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Genomics-based diversity analysis of natural vanilla populations is important in order to guide conservation efforts and genetic improvement through plant breeding. Vanilla is a cultivated, undomesticated spice that originated in Mesoamerica prior to spreading globally through vegetative cuttings. Vanilla extract from the commercial species, mainly V. planifolia and V. × tahitensis, is used around the world as an ingredient in foods, beverages, cosmetics, and pharmaceuticals. The global reliance on descendants of a few foundational clones in commercial production has resulted in an industry at heightened risk of catastrophic failure due to extremely narrow genetic diversity. Conversely, national and institutional collections including those near the center of cultivation contain previously undiscovered diversity that could bolster the genetic improvement of vanilla and guide conservation efforts. Towards this goal, an international vanilla genotyping effort generated and analyzed 431,204 single nucleotide polymorphisms among 412 accessions and 27 species from eight collections. Phylogenetic and STRUCTURE analysis sorted vanilla by species and identified hybrid accessions. Principal Component Analysis and the Fixation Index (FST) were used to refine relationships among accessions and showed differentiation among species. Analysis of the commercial species split V. planifolia into three types with all V. × tahitensis accessions being most similar to V. planifolia type 2. Finally, an in-depth analysis of V. × tahitensis identified seven V. planifolia and six V. odorata accessions as most similar to the estimated parental genotypes providing additional data in support of the current hybrid theory. The prevalence of probable V. × tahitensis parental accessions from Belize suggests that V. × tahitensis could have originated from this area and highlights the need for vanilla conservation throughout Central and South America. The genetic groupings among accessions, particularly for V. planifolia, can now be used to focus breeding efforts on fewer accessions that capture the greatest diversity., (Copyright © 2021 Elsevier B.V. All rights reserved.)

A growing body of evidence currently proposes that deep learning approaches can serve as an essential cornerstone for the diagnosis and prediction of Alzheimer's disease (AD). In light of the latest advancements in neuroimaging and genomics, numerous deep learning models are being exploited to distinguish AD from normal controls and/or to distinguish AD from mild cognitive impairment in recent research studies. In this review, we focus on the latest developments for AD prediction using deep learning techniques in cooperation with the principles of neuroimaging and genomics. First, we narrate various investigations that make use of deep learning algorithms to establish AD prediction using genomics or neuroimaging data. Particularly, we delineate relevant integrative neuroimaging genomics investigations that leverage deep learning methods to forecast AD on the basis of incorporating both neuroimaging and genomics data. Moreover, we outline the limitations as regards to the recent AD investigations of deep learning with neuroimaging and genomics. Finally, we depict a discussion of challenges and directions for future research. The main novelty of this work is that we summarize the major points of these investigations and scrutinize the similarities and differences among these investigations.

Despite the importance of climate-adjusted provenancing to mitigate the effects of environmental change, climatic considerations alone are insufficient when restoring highly degraded sites. Here we propose a comprehensive landscape genomic approach to assist the restoration of moderately disturbed and highly degraded sites. To illustrate it we employ genomic data sets comprising thousands of single nucleotide polymorphisms from two plant species suitable for the restoration of iron-rich Amazonian Savannas. We first use a subset of neutral loci to assess genetic structure and determine the genetic neighbourhood size. We then identify genotype-phenotype-environment associations, map adaptive genetic variation, and predict adaptive genotypes for restoration sites. Whereas local provenances were found optimal to restore a moderately disturbed site, a mixture of genotypes seemed the most promising strategy to recover a highly degraded mining site. We discuss how our results can help define site-adjusted provenancing strategies, and argue that our methods can be more broadly applied to assist other restoration initiatives., (© 2020 John Wiley & Sons Ltd.)

Objective: Clinical data of patients' measurements and treatment history stored in electronic health record (EHR) systems are starting to be mined for better treatment options and disease associations. A primary challenge associated with utilizing EHR data is the considerable amount of missing data. Failure to address this issue can introduce significant bias in EHR-based research. Currently, imputation methods rely on correlations among the structured phenotype variables in the EHR. However, genetic studies have shown that many EHR-based phenotypes have a heritable component, suggesting that measured genetic variants might be useful for imputing missing data. In this article, we developed a computational model that incorporates patients' genetic information to perform EHR data imputation., Materials and Methods: We used the individual single nucleotide polymorphism's association with phenotype variables in the EHR as input to construct a genetic risk score that quantifies the genetic contribution to the phenotype. Multiple approaches to constructing the genetic risk score were evaluated for optimal performance. The genetic score, along with phenotype correlation, is then used as a predictor to impute the missing values., Results: To demonstrate the method performance, we applied our model to impute missing cardiovascular related measurements including low-density lipoprotein, heart failure, and aortic aneurysm disease in the electronic Medical Records and Genomics data. The integration method improved imputation's area-under-the-curve for binary phenotypes and decreased root-mean-square error for continuous phenotypes., Conclusion: Compared with standard imputation approaches, incorporating genetic information offers a novel approach that can utilize more of the EHR data for better performance in missing data imputation., (© The Author(s) 2019. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

Using whole-genome sequence (WGS) data are supposed to be optimal for genome-wide association studies and genomic predictions. However, sequencing thousands of individuals of interest is expensive. Imputation from single nucleotide polymorphisms panels to WGS data is an attractive approach to obtain highly reliable WGS data at low cost. Here, we conducted a genotype imputation study with a combined reference panel in yellow-feather dwarf broiler population. The combined reference panel was assembled by sequencing 24 key individuals of a yellow-feather dwarf broiler population (internal reference panel) and WGS data from 311 chickens in public databases (external reference panel). Three scenarios were investigated to determine how different factors affect the accuracy of imputation from 600 K array data to WGS data, including: genotype imputation with internal, external and combined reference panels; the number of internal reference individuals in the combined reference panel; and different reference sizes and selection strategies of an external reference panel. Results showed that imputation accuracy from 600 K to WGS data were 0.834±0.012, 0.920±0.007 and 0.982±0.003 for the internal, external and combined reference panels, respectively. Increasing the reference size from 50 to 250 improved the accuracy of genotype imputation from 0.848 to 0.974 for the combined reference panel and from 0.647 to 0.917 for the external reference panel. The selection strategies for the external reference panel had no impact on the accuracy of imputation using the combined reference panel. However, if only an external reference panel with reference size >50 was used, the selection strategy of minimizing the average distance to the closest leaf had the greatest imputation accuracy compared with other methods. Generally, using a combined reference panel provided greater imputation accuracy, especially for low-frequency variants. In conclusion, the optimal imputation strategy with a combined reference panel should comprehensively consider genetic diversity of the study population, availability and properties of external reference panels, sequencing and computing costs, and frequency of imputed variants. This work sheds light on how to design and execute genotype imputation with a combined external reference panel in a livestock population.

The Cryptococcus neoformans species complex is a model organism for fungal studies. Many studies have used two strains, JEC20 and JEC21, and their derivatives. These two strains were obtained through 10 rounds of backcrosses and have been assumed near identical except at the mating-type locus. Here we obtained and compared the JEC20 genome sequence with the published "JEC21" genome. Our comparison revealed 5322 single nucleotide polymorphisms (SNPs) with the majority (N = 3816, 71.7%) located in three genomic regions, including the previously noted mating-type region. The remaining 1506 SNPs (28.3%) were distributed throughout all 14 chromosomes, predominantly at chromosomal ends. To study the potential effects of these three SNP-rich regions on phenotypes, 24 progenies from the JEC20 × JEC21 cross representing eight recombinant genotypes were analyzed for their mating ability, melanin production, capsule formation, and growths at 30 °C and 40 °C. Significant phenotypic variations were found among the progeny. However, the observed phenotypic variations could not be explained by the three SNP-rich regions. Further genome sequencing of our JEC21 and the 24 progenies revealed only six segregating SNPs outside of the three SNP-rich regions between JEC20 and JEC21, a result indicating that the 1500 SNPs identified in the published "JEC21" genome might be caused by sequencing errors and/or strain mixing. However, the six SNPs and the three SNP-rich regions could not explain the observed phenotypic variations. Our analyses suggest that spontaneous mutations accumulated under laboratory conditions could have significant effects on phenotypes and on our interpretations of experimental results.

Prostate cancer (PCa) is a very heterogeneous disease with unknown outcome at the time of first diagnosis. Multiple clinicopathological parameters andmodern imaging approaches are currently used to detect PCa and to assess the necessity of early or delayed treatment according to the predicted aggressiveness of the tumor. Despite regular adjustments of predictive systems based on histopathological factors such as the Gleason grading system or based on prostate MRI such as the Prostate Imaging Reporting and Data System (PIRADS) these tools for risk stratification of PCa patients still harbor significant limitations with regard to the accuracy of PCa outcome prediction. Therefore, great hopes have been placed on the use of biomolecular markers which might be more closely associated with the underlying biological characteristics of this tumor entity and able to predict the course of the disease better than clinical parameters. Such biomarkers are expected to serve as valuable tools not only to improve PCa diagnostics but also to enhance pre- and posttreatment risk stratification which could finally facilitate therapeutic decisions.In this review, current literature on genomic biomarkers used for PCa detection and early prediction of the tumor aggressiveness is examined. First, germline mutations and single nucleotide polymorphisms which might influence PCa onset are discussed in relation to the usefulness of targeted PCa screening approaches. Moreover, different urine- and tissue-based diagnostic tests assessing PCa-associated alterations on genetic, epigenetic and transcriptional level are reviewed. Most of these genomic biomarker assays were validated in large patient cohorts and their potential clinical usability could be proven. They provide useful diagnostic information to facilitate decisions with regard to screen men at risk to develop PCa or to repeat diagnostics in men with negative biopsy results, but persistent suspicion of cancer.Several assays can assist the early identification of patients with high-risk PCa, who potentially would need treatment, and may facilitate the selection of patients suitable for active surveillance. More evidence of the clinicalusability of such genomic PCa detection assays has to be provided by further prospective studies to support the transferability of these new diagnostic approaches to daily clinical practice.

Background: An increasing number of cancer patients die of cardiovascular diseases. The cardiotoxicity of chemotherapy is particularly important in triple-negative breast cancer (TNBC) with limited therapeutic options. Cardiac autophagy is an important mechanism of cardiotoxicity. This research was aimed to investigate the cardiotoxicity of chemotherapy in TNBC, screen the susceptible population, and determine the relationship between cardiotoxicity and autophagy-related polymorphisms., Methods: From a total of 2450 stage I-III TNBC patients, 147 met the inclusion criteria and finally recruited. Electrocardiography (ECG) was performed before most chemotherapy cycles, and echocardiography (UCG) was performed according to clinical needs. All ECG and UCG records were re-interpreted by cardiologists at the National Center for Cardiovascular Disease, Fuwai Hospital. According to the National Center for Biotechnology Information and the Catalog of Somatic Mutations in Cancer database, we selected 25 single nucleotide polymorphisms (SNPs) related to autophagy and genotyped the 147 TNBC patients. Paired-sample T tests, Chi squared tests, and logistic regression models were employed for the analysis., Results: Only 46 (31.3%) patients had normal ECG records after every chemotherapy cycle. Among the 16 patients who underwent UCG, 2 (12.5%) had a reversible decrease of left ventricular ejection fraction. The use of anthracyclines and excessive alcohol consumption were risk factors of ECG abnormalities. With the continuation of chemotherapy, heart rate gradually increased. Anthracyclines were associated with QRS duration abnormalities (P = 0.043). After genotyping for 25 autophagy-related SNPs, we found that the G allele of autophagy-related 13 (ATG13) rs10838611 was significantly associated with ECG abnormalities (odds ratio = 2.258, 95% confidence interval = 1.318-3.869; P = 0.003)., Conclusion: ECG abnormalities caused by chemotherapy are common in the real world. Autophagy-related SNPs are associated with chemotherapy-induced cardiotoxicity, thereby providing new evidence for autophagy as a cause of chemotherapy-induced cardiac damage.

Illumina and Thermo Fisher Scientific have developed assays that permit the sequencing of forensically relevant single nucleotide polymorphisms (SNPs), along with software to determine the associated genotypes. Currently there is no method to either independently confirm the genotypes determined using the manufacturer's software, or to compare genotypes and quality metrics among samples processed using both platforms. This paper outlines an automated workflow developed in CLC Genomics Workbench that permits accurate, fast and independent analysis of SNP sequence data from either platform. To facilitate the straightforward comparison of genotypes generated from both the manufacturer's software and the independent CLC analysis, a Python script was written. Data for a total of 323 forensically relevant ancestry, identity and phenotypic SNPs can be analyzed, and the resulting genotypes, coverage, quality flags and major allele frequencies are easily compared across samples and platforms., (© 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.)

Background: Targeted PCR amplicon sequencing (TAS) techniques provide a sensitive, scalable, and cost-effective way to query and identify closely related bacterial species and strains. Typically, this is accomplished by targeting housekeeping genes that provide resolution down to the family, genera, and sometimes species level. Unfortunately, this level of resolution is not sufficient in many applications where strain-level identification of bacteria is required (biodefense, forensics, clinical diagnostics, and outbreak investigations). Adding more genomic targets will increase the resolution, but the challenge is identifying the appropriate targets. VaST was developed to address this challenge by finding the minimum number of targets that, in combination, achieve maximum strain-level resolution for any strain complex. The final combination of target regions identified by the algorithm produce a unique haplotype for each strain which can be used as a fingerprint for identifying unknown samples in a TAS assay. VaST ensures that the targets have conserved primer regions so that the targets can be amplified in all of the known strains and it also favors the inclusion of targets with basal variants which makes the set more robust when identifying previously unseen strains., Results: We analyzed VaST's performance using a number of different pathogenic species that are relevant to human disease outbreaks and biodefense. The number of targets required to achieve full resolution ranged from 20 to 88% fewer sites than what would be required in the worst case and most of the resolution is achieved within the first 20 targets. We computationally and experimentally validated one of the VaST panels and found that the targets led to accurate phylogenetic placement of strains, even when the strains were not a part of the original panel design., Conclusions: VaST is an open source software that, when provided a set of variant sites, can find the minimum number of sites that will provide maximum resolution of a strain complex, and it has many different run-time options that can accommodate a wide range of applications. VaST can be an effective tool in the design of strain identification panels that, when combined with TAS technologies, offer an efficient and inexpensive strain typing protocol.

Genomic selection is arguably the most promising tool for improving genetic gain in domestic animals to emerge in the last few decades, but is an expensive process. The aim of this study was to evaluate the economic impact related to the implementation of genomic selection in a simulated dairy cattle population. The software QMSim was used to simulate genomic and phenotypic data. The simulated genome contained 30 chromosomes with 100 cm each, 1666 SNPs markers equally spread and 266 QTLs randomly designated for each chromosome. The numbers of markers and QTLs were designated according to information available from Animal QTL (http://www.animalgenome.org/QTLdb) and Bovine QTL (http://bovineqtl.tamu.edu/). The allelic frequency changes were assigned in a gamma distribution with alpha parameters equal to 0·4. Recurrent mutation rates of 1·0e-4 were assumed to apply to markers and QTLs. A historic population of 1000 individuals was generated and the total number of animals was reduced gradually along 850 generations until we obtained a number of 200 animals in the last generation, characterizing a bottleneck effect. Progenies were created along generations from random mating of the male and female gametes, assuming the same proportion of both genders. Than the population was extended for another 150 generations until we obtained 17 000 animals, with only 320 male individuals in the last generation. After this period a 25 year of selection was simulated taking into account a trait limited by sex with heritability of 0·30 (i.e. milk yield), one progeny/cow/year and variance equal to 1·0. Annually, 320 bulls were mated with 16 000 dams, assuming a replacement rate of 60 and 40% for males and females, respectively. Selection and discard criteria were based in four strategies to obtain the EBVs assuming as breeding objective to maximize milk yield. The progeny replaced the discarded animals creating an overlapping generation structure. The selection strategies were: RS is selection based on random values; PS is selection based on phenotypic values; Blup is selection based on EBVs estimated by BLUP; and GEBV is selection based on genomic estimated breeding values in one step, using high (GBlup) and low (GBlupi) density panels. Results indicated that the genetic evaluation using the aid of genomic information could provide better genetic gain rates in dairy cattle breeding programs as well as reduce the average inbreeding coefficient in the population. The economic viability indicators showed that only Blup and GBlup/GBlupi strategies, the ones that used milk control and genetic evaluation were economic viable, considering a discount rate of 6·32% per year.

With the advent of low cost, high-throughput genome sequencing technology, population genomic data sets are being generated for hundreds of species of pathogenic, industrial, and agricultural importance. The challenge is how best to analyze and visually display these complex data sets to yield intuitive representations capable of capturing complex evolutionary relationships. Here we present PopNet, a novel computational method that identifies regions of shared ancestry in the chromosomes of related strains through clustering patterns of genetic variation. These relationships are subsequently visualized within a network by a novel implementation of chromosome painting. We apply PopNet to three diverse populations that feature differential rates of recombination and demonstrate its ability to capture evolutionary relationships as well as associate traits to specific loci. Compared with existing tools, PopNet provides substantial advances by both removing the need to predefine a single reference genome that can bias interpretation of population structure, as well as its ability to visualize multiple evolutionary relationships, such as recombination events and shared ancestry, across hundreds of strains., (© The Author 2017. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Economically important reproduction traits in sheep, such as number of lambs weaned and litter size, are expressed only in females and later in life after most selection decisions are made, which makes them ideal candidates for genomic selection. Accurate genomic predictions would lead to greater genetic gain for these traits by enabling accurate selection of young rams with high genetic merit. The aim of this study was to design and evaluate the accuracy of a genomic prediction method for female reproduction in sheep using daughter trait deviations (DTD) for sires and ewe phenotypes (when individual ewes were genotyped) for three reproduction traits: number of lambs born (NLB), litter size (LSIZE) and number of lambs weaned. Genomic best linear unbiased prediction (GBLUP), BayesR and pedigree BLUP analyses of the three reproduction traits measured on 5340 sheep (4503 ewes and 837 sires) with real and imputed genotypes for 510 174 SNPs were performed. The prediction of breeding values using both sire and ewe trait records was validated in Merino sheep. Prediction accuracy was evaluated by across sire family and random cross-validations. Accuracies of genomic estimated breeding values (GEBVs) were assessed as the mean Pearson correlation adjusted by the accuracy of the input phenotypes. The addition of sire DTD into the prediction analysis resulted in higher accuracies compared with using only ewe records in genomic predictions or pedigree BLUP. Using GBLUP, the average accuracy based on the combined records (ewes and sire DTD) was 0.43 across traits, but the accuracies varied by trait and type of cross-validations. The accuracies of GEBVs from random cross-validations (range 0.17-0.61) were higher than were those from sire family cross-validations (range 0.00-0.51). The GEBV accuracies of 0.41-0.54 for NLB and LSIZE based on the combined records were amongst the highest in the study. Although BayesR was not significantly different from GBLUP in prediction accuracy, it identified several candidate genes which are known to be associated with NLB and LSIZE. The approach provides a way to make use of all data available in genomic prediction for traits that have limited recording., (© 2017 Stichting International Foundation for Animal Genetics.)

Background: The one-carbon metabolism pathway is vital in maintaining tissue homeostasis by driving the critical reactions of folate and methionine cycles. A myriad of genetic and epigenetic events mark the rate of reactions in a tissue-specific manner. Integration of these to predict and provide personalized health management requires robust computational tools that can process multiomics data. The DNA sequences that may determine the chain of biological events and the endpoint reactions within one-carbon metabolism genes remain to be comprehensively recorded. Hence, we designed the one-carbon metabolism database (1-CMDb) as a platform to interrogate its association with a host of human disorders., Methods: DNA sequence and network information of a total of 48 genes were extracted from a literature survey and KEGG pathway that are involved in the one-carbon folate-mediated pathway. The information generated, collected, and compiled for all these genes from the UCSC genome browser included the single nucleotide polymorphisms (SNPs), CpGs, copy number variations (CNVs), and miRNAs, and a comprehensive database was created. Furthermore, a significant correlation analysis was performed for SNPs in the pathway genes., Results: Detailed data of SNPs, CNVs, CpG islands, and miRNAs for 48 folate pathway genes were compiled. The SNPs in CNVs (9670), CpGs (984), and miRNAs (14) were also compiled for all pathway genes. The SIFT score, the prediction and PolyPhen score, as well as the prediction for each of the SNPs were tabulated and represented for folate pathway genes. Also included in the database for folate pathway genes were the links to 124 various phenotypes and disease associations as reported in the literature and from publicly available information., Conclusion: A comprehensive database was generated consisting of genomic elements within and among SNPs, CNVs, CpGs, and miRNAs of one-carbon metabolism pathways to facilitate (a) single source of information and (b) integration into large-genome scale network analysis to be developed in the future by the scientific community. The database can be accessed at http://slsdb.manipal.edu/ocm/., (© 2017 S. Karger AG, Basel.)

The recent development of interferon-free regimens based on direct-acting antivirals for the treatment of chronic hepatitis C virus infection has benefited many but not all patients. Some patients still experience treatment failure, possibly attributed to unknown host and viral factors, such as IFNL3 gene polymorphism. The present study assessed the prevalence of rs12979860-CC, rs12979860-CT, and rs12979860-TT genotypes of the IFNL3 gene, and its relationship with ancestry informative markers in 949 adult Brazilian healthy blood donors. Race was analyzed using ancestry informative markers as a surrogate for ancestry. IFNL3 gene was genotyped using the ABI TaqMan single nucleotide polymorphisms genotyping assays. The overall frequency of rs12979860-CC genotype was 36.9%. The contribution of African ancestry was significantly higher among donors from the northeast region in relation to southeast donors, whereas the influence of European ancestry was significantly higher in southeast donors. Donors with rs12979860-CC and rs12979860-CT genotypes had similar ancestry background. The contribution of African ancestry was higher among rs12979860-TT genotype donors in comparison to both rs12979860-CC and rs12979860-CT genotypes. The prevalence of rs12979860-CC genotype is similar to that found in the US, despite the Brazilian ancestry informative markers admixture. However, in terms of ancestry, rs12979860-CT genotype was much closer to rs12979860-CC individuals than to rs12979860-TT., (Copyright © 2016 Sociedade Brasileira de Infectologia. Published by Elsevier Editora Ltda. All rights reserved.)

The field of drug discovery has witnessed infinite development over the last decade with the demand for discovery of novel efficient lead compounds. Although the development of novel compounds in this field has seen large failure, a breakthrough in this area might be the establishment of personalized medicine. The trend of personalized medicine has shown stupendous growth being a hot topic after the successful completion of Human Genome Project and 1000 genomes pilot project. Genomic variant such as SNPs play a vital role with respect to inter individual's disease susceptibility and drug response. Hence, identification of such genetic variants has to be performed before administration of a drug. This process requires high-end techniques to understand the complexity of the molecules which might bring an insight to understand the compounds at their molecular level. To sustenance this, field of bioinformatics plays a crucial role in revealing the molecular mechanism of the mutation and thereby designing a drug for an individual in fast and affordable manner. High-end computational methods, such as molecular dynamics (MD) simulation has proved to be a constitutive approach to detecting the minor changes associated with an SNP for better understanding of the structural and functional relationship. The parameters used in molecular dynamic simulation elucidate different properties of a macromolecule, such as protein stability and flexibility. MD along with docking analysis can reveal the synergetic effect of an SNP in protein-ligand interaction and provides a foundation for designing a particular drug molecule for an individual. This compelling application of computational power and the advent of other technologies have paved a promising way toward personalized medicine. In this in-depth review, we tried to highlight the different wings of MD toward personalized medicine., (© 2016 Elsevier Inc. All rights reserved.)

Genomic resources are important and attainable for examining evolutionary change in divergent natural populations of nonmodel species. We utilized two next-generation sequencing (NGS) platforms, 454 and SOLiD 5500XL, to assemble low-coverage transcriptomes of the white-footed mouse (Peromyscus leucopus), a widespread and abundant native rodent in eastern North America. We sequenced liver mRNA transcripts from multiple individuals collected from urban populations in New York City and rural populations in undisturbed protected areas nearby and assembled a reference transcriptome using 1 080 065 954 SOLiD 5500XL (75 bp) reads and 3 052 640 454 GS FLX + reads. The reference contained 40 908 contigs with a N50 = 1044 bp and a total content of 30.06 Megabases (Mb). Contigs were annotated from Mus musculus (39.96% annotated) Uniprot databases. We identified 104 655 high-quality single nucleotide polymorphisms (SNPs) and 65 single sequence repeats (SSRs) with flanking primers. We also used normalized read counts to identify putative gene expression differences in 10 genes between populations. There were 19 contigs significantly differentially expressed in urban populations compared to rural populations, with gene function annotations generally related to the translation and modification of proteins and those involved in immune responses. The individual transcriptomes generated in this study will be used to investigate evolutionary responses to urbanization. The reference transcriptome provides a valuable resource for the scientific community using North American Peromyscus species as emerging model systems for ecological genetics and adaptation., (© 2014 John Wiley & Sons Ltd.)

Understanding a wider range of genotype-phenotype associations can be achieved through ecological and evolutionary studies of traditional laboratory models. Here, we conducted the first large-scale geographic analysis of genetic variation within and among wild zebrafish (Danio rerio) populations occurring in Nepal, India, and Bangladesh, and we genetically compared wild populations to several commonly used lab strains. We examined genetic variation at 1832 polymorphic EST-based single nucleotide polymorphisms (SNPs) and the cytb mitochondrial gene in 13 wild populations and three lab strains. Natural populations were subdivided into three major mitochondrial DNA clades with an average among-clade sequence divergence of 5.8%. SNPs revealed five major evolutionarily and genetically distinct groups with an overall FST of 0.170 (95% CI 0.105-0.254). These genetic groups corresponded to discrete geographic regions and appear to reflect isolation in refugia during past climate cycles. We detected 71 significantly divergent outlier loci (3.4%) and nine loci (0.5%) with significantly low FST values. Valleys of reduced heterozygosity, consistent with selective sweeps, surrounded six of the 71 outliers (8.5%). The lab strains formed two additional groups that were genetically distinct from all wild populations. An additional subset of outlier loci was consistent with domestication selection within lab strains. Substantial genetic variation that exists in zebrafish as a whole is missing from lab strains that we analysed. A combination of laboratory and field studies that incorporates genetic variation from divergent wild populations along with the wealth of molecular information available for this model organism provides an opportunity to advance our understanding of genetic influences on phenotypic variation for a vertebrate species., (© 2011 Blackwell Publishing Ltd.)

Premise Methods Results Discussion Leveraging DNA markers, particularly single‐nucleotide polymorphisms (SNPs), in parentage analysis, sib‐ship reconstruction, and genomic relatedness analysis can enhance plant breeding efficiency. However, the limited availability of genomic information, confined to the most commonly used species, hinders the broader application of SNPs in species of lower economic interest (e.g., most tree species). We explored the possibility of using universal target capture probes, namely Angiosperms353, to identify SNPs and assess their effectiveness in genomic relatedness analysis.We tested the approach in 11 tree species, six of which had a half‐sib family structure. Variants were called within species, and genomic relatedness analysis was conducted in species with two or more families. Scalability via amplicon sequencing was tested by designing primers and testing them in silico.Adequate SNPs for relatedness analysis were identified in all species. Relatedness values from Angiosperms353‐based SNPs highly correlated with those from thousands of genome‐wide DArTseq SNPs in Cordia africana, one of the species with a family structure. The in silico performance of designed primers demonstrated the potential for scaling up via amplicon sequencing.Utilizing universal target capture probes for SNP identification can help overcome the limitations of genomic information availability, thereby enhancing the application of genomic markers in breeding plant species with lower economic interest. [ABSTRACT FROM AUTHOR]

Cardiovascular diseases (CVDs) are complex, multifactorial conditions that require personalized assessment and treatment. Advancements in multi-omics technologies, namely RNA sequencing and whole-genome sequencing, have provided translational researchers with a comprehensive view of the human genome. The efficient synthesis and analysis of this data through integrated approach that characterizes genetic variants alongside expression patterns linked to emerging phenotypes, can reveal novel biomarkers and enable the segmentation of patient populations based on personalized risk factors. In this study, we present a cutting-edge methodology rooted in the integration of traditional bioinformatics, classical statistics, and multimodal machine learning techniques. Our approach has the potential to uncover the intricate mechanisms underlying CVD, enabling patient-specific risk and response profiling. We sourced transcriptomic expression data and single nucleotide polymorphisms (SNPs) from both CVD patients and healthy controls. By integrating these multi-omics datasets with clinical demographic information, we generated patient-specific profiles. Utilizing a robust feature selection approach, we identified a signature of 27 transcriptomic features and SNPs that are effective predictors of CVD. Differential expression analysis, combined with minimum redundancy maximum relevance feature selection, highlighted biomarkers that explain the disease phenotype. This approach prioritizes both biological relevance and efficiency in machine learning. We employed Combination Annotation Dependent Depletion scores and allele frequencies to identify variants with pathogenic characteristics in CVD patients. Classification models trained on this signature demonstrated high-accuracy predictions for CVD. The best performing of these models was an XGBoost classifier optimized via Bayesian hyperparameter tuning, which was able to correctly classify all patients in our test dataset. Using SHapley Additive exPlanations, we created risk assessments for patients, offering further contextualization of these predictions in a clinical setting. Across the cohort, RPL36AP37 and HBA1 were scored as the most important biomarkers for predicting CVDs. A comprehensive literature review revealed that a substantial portion of the diagnostic biomarkers identified have previously been associated with CVD. The framework we propose in this study is unbiased and generalizable to other diseases and disorders. [ABSTRACT FROM AUTHOR]

Body conformation traits are directly associated with longevity, fertility, health, and workability in dairy cows and have been under direct genetic selection for many decades in various countries worldwide. The main objectives of this study were to perform genome-wide association studies and functional enrichment analyses for fourteen body conformation traits using imputed high-density single nucleotide polymorphism (SNP) genotypes. The traits analyzed include body condition score (BCS), body depth (BD), bone quality (BQ), chest width (CW), dairy capacity (DC), foot angle (FAN), front legs view (FLV), heel depth (HDe), height at front end (HFE), locomotion (LOC), rear legs rear view (RLRV), rear legs side view (RLSV), stature (ST), and a composite feet and legs score index (FL) of Holstein cows scored in Canada. De-regressed estimated breeding values from a dataset of 39,135 North American Holstein animals were used as pseudo-phenotypes in the genome-wide association analyses. A mixed linear model was used to estimate the SNP effects, which ranged from 239,533 to 242,747 markers depending on the trait analyzed. Genes and quantitative trait loci (QTL) located up to 100 Kb upstream or downstream of the significant SNPs previously cited in the Animal QTLdb were detected, and functional enrichment analyses were performed for the candidate genes identified for each trait. A total of 20, 60, 13, 17, 27, 8, 7, 19, 4, 10, 13, 15, 7, and 13 genome-wide statistically significant SNPs for Bonferroni correction based on independent chromosomal segments were identified for BCS, BD, BQ, CW, DC, FAN, FLV, HDe, HFE, LOC, RLRV, RLSV, ST, and FL, respectively. The significant SNPs were located across the whole genome, except on chromosomes BTA24, BTA27, and BTA29. Four markers (for BCS, BD, HDe, and RLRV) were statistically significant when considering a much stricter threshold for the Bonferroni correction for multiple tests. Moreover, the genomic regions identified overlap with various QTL previously reported for the trait groups of exterior, health, meat and carcass, milk, production, and reproduction. The functional enrichment analyses revealed 27 significant gene ontology terms. These enriched genomic regions harbor various candidate genes previously reported as linked to bone development, metabolism, as well as infectious and immunological diseases. [ABSTRACT FROM AUTHOR]

Failure to consider population structure when managing harvested fishes increases the risk of stock depletion, yet empirical estimates of population structure are often lacking for important fishery species. In this study, we characterise genetic variation in single nucleotide polymorphisms (SNPs) to assess population structure for three harvested species of tropical snappers across the broad (up to 300 km wide) and extensive (~ 4000 km) continental shelf of north-western Australia. Comparisons across ~ 300 individuals per species, showed remarkably similar patterns of genetic structure among Lutjanus sebae (red emperor), L. malabaricus (saddletail snapper) and Pristipomoides multidens (goldband snapper) despite subtle differences in biological and ecological traits. Low levels of genetic subdivision were reflected in an isolation by distance relationship where genetic connectivity increased with geographic proximity. This indicates extensive but not unlimited dispersal across the north-western Australian shelf. Our findings provide evidence of connectivity between current management areas, violating the assumption of multiple independent stocks. Spatial stock assessment models may be more suitable for the management of these species however demographic connectivity rates cannot be accurately estimated from the conventional population genetic approaches applied in this study. We recommend that managers aim to maintain adequate spawning biomass across current management areas, and assess stocks at finer scales, where practical. [ABSTRACT FROM AUTHOR]

Background: Evidence from various cohort studies indicate a potential association between depressive disorder and benign prostatic hyperplasia (BPH), yet findings are inconsistent. This study employs bidirectional two‐sample Mendelian randomisation (MR) analysis to explore the causal relationship between BPH and major depressive disorder (MDD). Methods: Genetic variants strongly associated with MDD were extracted as instrumental variables conducted by the Psychiatric Genomics Consortium (PGC). Two sets of genetic variants associated with BPH were extracted from the recent FinnGen and Medical Research Council‐Integrative Epidemiology Unit Consortium of BPH as the discovery and replication stages, respectively. Bidirectional MR analysis employed methods such as inverse variance weighted, MR‐Egger, weighted median, maximum likelihood, and weighted mode. The inverse variance weighted method was primarily used to evaluate the causal relationship. Results: MR analysis in both the discovery and replication stages showed a significant causal relationship between MDD and the risk of BPH (discovery stages, odds ratio (OR) = 1.1146, 95% CI 1.0058–1.2353, P = 0.03852; replication stage, OR: 1.0042, 95% CI 1.0019–1.0065, P = 0.0004). No causal relationship was found between BPH and MDD risk in the reverse MR analysis. Conclusions: Our findings highlight a significant association between MDD and an increased risk of BPH development. Further investigation is needed to elucidate the underlying mechanisms linking depression and BPH. [ABSTRACT FROM AUTHOR]

Social Impact Statement: For hundreds of years, Agave marmorata plants have been used in the production of alcoholic beverages in Mexico. This species is very important in small‐scale rural economies because it is a large plant, yielding five liters of mezcal. However, the production of these beverages takes place when it reaches its reproductive stage, which takes up to 35 years. Due to its slow maturation and high demand, it is considered an endangered species. Therefore, as a conservation strategy, this study proposes the creation of nurseries, genetic breeding programs, and demographic monitoring of wild populations to counteract the extraction of wild plants and, the conservation of the genetic diversity. Summary: Agave marmorata Roezl., is an endemic species distributed in the states of Oaxaca and Puebla, Mexico, and locally is widely used to produce mezcal.We assessed the genomic diversity and differentiation using the RADseq method and 29,101 high‐quality single nucleotide polymorphisms (SNPs) in wild plants and grown under three different management types (cultivated, plants used as live fences, and young plants growing in nurseries). We examined the demographic history and used species distribution modeling to understand the future of A. marmorata under scenarios of climate change.We found high levels of genomic diversity (HS = 0.229) and moderate levels of inbreeding (FIS = 0.106 and Fhat3 = 0.190). The cultivated samples harbored less genetic diversity than the wild plants. Furthermore, we estimated low differentiation between cultivated and wild localities (FST = 0.037). In the wild samples, we identified two main genetic groups, one in the East and another in the West of its distribution area. This genetic structure possibly derived from a population contraction during the Pleistocene (~216,879.75 BP) and the formation of two refugia in small areas with climatic stability. Furthermore, the demographic reconstruction indicated that A. marmorata went through a recent population expansion event, with a large current Ne (Ne = 8,009). The future climate change models indicated contrasting possible changes in its distribution range, from an increase to the reduction of its suitable habitat, differences related to model parametrization, and future levels of CO2 production.We propose conservation measures for the different management types of the species while also considering the biotic and abiotic interactions of Agave marmorata. [ABSTRACT FROM AUTHOR]

Current interventions for oral/dental diseases heavily rely on operative/surgical procedures, while the discovery of novel drug targets may enable access to noninvasive pharmacotherapy. Therefore, this study aims to leverage large-scale data and Mendelian randomization (MR) techniques, utilizing genetic variants as instruments, to identify potential therapeutic targets for oral and dental diseases supported by genetic evidence. By intersecting 4,302 druggable genes with expression quantitative trait loci from 31,684 blood samples, we identified 2,580 druggable targets as exposures. Single nucleotide polymorphisms associated with dental disease/symptom traits were collected from FinnGen R9, the Gene–Lifestyle Interactions in Dental Endpoints consortium, and the UK Biobank to serve as outcomes for both discovery and replication purposes. Through MR analysis, we identified 43 druggable targets for various dental disease/symptom traits. To evaluate the viability of these targets, we replicated the analysis using circulating protein quantitative trait loci as exposures. Additionally, we conducted sensitivity, colocalization, Gene Ontology/Kyoto Encyclopedia of Genes and Genomes annotation, protein–protein interaction analyses, and validated dental trait–associated druggable gene expression in animal models. Among these targets, IL12RB1 (odds ratio [OR], 1.01; 95% confidence interval [CI], 1.01–1.01) and TNF (OR, 0.98; 95% CI, 0.97–0.99) exhibited therapeutic promise for oral ulcers, whereas CXCL10 (OR, 0.84; 95% CI, 0.76–0.91) was for periodontitis. Through a rigorous quality control and validation pipeline, our study yields compelling evidence for these druggable targets, which may enhance the clinical prognosis by developing novel drugs or repurposing existing ones. [ABSTRACT FROM AUTHOR]

Lung carcinoids (L‐CDs) are rare, poorly characterised neuroendocrine tumours (NETs). L‐CDs are more common in women and are not the consequence of cigarette smoking. They are classified histologically as typical carcinoids (TCs) or atypical carcinoids (ACs). ACs confer a worse survival. Histological classification is imperfect, and there is increasing interest in molecular markers. We therefore investigated global transcriptomic and epigenomic profiles of 15 L‐CDs resected with curative intent at Royal Brompton Hospital. We identified underlying mutations and structural abnormalities through whole‐exome sequencing (WES) and single nucleotide polymorphism (SNP) genotyping. Transcriptomic clustering algorithms identified two distinct L‐CD subtypes. These showed similarities either to pancreatic or neuroendocrine tumours at other sites and so were named respectively L‐CD‐PanC and L‐CD‐NeU. L‐CD‐PanC tumours featured upregulation of pancreatic and metabolic pathway genes matched by promoter hypomethylation of genes for beta cells and insulin secretion (p < 1 × 10−6). These tumours were centrally located and showed mutational signatures of activation‐induced deaminase/apolipoprotein B editing complex activity, together with genome‐wide DNA methylation loss enriched in repetitive elements (p = 2.2 × 10−16). By contrast, the L‐CD‐NeU group exhibited upregulation of neuronal markers (adjusted p < 0.01) and was characterised by focal spindle cell morphology (p = 0.04), peripheral location (p = 0.01), high mutational load (p = 2.17 × 10−4), recurrent copy number alterations, and enrichment for ACs. Mutations affected chromatin remodelling and SWI/SNF complex pathways. L‐CD‐NeU tumours carried a mutational signature attributable to aflatoxin and aristolochic acid (p = 0.05), suggesting a possible environmental exposure in their pathogenesis. Immunologically, myeloid and T‐cell markers were enriched in L‐CD‐PanC and B‐cell markers in L‐CD‐NeU tumours. The substantial epigenetic and non‐coding differences between L‐CD‐PanC and L‐CD‐NeU open new possibilities for biomarker selection and targeted treatment of L‐CD. © 2024 The Author(s). The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland. [ABSTRACT FROM AUTHOR]

Over the past three decades, significant advancements have been made in mutation enrichment methods, driven by the increasing need for precise and efficient identification of rare genetic variants associated with diseases. Mutation‐enrichment methods have emerged to boost sensitivity and enable easy detection of low‐frequency mutations. These methods are crucial in genomics research and clinical diagnostics, allowing for the detection of low‐frequency mutations within large genomic datasets. This review presents a summary of technological developments in rare mutation enrichment and emphasizes their mechanisms and applications in liquid biopsies. [ABSTRACT FROM AUTHOR]

Tepary bean (Phaseolus acutifolius A. Gray) is a diploid legume species (2n = 2x = 22). It is the most drought- and heat-tolerant crop of the genus Phaseolus. Tepary bean is native to the northern part of Mexico and the south-western part of the U.S. The lack of molecular markers associated with agronomic traits such as 100-seed weight and seed yield limit the development of elite tepary bean cultivars. Therefore, the objectives of this study were to evaluate tepary bean for 100-seed weight and yield, and identify single-nucleotide polymorphism (SNP) markers associated with these traits. A total of 230,000 high-quality SNPs obtained from the whole-genome resequencing of 153 tepary bean accessions were used for this study. For 100-seed weight, a total of 5 and 20 SNPs were found using a mixed linear model (MLM) and compressed mixed linear model (cMLM), respectively. A candidate gene, Phacu.CVR.002G320800.13, encoding the squamosa promoter-binding protein-like (SBP domain) transcription factor family protein was found to be associated with 100-seed weight. For seed yield, a total of one and eight SNPs were identified using an MLM and cMLM, respectively. Phacu.CVR.009G294200.1, encoding for peroxidase family protein, was identified as a candidate gene for seed yield. Both Phacu.CVR.002G320800.13 and Phacu.CVR.009G294200.1 are likely to be involved in seed development of tepary bean. This is one of the few studies investigating the genetics of 100-seed weight and seed yield in tepary bean. [ABSTRACT FROM AUTHOR]

Introduction: Genomic variant testing of tumors is a critical gateway for patients to access the full potential of personalized oncology therapeutics. Current methods such as next-generation sequencing are costly and challenging to interpret, while PCR assays are limited in the number of variants they can cover. We developed ASPYRE® (Allele-Specific PYrophosphorolysis RE action) technology to address the urgent need for rapid, accessible and affordable diagnostics informing actionable genomic target variants of a given cancer. The targeted ASPYRE-Lung panel for non-small cell carcinoma covers 114 variants in 11 genes (ALK, BRAF, EGFR, ERBB2, KRAS, RET, ROS1, MET & NTRK1/2/3) to robustly inform clinical management. The assay detects single nucleotide variants, insertions, deletions, and gene fusions from tissue-derived DNA and RNA simultaneously. Methods: We tested the limit of detection, specificity, analytical accuracy and analytical precision of ASPYRE-Lung using FFPE lung tissue samples from patients with non-small cell lung carcinoma, variant-negative FFPE tissue from healthy donors, and FFPE-based contrived samples with controllable variant allele fractions. Results: The sensitivity of ASPYRE-Lung was determined to be ≤ 3% variant allele fraction for single nucleotide variants and insertions or deletions, 100 copies for fusions, and 200 copies for MET exon 14 skipping. The specificity was 100% with no false positive results. The analytical accuracy test yielded no discordant calls between ASPYRE-Lung and expected results for clinical samples (via orthogonal testing) or contrived samples, and results were replicable across operators, reagent lots, runs, and real-time PCR instruments with a high degree of precision. Conclusions: The technology is simple and fast, requiring only four reagent transfer steps using standard laboratory equipment (PCR and qPCR instruments) with analysis via a cloud-based algorithm. The ASPYRE-Lung assay has the potential to be transformative in facilitating access to rapid, actionable molecular profiling of tissue for patients with non-small cell carcinoma. [ABSTRACT FROM AUTHOR]

Disorders of sexual development (DSDs) in dogs, similar to humans, arise from genetic mutations, gonadal differentiation, or phenotypic sex development. The French Bulldog, a breed that has seen a surge in popularity and demand, has also shown a marked increase in DSD incidence. This study aims to characterize the genetic underpinnings of DSDs in a French Bulldog named Brutus, exhibiting ambiguous genitalia and internal sexual anatomy, and to explore the impact of breeding practices on genetic diversity within the breed. We utilized a comprehensive approach combining conventional cytogenetics, molecular techniques, and deep sequencing to investigate the genetic profile of Brutus. The sequence data were compared to three other male French Bulldogs' genome sequences with typical reproductive anatomy, including Brutus's father and the canine reference genome (CanFam6). We found a Robertsonian fusion involving chromosome 23 previously reported in dogs as a causative mutation responsible for sex reversal syndrome. Our findings revealed a 22% mosaicism (78,XX/77,XX), the absence of the sex-determining region (SRY) gene, and the presence of 43 unique Single Nucleotide Variants (SNVs) not inherited from the father. Notably, the run of homozygosity (ROH) analysis showed Brutus has a higher number of homozygous segments compared to other Bulldogs, with a total length of these fragments 50% greater than the average, strongly suggesting this dog is the product of the mating between siblings. Although no direct causative genes for the DSD phenotype were identified, four candidate loci warrant further investigation. Our study highlighted the need for a better annotated and curated reference dog genome to define genes causative of any specific phenotype, suggests a potential genetic basis for the DSD phenotype in dogs, and underscores the consequences of uncontrolled breeding practices in French Bulldogs. These findings highlight the importance of implementing strategic genetic management to preserve genetic health and diversity in canine populations. [ABSTRACT FROM AUTHOR]

Climate change is motivating a reassessment of how seeds are selected for reforestation, as rapid environmental change can lead to local maladaptation in trees. Genetic association studies and past seed source climate both have the potential to help identify appropriate planting stock, but these techniques have not been compared and tested as part of an operational planting program. In this study, we combined an analysis of single nucleotide polymorphisms (SNPs) associated with environmental gradients in sugar pine (Pinus lambertiana) with an analysis of post‐fire seedling survival and growth in a restoration experiment. Our genotype–environment association (GEA) tests of 92 individuals from varying climates within CA revealed 829 SNPs (out of 300,604) with significant association with climate gradients, especially April snowpack. Of these, 323 either had annotations that suggested potential functional importance or were identified by two different methods. We then built Bayesian models of survival and growth for all seedlings in a separate post‐fire planting experiment, to test the relative predictive ability of source elevation (a common proxy for source climate) versus the proportion of seedling alleles expected to be locally advantageous based on GEA. Across three sites within the King Fire scar in Eldorado National Forest in 2017, 2018, and 2019, 1774 seedlings were planted. Of these, 206 had enough green needles in 2020 to allow sample collection, and 161 were successfully genotyped. We found that source elevation was generally better at predicting seedling performance than genotype indices, perhaps because of the limited scope of the association analysis. Seed sources from 500 to 1800 ft (152.4–548.6 m) lower in elevation, and one seed zone further south generally performed as well or better than local seed sources. This result, and those of similar previous studies, suggest that "climate matching" using past climate information for existing seed sourcing units is a reasonable starting point for finding seedlings suited to already‐altered planting site climate conditions. However, further tests with more extensive genomic and performance data may improve the utility of genotype information for seed selection. [ABSTRACT FROM AUTHOR]

Outbreaks of furunculosis cause significant losses in salmonid aquaculture worldwide. With a recent rise in antimicrobial resistance, regulatory measures to minimize the use of antibiotics in animal husbandry, including aquaculture, have increased scrutiny and availability of veterinary medical products to control this disease in production facilities. In such a regulatory environment, the utility of autogenous vaccines to assist with disease prevention and control as a veterinary-guided prophylactic measure is of high interest to the producers and veterinary services alike. However, evolving concepts of epidemiological units and epidemiological links need to be considered during approval and acceptance procedures for the application of autogenous vaccines in multiple aquaculture facilities. Here, we present the results of solid-state nanopore sequencing (Oxford Nanopore Technologies, ONT) performed on 54 isolates of Aeromonas salmonicida ssp. salmonicida sampled during clinical outbreaks of furunculosis in different aquaculture facilities from Bavaria, Germany, from 2017 to 2020. All of the performed analyses (phylogeny, single nucleotide polymorphism and 3D protein modeling for major immunogenic proteins) support a high probability that all studied isolates belong to the same epidemiological unit. Simultaneously, we describe a cost/effective method of whole genome analysis with the usage of ONT as a viable strategy to study outbreaks of other pathogens in the field of aquatic veterinary medicine for the purpose of developing the best autogenous vaccine candidates applicable to multiple aquaculture establishments. [ABSTRACT FROM AUTHOR]

The opportunistic pathogen Acinetobacter baumannii, carries variants of A. baumannii resistance islands (AbaR)-type genomic islands conferring multidrug resistance. Their pervasiveness in the species has remained enigmatic. The dissemination of AbaRs is intricately linked to their horizontal transfer via natural transformation, a process through which bacteria can import and recombine exogenous DNA, effecting allelic recombination, genetic acquisition, and deletion. In experimental populations of the closely related pathogenic Acinetobacter nosocomialis, we quantified the rates at which these natural transformation events occur between individuals. When integrated into a model of population dynamics, they lead to the swift removal of AbaRs from the population, contrasting with the high prevalence of AbaRs in genomes. Yet, genomic analyses show that nearly all AbaRs specifically disrupt comM, a gene encoding a helicase critical for natural transformation. We found that such disruption impedes gene acquisition, and deletion, while moderately impacting acquisition of single nucleotide polymorphism. A mathematical evolutionary model demonstrates that AbaRs inserted into comM gain a selective advantage over AbaRs inserted in sites that do not inhibit or completely inhibit transformation, in line with the genomic observations. The persistence of AbaRs can be ascribed to their integration into a specific gene, diminishing the likelihood of their removal from the bacterial genome. This integration preserves the acquisition and elimination of alleles, enabling the host bacterium--and thus its AbaR--to adapt to unpredictable environments and persist over the long term. This work underscores how manipulation of natural transformation by mobile genetic elements can drive the prevalence of multidrug resistance. [ABSTRACT FROM AUTHOR]

Objective: The purpose of this study was to investigate the epidemiological and genomic characteristics of Vibrio parahaemolyticus (V. parahaemolyticus) isolates from outbreaks in Huzhou, China. Methods: This study aims to analyze the epidemiological data on V. parahaemolyticus outbreaks reported in Huzhou from 2019 to 2023. A total of 70 V. parahaemolyticus outbreak isolates were collected. The antibiotic resistance, serotypes, molecular typing, and genomic characteristics of these isolates were analyzed. Results: Most outbreaks of V. parahaemolyticus infection occurred in the summer, and the majority of outbreaks occurred in restaurants and rural banquets. High resistance rates were observed for ampicillin (AMP, 24.29%), followed by tetracycline (TET, 15.71%) and trimethoprim-sulfamethoxazole (SXT, 15.71%). The newly emerged serotype O10:K4 became dominant from 2021 to 2023, with most isolates belonging to ST3. The resistance gene blaCARB was frequently detected among these isolates. The pulsed-field gel electrophoresis (PFGE) and whole-genome single-nucleotide polymorphisms (wgSNPs) effectively differentiated the nine outbreaks. Conclusion: The newly emerged serotype O10:K4 became dominant from 2021 to 2023, with most isolates being ST3. PFGE and WGS technologies provided reliable methods for typing and identifying V. parahaemolyticus for outbreaks. [ABSTRACT FROM AUTHOR]

Exploring genetic resources through genomic analyses has emerged as a powerful strategy to develop common bean (Phaseolus vulgaris L.) cultivars that are both productive and well-adapted to various environments. This study aimed to identify genomic regions linked to morpho-agronomic traits in Mesoamerican and Andean common bean accessions and to elucidate the proteins potentially involved in these traits. We evaluated 109 common bean accessions over three agricultural years, focusing on traits including the grain yield (YDSD), 100-seed weight (SW), number of seeds per pod (SDPD), number of pods per plant (PDPL), first pod insertion height (FPIH), plant height (PLHT), days to flowering (DF), and days to maturity (DPM). Using multilocus methods such as mrMLM, FASTmrMLM, FASTmrEMMA, ISIS EM-BLASSO, and pLARmEB, we identified 36 significant SNPs across all chromosomes (Pv01 to Pv11). Validating these SNPs and candidate genes in segregating populations is crucial for developing more productive common bean cultivars through marker-assisted selection. [ABSTRACT FROM AUTHOR]

Simple Summary: Sex chromosomal abnormalities are a major cause of reproductive failure in horses. While some horses with abnormal karyotypes exhibit reproductive abnormalities, others appear normal, remaining misdiagnosed until late age. The Pura Raza Español breeding program screens all horses for these abnormalities using genomics before adding them to the studbook. This process includes an initial assessment using the results of Short Tandem Repeat (STR) parentage testing, followed by confirmation using Single-Nucleotide Polymorphism (SNP) analysis for copy number aberrations. Hereby, we identified five new cases of 65,XXY syndrome among 27,330 foals over two breeding seasons. These horses were flagged as possibly affected by a chromosomal number aberration (CNA) due to abnormal STR and confirmed as 65,XXY by genomic analysis. All of them showed a male phenotype. One horse showed abnormal gonad development, whereas the others did not have visible abnormalities. This study represents the largest group of horses diagnosed with 65,XXY and emphasizes the importance of genomic screening for chromosomal testing. Sex chromosomal abnormalities are a well-established cause of reproductive failure in domestic horses. Because of its difficult diagnosis, the Pura Raza Español breeding program established a routine screening for chromosomal abnormalities in all the horses prior to enrolling in the studbook. This genomic procedure combines an initial assessment based on the results from Short Tandem Repeat (STR) parentage testing followed by a Single-Nucleotide Polymorphism (SNP) based copy number aberration (CNA) confirmative analysis in positive cases. Using this methodology, we identified five new individuals carrying a 65,XXY chromosomal number aberration (CNA) among 27,330 foals enrolled over the past two reproductive seasons. The animals were initially flagged as CNA candidates due to abnormal results in STR testing. Subsequent analysis genotyping using an STR sex-linked dedicated panel and a medium-density SNP array in ECAX and ECAY confirmed the diagnosis as 65,XXY carriers. Four cases (upon sample availability) underwent further analysis using in situ fluorescent hybridization with ECAX and ECAY probes, showing identical results. Phenotypic analysis revealed abnormal gonad development in one of the cases, showing that the remaining four had a normal reproductive morphology. To our knowledge, this represents the largest number of horses exhibiting the equine form of Klinefelter syndrome (65,XXY) reported to date. Our study highlights the importance of genomic screening in the accurate detection of chromosomal abnormalities in horses. [ABSTRACT FROM AUTHOR]

Background Given that obesity and insulin resistance play key roles in the pathogenesis of nonalcoholic fatty liver disease (NAFLD) and the connection between leptin and these metabolic diseases, the association between NAFLD and a leptin receptor gene (LEPR) polymorphism was examined. Methods In this genetic case-control association study, 144 biopsy-proven NAFLD patients and 144 controls were genotyped for the LEPR gene Gln223Arg (rs1137101) polymorphism using the polymerase chain reaction–restriction fragment length polymorphism method. Results The distributions of genotypes and alleles of Gln223Arg variant were in accordance with the Hardy-Weinberg equilibrium in the study groups (P >.05). Multivariate logistic regression analysis showed that the LEPR Gln223Arg Arg/Arg genotype was an independent risk factor for NAFLD; the Arg/Arg genotype, compared with the Gln/Gln genotype, was associated with a 2.09-fold increased risk for NAFLD (P =.036, odds ratio = 2.09 [95% CI = 1.31-5.95]). Conclusions We found that the LEPR Gln223Arg Arg/Arg genotype was independently associated with a more than 2-fold rise in biopsy-proven NAFLD risk. Our findings, however, need to be corroborated by further studies. [ABSTRACT FROM AUTHOR]

Aim: In northern environments, periods of isolation during Pleistocene glaciations and subsequent recolonization and secondary contact have had a significant influence on contemporary diversity of many species. The recent advent of high‐resolution genomic analyses allows unprecedented power to resolve genomic signatures of such events in northern species. Here, we provide the highest resolution genomic characterization of Atlantic salmon in North America to date to infer glacial refugia and the geographic scales of post‐glacial secondary contact. Location: North America. Taxon: Atlantic salmon, Salmo salar. Methods: Samples were collected for 5455 individuals from 148 populations, encompassing the majority of the Atlantic salmon's native range in North America, from Labrador to Maine. Individuals were genotyped using a 220K single nucleotide polymorphism array aligned to the Atlantic salmon (Salmo salar) genome. Spatial genetic structure (principal component analysis, k‐means clustering, admixture) was evaluated in conjunction with genomic comparisons of these identified lineages to infer the refugia during the last glacial maximum and regions of secondary contact following recolonization. Results: Spatial genomic analyses identified three phylogeographic groups, consistent with the northward recolonization from two southern glacial refugia in North America (a western Maritime lineage and an eastern Newfoundland and Labrador lineage), with subsequent differentiation of the eastern lineage into two separate groups. Secondary contact among these North American groups was observed within the northern Gulf of St. Lawrence, and evidence of trans‐Atlantic secondary contact was detected within the eastern Newfoundland and Labrador lineage. Comparison of groups from insular Newfoundland with those from mainland Labrador suggests genomic regions displaying high differentiation were characterized by elevated European admixture, suggesting a possible role of European secondary contact in population divergence. Main Conclusions: These findings present the first evidence suggesting that genomic diversity in extant North American Atlantic salmon populations has resulted from allopatric isolation in two glacial refugia followed by both regional and trans‐Atlantic recolonization and secondary contact and demonstrate the power of genomic tools to resolve historical drivers of diversity in wild populations. [ABSTRACT FROM AUTHOR]

Estimating heritabilities with large genomic models by established methods such as restricted maximum likelihood (REML) or Bayesian via Gibbs sampling is computationally expensive. Alternatively, heritability can be estimated indirectly by method R and by maximum predictivity, referred to as MaxPred here, at a much lower computing cost. By method R, the heritability used for predictions with whole and partial data is considered the best estimate when the predictions based on partial data are unbiased relative to those with the complete data. By MaxPred, the heritability estimate is the one that maximizes predictivity. This study compared heritability estimation with genomic information using average information REML (AI–REML), method R and MaxPred. A simulated population was generated with ten generations of 5000 animals each and an effective population size of 80. Each animal had one record for a trait with a heritability of 0.3, a phenotypic variance of 10.0 and was genotyped at 50 k SNP. In method R, the heritability estimate is found when the expectation of a regression coefficient is equal to one. The regression is the EBV of selection candidates calculated with the whole dataset regressed on the EBV of candidates calculated from a partial dataset. In this study, we used the GBLUP framework and therefore, GEBV was calculated. The partial dataset was created by removing the last generation of phenotypes. Predictivity was defined as the correlation between the adjusted phenotypes of the selection candidates and their GEBV calculated from the partial data. We estimated the heritability for populations that included between three and 10 generations. In every scenario, predictivity increased as more data was used and was the highest at the simulated heritability. However, the predictivity for all data subsets and all heritabilities compared did not differ more than 0.01, suggesting MaxPred is not the best indication for heritability estimation. For the whole dataset, the heritability was estimated as 0.30 ± 0.01, 0.26 ± 0.01 and 0.30 ± 0.04 for AI–REML without genomics, AI–REML with genomics and method R with genomics, respectively. Heritability estimation with genomics by method R reduced timing by 83%, implying a reduction in computing time from 9.5 to 1.6 h, on average, compared to AI–REML with genomics. Method R has the potential to estimate heritabilities with large genomic information at a low cost when many generations of animals are present; however, the standard error can be high when only a few iterations are used. [ABSTRACT FROM AUTHOR]

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Purpose: Understanding the mode of Mycobacterium tuberculosis (M. tuberculosis) transmission is crucial for disease prevention and control. Compared to traditional genotyping methods, whole genome sequencing (WGS) provides higher resolution and comprehensive genetic information, enabling the tracing of infection sources and determining of transmission routes to resolve extensive tuberculosis (TB) outbreaks. We conducted a ten-year study on the transmission of M. tuberculosis in a population in eastern China. Patients and Methods: We selected Lianyungang, an eastern city in China, as the study site. Patients diagnosed with active pulmonary TB from 2011 to 2020 were enrolled as the study subjects. We isolated and sequenced 2252 M. tuberculosis. Strains with pairwise genetic distances of less than 12 single nucleotide polymorphisms were defined as genomic clusters and which were considered recent transmissions. Kernel density estimation and K-function analysis were applied to explore the spatial distribution of recently transmitted strains. Results: After excluding non-tuberculous mycobacteria and duplicated samples, 2114 strains were included in the final analysis. These strains comprised lineage 2 (1593, 75.35%) and 4 (521, 24.65%). There were 672 clustered strains, with a recent transmission rate of 31.79%. The logistic regression model showed that the risk of recent transmission was high in students [adjusted odds ratio (aOR): 2.68, 95% confidence interval (CI): 1.63– 4.49, P< 0.001] and people infected with L2.2.1 strains (aOR: 1.59, 95% CI: 1.20– 2.12). Higher spatial aggregation of TB transmission has been concentrated in Haizhou, Donghai, and Guanyun for the past 10 years. Three outbreaks affecting 46 patients were spatially spaced, with 11 to 23 persons each. Different groups exhibited varying geographic distances between the initial and later cases. Conclusion: There are areas with a high risk of transmission for M. tuberculosis in the research site, and the risk varies among different populations. Accurate prevention strategies targeted at specific regions and key populations can help curb the prevalence of TB. [ABSTRACT FROM AUTHOR]

Bloodstream infections (BSIs) caused by Klebsiella pneumoniae (K. pneumoniae) are associated with high morbidity and mortality rates. This study presents a sequence type 25 (ST25) strain of hypermucoid K. pneumoniae A1 isolated from the blood of a patient with liver cirrhosis (LC) who succumbed to severe infections. We performed whole-genome sequencing of K. pneumoniae A1, which revealed virulence factors and antibiotic resistance genes. The strain harbors virulence genes encoding aerobactin, salmochelin, yersiniabactin, enterobactin, and rmpA. Additionally, the strain possessed five drug resistance genes: blaSHV-110, blaSHV-81, fosA6, OqxA, and OqxB. We further constructed a phylogenetic tree using 98 ST25 K. pneumoniae strains downloaded from NCBI together with K. pneumoniae A1. Phylogenetic analysis revealed that our isolated strain was closely related to a highly virulent strain isolated from a neonate in our region, differing by only 123 single nucleotide polymorphisms (SNPs). K. pneumoniae A1 is highly suspected to be Hypervirulent Klebsiella pneumoniae (hvKp). This study provided the first in-depth genomic analysis of ST25 K. pneumoniae in a patient with LC in China, highlighting the urgent need for early identification and diagnosis to combat this emerging threat. [ABSTRACT FROM AUTHOR]

Background: The metabolic syndrome consists of a combination of metabolic abnormalities and genetic predisposition that both contribute significantly to its development. Numerous studies have established a strong association between single nucleotide polymorphisms (SNPs) of the rs7903146 variant in the TCF7L2 gene and the metabolic syndrome (MetS) as well as type 2 diabetes. Objective: The aim of this study was to assess the impact of rs7903146 on MetS and its components. Methods: For this cross-sectional study, 325 individuals aged 25 to 86 who were selected from the baseline data of the Ahvaz cohort study were examined. Body mass index, blood pressure, fasting blood glucose, total cholesterol (TC), high-density lipoprotein cholesterol (HDL-C), and triglycerides (TG) were measured following standard protocols. MetS subjects were identified based on the National Cholesterol Education Program guidelines. Genotyping was conducted using the PCR-RFLP method. Results: Our findings revealed that individuals with the CT genotype of rs7903146 had an increased risk of MetS (OR 2.24; 95% CI, 1.26–3.98; p < 0.006). This genotype was also found to be associated with a higher risk of hypertension and low HDL cholesterol (p < 0.05). Moreover, plasma triglyceride levels were slightly higher in individuals with TT and CT genotypes, although not significantly so (p = 0.06). Conclusion: In conclusion, the CT genotype of the TCF7L2 rs7903146 polymorphism exhibited higher odds for MetS. While lifestyle factors and other genes are also implicated in MetS, our findings suggest that studying TCF7L2 polymorphisms in high-risk groups could contribute to the development of genotype-specific prevention or treatment strategies. However, further research is required to validate these results. [ABSTRACT FROM AUTHOR]

Polymorphisms in the cholesteryl ester transfer protein (CETP) gene are known to be strongly associated with increased cardiovascular risk, primarily through their effects on the lipid profile and consequently on atherosclerotic risk. The acute heart rate response (AHRR) to physical activity is closely related to individual cardiovascular health. This study aimed to investigate the effect of CETP gene polymorphisms on AHRR. Our analysis examines the association of five single nucleotide polymorphisms (SNPs; rs1532624, rs5882, rs708272, rs7499892, and rs9989419) and their haplotypes (H) in the CETP gene with AHRR in 607 people from the Hungarian population. Individual AHRR in the present study was assessed using the YMCA 3-min step test and was estimated as the difference between resting and post-exercise heart rate, i.e., delta heart rate (ΔHR). To exclude the direct confounding effect of the CETP gene on the lipid profile, adjustments for TG and HDL-C levels, next to conventional risk factors, were applied in the statistical analyses. Among the examined five SNPs, two showed a significant association with lower ΔHR (rs1532624—Cdominant: B = −8.41, p < 0.001; rs708272—Gdominant: B = −8.33, p < 0.001) and reduced the risk of adverse AHRR (rs1532624—Cdominant: OR = 0.44, p = 0.004; rs708272—Gdominant: OR = 0.43, p = 0.003). Among the ten haplotypes, two showed significant association with lower ΔHR (H3—CAGCA: B = −6.81, p = 0.003; H9—CGGCG: B = −14.64, p = 0.015) and lower risk of adverse AHRR (H3—CAGCA: OR = 0.58, p = 0.040; H9—CGGCG: OR = 0.05, p = 0.009) compared to the reference haplotype (H1—AGACG). Our study is the first to report a significant association between CETP gene polymorphisms and AHRR. It also confirms that the association of the CETP gene with cardiovascular risk is mediated by changes in heart rate in response to physical activity, in addition to its effect on lipid profile. [ABSTRACT FROM AUTHOR]

Background In Santiago, Chile, where typhoid had been hyperendemic (1977–1991), we investigated whether residual chronic carriers could be detected among household contacts of non-travel-related typhoid cases occurring during 2017–2019. Methods Culture-confirmed cases were classified as autochthonous (domestically acquired) versus travel/immigration related. Household contacts of cases had stool cultures and serum Vi antibody measurements to detect chronic Salmonella Typhi carriers. Whole genome sequences of acute cases and their epidemiologically linked chronic carrier isolates were compared. Results Five of 16 autochthonous typhoid cases (31.3%) were linked to 4 chronic carriers in case households; 2 cases (onsets 23 months apart) were linked to the same carrier. Carriers were women aged 69–79 years with gallbladder dysfunction and Typhi fecal excretion; 3 had highly elevated serum anti-Vi titers. Genomic analyses revealed close identity (≤11 core genome single-nucleotide polymorphism [SNP] differences) between case and epidemiologically linked carrier isolates; all were genotypes prevalent in 1980s Santiago. A cluster of 4 additional autochthonous cases unlinked to a carrier was identified based on genomic identity (0-1 SNPs). Travel/immigration isolate genotypes were typical for the countries of travel/immigration. Conclusions Although autochthonous typhoid cases in Santiago are currently rare, 5 of 16 such cases (31.3%) were linked to elderly chronic carriers identified among household contacts of cases. [ABSTRACT FROM AUTHOR]

Purpose: Alzheimer's disease (AD) is a common neurodegenerative disease, which can lead to cognitive impairment and dementia. Since AD is tightly associated with aging and cellular senescence, objective of this study was to investigate the association between senescence-related genes and proteins (SRGs and SRPs) and the development of AD. Design: The whole study was based on transcriptomic analysis of control and AD brain tissues and Mendelian randomization (MR) analysis. Methods: For transcriptomic analysis, GSE5281 dataset from GEO database contains the transcriptomic data of human brain tissues (n = 161) from control group and AD patients. The expression of SRGs in control and AD brain tissues were compared by Student's t test. For MR analysis, the instrumental singlenucleotide polymorphisms (SNPs) associated with 110 SRPs were filtered and selected from a large genome-wide association study (GWAS) for plasma proteome. The causality between plasma levels of SRPs and AD was explored using GWAS data of AD from Lambert et al. (17,008 cases and 37,154 controls) and further validated by using data from FinnGen consortium (6,489 patients and 170,489 controls). MR estimate was performed using the inversevariance weighted (IVW) method and the heterogeneity and pleiotropy of results were tested. Results: Transcriptomic analysis identified 36 up-regulated (including PLAUR) and 8 down-regulated SRGs in AD brain tissues. In addition, the MR results at both discovery and validation stages supported the causality between plasma levels of PLAUR (IVW-p = 3.04E-2, odds ratio [OR]= 1.15), CD55 (IVW-p = 1.56E-3, OR= 0.86), and SERPINE2 (IVW-p = 2.74E-2, OR= 0.91) and the risk of AD. Conclusion: Our findings identified that PLAUR, as an SRG, may take part in the development of AD and found that high plasma levels of PLAUR was associated with increased risk of AD, indicating that this gene was a risk factor for this disease and providing the rationale of existing drugs or new preventative and therapeutic strategies. [ABSTRACT FROM AUTHOR]

The Ethiopian core collection of the USDA-Agriculture Research Service, National Plant Germplasm System (NPGS) includes 376 accessions and a genomic characterization that revealed 151,210 single nucleotide polymorphisms. This collection, however, lacks phenotypic characterization for several important agriculture traits. A total of 330 accessions from this NPGS Ethiopian core set were evaluated for grain mould resistance response across four tropical environments. Most of the accessions in the NPGS Ethiopian collection showed susceptibility to grain mould based on the low emergence rate and high seed deterioration observed in the seeds. The population structure of the collection was not related to grain mould resistance response suggesting this germplasm originated in regions with low disease pressure. The analysis identified two accessions with high emergence (PI 457867 and PI 454221) and three (PI 455036, PI 455213 and PI 330821) with low seed degradation. Genome-wide association analysis found genomic regions in chromosome 1, 3 and 8 associated with the observed grain mould resistance variation. Candidate gene analysis within these three loci identified diseases resistance genes involved in pathogen recognition and signalling cascades of the plant immunity system. These five NPGS Ethiopian accessions are candidates for use in a pre-breeding germplasm programme to develop improved germplasm with grain mould resistance. [ABSTRACT FROM AUTHOR]

Background and Aim: Runs of homozygosity (ROH) is a biocomputational technique for identifying homozygous regions in the genomics of livestock. This study aimed to determine the ROH in Sumba Ongole (SO) bulls (n = 48) using the BovineSNP50K BeadChip. Materials and Methods: GenomeStudio 2.0 software was used to generate the BovineSNP50K BeadChip output. The ROH and ROH-based inbreeding coefficients (FROH) were determined using the detect RUNS R v4.1.0 package. Using the following filtering criteria, PLINK v1.90 software was used to perform genotype quality control: (1) Individuals and single-nucleotide polymorphism (SNPs) had call rates >0.95; (2) more than 0.05 was the minor allele frequency; (3) the list contained only SNPs linked to autosomes; and (4) SNPs that strongly deviated (p < 1e-6) from Hardy--Weinberg equilibrium were removed. Subsequently, 25,252 autosomal SNP markers were included in the ROH and FROH analyses. Results: In general, the number and length of ROH segments in pool animals were 149.77 ± 16.02 Mb and 486.13 ± 156.11 Mb, respectively. Furthermore, the ROH segments in the animals under study can be discriminated into two classes of 1-4 Mb (83.33%) and 4-8 Mb (16.67%). Subsequently, Bos taurus autosomes (BTA) 1, BTA6, and BTA14 had significant homozygous segments comprising 13 genes. Despite this, the average FROH in pool animals was 0.20 ± 0.06. Conclusion: These findings indicate that a recent inbreeding event in SO cattle occurred many generations ago. Furthermore, the candidate genes identified from the ROH analysis indicate phenotypic attributes associated with environmental adaptation and economic traits. [ABSTRACT FROM AUTHOR]

The complex topography of mountainous landscapes can create biogeographical barriers and promote allopatric speciation—even among morphologically cryptic species. However, these biogeographical barriers may not be permanent, and secondary contact between previously independently evolving populations can result in hybridization and introgression. In this study, we used genome-wide single nucleotide polymorphism data to re-examine a classic example of cryptic species in the Appalachian Mountains: the slimy salamander (Plethodon glutinosus) species complex. This group of morphologically similar terrestrial salamanders includes 14 species with parapatric distributions across the eastern USA. We focused on the Chattahoochee slimy salamander (P. chattahoochee) in northern Georgia and used a series of complementary phylogenomic and population genomic analyses to evaluate spatial genetic structure within this species and admixture with at least four other species of parapatric woodland salamanders. Our results highlight the utility of genomic data in clarifying species boundaries and underline the difficulty of species delimitation in organisms with complex evolutionary histories. [ABSTRACT FROM AUTHOR]