Single Nucleotide Polymorphism Data Research Articles

Accuracy of genomic prediction using multiple Atlantic salmon populations

BackgroundThe accuracy of genomic prediction is partly determined by the size of the reference population. In Atlantic salmon breeding programs, four parallel populations often exist, thus offering the opportunity to increase the size of the reference set by combining these populations. By allowing a reduction in the number of records per population, multi-population prediction can potentially reduce cost and welfare issues related to the recording of traits, particularly for diseases. In this study, we evaluated the accuracy of multi- and across-population prediction of breeding values for resistance to amoebic gill disease (AGD) using all single nucleotide polymorphisms (SNPs) on a 55K chip or a selected subset of SNPs based on the signs of allele substitution effect estimates across populations, using both linear and nonlinear genomic prediction (GP) models in Atlantic salmon populations. In addition, we investigated genetic distance, genetic correlation estimated based on genomic relationships, and persistency of linkage disequilibrium (LD) phase across these populations.ResultsThe genetic distance between populations ranged from 0.03 to 0.07, while the genetic correlation ranged from 0.19 to 0.99. Nonetheless, compared to within-population prediction, there was limited or no impact of combining populations for multi-population prediction across the various models used or when using the selected subset of SNPs. The estimates of across-population prediction accuracy were low and to some extent proportional to the genetic correlation estimates. The persistency of LD phase between adjacent markers across populations using all SNP data ranged from 0.51 to 0.65, indicating that LD is poorly conserved across the studied populations.ConclusionsOur results show that a high genetic correlation and a high genetic relationship between populations do not guarantee a higher prediction accuracy from multi-population genomic prediction in Atlantic salmon.

Genetic Diversity, Linkage Disequilibrium, and Population Structure in a Common Bean Reference Collection

An in-depth understanding of the extent and pattern of genetic diversity and population structure in crop populations is of paramount importance for any crop improvement program to efficiently promote the translation of genetic diversity into genetic gain. A reference collection of 150 common bean genotypes selected from the International Center for Tropical Agriculture’s global core collection was evaluated using single-nucleotide polymorphism (SNP) markers to quantify the amount of genetic diversity, linkage disequilibrium, and population structure. The cultivars and landraces of the collection were diverse and originated from 14 countries, and wild accessions were used as controls for each gene pool. The collection was genotyped using an SNP array, generating a total of 5398 locus calls distributed across the entire bean genome. The SNP data quality was checked, and two datasets were generated. The first dataset (Dataset_1) comprised a set of 5108 SNPs and 150 genotypes after filtering for 10% missing alleles and an MAF < 0.05. The second dataset (Dataset_2) comprised a set of 2300 SNPs that remained after removing any null-allele SNPs and LD pruning for a criterion of r2 < 0.2. Dataset_1 was used for a principal coordinate analysis (PCoA), phylogenetic relationship determination, an analysis of molecular variance (AMOVA), and a discriminant analysis of principal components. Dataset_2 was used for a population structure analysis using STRUCTURE software and is proposed for a genome-wide association study (GWAS). The population structure analysis split the reference collection into two subpopulations according to an Andean or Mesoamerican gene pool. The Mesoamerican populations displayed higher genetic differentiation and tended to split into more groups that were somewhat aligned with common bean races. Andean beans were characterized by a larger average LD but lower LD percentage, a small average genetic distance between members of the population, and a higher major allele frequency, which suggested narrower genetic diversity compared to the Mesoamerican gene pool. In conclusion, the results indicated the presence of high genetic diversity, which is useful for a GWAS. However, the presence of significant linkage disequilibrium requires that genetic distance be considered as a co-factor for any further genetic studies. Overall, the molecular variation observed in the genotypes shows that this reference collection is valuable as a genebank-derived diversity panel which is useful for marker trait association studies.

Runs of homozygosity analysis for selection signatures in the Yellow Korean native chicken.

Yellow Korean native chicken (KNC-Y) is one of the five pure Korean indigenous chicken breeds that were restored through a government project in 1992. KNC-Y is recognized for its superior egg production performance compared to other KNC lines. In this study, we performed runs of homozygosity (ROH) analysis to discover selection signatures associated with egg production traits in the KNC-Y population. A total of 675 DNA samples from KNC-Y were genotyped to generate single nucleotide polymorphism (SNP) data using custom 60K Affymetrix SNP chips. ROH analysis was performed using PLINK software, with predefined parameters set for the analysis. The threshold of ROH island was defined as the top 1% frequency of SNPs withing the ROH among the population. In the KNC-Y population, a total of 29,958 runs of homozygosity (ROH) fragments were identified. The average total length of ROH was 120.84 Mb, with each ROH fragment having an average length of 2.71 Mb. The calculated ROH-based inbreeding coefficient (FROH) was 0.13. Furthermore, we revealed the presence of ROH islands on chromosomes 1, 2, 4, 5, 7, 8, and 11. Within the identified regions, a total of 113 genes were annotated, and among them were genes related to economic traits, including PRMT3, ANO5, HDAC4, LSS, PLA2G4A, and PTGS2. Most of the overlapping quantitative trait locus (QTL) regions with ROH islands were found to be associated with production traits. This study conducted a comprehensive analysis of ROH in the KNC-Y population. Notably, among the findings, the PTGS2 gene is believed to play a crucial role in influencing the laying performance of KNC-Y.

Genetic diversity and population structure of four Anatolian sheep revealed by genome-wide ddRADseq data

This is the first wide-scale study to reveal next-generation sequencing-based and comprehensive study to reveal genetic diversity, population structure, and phylogenetic relationships of some of Türkiye's indigenous fat-tailed sheep breeds. A total of 80 animals belonging to Akkaraman (AKR), Güney Karaman (GKR), Morkaraman (MKR), and Karakaş (KRK) sheep breeds were genotyped with genome-wide of 296.097 single nucleotide polymorphisms (SNPs) data recovered from double digest restriction site-associated DNA sequencing (ddRADseq) libraries processed by Illumina HiSeq X Ten instrument. Minor allele frequency (MAF) ranged from 0.311 (MKR) to 0.316 (AKR and GKR). Observed heterozygosity (HO) values were higher than expected values (HE) across all populations with a mean of 0.307. Negative inbreeding coefficient (FIS) values were observed in four sheep populations varying between −0.034 (AKR) to −0.060 (MKR). Cross-entropy criterion-based ADMIXTURE analysis demonstrated that four Anatolian sheep breeds were represented by two ancestral populations in which GKR and AKR were distinct, while KRK and MKR were mixed populations. Genetic distance-based tree, discriminant analysis of principal components (DAPC), and ADMIXTURE analyses confirmed that GKR, which is believed to be derived from the AKR breed, has become genetically different from other sheep populations. Moreover, no migration from and into the GKR breed was detected, while a migration edge was drawn from the KRK and MKR clade into the AKR breed by the TreeMix algorithm. This study confirmed that high-density SNP data obtained from advanced next-generation sequencing (NGS) platforms are efficient in genetically distinguishing animal breeds, even varieties. It is recommended that further NGS-based studies could be carried out to better characterize different indigenous livestock populations raised in Türkiye.

Ability of Genomic Prediction to Bi-Parent-Derived Breeding Population Using Public Data for Soybean Oil and Protein Content.

Genomic selection (GS) is a marker-based selection method used to improve the genetic gain of quantitative traits in plant breeding. A large number of breeding datasets are available in the soybean database, and the application of these public datasets in GS will improve breeding efficiency and reduce time and cost. However, the most important problem to be solved is how to improve the ability of across-population prediction. The objectives of this study were to perform genomic prediction (GP) and estimate the prediction ability (PA) for seed oil and protein contents in soybean using available public datasets to predict breeding populations in current, ongoing breeding programs. In this study, six public datasets of USDA GRIN soybean germplasm accessions with available phenotypic data of seed oil and protein contents from different experimental populations and their genotypic data of single-nucleotide polymorphisms (SNPs) were used to perform GP and to predict a bi-parent-derived breeding population in our experiment. The average PA was 0.55 and 0.50 for seed oil and protein contents within the bi-parents population according to the within-population prediction; and 0.45 for oil and 0.39 for protein content when the six USDA populations were combined and employed as training sets to predict the bi-parent-derived population. The results showed that four USDA-cultivated populations can be used as a training set individually or combined to predict oil and protein contents in GS when using 800 or more USDA germplasm accessions as a training set. The smaller the genetic distance between training population and testing population, the higher the PA. The PA increased as the population size increased. In across-population prediction, no significant difference was observed in PA for oil and protein content among different models. The PA increased as the SNP number increased until a marker set consisted of 10,000 SNPs. This study provides reasonable suggestions and methods for breeders to utilize public datasets for GS. It will aid breeders in developing GS-assisted breeding strategies to develop elite soybean cultivars with high oil and protein contents.

In-Silico analysis of the structural and functional impact of SNPs in adhesion molecules associated with cardiovascular disease and the effect of potential phytochemicals on these molecules.

Adhesion molecules are one of the most important proteins playing a major role in the regulationof cell migration, apoptosis, and survival. While adhesion molecules play a pivotal role inpreserving cellular matrix and tissue integrity, these molecules are also involved in thepathogenesis of cardiovascular diseases. The cell adhesion molecules are involved inatherosclerotic plaque progression, deposition of fibrotic tissue, and thrombus formation. Selectins-E and -P and members of the immunoglobulin superfamily ICAM-1 and ICAM-3 are the majorgroups of the adhesion molecules involved in cardiovascular diseases.In the current study, computational analysis was performed to understand the effects of missensemutations on the structure and functions of the adhesion molecules. The computational studiescan lead to a better understanding of the role of single nucleotide polymorphic mutations on thefunctions of adhesion molecules. The single nucleotide polymorphism data of adhesion moleculeswas retrieved from gnomAD v2.1.1. Only the missense SNPs were selected, and the 3D proteinstructures of the adhesion molecules were retrieved from AlphaFold. The 3D protein structures ofthe adhesion molecules were mutated using USCF Chimera 1.14.The comparison of structural stability, structural change, and interaction with ligands wasperformed between the wild-type and mutant adhesion molecules by several bioinformatics tools.The R458W mutant of ICAM-1 was found to be destabilizing the structure of the protein.Similarly, the V88M mutant of ICAM-3 and H468Y mutant of E-selectin (SELE), along withG179R and G727Q mutants of P-selectin (SELP) have also shown structural deviations ascompared to wild-type molecules. The R367C mutant of ICAM-1, P15L mutant of ICAM-3, andT756P mutant of SELP showed a significant change in the hydrogen bonding interactions withtheir respective ligands as compared to the wild-type protein interactions with a deviation from theoriginal binding free energies. No significant changes in the hydrogen bonding interactions wereobserved in the G179R and G727Q mutants of SELP.Moreover, phytochemicals data of several medicinal plants which were effective againstcardiovascular diseases was retrieved from the Indian Medicinal Plants, Photochemistry, andTherapeutics database. The molecular docking was performed to analyze the effects ofphytochemicals on adhesion molecules. The 3,29-Dihydroxyolean-12-en-28-oic acid, aphytochemical of the Panax ginseng plant was found to have an inhibitory effect on the activesites of adhesion molecules ICAM-1, ICAM-3, SELE, and SELP. Furthermore, the results of thisstudy can be tested experimentally to confirm the effect of adhesion molecule mutations on theonset of cardiovascular diseases and the role of 3,29-Dihydroxyolean-12-en-28-oic acid as ageneral drug for the treatment of diseases caused by mutant adhesion molecules.

Genetic history of esophageal cancer group in southwestern China revealed by Y‐chromosome STRs and genomic evolutionary connection analysis

Genetic and environmental factors play crucial roles in the development of esophageal cancer (EC) and contribute uniquely or cooperatively to human cancer susceptibility. Sichuan is located in the interior of southwestern China, and the northern part of Sichuan is one of the regions with a high occurrence of EC. However, the factors influencing the high incidence rate of EC in the Sichuan Han Chinese population and its corresponding genetic background and origins are still poorly understood. Here, we utilized genome-wide single nucleotide polymorphisms (SNPs) and Y-chromosome short tandem repeats (Y-STRs) to characterize the genetic structure, connection, and origin of cancer groups and general populations. We generated Y-STR-based haplotype data from 214 Sichuan individuals, including the Han Chinese EC population and a control group of Han Chinese individuals. Our results, obtained from Y-STR-based population statistical methods (analysis of molecular variance (AMOVA), principal component analysis (PCA), and phylogenetic analysis), demonstrated that there was a genetic substructure difference between the EC population in the high-incidence area of northern Sichuan Province and the control population. Additionally, there was a strong genetic relationship between the EC population in the northern Sichuan high-incidence area and those at high risk in both the Fujian and Chaoshan areas. In addition, we obtained high-density SNP data from saliva samples of 60 healthy Han Chinese individuals from three high-prevalence areas of EC in China: Sichuan Nanchong, Fujian Quanzhou, and Henan Xinxiang. As inferred from the allele frequency of SNPs and sharing patterns of haplotype segments, the evolutionary history and admixture events suggested that the Han population from Nanchong in northern Sichuan Province shared a close genetic relationship with the Han populations from Xinxiang in Henan Province and Quanzhou in Fujian Province, both of which are regions with a high prevalence of EC. Our study illuminated the genetic profile and connection of the Northern Sichuan Han population and enriched the genomic resources and features of the Han Chinese populations in China, especially for the Y-STR genetic data of the Han Chinese EC population. Populations living in different regions with high incidences of EC may share similar genetic backgrounds, which offers new insights for further exploring the genetic mechanisms underlying EC.

Genome-wide association analysis for drought tolerance and component traits in groundnut gene pool

The potential production and productivity of groundnuts are limited due to severe drought stress associated with climate change. The current study aimed to identify genomic regions and candidate genes associated with drought tolerance and component traits for gene introgression and to guide marker-assisted breeding of groundnut varieties. Ninety-nine genetically diverse groundnut genotypes were phenotyped under drought-stressed and non-stressed field conditions in 2018/19 and 2019/20, and using the LeasyScan platform under non-stressed conditions in 2019/20 at the International Crops Research Institute for the Semi-Arid Tropics (ICRISAT)/India. The samples were genotyped using 48 K single nucleotide polymorphisms (SNPs) markers at the University of Georgia/USA. Phenotypic data was collected on 17 agronomic traits and subjected to statistical analyses. The SNP data were computed, and population structure was inferred using a Bayesian clustering method in Structure version 2.3.4, while linkage disequilibrium was calculated using the GAPIT program in R software. Marker-trait associations were deduced using Tassel 5.2.86. Significant phenotypic variations were recorded for drought tolerance and the assessed agronomic traits. GWAS analysis using PCA + K and Q + K models identified significant SNPs associated with leaf area (1 SNP), leaf area Index (1 SNP), specific leaf area (1 SNP), leaf relative water content (43 SNPs), number of primary branches (1 SNP) and hundred seed weight (1 SNP). Forty-seven and one marker-trait associations were detected under drought-stressed and non-stressed conditions, respectively. The candidate genes and markers identified in the current study are useful for accelerated groundnut breeding targeting drought tolerance and market-preferred traits.

SimHOEPI: A resampling simulator for generating single nucleotide polymorphism data with a high‐order epistasis model

SimHOEPI: A resampling simulator for generating single nucleotide polymorphism data with a high‐order epistasis model

Integrating Multi-omics Data for Alzheimer's Disease to Explore Its Biomarkers Via the Hypergraph-Regularized Joint Deep Semi-Non-Negative Matrix Factorization Algorithm.

Alzheimer's disease (AD) is a progressive and irreversible neurodegenerative disorder. Its etiology may be associated with genetic, environmental, and lifestyle factors. With the advancement of technology, the integration of genomics, transcriptomics, and imaging data related to AD allows simultaneous exploration of molecular information at different levels and their interaction within the organism. This paper proposes a hypergraph-regularized joint deep semi-non-negative matrix factorization (HR-JDSNMF) algorithm to integrate positron emission tomography (PET), single-nucleotide polymorphism (SNP), and gene expression data for AD. The method employs matrix factorization techniques to nonlinearly decompose the original data at multiple layers, extracting deep features from different omics data, and utilizes hypergraph mining to uncover high-order correlations among the three types of data. Experimental results demonstrate that this approach outperforms several matrix factorization-based algorithms and effectively identifies multi-omics biomarkers for AD. Additionally, single-cell RNA sequencing (scRNA-seq) data for AD were collected, and genes within significant modules were used to categorize different types of cell clusters into high and low-risk cell groups. Finally, the study extensively explores the differences in differentiation and communication between these two cell types. The multi-omics biomarkers unearthed in this study can serve as valuable references for the clinical diagnosis and drug target discovery for AD. The realization of the algorithm in this paper code is available at https://github.com/ShubingKong/HR-JDSNMF .

Shared Genetic Determinants of Schizophrenia and Autism Spectrum Disorder Implicate Opposite Risk Patterns: A Genome-Wide Analysis of Common Variants.

The synaptic pruning hypothesis posits that schizophrenia (SCZ) and autism spectrum disorder (ASD) may represent opposite ends of neurodevelopmental disorders: individuals with ASD exhibit an overabundance of synapses and connections while SCZ was characterized by excessive pruning of synapses and a reduction. Given the strong genetic predisposition of both disorders, we propose a shared genetic component, with certain loci having differential regulatory impacts. Genome-Wide single nucleotide polymorphism (SNP) data of European descent from SCZ (N cases = 53386, N controls = 77258) and ASD (N cases = 18381, N controls = 27969) were analyzed. We used genetic correlation, bivariate causal mixture model, conditional false discovery rate method, colocalization, Transcriptome-Wide Association Study (TWAS), and Phenome-Wide Association Study (PheWAS) to investigate the genetic overlap and gene expression pattern. We found a positive genetic correlation between SCZ and ASD (rg = .26, SE = 0.01, P = 7.87e-14), with 11 genomic loci jointly influencing both conditions (conjFDR <0.05). Functional analysis highlights a significant enrichment of shared genes during early to mid-fetal developmental stages. A notable genetic region on chromosome 17q21.31 (lead SNP rs2696609) showed strong evidence of colocalization (PP.H4.abf = 0.85). This SNP rs2696609 is linked to many imaging-derived brain phenotypes. TWAS indicated opposing gene expression patterns (primarily pseudogenes and long noncoding RNAs [lncRNAs]) for ASD and SCZ in the 17q21.31 region and some genes (LRRC37A4P, LINC02210, and DND1P1) exhibit considerable variation in the cerebellum across the lifespan. Our findings support a shared genetic basis for SCZ and ASD. A common genetic variant, rs2696609, located in the Chr17q21.31 locus, may exert differential risk regulation on SCZ and ASD by altering brain structure. Future studies should focus on the role of pseudogenes, lncRNAs, and cerebellum in synaptic pruning and neurodevelopmental disorders.

Risk relationship between inflammatory bowel disease and urolithiasis: A two-sample Mendelian randomization study.

The causal genetic relationship between common parenteral manifestations of inflammatory bowel disease (IBD) and urolithiasis remains unclear because their timing is difficult to determine. This study investigated the causal genetic association between IBD and urolithiasis using Mendelian randomization (MR) based on data from large population-based genome-wide association studies (GWASs). A two-sample MR analysis was performed to assess the potential relationship between IBD and urolithiasis. Specific single nucleotide polymorphism data were obtained from GWASs, including IBD (n = 59957) and its main subtypes, Crohn's disease (CD) (n = 40266) and ulcerative colitis (UC) (n = 45975). Summarized data on urolithiasis (n = 218792) were obtained from different GWAS studies. A random-effects model was analyzed using inverse-variance weighting, MR-Egger, and weighted medians. Genetic predisposition to IBD and the risk of urolithiasis were significantly associated [odds ratio (OR), 1.04 (95% confidence interval [CI], 1.00-.08), P = 0.01]. Consistently, the weighted median method yielded similar results [OR, 1.06 (95% CI, 1.00-1.12), P = 0.02]. The MR-Egger method also demonstrated comparable findings [OR, 1.02 (95% CI, 0.96-1.08), P = 0.45]. Both funnel plots and MR-Egger intercepts indicated no directional pleiotropic effects between IBD and urolithiasis. CD was strongly associated with it in its subtype analysis [OR, 1.04 (95% CI, 1.01-1.07), P = 0.01], and UC was also causally associated with urolithiasis, although the association was not significant [OR, 0.99 (95% CI, 0.95-1.03), P = 0.71]. A unidirectional positive causal correlation was identified between IBD and urolithiasis, with varying degrees of association observed among the different subtypes of IBD. Recognizing the increased incidence of urolithiasis in patients with IBD is crucial in clinical practice. Early detection and surveillance of IBD, improved patient awareness, adoption of preventive strategies, and promotion of collaborative efforts among healthcare providers regarding treatment methodologies are vital for improving patient outcomes.

Assessing the reproducibility of machine-learning-based biomarker discovery in Parkinson’s disease

Feature selection and machine learning algorithms can be used to analyze Single Nucleotide Polymorphisms (SNPs) data and identify potential disease biomarkers. Reproducibility of identified biomarkers is critical for them to be useful for clinical research; however, genotyping platforms and selection criteria for individuals to be genotyped affect the reproducibility of identified biomarkers. To assess biomarkers reproducibility, we collected five SNPs datasets from the database of Genotypes and Phenotypes (dbGaP) and explored several data integration strategies. While combining datasets can lead to a reduction in classification accuracy, it has the potential to improve the reproducibility of potential biomarkers. We evaluated the agreement among different strategies in terms of the SNPs that were identified as potential Parkinson’s disease (PD) biomarkers. Our findings indicate that, on average, 93% of the SNPs identified in a single dataset fail to be identified in other datasets. However, through dataset integration, this lack of replication is reduced to 62%. We discovered fifty SNPs that were identified at least twice, which could potentially serve as novel PD biomarkers. These SNPs are indirectly linked to PD in the literature but have not been directly associated with PD before. These findings open up new potential avenues of investigation.

The role of historical biogeography in shaping colour morph diversity in the common wall lizard.

The maintenance of polymorphisms often depends on multiple selective forces, but less is known on the role of stochastic or historical processes in maintaining variation. The common wall lizard (Podarcis muralis) is a colour polymorphic species in which local colour morph frequencies are thought to be modulated by natural and sexual selection. Here, we used genome-wide single-nucleotide polymorphism data to investigate the relationships between morph composition and population biogeography at a regional scale, by comparing morph composition with patterns of genetic variation of 54 populations sampled across the Pyrenees. We found that genetic divergence was explained by geographic distance but not by environmental features. Differences in morph composition were associated with genetic and environmental differentiation, as well as differences in sex ratio. Thus, variation in colour morph frequencies could have arisen via historical events and/or differences in the permeability to gene flow, possibly shaped by the complex topography and environment. In agreement with this hypothesis, colour morph diversity was positively correlated with genetic diversity and rates of gene flow and inversely correlated with the likelihood of the occurrence of bottlenecks. Concurrently, we did not find conclusive evidence for selection in the two colour loci. As an illustration of these effects, we observed that populations with higher proportions of the rarer yellow and yellow-orange morphs had higher genetic diversity. Our results suggest that processes involving a decay in overall genetic diversity, such as reduced gene flow and/or bottleneck events have an important role in shaping population-specific morph composition via non-selective processes.

Does Androgenic Alopecia Aggravate the Risk of Prostate Cancer? Evidence from Mendelian Randomization

BackgroundEpidemiological reports indicate a potential association between androgenic alopecia (AGA) and increased prostate cancer (PC) prevalence, but conflicting reports also exist. This study aims to elucidate the causality of AGA on PC risk using Mendelian randomization (MR) analysis. MethodsTwo-sample MR analyses utilised public genome-wide association studies summary data for single nucleotide polymorphisms associated with AGA. Four statistical methods were employed: Inverse variance weighted (IVW), MR-Egger, weighted median and weighted mode, with IVW as the preliminary estimation method. Additionally, sensitivity analyses were conducted to address pleiotropic bias. ResultsGenetically proxied AGA did not demonstrate a causal effect on PC risk (IVW P > 0.05). Consistently, complementary methods yielded results aligned with IVW. ConclusionsOur MR analysis indicates no causal relationship between genetically predicted AGA and PC risk, suggesting that observed associations in epidemiological studies may not be causal.

Genomic analysis reveals deep population divergence in the water snake Trimerodytes percarinatus (Serpentes, Natricidae).

Although several phylogeographic studies of Asian snakes have been conducted, most have focused on pitvipers, with non-venomous snakes, such as colubrids or natricids, remaining poorly studied. The Chinese keelback water snake (Trimerodytes percarinatus Boulenger) is a widespread, semiaquatic, non-venomous species occurring in China and southeastern Asia. Based on mitochondrial DNA (mtDNA) and single nucleotide polymorphism (SNP) data, we explored the population genetic structure, genetic diversity, and evolutionary history of this species. MtDNA-based phylogenetic analysis showed that T. percarinatus was composed of five highly supported and geographically structured lineages. SNP-based phylogenetic analysis, principal component analysis, and population structure analysis consistently revealed four distinct, geographically non-overlapping lineages, which was different from the mtDNA-based analysis in topology. Estimation of divergence dates and ancestral area of origin suggest that T. percarinatus originated ~12.68 million years ago (95% highest posterior density: 10.36-15.96 Mya) in a region covering southwestern China and Vietnam. Intraspecific divergence may have been triggered by the Qinghai-Xizang Plateau uplift. Population demographics and ecological niche modeling indicated that the effective population size fluctuated during 0.5 Mya and 0.002 Mya. Based on the data collected here, we also comment on the intraspecific taxonomy of T. percarinatus and question the validity of the subspecies T. p. suriki.

Phylogenetic relationships of Quercus species (Fagaceae) in Vietnam based on multiplexed inter simple sequence repeat genotyping by sequencing

Quercus is the largest genus of the family Fagaceae in the world and the second-largest in Vietnam, with approximately 50 species. However, the phylogeny study of the Quercus genus in Vietnam has yet to be thoroughly explored. In this study, we utilize the genome-wide single-nucleotide polymorphisms (SNPs) data obtained through Multiplexed Inter Simple Sequence Repeat Genotyping by sequencing (MIG-seq) to explore the phylogenetic relationships among Quercus species in Vietnam. The results of this study reveal that all Quercus species in Vietnam belong to subgenus Cerris and the phylogenetic analysis strongly supports the recognition of two infrageneric sections: Quercus and Ilex section for the Vietnamese Quercus. These results also confirm that the monophyly of Quercus in Vietnam is strongly supported by both morphological and molecular data. The results of this study also align entirely with previous research, indicating that Quercus species in Asia fall under the subgenus Cerris, comprising two sections: section Ilex and section Cyclobalanopsis.

The causal effect of iron status on risk of anxiety disorders: A two-sample Mendelian randomization study.

Observational studies had investigated the association of iron metabolism with anxiety disorders. The conclusions were inconsistent and not available to reveal the causal or reverse-causal association due to the confounding. In this study we estimated the potential causal effect of iron homeostasis markers on anxiety disorders using two-sample Mendelian randomization (MR) analysis. Summary data of single nucleotide polymorphisms (SNPs) associated with four iron-related biomarkers were extracted from a recent report about analysis of three genome-wide association study (GWAS), the sample size of which ranged from 131471 to 246139 individuals. The corresponding data for anxiety disorders were from Finngen database (20992 cases and 197800 controls). The analyses were mainly based on inverse variance weighted (IVW) method. In addition, the heterogeneity and pleiotropy of the results were assessed by Cochran's Q test and MR-Egger regression. Basing on IVW method, genetically predicted serum iron level, ferritin and transferrin had negative effects on anxiety disorders. The odd ratios (OR) of anxiety disorders per 1 standard deviation (SD) unit increment in iron status biomarkers were 0.922 (95% confidence interval (CI) 0.862-0.986; p = 0.018) for serum iron level, 0.873 (95% CI 0.790-0.964; p = 0.008) for log-transformed ferritin and 0.917 (95% CI 0.867-0.969; p = 0.002) for transferrin saturation. But no statical significance was found in the association of 1 SD unit increased total iron-binding capacity (TIBC) with anxiety disorders (OR 1.080; 95% CI 0.988-1.180; p = 0.091). The analyses were supported by pleiotropy test which suggested no pleiotropic bias. Our results indicated that genetically determined iron status biomarkers causally linked to the risk of anxiety disorders, providing valuable insights into the genetic research and clinical intervention of anxiety disorders.

Machine learning-based prediction of rheumatoid arthritis with development of ACPA autoantibodies in the presence of non-HLA genes polymorphisms.

Machine learning (ML) algorithms can handle complex genomic data and identify predictive patterns that may not be apparent through traditional statistical methods. They become popular tools for medical applications including prediction, diagnosis or treatment of complex diseases like rheumatoid arthritis (RA). RA is an autoimmune disease in which genetic factors play a major role. Among the most important genetic factors predisposing to the development of this disease and serving as genetic markers are HLA-DRB and non-HLA genes single nucleotide polymorphisms (SNPs). Another marker of RA is the presence of anticitrullinated peptide antibodies (ACPA) which is correlated with severity of RA. We use genetic data of SNPs in four non-HLA genes (PTPN22, STAT4, TRAF1, CD40 and PADI4) to predict the occurrence of ACPA positive RA in the Polish population. This work is a comprehensive comparative analysis, wherein we assess and juxtapose various ML classifiers. Our evaluation encompasses a range of models, including logistic regression, k-nearest neighbors, naïve Bayes, decision tree, boosted trees, multilayer perceptron, and support vector machines. The top-performing models demonstrated closely matched levels of accuracy, each distinguished by its particular strengths. Among these, we highly recommend the use of a decision tree as the foremost choice, given its exceptional performance and interpretability. The sensitivity and specificity of the ML models is about 70% that are satisfying. In addition, we introduce a novel feature importance estimation method characterized by its transparent interpretability and global optimality. This method allows us to thoroughly explore all conceivable combinations of polymorphisms, enabling us to pinpoint those possessing the highest predictive power. Taken together, these findings suggest that non-HLA SNPs allow to determine the group of individuals more prone to develop RA rheumatoid arthritis and further implement more precise preventive approach.

Scalepopgen: Bioinformatic Workflow Resources Implemented in Nextflow for Comprehensive Population Genomic Analyses.

Population genomic analyses such as inference of population structure and identifying signatures of selection usually involve the application of a plethora of tools. The installation of tools and their dependencies, data transformation, or series of data preprocessing in a particular order sometimes makes the analyses challenging. While the usage of container-based technologies has significantly resolved the problems associated with the installation of tools and their dependencies, population genomic analyses requiring multistep pipelines or complex data transformation can greatly be facilitated by the application of workflow management systems such as Nextflow and Snakemake. Here, we present scalepopgen, a collection of fully automated workflows that can carry out widely used population genomic analyses on the biallelic single nucleotide polymorphism data stored in either variant calling format files or the plink-generated binary files. scalepopgen is developed in Nextflow and can be run locally or on high-performance computing systems using either Conda, Singularity, or Docker. The automated workflow includes procedures such as (i) filtering of individuals and genotypes; (ii) principal component analysis, admixture with identifying optimal K-values; (iii) running TreeMix analysis with or without bootstrapping and migration edges, followed by identification of an optimal number of migration edges; (iv) implementing single-population and pair-wise population comparison-based procedures to identify genomic signatures of selection. The pipeline uses various open-source tools; additionally, several Python and R scripts are also provided to collect and visualize the results. The tool is freely available at https://github.com/Popgen48/scalepopgen.