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Deciphering the genetic basis of prostate-specific antigen (PSA) levels may improve their utility for prostate cancer (PCa) screening. Using genome-wide association study (GWAS) summary statistics from 95,768 PCa-free men, we conducted a transcriptome-wide association study (TWAS) to examine impacts of genetically predicted gene expression on PSA. Analyses identified 41 statistically significant (p 0.5: CCNA2 and HIST1H2BN. Six of the 20 identified genes are not known to impact PCa risk. Fine-mapping based on whole blood and prostate tissue revealed five protein-coding genes with evidence of causal relationships with PSA levels. Of these five genes, four exhibited evidence of colocalization and one was conditionally independent of previous GWAS findings. These results yield hypotheses that should be further explored to improve understanding of genetic factors underlying PSA levels.

Human humoral immune responses to SARS-CoV-2 vaccines exhibit substantial inter-individual variability and have been linked to vaccine efficacy. To elucidate the underlying mechanism behind this variability, we conducted a genome-wide association study (GWAS) on the anti-spike IgG serostatus of UK Biobank participants who were previously uninfected by SARS-CoV-2 and had received either the first dose (n = 54,066) or the second dose (n = 46,232) of COVID-19 vaccines. Our analysis revealed significant genome-wide associations between the IgG antibody serostatus following the initial vaccine and human leukocyte antigen (HLA) class II alleles. Specifically, the HLA-DRB1∗13:02 allele (MAF = 4.0%, OR = 0.75, p = 2.34e-16) demonstrated the most statistically significant protective effect against IgG seronegativity. This protective effect was driven by an alteration from arginine (Arg) to glutamic acid (Glu) at position 71 on HLA-DRβ1 (p = 1.88e-25), leading to a change in the electrostatic potential of pocket 4 of the peptide binding groove. Notably, the impact of HLA alleles on IgG responses was cell type specific, and we observed a shared genetic predisposition between IgG status and susceptibility/severity of COVID-19. These results were replicated within independent cohorts where IgG serostatus was assayed by two different antibody serology tests. Our findings provide insights into the biological mechanism underlying individual variation in responses to COVID-19 vaccines and highlight the need to consider the influence of constitutive genetics when designing vaccination strategies for optimizing protection and control of infectious disease across diverse populations.

Patients with chronic kidney disease (CKD) have increased oxidative stress and chronic inflammation, which may escalate the production of advanced glycation end‐products (AGEs). High soluble receptor for AGE (sRAGE) and low estimated glomerular filtration rate (eGFR) levels are associated with CKD and aging. We evaluated whether eGFR calculated from creatinine and cystatin C share pleiotropic genetic factors with sRAGE. We employed whole‐genome sequencing and correlated meta‐analyses on combined genome‐wide association study (GWAS) p‐values in 4182 individuals (age range: 24–110) from the Long Life Family Study (LLFS). We also conducted transcriptome‐wide association studies (TWAS) on whole blood in a subset of 1209 individuals. We identified 59 pleiotropic GWAS loci (p < 5 × 10−8) and 17 TWAS genes (Bonferroni‐p < 2.73 × 10−6) for eGFR traits and sRAGE. TWAS genes, LSP1 and MIR23AHG, were associated with eGFR and sRAGE located within GWAS loci, lncRNA‐KCNQ1OT1 and CACNA1A/CCDC130, respectively. GWAS variants were eQTLs in the kidney glomeruli and tubules, and GWAS genes predicted kidney carcinoma. TWAS genes harbored eQTLs in the kidney, predicted kidney carcinoma, and connected enhancer‐promoter variants with kidney function‐related phenotypes at p < 5 × 10−8. Additionally, higher allele frequencies of protective variants for eGFR traits were detected in LLFS than in ALFA‐Europeans and TOPMed, suggesting better kidney function in healthy‐aging LLFS than in general populations. Integrating genomic annotation and transcriptional gene activity revealed the enrichment of genetic elements in kidney function and aging‐related processes. The identified pleiotropic loci and gene expressions for eGFR and sRAGE suggest their underlying shared genetic effects and highlight their roles in kidney‐ and aging‐related signaling pathways. [ABSTRACT FROM AUTHOR]

Both genetic predispositions and exposures to stressors have collectively contributed to the development of major depressive disorder (MDD). To deep dive into their roles in MDD, our study aimed to examine which susceptible gene expression interacts with various dimensions of stressors in the MDD risk among a large population cohort. Data analyzed were from a longitudinal community-based cohort from Southwest Montreal, Canada (N = 1083). Latent profile models were used to identify distinct patterns of stressors for the study cohort. A transcriptome-wide association study (TWAS) method was performed to examine the interactive effects of three dimensions of stressors (threat, deprivation, and cumulative lifetime stress) and gene expression on the MDD risk in a total of 48 tissues from GTEx. Additional analyses were also conducted to further explore and specify these associations including colocalization, and fine-mapping analyses, in addition to enrichment analysis investigations based on TWAS. We identified 3321 genes linked to MDD at the nominal p -value <0.05 and found that different patterns of stressors can amplify the genetic susceptibility to MDD. We also observed specific genes and pathways that interacted with deprivation and cumulative lifetime stressors, particularly in specific brain tissues including basal ganglia, prefrontal cortex, brain amygdala, brain cerebellum, brain cortex, and the whole blood. Colocalization analysis also identified these genes as having a high probability of sharing MDD causal variants. The study cohort was composed exclusively of individuals of Caucasians, which restricts the generalizability of the findings to other ethnic population groups. The findings of the study unveiled significant interactions between potential tissue-specific gene expression × stressors in the MDD risk and shed light on the intricate etiological attributes of gene expression and specific stressors across the lifespan in MDD. These genetic and environmental attributes in MDD corroborate the vulnerability-stress theory and direct future stress research to have a closer examination of genetic predisposition and potential involvements of omics studies to specify the intricate relationships between genes and stressful environments. • Different stress dimensions, including threat, deprivation, and cumulative lifetime stress, increased MDD risk. • A total of 3321 genes with genetically predicted expression linked to MDD risk. • Specific genes/pathways interacted with deprivation and cumulative lifetime stressors in brain tissues. • Six genes were identified in co-localization between eQTL and GWAS. [ABSTRACT FROM AUTHOR]

Abstract Age at first farrowing (AFF) is a reproductive trait with low heritability and high importance in the pig industry. To enhance the statistical power of genome‐wide association study (GWAS) and further explore the genetic nature of AFF, we first conducted GWAS meta‐analysis using three Yorkshire populations, and then integrated the Pig Genotype‐Tissue Expression (PigGTEx) resources to interpret their potential regulatory mechanism. Additionally, we compared the AFF in pig with the age at first birth (AFB) of human using GWAS summary statistics. We detected 18 independent variants in GWAS meta‐analysis and 8 genes in gene‐based association analysis significantly associated with AFF. By integrating the PigGTEx resource, we conducted transcriptome‐wide association study (TWAS) and colocalization analysis on 34 pig tissues. In TWAS, we detected 18 significant gene‐tissue pairs, such as DCAF6 in uterus and CREG1 in blood. In colocalization, we found 111 potential candidate tissue‐gene pairs, such as GJD4 and LYPLAL1. We found that the homologous gene, CHST10, might be the potential candidate gene between humans in AFB and pigs in AFF. In conclusion, integrating GWAS meta‐analysis and PigGTEx resources is a meaningful way to decipher the genetic architecture of complex traits. We found that DCAF6, CREG1, GJD4, and LYPLAL1 are candidate genes with high reliability for AFF in swine. The comparative analysis showed that CHST10 might play a potentially critical role in AFB/AFF across human and pigs.

Abstract Background Salt sensitivity of blood pressure (SSBP) is an intermediate phenotype of hypertension and is a predictor of long-term cardiovascular events and death. However, the genetic structures of SSBP are uncertain, and it is difficult to precisely diagnose SSBP in population. So, we aimed to identify genes related to susceptibility to the SSBP, construct a risk evaluation model, and explore the potential functions of these genes. Methods and results A genome-wide association study of the systemic epidemiology of salt sensitivity (EpiSS) cohort was performed to obtain summary statistics for SSBP. Then, we conducted a transcriptome-wide association study (TWAS) of 12 tissues using FUSION software to predict the genes associated with SSBP and verified the genes with an mRNA microarray. The potential roles of the genes were explored. Risk evaluation models of SSBP were constructed based on the serial P value thresholds of polygenetic risk scores (PRSs), polygenic transcriptome risk scores (PTRSs) and their combinations of the identified genes and genetic variants from the TWAS. The TWAS revealed that 2605 genes were significantly associated with SSBP. Among these genes, 69 were differentially expressed according to the microarray analysis. The functional analysis showed that the genes identified in the TWAS were enriched in metabolic process pathways. The PRSs were correlated with PTRSs in the heart atrial appendage, adrenal gland, EBV-transformed lymphocytes, pituitary, artery coronary, artery tibial and whole blood. Multiple logistic regression models revealed that a PRS of P

The underlying pathogenesis of anxiety remain elusive, making the pinpointing of potential therapeutic and diagnostic biomarkers for anxiety paramount to its efficient treatment. We undertook a proteome-wide association study (PWAS), fusing human brain proteomes from both discovery (ROS/MAP; N = 376) and validation cohorts (Banner; N = 152) with anxiety genome-wide association study (GWAS) summary statistics. Complementing this, we executed transcriptome-wide association studies (TWAS) leveraging human brain transcriptomic data from the Common Mind Consortium (CMC) to discern the confluence of genetic influences spanning both proteomic and transcriptomic levels. We further scrutinized significant genes through a suite of methodologies. We discerned 14 genes instrumental in the genesis of anxiety through their specific cis -regulated brain protein abundance. Out of these, 6 were corroborated in the confirmatory PWAS, with 4 also showing associations with anxiety via their cis -regulated brain mRNA levels. A heightened confidence level was attributed to 5 genes (RAB27B, CCDC92, BTN2A1, TMEM106B, and DOC2A), taking into account corroborative evidence from both the confirmatory PWAS and TWAS, coupled with insights from mendelian randomization analysis and colocalization evaluations. A majority of the identified genes manifest in brain regions intricately linked to anxiety and predominantly partake in lysosomal metabolic processes. The limited scope of the brain proteome reference datasets, stemming from a relatively modest sample size, potentially curtails our grasp on the entire gamut of genetic effects. The genes pinpointed in our research present a promising groundwork for crafting therapeutic interventions and diagnostic tools for anxiety. • This study aims to identify causal factors of anxiety by integrating genetic, transcriptomic, and proteomic analyses. • We find 14 genes instrumental in the genesis of anxiety through their specific cis-regulated brain protein abundance. • CCDC92 acts as a multifaceted risk contributor to anxiety, potentially by modulating the brain's overall microenvironment. [ABSTRACT FROM AUTHOR]

Simple Summary: The journey of discovering melanoma—the most dangerous type of skin cancer—biomarkers is never-ending. Under that assumption, this study attempts to partially fill a gap in that journey by identifying melanoma-related biomarkers. This research paper studied the genetic and molecular profiling of melanoma. A transcriptome-wide association study (TWAS) was conducted to reveal the significant biomarkers associated with melanoma. A molecular network was constructed to represent the significant interactions between genes and microRNAs (miRNAs). The related biological processes, the enriched pathways, and the associated diseases were identified. The hotspot biomarkers were discovered to elucidate further the mechanism of these genes and miRNAs in melanoma pathogenesis. This work reveals the pathogenesis and biologically important molecular interactions of melanoma and provides new insights into the molecular mechanisms of melanoma. A transcriptome-wide association study (TWAS) was conducted on genome-wide association study (GWAS) summary statistics of malignant melanoma of skin (UK Biobank dataset) and The Cancer Genome Atlas-Skin Cutaneous Melanoma (TCGA-SKCM) gene expression weights to identify melanoma susceptibility genes. The GWAS included 2465 cases and 449,799 controls, while the gene expression testing was conducted on 103 cases. Afterward, a gene enrichment analysis was applied to identify significant TWAS associations. The melanoma's gene–microRNA (miRNA) regulatory network was constructed from the TWAS genes and their corresponding miRNAs. At last, a disease enrichment analysis was conducted on the corresponding miRNAs. The TWAS detected 27 genes associated with melanoma with p-values less than 0.05 (the top three genes are LOC389458 (RBAK), C16orf73 (MEIOB), and EIF3CL). After the joint/conditional test, one gene (AMIGO1) was dropped, resulting in 26 significant genes. The Gene Ontology (GO) biological process associated the extended gene set (76 genes) with protein K11-linked ubiquitination and regulation of cell cycle phase transition. K11-linked ubiquitin chains regulate cell division. Interestingly, the extended gene set was related to different skin cancer subtypes. Moreover, the enriched pathways were nsp1 from SARS-CoV-2 that inhibit translation initiation in the host cell, cell cycle, translation factors, and DNA repair pathways full network. The gene-miRNA regulatory network identified 10 hotspot genes with the top three: TP53, BRCA1, and MDM2; and four hotspot miRNAs: mir-16, mir-15a, mir-125b, and mir-146a. Melanoma was among the top ten diseases associated with the corresponding (106) miRNAs. Our results shed light on melanoma pathogenesis and biologically significant molecular interactions. [ABSTRACT FROM AUTHOR]

Depression, a common psychiatric illness and global public health problem, remains poorly understood across different life stages, which hampers the development of novel treatments. To identify new candidate genes for therapeutic development, we performed differential gene expression analysis of single-nucleus RNA sequencing data from the dorsolateral prefrontal cortex of older adults (n = 424) in relation to antemortem depressive symptoms. Additionally, we integrated genome-wide association study results for depression (n = 500,199) along with genetic tools for inferring the expression of 14,048 unique genes in 7 cell types and 52 cell subtypes to perform a transcriptome-wide association study of depression followed by Mendelian randomization. Our single-nucleus transcriptome-wide association study analysis identified 68 candidate genes for depression and showed the greatest number being in excitatory and inhibitory neurons. Of the 68 genes, 53 were novel compared to previous studies. Notably, gene expression in different neuronal subtypes had varying effects on depression risk. Traits with high genetic correlations with depression, such as neuroticism, shared more transcriptome-wide association study genes than traits that were not highly correlated with depression. Complementing these analyses, differential gene expression analysis across 52 neocortical cell subtypes showed that genes such as KCNN2 , SCAI , WASF3 , and SOCS6 were associated with late-life depressive symptoms in specific cell subtypes. These 2 sets of analyses illustrate the utility of large single-nucleus RNA sequencing data both to uncover genes whose expression is altered in specific cell subtypes in the context of depressive symptoms and to enhance the interpretation of well-powered genome-wide association studies so that we can prioritize specific susceptibility genes for further analysis and therapeutic development. [ABSTRACT FROM AUTHOR]

Background: Salt sensitivity of blood pressure (SSBP) is an intermediate phenotype of hypertension and is a predictor of long-term cardiovascular events and death. However, the genetic structures of SSBP are uncertain, and it is difficult to precisely diagnose SSBP in population. So, we aimed to identify genes related to susceptibility to the SSBP, construct a risk evaluation model, and explore the potential functions of these genes. Methods and results: A genome-wide association study of the systemic epidemiology of salt sensitivity (EpiSS) cohort was performed to obtain summary statistics for SSBP. Then, we conducted a transcriptome-wide association study (TWAS) of 12 tissues using FUSION software to predict the genes associated with SSBP and verified the genes with an mRNA microarray. The potential roles of the genes were explored. Risk evaluation models of SSBP were constructed based on the serial P value thresholds of polygenetic risk scores (PRSs), polygenic transcriptome risk scores (PTRSs) and their combinations of the identified genes and genetic variants from the TWAS. The TWAS revealed that 2605 genes were significantly associated with SSBP. Among these genes, 69 were differentially expressed according to the microarray analysis. The functional analysis showed that the genes identified in the TWAS were enriched in metabolic process pathways. The PRSs were correlated with PTRSs in the heart atrial appendage, adrenal gland, EBV-transformed lymphocytes, pituitary, artery coronary, artery tibial and whole blood. Multiple logistic regression models revealed that a PRS of P < 0.05 had the best predictive ability compared with other PRSs and PTRSs. The combinations of PRSs and PTRSs did not significantly increase the prediction accuracy of SSBP in the training and validation datasets. Conclusions: Several known and novel susceptibility genes for SSBP were identified via multitissue TWAS analysis. The risk evaluation model constructed with the PRS of susceptibility genes showed better diagnostic performance than the transcript levels, which could be applied to screen for SSBP high-risk individuals. [ABSTRACT FROM AUTHOR]

Abstract Background Male-pattern baldness (MPB) is the most common cause of hair loss in men. It can be categorized into three types: type 2 (T2), type 3 (T3), and type 4 (T4), with type 1 (T1) being considered normal. Although various MPB-associated genetic variants have been suggested, a comprehensive study for linking these variants to gene expression regulation has not been performed to the best of our knowledge. Results In this study, we prioritized MPB-related tissue panels using tissue-specific enrichment analysis and utilized single-tissue panels from genotype-tissue expression version 8, as well as cross-tissue panels from context-specific genetics. Through a transcriptome-wide association study and colocalization analysis, we identified 52, 75, and 144 MPB associations for T2, T3, and T4, respectively. To assess the causality of MPB genes, we performed a conditional and joint analysis, which revealed 10, 11, and 54 putative causality genes for T2, T3, and T4, respectively. Finally, we conducted drug repositioning and identified potential drug candidates that are connected to MPB-associated genes. Conclusions Overall, through an integrative analysis of gene expression and genotype data, we have identified robust MPB susceptibility genes that may help uncover the underlying molecular mechanisms and the novel drug candidates that may alleviate MPB.

Background: Male-pattern baldness (MPB) is the most common cause of hair loss in men. It can be categorized into three types: type 2 (T2), type 3 (T3), and type 4 (T4), with type 1 (T1) being considered normal. Although various MPB-associated genetic variants have been suggested, a comprehensive study for linking these variants to gene expression regulation has not been performed to the best of our knowledge. Results: In this study, we prioritized MPB-related tissue panels using tissue-specific enrichment analysis and utilized single-tissue panels from genotype-tissue expression version 8, as well as cross-tissue panels from context-specific genetics. Through a transcriptome-wide association study and colocalization analysis, we identified 52, 75, and 144 MPB associations for T2, T3, and T4, respectively. To assess the causality of MPB genes, we performed a conditional and joint analysis, which revealed 10, 11, and 54 putative causality genes for T2, T3, and T4, respectively. Finally, we conducted drug repositioning and identified potential drug candidates that are connected to MPB-associated genes. Conclusions: Overall, through an integrative analysis of gene expression and genotype data, we have identified robust MPB susceptibility genes that may help uncover the underlying molecular mechanisms and the novel drug candidates that may alleviate MPB. [ABSTRACT FROM AUTHOR]

Transcriptomics data have been integrated with genome-wide association studies (GWASs) to help understand disease/trait molecular mechanisms. The utility of metabolomics, integrated with transcriptomics and disease GWASs, to understand molecular mechanisms for metabolite levels or diseases has not been thoroughly evaluated. We performed probabilistic transcriptome-wide association and locus-level colocalization analyses to integrate transcriptomics results for 49 tissues in 706 individuals from the GTEx project, metabolomics results for 1,391 plasma metabolites in 6,136 Finnish men from the METSIM study, and GWAS results for 2,861 disease traits in 260,405 Finnish individuals from the FinnGen study. We found that genetic variants that regulate metabolite levels were more likely to influence gene expression and disease risk compared to the ones that do not. Integrating transcriptomics with metabolomics results prioritized 397 genes for 521 metabolites, including 496 previously identified gene-metabolite pairs with strong functional connections and suggested 33.3% of such gene-metabolite pairs shared the same causal variants with genetic associations of gene expression. Integrating transcriptomics and metabolomics individually with FinnGen GWAS results identified 1,597 genes for 790 disease traits. Integrating transcriptomics and metabolomics jointly with FinnGen GWAS results helped pinpoint metabolic pathways from genes to diseases. We identified putative causal effects of UGT1A1/UGT1A4 expression on gallbladder disorders through regulating plasma (E,E)-bilirubin levels, of SLC22A5 expression on nasal polyps and plasma carnitine levels through distinct pathways, and of LIPC expression on age-related macular degeneration through glycerophospholipid metabolic pathways. Our study highlights the power of integrating multiple sets of molecular traits and GWAS results to deepen understanding of disease pathophysiology.

Abstract Root system architecture plays a crucial role in nutrient and water absorption during rice production. Genetic improvement of the rice root system requires elucidating its genetic control. Genome-wide association studies (GWASs) have identified genomic regions responsible for rice root phenotypes. However, candidate gene prioritization around the peak region often suffers from low statistical power and resolution. Transcriptomics enables other statistical mappings, such as transcriptome-wide association study (TWAS) and expression GWAS (eGWAS), which improve candidate gene identification by leveraging the natural variation of the expression profiles. To explore the genes responsible for root phenotypes, we conducted GWAS, TWAS, and eGWAS for 12 root phenotypes in 57 rice accessions using 427,751 single nucleotide polymorphisms (SNPs) and the expression profiles of 16,901 genes expressed in the roots. The GWAS identified three significant peaks, of which the most significant peak responsible for seven root phenotypes (crown root length, crown root surface area, number of crown root tips, lateral root length, lateral root surface area, lateral root volume, and number of lateral root tips) was detected at 6,199,732 bp on chromosome 8. In the most significant GWAS peak region, OsENT1 was prioritized as the most plausible candidate gene because its expression profile was strongly negatively correlated with the seven root phenotypes. In addition to OsENT1, OsEXPA31, OsSPL14, OsDEP1, and OsDEC1 were identified as candidate genes responsible for root phenotypes using TWAS. Furthermore, a cis-eGWAS peak SNP was detected for OsDjA6, which showed the eighth strongest association with lateral root volume in the TWAS. The cis-eGWAS peak SNP for OsDjA6 was in strong linkage disequilibrium (LD) with a GWAS peak SNP on the same chromosome for lateral root volume and in perfect LD with another SNP variant in a putative cis-element at the 518 bp upstream of the gene. These candidate genes provide new insights into the molecular breeding of root system architecture.

Toward the identification of genetic basis of complex traits, transcriptome-wide association study (TWAS) is successful in integrating transcriptome data. However, TWAS is only applicable for common variants, excluding rare variants in exome or whole-genome sequences. This is partly because of the inherent limitation of TWAS protocols that rely on predicting gene expressions. Our previous research has revealed the insight into TWAS: the 2 steps in TWAS, building and applying the expression prediction models, are essentially genetic feature selection and aggregations that do not have to involve predictions. Based on this insight disentangling TWAS, rare variants' inability of predicting expression traits is no longer an obstacle. Herein, we developed "rare variant TWAS," or rvTWAS, that first uses a Bayesian model to conduct expression-directed feature selection and then uses a kernel machine to carry out feature aggregation, forming a model leveraging expressions for association mapping including rare variants. We demonstrated the performance of rvTWAS by thorough simulations and real data analysis in 3 psychiatric disorders, namely schizophrenia, bipolar disorder, and autism spectrum disorder. We confirmed that rvTWAS outperforms existing TWAS protocols and revealed additional genes underlying psychiatric disorders. Particularly, we formed a hypothetical mechanism in which zinc finger genes impact all 3 disorders through transcriptional regulations. rvTWAS will open a door for sequence-based association mappings integrating gene expressions. [ABSTRACT FROM AUTHOR]

Aim: Genome‐wide association studies (GWAS) have identified multiple susceptibility loci associated with insulin resistance (IR)‐relevant phenotypes. However, the genes responsible for these associations remain largely unknown. We aim to identify susceptibility genes for IR‐relevant phenotypes via a transcriptome‐wide association study. Materials and Methods: We conducted a large‐scale multi‐tissue transcriptome‐wide association study for IR (Insulin Sensitivity Index, homeostasis model assessment‐IR, fasting insulin) and lipid‐relevant traits (high‐density lipoprotein cholesterol, triglycerides, low‐density lipoprotein cholesterol and total cholesterol) using the largest GWAS summary statistics and precomputed gene expression weights of 49 human tissues. Conditional and joint analyses were implemented to identify significantly independent genes. Furthermore, we estimated the causal effects of independent genes by Mendelian randomization causal inference analysis. Results: We identified 1190 susceptibility genes causally associated with IR‐relevant phenotypes, including 58 genes that were not implicated in the original GWAS. Among them, 11 genes were further supported in differential expression analyses or a gene knockout mice database, such as KRIT1 showed both significantly differential expression and IR‐related phenotypic effects in knockout mice. Meanwhile, seven proteins encoded by susceptibility genes were targeted by clinically approved drugs, and three of these genes (H6PD, CACNB2 and DRD2) have been served as drug targets for IR‐related diseases/traits. Moreover, drug repurposing analysis identified four compounds with profiles opposing the expression of genes associated with IR risk. Conclusions: Our study provided new insights into IR aetiology and avenues for therapeutic development. [ABSTRACT FROM AUTHOR]

Root system architecture plays a crucial role in nutrient and water absorption during rice production. Genetic improvement of the rice root system requires elucidating its genetic control. Genome-wide association studies (GWASs) have identified genomic regions responsible for rice root phenotypes. However, candidate gene prioritization around the peak region often suffers from low statistical power and resolution. Transcriptomics enables other statistical mappings, such as transcriptome-wide association study (TWAS) and expression GWAS (eGWAS), which improve candidate gene identification by leveraging the natural variation of the expression profiles. To explore the genes responsible for root phenotypes, we conducted GWAS, TWAS, and eGWAS for 12 root phenotypes in 57 rice accessions using 427,751 single nucleotide polymorphisms (SNPs) and the expression profiles of 16,901 genes expressed in the roots. The GWAS identified three significant peaks, of which the most significant peak responsible for seven root phenotypes (crown root length, crown root surface area, number of crown root tips, lateral root length, lateral root surface area, lateral root volume, and number of lateral root tips) was detected at 6,199,732 bp on chromosome 8. In the most significant GWAS peak region, OsENT1 was prioritized as the most plausible candidate gene because its expression profile was strongly negatively correlated with the seven root phenotypes. In addition to OsENT1, OsEXPA31, OsSPL14, OsDEP1, and OsDEC1 were identified as candidate genes responsible for root phenotypes using TWAS. Furthermore, a cis-eGWAS peak SNP was detected for OsDjA6, which showed the eighth strongest association with lateral root volume in the TWAS. The cis-eGWAS peak SNP for OsDjA6 was in strong linkage disequilibrium (LD) with a GWAS peak SNP on the same chromosome for lateral root volume and in perfect LD with another SNP variant in a putative cis-element at the 518 bp upstream of the gene. These candidate genes provide new insights into the molecular breeding of root system architecture. [ABSTRACT FROM AUTHOR]

Abstract Background More than 200 asthma-associated genetic variants have been identified in genome-wide association studies (GWASs). Expression quantitative trait loci (eQTL) data resources can help identify causal genes of the GWAS signals, but it can be difficult to find an eQTL that reflects the disease state because most eQTL data are obtained from normal healthy subjects. Methods We performed a blood eQTL analysis using transcriptomic and genotypic data from 433 Korean asthma patients. To identify asthma-related genes, we carried out colocalization, Summary-based Mendelian Randomization (SMR) analysis, and Transcriptome-Wide Association Study (TWAS) using the results of asthma GWASs and eQTL data. In addition, we compared the results of disease eQTL data and asthma-related genes with two normal blood eQTL data from Genotype-Tissue Expression (GTEx) project and a Japanese study. Results We identified 340,274 cis-eQTL and 2,875 eGenes from asthmatic eQTL analysis. We compared the disease eQTL results with GTEx and a Japanese study and found that 64.1% of the 2,875 eGenes overlapped with the GTEx eGenes and 39.0% with the Japanese eGenes. Following the integrated analysis of the asthmatic eQTL data with asthma GWASs, using colocalization and SMR methods, we identified 15 asthma-related genes specific to the Korean asthmatic eQTL data. Conclusions We provided Korean asthmatic cis-eQTL data and identified asthma-related genes by integrating them with GWAS data. In addition, we suggested these asthma-related genes as therapeutic targets for asthma. We envisage that our findings will contribute to understanding the etiological mechanisms of asthma and provide novel therapeutic targets.

Abstract Background Spondyloarthritis (SpA) is a group of multifactorial bone diseases influenced by genetic factors, the environment and lifestyle. However, current studies have found a limited number of SpA-related genes, and the genetic and pathogenic mechanisms of SpA are still unclear. Methods A tissue-specific transcriptome-wide association study (TWAS) of SpA was performed using GWAS (including 3966 SpA patients and 448,298 controls) summary data and gene expression weights of whole blood and skeletal muscle. The SpA-associated genes identified by TWAS were further compared with the differentially expressed genes (DEGs) identified in the SpA gene expression profile acquired from the Gene Expression Omnibus database (GEO, GSE58667). Finally, functional enrichment and annotation analyses of the identified genes were performed. Results The TWAS detected 499 suggestive genes associated with SpA in whole blood and skeletal muscle, such as CTNNAL1 (P SM = 3.04 × 10−2, P WB = 9.58 × 10−3). The gene expression profile of SpA identified 20 candidate genes that overlapped in the TWAS data, such as MCM4 (P TWAS = 1.32 × 10−2, P DEG = 2.75 × 10−2) and KIAA1109 (P TWAS = 3.71 × 10−2, P DEG = 4.67 × 10−2). Enrichment analysis of the genes identified by TWAS identified 93 significant GO terms and 33 KEGG pathways, such as mitochondrion organization (GO: 0007005) and axon guidance (hsa04360). Conclusion We identified multiple candidate genes that were genetically related to SpA. Our study may provide novel clues regarding the genetic mechanism, diagnosis, and treatment of SpA.

Abstract Background While previous genome-wide association studies (GWAS) have identified multiple risk variants for migraine, there is a lack of evidence about how these variants contribute to the development of migraine. We employed an integrative pipeline to efficiently transform genetic associations to identify causal genes for migraine. Methods We conducted a proteome-wide association study (PWAS) by combining data from the migraine GWAS data with proteomic data from the human brain and plasma to identify proteins that may play a role in the risk of developing migraine. We also combined data from GWAS of migraine with a novel joint-tissue imputation (JTI) prediction model of 17 migraine-related human tissues to conduct transcriptome-wide association studies (TWAS) together with the fine mapping method FOCUS to identify disease-associated genes. Results We identified 13 genes in the human brain and plasma proteome that modulate migraine risk by regulating protein abundance. In addition, 62 associated genes not reported in previous migraine TWAS studies were identified by our analysis of migraine using TWAS and fine mapping. Five genes including ICA1L, TREX1, STAT6, UFL1, and B3GNT8 showed significant associations with migraine at both the proteome and transcriptome, these genes are mainly expressed in ependymal cells, neurons, and glial cells, and are potential target genes for prevention of neuronal signaling and inflammatory responses in the pathogenesis of migraine. Conclusions Our proteomic and transcriptome findings have identified disease-associated genes that may give new insights into the pathogenesis and potential therapeutic targets for migraine.

Aims: Lumbar spinal stenosis (LSS) is a common skeletal system disease that has been partly attributed to genetic variation. However, the correlation between genetic variation and pathological changes in LSS is insufficient, and it is difficult to provide a reference for the early diagnosis and treatment of the disease. Methods: We conducted a transcriptome-wide association study (TWAS) of spinal canal stenosis by integrating genome-wide association study summary statistics (including 661 cases and 178,065 controls) derived from Biobank Japan, and pre-computed gene expression weights of skeletal muscle and whole blood implemented in FUSION software. To verify the TWAS results, the candidate genes were furthered compared with messenger RNA (mRNA) expression profiles of LSS to screen for common genes. Finally, Metascape software was used to perform enrichment analysis of the candidate genes and common genes. Results: TWAS identified 295 genes with permutation p-values < 0.05 for skeletal muscle and 79 genes associated for the whole blood, such as RCHY1 (PTWAS = 0.001). Those genes were enriched in 112 gene ontology (GO) terms and five Kyoto Encyclopedia of Genes and Genomes pathways, such as ‘chemical carcinogenesis - reactive oxygen species’ (LogP value = −2.139). Further comparing the TWAS significant genes with the differentially expressed genes identified by mRNA expression profiles of LSS found 18 overlapped genes, such as interleukin 15 receptor subunit alpha (IL15RA) (PTWAS = 0.040, PmRNA = 0.010). Moreover, 71 common GO terms were detected for the enrichment results of TWAS and mRNA expression profiles, such as negative regulation of cell differentiation (LogP value = −2.811). Conclusion: This study revealed the genetic mechanism behind the pathological changes in LSS, and may provide novel insights for the early diagnosis and intervention of LSS. Cite this article: Bone Joint Res 2023;12(6):387–396.

Objectives: This study aimed to identify plasma proteins that are associated with and causative of breast cancer through Proteome and Transcriptome-wide association studies combining Mendelian Randomization.Methods: Utilizing high-throughput datasets, we designed a two-phase analytical framework aimed at identifying novel plasma proteins that are both associated with and causative of breast cancer. Initially, we conducted Proteome/Transcriptome-wide association studies (P/TWAS) to identify plasma proteins with significant associations. Subsequently, Mendelian Randomization was employed to ascertain the causation. The validity and robustness of our findings were further reinforced through external validation and various sensitivity analyses, including Bayesian colocalization, Steiger filtering, heterogeneity and pleiotropy. Additionally, we performed functional enrichment analysis of the identified proteins to better understand their roles in breast cancer and to assess their potential as druggable targets.Results: We identified 5 plasma proteins demonstrating strong associations and causative links with breast cancer. Specifically, PEX14 (OR = 1.201, p = 0.016) and CTSF (OR = 1.114, p < 0.001) both displayed positive and causal association with breast cancer. In contrast, SNUPN (OR = 0.905, p < 0.001), CSK (OR = 0.962, p = 0.038), and PARK7 (OR = 0.954, p < 0.001) were negatively associated with the disease. For the ER-positive subtype, 3 plasma proteins were identified, with CSK and CTSF exhibiting consistent trends, while GDI2 (OR = 0.920, p < 0.001) was distinct to this subtype. In ER-negative subtype, PEX14 (OR = 1.645, p < 0.001) stood out as the sole protein, even showing a stronger causal effect compared to breast cancer. These associations were robustly supported by colocalization and sensitivity analyses.Conclusion: Integrating multiple data dimensions, our study successfully pinpointed plasma proteins significantly associated with and causative of breast cancer, offering valuable insights for future research and potential new biomarkers and therapeutic targets.

ObjectivesEpidemiological studies suggested a potential connection between education and autoimmune disorders. This study investigated the possible cause-and-effect relationship using a Mendelian randomization approach.MethodsWe explored the causality between four education traits (n = 257,841~1,131,881) and 22 autoimmune diseases. The mediating role of smoking (632,802 individuals), BMI (681,275 individuals), alcohol (335,394 individuals), and income (397,751 individuals) was also investigated. Transcriptome-wide association study (TWAS) and enriched signaling pathways analysis were used to investigate the underlying biological mechanisms.ResultsEspecially, higher cognitive performance was protective for psoriasis (odds ratio (OR) = 0.69, 95% confidence interval (CI) = 0.60-0.79, p = 6.12×10-8), rheumatoid arthritis (RA) (OR = 0.75, 95% CI = 0.67-0.83, p = 4.62×10-6), and hypothyroidism (OR = 0.83, 95% CI = 0.77-0.90, p = 9.82×10-6). Higher levels of educational attainment decreased risks of psoriasis (OR = 0.61, 95% CI = 0.52-0.72, p = 1.12×10-9), RA (OR = 0.68, 95% CI = 0.59-0.79, p = 1.56×10-7), and hypothyroidism (OR = 0.80, 95% CI = 0.72-0.88, p = 5.00×10-6). The completion of highest-level math class genetically downregulates the incidence of psoriasis (OR = 0.66, 95% CI = 0.58-0.76, p = 2.47×10-9), RA (OR = 0.71, 95% CI = 0.63-0.81, p = 5.28×10-8), and hypothyroidism (OR = 0.85, 95% CI = 0.79-0.92, p = 8.88×10-5). Higher self-reported math ability showed protective effects on Crohn’s disease (CD) (OR = 0.67, 95% CI = 0.55-0.81, p = 4.96×10-5), RA (OR = 0.76, 95% CI = 0.67-0.87, p = 5.21×10-5), and psoriasis (OR = 0.76, 95% CI = 0.65-0.88, p = 4.08×10-4). Protein modification and localization, response to arsenic-containing substances may participate in the genetic association of cognitive performance on UC, RA, psoriasis, and hypothyroidism. According to mediation analyses, BMI, smoking, and income served as significant mediators in the causal connection between educational traits and autoimmune diseases.ConclusionHigher levels of education-related factors have a protective effect on the risk of several autoimmune disorders. Reducing smoking and BMI and promoting income equality can mitigate health risks associated with low education levels.

Background: Spondyloarthritis (SpA) is a group of multifactorial bone diseases influenced by genetic factors, the environment and lifestyle. However, current studies have found a limited number of SpA-related genes, and the genetic and pathogenic mechanisms of SpA are still unclear. Methods: A tissue-specific transcriptome-wide association study (TWAS) of SpA was performed using GWAS (including 3966 SpA patients and 448,298 controls) summary data and gene expression weights of whole blood and skeletal muscle. The SpA-associated genes identified by TWAS were further compared with the differentially expressed genes (DEGs) identified in the SpA gene expression profile acquired from the Gene Expression Omnibus database (GEO, GSE58667). Finally, functional enrichment and annotation analyses of the identified genes were performed. Results: The TWAS detected 499 suggestive genes associated with SpA in whole blood and skeletal muscle, such as CTNNAL1 (PSM = 3.04 × 10−2, PWB = 9.58 × 10−3). The gene expression profile of SpA identified 20 candidate genes that overlapped in the TWAS data, such as MCM4 (PTWAS = 1.32 × 10−2, PDEG = 2.75 × 10−2) and KIAA1109 (PTWAS = 3.71 × 10−2, PDEG = 4.67 × 10−2). Enrichment analysis of the genes identified by TWAS identified 93 significant GO terms and 33 KEGG pathways, such as mitochondrion organization (GO: 0007005) and axon guidance (hsa04360). Conclusion: We identified multiple candidate genes that were genetically related to SpA. Our study may provide novel clues regarding the genetic mechanism, diagnosis, and treatment of SpA. Highlights: This is the first study to identify susceptibility genes associated with spondyloarthritis (SpA), revealing 499 suggestive genes by TWAS analysis. The TWAS results were further verified by comparing the gene expression profiles. This study provides novel clues for understanding the genetic mechanism of SpA, especially focusing on aberrant gene expression in the pathogenesis of SpA. [ABSTRACT FROM AUTHOR]

Aims/hypothesis: This study aimed to assess the causal relationship between visceral obesity and type 2 diabetes and subsequently to screen visceral adipose tissue (VAT)-specific targets for type 2 diabetes. Methods: We examined the causal relationship between VAT and type 2 diabetes using bidirectional Mendelian randomisation (MR) followed by multivariable MR. We conducted a transcriptome-wide association study (TWAS) leveraging prediction models and a large-scale type 2 diabetes genome-wide association study (74,124 cases and 824,006 controls) to identify candidate genes in VAT and used summary-data-based MR (SMR) and co-localisation analysis to map causal genes. We performed enrichment and single-cell RNA-seq analyses to determine the cell-specific localisation of the TWAS-identified genes. We also conducted knockdown experiments in 3T3-L1 pre-adipocytes. Results: MR analyses showed a causal relationship between genetically increased VAT mass and type 2 diabetes (inverse-variance weighted OR 2.48 [95% CI 2.21, 2.79]). Ten VAT-specific candidate genes were associated with type 2 diabetes after Bonferroni correction, including five causal genes supported by SMR and co-localisation: PABPC4 (1p34.3); CCNE2 (8q22.1); HAUS6 (9p22.1); CWF19L1 (10q24.31); and CCDC92 (12q24.31). Combined with enrichment analyses, clarifying cell-type specificity with single-cell RNA-seq data indicated that most TWAS-identified candidate genes appear more likely to be associated with adipocytes in VAT. Knockdown experiments suggested that Pabpc4 likely contributes to regulating differentiation and energy metabolism in 3T3-L1 adipocytes. Conclusions/interpretation: Our findings provide new insights into the genetic basis and biological processes of the association between VAT accumulation and type 2 diabetes and warrant investigation through further functional studies to validate these VAT-specific candidate genes. [ABSTRACT FROM AUTHOR]

Background: More than 200 asthma-associated genetic variants have been identified in genome-wide association studies (GWASs). Expression quantitative trait loci (eQTL) data resources can help identify causal genes of the GWAS signals, but it can be difficult to find an eQTL that reflects the disease state because most eQTL data are obtained from normal healthy subjects. Methods: We performed a blood eQTL analysis using transcriptomic and genotypic data from 433 Korean asthma patients. To identify asthma-related genes, we carried out colocalization, Summary-based Mendelian Randomization (SMR) analysis, and Transcriptome-Wide Association Study (TWAS) using the results of asthma GWASs and eQTL data. In addition, we compared the results of disease eQTL data and asthma-related genes with two normal blood eQTL data from Genotype-Tissue Expression (GTEx) project and a Japanese study. Results: We identified 340,274 cis-eQTL and 2,875 eGenes from asthmatic eQTL analysis. We compared the disease eQTL results with GTEx and a Japanese study and found that 64.1% of the 2,875 eGenes overlapped with the GTEx eGenes and 39.0% with the Japanese eGenes. Following the integrated analysis of the asthmatic eQTL data with asthma GWASs, using colocalization and SMR methods, we identified 15 asthma-related genes specific to the Korean asthmatic eQTL data. Conclusions: We provided Korean asthmatic cis-eQTL data and identified asthma-related genes by integrating them with GWAS data. In addition, we suggested these asthma-related genes as therapeutic targets for asthma. We envisage that our findings will contribute to understanding the etiological mechanisms of asthma and provide novel therapeutic targets. [ABSTRACT FROM AUTHOR]

BackgroundThe etiology of frontotemporal dementia (FTD) is poorly understood. To identify genes with predicted expression levels associated with FTD, we integrated summary statistics with external reference gene expression data using a transcriptome-wide association study approach.MethodsFUSION software was used to leverage FTD summary statistics (all FTD: n = 2154 cases, n = 4308 controls; behavioral variant FTD: n = 1337 cases, n = 2754 controls; semantic dementia: n = 308 cases, n = 616 controls; progressive nonfluent aphasia: n = 269 cases, n = 538 controls; FTD with motor neuron disease: n = 200 cases, n = 400 controls) from the International FTD-Genomics Consortium with 53 expression quantitative loci tissue type panels (n = 12,205; 5 consortia). Significance was assessed using a 5% false discovery rate threshold.ResultsWe identified 73 significant gene-tissue associations for FTD, representing 44 unique genes in 34 tissue types. Most significant findings were derived from dorsolateral prefrontal cortex splicing data (n = 19 genes, 26%). The 17q21.31 inversion locus contained 23 significant associations, representing 6 unique genes. Other top hits included SEC22B (a gene involved in vesicle trafficking), TRGV5, and ZNF302. A single gene finding (RAB38) was observed for behavioral variant FTD. For other clinical subtypes, no significant associations were observed.ConclusionsWe identified novel candidate genes (e.g., SEC22B) and previously reported risk regions (e.g., 17q21.31) for FTD. Most significant associations were observed in dorsolateral prefrontal cortex splicing data despite the modest sample size of this reference panel. This suggests that our findings are specific to FTD and are likely to be biologically relevant highlights of genes at different FTD risk loci that are contributing to the disease pathology.

BackgroundHumans and viruses have co-evolved for millennia resulting in a complex host genetic architecture. Understanding the genetic mechanisms of immune response to viral infection provides insight into disease etiology and therapeutic opportunities.MethodsWe conducted a comprehensive study including genome-wide and transcriptome-wide association analyses to identify genetic loci associated with immunoglobulin G antibody response to 28 antigens for 16 viruses using serological data from 7924 European ancestry participants in the UK Biobank cohort.ResultsSignals in human leukocyte antigen (HLA) class II region dominated the landscape of viral antibody response, with 40 independent loci and 14 independent classical alleles, 7 of which exhibited pleiotropic effects across viral families. We identified specific amino acid (AA) residues that are associated with seroreactivity, the strongest associations presented in a range of AA positions within DRβ1 at positions 11, 13, 71, and 74 for Epstein-Barr virus (EBV), Varicella zoster virus (VZV), human herpesvirus 7, (HHV7), and Merkel cell polyomavirus (MCV). Genome-wide association analyses discovered 7 novel genetic loci outside the HLA associated with viral antibody response (P

Background: While previous genome-wide association studies (GWAS) have identified multiple risk variants for migraine, there is a lack of evidence about how these variants contribute to the development of migraine. We employed an integrative pipeline to efficiently transform genetic associations to identify causal genes for migraine. Methods: We conducted a proteome-wide association study (PWAS) by combining data from the migraine GWAS data with proteomic data from the human brain and plasma to identify proteins that may play a role in the risk of developing migraine. We also combined data from GWAS of migraine with a novel joint-tissue imputation (JTI) prediction model of 17 migraine-related human tissues to conduct transcriptome-wide association studies (TWAS) together with the fine mapping method FOCUS to identify disease-associated genes. Results: We identified 13 genes in the human brain and plasma proteome that modulate migraine risk by regulating protein abundance. In addition, 62 associated genes not reported in previous migraine TWAS studies were identified by our analysis of migraine using TWAS and fine mapping. Five genes including ICA1L, TREX1, STAT6, UFL1, and B3GNT8 showed significant associations with migraine at both the proteome and transcriptome, these genes are mainly expressed in ependymal cells, neurons, and glial cells, and are potential target genes for prevention of neuronal signaling and inflammatory responses in the pathogenesis of migraine. Conclusions: Our proteomic and transcriptome findings have identified disease-associated genes that may give new insights into the pathogenesis and potential therapeutic targets for migraine. [ABSTRACT FROM AUTHOR]

Prostate cancer (PCa) represents a huge public health burden among men. Many susceptibility genetic factors for PCa still remain unknown. In this study, we performed a large splicing transcriptome-wide association study (spTWAS) using three modeling strategies to develop alternative splicing genetic prediction models for identifying novel susceptibility loci and splicing introns for PCa risk by assessing 79,194 cases and 61,112 controls of European ancestry in the PRACTICAL, CRUK, CAPS, BPC3, and PEGASUS consortia. We identified 120 splicing introns of 97 genes showing an association with PCa risk at false discovery rate (FDR)-corrected threshold (FDR <0.05). Of them, 33 genes were enriched in PCa-related diseases and function categories. Fine-mapping analysis suggested that 21 splicing introns of 19 genes were likely causally associated with PCa risk. Thirty-five splicing introns of 34 novel genes were identified to be related to PCa susceptibility for the first time, and 11 of the genes were enriched in a cancer-related network. Our study identified novel loci and splicing introns associated with PCa risk, which can improve our understanding of the etiology of this common malignancy. [ABSTRACT FROM AUTHOR]

Genome-wide association studies (GWASs) have identified more than 200 genomic loci for breast cancer risk, but specific causal genes in most of these loci have not been identified. In fact, transcriptome-wide association studies (TWASs) of breast cancer performed using gene expression prediction models trained in breast tissue have yet to clearly identify most target genes. To identify candidate genes, we performed a GWAS analysis in a breast cancer dataset from UK Biobank (UKB) and combined the results with the GWAS results of the Breast Cancer Association Consortium (BCAC) by a meta-analysis. Using the summary statistics from the meta-analysis, we performed a joint TWAS analysis that combined TWAS signals from multiple tissues. We used expression prediction models trained in 11 tissues that are potentially relevant to breast cancer from the Genotype-Tissue Expression (GTEx) data. In the GWAS analysis, we identified eight loci distinct from those reported previously. In the TWAS analysis, we identified 309 genes at 108 genomic loci to be significantly associated with breast cancer at the Bonferroni threshold. Of these, 17 genes were located in eight regions that were at least 1 Mb away from published GWAS hits. The remaining TWAS-significant genes were located in 100 known genomic loci from previous GWASs of breast cancer. We found that 21 genes located in known GWAS loci remained statistically significant after conditioning on previous GWAS index variants. Our study provides insights into breast cancer genetics through mapping candidate target genes in a large proportion of known GWAS loci and discovering multiple new loci. Gao and colleagues identify eight loci of breast cancer susceptibility in a genome-wide association study. By leveraging gene expression data from multiple tissues, they also discover 309 breast cancer susceptibility genes in a transcriptome-wide association study. Known genetic risk variants could act on these genes to cause breast cancer. [ABSTRACT FROM AUTHOR]

Introduction:Humans and viruses have co-evolved for millennia resulting in a complex host genetic architecture. Understanding the genetic mechanisms of immune response to viral infection provides insight into disease etiology and informs public health interventions. Methods:We conducted a comprehensive study linking germline genetic variation and gene expression with antibody response to 28 antigens for 16 viruses using serological data from 7924 participants in the UK Biobank cohort. Using test results from 2010 UK Biobank subjects, we also investigated genetic determinants of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. Results:Signals in human leukocyte antigen (HLA) class II region dominated the landscape of viral antibody response, with 40 independent loci and 14 independent classical alleles, 7 of which exhibited pleiotropic effects across viral families. Genome-wide association analyses discovered 7 novel genetic loci associated with viral antibody response (P

AimsDegenerative cervical spondylosis (DCS) is a common musculoskeletal disease that encompasses a wide range of progressive degenerative changes and affects all components of the cervical spine. DCS imposes very large social and economic burdens. However, its genetic basis remains elusive.MethodsPredicted whole-blood and skeletal muscle gene expression and genome-wide association study (GWAS) data from a DCS database were integrated, and functional summary-based imputation (FUSION) software was used on the integrated data. A transcriptome-wide association study (TWAS) was conducted using FUSION software to assess the association between predicted gene expression and DCS risk. The TWAS-identified genes were verified via comparison with differentially expressed genes (DEGs) in DCS RNA expression profiles in the Gene Expression Omnibus (GEO) (Accession Number: GSE153761). The Functional Mapping and Annotation (FUMA) tool for genome-wide association studies and Meta tools were used for gene functional enrichment and annotation analysis.ResultsThe TWAS detected 420 DCS genes with p < 0.05 in skeletal muscle, such as ribosomal protein S15A (RPS15A) (PTWAS = 0.001), and 110 genes in whole blood, such as selectin L (SELL) (PTWAS = 0.001). Comparison with the DCS RNA expression profile identified 12 common genes, including Apelin Receptor (APLNR) (PTWAS = 0.001, PDEG = 0.025). In total, 148 DCS-enriched Gene Ontology (GO) terms were identified, such as mast cell degranulation (GO:0043303); 15 DCS-enriched Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were identified, such as the sphingolipid signalling pathway (ko04071). Nine terms, such as degradation of the extracellular matrix (R-HSA-1474228), were common to the TWAS enrichment results and the RNA expression profile.ConclusionOur results identify putative susceptibility genes; these findings provide new ideas for exploration of the genetic mechanism of DCS development and new targets for preclinical intervention and clinical treatment.Cite this article: Bone Joint Res 2023;12(1):80–90.

Background: Intraocular pressure (IOP) is a major modifiable risk factor for glaucoma. However, the mechanisms underlying the controlling of IOP remain to be elucidated. Objective: To prioritize genes that are pleiotropically associated with IOP. Methods: We adopted a two-sample Mendelian randomization method, named summary-based Mendelian randomization (SMR), to examine the pleiotropic effect of gene expression on IOP. The SMR analyses were based on summarized data from a genome-wide association study (GWAS) on IOP. We conducted separate SMR analyses using Genotype-Tissue Expression (GTEx) and Consortium for the Architecture of Gene Expression (CAGE) expression quantitative trait loci (eQTL) data. Additionally, we performed a transcriptome-wide association study (TWAS) to identify genes whose cis-regulated expression levels were associated with IOP. Results: We identified 19 and 25 genes showing pleiotropic association with IOP using the GTEx and CAGE eQTL data, respectively. RP11-259G18.3 (PSMR = 2.66 × 10−6), KANSL1-AS1 (PSMR = 2.78 × 10−6), and RP11-259G18.2 (PSMR = 2.91 × 10−6) were the top three genes using the GTEx eQTL data. LRRC37A4 (PSMR = 1.19 × 10−5), MGC57346 (PSMR = 1.19 × 10−5), and RNF167 (PSMR = 1.53 × 10−5) were the top three genes using the CAGE eQTL data. Most of the identified genes were found in or near the 17q21.31 genomic region. Additionally, our TWAS analysis identified 18 significant genes whose expression was associated with IOP. Of these, 12 and 4 were also identified by the SMR analysis using the GTEx and CAGE eQTL data, respectively. Conclusions: Our findings suggest that the 17q21.31 genomic region may play a critical role in the regulation of IOP. [ABSTRACT FROM AUTHOR]

How genome-wide associated loci confer risk for Parkinson's disease is unclear. We aim to reveal causal genes through effects on brain proteins to provide new pathogenesis insights for Parkinson's disease. Proteome-wide and transcriptome-wide associations were determined by functional summary-based imputation leveraging data from genome-wide association summary (56 306 Europeans, 1.4 million controls), brain proteomes (528 cases from 2 separate data sets), and transcriptome (452 cases), followed by Mendelian randomization, Bayesian colocalization, cell-type-specific and brain regional expression, and drug–gene interaction analyses. As a result, genetically regulated protein abundances of 11 genes were associated with Parkinson's disease. Five genes (CD38 , GPNMB , TMEM175 , RAB7L1 , and HIP1R) were colocalized. Four genes (GPNMB , SEC23IP , CD38 , and DGKQ) demonstrated Mendelian randomized correlations (p < 8.10 × 10−5). Higher GPNMB level (1.47, 1.28–1.68) and lower CD38 level (0.319, 0.24–0.43) were causally associated with higher risk of Parkinson's disease, consistent with transcriptomic evaluations. CD38 and GPNMB were preferentially enriched in astrocytes and oligodendrocyte precursor cells, respectively. And CD38 and GPNMB were suggested to be the targets of many oncological drugs from Drug–Gene Interaction database. In conclusion, utilizing multidimensional data, GPNMB and CD38 were prioritized as the causal genes of Parkinson's disease, crucial for mechanistic and therapeutic investigations. [ABSTRACT FROM AUTHOR]

Objective: We explore the candidate susceptibility genes for influenza A virus (IAV), measles, rubella, and mumps and their underlying biological mechanisms. Methods: We downloaded the genome-wide association study summary data of four virus-specific immunoglobulin G (IgG) level data sets (anti-IAV IgG, anti-measles IgG, anti-rubella IgG, and anti-mumps virus IgG levels) and integrated them with reference models of three potential tissues from the Genotype-Tissue Expression (GTEx) project, namely, whole blood, lung, and transformed fibroblast cells, to identify genes whose expression is predicted to be associated with IAV, measles, mumps, and rubella. Results: We identified 19 significant genes (ULK4, AC010132.11, SURF1, NIPAL2, TRAP1, TAF1C, AC000078.5, RP4-639F20.1, RMDN2, ATP1B3, SRSF12, RP11-477D19.2, TFB1M, XXyac-YX65C7_A.2, TAF1C, PCGF2, and BNIP1) associated with IAV at a Bonferroni-corrected threshold of p < 0.05; 14 significant genes (SOAT1, COLGALT2, AC021860.1, HCG11, METTL21B, MRPL10, GSTM4, PAQR6, RP11-617D20.1, SNX8, METTL21B, ANKRD27, CBWD2, and TSFM) associated with measles at a Bonferroni-corrected threshold of p < 0.05; 15 significant genes (MTOR, LAMC1, TRIM38, U91328.21, POLR2J, SCRN2, Smpd4, UBN1, CNTROB, SCRN2, HOXB-AS1, SLC14A1, AC007566.10, AC093668.2, and CPD) associated with mumps at a Bonferroni-corrected threshold of p < 0.05; and 13 significant genes (JAGN1, RRP12, RP11-452K12.7, CASP7, AP3S2, IL17RC, FAM86HP, AMACR, RRP12, PPP2R1B, C11orf1, DLAT, and TMEM117) associated with rubella at a Bonferroni-corrected threshold of p < 0.05. Conclusions: We have identified several candidate genes for IAV, measles, mumps, and rubella in multiple tissues. Our research may further our understanding of the pathogenesis of infectious respiratory diseases. [ABSTRACT FROM AUTHOR]

Alzheimer's disease (AD) is a common neurodegenerative disease in aging individuals. Alternative splicing is reported to be relevant to AD development while their roles in etiology of AD remain largely elusive. We performed a comprehensive splicing transcriptome-wide association study (spTWAS) using intronic excision expression genetic prediction models of 12 brain tissues developed through three modelling strategies, to identify candidate susceptibility splicing introns for AD risk. A total of 111,326 (46,828 proxy) cases and 677,663 controls of European ancestry were studied. We identified 343 associations of 233 splicing introns (143 genes) with AD risk after Bonferroni correction (0.05/136,884 = 3.65 × 10−7). Fine-mapping analyses supported 155 likely causal associations corresponding to 83 splicing introns of 55 genes. Eighteen causal splicing introns of 15 novel genes (EIF2D, WDR33, SAP130, BYSL, EPHB6, MRPL43, VEGFB, PPP1R13B, TLN2, CLUHP3, LRRC37A4P, CRHR1, LINC02210, ZNF45-AS1, and XPNPEP3) were identified for the first time to be related to AD susceptibility. Our study identified novel genes and splicing introns associated with AD risk, which can improve our understanding of the etiology of AD.

Summary: Background: The identification of new uses for existing drug therapies has the potential to identify treatments for comorbid conditions that have the added benefit of glycemic control while also providing a rapid, low-cost approach to drug (re)discovery. Methods: We developed and tested a genetically-informed drug-repurposing pipeline for diabetes management. This approach mapped genetically-predicted gene expression signals from the largest genome-wide association study for type 2 diabetes mellitus to drug targets using publicly available databases to identify drug–gene pairs. These drug–gene pairs were then validated using a two-step approach: 1) a self-controlled case-series (SCCS) using electronic health records from a discovery and replication population, and 2) Mendelian randomization (MR). Findings: After filtering on sample size, 20 candidate drug–gene pairs were validated and various medications demonstrated evidence of glycemic regulation including two anti-hypertensive classes: angiotensin-converting enzyme inhibitors as well as calcium channel blockers (CCBs). The CCBs demonstrated the strongest evidence of glycemic reduction in both validation approaches (SCCS HbA1c and glucose reduction: −0.11%, p = 0.01 and −0.85 mg/dL, p = 0.02, respectively; MR: OR = 0.84, 95% CI = 0.81, 0.87, p = 5.0 x 10–25). Interpretation: Our results support CCBs as a strong candidate medication for blood glucose reduction in addition to cardiovascular disease reduction. Further, these results support the adaptation of this approach for use in future drug-repurposing efforts for other conditions. Funding: National Institutes of Health, Medical Research Council Integrative Epidemiology Unit at the University of Bristol, UK Medical Research Council, American Heart Association, and Department of Veterans Affairs (VA) Informatics and Computing Infrastructure and VA Cooperative Studies Program.

Dupuytren's disease is a common inherited tissue-specific fibrotic disorder, characterized by progressive and irreversible fibroblastic proliferation affecting the palmar fascia of the hand. Although genome-wide association study (GWAS) have identified 24 genomic regions associated with Dupuytrens risk, the biological mechanisms driving signal at these regions remain elusive. We identify potential biological mechanisms for Dupuytren's disease by integrating the most recent, largest GWAS (3,871 cases and 4,686 controls) with eQTLs (47 tissue panels from five consortia, total n = 3,975) to perform a transcriptome-wide association study. We identify 43 tissue-specific gene associations with Dupuytren's risk, including one in a novel risk region. We also estimate the genome-wide genetic correlation between Dupuytren's disease and 45 complex traits and find significant genetic correlations between Dupuytren's disease and body mass index (BMI), type II diabetes, triglycerides, and high-density lipoprotein (HDL), suggesting a shared genetic etiology between these traits. We further examine local genetic correlation to identify 8 and 3 novel regions significantly correlated with BMI and HDL respectively. Our results are consistent with previous epidemiological findings showing that lower BMI increases risk for Dupuytren's disease. These 12 novel risk regions provide new insight into the biological mechanisms of Dupuytren's disease and serve as a starting point for functional validation.

Neuropathic pain is a leading cause of high-impact pain, is often disabling and is poorly managed by current therapeutics. Here we focused on a unique group of neuropathic pain patients undergoing thoracic vertebrectomy where the dorsal root ganglia is removed as part of the surgery allowing for molecular characterization and identification of mechanistic drivers of neuropathic pain independently of preclinical models. Our goal was to quantify whole transcriptome RNA abundances using RNA-seq in pain-associated human dorsal root ganglia from these patients, allowing comprehensive identification of molecular changes in these samples by contrasting them with non-pain-associated dorsal root ganglia. We sequenced 70 human dorsal root ganglia, and among these 50 met inclusion criteria for sufficient neuronal mRNA signal for downstream analysis. Our expression analysis revealed profound sex differences in differentially expressed genes including increase of IL1B , TNF , CXCL14 and OSM in male and CCL1 , CCL21 , PENK and TLR3 in female dorsal root ganglia associated with neuropathic pain. Coexpression modules revealed enrichment in members of JUN-FOS signalling in males and centromere protein coding genes in females. Neuro-immune signalling pathways revealed distinct cytokine signalling pathways associated with neuropathic pain in males (OSM , LIF , SOCS1) and females (CCL1 , CCL19 , CCL21). We validated cellular expression profiles of a subset of these findings using RNAscope in situ hybridization. Our findings give direct support for sex differences in underlying mechanisms of neuropathic pain in patient populations. [ABSTRACT FROM AUTHOR]

By combining data from 160,500 individuals with breast cancer and 226,196 controls of Asian and European ancestry, we conducted genome- and transcriptome-wide association studies of breast cancer. We identified 222 genetic risk loci and 137 genes that were associated with breast cancer risk at a p < 5.0 × 10−8 and a Bonferroni-corrected p < 4.6 × 10−6, respectively. Of them, 32 loci and 15 genes showed a significantly different association between ER-positive and ER-negative breast cancer after Bonferroni correction. Significant ancestral differences in risk variant allele frequencies and their association strengths with breast cancer risk were identified. Of the significant associations identified in this study, 17 loci and 14 genes are located 1Mb away from any of the previously reported breast cancer risk variants. Pathways analyses including 221 putative risk genes identified multiple signaling pathways that may play a significant role in the development of breast cancer. Our study provides a comprehensive understanding of and new biological insights into the genetics of this common malignancy. Using data from 386,000 Asian- and European-ancestry women, we conducted extensive genome- and transcriptome-wide association studies that identified 222 risk loci and 137 genes in association with breast cancer risk. These studies, along with pathway analyses, provide a comprehensive understanding of and new biological insights into the genetics of breast cancer. [ABSTRACT FROM AUTHOR]

Abstract Background Genome-wide association studies (GWAS) have revealed numerous loci associated with stroke. However, the underlying mechanisms at these loci in the pathogenesis of stroke and effective stroke drug targets are elusive. Therefore, we aimed to identify causal genes in the pathogenesis of stroke and its subtypes. Methods Utilizing multidimensional high-throughput data generated, we integrated proteome-wide association study (PWAS), transcriptome-wide association study (TWAS), Mendelian randomization (MR), and Bayesian colocalization analysis to prioritize genes that contribute to stroke and its subtypes risk via affecting their expression and protein abundance in brain and blood. Results Our integrative analysis revealed that ICA1L was associated with small-vessel stroke (SVS), according to robust evidence at both protein and transcriptional levels based on brain-derived data. We also identified NBEAL1 that was causally related to SVS via its cis-regulated brain expression level. In blood, we identified 5 genes (MMP12, SCARF1, ABO, F11, and CKAP2) that had causal relationships with stroke and stroke subtypes. Conclusions Together, via using an integrative analysis to deal with multidimensional data, we prioritized causal genes in the pathogenesis of SVS, which offered hints for future biological and therapeutic studies.

Bipolar disorder (BD) is a debilitating neuropsychiatric disorder, which is associated with genetic variation through "vast but mixed" Genome-Wide Association Studies (GWAS). Transcriptome-Wide Association Study (TWAS) is more effective in explaining genetic factors that influence complex diseases and can help identifying risk genes more reliably. So, this study aims to identify potential BD risk genes in pedigrees with TWAS. We conducted a TWAS analysis with expression quantitative trait loci (eQTL) analysis on extended BD pedigrees, and the BD genome-wide association study (GWAS) summary data acquired from the Psychiatric Genomics Consortium (PGC). Furthermore, the BD-associated genes identified by TWAS were validated by mRNA expression profiles from the Gene Expression Omnibus (GEO) Datasets (GSE23848 and GSE46416). Functional enrichment and annotation analysis were implemented by RStudio (version 4.2.0). TWAS identified 362 genes with P value < 0.05, and 18 genes remain significant after Bonferroni correction, such as SEMA3G (P TWAS =1.07 × 10-11), ALOX5AP (P TWAS =3.12 × 10-8), and PLEC (P TWAS =1.27 × 10-7). Further 6 overlapped genes were detected in integrative analysis, such as UQCRB (P TWAS =0.0020, P mRNA =0.0000), TMPRSS9 (P TWAS =0.0405, P mRNA =0.0032), and SNX10 (P TWAS =0.0104, P mRNA =0.0015). Using genes identified by TWAS, Gene Ontology (GO) enrichment analysis identified 40 significant GO terms, such as mitochondrial ATP synthesis coupled electron transport, mitochondrial respiratory, aerobic electron transport chain, oxidative phosphorylation, mitochondrial membrane proteins, and ubiquinone activity. The Kyoto Encyclopedia of Genes and Genomes (KEGG) Pathway enrichment analysis identified significant 15 pathways for BD, such as Oxidative phosphorylation, endocannabinoids signaling, neurodegeneration, and reactive oxide species. We found a set of BD-associated genes and pathways, validating the important role of neurodevelopmental abnormalities, inflammatory responses, and mitochondrial dysfunction in the pathology of BD, offering novel information for comprehending the genetic basis of BD. [ABSTRACT FROM AUTHOR]

Genome-wide association study (GWAS) of Juvenile idiopathic arthritis (JIA) suffers from low power due to limited sample size and the interpretation challenge due to most signals located in non-coding regions. Gene-level analysis could alleviate these issues. Using GWAS summary statistics, we performed two typical gene-level analysis of JIA, transcriptome-wide association studies (TWAS) using FUnctional Summary-based ImputatiON (FUSION) and gene-based analysis using eQTL Multi-marker Analysis of GenoMic Annotation (eMAGMA), followed by comprehensive enrichment analysis. Among 33 overlapped significant genes from these two methods, 11 were previously reported, including TYK2 (PFUSION = 5.12 × 10−6, PeMAGMA = 1.94 × 10−7 for whole blood), IL-6R (PFUSION = 8.63 × 10−7, PeMAGMA = 2.74 × 10−6 for cells EBV-transformed lymphocytes), and Fas (PFUSION = 5.21 × 10−5, PeMAGMA = 1.08 × 10−6 for muscle skeletal). Some newly plausible JIA-associated genes are also reported, including IL-27 (PFUSION = 2.10 × 10−7, PeMAGMA = 3.93 × 10−8 for Liver), LAT (PFUSION = 1.53 × 10−4, PeMAGMA = 4.62 × 10−7 for Artery Aorta), and MAGI3 (PFUSION = 1.30 × 10−5, PeMAGMA = 1.73 × 10−7 for Muscle Skeletal). Enrichment analysis further highlighted 4 Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways and 10 Gene Ontology (GO) terms. Our findings can benefit the understanding of genetic determinants and potential therapeutic targets for JIA. [ABSTRACT FROM AUTHOR]

Global genetic correlation analysis has provided valuable insight into the shared genetic basis between psychiatric and substance use disorders. However, little is known about which regions disproportionately contribute to the global correlation. We used Local Analysis of [co]Variant Annotation to calculate bivariate local genetic correlations across 2495 approximately equal-sized, semi-independent genomic regions for 20 psychiatric and substance use phenotypes. We performed a transcriptome-wide association study using expression weights from the prefrontal cortex to identify risk genes for each phenotype, followed by probabilistic fine-mapping to prioritize credible causal genes within each bivariate locus. We detected 80 significant (p < 2.08 × 10−6) bivariate local genetic correlations across 61 loci. The expression effect directions for risk genes within each bivariate locus were largely consistent with the local correlation coefficients, suggesting that genetically regulated gene expression may be used in the functional interpretation of local genetic correlations. Probabilistic fine-mapping identified several genes that may drive pleiotropic mechanisms for genetically correlated phenotypes. For example, we confirmed a local genetic correlation between schizophrenia and smoking behavior at 15q25 and prioritized PSMA4 as the most credible gene candidate underlying both phenotypes. Our study reveals previously unreported local bivariate genetic correlations between psychiatric and substance use phenotypes, which we fine-mapped to identify shared credible causal genes underlying genetically correlated phenotypes. [ABSTRACT FROM AUTHOR]

To obtain additional insight into the genetic factors of attention deficit hyperactivity disorder (ADHD). First, we performed a transcriptome-wide association study (TWAS) integrating human cerebellum-specific variant-expression/splicing correlations to identify ADHD susceptibility genes. Then, the associations between expression/splicing quantitative trait loci (eQTLs/sQTLs) of the transcriptome-wide significant genes and ADHD were observed in a case-control study of Han Chinese children. Furthermore, dual luciferase reporter gene assays were performed to validate the regulatory function of ADHD risk variants. Additionally, the transcription level of target genes in blood was detected by real-time quantitative polymerase chain reaction (RT-qPCR) assay. TWAS identified that the genetically regulated expression of MANBA in the cerebellum was significantly associated with ADHD risk. Furthermore, we observed a higher risk of ADHD and more severe clinical symptoms in subjects harbouring heterozygous (TC) or mutant homozygous (TT) genotypes of MANBA rs1054037 than CC carriers. The dual luciferase reporter gene assay revealed that the mutation of rs1054037(C > T) potentially upregulated MANBA expression by eliminating the binding site for hsa-miR-5591-3P. Finally, RT-qPCR showed that MANBA expression in blood samples of patients was significantly higher than that of controls. Taken together, these results suggest a role of MANBA in the development of ADHD. [ABSTRACT FROM AUTHOR]

Genome-wide association studies (GWAS) statistically assess the association between tens of millions of genetic variants in the whole genome and a phenotype of interest. Genome-wide association studies enable the elucidation of polygenic inheritance of cancer, in which myriad low-penetrance genetic variants collectively contribute to a substantial proportion of the heritable susceptibility. In addition to the robust genotype-phenotype associations provided by GWAS, combining GWAS data with functional genomic datasets or sophisticated statistical genetic methods unlocks deeper insights. Integrating genotype and molecular phenotyping data facilitates functional characterization of GWAS association signals through molecular quantitative trait loci mapping and transcriptome-wide association studies. Furthermore, aggregating genome-wide polygenic signals, including subthreshold associations, enables one to estimate genetic correlations across diverse phenotypes and helps in clinical risk predictions by evaluating polygenic risk scores. In this review, we begin by summarizing the rationale for GWAS of cancer, introduce recent methodological updates in the GWAS-derived downstream analyses, and demonstrate their applications to GWAS of cancers., (© 2024 The Author(s). Cancer Science published by John Wiley & Sons Australia, Ltd on behalf of Japanese Cancer Association.)

Background: Periodontitis arises from a multifaceted interplay of environmental variables and genetic susceptibility, where microbial infection plays an indispensable part. Epstein-Barr virus (EBV) exposure has long been considered associated with periodontitis activity; however, the causal relationship and genetic connection between them remain unknown., Methods: Within a life-course context, our study employed comprehensive Mendelian randomization (MR) methods, including univariable, multivariable, Bayesian model averaging, and reverse MR, to investigate the causal association between EBV exposure and periodontitis. Additionally, linkage disequilibrium score regression and colocalization analysis were utilized to assess the cross-trait genetic correlations, followed by transcriptome-wide association and enrichment analysis to discern the genetic-phenotypic biological profiles., Results: Heightened levels of EBV antibodies, particularly early antigen diffuses (which serve as indicators of early infection or reactivation), are associated with an increased risk of periodontitis (odds ratio [OR]: 1.27 [1.09-1.47], p = 6.05 × 10 -3 ) and demonstrate a significant genetic correlation (p = 4.11 × 10 -3 ). This pathogenesis may involve the high-confidence causal gene RNASEK located in 17p13.1. Genetically predicted early-life anti-EBV immunoglobulin G (IgG) levels are correlated to a reduced periodontitis risk (OR: 0.89 [0.82-0.97], p = 1.76 × 10 -3 )., Conclusions: The present study highlights the impact of life-course EBV exposure and its genetic hallmark on periodontitis, providing novel perspectives into the underlying pathogenesis and management strategies for EBV-related periodontitis. These findings underscore diverse clinical and public health implications, encompassing antiviral therapies, viral vaccination strategies, and tailored interventions for individualized periodontitis management. Further research is required to validate and expand upon our findings., Plain Language Summary: Periodontitis is a chronic inflammatory disease driven by interactions between microbial pathogens and the host immune system. While bacteria have traditionally been the focus of research, recent studies highlight the significance of virus-bacteria interactions, particularly the role of Epstein-Barr virus (EBV)-a herpesvirus infecting over 90% of the global population-in the development of periodontitis. However, the underlying causal and genetic mechanisms remain unclear. Our study employed genome-wide multi-omics approaches to investigate the link between EBV exposure and periodontitis. We found that recent EBV infection or reactivation increases the risk of periodontitis, whereas early-life exposure, possibly enabling immune resistance, may reduce it. Essential genes were identified as potential mediators, including CRTC3-AS1, HLA-DQA1, and RNASEK. These findings provide novel insights into the EBV-periodontitis connection. For example, viral testing and control could benefit patients unresponsive to standard bacterial treatments, and early viral exposure via vaccination might reduce the risk of periodontitis. Further clinical studies are required to elucidate these underlying mechanisms and the contribution of virus-bacteria interactions., (© 2024 American Academy of Periodontology.)

Studying the genetic basis of leaf wax composition and its correlation with leaf cuticular conductance (gc) is crucial for improving crop productivity. The leaf cuticle, which comprises a cutin matrix and various waxes, functions as an extracellular hydrophobic layer, protecting against water loss upon stomatal closure. To address the limited understanding of genes associated with the natural variation of adult leaf cuticular waxes and their connection to gc, we conducted statistical genetic analyses using leaf transcriptomic, metabolomic, and physiological data sets collected from a maize (Zea mays L.) panel of ∼300 inbred lines. Through a random forest analysis with 60 cuticular wax traits, it was shown that high molecular weight wax esters play an important role in predicting gc. Integrating results from genome-wide and transcriptome-wide studies (GWAS and TWAS) via a Fisher's combined test revealed 231 candidate genes detected by all three association tests. Among these, 11 genes exhibit known or predicted roles in cuticle-related processes. Throughout the genome, multiple hotspots consisting of GWAS signals for several traits from one or more wax classes were discovered, identifying four additional plausible candidate genes and providing insights into the genetic basis of correlated wax traits. Establishing a partially shared genetic architecture, we identified 35 genes for both gc and at least one wax trait, with four considered plausible candidates. Our study enhances the understanding of how adult leaf cuticle wax composition relates to gc and implicates both known and novel candidate genes as potential targets for optimizing productivity in maize., (© The Author(s) 2024. Published by Oxford University Press on behalf of The Genetics Society of America.)