Using GWAS and Machine Learning to Identify and Predict Genetic Variants Associated with Foodborne Bacteria Phenotypic Traits.

One of the main challenges in food microbiology is to prevent the risk of outbreaks by avoiding the distribution of food contaminated by bacteria. This requires constant monitoring of the circulating strains throughout the food production chain. Bacterial genomes contain signatures of natural evolution and adaptive markers that can be exploited to better understand the behavior of pathogen in the food industry. The monitoring of foodborne strains can therefore be facilitated by the use of these genomic markers capable of rapidly providing essential information on isolated strains, such as the source of contamination, risk of illness, potential for biofilm formation, and tolerance or resistance to biocides. The increasing availability of large genome datasets is enhancing the understanding of the genetic basis of complex traits such as host adaptation, virulence, and persistence. Genome-wide association studies have shown very promising results in the discovery of genomic markers that can be integrated into rapid detection tools. In addition, machine learning has successfully predicted phenotypes and classified important traits. Genome-wide association and machine learning tools have therefore the potential to support decision-making circuits intending at reducing the burden of foodborne diseases. The aim of this chapter review is to provide knowledge on the use of these two methods in food microbiology and to recommend their use in the field.

Association of a composite trait for anthropometrics, adiposity and energy expenditure with cardiometabolic diseases: An age-stratified cohort and genetic risk score analysis.

AIM: Various anthropometric measures capture distinct as well as overlapping characteristics of an individual's body composition. To characterize independent body composition measures, we aimed to reduce easily-obtainable individual measures reflecting adiposity, anthropometrics and energy expenditure into fewer independent constructs, and to assess their potential sex- and age-specific relation with cardiometabolic diseases. METHODS: Analyses were performed within European ancestry participants from UK Biobank (N = 418,963, mean age 58.0 years, 56% women). Principal components (PC) analyses were used for the dimension reduction of 11 measures of adiposity, anthropometrics and energy expenditure. PCs were studied in relation to incident type 2 diabetes mellitus (T2D) and coronary artery disease (CAD). Multivariable-adjusted Cox regression analyses, adjusted for confounding factors, were performed in all and stratified by age. Genome-wide association studies were performed in half of the cohort (N = 156,295) to identify genetic variants as instrumental variables. Genetic risk score analyses were performed in the other half of the cohort stratified by age of disease onset (N = 156,295). RESULTS: We identified two PCs, of which PC1 reflected lower overall adiposity (negatively correlated with all adiposity aspects) and PC2 reflected more central adiposity (mainly correlated with higher waist-hip ratio, but with lower total body fat) and increased height, collectively capturing 87.8% of the total variance. Similar to that observed in the multivariable-adjusted regression analyses, we found associations between the PC1 genetic risk score and lower risks of CAD and T2D [CAD cases <50 years, odds ratio: 0.91 (95% confidence interval 0.87, 0.94) per SD; T2D cases <50 years, odds ratio: 0.76 (0.72, 0.81)], which attenuated with higher age (p-values 8.13E-4 and 2.41E-6, respectively). No associations were found for PC2. CONCLUSIONS: The consistently observed weaker associations of the composite traits with cardiometabolic disease suggests the need for age-specific cardiometabolic disease prevention strategies.

Genome-wide characterization of nitric oxide-induced NBS-LRR genes from Arabidopsis thaliana and their association in monocots and dicots.

BACKGROUND: Nitric oxide (NO) is pivotal in regulating the activity of NBS-LRR specific R genes, crucial components of the plant's immune system. It is noteworthy that previous research has not included a genome-wide analysis of NO-responsive NBS-LRR genes in plants. RESULTS: The current study examined 29 NO-induced NBS-LRR genes from Arabidopsis thaliana, along with two monocots (rice and maize) and two dicots (soybean and tomato) using genome-wide analysis tools. These NBS-LRR genes were subjected to comprehensive characterization, including analysis of their physio-chemical properties, phylogenetic relationships, domain and motif identification, exon/intron structures, cis-elements, protein-protein interactions, prediction of S-Nitrosylation sites, and comparison of transcriptomic and qRT-PCR data. Results showed the diverse distribution of NBS-LRR genes across chromosomes, and variations in amino acid number, exons/introns, molecular weight, and theoretical isoelectric point, and they were found in various cellular locations like the plasma membrane, cytoplasm, and nucleus. These genes predominantly harbor the NB-ARC superfamily, LRR, LRR_8, and TIR domains, as also confirmed by motif analysis. Additionally, they feature species-specific PLN00113 superfamily and RX-CC_like domain in dicots and monocots, respectively, both responsive to defense against pathogen attacks. The NO-induced NBS-LRR genes of Arabidopsis reveal the presence of cis-elements responsive to phytohormones, light, stress, and growth, suggesting a wide range of responses mediated by NO. Protein-protein interactions, coupled with the prediction of S-Nitrosylation sites, offer valuable insights into the regulatory role of NO at the protein level within each respective species. CONCLUSION: These above findings aimed to provide a thorough understanding of the impact of NO on NBS-LRR genes and their relationships with key plant species.

Optimizing and benchmarking polygenic risk scores with GWAS summary statistics.

BACKGROUND: Polygenic risk score (PRS) is a major research topic in human genetics. However, a significant gap exists between PRS methodology and applications in practice due to often unavailable individual-level data for various PRS tasks including model fine-tuning, benchmarking, and ensemble learning. RESULTS: We introduce an innovative statistical framework to optimize and benchmark PRS models using summary statistics of genome-wide association studies. This framework builds upon our previous work and can fine-tune virtually all existing PRS models while accounting for linkage disequilibrium. In addition, we provide an ensemble learning strategy named PUMAS-ensemble to combine multiple PRS models into an ensemble score without requiring external data for model fitting. Through extensive simulations and analysis of many complex traits in the UK Biobank, we demonstrate that this approach closely approximates gold-standard analytical strategies based on external validation, and substantially outperforms state-of-the-art PRS methods. CONCLUSIONS: Our method is a powerful and general modeling technique that can continue to combine the best-performing PRS methods out there through ensemble learning and could become an integral component for all future PRS applications.

Genetic correlation between chronic sinusitis and autoimmune diseases.

Objective: The association between autoimmune diseases and chronic rhinosinusitis in observational studies remains unclear. This study aimed to explore the genetic correlation between chronic rhinosinusitis and autoimmune diseases. Methods: We employed Mendelian randomization (MR) analysis and linkage disequilibrium score regression (LDSC) to investigate causal relationships and genetic correlations between autoimmune phenotypes and chronic rhinosinusitis. Additionally, transcriptome-wide association (TWAS) analysis was conducted to identify the shared genes between the two conditions to demonstrate their relationship. The CRS GWAS (genome-wide association study) data and other autoimmune diseases were retrieved from ieuOpenGWAS (https://gwas.mrcieu.ac.uk/), the FinnGen alliance (https://r8.finngen.fi/), the UK Biobank (https://www.ukbiobank.ac.uk/), and the EBI database (https://www.ebi.ac.uk/). Results: Utilizing a bivariate two-sample Mendelian randomization approach, our findings suggest a significant association of chronic rhinosinusitis with various autoimmune diseases, including allergic rhinitis (p = 9.55E-10, Odds Ratio [OR] = 2,711.019, 95% confidence interval [CI] = 261.83391-28,069.8), asthma (p = 1.81E-23, OR = 33.99643, 95%CI = 17.52439-65.95137), rheumatoid arthritis (p = 9.55E-10, OR = 1.115526, 95%CI = 1.0799484-1.1522758), hypothyroidism (p = 2.08828E-2, OR = 4.849254, 95%CI = 1.7154455-13.707962), and type 1 diabetes (p = 2.08828E-2, OR = 01.04849, 95%CI = 1.0162932-1.0817062). LDSC analysis revealed a genetic correlation between the positive autoimmune phenotypes mentioned above and chronic rhinosinusitis: AR (rg = 0.344724754, p = 3.94E-8), asthma (rg = 0.43703672, p = 1.86E-10), rheumatoid arthritis (rg = 0.27834931, p = 3.5376E-2), and hypothyroidism (rg = -0.213201473, p = 3.83093E-4). Utilizing the Transcriptome-Wide Association Studies (TWAS) approach, we identified several genes commonly associated with both chronic rhinosinusitis and autoimmune diseases. Genes such as TSLP/WDR36 (Chromosome 5, top SNP: rs1837253), ORMDL3 (Chromosome 13, top SNP: rs11557467), and IL1RL1/IL18R1 (Chromosome 2, top SNP: rs12905) exhibited a higher degree of consistency in their shared involvement across atopic dermatitis (AT), allergic rhinitis (AR), and chronic rhinosinusitis (CRS). Conclusion: Current evidence suggests a genetic correlation between chronic rhinosinusitis and autoimmune diseases like allergic rhinitis, asthma, rheumatoid arthritis, hypothyroidism, and type 1 diabetes. Further research is required to elucidate the mechanisms underlying these associations.

Association between genetic polymorphisms in chromosome region 9q21 and pelvic organ prolapse in Northwestern Chinese women.

This study aimed to explore the association between genetic polymorphisms in the chromosome region 9q21 and the risk of pelvic organ prolapse (POP) in Northwestern Chinese women. A case-control study was conducted with 241 POP patients and 268 healthy controls, analyzing ten single nucleotide polymorphisms (SNPs) across five genes using PCR amplification and Sequenom MassArray. The results revealed significant associations between three SNPs-rs2297002 in GOLM1, rs7450 in MAK10, and rs3814535 in TLE1-and POP. Specifically, the TC genotype of rs2297002, the GA genotype of rs7450, and the AA genotype of rs3814535 were linked to an increased or decreased risk of POP. The study suggests that these genetic variants might contribute to the pathogenesis of POP in this population, offering potential markers for early diagnosis and further investigation into the molecular mechanisms underlying POP.

Cette étude visait à explorer l'association entre les polymorphismes génétiques dans la région chromosomique 9q21 et le risque de prolapsus des organes pelviens (POP) chez les femmes chinoises du nord-ouest. Une étude cas-témoins a été menée auprès de 241 patientes atteintes de POP et de 268 témoins sains, analysant dix polymorphismes nucléotidiques simples (SNP) sur cinq gènes à l'aide de l'amplification par PCR et du Sequenom MassArray. Les résultats ont révélé des associations significatives entre trois SNP (rs2297002 dans GOLM1, rs7450 dans MAK10 et rs3814535 dans TLE1) et le POP. Plus précisément, le génotype TC de rs2297002, le génotype GA de rs7450 et le génotype AA de rs3814535 étaient liés à un risque accru ou réduit de POP. L'étude suggère que ces variantes génétiques pourraient contribuer à la pathogenèse du POP dans cette population, offrant des marqueurs potentiels pour un diagnostic précoce et une étude plus approfondie des mécanismes moléculaires sous-jacents au POP.

Assessing the causal relationship between non-small cell lung cancer and sepsis: a Mendelian randomization study.

BACKGROUND: A Two-sample Mendelian randomization (MR) Analysis was used to assess the causal relationship between non-small cell lung cancer (NSCLC) and sepsis. METHOD: Single nucleotide polymorphisms (SNPs) closely associated with NSCLC were utilized as instrumental variables (IVs) in this study. The Inverse Variance Weighted (IVW) method was used as the primary method for MR analysis, supplemented by the Weighted median, Weighted model, and MR-Egger regression method. Sensitivity analysis was conducted to improve result robustness, and data from various sources were validated and integrated. Bonferroni tests were applied to adjust for multiple comparisons. RESULTS: After Bonferroni tests correcting the combined results, MR analysis revealed a significant association between genetically predicted NSCLC and an increased susceptibility to sepsis (odds ratios [OR]: 1.140, 95% confidence interval [CI]: 1.085-1.199, P = 2.61 × 10- 7). The combined results demonstrated that NSCLC is associated with a heightened risk of sepsis in patients under 75 years of age (OR: 1.085, 95%CI: 1.037-1.353, P = 3.84 × 10- 4). Furthermore, lung adenocarcinoma (LUAD) was found to be potentially associated with an increased susceptibility to sepsis (OR: 1.040, 95% CI: 1.009-1.073, P = 1.16 × 10- 2). These results withstood multiple sensitivity analyses, demonstrating their robustness. CONCLUSION: This study confirms that NSCLC can significantly increase susceptibility to sepsis at the genetic level, providing valuable insights for the early identification of individuals at risk for sepsis.

Genome-wide association study of cassava brown streak disease resistance in cassava germplasm conserved in South America.

Cassava (Manihot esculenta Crantz) is a vital carbohydrate source for over 800 million people globally, yet its production in East Africa is severely affected by cassava brown streak disease (CBSD). Genebanks, through ex-situ conservation, play a pivotal role in preserving crop diversity, providing crucial resources for breeding resilient and disease-resistant crops. This study genotyped 234 South American cassava accessions conserved at the CIAT genebank, previously phenotyped for CBSD resistance by an independent group, to perform a genome-wide association analysis (GWAS) to identify genetic variants associated with CBSD resistance. Our GWAS identified 35 single nucleotide polymorphism (SNP) markers distributed across various chromosomes, associated with disease severity or the presence/absence of viral infection. Markers were annotated within or near genes previously identified with functions related to pathogen recognition and immune response activation. Using the SNP candidates, we screened the world's largest cassava collection for accessions with a higher frequency of favorable genotypes, proposing 35 accessions with potential resistance to CBSD. Our results provide insights into the genetics of CBSD resistance and highlight the importance of genetic resources to equip breeders with the raw materials needed to develop new crop varieties resistant to pests and diseases.

Tabular deep learning: a comparative study applied to multi-task genome-wide prediction.

PURPOSE: More accurate prediction of phenotype traits can increase the success of genomic selection in both plant and animal breeding studies and provide more reliable disease risk prediction in humans. Traditional approaches typically use regression models based on linear assumptions between the genetic markers and the traits of interest. Non-linear models have been considered as an alternative tool for modeling genomic interactions (i.e. non-additive effects) and other subtle non-linear patterns between markers and phenotype. Deep learning has become a state-of-the-art non-linear prediction method for sound, image and language data. However, genomic data is better represented in a tabular format. The existing literature on deep learning for tabular data proposes a wide range of novel architectures and reports successful results on various datasets. Tabular deep learning applications in genome-wide prediction (GWP) are still rare. In this work, we perform an overview of the main families of recent deep learning architectures for tabular data and apply them to multi-trait regression and multi-class classification for GWP on real gene datasets. METHODS: The study involves an extensive overview of recent deep learning architectures for tabular data learning: NODE, TabNet, TabR, TabTransformer, FT-Transformer, AutoInt, GANDALF, SAINT and LassoNet. These architectures are applied to multi-trait GWP. Comprehensive benchmarks of various tabular deep learning methods are conducted to identify best practices and determine their effectiveness compared to traditional methods. RESULTS: Extensive experimental results on several genomic datasets (three for multi-trait regression and two for multi-class classification) highlight LassoNet as a standout performer, surpassing both other tabular deep learning models and the highly efficient tree based LightGBM method in terms of both best prediction accuracy and computing efficiency. CONCLUSION: Through series of evaluations on real-world genomic datasets, the study identifies LassoNet as a standout performer, surpassing decision tree methods like LightGBM and other tabular deep learning architectures in terms of both predictive accuracy and computing efficiency. Moreover, the inherent variable selection property of LassoNet provides a systematic way to find important genetic markers that contribute to phenotype expression.

Integrating targeted genetic markers to genotyping-by-sequencing for an ultimate genotyping tool.

New selection methods, using trait-specific markers (marker-assisted selection (MAS)) and/or genome-wide markers (genomic selection (GS)), are becoming increasingly widespread in breeding programs. This new era requires innovative and cost-efficient solutions for genotyping. Reduction in sequencing cost has enhanced the use of high-throughput low-cost genotyping methods such as genotyping-by-sequencing (GBS) for genome-wide single-nucleotide polymorphism (SNP) profiling in large breeding populations. However, the major weakness of GBS methodologies is their inability to genotype targeted markers. Conversely, targeted methods, such as amplicon sequencing (AmpSeq), often face cost constraints, hindering genome-wide genotyping across a large cohort. Although GBS and AmpSeq data can be generated from the same sample, an efficient method to achieve this is lacking. In this study, we present the Genome-wide & Targeted Amplicon (GTA) genotyping platform, an innovative way to integrate multiplex targeted amplicons into the GBS library preparation to provide an all-in-one cost-effective genotyping solution to breeders and research communities. Custom primers were designed to target 23 and 36 high-value markers associated with key agronomical traits in soybean and barley, respectively. The resulting multiplex amplicons were compatible with the GBS library preparation enabling both GBS and targeted genotyping data to be produced efficiently and cost-effectively. To facilitate data analysis, we have introduced Fast-GBS.v3, a user-friendly bioinformatic pipeline that generates comprehensive outputs from data obtained following sequencing of GTA libraries. This high-throughput low-cost approach will greatly facilitate the application of DNA markers as it provides required markers for both MAS and GS in a single assay.

A genome-wide Association study of the Count of Codeine prescriptions.

Opioid prescription records in existing electronic health record (EHR) databases are a potentially useful, high-fidelity data source for opioid use-related risk phenotyping in genetic analyses. Prescriptions for codeine derived from EHR records were used as targeting traits by screening 16 million patient-level medication records. Genome-wide association analyses were then conducted to identify genomic loci and candidate genes associated with different count patterns of codeine prescriptions. Both low- and high-prescription counts were captured by developing 8 types of phenotypes with selected ranges of prescription numbers to reflect potentially different levels of opioid risk severity. We identified one significant locus associated with low-count codeine prescriptions (1, 2 or 3 prescriptions), while up to 7 loci were identified for higher counts (≥ 4, ≥ 5, ≥6, or ≥ 7 prescriptions), with a strong overlap across different thresholds. We identified 9 significant genomic loci with all-count phenotype. Further, using the polygenic risk approach, we identified a significant correlation (Tau = 0.67, p = 0.01) between an externally derived polygenic risk score for opioid use disorder and numbers of codeine prescriptions. As a proof-of-concept study, our research provides a novel and generalizable phenotyping pipeline for the genomic study of opioid-related risk traits.

Genome-wide identification of the endonuclease family genes implicates potential roles of TaENDO23 in drought-stressed response and grain development in wheat.

BACKGROUND: Endonucleases play a crucial role in plant growth and stress response by breaking down nuclear DNA. However, the specific members and biological functions of the endonuclease encoding genes in wheat remain to be determined. RESULTS: In this study, we identified a total of 26 TaENDO family genes at the wheat genome-wide level. These genes were located on chromosomes 2 A, 2B, 2D, 3 A, 3B, and 3D and classified into four groups, each sharing similar gene structures and conserved motifs. Furthermore, we identified diverse stress-response and growth-related cis-elements in the promoter of TaENDO genes, which were broadly expressed in different organs, and several TaENDO genes were significantly induced under drought and salt stresses. We further examined the biological function of TaENDO23 gene since it was rapidly induced under drought stress and exhibited high expression in spikes and grains. Subcellular localization analysis revealed that TaENDO23 was localized in the cytoplasm of wheat protoplasts. qRT-PCR results indicated that the expression of TaENDO23 increased under PEG6000 and abscisic acid treatments, but decreased under NaCl treatment. TaENDO23 mainly expressed in leaves and spikes. A kompetitive allele-specific PCR (KASP) marker was developed to identify single nucleotide polymorphisms in TaENDO23 gene in 256 wheat accessions. The alleles with TaENDO23-HapI haplotypes had higher grain weight and size compared to TaENDO23-HapII. The geographical and annual frequency distributions of the two TaENDO23 haplotypes revealed that the elite haplotype TaENDO23-HapI was positively selected in the wheat breeding process. CONCLUSION: We systematically analyzed the evolutionary relationships, gene structure characteristics, and expression patterns of TaENDO genes in wheat. The expression of TaENDO23, in particular, was induced under drought stress, mainly expressed in the leaves and grains. The KASP marker of TaENDO23 gene successfully distinguished between the wheat accessions, revealing TaENDO23-HapI as the elite haplotype associated with improved grain weight and size. These findings provide insights into the evolution and characteristics of TaENDO genes at the genome-wide level in wheat, laying the foundation for further biological analysis of TaENDO23 gene, especially in response to drought stress and grain development.

Identification of Key Genes Controlling Sugar and Organic Acid Accumulation in Wampee Fruit (Clausena lansium) via Genome Assembly and Genome-wide Association Analysis.

Wampee (Clausena lansium) is an economically significant subtropical fruit tree widely cultivated in Southern China. To provide high-quality genomic resources for C. lansium, we report a chromosome-level genome sequence for the "JinFeng" cultivar. The 297.1 Mb C. lansium genome contained nine chromosomes with a scaffold N50 of 29.2 Mb and encoded 23,468 protein-coding genes. Selective sweep analysis between sweet and sour C. lansium varieties and genome-wide association analysis identified 14 candidate genes putatively involved in sugar and acid accumulation. ClERF061, encoding an ethylene response factor, and ClSWEET7, encoding a Sugars Will Eventually be Exported Transporters (SWEET) family protein, were proposed as key regulators of the sweet and sour tastes of the wampee fruit. ClERF061 and ClSWEET7 overexpression in tomatoes increased the total sugar and acid content in fruits. ClSWEET7 promoter activation by ClERF061 was confirmed via Nicotiana benthamiana transient expression. Our study provides valuable genomic resources for C. lansium genetics and breeding.

A genome-wide gene-environmental interaction study identified novel loci for the relationship between ambient air pollution exposure and depression, anxiety.

BACKGROUND: Genetic factors and environmental exposures, including air pollution, contribute to the risk of depression and anxiety. While the association between air pollution and depression and anxiety has been established in the UK Biobank, there has been limited research exploring this relationship from a genetic perspective. METHODS: Based on individual genotypic and phenotypic data from a cohort of 104,385 participants in the UK Biobank, a polygenic risk score for depression and anxiety was constructed to explore the joint effects of nitric oxide (NO), nitrogen dioxide (NO2), particulate matter (PM) with a diameter of ⩽2.5 µm (PM2.5) and 2.5-10 µm (PMcoarse) with depression and anxiety by linear and logistic regression models. Subsequently, a genome-wide gene-environmental interaction study (GWEIS) was performed using PLINK 2.0 to identify the genes interacting with air pollution for depression and anxiety. RESULTS: A substantial risk of depression and anxiety development was detected in participants exposed to the high air pollution concomitantly with high genetic risk. GWEIS identified 166, 23, 18, and 164 significant candidate loci interacting with NO, NO2, PM2.5, and PMcoarse for Patient Health Questionnaire-9 (PHQ-9) score, and detected 44, 10, 10, and 114 candidate loci associated with NO, NO2, PM2.5, and PMcoarse for General Anxiety Disorder (GAD-7) score, respectively. And some significant genes overlapped among four air pollutants, like TSN (rs184699498, PNO2 = 3.47 × 10-9; rs139212326, PPM2.5 = 1.51 × 10-8) and HSP90AB7P(rs150987455, PNO2 = 1.63 × 10-11; rs150987455, PPM2.5 = 7.64 × 10-11), which were common genes affecting PHQ-9 score for both NO2 and PM2.5. CONCLUSION: Our study identified the joint effects of air pollution with genetic susceptibility on the risk of depression and anxiety, and provided several novel candidate genes for the interaction, contributing to an understanding of the genetic architecture of depression and anxiety.

Different genome-wide DNA methylation patterns in CD4+ T lymphocytes and CD14+ monocytes characterize relapse and remission of multiple sclerosis: Focus on GNAS.

BACKGROUND: Relapsing-remitting multiple sclerosis (RRMS) is a most common form of multiple sclerosis in which periods of neurological worsening are followed by periods of clinical remission. RRMS relapses are caused by an acute autoimmune inflammatory process, which can occur in any area of the central nervous system. Although development of exacerbation cannot yet be accurately predicted, various external factors are known to affect its risk. These factors may trigger the pathological process through epigenetic mechanisms of gene expression regulation, first of all, through changes in DNA methylation. METHODS: In the present work, we for the first time analyzed genome-wide DNA methylation patterns in CD4+ T lymphocytes and CD14+ monocytes of the same RRMS patients in relapse and remission. The effects of the differential methylation on gene expression were studied using qPCR. RESULTS: We found 743 differentially methylated CpG positions (DMPs) in CD4+ cells and only 113 DMPs in CD14+ cells. They were mostly hypermethylated in RRMS relapse in both cell populations. However, the proportion of hypermethylated DMPs (as well as DMPs located within or in close proximity to CpG islands) was significantly higher in CD4+ T lymphocytes. In CD4+ and CD14+ cells we identified 469 and 67 DMP-containing genes, respectively; 25 of them were common for two cell populations. When we conducted a search for differentially methylated genomic regions (DMRs), we found a CD4+ specific DMR hypermethylated in RRMS relapse (adj. p = 0.03) within the imprinted GNAS locus. Total level of the protein-coding GNAS transcripts in CD4+ T cells decreased significantly in the row from healthy control to RRMS remission and then to RRMS relapse (adj. p = 3.1 × 10-7 and 0.011, respectively). CONCLUSION: Our findings suggest that the epigenetic mechanism of DNA methylation in immune cells contributes to the development of RRMS relapse. Further studies are now required to validate these results and shed light on the molecular mechanisms underlying the observed GNAS methylation and expression changes.

New insights on genetic background of major diabetic vascular complications.

BACKGROUND: By 2045, it is expected that 693 million individuals worldwide will have diabetes and with greater risk of morbidity, mortality, loss of vision, renal failure, and a decreased quality of life due to the devastating effects of macro- and microvascular complications. As such, clinical variables and glycemic control alone cannot predict the onset of vascular problems. An increasing body of research points to the importance of genetic predisposition in the onset of both diabetes and diabetic vascular complications. OBJECTIVES: Purpose of this article is to review these approaches and narrow down genetic findings for Diabetic Mellitus and its consequences, highlighting the gaps in the literature necessary to further genomic discovery. MATERIAL AND METHODS: In the past, studies looking for genetic risk factors for diabetes complications relied on methods such as candidate gene studies, which were rife with false positives, and underpowered genome-wide association studies, which were constrained by small sample sizes. RESULTS: The number of genetic findings for diabetes and diabetic complications has over doubled due to the discovery of novel genomics data, including bioinformatics and the aggregation of global cohort studies. Using genetic analysis to determine whether diabetes individuals are at the most risk for developing diabetic vascular complications (DVC) might lead to the development of more accurate early diagnostic biomarkers and the customization of care plans. CONCLUSIONS: A newer method that uses extensive evaluation of single nucleotide polymorphisms (SNP) in big datasets is Genome-Wide Association Studies (GWAS).

Development of genomic and genetic resources facilitating molecular genetic studies on untapped Myanmar rice germplasms.

To counteract the growing population and climate changes, resilient varieties adapted to regional environmental changes are required. Landraces are valuable genetic resources for achieving this goal. Recent advances in sequencing technology have enabled national seed/gene banks to share genomic and genetic information from their collections including landraces, promoting the more efficient utilization of germplasms. In this study, we developed genomic and genetic resources for Myanmar rice germplasms. First, we assembled a diversity panel consisting of 250 accessions representing the genetic diversity of Myanmar indica varieties, including an elite lowland variety, Inn Ma Yebaw (IMY). Our population genetic analyses illustrated that the diversity panel represented Myanmar indica varieties well without any apparent population structure. Second, de novo genome assembly of IMY was conducted. The IMY assembly was constructed by anchoring 2888 contigs, which were assembled from 30× coverage of long reads, into 12 chromosomes. Although many gaps existed in the IMY genome assembly, our quality assessments indicated high completeness in the gene-coding regions, identical to other near-gap-free assemblies. Together with dense variant information, the diversity panel and IMY genome assembly will facilitate deeper genetic research and breeding projects that utilize the untapped Myanmar rice germplasms.

Corrigendum: Genome-wide association study identifying variants related to performance and injury in high-performance athletes.

[This corrects the article DOI: 10.1177/15353702231198068.].

Genome-wide identification of the histone modification gene family in Aquilaria sinensis and functional analysis of several HMs in response to MeJA and NaCl stress.

Histone modifications (HMs) play various roles in growth, development, and resistance to abiotic stress. However, HMs have been systematically identified in a few plants, and identification of HMs in medicinal plants is very rare. Aquilaria sinensis is a typical stress-induced medicinal plant, in which HMs remain unexplored. We conducted a comprehensive study to identify HMs and obtained 123 HMs. To conduct evolutionary analysis, we constructed phylogenetic trees and analyzed gene structures. To conduct functional analysis, we performed promoter, GO, and KEGG analyses and ortholog analyses against AtHMs. Based on the expression profiles of different tissues and different layers of Agar-Wit, some HMs of A. sinensis (AsHMs) were predicted to be involved in the formation of agarwood, and their response to MeJA and NaCl stress was tested by qRT-PCR analysis. By analyzing the enrichment of H3K4me3, H3K27me3, and H4K5ac in the promoter regions of two key sesquiterpene synthase genes, AsTPS13/18, we hypothesized that AsHMs play important roles in the synthesis of agarwood sesquiterpenes. We confirmed this hypothesis by conducting RNAi transgenic interference experiments. This study provided valuable information and important biological theories for studying epigenetic regulation in the formation of agarwood. It also provided a framework for conducting further studies on the biological functions of HMs.

Immune cell traits and causal relationships with cholecystitis: a mendelian randomization analysis.

Cholecystitis, characterized by inflammation of the gallbladder, is intricately linked to immune cells and the cytokines they produce. Despite this association, the specific contributions of immune cells to the onset and progression of cholecystitis remain to be fully understood. To delineate this relationship, we utilized the Mendelian randomization (MR) method to scrutinize the causal connections between 731 immune cell phenotypes and cholecystitis. By conducting MR analysis on 731 immune cell markers from public datasets, this study seeks to understand their potential impact on the risk of cholecystitis. It aims to elucidate the interactions between immune phenotypes and the disease, aiming to lay the groundwork for advancing precision medicine and developing effective treatment strategies for cholecystitis. Taking immune cell phenotypes as the exposure factor and cholecystitis as the outcome event, this study used single nucleotide polymorphisms (SNPs) closely associated with both immune cell phenotypes and cholecystitis as genetic instrumental variables. We conducted a two-sample MR analysis on genome-wide association studies (GWAS) data. Our research thoroughly examined 731 immune cell markers, to determine potential causal relationships with susceptibility to cholecystitis. Sensitivity analyses were performed to ensure the robustness of our findings, excluding the potential impacts of heterogeneity and pleiotropy. To avoid reverse causality, we conducted reverse MR analyses with cholecystitis as the exposure factor and immune cell phenotypes as the outcome event. Among the 731 immune phenotypes, our study identified 21 phenotypes with a causal relationship to cholecystitis (P < 0.05). Of these, eight immune phenotypes exhibited a protective effect against cholecystitis (odds ratio (OR) < 1), while the other 13 immune phenotypes were associated with an increased risk of developing cholecystitis (OR > 1). Additionally, employing the false discovery rate (FDR) method at a significance level of 0.2, no significant causal relationship was found between cholecystitis and immune phenotypes. Our research has uncovered a significant causal relationship between immune cell phenotypes and cholecystitis. This discovery not only enhances our understanding of the role of immune cells in the onset and progression of cholecystitis but also establishes a foundation for developing more precise biomarkers and targeted therapeutic strategies. It provides a scientific basis for more effective and personalized treatments in the future. These findings are expected to substantially improve the quality of life for patients with cholecystitis and mitigate the impact of the disease on patients and their families.