Optimal variable identification for accurate detection of causal expression Quantitative Trait Loci with applications in heart-related diseases.

Gene expression plays a pivotal role in various diseases, contributing significantly to their mechanisms. Most GWAS risk loci are in non-coding regions, potentially affecting disease risk by altering gene expression in specific tissues. This expression is notably tissue-specific, with genetic variants substantially influencing it. However, accurately detecting the expression Quantitative Trait Loci (eQTL) is challenging due to limited heritability in gene expression, extensive linkage disequilibrium (LD), and multiple causal variants. The single variant association approach in eQTL analysis is limited by its susceptibility to capture the combined effects of multiple variants, and a bias towards common variants, underscoring the need for a more robust method to accurately identify causal eQTL variants. To address this, we developed an algorithm, CausalEQTL, which integrates L 0 +L 1 penalized regression with an ensemble approach to localize eQTL, thereby enhancing prediction performance precisely. Our results demonstrate that CausalEQTL outperforms traditional models, including LASSO, Elastic Net, Ridge, in terms of power and overall performance. Furthermore, analysis of heart tissue data from the GTEx project revealed that eQTL sites identified by our algorithm provide deeper insights into heart-related tissue eQTL detection. This advancement in eQTL mapping promises to improve our understanding of the genetic basis of tissue-specific gene expression and its implications in disease. The source code and identified causal eQTLs for CausalEQTL are available on GitHub: https://github.com/zhc-moushang/CausalEQTL.

The Benefits of Permutation-Based Genome-Wide Association Studies.

Linear mixed models (LMMs) are a commonly used method for genome-wide association studies (GWAS) that aim to detect associations between genetic markers and phenotypic measurements in a population of individuals while accounting for population structure and cryptic relatedness. In a standard GWAS, hundreds of thousands to millions of statistical tests are performed, requiring control for multiple hypothesis testing. Typically, static corrections that penalize the number of tests performed are used to control for the family-wise error rate, which is the probability of making at least one false positive. However, it has been shown that in practice this threshold is too conservative for normally distributed phenotypes and not stringent enough for non-normally distributed phenotypes. Therefore, permutation-based LMM approaches have recently been proposed to provide a more realistic threshold that takes phenotypic distributions into account. In this work, we will discuss the advantages of permutation-based GWAS approaches, including new simulations and results from a re-analysis of all publicly available Arabidopsis thaliana phenotypes from the AraPheno database.

Causal association between immune cells and lung cancer risk: a two-sample bidirectional Mendelian randomization analysis.

Background: Previous studies have highlighted the crucial role of immune cells in lung cancer development; however, the direct link between immunophenotypes and lung cancer remains underexplored. Methods: We applied two-sample Mendelian randomization (MR) analysis, using genetic variants as instruments to determine the causal influence of exposures on outcomes. This method, unlike traditional randomized controlled trials (RCTs), leverages genetic variants inherited randomly at conception, thus reducing confounding and preventing reverse causation. Our analysis involved three genome-wide association studies to assess the causal impact of 731 immune cell signatures on lung cancer using genetic instrumental variables (IVs). We initially used the standard inverse variance weighted (IVW) method and further validated our findings with three supplementary MR techniques (MR-Egger, weighted median, and MR-PRESSO) to ensure robustness. We also conducted MR-Egger intercept and Cochran's Q tests to assess heterogeneity and pleiotropy. Additionally, reverse MR analysis was performed to explore potential causality between lung cancer subtypes and identified immunophenotypes, using R software for all statistical calculations. Results: Our MR analysis identified 106 immune signatures significantly associated with lung cancer. Notably, we found five suggestive associations across all sensitivity tests (P<0.05): CD25 on IgD- CD24- cells in small cell lung carcinoma (ORIVW =0.885; 95% CI: 0.798-0.983; P IVW =0.022); CD27 on IgD+ CD24+ cells in lung squamous cell carcinoma (ORIVW =1.054; 95% CI: 1.010-1.100; P IVW =0.015); CCR2 on monocyte cells in lung squamous cell carcinoma (ORIVW =0.941; 95% CI: 0.898-0.987; P IVW =0.012); CD123 on CD62L+ plasmacytoid dendritic cells (ORIVW =0.958; 95% CI: 0.924-0.992; P IVW =0.017) as well as on plasmacytoid dendritic cells (ORIVW =0.958; 95% CI: 0.924-0.992; P IVW =0.017) in lung squamous cell carcinoma. Conclusion: This study establishes a significant genomic link between immune cells and lung cancer, providing a robust basis for future clinical research aimed at lung cancer management.

Pleiotropic effects on Sarcopenia subphenotypes point to potential molecular markers for the disease.

Sarcopenia is a progressive age-related muscle disease characterized by low muscle strength, quantity and quality, and low physical performance. The clinical overlap between these subphenotypes (reduction in muscle strength, quantity and quality, and physical performance) was evidenced, but the genetic overlap is still poorly investigated. Herein, we investigated whether there is a genetic overlap amongst sarcopenia subphenotypes in the search for more effective molecular markers for this disease. For that, a Bioinformatics approach was used to identify and characterize pleiotropic effects at the genome, loci and gene levels using Genome-wide association study results. As a result, a high genetic correlation was identified between gait speed and muscle strength (rG=0.5358, p=3.39 × 10-8). Using a Pleiotropy-informed conditional and conjunctional false discovery rate method we identified two pleiotropic loci for muscle strength and gait speed, one of them was nearby the gene PHACTR1. Moreover, 11 pleiotropic loci and 25 genes were identified for muscle mass and muscle strength. Lastly, using a gene-based GWAS approach three candidate genes were identified in the overlap of the three Sarcopenia subphenotypes: FTO, RPS10 and CALCR. The current study provides evidence of genetic overlap and pleiotropy among sarcopenia subphenotypes and highlights novel candidate genes and molecular markers associated with the risk of sarcopenia.

Utility of polygenic risk scores to aid in the diagnosis of rheumatic diseases.

Rheumatic diseases (RDs) are characterized by autoimmunity and autoinflammation and are recognized as complex due to the interplay of multiple genetic, environmental, and lifestyle factors in their pathogenesis. The rapid advancement of genome-wide association studies (GWASs) has enabled the identification of numerous single nucleotide polymorphisms (SNPs) associated with RD susceptibility. Based on these SNPs, polygenic risk scores (PRSs) have emerged as promising tools for quantifying genetic risk in this disease group. This chapter reviews the current status of PRSs in assessing the risk of RDs and discusses their potential to improve the accuracy of the diagnosis of these complex diseases through their ability to discriminate among different RDs. PRSs demonstrate a high discriminatory capacity for various RDs and show potential clinical utility. As GWASs continue to evolve, PRSs are expected to enable more precise risk stratification by integrating genetic, environmental, and lifestyle factors, thereby refining individual risk predictions and advancing disease management strategies.

Identification of novel drug targets for multiple sclerosis by integrating plasma genetics and proteomes.

BACKGROUND: Genome-wide association studies (GWAS) have revealed numerous loci associated with multiple sclerosis (MS). However, the challenge lies in deciphering the mechanisms by which these loci influence the target traits. Here, we employed an integrative analytical pipeline to efficiently transform genetic associations to identify novel proteins for MS. METHODS: We systematically integrated MS GWAS data (N = 115,803) with human plasma proteome data (N = 7213) and conducted proteome-wide association studies (PWAS) to identify MS-associated pathogenic proteins. Following this, we employed Mendelian randomization and Bayesian colocalization analyses to verify the causal relationship between these significant plasma proteins and MS. Lastly, we utilized the Drug-Gene Interaction Database (DGIdb) to identify potential drug targets for MS. RESULTS: The PWAS identified 25 statistically significant cis-regulated plasma proteins associated with MS at a false discovery rate of P < 0.05. Further analysis revealed that the abundance of 7 of these proteins (PLEK, TNXB, CASP3, CD59, CR1, TAPBPL, ATXN3) was causally related to the incidence of MS. Our findings indicated that genetically predicted higher levels of TNXB and CD59 were associated with a lower risk of MS, whereas higher levels of PLEK, CASP3, CR1, TAPBPL, and ATXN3 were associated with an increased risk of MS. Three plasma proteins (PLEK, CR1, CD59) were validated by colocalization analysis. Among these, CR1 was prioritized as a target for Eculizumab due to its significant association with MS risk. Additionally, PLEK, CR1, and CD59 were identified as druggable target genes. CONCLUSIONS: Our proteomic analysis has identified PLEK, CR1, and CD59 as potential drug targets for MS treatment. Developing pharmacological inducers or inhibitors for these proteins could pave the way for new therapeutic approaches, potentially improving outcomes for MS patients.

GWarrange: a pre- and post- genome-wide association studies pipeline for detecting phenotype-associated genome rearrangement events.

The use of k-mers to capture genetic variation in bacterial genome-wide association studies (bGWAS) has demonstrated its effectiveness in overcoming the plasticity of bacterial genomes by providing a comprehensive array of genetic variants in a genome set that is not confined to a single reference genome. However, little attempt has been made to interpret k-mers in the context of genome rearrangements, partly due to challenges in the exhaustive and high-throughput identification of genome structure and individual rearrangement events. Here, we present GWarrange, a pre- and post-bGWAS processing methodology that leverages the unique properties of k-mers to facilitate bGWAS for genome rearrangements. Repeat sequences are common instigators of genome rearrangements through intragenomic homologous recombination, and they are commonly found at rearrangement boundaries. Using whole-genome sequences, repeat sequences are replaced by short placeholder sequences, allowing the regions flanking repeats to be incorporated into relatively short k-mers. Then, locations of flanking regions in significant k-mers are mapped back to complete genome sequences to visualise genome rearrangements. Four case studies based on two bacterial species (Bordetella pertussis and Enterococcus faecium) and a simulated genome set are presented to demonstrate the ability to identify phenotype-associated rearrangements. GWarrange is available at https://github.com/DorothyTamYiLing/GWarrange.

Methods for multiancestry genome-wide association study meta-analysis.

Genome-wide association studies (GWAS) have significantly enhanced our understanding of the genetic basis of complex diseases. Despite the technological advancements, gaps in our understanding remain, partly due to small effect sizes and inadequate coverage of genetic variation. Multiancestry GWAS meta-analysis (MAGMA) addresses these challenges by integrating genetic data from diverse populations, thereby increasing power to detect loci and improving fine-mapping resolution to identify causal variants across different ancestry groups. This review provides an overview of the protocols, statistical methods, and software of MAGMA, as well as highlighting some challenges associated with this approach.

Discovering novel genomic regions explaining adaptation of bread wheat to conservation agriculture through GWAS.

To sustainably increase wheat yield to meet the growing world population's food demand in the face of climate change, Conservation Agriculture (CA) is a promising approach. Still, there is a lack of genomic studies investigating the genetic basis of crop adaptation to CA. To dissect the genetic architecture of 19 morpho-physiological traits that could be involved in the enhanced adaptation and performance of genotypes under CA, we performed GWAS to identify MTAs under four contrasting production regimes viz., conventional tillage timely sown (CTTS), conservation agriculture timely sown (CATS), conventional tillage late sown (CTLS) and conservation agriculture late sown (CALS) using an association panel of 183 advanced wheat breeding lines along with 5 checks. Traits like Phi2 (Quantum yield of photosystem II; CATS:0.37, CALS: 0.31), RC (Relative chlorophyll content; CATS:55.51, CALS: 54.47) and PS1 (Active photosystem I centers; CATS:2.45, CALS: 2.23) have higher mean values in CA compared to CT under both sowing times. GWAS identified 80 MTAs for the studied traits across four production environments. The phenotypic variation explained (PVE) by these QTNs ranged from 2.15 to 40.22%. Gene annotation provided highly informative SNPs associated with Phi2, NPQ (Quantum yield of non-photochemical quenching), PS1, and RC which were linked with genes that play crucial roles in the physiological adaptation under both CA and CT. A highly significant SNP AX94651261 (9.43% PVE) was identified to be associated with Phi2, while two SNP markers AX94730536 (30.90% PVE) and AX94683305 (16.99% PVE) were associated with NPQ. Identified QTNs upon validation can be used in marker-assisted breeding programs to develop CA adaptive genotypes.

Causal inference in the field of arrhythmia: an introduction to Mendelian randomization.

Mendelian randomization (MR) uses genetic variants associated with an exposure (e.g., high blood pressure) as instrumental variables to test causal effects on an outcome (e.g., atrial fibrillation (AF)). By leveraging the random assortment of genetic variants during gamete formation, MR reduces biases like confounding and reverse causation. We screened 391 papers, examining 278 that applied MR to investigate arrhythmia and, among others, cardiovascular traits, lifestyle, behavioral traits, body composition. Our analysis focused on MR studies of arrhythmia and cardiovascular traits. Key findings highlight high systolic blood pressure, low resting heart rate, elevated cardiac troponin I levels, coronary artery disease, and heart failure as risk factors for AF, while AF itself increases heart failure risk. As genetic data becomes more accessible, MR's relevance grows. Sensitivity analyses and integrating MR with other methodologies in a triangulation framework enhance the robustness of causal inferences by navigating different biases.

Potential causal association between serum vitamin D levels and intervertebral disc degeneration: A mendelian randomization study.

OBJECTIVES: Intervertebral disc degeneration (IDD) is a prevalent musculoskeletal disorder with substantial implications for disability and healthcare expenditures. The role of serum vitamin D (25-Hydroxyvitamin D, 25(OH)D) levels in the pathogenesis of various musculoskeletal conditions has been explored in prior observational studies, suggesting a potential association. While previous observational studies have suggested an association between the two conditions, it might confound the effect of 25(OH)D on IDD. This Mendelian randomization (MR) study seeks to elucidate the causal relationship between 25(OH)D and IDD. METHODS: We performed a MR analysis using summary-level data from genome-wide association studies (GWAS) of 25(OH)D (sample size = 441,291 European) and IDD (sample size = 336,439 (cases = 41,669, controls = 294,770) European). Single nucleotide polymorphisms (SNPs) significantly associated with 25(OH)D (p < 5 × 10-8) were selected as instrumental variables. The associations between genetically predicted 25(OH)D and IDD were estimated using the inverse-variance weighted (IVW) method, with sensitivity analyses employing the weighted median, MR-Egger, and MR-PRESSO approaches to assess the robustness of the findings. RESULTS: In the primary IVW analysis, genetically predicted 25(OH)D was unrelated associated with IDD (odds ratio (OR) = 0.9671, 95% confidence interval (CI): 0.8956-1.0444, p = 0.39). The results remained consistent across the sensitivity analyses, and no significant directional pleiotropy was detected (MR-Egger intercept: p = 0.64). CONCLUSIONS: This study found no obvious evidence that 25(OH)D is causally associated with IDD risks. We call for larger sample size studies to further unravel the potential causal relationship and the exact mechanism.

A single-blinded, randomized, parallel intervention to evaluate genetics and omics-based personalized nutrition in general population via an e-commerce tool: The PREVENTOMICS e-commerce study.

BACKGROUND: Personalized nutrition (PN) has been proposed as a strategy to increase the effectiveness of dietary recommendations and ultimately improve health status. OBJECTIVES: We aimed to assess whether including omics-based PN in an e-commerce tool improves dietary behavior and metabolic profile in general population. METHODS: A 21-wk parallel, single-blinded, randomized intervention involved 193 adults assigned to a control group following Mediterranean diet recommendations (n = 57, completers = 36), PN (n = 70, completers = 45), or personalized plan (PP, n = 68, completers = 53) integrating a behavioral change program with PN recommendations. The intervention used metabolomics, proteomics, and genetic data to assist participants in creating personalized shopping lists in a simulated e-commerce retailer portal. The primary outcome was the Mediterranean diet adherence screener (MEDAS) score; secondary outcomes included biometric and metabolic markers and dietary habits. RESULTS: Volunteers were categorized with a scoring system based on biomarkers of lipid, carbohydrate metabolism, inflammation, oxidative stress, and microbiota, and dietary recommendations delivered accordingly in the PN and PP groups. The intervention significantly increased MEDAS scores in all volunteers (control-3 points; 95% confidence interval [CI]: 2.2, 3.8; PN-2.7 points; 95% CI: 2.0, 3.3; and PP-2.8 points; 95% CI: 2.1, 3.4; q < 0.001). No significant differences were observed in dietary habits or health parameters between PN and control groups after adjustment for multiple comparisons. Nevertheless, personalized recommendations significantly (false discovery rate < 0.05) and selectively enhanced the scores calculated with biomarkers of carbohydrate metabolism (ß: -0.37; 95% CI: -0.56, -0.18), oxidative stress (ß: -0.37; 95% CI: -0.60, -0.15), microbiota (ß: -0.38; 95% CI: -0.63, -0.15), and inflammation (ß: -0.78; 95% CI: -1.24, -0.31) compared with control diet. CONCLUSIONS: Integration of personalized strategies within an e-commerce-like tool did not enhance adherence to Mediterranean diet or improved health markers compared with general recommendations. The metabotyping approach showed promising results and more research is guaranteed to further promote its application in PN. This trial was registered at clinicaltrials.gov as NCT04641559 (https://clinicaltrials.gov/study/NCT04641559?cond=NCT04641559&rank=1).

Investigation of the genetic aetiology of Lewy body diseases with and without dementia.

Up to 80% of Parkinson's disease patients develop dementia, but time to dementia varies widely from motor symptom onset. Dementia with Lewy bodies presents with clinical features similar to Parkinson's disease dementia, but cognitive impairment precedes or coincides with motor onset. It remains controversial whether dementia with Lewy bodies and Parkinson's disease dementia are distinct conditions or represent part of a disease spectrum. The biological mechanisms underlying disease heterogeneity, in particular the development of dementia, remain poorly understood, but will likely be the key to understanding disease pathways and, ultimately, therapy development. Previous genome-wide association studies in Parkinson's disease and dementia with Lewy bodies/Parkinson's disease dementia have identified risk loci differentiating patients from controls. We collated data for 7804 patients of European ancestry from Tracking Parkinson's, The Oxford Discovery Cohort, and Accelerating Medicine Partnership-Parkinson's Disease Initiative. We conducted a discrete phenotype genome-wide association study comparing Lewy body diseases with and without dementia to decode disease heterogeneity by investigating the genetic drivers of dementia in Lewy body diseases. We found that risk allele rs429358 tagging APOEe4 increases the odds of developing dementia, and that rs7668531 near the MMRN1 and SNCA-AS1 genes and an intronic variant rs17442721 tagging LRRK2 G2019S on chromosome 12 are protective against dementia. These results should be validated in autopsy-confirmed cases in future studies.

Causal relationship between type 2 diabetes and glioblastoma: bidirectional Mendelian randomization analysis.

As the prevalence of Type 2 Diabetes Mellitus (T2DM) and Glioblastoma (GBM) rises globally, the relationship between T2DM and GBM remains controversial. This study aims to investigate whether genetically predicted T2DM is causally associated with GBM. We performed bidirectional Mendelian randomization (MR) analysis using data from genome-wide studies on T2DM (N = 62,892) and GBM (N = 218,792) in European populations. The results of the inverse-variance weighted (IVW) approach served as the primary outcomes. We applied Cochran's Q test and MR-Egger regression for heterogeneity assessment. Leave-one-out analysis was used to evaluate whether any single SNP significantly influenced the observed effect. Our findings reveal a significant causal association between T2DM and an increased risk of GBM (OR [95% CI] 1.70 [1.09, 2.65], P = 0.019). Conversely, the reverse association between T2DM and GBM was insignificant (OR [95% CI] 1.00 [0.99, 1.01], P = 0.408) (P > 0.40). Furthermore, the results from Cochran's Q-test and funnel plots in the MR-Egger method indicated no evidence of pleiotropy between the SNPs and GBM. Additionally, we mapped causal SNPs to genes and identified 10 genes, including MACF1, C1orf185, PTGFRN, NOTCH2, ABCB10, GCKR, THADA, RBMS1, SPHKAP, and PPARG, located on chromosomes 1, 2, and 3. These genes are involved in key biological processes such as the BMP signaling pathway and various metabolic pathways relevant to both conditions. This study provides robust evidence of a significant causal relationship between T2DM and an increased risk of GBM. The identified SNP-mapped genes highlight potential biological mechanisms underlying this association.

The associations between dysregulation of human blood metabolites and lung cancer risk: evidence from genetic data.

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

Non-coding variants impact cis-regulatory coordination in a cell type-specific manner.

BACKGROUND: Interactions among cis-regulatory elements (CREs) play a crucial role in gene regulation. Various approaches have been developed to map these interactions genome-wide, including those relying on interindividual epigenomic variation to identify groups of covariable regulatory elements, referred to as chromatin modules (CMs). While CM mapping allows to investigate the relationship between chromatin modularity and gene expression, the computational principles used for CM identification vary in their application and outcomes. RESULTS: We comprehensively evaluate and streamline existing CM mapping tools and present guidelines for optimal utilization of epigenome data from a diverse population of individuals to assess regulatory coordination across the human genome. We showcase the effectiveness of our recommended practices by analyzing distinct cell types and demonstrate cell type specificity of CRE interactions in CMs and their relevance for gene expression. Integration of genotype information revealed that many non-coding disease-associated variants affect the activity of CMs in a cell type-specific manner by affecting the binding of cell type-specific transcription factors. We provide example cases that illustrate in detail how CMs can be used to deconstruct GWAS loci, assess variable expression of cell surface receptors in immune cells, and reveal how genetic variation can impact the expression of prognostic markers in chronic lymphocytic leukemia. CONCLUSIONS: Our study presents an optimal strategy for CM mapping and reveals how CMs capture the coordination of CREs and its impact on gene expression. Non-coding genetic variants can disrupt this coordination, and we highlight how this may lead to disease predisposition in a cell type-specific manner.

Regulation of MicroRNA-4697-3p by Parkinson's disease-associated SNP rs329648 and its impact on SNCA112 mRNA.

BACKGROUND: Parkinson's disease (PD) is a common neurodegenerative disorder characterized by a multifaceted genetic foundation. Genome-Wide Association Studies (GWAS) have played a crucial role in pinpointing genetic variants linked to PD susceptibility. Current study aims to delve into the mechanistic aspects through which the PD-associated Single Nucleotide Polymorphism (SNP) rs329648, identified in prior GWAS, influences the pathogenesis of PD. METHODS AND RESULTS: Employing the CRISPR/Cas9-mediated genome editing mechanism, we demonstrated the association of the disease-associated allele of rs329648 with increased expression of miR-4697-3p in differentiated SH-SY5Y cells. We revealed that miR-4697-3p contributes to the formation of high molecular weight complexes of α-Synuclein (α-Syn), indicative of α-Syn aggregate formation, as evidenced by Western blot analysis. Furthermore, our study unveiled that miR-4697-3p elevates SNCA112 mRNA levels. The resultant protein product, α-Syn 112, a variant of α-Syn with 112 amino acids, is recognized for augmenting α-Syn aggregation. Notably, this regulatory effect minimally impacts the levels of full-length SNCA140 mRNA, as evidenced by qRT-PCR. Additionally, we observed a correlation between the disease-associated allele and miR-4697-3p with increased cell death, substantiated by assessments including cell viability assays, alterations in cell morphology, and TUNEL assays. CONCLUSION: Our research reveals that the disease-associated allele of rs329648 is linked to higher levels of miR-4697-3p. This increase in miR-4697-3p leads to elevated SNCA112 mRNA levels, consequently promoting the formation of α-Syn aggregates. Furthermore, miR-4697-3p appears to play a role in increased cell death, potentially contributing to the pathogenesis of PD.

PYPE: A pipeline for phenome-wide association and Mendelian randomization in investigator-driven biobank scale analysis.

Phenome-wide association studies (PheWASs) serve as a way of documenting the relationship between genotypes and multiple phenotypes, helping to uncover unexplored genotype-phenotype associations (known as pleiotropy). Secondly, Mendelian randomization (MR) can be harnessed to make causal statements about a pair of phenotypes by comparing their genetic architecture. Thus, approaches that automate both PheWASs and MR can enhance biobank-scale analyses, circumventing the need for multiple tools by providing a comprehensive, end-to-end tool to drive scientific discovery. To this end, we present PYPE, a Python pipeline for running, visualizing, and interpreting PheWASs. PYPE utilizes input genotype or phenotype files to automatically estimate associations between the chosen independent variables and phenotypes. PYPE can also produce a variety of visualizations and can be used to identify nearby genes and functional consequences of significant associations. Finally, PYPE can identify possible causal relationships between phenotypes using MR under a variety of causal effect modeling scenarios.

Additive and Dominance Genome-Wide Association Studies Reveal the Genetic Basis of Heterosis Related to Growth Traits of Duhua Hybrid Pigs.

Heterosis has been extensively used for pig genetic breeding and production, but the genetic basis of heterosis remains largely elusive. Crossbreeding between commercial and native breeds provides a good model to parse the genetic basis of heterosis. This study uses Duhua hybrid pigs, a crossbreed of Duroc and Liangguang small spotted pigs, as materials to explore the genetic basis underlying heterosis related to growth traits at the genomic level. The mid-parent heterosis (MPH) analysis showed heterosis of this Duhua offspring on growth traits. In this study, we examined the impact of additive and dominance effects on 100 AGE (age adjusted to 100 kg) and 100 BF (backfat thickness adjusted to 100 kg) of Duhua hybrid pigs. Meanwhile, we successfully identified SNPs associated with growth traits through both additive and dominance GWASs (genome-wide association studies). These findings will facilitate the subsequent in-depth studies of heterosis in the growth traits of Duhua pigs.

Causal relationship between Helicobacter pylori infection and IgA nephropathy: a bidirectional two-sample mendelian randomization study.

IgA nephropathy (IgAN) is one of the most common primary glomerulonephritis, and serum Helicobacter pylori (H. pylori) antibody levels are increased in patients with IgA N, but the role of H. pylori infection in the pathogenesis of IgAN is unclear. In this study, we investigated whether there is a causal relationship and reverse causality between IgAN and H. pylori infection by using a bidirectional two-sample Mendelian randomization (MR) analysis. This study was estimated using inverse variance weighted (IVW), MR-Egger and weighted median methods, with the IVW method having the strongest statistical efficacy. Seven common serum H. pylori antibodies were selected as exposure factors for positive MR analysis. The results showed that there was no evidence of a causal relationship between H. pylori infection and IgAN. Reverse MR analysis showed that there was also no evidence that the occurrence of IgAN leads to an increased risk of H. pylori infection.