Genome-wide Association Research Articles

Genome wide association studies are enriched for interacting genes.

Background : With recent advances in single cell technology, high-throughput methods provide unique insight into disease mechanisms and more importantly, cell type origin. Here, we used multi-omics data to understand how genetic variants from genome-wide association studies influence development of disease. We show in principle how to use genetic algorithms with normal, matching pairs of single-nucleus RNA- and ATAC-seq, genome annotations, and protein-protein interaction data to describe the genes and cell types collectively and their contribution to increased risk. Results : We used genetic algorithms to measure fitness of gene-cell set proposals against a series of objective functions that capture data and annotations. The highest information objective function captured protein-protein interactions. We observed significantly greater fitness scores and subgraph sizes in foreground vs. matching sets of control variants. Furthermore, our model reliably identified known targets and ligand-receptor pairs, consistent with prior studies. Conclusions : Our findings suggested that application of genetic algorithms to association studies can generate a coherent cellular model of risk from a set of susceptibility variants. Further, we showed, using breast cancer as an example, that such variants have a greater number of physical interactions than expected due to chance.

Association between antithrombotic agents use and hepatocellular carcinoma risk: a two-sample mendelian randomization analysis

BackgroundHepatocellular carcinoma (HCC) is the most common primary liver cancer worldwide. Multiple observational studies demonstrated a negative association between the use of antithrombotic agents and the risk of HCC. However, the precise causal relationship between these factors remains uncertain. Therefore, our study used a two-sample Mendelian randomization (MR) analysis to assess the causal link between these two factors.MethodThe summary statistics of single nucleotide polymorphisms (SNPs) associated with the use of antithrombotic agents were acquired from a genome-wide association study (GWAS) performed on individuals of European descent. A two-sample MR analysis was performed using the inverse variance weighting (IVW), the weighted median estimate, the MR-Egger regression, and the weighted-mode estimate. Sensitivity analysis of the primary findings was performed using MR-PRESSO, MR-Egger regression, Cochran’s Q test, and Leave-one-out analysis.ResultsTen SNPs associated with the use of antithrombotic agents were selected as instrumental variables. The MR analysis performed using the four methods mentioned above revealed a negative causal association between the use of antithrombotic agents and HCC. Univariate MR estimates based on the inverse variance weighting (IVW) method suggested a negative causal association between the use of antithrombotic agents and HCC [odds ratio (OR) 0.444, 95% confidence interval (CI) 0.279 to 0.707, P = 0.001]. The other methods also produced similar results. No heterogeneity and horizontal pleiotropy were found.ConclusionOur findings suggested an inverse causal association of antithrombotic agents with the risk of HCC.

Validation of QTLs associated with corn borer resistance and grain yield: implications in maize breeding.

Validations of previously detected quantitative trait loci (QTLs) to assess their reliability are crucial before implementing breeding programs. The objective of this study was to determine the reliability and practical usefulness of previously reported QTLs for resistance to stem tunneling by the Mediterranean stem borer (MSB) and yield. These authors used approximately 600 recombinant inbred lines (RILs) from a multiparent advanced generation intercross (MAGIC) population to map QTL using a genome-wide association study (GWAS) approach. We identified RILs situated at the extremes of resistance and yield distributions within the whole MAGIC, and those QTLs were evaluated per se and crossed to a tester (A638) using lattice designs. In each set, a significant single-nucleotide polymorphism (SNP) was considered validated if (1) the same SNP was associated with the trait with a p-value < 0.02, or (2) within a ±2-Mbp interval, an SNP associated with the trait exhibited a p-value < 0.02 and demonstrated linkage disequilibrium (r2 > 0.2) with the SNPs previously reported. The novel QTL validation approach was implemented using improved experimental designs that led to higher heritability estimates for both traits compared to those estimated with the whole MAGIC population. The procedure used allowed us to jointly validate several QTL and to ascertain their possible contribution to hybrid improvement. Specifically, nearly three-quarters of the QTLs for tunnel length were confirmed. Notably, QTLs located in the genomic region 6.05-6.07 were consistently validated across different sets and have been previously linked to resistance against stalk tunneling in various mapping populations. For grain yield, approximately 10 out of 16 QTLs were validated. The validation rate for yield was lower than for tunnel length, likely due to the influence of dominance and/or epistatic effects. Overall, 9 out of 21 QTLs for tunnel length and 6 out of 17 QTLs for grain yield identified in our previous research were validated across both validation sets, indicating a moderate genetic correlation between per se and testcross performance of inbred lines. These findings offer insights into the reliability of QTL and genomic predictions, both derived from assessments conducted on the entire MAGIC population. Genomic predictions for tunnel length based on inbred line evaluations could be useful to develop more resistant hybrids; meanwhile, genomic prediction for yield could only be valid in a homozygous background.

Identification of Genetic Loci Associated With Intracerebral Hemorrhage Using a Multitrait Analysis Approach.

Genome-wide association studies (GWASs) have only 2 loci associated with spontaneous intracerebral hemorrhage (ICH): APOE for lobar and 1q22 for nonlobar ICH. We aimed to discover new loci through an analysis that combines correlated traits (multi-trait analysis of GWAS [MTAG]) and explore a gene-based analysis, transcriptome-wide association study (TWAS), and proteome-wide association study (PWAS) to understand the biological mechanisms of spontaneous ICH providing potential therapeutic targets. We use the published MTAG of ICH (patients with spontaneous intraparenchymal bleeding) and small-vessel ischemic stroke. For all ICH, lobar ICH, and nonlobar ICH, a pairwise MTAG combined ICH with traits related to cardiovascular risk factors, cerebrovascular diseases, or Alzheimer disease (AD). For the analysis, we assembled those traits with a genetic correlation ≥0.3. A new MTAG combining multiple traits was performed with those traits whose pairwise MTAG yielded new GWAS-significant single nucleotide polymorphisms (SNPs), with a posterior-probability of model 3 (GWAS-pairwise) ≥0.6. We perform TWAS and PWAS that correlate the genetic component of expression or protein levels with the genetic component of a trait. We use the ICH cohort from UK Biobank as replication. For all ICH (1,543 ICH, 1,711 controls), the mean age was 72 ± 2 in cases and 70 ± 2 in controls, and half of them were women. Replication cohort: 700 ICH and 399,717 controls. Novel loci were found only for all ICH (the trait containing lobar and nonlobar ICH), combining data of ICH and small vessel stroke, white matter hyperintensities volume, fractional anisotropy, mean diffusivity, and AD. We replicated 6 SNPs belonging to 2q33.2 (ICA1L, β = 0.20, SE = 0.03, p value = 8.91 × 10-12), 10q24.33 (OBFC1, β = -0.12, SE = 0.02, p value = 1.67 × 10-8), 13q34 (COL4A2, β = 0.02, SE = 0.02, p value = 2.34 × 10-11), and 19q13.32 (APOC1, β = -0.19, SE = 0.03, p value = 1.38 × 10-12; APOE, β = 0.21, SE = 0.03, p value = 2.70 × 10-11; PVRL2:CTB-129P6.4, β = 0.15, SE = 0.03, p value = 1.38 × 10-8); 2 genes (SH3PXD2A, Z-score = 4.83, p value = 6.67 × 10-7; and APOC1, Z-score: = 5.11, p value = 1.60 × 10-7); and ICA1L transcript (Z-score = 6.8, p value = 9.1 × 10-12) and protein levels (Z-score = -5.8, p value = 6.7 × 10-9). Our results reinforce the role of APOE in ICH risk, replicate previous ICH-associated loci (2q33 and 13q34), and point to new ICH associations with OBFC1, PVRL2:CTB-129P6.4, APOC1, and SH3PXD2A. Our study used data from European subjects, our main limitation. These molecules could be potential targets for future studies for modulating ICH risk.

Identification of osteoporosis genes using family studies.

Osteoporosis is a multifactorial bone disease characterised by reduced bone mass and increased fracture risk. Family studies have made significant contribution in unravelling the genetics of osteoporosis. Yet, most of the underlying molecular and biological mechanisms remain unknown prompting the need for further studies. This review outlines the proper phenotyping and advanced genetic techniques in the form of high-throughput DNA sequencing used to identify genetic factors underlying monogenic osteoporosis in a family-based setting. The steps related to variant filtering prioritisation and curation are also described. From an evolutionary perspective, deleterious risk variants with higher penetrance tend to be rare as a result of negative selection. High-throughput sequencing (HTS) can identify rare variants with large effect sizes which are likely to be missed by candidate gene analysis or genome-wide association studies (GWAS) wherein common variants with small to moderate effect sizes are identified. We also describe the importance of replicating implicated genes, and possibly variants, identified following HTS to confirm their causality. Replication of the gene in other families, singletons or independent cohorts confirms that the shortlisted genes and/or variants are indeed causal. Furthermore, novel genes and/or variants implicated in monogenic osteoporosis require a thorough validation by means of in vitro and in vivo assessment. Therefore, analyses of families can continue to elucidate the genetic architecture of osteoporosis, paving the way for improved diagnostic and therapeutic strategies.

Ambient air pollution and risk of allergic respiratory diseases in European and East Asian populations: A Mendelian randomization study

BackgroundAmbient air pollution has become a challenging global health issue since industrialization, especially affecting respiratory diseases. However, the causal link between air pollution and allergic respiratory diseases (ARDs) remains unclear due to confounding factors in conventional epidemiological studies across different populations. Thus, we aimed to clarify the causal associations between air pollution and ARDs in European and East Asian populations using Mendelian randomization (MR). MethodsMR utilizes genetic variants and provides a satisfactory level of causal evidence. Genetic data for exposures (PM2.5, PM2.5 absorbance, PM10, PMcoarse, NO2 and NOx) and outcomes (allergic rhinitis, chronic rhinosinusitis, asthma, and obesity related asthma) were obtained from genome-wide association studies. Instrumental variables were strictly filtered based on core assumptions. Two-sample MR and sensitivity analyses were conducted separately for European and East Asian populations. ResultsPMcoarse was causally associated with an increased risk of chronic rhinosinusitis (OR = 1.588 [1.002–2.518]; p = 0.049) and obesity related asthma (OR = 1.956 [1.012–3.780]; p = 0.046) in European population, and PM10 was associated with a decreased risk of allergic rhinitis in East Asian population (OR = 0.882 [0.798–0.974]; p = 0.013). No heterogeneity or pleiotropy was detected in any significant causal association. ConclusionOur findings indicate that ambient air pollution has opposite impacts on the etiology of ARDs in European and East Asian populations, which provides evidence for decisions on public policies and suggests that different responses to environmental factors such as air pollution may contribute to racial heterogeneity of ARDs.

Genetic insight into the relationship between inflammatory bowel disease and Clostridioides difficile infection.

Patients with inflammatory bowel disease (IBD) are at increased risk of Clostridioides difficile infection (CDI). Herein, we aimed to determine if genetic risk contributes to this observed association. We carried out a genome-wide association study (GWAS) analysis in the Michigan Genomics Initiative and the United Kingdom Biobank for CDI based on ICD codes and meta-analyzed these results with similar publicly accessible GWAS summary statistics from Finngen. Conditional and joint multi-SNP analyses were used to identify independent associations. Imputation of the human leukocyte antigen (HLA) region with fine mapping was used to try to identify causal HLA allele groups. Two-sample bidirectional Mendelian randomization (MR) was implemented to determine causal relationships between IBD and CDI. A total of 3,500 cases of CDI and 674,323 controls were meta-analyzed, revealing one genome-wide significant variant for CDI, HLA-C;LINC02571-rs3134745-C (P = 4.27E-08), which annotated to the major histocompatibility complex on chromosome 6. While fine mapping did not identify a statistically significant HLA allele group, there was a suggestive signal for HLA-B*35:01 (P = 4.74e-04). Using two-sample MR, genetically predicted IBD was associated with increased risk of CDI (MR Egger [odds ratio {OR} 1.16, 95% confidence interval {CI} 1.02-1.31]). Subset analysis revealed that risk was primarily driven by genetically predicted ulcerative colitis (MR Egger [OR 1.22, 95% CI 1.05-1.41]). These results highlight the importance of the host immune response in CDI pathogenesis, help explain the observed relationship between IBD and CDI, and open new avenues for targeted treatment of CDI in IBD.IMPORTANCEData from this paper (i) provide reproducible evidence that susceptibility CDI is genetically mediated, (ii) highlight genetic risk as a mechanism for the increased risk of CDI in patients with inflammatory bowel disease, and (iii) point toward anti-interleukin-23 therapy as a common therapeutic strategy.

Implementation of a genotyped African population cohort, with virtual follow-up: A feasibility study in the Western Cape Province, South Africa

Background There is limited knowledge regarding African genetic drivers of disease due to prohibitive costs of large-scale genomic research in Africa. Methods We piloted a cost-effective, scalable virtual genotyped cohort in South Africa, with participant recruitment using a tiered informed consent model and DNA collection by buccal swab. Genotype data was generated using the H3Africa Illumina micro-array, and phenotype data was derived from routine health data of participants. We demonstrated feasibility of nested case control genome wide association studies using these data for phenotypes type 2 diabetes mellitus (T2DM) and severe COVID-19. Results 2267346 variants were analysed in 459 participant samples. 78.6% of SNPs and 74% of samples passed quality control (QC). Principal component analysis showed extensive ancestry admixture in study participants. For 1780 published COVID-19-associated variants, 3 SNPs in the pre-imputation data and 23 SNPS in the imputed data were significantly associated with severe COVID-19 cases compared to controls. For 2755 published T2DM associated variants, 69 SNPs in the pre-imputation data and 419 SNPs in the imputed data were significantly associated with T2DM cases when compared to controls. Conclusions The results shown here are illustrative of what will be possible as the cohort expands in the future. Here we demonstrate the feasibility of this approach, recognising that the findings presented here are preliminary and require further validation once we have a sufficient sample size to improve statistical significance of findings. We implemented a genotyped population cohort with virtual follow up data in a resource-constrained African environment, demonstrating feasibility for scale up and novel health discoveries through nested case-control studies.

Walking pace is a protective factor for rheumatoid arthritis: a mendelian randomization study

Walking pace is a simple and functional form of exercise and a strong predictor of health, but little is known about its causal association with rheumatoid arthritis. This study aimed to investigate the causal effect of WP on the developing RA using Mendelian randomization analysis. The genetic variation associated with WP was selected as an instrumental variable from the latest genome-wide association studies. Summary-level data for the outcomes were obtained from the corresponding GWAS. The inverse-variance weighted method was used as the primary MR analysis. The results were further tested using a multivariable MR approach based on Bayesian model averaging. Confounders (BMI, SMK, HBP, TD) with close associations with RA were included in the analysis. An observational study with individual data from UK Biobank was performed to reinforce our findings. The MR results indicated the significant inverse associations of WP with the risk of RA (odds ratio (OR), 0.31; 95% confidence interval (CI), 0.15, 0.62; p = 1.05 × 10 −3). After adjusting for the risk factors, the associations for WP and RA did not change substantially. Observational study results demonstrated the same effect of WP on reducing the risk of RA. The Mendelian randomization analysis and observational study provide evidence suggesting that walking pace is a protective factor for rheumatoid arthritis. Given its simple measurement, walking pace may be a pragmatic target for interventions.

Analyses of GWAS signal using GRIN identify additional genes contributing to suicidal behavior

Genome-wide association studies (GWAS) identify genetic variants underlying complex traits but are limited by stringent genome-wide significance thresholds. We present GRIN (Gene set Refinement through Interacting Networks), which increases confidence in the expanded gene set by retaining genes strongly connected by biological networks when GWAS thresholds are relaxed. GRIN was validated on both simulated interrelated gene sets as well as multiple GWAS traits. From multiple GWAS summary statistics of suicide attempt, a complex phenotype, GRIN identified additional genes that replicated across independent cohorts and retained biologically interrelated genes despite a relaxed significance threshold. We present a conceptual model of how these retained genes interact through neurobiological pathways that may influence suicidal behavior, and identify existing drugs associated with these pathways that would not have been identified under traditional GWAS thresholds. We demonstrate GRIN’s utility in boosting GWAS results by increasing the number of true positive genes identified from GWAS results.

DGRPool, a web tool leveraging harmonized Drosophila Genetic Reference Panel phenotyping data for the study of complex traits

Genome-wide association studies have advanced our understanding of complex traits, but studying how a GWAS variant can affect a specific trait in the human population remains challenging due to environmental variability. Drosophila melanogaster is in this regard an excellent model organism for studying the relationship between genetic and phenotypic variation due to its simple handling, standardized growth conditions, low cost, and short lifespan. The Drosophila Genetic Reference Panel (DGRP) in particular has been a valuable tool for studying complex traits, but proper harmonization and indexing of DGRP phenotyping data is necessary to fully capitalize on this resource. To address this, we created a web tool called DGRPool (dgrpool.epfl.ch), which aggregates phenotyping data of 1034 phenotypes across 135 DGRP studies in a common environment. DGRPool enables users to download data and run various tools such as genome-wide (GWAS) and phenome-wide (PheWAS) association studies. As a proof-of-concept, DGRPool was used to study the longevity phenotype and uncovered both established and unexpected correlations with other phenotypes such as locomotor activity, starvation resistance, desiccation survival, and oxidative stress resistance. DGRPool has the potential to facilitate new genetic and molecular insights of complex traits in Drosophila and serve as a valuable, interactive tool for the scientific community.

DGRPool, a web tool leveraging harmonized Drosophila Genetic Reference Panel phenotyping data for the study of complex traits.

Genome-wide association studies have advanced our understanding of complex traits, but studying how a GWAS variant can affect a specific trait in the human population remains challenging due to environmental variability. Drosophila melanogaster is in this regard an excellent model organism for studying the relationship between genetic and phenotypic variation due to its simple handling, standardized growth conditions, low cost, and short lifespan. The Drosophila Genetic Reference Panel (DGRP) in particular has been a valuable tool for studying complex traits, but proper harmonization and indexing of DGRP phenotyping data is necessary to fully capitalize on this resource. To address this, we created a web tool called DGRPool (dgrpool.epfl.ch), which aggregates phenotyping data of 1034 phenotypes across 135 DGRP studies in a common environment. DGRPool enables users to download data and run various tools such as genome-wide (GWAS) and phenome-wide (PheWAS) association studies. As a proof-of-concept, DGRPool was used to study the longevity phenotype and uncovered both established and unexpected correlations with other phenotypes such as locomotor activity, starvation resistance, desiccation survival, and oxidative stress resistance. DGRPool has the potential to facilitate new genetic and molecular insights of complex traits in Drosophila and serve as a valuable, interactive tool for the scientific community.

Discovering Novel Loci of Chronic Kidney Disease via Principal Component Analysis based Multiple-trait Genome‑wide Association Study.

Introduction Chronic kidney diseases (CKD) encompass a spectrum of complex pathophysiological processes. While numerous genome-wide association studies (GWASs) have focused on individual traits such as albuminuria, estimated glomerular filtration rate (eGFR), and eGFR change, there remains a paucity of genetic studies integrating these traits collectively for comprehensive evaluation. Methods In this study, we performed individual GWASs for albuminuria, baseline eGFR, and eGFR slope utilizing data from non-diabetic individuals enrolled from the Taiwan Biobank (TWB). Subsequently, we employed principal component analysis to transform these three quantitative traits into principal components (PCs) and performed GWAS based on these principal components (PC-based GWAS). Results The individual GWAS analyses of albuminuria, baseline eGFR, and eGFR slope identified 10, 13, and 210 candidate loci respectively, with 2, 3, and 99 of them representing previously reported loci. PC-based GWAS identified additional 20 novel candidate loci linked to CKD (P-values ranging from 5.8 × 10-7 to 9.1 × 10-6). Notably, 4 of these 20 single nucleotide polymorphisms (rs9332641, rs10737429, rs117231653, and rs73360624) exhibited significant associations with kidney expression quantitative trait loci. Conclusion To our knowledge, this study represents the first PC-based GWAS integrating albuminuria, baseline eGFR, and eGFR slope. Our approach found 20 novel candidate loci suggestively associated with CKD, underscoring the value of integrating multiple kidney traits in unraveling the pathophysiology of this complex disorder.

Shared Genetic Architecture and Causal Relationship between Serum 25-Hydroxyvitamin D and Bone Mineral Density.

Despite the well-established regulatory role of vitamin D in maintaining bone health, little is known about the shared genetics and causality of the association between serum 25-hydroxyvitamin D (25OHD) and bone mineral density (BMD). Leveraging individual-level data from the UK Biobank (UKB) cohort and summary-level data from the genome-wide association studies (GWASs) conducted on European individuals for serum 25OHD (N = 417,580) and estimated heel BMD (eBMD, N = 426,824), we systematically elucidated the shared genetic architecture underlying serum 25OHD and eBMD through a comprehensive genome-wide cross-trait design. Despite a lack of global genetic correlation (rg = -0.001, P = 0.95), a significant local signal was discovered at 5p11-5q11.9. Two-sample Mendelian randomization (MR) indicated no causal association in the overall population (β = 0.003, 95% CI = -0.04∼0.03, P = 0.93), while positive causal effects were observed in males (β = 0.005, 95% CI = 0.00∼0.01, P = 0.03) and the elderly (β = 0.009, 95% CI = 0.00∼0.02, P = 0.01) according to one-sample MR. A total of 49 pleiotropic SNPs, with 4 novel SNPs (rs1077151, rs79873740, rs12150353, and rs4760401), were identified, and a total of 95 gene-tissue pairs exhibited overlap, predominantly enriched in the nervous, digestive, exo-/endocrine and cardiovascular systems. Protein-protein interaction analysis identified RPS9 and RPL7A as hub genes. This study illuminates the potential health benefits of enhancing serum 25OHD levels to mitigate the risk of osteoporosis among males and the elderly. It also unveils a shared genetic basis between serum 25OHD and eBMD, offering valuable insights into the intricate biological pathways.

Machine Learning-Based Detection of Bladder Cancer by Urine cfDNA Fragmentation Hotspots that Capture Cancer-Associated Molecular Features.

cfDNA fragmentomics-based liquid biopsy is a potential option for noninvasive bladder cancer (BLCA) detection that remains an unmet clinical need. We assessed the diagnostic performance of cfDNA hotspot-driven machine-learning models in a cohort of 55 BLCA patients, 51 subjects with benign conditions, and 11 healthy volunteers. We further performed functional bioinformatics analysis for biological understanding and interpretation of the tool's diagnostic capability. Urinary cfDNA hotspots-based machine-learning model enabled effective BLCA detection, achieving high performance (area under curve 0.96) and an 87% sensitivity at 100% specificity. It outperformed models using other cfDNA-derived features. In stage-stratified analysis, the sensitivity at 100% specificity of the urine hotspots-based model was 71% and 92% for early (low-grade Ta and T1) and advanced (high-grade T1 and muscle-invasive) disease, respectively. Biologically, cfDNA hotspots effectively retrieved regulatory elements and were correlated with the cell of origin. Urine cfDNA hotspots specifically captured BLCA-related molecular features, including key functional pathways, chromosome loci associated with BLCA risk as identified in genome-wide association studies, or presenting frequent somatic alterations in BLCA tumors, and the transcription factor regulatory landscape. Our findings support the applicability of urine cfDNA fragmentation hotspots for noninvasive BLCA diagnosis, as well as for future translational study regarding its molecular pathology and heterogeneity.

Comprehensive multi-omics integration uncovers mitochondrial gene signatures for prognosis and personalized therapy in lung adenocarcinoma

The therapeutic efficacy of lung adenocarcinoma (LUAD), the most prevalent histological subtype of primary lung cancer, remains inadequate, with accurate prognostic assessment posing significant challenges. This study sought to elucidate the prognostic significance of mitochondrial-related genes in LUAD through an integrative multi-omics approach, aimed at developing personalized therapeutic strategies. Utilizing transcriptomic and single-cell RNA sequencing (scRNA-seq) data, alongside clinical information from publicly available databases, we first applied dimensionality reduction and clustering techniques to the LUAD single-cell dataset, focusing on the subclassification of fibroblasts, epithelial cells, and T cells. Mitochondrial-related prognostic genes were subsequently identified using TCGA-LUAD data, and LUAD cases were stratified into distinct molecular subtypes through consensus clustering, allowing for the exploration of gene expression profiles and clinical feature distributions across subtypes. By leveraging an ensemble of machine learning algorithms, we developed an Artificial Intelligence-Derived Prognostic Signature (AIDPS) model based on mitochondrial-related genes and validated its prognostic accuracy across multiple independent datasets. The AIDPS model demonstrated robust predictive power for LUAD patient outcomes, revealing significant differences in responses to immunotherapy and chemotherapy, as well as survival outcomes between risk groups. Furthermore, we conducted comprehensive analyses of tumor mutation burden (TMB), immune microenvironment characteristics, and genome-wide association study (GWAS) data, providing additional insights into the mechanistic roles of mitochondrial-related genes in LUAD pathogenesis. This study not only offers a novel approach to improving prognostic assessments in LUAD but also establishes a strong foundation for the development of personalized therapeutic interventions.

Causal relationships between the gut microbiota, inflammatory cytokines, and alcoholic liver disease: a Mendelian randomization analysis.

Previous studies have suggested a potential association between gut microbiota and the development of alcohol-related liver disease (ALD). However, the causal relationship between gut microbiota and ALD, as well as the role of inflammatory cytokines as mediators, remains unclear. This study aims to explore the causal relationship between gut microbiota and ALD using Mendelian randomization (MR) methods, and to analyze the mediating role of inflammatory cytokines. Gut microbiota, 91 inflammatory cytokines, and ALD were identified from summary data of large-scale genome-wide association studies (GWAS). MR was employed to investigate the causal relationship between gut microbiota, cytokines, and ALD, with the inverse variance-weighted method (IVW) as the primary statistical approach. Additionally, we examined whether inflammatory cytokines act as mediating factors in the pathway from gut microbiota to ALD. The IVW results confirmed two positive and one negative causal effect between genetic liability in the gut microbiota and ALD. Escherichia coli (P= 0.003) was identified as a protective factor for ALD, while Roseburia hominis (P=0.023) and Family Porphyromonadaceae (P=0.038) were identified as risk factors for ALD. Furthermore, there were five positive and two negative causal effects between inflammatory cytokines and ALD, with CUB domain-containing protein 1 (P= 0.035), Macrophage colony-stimulating factor 1 (P=0.047), Cystatin D (P = 0.035), Fractalkine (P=0.000000038), Monocyte chemoattractant protein-1 (P=0.004) positively associated with ALD onset. CD40L receptor (P= 0.044) and Leukemia inhibitory factor (P = 0.024) exhibited protective effects against ALD. Mediation MR analysis indicated that CUB domain-containing protein 1 (mediation proportion=1.6%, P=0.035), Cystatin D (mediation proportion=1.5%, P=0.012), and Monocyte chemoattractant protein-1 (mediation proportion=3.3%, P=0.005) mediated the causal effect of Roseburia hominis on ALD. In conclusion, our study supports a causal relationship among gut microbiota, inflammatory cytokines and ALD, with inflammatory cytokines potentially acting as mediating factors in the pathway from gut microbiota to ALD.

GWAS for Drought Resilience Traits in Red Clover (Trifolium pratense L.)

Red clover (Trifolium pratense L.) is a well-appreciated grassland crop in temperate climates but suffers from increasingly frequent and severe drought periods. Molecular markers for drought resilience (DR) would benefit breeding initiatives for red clover, as would a better understanding of the genes involved in DR. Two previous studies, as follows, have: (1) identified phenotypic DR traits in a diverse set of red clover accessions; and (2) produced genotypic data using a pooled genotyping-by-sequencing (GBS) approach in the same collection. In the present study, we performed genome-wide association studies (GWAS) for DR using the available phenotypic and genotypic data. Single nucleotide polymorphism (SNP) calling was performed using GBS data and the following two red clover genome assemblies: the recent HEN-17 assembly and the Milvus assembly. SNP positions with significant associations were used to delineate flanking regions in both genome assemblies, while functional annotations were retrieved from Medicago truncatula orthologs. GWAS revealed 19 significant SNPs in the HEN-17-derived SNP set, explaining between 5.3 and 23.2% of the phenotypic variation per SNP–trait combination for DR traits. Among the genes in the SNP-flanking regions, we identified candidate genes related to cell wall structuring, genes encoding sugar-modifying proteins, an ureide permease gene, and other genes linked to stress metabolism pathways. GWAS revealed 29 SNPs in the Milvus-derived SNP set that explained substantially more phenotypic variation for DR traits, between 5.3 and 42.3% per SNP–trait combination. Candidate genes included a DEAD-box ATP-dependent RNA helicase gene, a P-loop nucleoside triphosphate hydrolase gene, a Myb/SANT-like DNA-binding domain protein, and an ubiquitin–protein ligase gene. Most accessions in this study are genetically more closely related to the Milvus genotype than to HEN-17, possibly explaining how the Milvus-derived SNP set yielded more robust associations. The Milvus-derived SNP set pinpointed 10 genomic regions that explained more than 25% of the phenotypic variation for DR traits. A possible next step could be the implementation of these SNP markers in practical breeding programs, which would help to improve DR in red clover. Candidate genes could be further characterized in future research to unravel drought stress resilience in red clover in more detail.

Helicobacter pylori infection and inflammatory bowel disease: a 2-sample Mendelian randomization study.

Observational studies have discovered a contradictory phenomenon between Helicobacter pylori (H. pylori) infection and inflammatory bowel disease (IBD). The study aimed to confirm the causal association between H. pylori and IBD, including ulcerative colitis (UC) and Crohn's disease (CD). We conducted a Mendelian randomization (MR) study with two sample Genome-Wide Association Studies (GWAS) to determine whether there is a causal relationship between H. pylori infection and IBD, as well as the possible pathogenic factors that may be involved. The reliability of the main MR assumptions was examined through a series of sensitivity analyses. Two genetic variants (SNPs) previously identified were employed as instrumental variables (IVs) for H. pylori infection. GWAS data for IBD, UC, and CD were obtained from the recent DF10 release10 of the FinnGen study. Our findings indicated a significant association between H. pylori seropositivity and an increased risk of IBD and UC (IBD: OR: 1.16, 95% CI, 1.03-1.31, P < 0.05; UC: OR: 1.22, 95% CI, 1.08-1.37, P < 0.001) while no causal relationship with CD (P > 0.05). Analysis of the main virulence pathogenic factors revealed a causal relationship between cytotoxin-associated protein A (CagA) and IBD and UC (IBD: OR: 1. 06, 95% CI, 1.001-1.11, P < 0.05; UC: OR: 1.07, 95% CI, 1.004-1.14, P < 0.05), while no correlation was found for vacuolar cytotoxin A (VacA) (P > 0.05). After applying the False Discovery Rate (FDR) correction, the causal relationship between CagA and the risk of IBD or UC was no longer statistically significant. This study suggests a potential causal relationship between H. pylori infection and IBD, particularly UC. The effect may be more pronounced in individuals with previous H. pylori infections.

A comprehensive study of genetic regulation and disease associations of plasma circulatory microRNAs using population-level data.

MicroRNAs (miRNAs) are small non-coding RNAs that post-transcriptionally regulate gene expression. Perturbations in plasma miRNA levels are known to impact disease risk and have potential as disease biomarkers. Exploring the genetic regulation of miRNAs may yield new insights into their important role in governing gene expression and disease mechanisms. We present genome-wide association studies of 2083 plasma circulating miRNAs in 2178 participants of the Rotterdam Study to identify miRNA-expression quantitative trait loci (miR-eQTLs). We identify 3292 associations between 1289 SNPs and 63 miRNAs, of which 65% are replicated in two independent cohorts. We demonstrate that plasma miR-eQTLs co-localise with gene expression, protein, and metabolite-QTLs, which help in identifying miRNA-regulated pathways. We investigate consequences of alteration in circulating miRNA levels on a wide range of clinical conditions in phenome-wide association studies and Mendelian randomisation using the UK Biobank data (N = 423,419), revealing the pleiotropic and causal effects of several miRNAs on various clinical conditions. In the Mendelian randomisation analysis, we find a protective causal effect of miR-1908-5p on the risk of benign colon neoplasm and show that this effect is independent of its host gene (FADS1). This study enriches our understanding of the genetic architecture of plasma miRNAs and explores the signatures of miRNAs across a wide range of clinical conditions. The integration of population-based genomics, other omics layers, and clinical data presents opportunities to unravel potential clinical significance of miRNAs and provides tools for novel miRNA-based therapeutic target discovery.