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.

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.

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).

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 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.

Identification of genomic regions and genes associated with subclinical ketosis in periparturient dairy cows.

Subclinical ketosis (SCK) is a prevalent metabolic disorder that occurs during the transition to lactation period. It is defined as a high blood concentration of ketone bodies (beta-hydroxybutyric acid f ≥ 1.2 mmol/L) within the first few weeks of lactation, and often presents without clinical signs. SCK is mainly caused by negative energy balance (NEB). The objective of this study is to identify single nucleotide polymorphisms (SNPs) associated with SCK using genome-wide association studies (GWAS), and to predict the biological functions of proximal genes using gene-set enrichment analysis (GSEA). Blood samples were collected from 112 Holstein cows between 5 and 18 days postpartum to determine the incidence of SCK. Genomic DNA extracted from both SCK and healthy cows was examined using the Illumina Bovine SNP50K BeadChip for genotyping. GWAS revealed 194 putative SNPs and 163 genes associated with those SNPs. Additionally, GSEA showed that the genes retrieved by Database for Annotation, Visualization, and Integrated Discovery (DAVID) belonged to calcium signaling, starch and sucrose, immune network, and metabolic pathways. Furthermore, the proximal genes were found to be related to germ cell and early embryo development. In summary, this study proposes several feasible SNPs and genes associated with SCK through GWAS and GSEA. These candidates can be utilized in selective breeding programs to reduce the genetic risk for SCK and subfertility in high-performance dairy cows.

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.

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.

Genetics of causal relationships between circulating inflammatory proteins and postherpetic neuralgia: a bidirectional Mendelian randomization study.

Objective: According to data from several observational studies, there is a strong association between circulating inflammatory cytokines and postherpetic neuralgia (PHN), but it is not clear whether this association is causal or confounding; therefore, the main aim of the present study was to analyze whether circulating inflammatory proteins have a bidirectional relationship with PHN at the genetic inheritance level using a Mendelian randomization (MR) study. Methods: The Genome-Wide Association Study (GWAS) database was used for our analysis. We gathered data on inflammation-related genetic variation from three GWASs of human cytokines. These proteins included 91 circulating inflammatory proteins, tumor necrosis factor-alpha (TNF-α), macrophage inflammatory protein 1b (MIP-1b), and CXC chemokine 13 (CXCL13). The PHN dataset was obtained from the FinnGen biobank analysis round 5, and consisted of 1,413 cases and 275,212 controls. We conducted a two-sample bidirectional MR study using the TwoSampleMR and MRPRESSO R packages (version R.4.3.1). Our main analytical method was inverse variance weighting (IVW), and we performed sensitivity analyses to assess heterogeneity and pleiotropy, as well as the potential influence of individual SNPs, to validate our findings. Results: According to our forward analysis, five circulating inflammatory proteins were causally associated with the development of PHN: interleukin (IL)-18 was positively associated with PHN, and IL-13, fibroblast growth factor 19 (FGF-19), MIP-1b, and stem cell growth factor (SCF) showed reverse causality with PHN. Conversely, we found that PHN was closely associated with 12 inflammatory cytokines, but no significant correlation was found among the other inflammatory factors. Among them, only IL-18 had a bidirectional causal relationship with PHN. Conclusion: Our research advances the current understanding of the role of certain inflammatory biomarker pathways in the development of PHN. Additional verification is required to evaluate the viability of these proteins as targeted inflammatory factors for PHN-based treatments.

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.

Bidirectional causality between idiopathic sudden sensorineural hearing loss and depression: a Mendelian randomization study.

Idiopathic Sudden Sensorineural Hearing Loss (ISSHL) is a sudden onset, unexplained sensorineural hearing loss. Depression is a common mental disorder and a leading cause of disability. Here, We used a two-sample Mendelian randomization approach using pooled statistics from genome-wide association studies of ISSHL (1491 cases, 196,592 controls) and depression (23,424 cases, 192,220 controls) in European populations. This study investigated the bidirectional relationship between single nucleotide polymorphisms associated with depression and ISSHL using inverse variance weighting.Additional sensitivity analyses, such as Mendelian randomization-Egger (MR-Egger), weighted median estimates, and leave-one-out analysis, were performed to assess the reliability of the findings. Significant causal association between genetic susceptibility to ISSHL and depression in a random-effects IVW approach (OR = 1.037, 95% CI = 1.004-1.072, P = 0.030). In contrast, genetic depression was not risk factors for ISSHL (OR = 1.134, 95% CI = 0.871-1.475, P = 0.350). After validation by different MR methods and the sensitivity analysis, all of the above results are consistent. The evidence we have gathered suggests a causal relationship between ISSHL and depression. The presence of the former induces or further exacerbates the latter, whereas a similar situation does not exist when the latter is an influencing factor.

Distinct biological signature and modifiable risk factors underlie the comorbidity between major depressive disorder and cardiovascular disease.

Major depressive disorder (MDD) and cardiovascular disease (CVD) are often comorbid, resulting in excess morbidity and mortality. Here we show that CVDs share most of their genetic risk factors with MDD. Multivariate genome-wide association analysis of shared genetic liability between MDD and atherosclerotic CVD revealed seven loci and distinct patterns of tissue and brain cell-type enrichments, suggesting the involvement of the thalamus. Part of the genetic overlap was explained by shared inflammatory, metabolic and psychosocial or lifestyle risk factors. Our data indicated causal effects of genetic liability to MDD on CVD risk, but not from most CVDs to MDD, and showed that the causal effects were partly explained by metabolic and psychosocial or lifestyle factors. The distinct signature of MDD-atherosclerotic CVD comorbidity suggests an immunometabolic subtype of MDD that is more strongly associated with CVD than overall MDD. In summary, we identified biological mechanisms underlying MDD-CVD comorbidity and modifiable risk factors for prevention of CVD in individuals with MDD.

Schizophrenia and risk preference: a bidirectional two-sample mendelian randomization study.

Increasing evidence shows that risk preference is associated with schizophrenia. However, the causality and direction of this association are not clear; Therefore, we used Mendelian randomization (MR) to examine the potential bidirectional relationship between risk preference and schizophrenia. Genome-wide association studies (GWAS) summary data on risk preference of 939,908 participants from the UK Biobank and 23andMe were used to identify general risk preference. Data from 320,404 subjects (76,755 cases and 243,649 controls) from The Psychiatric Genomics Consortium were used to identify schizophrenia. The weighted median (WM), the inverse variance weighted (IVW), and the Mendelian randomization-Egger (MR-Egger) methods were used for the MR analysis to estimate the causal effect and detect the directional pleiotropy. The GWAS summary data were respectively from two combined samples, containing 939,908 and 320,404 subjects of European ancestry. Mendelian randomization evidence suggested that risk preference was associated with increased onset of schizophrenia (OR = 2.84, 95CI%: 1.77-4.56, P = 1.58*10 - 5) and that schizophrenia was also associated with raised risk preference (OR = 1.11, 95CI%: 1.07-1.15, P = 7.98*10 - 8). With the use of large-scale GWAS data, robust evidence suggests an interaction between risk preference and schizophrenia. This also indicates that early identification of and intervention for increased risk preference may improve the prognosis of schizophrenia.

Relationship between low molecular weight heparin calcium therapy and prognosis in severe acute kidney injury in sepsis: Mendelian randomized analysis and retrospective study.

Background: Sepsis-associated acute kidney injury (SA-AKI) poses an independent risk for mortality due to the absence of highly sensitive biomarkers and a specific treatment plan. Objective: Investigate the association between low molecular weight heparin (LMWH) calcium therapy and prognosis in critically ill SA-AKI patients, and assess the causal relationship through Mendelian randomization (MR) analysis. Methods: A single-center, retrospective, cross-sectional study included 90 SA-AKI patients and 30 septic patients without acute kidney injury (AKI) from the intensive care unit (ICU) of the First Hospital of Lanzhou University. SA-AKI patients were categorized into control or LMWH groups based on LMWH calcium usage. Primary outcome was renal function recovery, with secondary outcomes including 28-day mortality, ICU stay length, number of renal replacement therapy (RRT) recipients, and 90-day survival. MR and related sensitivity analyses explored causal effects. Results: The combination of heparin-binding protein (HBP), heparanase (HPA), and neutrophil gelatinase-associated lipocalin (NGAL) demonstrated high diagnostic value for SA-AKI. MR analysis suggested a potential causal link between gene-predicted HBP and AKI (OR: 1.369, 95%CI: 1.040-1.801, p = 0.024). In the retrospective study, LMWH-treated patients exhibited improved renal function, reduced levels of HPA, HBP, Syndecan-1, and inflammation, along with enhanced immune function compared to controls. However, LMWH did not impact 28-day mortality, 90-day survival, or ICU stay length. Conclusion: LMWH could enhance renal function in SA-AKI patients. MR analysis supports this causal link, underscoring the need for further validation in randomized controlled trials.

A Mendelian randomization study between metabolic syndrome and its components with prostate cancer.

Previous research has produced inconsistent findings concerning the connection between metabolic syndrome and prostate cancer. It is challenging for observational studies to establish a conclusive causal relationship between the two. However, Mendelian randomization can provide stronger evidence of causality in this context. To examine the causal link between a metabolic composite and its components with prostate cancer, we performed a two-sample Mendelian randomization (MR) study utilizing aggregated data from genome-wide association studies, followed by meta-analyses. In our study, we employed inverse variance weighting as the primary method for MR analysis. Additionally, we assessed potential sources of heterogeneity and horizontal pleiotropy through the Cochran's Q test and MR-Egger regression. Moreover, we used multivariate MR to determine whether smoking versus alcohol consumption had an effect on the outcomes. We found no causal relationship between metabolic syndrome and its components and prostate cancer(MetS, odds ratio [OR] = 0.95, 95% confidence interval [CI] = 0.738-1.223, p = 0.691; TG, [OR] = 1.02, 95%[CI] = 0.96-1.08, p = 0.59); HDL, [OR] = 1.02, 95% [CI] = 0.97-1.07, p = 0.47; DBP, [OR] = 1.00, 95%[CI] = 0.99-1.01, p = 0.87; SBP, [OR] = 1.00, 95%[CI] = 0.99-1.00, p = 0.26; FBG [OR] = 0.92, 95%[CI] = 0.81-1.05, p = 0.23; WC, [OR] = 0.93, 95%[CI] = 0.84-1.03, p = 0.16). Finally, the MVMR confirms that the metabolic syndrome and its components are independent of smoking and alcohol consumption in prostate cancer. We didn't find significant evidence to determine a causal relationship between the metabolic syndrome and its components and prostate cancer through MR analysis. Further research is necessary to explore the potential pathogenesis between the two diseases.

Genetic overlap between psoriatic arthritis and ankylosing spondylitis in a Chinese population.

Previous studies reveal that psoriatic arthritis (PsA) and ankylosing spondylitis (AS) share susceptibility genes, such as HLA-B27, demonstrating a degree of genetic overlap between these diseases. Recent studies have identified a number of novel AS and PsA genetic susceptibility loci, but data on these loci in Chinese PsA patients are limited. To identify candidate genes that confer susceptibility to PsA in Chinese patients with PsA, psoriasis vulgaris (PsV), and healthy controls. Sixteen susceptibility loci, reported in a genome-wide association study of AS, and nine susceptibility loci, reported in candidate gene studies of PsA, were examined. Single-nucleotide polymorphisms (SNPs) were genotyped in 503 patients with PsA, 496 patients with PsV, and 979 healthy controls using the SNPscanTM multiplex SNP genotyping platform. PLINK software and logistic regression analysis were used to estimate the statistical significance of associations. PPP2R3C (rs8006884) was shown to significantly associate with PsA+PsV (p = 1.92×10-3, OR = 1.28) and was suggested to associate with PsV (p = 0.03, OR = 1.19). A suggestive association was also observed between IL-23R (rs12141575) and PsA as well as with axial PsA based on subtype analysis, KIF3A (rs2897442) and PsV, and ERN1 (rs196941) or IFIH1 (rs984971) and axial PsA. Our results suggest that PPP2R3C confers susceptibility to PsA and PsV, and that this gene may be related to the pathogenesis of psoriatic lesions and arthritis. Moreover, our results indicate a possible association between IL-23R, ERN1, or IFIH1 and subtypes of PsA, and between KIF3A and PsV.

FastBiCmrMLM: a fast and powerful compressed variance component mixed logistic model for big genomic case-control genome-wide association study.

Large sample datasets have been regarded as the primary basis for innovative discoveries and the solution to missing heritability in genome-wide association studies. However, their computational complexity cannot consider all comprehensive effects and all polygenic backgrounds, which reduces the effectiveness of large datasets. To address these challenges, we included all effects and polygenic backgrounds in a mixed logistic model for binary traits and compressed four variance components into two. The compressed model combined three computational algorithms to develop an innovative method, called FastBiCmrMLM, for large data analysis. These algorithms were tailored to sample size, computational speed, and reduced memory requirements. To mine additional genes, linkage disequilibrium markers were replaced by bin-based haplotypes, which are analyzed by FastBiCmrMLM, named FastBiCmrMLM-Hap. Simulation studies highlighted the superiority of FastBiCmrMLM over GMMAT, SAIGE and fastGWA-GLMM in identifying dominant, small α (allele substitution effect), and rare variants. In the UK Biobank-scale dataset, we demonstrated that FastBiCmrMLM could detect variants as small as 0.03% and with α ≈ 0. In re-analyses of seven diseases in the WTCCC datasets, 29 candidate genes, with both functional and TWAS evidence, around 36 variants identified only by the new methods, strongly validated the new methods. These methods offer a new way to decipher the genetic architecture of binary traits and address the challenges outlined above.

Genome wide association studies on seven yield-related traits of 183 rice varieties in Bangladesh.

Rice genetic diversity is regulated by multiple genes and is largely dependent on various environmental factors. Uncovering the genetic variations associated with the diversity in rice populations is the key to breed stable and high yielding rice varieties. We performed genome wide association studies (GWASs) on seven rice yielding traits (grain length, grain width, grain weight, panicle length, leaf length, leaf width, and leaf angle) based on a population of 183 rice landraces of Bangladesh. Our GWASs reveal various chromosomal regions and candidate genes that are associated with different traits in Bangladeshi rice varieties. Noteworthy was the recurrent implication of chromosome 10 in all three grain-shape-related traits (grain length, grain width, and grain weight), indicating its pivotal role in shaping rice grain morphology. Our study also underscores the involvement of transposon gene families across these three traits. For leaf related traits, chromosome 10 was found to harbor regions that are significantly associated with leaf length and leaf width. The results of these association studies support previous findings as well as provide additional insights into the genetic diversity of rice. This is the first known GWAS study on various yield-related traits in the varieties of Oryza sativa available in Bangladesh-the fourth largest rice-producing country. We believe this study will accelerate rice genetics research and breeding stable high-yielding rice in Bangladesh.