Loci Datasets Research Articles

Uncovering immune cell-associated genes in breast cancer: based on summary data-based Mendelian randomized analysis and colocalization study

BackgroundBreast cancer, which is the most prevalent form of cancer among women globally, encompasses various subtypes that demand distinct treatment approaches. The tumor microenvironment and immune response are of crucial significance in the development and progression of breast cancer. Nevertheless, there has been scant evidence concerning the genes within breast cancer - specific immune cells.MethodsWe utilized summary data-based Mendelian randomization (SMR) to identify genes associated with breast cancer by utilizing expression quantitative trait loci (eQTL) datasets for 14 different immune cell types and genome-wide association studies (GWAS) for overall breast cancer and its subtypes. Furthermore, colocalization analysis was carried out to evaluate whether the observed association in SMR analyses is influenced by the same causal variant. Replication analysis and bulk RNA sequencing (bulkRNA-seq) analysis were employed to validate promising immune genes as potential drug targets.ResultsAfter correcting for the rate of false discovery, we discovered a total of 17 genes in 9 immune cell types that were significantly associated with overall breast cancer and its subtypes. The genes KCNN4, L3MBTL3, ZBTB38, MDM4, and TNFSF10 were identified in overall breast cancer and its subtypes. Colocalization analyses provided robust evidence in support of these associations. Notably, the KCNN4 gene in non-classical MONOcytes (MONOnc) was further validated through replication analysis and bulkRNA-seq analysis.ConclusionIn summary, our research has revealed a repertoire of genes within diverse immune cells associated with breast cancer. KCNN4 gene in non-classical MONOcytes (MONOnc) exhibited a negative association with overall breast cancer and its subtypes, which was identified as a potential drug target for breast cancer, opening up new avenues for therapeutic interventions.

Genomics-driven integrative analysis highlights immune-related plasma proteins for psychiatric disorders

BackgroundGenome-wide association studies (GWAS) have identified numerous variants associated with psychiatric disorders. However, it remains largely unknown on how GWAS risk variants contribute to psychiatric disorders. MethodsThrough integrating two largest, publicly available, independent protein quantitative trait loci datasets of plasma protein and nine large-scale GWAS summary statistics of psychiatric disorders, we first performed proteome-wide association study (PWAS) to identify psychiatric disorders-associated plasma proteins, followed by enrichment analysis to reveal the underlying biological processes and pathways. Then, we conducted Mendelian randomization (MR) and Bayesian colocalization (COLOC) analyses, with both discovery and parallel replication datasets, to further identify protein-disorder pairs with putatively causal relationships. We finally prioritized the potential drug targets using Drug Gene Interaction Database. ResultsPWAS totally identified 112 proteins, which were significantly enriched in biological processes relevant to immune regulation and response to stimulus including regulation of immune system process (adjusted P = 1.69 × 10−7) and response to external stimulus (adjusted P = 4.13 × 10−7), and viral infection related pathways, including COVID-19 (adjusted P = 2.94 × 10−2). MR and COLOC analysis further identified 26 potentially causal protein-disorder pairs in both discovery and replication analysis. Notably, eight protein-coding genes were immune-related, such as IRF3, CSK, and ACE, five among 16 druggable genes were reported to interact with drugs, including ACE, CSK, PSMB4, XPNPEP1, and MICB. ConclusionsOur findings highlighted the immunological hypothesis and identified potentially causal plasma proteins for psychiatric disorders, providing biological insights into the pathogenesis and benefit the development of preventive or therapeutic drugs for psychiatric disorders.

Identification of JAZF1, KNOP1, and PLEKHA1 as causally associated genes and drug targets for Alzheimer's disease: a summary data-based Mendelian randomization study.

There is a growing body of evidence indicating the significant role of the immune system and immune cells in the progression of Alzheimer's disease (AD). However, the exact role of genes from various immune cell types in AD remains unclear. We aimed to utilize summary data-based Mendelian randomization (SMR) to explore the potential causal relationships between genes in specific immune cells and the risk of AD. By utilizing data sets of expression quantitative trait loci (eQTL) for 14 different immune cell types and large-scale AD genome-wide association study (GWAS), we employed SMR to identify key genes associated with AD within specific immune cells. Sensitivity analyses, including F-statistic, colocalization, and assessment of horizontal pleiotropy, were further conducted to validate the discovered genes. In addition, replication analyses were performed in AD GWAS from the FinnGen consortium. Finally, we further identified existing drugs that target or interact with the druggable genes and reviewed the studies about the associations between these drugs and AD. SMR analysis revealed 342 genes associated with AD across 14 immune cell types. Further sensitivity analyses identified nine genes, CTSH, FCER1G, FNBP4, HLA-E, JAZF1, KNOP1, PLEKHA1, RP11-960L18.1, and ZNF638 that had significant associations with AD across nine specific immune cell types. JAZF1, KNOP1 and PLEKHA1 were replicated in an independent analysis usingthe GWAS data. The review on gene-related drugs also supported these findings. Our research suggests that the expression of the genes JAZF1, KNOP1, and PLEKHA1 in specific immune cell types is related to the risk of AD.

Enhancing Gene Expression Predictions Using Deep Learning and Functional Annotations.

Transcriptome-wide association studies (TWAS) aim to uncover genotype-phenotype relationships through a two-stage procedure: predicting gene expression from genotypes using an expression quantitative trait locus (eQTL) data set, then testing the predicted expression for trait associations. Accurate gene expression prediction in stage 1 is crucial, as it directly impacts the power to identify associations in stage 2. Currently, the first stage of such studies is primarily conducted using linear models like elastic net regression, which fail to capture the nonlinear relationships inherent in biological systems. Deep learning methods have the potential to model such nonlinear effects, but have yet to demonstrably outperform linear methods at this task. To address this gap, we propose a new deep learning architecture to predict gene expression from genotypic variation across individuals. Our method utilizes a learnable input scaling layer in conjunction with a convolutional encoder to capture nonlinear effects and higher-order interactions without compromising on interpretability. We further augment this approach to allow for parameter sharing across multiple networks, enabling us to utilize prior information for individual variants in the form of functional annotations. Evaluations on real-world genomic data show that our method consistently outperforms elastic net regression across a large set of heritable genes. Furthermore, our model statistically significantly improved predictive performance by leveraging functional annotations, whereas elastic net regression failed to show equivalent gains when using the same information, suggesting that our method can capture nonlinear functional information beyond the capability of linear models.

Identification of Potential Causal Genes for Neurodegenerative Diseases by Mitochondria-Related Genome-Wide Mendelian Randomization.

Current research lacks comprehensive investigations into the potential causal link between mitochondrial-related genes and the risk of neurodegenerative diseases (NDDs). We aimed to identify potential causative genes for five NDDs through an examination of mitochondrial-related gene expression levels. Through the integration of summary statistics from expression quantitative trait loci (eQTL) datasets (human blood and brain tissue), mitochondrial DNA copy number (mtDNA-CN), and genome-wide association studies (GWAS) datasets of five NDDs from European ancestry, we conducted a Mendelian randomization (MR) analysis to explore the potential causal relationship between mitochondrial-related genes and Alzheimer's disease (AD), Parkinson's disease (PD), amyotrophic lateral sclerosis (ALS), frontotemporal dementia (FTD), and Lewy body dementia (LBD). Sensitivity analysis and Bayesian colocalization were employed to validate this causal relationship. Through MR analysis, we have identified potential causal relationships between 12 mitochondria-related genes and AD, PD, ALS, and FTD overlapping with motor neuron disease (FTD_MND) in human blood or brain tissue. Bayesian colocalization analysis further confirms 9 causal genes, including NDUFS2, EARS2, and MRPL41 for AD; NDUFAF2, MALSU1, and METTL8 for PD; MYO19 and MRM1 for ALS; and FASTKD1 for FTD_MND. Importantly, in both human blood and brain tissue, NDUFS2 exhibits a significant pathogenic effect on AD, while NDUFAF2 demonstrates a robust protective effect on PD. Additionally, the mtDNA-CN plays a protected role in LBD (OR = 0.62, p = 0.031). This study presents evidence establishing a causal relationship between mitochondrial dysfunction and NDDs. Furthermore, the identified candidate genes may serve as potential targets for drug development aimed at preventing NDDs.

Pinpointing Novel Plasma and Brain Proteins for Common Ocular Diseases: A Comprehensive Cross-Omics Integration Analysis.

The pathogenesis of ocular diseases (ODs) remains unclear, although genome-wide association studies (GWAS) have identified numerous associated genetic risk loci. We integrated protein quantitative trait loci (pQTL) datasets and five large-scale GWAS summary statistics of ODs under a cutting-edge systematic analytic framework. Proteome-wide association studies (PWAS) identified plasma and brain proteins associated with ODs, and 11 plasma proteins were identified by Mendelian randomization (MR) and colocalization (COLOC) analyses as being potentially causally associated with ODs. Five of these proteins (protein-coding genes ECI1, LCT, and NPTXR for glaucoma, WARS1 for age-related macular degeneration (AMD), and SIGLEC14 for diabetic retinopathy (DR)) are newly reported. Twenty brain-protein-OD pairs were identified by COLOC analysis. Eight pairs (protein-coding genes TOM1L2, MXRA7, RHPN2, and HINT1 for senile cataract, WARS1 and TDRD7 for AMD, STAT6 for myopia, and TPPP3 for DR) are newly reported in this study. Phenotype-disease mapping analysis revealed 10 genes related to the eye/vision phenotype or ODs. Combined with a drug exploration analysis, we found that the drugs related to C3 and TXN have been used for the treatment of ODs, and another eight genes (GSTM3 for senile cataract, IGFBP7 and CFHR1 for AMD, PTPMT1 for glaucoma, EFEMP1 and ACP1 for myopia, SIRPG and CTSH for DR) are promising targets for pharmacological interventions. Our study highlights the role played by proteins in ODs, in which brain proteins were taken into account due to the deepening of eye-brain connection studies. The potential pathogenic proteins finally identified provide a more reliable reference range for subsequent medical studies.

Data-driven guidelines for phylogenomic analyses using SNP data.

There is a general lack of consensus on the best practices for filtering of single-nucleotide polymorphisms (SNPs) and whether it is better to use SNPs or include flanking regions (full "locus") in phylogenomic analyses and subsequent comparative methods. Using genotyping-by-sequencing data from 22 Glycine species, we assessed the effects of SNP vs. locus usage and SNP retention stringency. We compared branch length, node support, and divergence time estimation across 16 datasets with varying amounts of missing data and total size. Our results revealed five aspects of phylogenomic data usage that may be generally applicable: (1) tree topology is largely congruent across analyses; (2) filtering strictly for SNP retention (e.g., 90-100%) reduces support and can alter some inferred relationships; (3) absolute branch lengths vary by two orders of magnitude between SNP and locus datasets; (4) data type and branch length variation have little effect on divergence time estimation; and (5) phylograms alter the estimation of ancestral states and rates of morphological evolution. Using SNP or locus datasets does not alter phylogenetic inference significantly, unless researchers want or need to use absolute branch lengths. We recommend against using excessive filtering thresholds for SNP retention to reduce the risk of producing inconsistent topologies and generating low support.

Cross-Phenotype GWAS Supports Shared Genetic Susceptibility to Systemic Sclerosis and Primary Biliary Cholangitis.

An increased risk of primary biliary cholangitis (PBC) has been reported in patients with systemic sclerosis (SSc). Our study aims to investigate the shared genetic susceptibility between the two disorders and to define candidate causal genes using cross-phenotype GWAS meta-analysis. We performed cross-phenotype GWAS meta-analysis and colocalization analysis for SSc and PBC. We performed both genome-wide and locus-based analysis, including tissue and pathway enrichment analyses, fine-mapping, colocalization analyses with expression quantitative trait loci (eQTL) and protein quantitative trait loci (pQTL) datasets, and phenome-wide association studies (PheWAS). Finally, we used an integrative approach to prioritize candidate causal genes from the novel loci. We detected a strong genetic correlation between SSc and PBC (rg = 0.84, p = 1.7 × 10-6). In the cross-phenotype GWAS meta-analysis, we identified 44 non-HLA loci that reached genome-wide significance (p < 5 × 10-8). Evidence of shared causal variants between SSc and PBC was found for nine loci, five of which were novel. Integrating multiple sources of evidence, we prioritized CD40, ERAP1, PLD4, SPPL3, and CCDC113 as novel candidate causal genes. The CD40 risk locus colocalized with trans-pQTLs of multiple plasma proteins involved in B cell function. Our study supports a strong shared genetic susceptibility between SSc and PBC. Through cross-phenotype analyses, we have prioritized several novel candidate causal genes and pathways for these disorders.

Evaluating the associations between intelligence quotient and multi-tissue proteome from the brain, CSF and plasma.

Intelligence quotient is a vital index to evaluate the ability of an individual to think rationally, learn from experience and deal with the environment effectively. However, limited efforts have been paid to explore the potential associations of intelligence quotient traits with the tissue proteins from the brain, CSF and plasma. The information of protein quantitative trait loci was collected from a recently released genome-wide association study conducted on quantification data of proteins from the tissues including the brain, CSF and plasma. Using the individual-level genotypic data from the UK Biobank cohort, we calculated the polygenic risk scores for each protein based on the protein quantitative trait locus data sets above. Then, Pearson correlation analysis was applied to evaluate the relationships between intelligence quotient traits (including 120 330 subjects for 'fluid intelligence score' and 38 949 subjects for 'maximum digits remembered correctly') and polygenic risk scores of each protein in the brain (17 protein polygenic risk scores), CSF (116 protein polygenic risk scores) and plasma (59 protein polygenic risk scores). The Bonferroni corrected P-value threshold was P < 1.30 × 10-4 (0.05/384). Finally, Mendelian randomization analysis was conducted to test the causal relationships between 'fluid intelligence score' and pre-specific proteins from correlation analysis results. Pearson correlation analysis identified significant association signals between the protein of macrophage-stimulating protein and fluid intelligence in brain and CSF tissues (P brain = 1.21 × 10-8, P CSF = 1.10 × 10-7), as well as between B-cell lymphoma 6 protein and fluid intelligence in CSF (P CSF = 1.23 × 10-4). Other proteins showed close-to-significant associations with the trait of 'fluid intelligence score', such as plasma protease C1 inhibitor (P CSF = 4.19 × 10-4, P plasma = 6.97 × 10-4), and with the trait of 'maximum digits remembered correctly', such as tenascin (P plasma = 3.42 × 10-4). Additionally, Mendelian randomization analysis results suggested that macrophage-stimulating protein (Mendelian randomization-Egger: β = 0.54, P = 1.64 × 10-61 in the brain; β = 0.09, P = 1.60 × 10-12 in CSF) had causal effects on fluid intelligence score. We observed functional relevance of specific tissue proteins to intelligence quotient and identified several candidate proteins, such as macrophage-stimulating protein. This study provided a novel insight to the relationship between tissue proteins and intelligence quotient traits.

Integrating single cell expression quantitative trait loci summary statistics to understand complex trait risk genes

Transcriptome-wide association study (TWAS) is a popular approach to dissect the functional consequence of disease associated non-coding variants. Most existing TWAS use bulk tissues and may not have the resolution to reveal cell-type specific target genes. Single-cell expression quantitative trait loci (sc-eQTL) datasets are emerging. The largest bulk- and sc-eQTL datasets are most conveniently available as summary statistics, but have not been broadly utilized in TWAS. Here, we present a new method EXPRESSO (EXpression PREdiction with Summary Statistics Only), to analyze sc-eQTL summary statistics, which also integrates 3D genomic data and epigenomic annotation to prioritize causal variants. EXPRESSO substantially improves existing methods. We apply EXPRESSO to analyze multi-ancestry GWAS datasets for 14 autoimmune diseases. EXPRESSO uniquely identifies 958 novel gene x trait associations, which is 26% more than the second-best method. Among them, 492 are unique to cell type level analysis and missed by TWAS using whole blood. We also develop a cell type aware drug repurposing pipeline, which leverages EXPRESSO results to identify drug compounds that can reverse disease gene expressions in relevant cell types. Our results point to multiple drugs with therapeutic potentials, including metformin for type 1 diabetes, and vitamin K for ulcerative colitis.

Whole Snake Genomes from Eighteen Families of Snakes (Serpentes: Caenophidia) and Their Applications to Systematics.

We present genome assemblies for 18 snake species representing 18 families (Serpentes: Caenophidia): Acrochordus granulatus, Aparallactus werneri, Boaedon fuliginosus, Calamaria suluensis, Cerberus rynchops, Grayia smithii, Imantodes cenchoa, Mimophis mahfalensis, Oxyrhabdium leporinum, Pareas carinatus, Psammodynastes pulverulentus, Pseudoxenodon macrops, Pseudoxyrhopus heterurus, Sibynophis collaris, Stegonotus admiraltiensis, Toxicocalamus goodenoughensis, Trimeresurus albolabris, and Tropidonophis doriae. From these new genome assemblies, we extracted thousands of loci commonly used in systematic and phylogenomic studies on snakes, including target-capture datasets composed of UCEs and AHEs, as well as traditional Sanger loci. Phylogenies inferred from the two target-capture loci datasets were identical with each other, and strongly congruent with previously published snake phylogenies. To show additional utility of these non-model genomes for investigative evolutionary research, we mined the genome assemblies of two New Guinea island endemics in our dataset (Stegonotus admiraltiensis and Tropidonophis doriae) for the ATP1a3 gene, a thoroughly researched indicator of resistance to toad toxin ingestion by squamates. We find that both these snakes possess the genotype for toad toxin resistance despite their endemism to New Guinea, a region absent of any toads until the human-mediated introduction of Cane Toads in the 1930s. These species possess identical substitutions that suggest the same bufotoxin resistance as their Australian congenerics (Stegonotus cucullatus and Tropidonophis mairii) which forage on invasive Cane Toads. Herein, we show the utility of short-read high coverage genomes, as well as improving the deficit of available squamate genomes with associated voucher specimens.

High-resolution African HLA resource uncovers HLA-DRB1 expression effects underlying vaccine response.

How human genetic variation contributes to vaccine effectiveness in infants is unclear, and data are limited on these relationships in populations with African ancestries. We undertook genetic analyses of vaccine antibody responses in infants from Uganda (n = 1391), Burkina Faso (n = 353) and South Africa (n = 755), identifying associations between human leukocyte antigen (HLA) and antibody response for five of eight tested antigens spanning pertussis, diphtheria and hepatitis B vaccines. In addition, through HLA typing 1,702 individuals from 11 populations of African ancestry derived predominantly from the 1000 Genomes Project, we constructed an imputation resource, fine-mapping class II HLA-DR and DQ associations explaining up to 10% of antibody response variance in our infant cohorts. We observed differences in the genetic architecture of pertussis antibody response between the cohorts with African ancestries and an independent cohort with European ancestry, but found no in silico evidence of differences in HLA peptide binding affinity or breadth. Using immune cell expression quantitative trait loci datasets derived from African-ancestry samples from the 1000 Genomes Project, we found evidence of differential HLA-DRB1 expression correlating with inferred protection from pertussis following vaccination. This work suggests that HLA-DRB1 expression may play a role in vaccine response and should be considered alongside peptide selection to improve vaccine design.

Genome composition and GC content influence loci distribution in reduced representation genomic studies

BackgroundGenomic architecture is a key evolutionary trait for living organisms. Due to multiple complex adaptive and neutral forces which impose evolutionary pressures on genomes, there is a huge variability of genomic features. However, their variability and the extent to which genomic content determines the distribution of recovered loci in reduced representation sequencing studies is largely unexplored.ResultsHere, by using 80 genome assemblies, we observed that whereas plants primarily increase their genome size by expanding their intergenic regions, animals expand both intergenic and intronic regions, although the expansion patterns differ between deuterostomes and protostomes. Loci mapping in introns, exons, and intergenic categories obtained by in silico digestion using 2b-enzymes are positively correlated with the percentage of these regions in the corresponding genomes, suggesting that loci distribution mostly mirrors genomic architecture of the selected taxon. However, exonic regions showed a significant enrichment of loci in all groups regardless of the used enzyme. Moreover, when using selective adaptors to obtain a secondarily reduced loci dataset, the percentage and distribution of retained loci also varied. Adaptors with G/C terminals recovered a lower percentage of selected loci, with a further enrichment of exonic regions, while adaptors with A/T terminals retained a higher percentage of loci and slightly selected more intronic regions than expected.ConclusionsOur results highlight how genome composition, genome GC content, RAD enzyme choice and use of base-selective adaptors influence reduced genome representation techniques. This is important to acknowledge in population and conservation genomic studies, as it determines the abundance and distribution of loci.

Untangling deep‐sea corals systematics: Description of a new family, Stephanocyathidae (Anthozoa, Scleractinia), through a genomic approach

AbstractOnce considered the most speciose mostly azooxanthellate scleractinian group, the family Caryophylliidae is found to be highly polyphyletic based on molecular data and is undergoing a process of systematic revision. High‐throughput sequencing methods coupled with morphological analyses have facilitated revision of several scleractinian lineages, including the aforementioned family. In previous studies that relied on few mitochondrial and nuclear markers, the caryophylliid genera Stephanocyathus and Vaughanella were phylogenetically recovered in separate clades from the lineage that includes the type genus of the family, Caryophyllia, which is considered the ‘true’ Caryophylliidae. To help untangle the relationship among taxa of this family, here a new deep‐sea scleractinian family (Stephanocyathidae Vaga, Cairns &amp; Kitahara fam. nov.) is proposed based on phylogenomic reconstructions coupled with molecular features, specifically gene order, of the complete mitochondrial genome. Evolutionary reconstructions were based on both mitochondrial and nuclear ultraconserved elements (UCEs) and exon loci data sets and confirmed the divergent position of the genera Stephanocyathus and Vaughanella. The new family shows a specific gene transposition in the mitochondrial genome, not present in the ‘true’ caryophylliid lineage, but instead already observed for the species Paraconotrochus antarcticus, recovered as sister taxon of the here proposed new family. Although its phylogenetic position is unknown, the genus Ericiocyathus is also added to the new family, based on macromorphological similarities. This study represents a step forward in our understanding of deep‐sea corals relationships and provide further information (e.g., mitochondrial gene order) that will aid in future efforts of assessing the systematic of caryophylliid lineages.

Revealing genes associated with cervical cancer in distinct immune cells: A comprehensive Mendelian randomization analysis.

Human papillomavirus can be contracted by sexually active women. However, only a small proportion of these infections persist and have the potential to progress into cervical cancers, indicating a significant involvement of the immune system in cervical cancer development. Despite this, our understanding of the precise contributions of genes from different immune cell types in cervical cancers remains limited. Therefore, the primary objective of our study was to investigate the potential causal relationships between specific immune cell genes and the development of cervical cancers. By accessing expression quantitative trait loci datasets of 14 distinct immune cell types and genome wide association study of cervical cancers, we employed the summary data-based Mendelian randomization (SMR) along with multi-single nucleotide polymorphism (SNP)-based SMR to identify significant genes associated with cervical cancers. Colocalization analysis was further conducted to explore the shared genetic causality. A total of 10 genes across 11 immune cell types (26 significant gene-trait associations) were found to be associated with cervical cancers after false discovery rate correction. Notably, the ORMDL3, BRK1 and HMGN1 gene expression levels showed significant association with cervical cancer in specific immune cell types, respectively. These associations were supported by strong evidence of colocalization analyses. Our study has identified several genes in different immune cells that were associated with cervical cancer. However, further research is necessary to confirm these findings and provide more comprehensive insights into the association between these gene expressions and cervical cancer risk.

Brain tissue- and cell type-specific eQTL Mendelian randomization reveals efficacy of FADS1 and FADS2 on cognitive function

Epidemiological studies suggested an association between omega-3 fatty acids and cognitive function. However, the causal role of the fatty acid desaturase (FADS) gene, which play a key role in regulating omega-3 fatty acids biosynthesis, on cognitive function is unclear. Hence, we used two-sample Mendelian randomization (MR) to estimate the gene-specific causal effect of omega-3 fatty acids (N = 114,999) on cognitive function (N = 300,486). Tissue- and cell type-specific effects of FADS1/FADS2 expression on cognitive function were estimated using brain tissue cis-expression quantitative trait loci (cis-eQTL) datasets (GTEx, N ≤ 209; MetaBrain, N ≤ 8,613) and single cell cis-eQTL data (N = 373), respectively. These causal effects were further evaluated in whole blood cis-eQTL data (N ≤ 31,684). A series of sensitivity analyses were conducted to validate MR assumptions. Leave-one-out MR showed a FADS gene-specific effect of omega-3 fatty acids on cognitive function [β = −1.3 × 10−2, 95% confidence interval (CI) (−2.2 × 10−2, −5 × 10−3), P = 2 × 10−3]. Tissue-specific MR showed an effect of increased FADS1 expression in cerebellar hemisphere and FADS2 expression in nucleus accumbens basal ganglia on maintaining cognitive function, while decreased FADS1 expression in nine brain tissues on maintaining cognitive function [colocalization probability (PP.H4) ranged from 71.7% to 100.0%]. Cell type-specific MR showed decreased FADS1/FADS2 expression in oligodendrocyte was associated with maintaining cognitive function (PP.H4 = 82.3%, respectively). Increased FADS1/FADS2 expression in whole blood showed an effect on cognitive function maintenance (PP.H4 = 86.6% and 88.4%, respectively). This study revealed putative causal effect of FADS1/FADS2 expression in brain tissues and blood on cognitive function. These findings provided evidence to prioritize FADS gene as potential target gene for maintenance of cognitive function.

Exploring Pathogenic Genes in Frozen Shoulder through weighted gene co-expression network analysis and Mendelian Randomization.

Background: Frozen shoulder (FS) is characterized by the thickening and fibrosis of the joint capsule, leading to joint contracture and a reduction in joint volume. The precise etiology responsible for these pathological changes remains elusive. Therefore, the primary aim of this study was to explore the potential involvement of pathogenic genes in FS and analyze their underlying roles in the disease progression. Methods: Differential expression analysis and weighted gene co-expression network analysis (WGCNA) were employed to investigate co-expressed genes potentially associated with FS. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses were conducted to elucidate the potential roles of these co-expressed genes. Subsequently, Mendelian randomization (MR) analysis was performed using expression quantitative trait loci datasets for the co-expressed genes, combined with summary statistics from the genome-wide association study of FS, aiming to identify key genes causally associated with FS. The identified key genes were further validated through reverse transcription-quantitative PCR (RT-qPCR). Additionally, a nomogram model and receiver operating characteristic (ROC) curves were established to assess the diagnostic value of the hub genes. Furthermore, the infiltration of immune cells was evaluated using the CIBERSORT algorithm and the relationship between key genes and immune-infiltrating cells was analyzed. Results: 295 overlapping co-expressed genes were identified by intersecting the differentially expressed genes with the hub genes obtained from associated modules identified through WGCNA. Utilizing MR analysis, four key genes, namely ADAMTS1, NR4A2, PARD6G and SMKR1, were found to exhibit positive causal relationships with FS, which were subsequently validated through RT-qPCR analysis. Moreover, the diagnostic value of these four key genes was demonstrated through the development of a nomogram model and the construction of ROC curves. Notably, a causal relationship between ADAMTS1 and immune cell infiltration in FS was observed. Conclusion: Our study suggested genetic predisposition to higher expression levels of ADAMTS1, NR4A2, PARD6G and SMKR1, was associated with an increased risk of FS. Further investigations elucidating the functional roles of these genes will enhance our understanding of the pathogenesis of FS and may facilitate the development of targeted treatment strategies.

Alzheimer’s disease genetics and real‐world patient data fuel target and drug discovery in European and African American ancestries

AbstractBackgroundTranslation of massive genetic and genomic data into Alzheimer’s target and drug discovery has not materialized. One challenge in this process is ethnicity‐based differences in the field of Alzheimer’s disease (AD). For example, African Americans (AAs) and European Americans (EAs) differ in AD prevalence, risk factors, and symptomatic presentation, and AAs have historically been less frequently enrolled in AD clinical trials.MethodWe conducted ancestry genetic‐specific target and drug discovery by gathering large genetic data from a diverse population with real‐world patient data to boost the likelihood of identifying disease‐modifying drug targets and treatments for AD. We analyzed genetic data from 275,540 AD cases and 1.55 million controls across 7 AD genome‐wide association studies (GWAS) datasets for both EA and AA ancestries, using Mendelian randomization. We utilized the genetic instruments from 9 expression quantitative trait loci (eQTL) and 3 protein quantitative trait loci (pQTL) datasets from three large human brain biobanks: a) Mount Sinai Brain Bank (MSBB), b) Mayo clinic (Mayo), and c) Religious Orders Study (ROS) or the Rush Memory and Aging Project (MAP) (ROS/MAP).ResultWe identified 25 drug targets in EAs and 6 new drug targets in AAs among 1,176 investigated druggable targets. Notably, the inflammatory target of epoxide hydrolase 2 (EPHX2) emerged as a potent AD target in EAs, and treatment of transgenic TgF344‐AD rats with an EPHX2 inhibitor was therapeutic. Among 6 AA‐specific targets, we identified that TRPV3 is a potent drug target for AA individuals with AD, and further replicated our finding in AA‐specific eQTL data. We additionally identified 23 candidate drugs associated with reduced risk of AD in mild cognitive impairment (MCI) patients after analysis of ∼80 million electronic health records. Using a propensity score‐matched design, we identified that usage of either apixaban (hazard ratio [HR] = 0.74, 95% confidence interval [CI] 0.69 – 0.80) or amlodipine (HR = 0.91, 95% CI 0.88 – 0.94) were both significantly associated with reduced progression to AD in people with MCI.ConclusionCombining genetics and real‐world patient data has identified ancestry‐specific therapeutic targets and medicines for AD.

Expanding causal genes for Parkinson’s disease via multi-omics analysis

Genome‑wide association studies (GWASs) have revealed numerous loci associated with Parkinson’s disease (PD). However, some potential causal/risk genes were still not revealed and no etiological therapies are available. To find potential causal genes and explore genetically supported drug targets for PD is urgent. By integrating the expression quantitative trait loci (eQTL) and protein quantitative trait loci (pQTL) datasets from multiple tissues (blood, cerebrospinal fluid (CSF) and brain) and PD GWAS summary statistics, a pipeline combing Mendelian randomization (MR), Steiger filtering analysis, Bayesian colocalization, fine mapping, Protein-protein network and enrichment analysis were applied to identify potential causal genes for PD. As a result, GPNMB displayed a robust causal role for PD at the protein level in the blood, CSF and brain, and transcriptional level in the brain, while the protective role of CD38 (in brain pQTL and eQTL) was also identified. We also found inconsistent roles of DGKQ on PD between protein and mRNA levels. Another 9 proteins (CTSB, ARSA, SEC23IP, CD84, ENTPD1, FCGR2B, BAG3, SNCA, FCGR2A) were associated with the risk for PD based on only a single pQTL after multiple corrections. We also identified some proteins’ interactions with known PD causative genes and therapeutic targets. In conclusion, this study suggested GPNMB, CD38, and DGKQ may act in the pathogenesis of PD, but whether the other proteins involved in PD needs more evidence. These findings would help to uncover the genes underlying PD and prioritize targets for future therapeutic interventions.

Occurrence of leaf spot caused by Stemphylium vesicarium on Red Clover (Trifolium pratense) in China.

Red clover (Trifolium pratense) is becoming increasingly important in grassland systems (sheep and beef) due to its high productivity, protein content and nitrogen-fixing ability which allows lower N fertilizer use (Murray et al. 2007). In June 2021, an unidentified foliar disease occurred seriously in experimental fields of red clover (25.61°N, 102.48°E) in Kunming city, Yunnan Province, China. Disease incidence was approximately 55% with an average disease severity of 41.1% in the fields (about 1.5 ha). Initially, the symptoms consisted of cicular to oval leaf spots with dark brown edges. In severely diseased plots, the diseased spots continued to expand and fuse along the leaf veins, forming long and narrow leaf spots, and the middle of the leaves were decayed and hollow. To isolate the pathogen, small pieces (3×3 mm) from the margin of infected lesions were surface-sterilized in 75% ethanol solution for 30 s, 1% NaClO solution for 90 s, rinsed three times with sterilized distilled water, air dried, and plated onto potato dextrose agar (PDA). The same fungus was isolated in 90% of the samples and purified by transferring the hyphal tip from the edge of colonies to fresh PDA plates. The colonies produced relatively dense light-grayish aerial mycelium in the early stage, which turned grayish upon mature, and then dark brown on the back of the colonies with dense small black patches. Conidiophores were unbranched, straight or flexuous, accompanied by slight or obvious swelling of its apical cells. Conidia were olive brown, irregularly round, 1 to 3 transverse septa, 0 to 2 longitudinal or oblique septa, and measured 16.0 - (24.8)- 30.1 × 13.9 - (21.4) - 28.6 µm (n = 50). Morphological characteristics were consistent with those described for Stemphylium vesicarium (Simmons, 1967). For molecular identification, total genomic DNA was isolated from mycelia collected from 6 day-old colonies of three representative isolates LZQF-1-LZQF-3 using the Omega D3195 fungal genomic DNA extraction kit. Fragments of three genes, including those encoding the ITS region of rDNA, calmodulin gene (cmdA), and glyceraldehyde-3-phosphate dehydrogenase (gpd) regions of isolates LZQF-1-LZQF-3 were amplified by the primers described previously (Woudenberg et al. 2017) and sequenced. Resulting sequences of representative strain LZQF-1 were deposited in GenBank with accession numbers of OL615093 (ITS), OL624541 (cmdA) and OL624538 (gpd), respectively. A nucleotide BLAST search revealed ITS, cmdA and gpd sequences to be 100%, 100% and 99.0% similar to the corresponding sequences (accessions numbers KU850563.1, KU850853.1 and KU850710.1) of ex-type strain CBS 370.51 of S. vesicarium. The three locus datasets were combined by SequenceMatrix 1.8 (Vaidya et al. 2011), and the strain LZQF-1-LZQF-3 and S. vesicarium (CBS 192.86, CBS 370.51 and CBS 205.82) formed a subclade with 99% bootstrap support. Pathogenicity tests were performed on LZQF-1 in a greenhouse at 18 °C-26 °C with 75% relative humidity. Three pots each containing three 50-day-old red clover were sprayed with the conidial suspension (3 × 105 conidia/ml) while another three pots were sprayed with sterile water as a control. Two weeks later, the inoculated leaves showed oval leaf spots with dark brown edges, distinctive symptoms of S. vesicarium infection. S. vesicarium was reisolated and confirmed by morphological and molecular features. There were no symptoms on the control leaves. S. vesicarium as a pathogen causing leaf spot disease has been reported on red clover in Canada (Jasalavich et al. 1995) and in Russia (Babuschkina et al. 1995). To our knowledge, this is the first report of leaf spot of red clover caused by S. vesicarium in China. The identification of Stemphylium leaf spot lays the groundwork for future investigations into epidemiology and management of S. vesicarium on red clover.