating a weighted genomic relationship matrix using the posterior probabilities as weights; and (5) performing genomic prediction using the weighted genomic relationship matrix. Using high-density genotypes and milk, fat, protein and somatic cell count phenotypes on dairy cows, GWABLUP was compared to GBLUP, GBLUP (topSNPs) with extra weights for GWAS top-SNPs, and BayesGC, i.e. a Bayesian variable selection model. The GWAS resulted in six, five, four, and three genome-wide significant peaks for milk, fat and protein yield and somatic cell count, respectively. GWABLUP genomic predictions were 10, 6, 7 and 1% more reliable than those of GBLUP for milk, fat and protein yield and somatic cell count, respectively. It was also more reliable than GBLUP (topSNPs) for all four traits, and more reliable than BayesGC for three of the traits. Although GWABLUP showed a tendency towards inflation bias for three of the traits, this was not statistically significant. In a multitrait analysis, GWABLUP yielded the highest accuracy for two of the traits. However, for SCC, which was relatively unrelated to the yield traits, including yield trait GWAS-results reduced the reliability compared to a single trait analysis.ConclusionsGWABLUP uses GWAS results to differentially weigh all the SNPs in a weighted GBLUP genomic prediction analysis. GWABLUP yielded up to 10% and 13% more reliable genomic predictions than GBLUP for single and multitrait analyses, respectively. Extension of GWABLUP to single-step analyses is straightforward.

Imagenetics for Precision Medicine in Dilated Cardiomyopathy.

Dilated cardiomyopathy (DCM) is a common heart muscle disorder of nonischemic etiology associated with heart failure development and the risk of malignant ventricular arrhythmias and sudden cardiac death. A tailored approach to risk stratification and prevention of sudden cardiac death is required in genetic DCM given its variable presentation and phenotypic severity. Currently, advances in cardiogenetics have shed light on disease mechanisms, the complex genetic architecture of DCM, polygenic contributors to disease susceptibility and the role of environmental triggers. Parallel advances in imaging have also enhanced disease recognition and the identification of the wide spectrum of phenotypes falling under the DCM umbrella. Genotype-phenotype associations have been also established for specific subtypes of DCM, such as DSP (desmoplakin) or FLNC (filamin-C) cardiomyopathy but overall, they remain elusive and not readily identifiable. Also, despite the accumulated knowledge on disease mechanisms, certain aspects remain still unclear, such as which patients with DCM are at risk for disease progression or remission after treatment. Imagenetics, that is, the combination of imaging and genetics, is expected to further advance research in the field and contribute to precision medicine in DCM management and treatment. In the present article, we review the existing literature in the field, summarize the established knowledge and emerging data on the value of genetics and imaging in establishing genotype-phenotype associations in DCM and in clinical decision making for DCM patients.

A comparison of genetic and genomic breeding values in Saanen and Alpine goats

Nowadays, several countries are developing or adopting genomic selection in the dairy goat sector. The most used method to estimate breeding values is Single-Step Genomic Best Linear Unbiased Prediction (ssGBLUP) which offers several advantages in terms of computational process and accuracy of the estimated breeding values (EBVs). Saanen and Alpine are the predominant dairy goat breeds in Italy, and both have similar breeding programs where EBVs for productive traits are currently calculated using BLUP. This work describes the implementation of genomic selection for these two breeds in Italy, aligning with the selection practices already carried out in the international landscape. The available dataset included 3 611 genotyped animals, 11 470 lactation records, five traits (milk, protein and fat yields, and fat and protein percentages), and three-generation pedigrees. EBVs were estimated using BLUP, GBLUP, and ssGBLUP both with single and multiple trait approaches. The methods were compared in terms of correlation between EBVs and genetic trends. Results were also validated with the linear regression method excluding part of the phenotypic data. In both breeds, EBVs and GEBVs were strongly correlated and the trend of each trait was similar comparing the three methods. The average increase in accuracy across traits and methods amounted to +13 and +10% from BLUP to ssGBLUP for Alpine and Saanen breeds, respectively. Results indicated higher prediction accuracy and correlation for GBLUP and ssGBLUP compared to BLUP, implying that the use of genotypes increases the accuracy of EBVs, particularly in the absence of phenotypic data. Therefore, ssGBLUP is likely to be the most effective method to enhance genetic gain in Italian Saanen and Alpine goats.

Hilly, Semi-Mountainous and Mountainous Areas Harbor Landraces Diversity: The Case of Messinia (Peloponnese-Greece)

Landraces are identified for their genetic diversity and heritage value. Geographically isolated areas are gradually recognized for their agricultural diversity and importance in conservation. One of the consequences of agricultural intensification is an increase in crop uniformity and hence a reduction in the landraces’ genetic resources. Messinia, located in Southwest Peloponnese, Greece, is characterized by a diverse terrain and smallholdings. The geomorphological character of the area and farmers’ selections have led to new landraces (local varieties) with unique traits and specific adaptations. A total of 110 villages/settlements were visited between 2013–2016, aiming to explore the existing wealth of landraces and the degree of genetic erosion. The genetic material collected and the testimonies extracted from local communities were compared to data from past expeditions, gene banks, and portal databases. Of the 427 collected samples, the majority belonged to annual vegetable or pulse species, indicating the genetic diversity of the groups, which was likely related to their culinary value. Perennial crops are priced both as commercial and staple crops, with olive trees dominating the agricultural landscape. Genetic erosion and production decline were noticed for cereals. It is concluded that socio-cultural and agricultural trends have a strong influence on the survival of landraces. Without exhausting the agricultural wealth of the region, the present study suggests that Messinia is an agrobiodiversity hotspot that includes neglected crops.

Factors influencing ovum pick-up technique results in cattle

The Ovum Pick-Up (OPU) technique, which is used in in vitro embryo production (IVP) to retrieve immature oocytes from live donor animals, is one of the most important biotechnological procedures used in cattle breeding. The most important advantage of this technology is that it allows for the reproducible retrieval of immature oocytes from living donor animals. It is particularly useful in dairy cattle breeding to address infertility issues and boost the production of superior animals with high genetic value. The OPU technique offers several advantages, including its applicability to cows ranging from six-month-old calves to the first three months of pregnancy, its effectiveness in animals with genital tract infection or acyclic cattle, and its ability to yield a higher number of embryos within the same period compared to the Multiple Ovulation and Embryo Transfer (MOET) technique. Understanding and improving the technical and biological factors influencing the OPU procedure is necessary to increase and optimize donor animal use in IVP and the number of quality oocytes obtained. This review aims to examine the specifics of the OPU approach and the factors influencing its performance in light of contemporary literature, as well as to propose fresh ideas to researchers.

Impacts of reference population size and methods on the accuracy of genomic prediction for fleece traits in Inner Mongolia Cashmere Goats.

Inner Mongolia Cashmere Goats (IMCGs) are famous for its cashmere quality and it's a unique genetic resource in China. Therefore, it is necessary to use genomic selection to improve the accuracy of selection for fleece traits in Inner Mongolia cashmere goats. The aim of this study was to determine the effect of methods (GBLUP, BayesA, BayesB, Bayesian LASSO, Bayesian Ridge Region) and the reference population size on accuracy of genomic selection in IMCGs. This study fully utilizes the pedigree and phenotype records of fleece traits in 2255 individuals, genotype of 50794 SNPs after quality control, and environmental data to perform genomic selection of fleece traits. Then GBLUP and Bayes series methods (BayesA, BayesB, Bayesian LASSO, Bayesian Ridge Region) were used to perform estimates of genetic parameter and genomic breeding value. And the accuracy of genomic estimated breeding value (GEBV) is evaluated using the five-fold cross validation method. And the analysis of variance and multiple comparison methods were used to determine the best method for genomic selection in fleece traits of IMCGs. Further the different reference population sizes (500, 1000, 1500, and 2000) was set. Then the best method was applied to estimate genome breeding values, and evaluate the impact of reference population sizes on the accuracy of genome selection for fleece traits in IMCGs. It was found that the genomic prediction accuracy for each fleece trait in IMCGs by GBLUP method is highest, and it is significantly higher than that obtained by Bayesian method. The accuracy of breeding value estimation is 58.52% -68.49%. Also, it was found that the size of the reference population has a significant impact on the accuracy of genome prediction of fleece traits. When the reference population size is 2000, the accuracy of genomic prediction for each fleece trait is significantly higher than other levels, with accuracy of 55.47% -67.87%. This provides a theoretical basis for design a reasonable genome selection plan for Inner Mongolia cashmere goats in the later stag.

Convolutional neural network based data interpretable framework for Alzheimer’s treatment planning

Alzheimer’s disease (AD) is a neurological disorder that predominantly affects the brain. In the coming years, it is expected to spread rapidly, with limited progress in diagnostic techniques. Various machine learning (ML) and artificial intelligence (AI) algorithms have been employed to detect AD using single-modality data. However, recent developments in ML have enabled the application of these methods to multiple data sources and input modalities for AD prediction. In this study, we developed a framework that utilizes multimodal data (tabular data, magnetic resonance imaging (MRI) images, and genetic information) to classify AD. As part of the pre-processing phase, we generated a knowledge graph from the tabular data and MRI images. We employed graph neural networks for knowledge graph creation, and region-based convolutional neural network approach for image-to-knowledge graph generation. Additionally, we integrated various explainable AI (XAI) techniques to interpret and elucidate the prediction outcomes derived from multimodal data. Layer-wise relevance propagation was used to explain the layer-wise outcomes in the MRI images. We also incorporated submodular pick local interpretable model-agnostic explanations to interpret the decision-making process based on the tabular data provided. Genetic expression values play a crucial role in AD analysis. We used a graphical gene tree to identify genes associated with the disease. Moreover, a dashboard was designed to display XAI outcomes, enabling experts and medical professionals to easily comprehend the prediction results.