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In evolving populations where the rate of beneficial mutations is large, subpopulations of individuals with competing beneficial mutations can be maintained over long times. Evolution with this kind of clonal structure is commonly observed in a wide range of microbial and viral populations. However, it can be difficult to completely resolve clonal dynamics in data. This is due to limited read lengths in high-throughput sequencing methods, which are often insufficient to directly measure linkage disequilibrium or determine clonal structure. Here, we develop a method to infer clonal structure using correlated allele frequency changes in time-series sequence data. Simulations show that our method recovers true, underlying clonal structures when they are known and accurately estimates linkage disequilibrium. This information can then be combined with other inference methods to improve estimates of the fitness effects of individual mutations. Applications to data suggest novel clonal structures in an E. coli long-term evolution experiment, and yield improved predictions of the effects of mutations on bacterial fitness and antibiotic resistance. Moreover, our method is computationally efficient, requiring orders of magnitude less run time for large data sets than existing methods. Overall, our method provides a powerful tool to infer clonal structures from data sets where only allele frequencies are available, which can also improve downstream analyses.

Abstract Modern medicine has produced large genetic datasets of high dimensions through advanced gene sequencing technology, and processing these data is of great significance for clinical decision-making. Gene selection (GS) is an important data preprocessing technique that aims to select a subset of feature information to improve performance and reduce data dimensionality. This study proposes an improved wrapper GS method based on forensic-based investigation (FBI). The method introduces the search mechanism of the slime mould algorithm in the FBI to improve the original FBI; the newly proposed algorithm is named SMA_FBI; then GS is performed by converting the continuous optimizer to a binary version of the optimizer through a transfer function. In order to verify the superiority of SMA_FBI, experiments are first executed on the 30-function test set of CEC2017 and compared with 10 original algorithms and 10 state-of-the-art algorithms. The experimental results show that SMA_FBI is better than other algorithms in terms of finding the optimal solution, convergence speed, and robustness. In addition, BSMA_FBI (binary version of SMA_FBI) is compared with 8 binary algorithms on 18 high-dimensional genetic data from the UCI repository. The results indicate that BSMA_FBI is able to obtain high classification accuracy with fewer features selected in GS applications. Therefore, SMA_FBI is considered an optimization tool with great potential for dealing with global optimization problems, and its binary version, BSMA_FBI, can be used for GS tasks.

Abstract Background The long-term impacts of COVID-19 on human health are a major concern, yet comprehensive evaluations of its effects on various health conditions are lacking. Methods This study aims to evaluate the role of various diseases in relation to COVID-19 by analyzing genetic data from a large-scale population over 2,000,000 individuals. A bidirectional two-sample Mendelian randomization approach was used, with exposures including COVID-19 susceptibility, hospitalization, and severity, and outcomes encompassing 86 different diseases or traits. A reverse Mendelian randomization analysis was performed to assess the impact of these diseases on COVID-19. Results Our analysis identified causal relationships between COVID-19 susceptibility and several conditions, including breast cancer (OR = 1.0073, 95% CI = 1.0032–1.0114, p = 5 × 10 − 4), ER + breast cancer (OR = 0.5252, 95% CI = 0.3589–0.7685, p = 9 × 10 − 4), and heart failure (OR = 1.0026, 95% CI = 1.001–1.0042, p = 0.002). COVID-19 hospitalization was causally linked to heart failure (OR = 1.0017, 95% CI = 1.0006–1.0028, p = 0.002) and Alzheimer’s disease (OR = 1.5092, 95% CI = 1.1942–1.9072, p = 0.0006). COVID-19 severity had causal effects on primary biliary cirrhosis (OR = 2.6333, 95% CI = 1.8274–3.7948, p = 2.059 × 10 − 7), celiac disease (OR = 0.0708, 95% CI = 0.0538–0.0932, p = 9.438 × 10–80), and Alzheimer’s disease (OR = 1.5092, 95% CI = 1.1942–1.9072, p = 0.0006). Reverse MR analysis indicated that rheumatoid arthritis, diabetic nephropathy, multiple sclerosis, and total testosterone (female) influence COVID-19 outcomes. We assessed heterogeneity and horizontal pleiotropy to ensure result reliability and employed the Steiger directionality test to confirm the direction of causality. Conclusions This study provides a comprehensive analysis of the causal relationships between COVID-19 and diverse health conditions. Our findings highlight the long-term impacts of COVID-19 on human health, emphasizing the need for continuous monitoring and targeted interventions for affected individuals. Future research should explore these relationships to develop comprehensive healthcare strategies.

Abstract Population-based studies of human mitochondrial genetic diversity often require the classification of mitochondrial DNA (mtDNA) haplotypes into more than 5400 described haplogroups, and further grouping those into hierarchically higher haplogroups. Such secondary haplogroup groupings (e.g., “macro-haplogroups”) vary across studies, as they depend on the sample quality, technical factors of haplogroup calling, the aims of the study, and the researchers' understanding of the mtDNA haplogroup nomenclature. Retention of historical nomenclature coupled with a growing number of newly described mtDNA lineages results in increasingly complex and inconsistent nomenclature that does not reflect phylogeny well. This “clutter” leaves room for grouping errors and inconsistencies across scientific publications, especially when the haplogroup names are used as a proxy for secondary groupings, and represents a source for scientific misinterpretation. Here we explore the effects of phylogenetically insensitive secondary mtDNA haplogroup groupings, and the lack of standardized secondary haplogroup groupings on downstream analyses and interpretation of genetic data. We demonstrate that frequency-based analyses produce inconsistent results when different secondary mtDNA groupings are applied, and thus allow for vastly different interpretations of the same genetic data. The lack of guidelines and recommendations on how to choose appropriate secondary haplogroup groupings presents an issue for the interpretation of results, as well as their comparison and reproducibility across studies. To reduce biases originating from arbitrarily defined secondary nomenclature-based groupings, we suggest that future updates of mtDNA phylogenies aimed for the use in mtDNA haplogroup nomenclature should also provide well-defined and standardized sets of phylogenetically meaningful algorithm-based secondary haplogroup groupings such as “macro-haplogroups”, “meso-haplogroups”, and “micro-haplogroups”. Ideally, each of the secondary haplogroup grouping levels should be informative about different human population history events. Those phylogenetically informative levels of haplogroup groupings can be easily defined using TreeCluster, and then implemented into haplogroup callers such as HaploGrep3. This would foster reproducibility across studies, provide a grouping standard for population-based studies, and reduce errors associated with haplogroup nomenclatures in future studies.

The exponential expansion of consumer genetic testing has led to an accumulation of massive genomic data sets owned by governments and firms. The prospect of leveraging genetic data for enhancing consumers' health, well-being, and satisfaction through improved personalization, segmentation, and targeting is promising. Nonetheless, this potential has not been studied empirically to date, and it is unknown whether and when firms should invest resources into incorporating genetic data into strategies and processes. The authors address this gap in a study of taste preferences, important drivers of food and beverage consumption. Using a large U.K.-based sample, they find that with sample sizes currently available, genetic data are expected to significantly improve prediction of taste preferences above traditionally used metrics such as demographics, behavioral variables, and even past consumption, especially for tastes that are uncommon in the local diet (e.g., spicy, sour), as they are less expressed behaviorally. The authors conclude that genetic data show immense promise for prediction-based applications when other data sources are limited or uninformative. These findings could have significant implications for public health initiatives, potentially aiding development of personalized nutrition plans and dietary interventions. [ABSTRACT FROM AUTHOR]

Abstract The incidence of affective disorders, of which major depression disorder (MDD) and bipolar disorder (BD) are two main types, has increased rapidly in recent years. They significantly impact patients, their families, and society. However, while affective disorders have become a major issue worldwide, their pathogenesis remains unclear. In the last 6 years, research using magnetic resonance imaging (MRI) and genetic data has gained prominence in understanding their pathophysiology and etiology. This systematic review collected the studies of MDD and BD research published between January 1, 2018, and February 1, 2024, focusing on studies using MRI and genetic data and indexed in the Web of Science and PubMed database. It aims to investigate the similarities and differences in their imaging phenotypes and underlying molecular bases. After exclusions, a total of 80 articles were included in this review. Research on MDD and BD reveals the critical role of epigenetic modifications, such as DNA methylation, in brain structure and function changes. The genes and pathways implicated in MDD are directly associated with depressive symptoms. In contrast, those implicated in BD are associated with mood regulation and cognitive functions. In addition, functional imaging studies have revealed that abnormalities in MDD are frequently concentrated in regions involved in emotion regulation and stress response. In contrast, those in BD are frequently concentrated in the neural circuits related to reward processing and emotional stability. Further multimodal and multiscale studies are needed to advance the field of mood disorder research.

Several regression models for predicting returns of pink salmon in the Kamchatka region are presented. The data for 1990–2023 were analyzed. Among available climatic and oceanological indices, the most suitable for using as predictors for forecasting of pink salmon returns were the Pacific Decadal Oscillation (PDO) index, Western Pacific Cyclonic Index (WP), Arctic Oscillation (AO) index, and the sea surface temperature anomaly in the North Pacific. Multi-dimensional models of the «stock–recruitment» type were built on identified statistical patterns, which allowed to estimate potential abundance of the pink salmon returns to northeastern and western Kamchatka. Besides, methods for predicting the abundance of pink salmon returns on the data of fish counting in the sea are considered, using the materials of TINRO trawl surveys conducted in the Bering and Okhotsk Seas in the fall seasons of 2012–2023. To determine the abundance of pink salmon originated from West Kamchatka, genetic identification of regional composition of juveniles in mixed trawl catches was used. All tested methods have a high level of determination, but simpler regressive models are more prospective for practical forecasting of general trend in dynamics of pink salmon stocks in the Kamchatka region due to very weak generalization ability of more complicated models.

Abstract Background Metabolic dysregulation is recognized as a significant hallmark of cancer progression. Although numerous studies have linked specific metabolic pathways to cancer incidence, the causal relationship between blood metabolites and lung cancer risk remains unclear. Methods Genomic data from 29,266 lung cancer patients and 56,450 control individuals from the Transdisciplinary Research in Cancer of the Lung and the International Lung Cancer Consortium (TRICL-ILCCO) were utilized, and findings were replicated using additional data from the FinnGen consortium. The analysis focused on the associations between 486 blood metabolites and the susceptibility to overall lung cancer and its three major clinical subtypes. Various Mendelian randomization methods, including inverse-variance weighting, weighted median estimation, and MR-Egger regression, were employed to ensure the robustness of our findings. Results A total of 19 blood metabolites were identified with significant associations with lung cancer risk. Specifically, oleate (OR per SD = 2.56, 95% CI: 1.51 to 4.36), 1-arachidonoylglyceropholine (OR = 1.79, 95% CI: 1.22 to 2.65), and arachidonate (OR = 1.67, 95% CI: 1.16 to 2.40) were associated with a higher risk of lung cancer. Conversely, 1-linoleoylglycerophosphoethanolamine (OR = 0.57, 95% CI: 0.40 to 0.82), ADpSGEGDFXAEGGGVR, a fibrinogen cleavage peptide (OR = 0.60, 95% CI: 0.47 to 0.77), and isovalerylcarnitine (OR = 0.62, 95% CI: 0.49 to 0.78) were associated with a lower risk of lung cancer. Notably, isoleucine (OR = 9.64, 95% CI: 2.55 to 36.38) was associated with a significantly higher risk of lung squamous cell cancer, while acetyl phosphate (OR = 0.11, 95% CI: 0.01 to 0.89) was associated with a significantly lower risk of small cell lung cancer. Conclusion This study reveals the complex relationships between specific blood metabolites and lung cancer risk, highlighting their potential as biomarkers for lung cancer prevention, screening, and treatment. The findings not only deepen our understanding of the metabolic mechanisms of lung cancer but also provide new insights for future treatment strategies.

Abstract Background The interferon‐γ (IFN‐γ) signaling pathway is activated in systemic lupus erythematosus (SLE). This study aimed to assess the causal association between IFN‐γ, IFN‐γ receptor 1 (IFN‐γR1), and IFN‐γR2 and SLE using a bidirectional Mendelian randomization (MR) design. Methods Genetic instruments for exposure to IFN‐γ, IFN‐γR1, and IFN‐γR2 were derived from a large genome‐wide association study (GWAS) that included a sample size of 3301 participants. Instrumental variables for SLE were selected from another independent GWAS analysis comprising 5201 cases and 6099 controls with European ancestry. Bidirectional two‐sample MR was performed using inverse variance weighting, MR‐Egger regression, and weighted median methods. A series of sensitivity analyses were conducted to assess the robustness of the results. Results The inverse variance weighting showed that IFN‐γ had a positive causal association with the risk of SLE (odd ratio [OR] = 1.24, 95% confidence interval [CI]: 1.03–1.47, p = 0.018). IFN‐γR2 levels were not associated with SLE risk after adjustment for multiple comparisons (OR = 0.85, 95% CI: 0.73–0.99, p = 0.034). No genetic association was also detected between IFN‐γR1 and SLE (OR = 0.97, 95% CI: 0.79–1.19, p = 0.768). Evidence from bidirectional MR did not support reverse causality. The weighted median regression also showed directionally similar estimates. Conclusion Higher levels of IFN‐γ are significantly associated with an increased risk of SLE, providing insights into the pathogenesis of SLE.

Effective counteraction to biological threats, both natural and man-made, requires the availability of means and methods for rapid and reliable microorganism identification and a comprehensive study of their basic biological properties. Over the past decade, the arsenal of domestic microbiologists has been supplemented by numerous methods for analyzing the genomes of pathogens, primarily based on nucleic acid sequencing. The purpose of this work is to provide the reader with information about capabilities of modern technical and methodological arsenal used for in-depth molecular genetic study of microorganisms, including bioinformatics solutions used for the genetic data analysis. The source base for this research is English-language scientific literature available via the Internet, bioinformation software documentation. The research method is an analysis of scientific sources from the general to the specific. We considered the features of sequencing platforms, the main stages of genetic information analysis, current bioinformation utilities, their interaction and organization into a single workflow. Results and discussion. The performance of modern genetic analyzers allows for complete decoding of the bacterial genome within one day, including the time required to prepare the sample for research. The key factor that largely determines the effectiveness of the genetic analysis methods used is the competent use of the necessary bioinformatics software utilities. Standard stages of primary genetic data analysis are assessment of the quality control, data preprocessing, mapping to a reference genome or de novo genome assembly, genome annotation, typing and identification of significant genetic determinants (resistance to antibacterial drugs, pathogenicity factors, etc.), phylogenetic analysis. For each stage bioinformation utilities have been developed, differing in implemented analysis algorithms. Conclusion. Open source utilities that do not require access to remote resources for their operation are of greatest interest due to activities specifics of NBC protection corps units.

Classifiers trained on high-dimensional data, such as genetic datasets, often encounter situations where the number of features exceeds the number of objects. In these cases, classifiers typically rely on a small subset of features. For a robust algorithm, this subset should remain relatively stable with minor changes in the training data, such as the replacement of a few samples. While the stability of feature selection is a common focus in studies of feature selection algorithms, it is less emphasized in classifier evaluation, where only metrics such as accuracy are commonly used. We investigate the importance of feature selection stability through an empirical study of four classifiers (logistic regression, support vector machine, convex and piecewise Linear, and Random Forest) on seven high dimensional, publicly available, gene datasets. We measure the stability of feature selection using Lustgarten, Nogueira and Jaccard Index measures. We employed our own cross-validation procedure that guarantees a difference of exactly p objects between any two training sets which allows us to control the level of disturbance in the data. Our results show the existence of a drop in feature selection stability when we increase disturbance in the data in all 28 experiment configurations (seven datasets and four classifiers). The relationship is not linear, but resembles more of a hyperbolic pattern. In the evaluation of the tested classifiers, logistic regression demonstrated the highest stability. It was followed in order by support vector machine, convex and piecewise linear, with Random Forest exhibiting the lowest stability among them. This work provides evidence that all tested classifiers are very sensitive to even small changes in learning data in terms of features used by the model, while showing almost no sensitivity in terms of accuracy. The data and codes to reproduce the results in the article are available publicly on GitHub: https://github.com/tlukaszuk/feature-selection-stability-in-classifier-evaluation.

ABSTRACT Oncorhynchus mykiss is a partially migratory salmonid species, and many migratory populations (known as steelhead) have declined in recent decades in the western United States and Canada. Closely related resident populations (known as rainbow trout) may be an effective resource in the recovery of these declining migratory populations. However, the extent to which different populations of resident rainbow trout produce migratory individuals and how likely these individuals are to return as adults to spawn remains unknown. One limitation to answering these questions is the identification of loci that accurately segregate between migratory and resident populations. To address this limitation, we used existing genomic data from a well‐studied population of O. mykiss from Southeastern Alaska (Sashin Creek) to identify loci that segregate between phenotypes. We then utilized Double Mismatch Allele‐Specific qPCR (DMAS‐qPCR) to genotype 233 smolts out‐migrating from Sashin Creek and 99 returning adult steelhead trout across a five‐year period to determine (a) the origin of out‐migrating smolts and returning adults and (b) to quantify the extent to which the resident population contributes to the migratory population. Our results show that 37.3% of out‐migrating smolts were produced from resident parents, whereas 19.3% of returning adults had resident parents. Ultimately, these results demonstrate that resident populations of rainbow trout produce migrant offspring that successfully complete their migration and return to spawn, increasing population sizes and likely improving genetic diversity. Therefore, conservation efforts should consider landlocked resident populations for producing smolts when developing recovery plans for migratory steelhead populations.

As global temperatures continue to rise, high summer temperatures severely affect crop growth, reducing yield and quality. The projections of annual declines in crop yield require more in-depth multidisciplinary studies on plant tolerance to abiotic stresses. As tomato is a major crop in the Mediterranean region, its response to heat stress has become important to be addressed in order to identify those traits affecting stress tolerance. In this study, physiological and genomic analyses were performed on two heat-tolerant genotypes (LA3120 and E42) grown at high temperatures during the entire life cycle. The results showed that heat stress diversely affected gas exchange and fluorescence parameters in the two genotypes. In particular, E42 regulated the photosynthetic machinery under heat stress by modulating the electron transport chain, whereas LA3120 was less affected by the applied stress. Genotyping data obtained from a GBS (genotyping by sequencing) analysis were used to explore the genetic variability of both genotypes with the aim of identifying candidate genes that might regulate their stress response. These results further deepen our understanding of the physiological mechanisms activated in response to heat stress and allowed to select key traits that could be used in breeding program to select thermotolerant tomato genotypes.

Abstract Background Alzheimer’s Disease (AD) is a progressive memory disorder that causes irreversible cognitive decline. Given that there is currently no cure, it is critical to detect AD in its early stage during the disease progression. Recently, many statistical learning methods have been presented to identify cognitive decline with temporal data, but few of these methods integrate heterogeneous phenotype and genetic information together to improve the accuracy of prediction. In addition, many of these models are often unable to handle incomplete temporal data; this often manifests itself in the removal of records to ensure consistency in the number of records across participants. Results To address these issues, in this work we propose a novel approach to integrate the genetic data and the longitudinal phenotype data to learn a fixed-length “enriched” biomarker representation derived from the temporal heterogeneous neuroimaging records. Armed with this enriched representation, as a fixed-length vector per participant, conventional machine learning models can be used to predict clinical outcomes associated with AD. Conclusion The proposed method shows improved prediction performance when applied to data derived from Alzheimer’s Disease Neruoimaging Initiative cohort. In addition, our approach can be easily interpreted to allow for the identification and validation of biomarkers associated with cognitive decline.

Museum collections are an important source for resolving taxonomic issues and species delimitation. Type specimens as name-bearing specimens, traditionally used in morphology-based taxonomy, are, due to the progress in historical DNA methodology, increasingly used in molecular taxonomic studies. Museum collections are subject to constant deterioration and major disasters. The digitisation of collections offers a partial solution to these problems and makes museum collections more accessible to the wider scientific community. The Extended Specimen Approach (ESA) is a method of digitisation that goes beyond the physical specimen to include the historical information stored in the collection. The collections of the Natural History Museum Vienna represent one of the largest non-university research centres in Europe and, due to their size and numerous type specimens, are frequently used for taxonomic studies by visiting and resident scientists. Recently, a version of ESA was presented in the common catalogue of the Fish and Evertebrata Varia collections and extended to include genetic information on type specimens in a case study of a torpedo ray. Here the case study was extended to a heterogeneous selection of historical type series from different collections with the type locality of Vienna. The goal was to apply the ESA, including genetic data on a selected set of type material: three parasitic worms, three myriapods, two insects, twelve fishes, and one bird species. Five hundred digital items (photographs, X-rays, scans) were produced, and genetic analysis was successful in eleven of the 21 type series. In one case a complete mitochondrial genome was assembled, and in another case ten short fragments (100–230 bp) of the cytochrome oxidase I gene were amplified and sequenced. For five type series, genetic analysis confirmed their taxonomic status as previously recognised synonyms, and for one the analysis supported its status as a distinct species. For two species, genetic information was provided for the first time. This catalogue thus demonstrates the usefulness of ESA in providing digitised data of types that can be easily made available to scientists worldwide for further study.