Single Nucleotide Polymorphism Data Research Articles

Blood metabolomic and postpartum depression: a mendelian randomization study

BackgroundPostpartum depression is a complex mental health condition that often occurs after childbirth and is characterized by persistent sadness, anxiety, and fatigue. Recent research suggests a metabolic component to the disorder. This study aims to investigate the causal relationship between blood metabolites and postpartum depression using mendelian randomization (MR).MethodsThis study used a bi-directional MR framework to investigate the causal relationship between 1,400 metabolic biomarkers and postpartum depression. We used two specific genome-wide association studies datasets: one with single nucleotide polymorphisms data from mothers diagnosed with postpartum depression and another with blood metabolite data, both of which focused on people of European ancestry. Genetic variants were chosen as instrumental variables from both datasets using strict criteria to improve the robustness of the MR analysis. The combination of these datasets enabled a thorough examination of genetic influences on metabolic profiles associated with postpartum depression. Statistical analyses were conducted using techniques such as inverse variance weighting, weighted median, and model-based estimation, which enabled rigorous causal inference from the observed associations. postpartum depression was defined using endpoint definitions approved by the FinnGen study’s clinical expert groups, which included leading experts in their respective medical fields.ResultsThe MR analysis identified seven metabolites that could be linked to postpartum depression. Out of these, one metabolite was found to be protective, while six were associated with an increased risk of developing the condition. The results were consistent across multiple MR methods, indicating a significant correlation.ConclusionsThis study emphasizes the potential of metabolomics for understanding postpartum depression. The discovery of specific metabolites associated with the condition sheds new insights on its pathophysiology and opens up possibilities for future research into targeted treatment strategies.

Computing linkage disequilibrium aware genome embeddings using autoencoders.

The completion of the genome has paved the way for genome-wide association studies (GWAS), which explained certain proportions of heritability. GWAS are not optimally suited to detect non-linear effects in disease risk, possibly hidden in non-additive interactions (epistasis). Alternative methods for epistasis detection using, e.g. deep neural networks (DNNs) are currently under active development. However, DNNs are constrained by finite computational resources, which can be rapidly depleted due to increasing complexity with the sheer size of the genome. Besides, the curse of dimensionality complicates the task of capturing meaningful genetic patterns for DNNs; therefore necessitates dimensionality reduction. We propose a method to compress single nucleotide polymorphism (SNP) data, while leveraging the linkage disequilibrium (LD) structure and preserving potential epistasis. This method involves clustering correlated SNPs into haplotype blocks and training per-block autoencoders to learn a compressed representation of the block's genetic content. We provide an adjustable autoencoder design to accommodate diverse blocks and bypass extensive hyperparameter tuning. We applied this method to genotyping data from Project MinE, and achieved 99% average test reconstruction accuracy-i.e. minimal information loss-while compressing the input to nearly 10% of the original size. We demonstrate that haplotype-block based autoencoders outperform linear Principal Component Analysis (PCA) by approximately 3% chromosome-wide accuracy of reconstructed variants. To the extent of our knowledge, our approach is the first to simultaneously leverage haplotype structure and DNNs for dimensionality reduction of genetic data. Data are available for academic use through Project MinE at https://www.projectmine.com/research/data-sharing/, contingent upon terms and requirements specified by the source studies. Code is available at https://github.com/gizem-tas/haploblock-autoencoders.

Neutral processes and taxonomic scale drive beta species-genetic diversity correlations in a submesophotic tropical reef fish.

If similar evolutionary forces maintain intra- and interspecific diversity, patterns of diversity at both levels of biological organization can be expected to covary across space. Although this prediction of a positive species-genetic diversity correlation (SGDC) has been tested for several taxa in natural landscapes, no study has yet evaluated the influence of the community delineation on these SGDCs. In this study, we focused on tropical fishes of the Indo-Pacific Ocean, using range-wide single nucleotide polymorphism data for a deep-sea fish (Etelis coruscans) and species presence data of 4878 Teleostei species. We investigated whether a diversity continuum occurred, for different community delineations (subfamily, family, order and class) and spatial extents, and which processes explained these diversity patterns. We found no association between genetic diversity and species richness (α-SGDC), regardless of the community and spatial extent. In contrast, we evidenced a positive relationship between genetic and species dissimilarities (β-SGDC) when the community was defined at the subfamily or family level of the species of interest, and when the Western Indian Ocean was excluded. This relationship was related to the imprint of dispersal processes across levels of biological organization in Lutjanidae. However, this positive β-SGDC was lost when considering higher taxonomic communities and at the scale of the entire Indo-Pacific, suggesting different responses of populations and communities to evolutionary processes at these scales. This study provides evidence that the taxonomic scale at which communities are defined and the spatial extent are pivotal to better understand the processes shaping diversity across levels of biological organization.

Effect of serum uric acid on the risk of aortic aneurysm and dissection: A mendelian randomization analysis

Aortic aneurysm and dissection (AAD) are severe vascular diseases with high mortality rates. However, the causal relationship between serum uric acid levels and the occurrence of AAD remains a subject of controversy. To address this issue, we conducted a two-sample Mendelian randomization (MR) analysis to investigate whether there is a causal association between these factors. We obtained single-nucleotide polymorphisms (SNPs) data related to serum uric acid levels from the FinnGen study and data on AAD from the UK Biobank. Various two-sample MR methods, including inverse variance weighted (IVW) analysis, MR-Egger regression analysis, weighted median analysis, and contamination mixture method, were employed to assess the causal relationship between serum uric acid and the risk of AAD. Sensitivity analysis was conducted to evaluate the stability and reliability of the results. The findings revealed a positive association between serum uric acid levels and the risk of aortic aneurysm (AA) (odds ratio [OR] = 1.200, 95 % confidence interval [CI]: 1.020–1.400, P = 0.0239). However, no significant correlation was observed between serum uric acid levels and the occurrence of aortic dissection (AD) (OR = 0.893, 95 % CI = 0.602–1.326, P = 0.576). Our study, which employed MR analysis, identified a positive association between serum uric acid levels and the risk of AA. However, we did not observe a significant correlation with AD.

HaploMAGIC: Accurate phasing and detection of recombination in multiparental populations despite genotyping errors.

Recombination is a key mechanism in breeding for promoting genetic variability. Multiparental populations constitute an excellent platform for precise genotype phasing, identification of genome-wide crossovers, estimation of recombination frequencies and construction of recombination maps. Here, we introduce haploMAGIC, a pipeline to detect crossovers in multiparental populations with single-nucleotide polymorphism (SNP) data by exploiting the pedigree relationships for accurate genotype phasing and inference of grandparental haplotypes. haploMAGIC applies filtering to prevent false positive crossovers due to genotyping errors, a common problem in high-throughput SNP analysis of complex plant genomes. Hence it discards haploblocks not reaching a specified minimum number of informative alleles. A performance analysis using populations simulated with AlphaSimR revealed that haploMAGIC improves upon existing methods of crossover detection in terms of recall and precision, most notably when genotyping error rates are high. Furthermore, we constructed recombination maps using haploMAGIC with high-resolution genotype data from two large multi-parental populations of winter rapeseed (Brassica napus). The results demonstrate the applicability of the pipeline in real-world scenarios and showed good correlations in recombination frequency compared with alternative software. Therefore, we propose haploMAGIC as an accurate tool at crossover detection with multiparental populations that shows robustness against genotyping errors.

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.

Causal association of circulating metabolites with diabetic retinopathy: a bidirectional Mendelian randomization analysis.

The retina is a highly metabolically active tissue, and there is a lack of clarity about the relationship between metabolites and diabetic retinopathy (DR). This study used two-sample bidirectional Mendelian randomization (MR) analyses to identify causal relationships between metabolites and DR. Genetic variants were selected from the open-access Genome-Wide Association Studies (GWAS) summary database as proxies for the 1400 most recently published metabolites. MR analysis was performed to examine associations between these metabolite traits and DR. Single nucleotide polymorphism (SNP) data that were significantly associated with exposure were screened through association analysis. Validated instrumental variables (IVs) were obtained by removing SNPs with linkage disequilibrium (LD) and F-statistic values below 10. MR analyses were performed using the inverse variance weighted (IVW) method as the primary approach. The robustness of the results was verified by sensitivity analyses, including assessments of heterogeneity, horizontal pleiotropy, and the leave-one-out method. In the IVW approach and in the primary analysis of several sensitivity analyses, genetically determined glycolithocholate sulfate levels, androstenediol (3 beta, 17 beta) monosulfate (1) levels, 1-stearoyl-2-arachidonoyl-GPE (18:0/20:4) levels, 1-oleoyl-2-arachidonoyl-GPE (18:1/20:4) levels, 1-oleoyl-2-linoleoyl-GPE (18:1/18:2) levels, X-26109 levels, N6-methyllysine levels, (N6,N6-dimethyllysine levels), and (N2-acetyl,N6,N6-dimethyllysine levels) were negatively associated with the risk of DR. 5-hydroxymethyl-2-furoylcarnitine levels and the glutamate-to-alanine ratio were positively associated with the risk of DR. No reverse causal association was found between DR and metabolites. This MR study suggests that nine metabolites may have a protective effect in DR, while two metabolites may be associated with an increased risk of DR. However, further research is needed to confirm these findings. Supplementation with beneficial metabolites may reduce DR risk and could potentially be a novel therapeutic approach to DR treatment.

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.

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.

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.

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. Materials and methodsTwo-sample MR analyses utilized public genome-wide association studies summary data for single-nucleotide polymorphisms associated with AGA. Four statistical methods were used: 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.

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.