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Objectives: Systemic lupus erythematosus (SLE) is a heterogeneous autoimmune disease with an unsatisfactory state of treatment. We aim to explore novel targets for SLE from a genetic standpoint. Methods: Cis-expression quantitative trait loci (eQTLs) for whole blood from 31,684 samples provided by the eQTLGen Consortium as well as two large SLE cohorts were utilized for screening and validating genes causally associated with SLE. Colocalization analysis was employed to further investigate whether changes in the expression of risk genes, as indicated by GWAS signals, influence the occurrence and development of SLE. Targets identified for drug development were evaluated for potential side effects using a phenome-wide association study (PheWAS). Based on the multiple databases, we explored the interactions between drugs and genes for drug prediction and the assessment of current medications. Results: The analysis comprised 5427 druggable genes in total. The two-sample Mendelian randomization (MR) in the discovery phase identified 20 genes causally associated with SLE and validated 8 genes in the replication phase. Colocalization analysis ultimately identified five genes (BLK, HIST1H3H, HSPA1A, IL12A, NEU1) with PPH4 > 0.8. PheWAS further indicated that drugs acting on BLK and IL12A are less likely to have potential side effects, while HSPA1A and NEU1 were associated with other traits. Four genes (BLK, HSPA1A, IL12A, NEU1) have been targeted for drug development in autoimmune diseases and other conditions. Conclusions:.This study identified five genes as therapeutic targets for SLE. Repurposing and developing drugs targeting these genes is anticipated to improve the existing treatment state for SLE. Key Points • We identified five gene targets of priority for the treatment of SLE, with BLK and IL12A indicating fewer side effects. • Among the existing drugs that target these candidate genes, Ustekinumab, Ebdarokimab, and Briakinumab (targeting the IL12 gene) and CD24FC (targeting HSPA1A) may potentially be repurposed for the treatment of SLE. [ABSTRACT FROM AUTHOR]

Phantom tooth pain (PTP) is one type of non-odontogenic neuropathic toothache, which rarely occurs after appropriate pulpectomy or tooth extraction. The cause of PTP is unknown. We investigated pain-related genetic factors that are associated with PTP. Four pain-associated genes, including G protein-coupled receptor 158 (GPR158) and phosphoribosyl transferase domain containing 1 (PRTFDC1), are adjacent to each other on the human genome. Some of these four genes or their genomic region may be related to PTP. We statistically analyzed associations between single-nucleotide polymorphisms (SNPs) in the genomic region and PTP in patients with PTP (PTP group), other orofacial pain (OFP group), and healthy control subjects. We then performed a database search of expression quantitative trait loci (eQTLs). For the seven SNPs that were significantly associated with PTP even after Bonferroni correction, we focused on the rs12411980 tag SNP (p = 9.42 × 10−4). Statistical analyses of the PTP group and healthy subject groups (group labels: NOC and TD) revealed that the rate of the GG genotype of the rs12411980 SNP was significantly higher in the PTP group than in the healthy subject groups (PTP group vs. NOC group: p = 2.92 × 10−4, PTP group vs. TD group: p = 5.46 × 10−4; percentage of GG: 30% in PTP group, 12% in NOC group, 11% in TD group). These results suggest that the GG genotype of the rs12411980 SNP is more susceptible to PTP. The rs2765697 SNP that is in strong linkage disequilibrium with the rs12411980 SNP is an eQTL that is associated with higher PRTFDC1 expression in the minor allele homozygotes in the healthy subject groups of the rs2765697 SNP. Thus, PRTFDC1 expression similarly increases in the minor allele homozygotes (GG genotype) in the healthy subject groups of the rs12411980 SNP, which would lead to greater susceptibility to PTP. [ABSTRACT FROM AUTHOR]

Summary Our study utilized genome‐wide association studies (GWAS) to link nucleotide variants to traits in Populus trichocarpa, a species with rapid linkage disequilibrium decay. The aim was to overcome the challenge of interpreting statistical associations at individual loci without sufficient biological context, which often leads to reliance solely on gene annotations from unrelated model organisms. We employed an integrative approach that included GWAS targeting multiple traits using three individual techniques for lignocellulose phenotyping, expression quantitative trait loci (eQTL) analysis to construct transcriptional regulatory networks around each candidate locus and co‐expression analysis to provide biological context for these networks, using lignocellulose biosynthesis in Populus trichocarpa as a case study. The research identified three candidate genes potentially involved in lignocellulose formation, including one previously recognized gene (Potri.005G116800/VND1, a critical regulator of secondary cell wall formation) and two genes (Potri.012G130000/AtSAP9 and Potri.004G202900/BIC1) with newly identified putative roles in lignocellulose biosynthesis. Our integrative approach offers a framework for providing biological context to loci associated with trait variation, facilitating the discovery of new genes and regulatory networks. [ABSTRACT FROM AUTHOR]

Background: This study aims to prioritize genes potentially involved in multifactorial or causal relationships with gout. Methods: Using the Summary Data-based Mendelian Randomization (SMR) approach, this research analyzed expression quantitative trait loci (eQTL) data from blood and renal tissues and genome-wide association study (GWAS) data related to gout. It sought to identify genetic loci potentially involved in gout. Heterogeneity testing was conducted with the HEIDI test, and results were adjusted for the False Discovery Rate (FDR). Blood cis-eQTL data were sourced from the eQTLGen Consortium's summary-level data, and renal tissue data came from the V8 release of the GTEx eQTL summary data. Gout GWAS data was sourced from the FinnGen Documentation of the R10 release. Result: SMR analysis identified 14 gene probes in the eQTLGen blood summarylevel data significantly associated with gout. The top five ranked genes are: ENSG00000169231 (labeled THBS3, PSMR = 4.16 x 10--13), ENSG00000231064 (labeled THBS3-AS1, PSMR = 1.88 x 10--8), ENSG00000163463 (labeled KRTCAP2, PSMR = 3.88 x 10--6), ENSG00000172977 (labeled KAT5, PSMR = 1.70 x 10--5), and ENSG00000161395 (labeled PGAP3, PSMR = 3.24 x 10--5). Notably, increased expression of KRTCAP2 and PGAP3 is associated with an increased risk of gout, whereas increased expression of THBS3, THBS3-AS1, and KAT5 is associated with a reduced gout risk. No significant gene associations with gout were observed in renal tissue, likely due to the limited sample size of kidney tissue. Conclusion: Our findings have highlighted several genes potentially involved in the pathogenesis of gout. These results offer valuable insights into the mechanisms of gout and identify potential therapeutic targets for its treatment. [ABSTRACT FROM AUTHOR]

Background: Preeclampsia (PE) is a global pregnancy concern, characterized by hypertension with an unclear etiology. This study employs Mendelian randomization (MR) and single-cell RNA sequencing (scRNA-seq) to clarify its genetic and molecular roots, offering insights into diagnosis and treatment avenues. Methods: We integrated PE-specific genome-wide association study (GWAS) data, expression and protein quantitative trait loci (eQTL and pQTL) data, and single-cell data from peripheral blood mononuclear cells (PBMCs). We identified highly variable genes using single-cell information and employed MR to determine potential causality. We also combined pQTL and GWAS data, discerned genes positively associated with PE through scRNA-seq, and leveraged the Enrichr platform to unearth drug-gene interactions. Results: Our scRNA-seq pinpointed notable cell type distribution variances, especially in T helper cells (Th cells), between PE and control groups. We unveiled 591 highly variable genes and 6 directly PE-associated genes. Although MR revealed correlations with PE risk, pQTL analysis was inconclusive due to data constraints. Using DSigDB, 93 potential therapeutic agents, like Retinoic acid targeting core genes (IFITM3, NINJ1, COTL1, CD69, and YWHAZ), emerged as prospective multi-target treatments. Conclusion: Utilizing MR and scRNA-seq, this study underscores significant cellular disparities, particularly in Th cells, and identifies crucial genes related to PE. Despite some limitations, these genes have been revealed in PE's underlying mechanism. Potential therapeutic agents, such as Retinoic acid, suggest promising treatment pathways. [ABSTRACT FROM AUTHOR]

Accumulative evidence indicates a critical role of mitochondrial function in autism spectrum disorders (ASD), implying that ASD risk may be linked to mitochondrial dysfunction due to DNA (mtDNA) variations. Although a few studies have explored the association between mtDNA variations and ASD, the role of mtDNA in ASD is still unclear. Here, we aimed to investigate whether mitochondrial DNA haplogroups are associated with the risk of ASD. Two European cohorts and an Ashkenazi Jewish (AJ) cohort were analyzed, including 2,062 ASD patients in comparison with 4,632 healthy controls. DNA samples were genotyped using Illumina HumanHap550/610 and Illumina 1M arrays, inclusive of mitochondrial markers. Mitochondrial DNA (mtDNA) haplogroups were identified from genotyping data using HaploGrep2. A mitochondrial genome imputation pipeline was established to detect mtDNA variants. We conducted a case-control study to investigate potential associations of mtDNA haplogroups and variants with the susceptibility to ASD. We observed that the ancient adaptive mtDNA haplogroup K was significantly associated with decreased risk of ASD by the investigation of 2 European cohorts including a total of 2,006 cases and 4,435 controls (odds ratio = 0.64, P= 1.79 × 10–5), and we replicated this association in an Ashkenazi Jewish (AJ) cohort including 56 cases and 197 controls (odds ratio = 0.35, P = 9.46 × 10–3). Moreover, we demonstrate that the mtDNA variants rs28358571, rs28358584, and rs28358280 are significantly associated with ASD risk. Further expression quantitative trait loci (eQTLs) analysis indicated that the rs28358584 and rs28358280 genotypes are associated with expression levels of nearby genes in brain tissues, suggesting those mtDNA variants may confer risk for ASD via regulation of expression levels of genes encoded by the mitochondrial genome. This study helps to shed light on the contribution of mitochondria in ASD and provides new insights into the genetic mechanism underlying ASD, suggesting the potential involvement of mtDNA-encoded proteins in the development of ASD. Increasing evidence indicates that mitochondrial dysfunction may be linked to autism spectrum disorder (ASD). This study investigated potential associations of mitochondrial DNA (mtDNA) variants in 2 European and Ashkenazi Jewish cohorts including 2,062 individuals with ASD and 4,632 healthy controls. Researchers found that the ancient mtDNA haplogroup K was linked to a reduced risk of ASD in both European and Ashkenazi Jewish populations. Additionally, specific mtDNA variants were associated with ASD risk and were shown to influence the expression of nearby genes in the brain. These findings highlight the potential involvement of mtDNA in ASD development, offering new insights into the genetic mechanisms underlying the disorder. [ABSTRACT FROM AUTHOR]

Transcriptome-wide association studies (TWAS) goal is to better understand the etiology of diseases and develop preventative and therapeutic approaches by examining the connections between genetic variants and phenotypes while overcoming the limitations of the genome-wide association study (GWAS). It is a valuable complement to GWAS, reducing the negative effects of multiple tests and enabling a more thorough investigation of gene expression patterns in various tissues. A systematic review is presented in this paper to identify articles that utilize TWAS to understand the genetic factors behind complex diseases. A detailed selection process was carried out using standard PRISMA criteria to select relevant articles for the review. Twenty-five articles passed the inclusion criteria and were selected for additional review. The studies cover a diverse range of disorders, including Tourette's syndrome, Alzheimer's disease, rheumatoid arthritis, and major depression. Leveraging gene expression data from different tissues and populations, these investigations successfully identified novel genes and pathways associated with the studied conditions. The collective findings highlight the transformative impact of integrative genomics in advancing our understanding of complex diseases, providing insights into potential therapeutic targets, and laying the foundation for precision medicine approaches. [ABSTRACT FROM AUTHOR]

Background: Alzheimer's disease (AD) is the highest risk of COVID‐19 infection, hospitalization, and mortality. However, it remains largely unclear about the link between AD and COVID‐19 outcomes. ACE2 is an entry receptor for SARS‐CoV‐2. Circulating ACE2 is a novel biomarker of death and associated with COVID‐19 outcomes. Methods: Here, we explored the shared genetics and causal association between AD and plasma ACE2 levels using large‐scale genome‐wide association study, gene expression, expression quantitative trait loci, and high‐throughput plasma proteomic profiling datasets. Results: We found a significant causal effect of genetically increased circulating ACE2 on increased risk of AD. Cross‐trait association analysis identified 19 shared genetic variants, and three variants rs3104412, rs2395166, and rs3135344 at chromosome 6p21.32 were associated with COVID‐19 infection, hospitalization, and severity. We mapped 19 variants to 117 genes, which were significantly upregulated in lung, spleen, and small intestine, downregulated in brain tissues, and involved in immune system, immune disease, and infectious disease pathways. The plasma proteins corresponding to LST1, AGER, TNXB, and APOC1 were predominantly associated with COVID‐19 infection, ventilation, and death. Conclusion: Together, our findings suggest the shared genetics and causal association between AD and plasma ACE2 levels, which may partially explain the link between AD and COVID‐19. [ABSTRACT FROM AUTHOR]

Abstract Transcriptome-wide association studies (TWAS) goal is to better understand the etiology of diseases and develop preventative and therapeutic approaches by examining the connections between genetic variants and phenotypes while overcoming the limitations of the genome-wide association study (GWAS). It is a valuable complement to GWAS, reducing the negative effects of multiple tests and enabling a more thorough investigation of gene expression patterns in various tissues. A systematic review is presented in this paper to identify articles that utilize TWAS to understand the genetic factors behind complex diseases. A detailed selection process was carried out using standard PRISMA criteria to select relevant articles for the review. Twenty-five articles passed the inclusion criteria and were selected for additional review. The studies cover a diverse range of disorders, including Tourette’s syndrome, Alzheimer’s disease, rheumatoid arthritis, and major depression. Leveraging gene expression data from different tissues and populations, these investigations successfully identified novel genes and pathways associated with the studied conditions. The collective findings highlight the transformative impact of integrative genomics in advancing our understanding of complex diseases, providing insights into potential therapeutic targets, and laying the foundation for precision medicine approaches.

Tissue-specific gene expression and gene regulation lead to a better understanding of tissue-specific physiology and pathophysiology. We analyzed the transcriptome and genetic regulatory profiles of two distinct gastric sites, corpus and antrum, to identify tissue-specific gene expression and its regulation. Gastric corpus and antrum mucosa biopsies were collected during routine gastroscopies from up to 431 healthy individuals. We obtained genotype and transcriptome data and performed transcriptome profiling and expression quantitative trait locus (eQTL) studies. We further used data from genome-wide association studies (GWAS) of various diseases and traits to partition their heritability and to perform transcriptome-wide association studies (TWAS). The transcriptome data from corpus and antral mucosa highlights the heterogeneity of gene expression in the stomach. We identified enriched pathways revealing distinct and common physiological processes in gastric corpus and antrum. Furthermore, we found an enrichment of the single nucleotide polymorphism (SNP)-based heritability of metabolic, obesity-related, and cardiovascular traits and diseases by considering corpus- and antrum-specifically expressed genes. Particularly, we could prioritize gastric-specific candidate genes for multiple metabolic traits, like NQO1 which is involved in glucose metabolism, MUC1 which contributes to purine and protein metabolism or RAB27B being a regulator of weight and body composition. Our findings show that gastric corpus and antrum vary in their transcriptome and genetic regulatory profiles indicating physiological differences which are mostly related to digestion and epithelial protection. Moreover, our findings demonstrate that the genetic regulation of the gastric transcriptome is linked to biological mechanisms associated with metabolic, obesity-related, and cardiovascular traits and diseases. [ABSTRACT FROM AUTHOR]

Introduction: Peripheral vascular atherosclerosis (PVA) is a chronic inflammatory disease characterized by lipid accumulation in blood vessel walls, leading to vessel narrowing and inadequate blood supply. However, the molecular mechanisms underlying PVA remain poorly understood. In this study, we employed a combination of Mendelian randomization (MR) analysis and integrated transcriptomics to identify the critical gene signature associated with PVA. Methods: This study utilized three public datasets (GSE43292, GSE100927 and GSE28829) related to peripheral vascular atherosclerosis obtained from the Gene Expression Omnibus database. Instrumental variables (IVs) were identified through expression quantitative trait loci (eQTL) analysis, and two-sample MR analysis was performed using publicly available summary statistics. Disease critical genes were identified based on odds ratios and intersected with differentially expressed genes in the disease dataset. GSE28829 dataset was used to validate the screened disease critical genes. Functional enrichment analysis, GSEA analysis, and immune cell infiltration analysis were performed to further characterize the role of these genes in peripheral vascular atherosclerosis. Results: A total of 26,152 gene-related SNPs were identified as IVs, and 242 disease-associated genes were identified through MR analysis. Ten disease critical genes (ARHGAP25, HCLS1, HVCN1, RBM47, LILRB1, PLAU, IFI44L, IL1B, IFI6, and CFL2) were significantly associated with peripheral vascular atherosclerosis. Functional enrichment analysis using KEGG pathways revealed enrichment in the NF-kappa B signaling pathway and osteoclast differentiation. Gene set enrichment analysis further demonstrated functional enrichment of these genes in processes related to vascular functions and immune system activation. Additionally, immune cell infiltration analysis showed differential ratios of B cells and mast cells between the disease and control groups. The correlations analysis highlights the intricate interplay between disease critical genes and immune cells associated with PVA. Conclusion: In conclusion, this study provides new insights into the molecular mechanisms underlying PVA by identifying ten disease critical genes associated with the disease. These findings, supported by differential expression, functional enrichment, and immune system involvement, emphasize the role of these genes in vascular function and immune cell interactions in the context of PVA. These findings contribute to a better understanding of PVA pathogenesis and offer potential targets for further mechanistic exploration and therapeutic interventions. [ABSTRACT FROM AUTHOR]

For sessile plants, gene expression plays a pivotal role in responding to salinity stress by activating or suppressing specific genes. However, our knowledge of genetic variations governing gene expression in response to salt stress remains limited in natural germplasm. Through transcriptome analysis of the Global Mini-Core Rice Collection consisting of a panel of 202 accessions, we identified 22   345 and 27   610 expression quantitative trait loci associated with the expression of 7787 and 9361 eGenes under normal and salt-stress conditions, respectively, leveraging the super pan-genome map. Notably, combined with genome-wide association studies, we swiftly pinpointed the potential candidate gene STG5 —a major salt-tolerant locus known as qSTS5. Intriguingly, STG5 is required for maintaining Na+/K+ homeostasis by directly regulating the transcription of multiple members of the OsHKT gene family. Our study sheds light on how genetic variants influence the dynamic changes in gene expression responding to salinity stress and provides a valuable resource for the mining of salt-tolerant genes in the future. [ABSTRACT FROM AUTHOR]

Introduction: Growing evidence highlights a potential genetic overlap between Alzheimer's disease (AD) and Parkinson's disease (PD); however, the role of the PD risk variant rs6430538 in AD remains unclear. Methods: In Stage 1, we investigated the risk associated with the rs6430538C allele in seven large-scale AD genome-wide association study (GWAS) cohorts. In Stage 2, we performed expression quantitative trait loci (eQTL) analysis to calculate the cis-regulated eFFect of rs6430538 on TMEM163 in both AD and neuropathologically normal samples. Stage 3 involved evaluating the differential expression of TMEM163 in 4 brain tissues from AD cases and controls. Finally, in Stage 4, we conducted a transcriptome-wide association study (TWAS) to identify any association between TMEM163 expression and AD. Results: The results showed that genetic variant rs6430538C allele might increase the risk of AD. eQTL analysis revealed that rs6430538 up-regulated TMEM163 expression in AD brain tissue, but down-regulated its expression in normal samples. Interestingly, TMEM163 showed differential expression in entorhinal cortex (EC) and temporal cortex (TCX). Furthermore, the TWAS analysis indicated strong associations between TMEM163 and AD in various tissues. Discussion: In summary, our findings suggest that rs6430538 may influence AD by regulating TMEM163 expression. These discoveries may open up new opportunities for therapeutic strategies targeting AD. [ABSTRACT FROM AUTHOR]

BackgroundAtrial fibrillation (AF) is a common persistent arrhythmia characterized by rapid and chaotic atrial electrical activity, potentially leading to severe complications such as thromboembolism, heart failure, and stroke, significantly affecting patient quality of life and safety. As the global population ages, the prevalence of AF is on the rise, placing considerable strains on individuals and healthcare systems. This study utilizes bioinformatics and Mendelian Randomization (MR) to analyze transcriptome data and genome-wide association study (GWAS) summary statistics, aiming to identify biomarkers causally associated with AF and explore their potential pathogenic pathways.MethodsWe obtained AF microarray datasets GSE41177 and GSE79768 from the Gene Expression Omnibus (GEO) database, merged them, and corrected for batch effects to pinpoint differentially expressed genes (DEGs). We gathered exposure data from expression quantitative trait loci (eQTL) and outcome data from AF GWAS through the IEU Open GWAS database. We employed inverse variance weighting (IVW), MR-Egger, weighted median, and weighted model approaches for MR analysis to assess exposure-outcome causality. IVW was the primary method, supplemented by other techniques. The robustness of our results was evaluated using Cochran's Q test, MR-Egger intercept, MR-PRESSO, and leave-one-out sensitivity analysis. A “Veen” diagram visualized the overlap of DEGs with significant eQTL genes from MR analysis, referred to as common genes (CGs). Additional analyses, including Gene Ontology (GO) enrichment, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways, and immune cell infiltration studies, were conducted on these intersecting genes to reveal their roles in AF pathogenesis.ResultsThe combined dataset revealed 355 differentially expressed genes (DEGs), with 228 showing significant upregulation and 127 downregulated. Mendelian randomization (MR) analysis identified that the autocrine motility factor receptor (AMFR) [IVW: OR = 0.977; 95% CI, 0.956–0.998; P = 0.030], leucine aminopeptidase 3 (LAP3) [IVW: OR = 0.967; 95% CI, 0.934–0.997; P = 0.048], Rab acceptor 1 (RABAC1) [IVW: OR = 0.928; 95% CI, 0.875–0.985; P = 0.015], and tryptase beta 2 (TPSB2) [IVW: OR = 0.971; 95% CI, 0.943–0.999; P = 0.049] are associated with a reduced risk of atrial fibrillation (AF). Conversely, GTPase-activating SH3 domain-binding protein 2 (G3BP2) [IVW: OR = 1.030; 95% CI, 1.004–1.056; P = 0.024], integrin subunit beta 2 (ITGB2) [IVW: OR = 1.050; 95% CI, 1.017–1.084; P = 0.003], glutaminyl-peptide cyclotransferase (QPCT) [IVW: OR = 1.080; 95% CI, 1.010–0.997; P = 1.154], and tripartite motif containing 22 (TRIM22) [IVW: OR = 1.048; 95% CI, 1.003–1.095; P = 0.035] are positively associated with AF risk. Sensitivity analyses indicated a lack of heterogeneity or horizontal pleiotropy (P > 0.05), and leave-one-out analysis did not reveal any single nucleotide polymorphisms (SNPs) impacting the MR results significantly. GO and KEGG analyses showed that CG is involved in processes such as protein polyubiquitination, neutrophil degranulation, specific and tertiary granule formation, protein-macromolecule adaptor activity, molecular adaptor activity, and the SREBP signaling pathway, all significantly enriched. The analysis of immune cell infiltration demonstrated associations of CG with various immune cells, including plasma cells, CD8T cells, resting memory CD4T cells, regulatory T cells (Tregs), gamma delta T cells, activated NK cells, activated mast cells, and neutrophils.ConclusionBy integrating bioinformatics and MR approaches, genes such as AMFR, G3BP2, ITGB2, LAP3, QPCT, RABAC1, TPSB2, and TRIM22 are identified as causally linked to AF, enhancing our understanding of its molecular foundations. This strategy may facilitate the development of more precise biomarkers and therapeutic targets for AF diagnosis and treatment.

Abstract Background The term eGene has been applied to define a gene whose expression level is affected by at least one independent expression quantitative trait locus (eQTL). It is both theoretically and empirically important to identify eQTLs and eGenes in genomic studies. However, standard eGene detection methods generally focus on individual cis-variants and cannot efficiently leverage useful knowledge acquired from auxiliary samples into target studies. Methods We propose a multilocus-based eGene identification method called TLegene by integrating shared genetic similarity information available from auxiliary studies under the statistical framework of transfer learning. We apply TLegene to eGene identification in ten TCGA cancers which have an explicit relevant tissue in the GTEx project, and learn genetic effect of variant in TCGA from GTEx. We also adopt TLegene to the Geuvadis project to evaluate its usefulness in non-cancer studies. Results We observed substantial genetic effect correlation of cis-variants between TCGA and GTEx for a larger number of genes. Furthermore, consistent with the results of our simulations, we found that TLegene was more powerful than existing methods and thus identified 169 distinct candidate eGenes, which was much larger than the approach that did not consider knowledge transfer across target and auxiliary studies. Previous studies and functional enrichment analyses provided empirical evidence supporting the associations of discovered eGenes, and it also showed evidence of allelic heterogeneity of gene expression. Furthermore, TLegene identified more eGenes in Geuvadis and revealed that these eGenes were mainly enriched in cells EBV transformed lymphocytes tissue. Conclusion Overall, TLegene represents a flexible and powerful statistical method for eGene identification through transfer learning of genetic similarity shared across auxiliary and target studies.

Abstract Background Mapping expression quantitative trait loci (eQTLs) in skeletal muscle tissue in pigs is crucial for understanding the relationship between genetic variation and phenotypic expression of carcass traits in meat animals. Therefore, the primary objective of this study was to evaluate the impact of different sets of single nucleotide polymorphisms (SNP), including scenarios removing SNPs pruned for linkage disequilibrium (LD) and SNPs derived from SNP chip arrays and RNA-seq data from liver, brain, and skeletal muscle tissues, on the identification of eQTLs in the Longissimus lumborum tissue, associated with carcass and body composition traits in Large White pigs. The SNPs identified from muscle mRNA were combined with SNPs identified in the brain and liver tissue transcriptomes, as well as SNPs from the GGP Porcine 50 K SNP chip array. Cis- and trans-eQTLs were identified based on the skeletal muscle gene expression level, followed by functional genomic analyses and statistical associations with carcass and body composition traits in Large White pigs. Results The number of cis- and trans-eQTLs identified across different sets of SNPs (scenarios) ranged from 261 to 2,539 and from 29 to 13,721, respectively. Furthermore, 6,180 genes were modulated by eQTLs in at least one of the scenarios evaluated. The eQTLs identified were not significantly associated with carcass and body composition traits but were significantly enriched for many traits in the “Meat and Carcass” type QTL. The scenarios with the highest number of cis- (n = 304) and trans- (n = 5,993) modulated genes were the unpruned and LD-pruned SNP set scenarios identified from the muscle transcriptome. These genes include 84 transcription factor coding genes. Conclusions After LD pruning, the set of SNPs identified based on the transcriptome of the skeletal muscle tissue of pigs resulted in the highest number of genes modulated by eQTLs. Most eQTLs are of the trans type and are associated with genes influencing complex traits in pigs, such as transcription factors and enhancers. Furthermore, the incorporation of SNPs from other genomic regions to the set of SNPs identified in the porcine skeletal muscle transcriptome contributed to the identification of eQTLs that had not been identified based on the porcine skeletal muscle transcriptome alone.

Introduction: The heterogeneity of outcomes after Mycobacterium tuberculosis (Mtb) exposure is a conundrum associated with millennia of host-pathogen coevolution. We hypothesized that human myeloid cells contain genetically encoded, Mtb-specific responses that regulate critical steps in tuberculosis (TB) pathogenesis. Methods: We mapped genome-wide expression quantitative trait loci (eQTLs) in Mtb-infected monocytes with RNAseq from 80 Ugandan household contacts of pulmonary TB cases to identify monocyte-specific, Mtb-dependent eQTLs and their association with cytokine expression and clinical resistance to tuberculin skin test (TST) and interferon-g release assay (IGRA) conversion. Results: cis-eQTLs (n=1,567) were identified in Mtb-infected monocytes (FDR<0.01), including 29 eQTLs in 16 genes which were Mtb-dependent (significant for Mtb:genotype interaction [FDR<0.1], but not classified as eQTL in uninfected condition [FDR=0.01]). A subset of eQTLs were associated with Mtbinduced cytokine expression (n=8) and/or clinical resistance to TST/IGRA conversion (n=1). Expression of BMP6, an Mtb-dependent eQTL gene, was associated with IFNB1 induction in Mtb-infected and DNA ligand-induced cells. Network and enrichment analyses identified fatty acid metabolism as a pathway associated with eQTL genes. Discussion: These findings suggest that monocyte genes contain Mtb-dependent eQTLs, including a subset associated with cytokine expression and/or clinical resistance to TST/IGRA conversion, providing insight into immunogenetic pathways regulating susceptibility to Mtb infection and TB pathogenesis. [ABSTRACT FROM AUTHOR]

Background: The term eGene has been applied to define a gene whose expression level is affected by at least one independent expression quantitative trait locus (eQTL). It is both theoretically and empirically important to identify eQTLs and eGenes in genomic studies. However, standard eGene detection methods generally focus on individual cis-variants and cannot efficiently leverage useful knowledge acquired from auxiliary samples into target studies. Methods: We propose a multilocus-based eGene identification method called TLegene by integrating shared genetic similarity information available from auxiliary studies under the statistical framework of transfer learning. We apply TLegene to eGene identification in ten TCGA cancers which have an explicit relevant tissue in the GTEx project, and learn genetic effect of variant in TCGA from GTEx. We also adopt TLegene to the Geuvadis project to evaluate its usefulness in non-cancer studies. Results: We observed substantial genetic effect correlation of cis-variants between TCGA and GTEx for a larger number of genes. Furthermore, consistent with the results of our simulations, we found that TLegene was more powerful than existing methods and thus identified 169 distinct candidate eGenes, which was much larger than the approach that did not consider knowledge transfer across target and auxiliary studies. Previous studies and functional enrichment analyses provided empirical evidence supporting the associations of discovered eGenes, and it also showed evidence of allelic heterogeneity of gene expression. Furthermore, TLegene identified more eGenes in Geuvadis and revealed that these eGenes were mainly enriched in cells EBV transformed lymphocytes tissue. Conclusion: Overall, TLegene represents a flexible and powerful statistical method for eGene identification through transfer learning of genetic similarity shared across auxiliary and target studies. [ABSTRACT FROM AUTHOR]

Transposable elements (TEs) are repetitive elements which make up around 45% of the human genome. A class of TEs, known as SINE-VNTR-Alu (SVA), demonstrate the capacity to mobilise throughout the genome, resulting in SVA polymorphisms for their presence or absence within the population. Although studies have previously highlighted the involvement of TEs within neurodegenerative diseases, such as Parkinson's disease and amyotrophic lateral sclerosis (ALS), the exact mechanism has yet to be identified. In this study, we used whole-genome sequencing and RNA sequencing data of ALS patients and healthy controls from the New York Genome Centre ALS Consortium to elucidate the influence of reference SVA elements on gene expressions genome-wide within central nervous system (CNS) tissues. To investigate this, we applied a matrix expression quantitative trait loci analysis and demonstrate that reference SVA insertion polymorphisms can significantly modulate the expression of numerous genes, preferentially in the trans position and in a tissue-specific manner. We also highlight that SVAs significantly regulate mitochondrial genes as well as genes within the HLA and MAPT loci, previously associated within neurodegenerative diseases. In conclusion, this study continues to bring to light the effects of polymorphic SVAs on gene regulation and further highlights the importance of TEs within disease pathology. [ABSTRACT FROM AUTHOR]

Soybean [Glycine max (L.) Merr.] is a short-day crop for which breeders want to expand the cultivation range to more northern agro-environments by introgressing alleles involved in early reproductive traits. To do so, we investigated quantitative trait loci (QTL) and expression quantitative trait loci (eQTL) regions comprised within the E8 locus, a large undeciphered region (~7.0 Mbp to 44.5 Mbp) associated with early maturity located on chromosome GM04. We used a combination of two mapping algorithms, (i) inclusive composite interval mapping (ICIM) and (ii) genome-wide composite interval mapping (GCIM), to identify major and minor regions in two soybean populations (QS15524F2:F3 and QS15544RIL) having fixed E1, E2, E3, and E4 alleles. Using this approach, we identified three main QTL regions with high logarithm of the odds (LODs), phenotypic variation explained (PVE), and additive effects for maturity and pod-filling within the E8 region: GM04:16,974,874-17,152,230 (E8-r1); GM04:35,168,111-37,664,017 (E8-r2); and GM04:41,808,599-42,376,237 (E8-r3). Using a five-step variant analysis pipeline, we identified Protein far-red elongated hypocotyl 3 (Glyma.04G124300; E8-r1), E 1 - l ike-a (Glyma.04G156400; E8-r2), Light-harvesting chlorophyll-protein complex I subunit A4 (Glyma.04G167900; E8-r3), and Cycling dof factor 3 (Glyma.04G168300; E8-r3) as the most promising candidate genes for these regions. A combinatorial eQTL mapping approach identified significant regulatory interactions for 13 expression traits (e-traits), including Glyma.04G050200 (Early flowering 3/E6 locus), with the E8-r3 region. Four other important QTL regions close to or encompassing major flowering genes were also detected on chromosomes GM07, GM08, and GM16. In GM07:5,256,305-5,404,971, a missense polymorphism was detected in the candidate gene Glyma.07G058200 (Protein suppressor of PHYA-105). These findings demonstrate that the locus known as E8 is regulated by at least three distinct genomic regions, all of which comprise major flowering genes. [ABSTRACT FROM AUTHOR]

Background: Mapping expression quantitative trait loci (eQTLs) in skeletal muscle tissue in pigs is crucial for understanding the relationship between genetic variation and phenotypic expression of carcass traits in meat animals. Therefore, the primary objective of this study was to evaluate the impact of different sets of single nucleotide polymorphisms (SNP), including scenarios removing SNPs pruned for linkage disequilibrium (LD) and SNPs derived from SNP chip arrays and RNA-seq data from liver, brain, and skeletal muscle tissues, on the identification of eQTLs in the Longissimus lumborum tissue, associated with carcass and body composition traits in Large White pigs. The SNPs identified from muscle mRNA were combined with SNPs identified in the brain and liver tissue transcriptomes, as well as SNPs from the GGP Porcine 50 K SNP chip array. Cis- and trans-eQTLs were identified based on the skeletal muscle gene expression level, followed by functional genomic analyses and statistical associations with carcass and body composition traits in Large White pigs. Results: The number of cis- and trans-eQTLs identified across different sets of SNPs (scenarios) ranged from 261 to 2,539 and from 29 to 13,721, respectively. Furthermore, 6,180 genes were modulated by eQTLs in at least one of the scenarios evaluated. The eQTLs identified were not significantly associated with carcass and body composition traits but were significantly enriched for many traits in the "Meat and Carcass" type QTL. The scenarios with the highest number of cis- (n = 304) and trans- (n = 5,993) modulated genes were the unpruned and LD-pruned SNP set scenarios identified from the muscle transcriptome. These genes include 84 transcription factor coding genes. Conclusions: After LD pruning, the set of SNPs identified based on the transcriptome of the skeletal muscle tissue of pigs resulted in the highest number of genes modulated by eQTLs. Most eQTLs are of the trans type and are associated with genes influencing complex traits in pigs, such as transcription factors and enhancers. Furthermore, the incorporation of SNPs from other genomic regions to the set of SNPs identified in the porcine skeletal muscle transcriptome contributed to the identification of eQTLs that had not been identified based on the porcine skeletal muscle transcriptome alone. [ABSTRACT FROM AUTHOR]

Objective: To provide novel insights into the aetiology of non-syndromic cleft lip with or without cleft palate (NSCL/P) by integrating multi-omics data and exploring susceptibility genes associated with NSCL/P. Methods: A two-stage genome-wide association study (GWAS) of NSCL/P was performed, involving a total of 1,069 cases and 1,724 controls. Using promoter capture Hi-C (pCHi-C) datasets in human embryonic stem cells (hESC) and chromatin immunoprecipitation sequencing (ChIP-seq) in craniofacial tissues, we filtered out single nucleotide polymorphisms (SNPs) with active cis-regulation and their target genes. Additionally, we employed expression quantitative trait loci (eQTL) analysis to identify candidate genes. Results: Thirteen SNPs were identified as cis-regulation units associated with the risk of NSCL/P. Five of these were proven to be active in chromatin states in early human craniofacial development (rs7218002: odds ratio [OR] 1.50, P = 8.14E-08; rs835367: OR 0.78, P = 3.48E05; rs77022994: OR 0.55, P = 1.05E-04; rs961470: OR 0.73, P = 1.38E-04; rs17314727: OR 0.73, P = 1.85E-04). Additionally, pCHi-C and eQTL analysis prioritised three candidate genes (rs7218002: NTN1, rs835367: FGGY, LINC01135). NTN1 and FGGY were expressed in mouse orofacial development. Deficiencies in NTN1, FGGY and LINC01135 were associated with cleft palate and cleft lip, abnormal facial shape and bifid uvula, and abnormality of the face, respectively. Conclusion: Our study identified five SNPs (rs7218002, rs835367, rs77022994, rs961470 and rs17314727) and three susceptibility genes (NTN1, FGGY and LINC01135) associated with NSCL/P. These findings contribute to a better understanding of the genetic factors involved. [ABSTRACT FROM AUTHOR]

Genome-wide association study (GWAS) analysis has exposed that genetic factors play important roles in COVID-19. Whereas a deeper understanding of the underlying mechanism of COVID-19 was hindered by the lack of expression of quantitative trait loci (eQTL) data specific for disease. To this end, we identified COVID-19-specific cis-eQTLs by integrating nucleotide sequence variations and RNA-Seq data from COVID-19 samples. These identified eQTLs have different regulatory effect on genes between patients and controls, indicating that SARS-CoV-2 infection may cause alterations in the human body's internal environment. Individuals with the TT genotype in the rs1128320 region seemed more susceptible to SARS-CoV-2 infection and developed into severe COVID-19 due to the abnormal expression of IFITM1. We subsequently discovered potential causal genes, of the result, a total of 48 genes from six tissues were identified. siRNA-mediated depletion assays in SARS-CoV-2 infection proved that 14 causal genes were directly associated with SARS-CoV-2 infection. These results enriched existing research on COVID-19 causal genes and provided a new sight in the mechanism exploration for COVID-19.

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 Monocytes are key mediators of innate immunity to infection, undergoing profound and dynamic changes in epigenetic state and immune function which are broadly protective but may be dysregulated in disease. Here, we aimed to advance understanding of epigenetic regulation following innate immune activation, acutely and in endotoxin tolerant states. Methods We exposed human primary monocytes from healthy donors (n = 6) to interferon-γ or differing combinations of endotoxin (lipopolysaccharide), including acute response (2 h) and two models of endotoxin tolerance: repeated stimulations (6 + 6 h) and prolonged exposure to endotoxin (24 h). Another subset of monocytes was left untreated (naïve). We identified context-specific regulatory elements based on epigenetic signatures for chromatin accessibility (ATAC-seq) and regulatory non-coding RNAs from total RNA sequencing. Results We present an atlas of differential gene expression for endotoxin and interferon response, identifying widespread context specific changes. Across assayed states, only 24–29% of genes showing differential exon usage are also differential at the gene level. Overall, 19.9% (6,884 of 34,616) of repeatedly observed ATAC peaks were differential in at least one condition, the majority upregulated on stimulation and located in distal regions (64.1% vs 45.9% of non-differential peaks) within which sequences were less conserved than non-differential peaks. We identified enhancer-derived RNA signatures specific to different monocyte states that correlated with chromatin accessibility changes. The endotoxin tolerance models showed distinct chromatin accessibility and transcriptomic signatures, with integrated analysis identifying genes and pathways involved in the inflammatory response, detoxification, metabolism and wound healing. We leveraged eQTL mapping for the same monocyte activation states to link potential enhancers with specific genes, identifying 1,946 unique differential ATAC peaks with 1,340 expression associated genes. We further use this to inform understanding of reported GWAS, for example involving FCHO1 and coronary artery disease. Conclusion This study reports context-specific regulatory elements based on transcriptomic profiling and epigenetic signatures for enhancer-derived RNAs and chromatin accessibility in immune tolerant monocyte states, and demonstrates the informativeness of linking such elements and eQTL to inform future mechanistic studies aimed at defining therapeutic targets of immunosuppression and diseases.

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]

Background: Monocytes are key mediators of innate immunity to infection, undergoing profound and dynamic changes in epigenetic state and immune function which are broadly protective but may be dysregulated in disease. Here, we aimed to advance understanding of epigenetic regulation following innate immune activation, acutely and in endotoxin tolerant states. Methods: We exposed human primary monocytes from healthy donors (n = 6) to interferon-γ or differing combinations of endotoxin (lipopolysaccharide), including acute response (2 h) and two models of endotoxin tolerance: repeated stimulations (6 + 6 h) and prolonged exposure to endotoxin (24 h). Another subset of monocytes was left untreated (naïve). We identified context-specific regulatory elements based on epigenetic signatures for chromatin accessibility (ATAC-seq) and regulatory non-coding RNAs from total RNA sequencing. Results: We present an atlas of differential gene expression for endotoxin and interferon response, identifying widespread context specific changes. Across assayed states, only 24–29% of genes showing differential exon usage are also differential at the gene level. Overall, 19.9% (6,884 of 34,616) of repeatedly observed ATAC peaks were differential in at least one condition, the majority upregulated on stimulation and located in distal regions (64.1% vs 45.9% of non-differential peaks) within which sequences were less conserved than non-differential peaks. We identified enhancer-derived RNA signatures specific to different monocyte states that correlated with chromatin accessibility changes. The endotoxin tolerance models showed distinct chromatin accessibility and transcriptomic signatures, with integrated analysis identifying genes and pathways involved in the inflammatory response, detoxification, metabolism and wound healing. We leveraged eQTL mapping for the same monocyte activation states to link potential enhancers with specific genes, identifying 1,946 unique differential ATAC peaks with 1,340 expression associated genes. We further use this to inform understanding of reported GWAS, for example involving FCHO1 and coronary artery disease. Conclusion: This study reports context-specific regulatory elements based on transcriptomic profiling and epigenetic signatures for enhancer-derived RNAs and chromatin accessibility in immune tolerant monocyte states, and demonstrates the informativeness of linking such elements and eQTL to inform future mechanistic studies aimed at defining therapeutic targets of immunosuppression and diseases. [ABSTRACT FROM AUTHOR]

Soybean [Glycine max (L.) Merr.] is a short-day crop for which breeders want to expand the cultivation range to more northern agro-environments by introgressing alleles involved in early reproductive traits. To do so, we investigated quantitative trait loci (QTL) and expression quantitative trait loci (eQTL) regions comprised within the E8 locus, a large undeciphered region (~7.0 Mbp to 44.5 Mbp) associated with early maturity located on chromosome GM04. We used a combination of two mapping algorithms, (i) inclusive composite interval mapping (ICIM) and (ii) genome-wide composite interval mapping (GCIM), to identify major and minor regions in two soybean populations (QS15524F2:F3 and QS15544RIL) having fixed E1, E2, E3, and E4 alleles. Using this approach, we identified three main QTL regions with high logarithm of the odds (LODs), phenotypic variation explained (PVE), and additive effects for maturity and pod-filling within the E8 region: GM04:16,974,874-17,152,230 (E8-r1); GM04:35,168,111-37,664,017 (E8-r2); and GM04:41,808,599-42,376,237 (E8-r3). Using a five-step variant analysis pipeline, we identified Protein far-red elongated hypocotyl 3 (Glyma.04G124300; E8-r1), E1-like-a (Glyma.04G156400; E8-r2), Light-harvesting chlorophyll-protein complex I subunit A4 (Glyma.04G167900; E8-r3), and Cycling dof factor 3 (Glyma.04G168300; E8-r3) as the most promising candidate genes for these regions. A combinatorial eQTL mapping approach identified significant regulatory interactions for 13 expression traits (e-traits), including Glyma.04G050200 (Early flowering 3/E6 locus), with the E8-r3 region. Four other important QTL regions close to or encompassing major flowering genes were also detected on chromosomes GM07, GM08, and GM16. In GM07:5,256,305-5,404,971, a missense polymorphism was detected in the candidate gene Glyma.07G058200 (Protein suppressor of PHYA-105). These findings demonstrate that the locus known as E8 is regulated by at least three distinct genomic regions, all of which comprise major flowering genes.

Accurate gene expression is fundamental for sustaining life, enabling adaptive responses to routine tasks and management of urgent cellular environments. RNA polymerases (RNAP I, RNAP II, and RNAP III) and ribosomal proteins (RPs) play pivotal roles in the precise synthesis of proteins from DNA sequences. In this review, we briefly examined the structure and function of their constituent proteins and explored to characterize these proteins and the genes encoding them, particularly in terms of their expression quantitative trait loci (eQTL) associated with complex human traits. We gathered a comprehensive set of 4007 genome-wide association study (GWAS) signal–eQTL pairs, aligning GWAS Catalog signals with eQTLs across various tissues for the genes involved. These pairs spanned 16 experimental factor ontology (EFO) parent terms defined in European Bioinformatics Institute (EBI). A substantial majority (83.4%) of the pairs were attributed to the genes encoding RPs, especially RPS26 (32.9%). This large proportion was consistent across all tissues (15.5~81.9%), underscoring its extensive impact on complex human traits. Notably, these proportions of EFO terms differed significantly (p < 0.0031) from those for RNAPs. Brain-specific pairs for POLR3H, a component of RNAP III, were implicated in neurological disorders. The largest number of pairs in RNAP I was found for POLR1H, encoding RPA12, a built-in transcription factor essential for high transcriptional efficiency of RNAP I. RNAP II-related pairs were less abundant, with unique structural organization featuring minimal subunits for flexible transcription of a diverse range of genes with customized dissociable subunits. For instance, RPB4 encoded by POLR2D, the RNAP II gene with the most pairs, forms its dissociable stalk module with RPB7. This study provides insightful genetic characteristics of RPs and RNAPs, with a priority emphasis on RPS26, POLR1H, POLR2D, and POLR3H, for future studies on the impact of individual genetic variation on complex human traits.

Genome-wide association studies (GWAS) have identified a large number of genetic loci for coronary artery disease (CAD), with many located close to genes associated with traditional CAD risk pathways, such as lipid metabolism and inflammation. It is becoming evident with recent CAD GWAS meta-analyses that vascular pathways are also highly enriched and present an opportunity for novel therapeutics. This review examines GWAS-enriched vascular gene loci, the pathways involved and their potential role in CAD pathogenesis. The functionality of variants is explored from expression quantitative trait loci, massively parallel reporter assays and CRISPR-based gene-editing tools. We discuss how this research may lead to novel therapeutic tools to treat cardiovascular disorders. [ABSTRACT FROM AUTHOR]

Introduction: Asthma is a heterogeneous inflammatory disease often associated with other complex phenotypes. Identifying asthma-associated diseases and uncovering the molecular mechanisms mediating their interaction can help detangle the heterogeneity of asthma. Network analysis is a powerful approach for untangling such inter-disease relationships. Methods: Here, we integrated information on physical contacts between common single nucleotide polymorphisms (SNPs) and gene expression with expression quantitative trait loci (eQTL) data from the lung and whole blood to construct two tissue-specific spatial gene regulatory networks (GRN). We then located the asthma GRN (level 0) within each tissue-specific GRN by identifying the genes that are functionally affected by asthma-associated spatial eQTLs. Curated protein interaction partners were subsequently identified up to four edges or levels away from the asthma GRN. The eQTLs spatially regulating genes on levels 0-4 were queried against the GWAS Catalog to identify the traits enriched (hypergeometric test; FDR ≤ 0.05) in each level. Results: We identified 80 and 82 traits significantly enriched in the lung and blood GRNs, respectively. All identified traits were previously reported to be comorbid or associated (positively or negatively) with asthma (e.g., depressive symptoms and lung cancer), except 8 traits whose association with asthma is yet to be confirmed (e.g., reticulocyte count). Our analysis additionally pinpoints the variants and genes that link asthma to the identified asthma-associated traits, a subset of which was replicated in a comorbidity analysis using health records of 26,781 asthma patients in New Zealand. Discussion: Our discovery approach identifies enriched traits in the regulatory space proximal to asthma, in the tissue of interest, without a priori selection of the interacting traits. The predictions it makes expand our understanding of possible shared molecular interactions and therapeutic targets for asthma, where no cure is currently available. [ABSTRACT FROM AUTHOR]

Background We evaluated the relationship between response to efflux pump inhibition in fluoroquinolone-resistant Mycobacterium tuberculosis (Mtb) isolates and differences in gene expression and expression quantitative trait loci (eQTL). Methods We determined ofloxacin minimum inhibitory concentration (MIC) for ofloxacin-resistant and -susceptible Mtb isolates without and with the efflux pump inhibitor verapamil. We performed RNA sequencing (RNA-seq), whole genome sequencing (WGS), and eQTL analysis, focusing on efflux pump, transport, and secretion-associated genes. Results Of 42 ofloxacin-resistant Mtb isolates, 27 had adequate WGS coverage and acceptable RNA-seq quality. Of these 27, 7 had >2-fold reduction in ofloxacin MIC with verapamil; 6 had 2-fold reduction, and 14 had <2-fold reduction. Five genes (including Rv0191) had significantly increased expression in the MIC fold change >2 compared to <2 groups. Among regulated genes, 31 eQTLs (without ofloxacin) and 35 eQTLs (with ofloxacin) had significant allele frequency differences between MIC fold change >2 and <2 groups. Of these, Rv1410c , Rv2459 , and Rv3756c (without ofloxacin) and Rv0191 and Rv3756c (with ofloxacin) have previously been associated with antituberculosis drug resistance. Conclusions In this first reported eQTL analysis in Mtb , Rv0191 had increased gene expression and significance in eQTL analysis, making it a candidate for functional evaluation of efflux-mediated fluoroquinolone resistance in Mtb. [ABSTRACT FROM AUTHOR]

SUMMARY: Maize (Zea mays L.) is a major staple crop worldwide, and during modern maize breeding, cultivars with increased tolerance to high‐density planting and higher yield per plant have contributed significantly to the increased yield per unit land area. Systematically identifying key agronomic traits and their associated genomic changes during modern maize breeding remains a significant challenge because of the complexity of genetic regulation and the interactions of the various agronomic traits, with most of them being controlled by numerous small‐effect quantitative trait loci (QTLs). Here, we performed phenotypic and gene expression analyses for a set of 137 elite inbred lines of maize from different breeding eras in China. We found four yield‐related traits are significantly improved during modern maize breeding. Through gene‐clustering analyses, we identified four groups of expressed genes with distinct trends of expression pattern change across the historical breeding eras. In combination with weighted gene co‐expression network analysis, we identified several candidate genes regulating various plant architecture‐ and yield‐related agronomic traits, such as ZmARF16, ZmARF34, ZmTCP40, ZmPIN7, ZmPYL10, ZmJMJ10, ZmARF1, ZmSWEET15b, ZmGLN6 and Zm00001d019150. Further, by combining expression quantitative trait loci (eQTLs) analyses, correlation coefficient analyses and population genetics, we identified a set of candidate genes that might have been under selection and contributed to the genetic improvement of various agronomic traits during modern maize breeding, including a number of known key regulators of plant architecture, flowering time and yield‐related traits, such as ZmPIF3.3, ZAG1, ZFL2 and ZmBES1. Lastly, we validated the functional variations in GL15, ZmPHYB2 and ZmPYL10 that influence kernel row number, flowering time, plant height and ear height, respectively. Our results demonstrates the effectiveness of our combined approaches for uncovering key candidate regulatory genes and functional variation underlying the improvement of important agronomic traits during modern maize breeding, and provide a valuable genetic resource for the molecular breeding of maize cultivars with tolerance for high‐density planting. Significance Statement: Our results provide insights into the genetic improvement of maize inbred lines during modern maize breeding and reveal potential valuable targets for molecular breeding of maize cultivars tolerant of high‐density planting in the future. [ABSTRACT FROM AUTHOR]

Summary: Accurate imputation of tissue-specific gene expression can be a powerful tool for understanding the biological mechanisms underlying human complex traits. Existing imputation methods can be grouped into two categories according to the types of predictors used. The first category uses genotype data, while the second category uses whole-blood expression data. Both data types can be easily collected from blood, avoiding invasive tissue biopsies. In this study, we attempted to build an optimal predictive model for imputing tissue-specific gene expression by combining the genotype and whole-blood expression data. We first evaluated the imputation performance of each standalone model (using genotype data [GEN model] and using whole-blood expression data [WBE model]) using their respective data types across 47 human tissues. The WBE model outperformed the GEN model in most tissues by a large gain. Then, we developed several combined models that leverage both types of predictors to further improve imputation performance. We tried various strategies, including utilizing a merged dataset of the two data types (MERGED models) and integrating the imputation outcomes of the two standalone models (inverse variance-weighted [IVW] models). We found that one of the MERGED models noticeably outperformed the standalone models. This model involved a fixed ratio between the two regularization penalty factors for the two predictor types so that the contribution of the whole-blood transcriptome is upweighted compared with the genotype. Our study suggests that one can improve the imputation of tissue-specific gene expression by combining the genotype and whole-blood expression, but the improvement can be largely dependent on the combination strategy chosen.

Background: Observational studies have shown a bidirectional association between chronic obstructive pulmonary disease (COPD) and gastroesophageal reflux disease (GERD), but it is not clear whether this association is causal. In our previous study, we found that depression was a hot topic of research in the association between COPD and GERD. Is major depressive disorder (MDD) a mediator of the association between COPD and GERD? Here, we evaluated the causal association between COPD, MDD, and GERD using Mendelian randomization (MR) study. Methods: Based on the FinnGen, United Kingdom Biobank, and Psychiatric Genomics Consortium (PGC) databases, we obtained genome-wide association study (GWAS) summary statistics for the three phenotypes from 315,123 European participants (22,867 GERD cases and 292,256 controls), 462,933 European participants (1,605 COPD cases and 461,328 controls), and 173,005 European participants (59,851 MDD cases and 113,154 controls), respectively. To obtain more instrumental variables to reduce bias, we extracted relevant single-nucleotide polymorphisms (SNPs) for the three phenotypes from published meta-analysis studies. Bidirectional MR and expression quantitative trait loci (eQTL)-MR were performed using the inverse variance weighting method to assess the causal association between GERD, MDD, and COPD. Results: There was no evidence of a causal effect between GERD and COPD in the bidirectional MR analysis [forward MR for GERD on COPD: odds ratios (OR) = 1.001, p = 0.270; reverse MR for COPD on GERD: OR = 1.021, p = 0.303]. The causal effect between GERD and MDD appeared to be bidirectional (forward MR for GERD on MDD: OR = 1.309, p = 0.006; reverse MR for MDD on GERD: OR = 1.530, p < 0.001), while the causal effect between MDD and COPD was unidirectional (forward MR for MDD on COPD: OR = 1.004, p < 0.001; reverse MR for COPD on MDD: OR = 1.002, p = 0.925). MDD mediated the effect of GERD on COPD in a unidirectional manner (OR = 1.001). The results of the eQTL-MR were consistent with those of the bidirectional MR. Conclusion: MDD appears to play a vital role in the effect of GERD on COPD. However, we have no evidence of a direct causal association between GERD and COPD. There is a bidirectional causal association between MDD and GERD, which may accelerate the progression from GERD to COPD. [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.

The risk of endometriosis (EM), which is a common complex gynaecological disease, is related to genetic predisposition. However, it is unclear how genetic variants confer the risk of EM. Here, via Sherlock integrative analysis, we combined large-scale genome-wide association studies (GWAS) summary statistics on EM (N = 245,494) with a blood-based eQTL dataset (N = 1490) to identify EM risk-related genes. For validation, we leveraged two independent eQTL datasets (N = 769) for integration with the GWAS data. Thus, we prioritised 14 genes, including GIMAP4, TOP3A, and NMNAT3, which showed significant association with susceptibility to EM. We also utilised two independent methods, Multi-marker Analysis of GenoMic Annotation and S-PrediXcan, to further validate the EM risk-associated genes. Moreover, protein–protein interaction network analysis showed the 14 genes were functionally connected. Functional enrichment analyses further demonstrated that these genes were significantly enriched in metabolic and immune-related pathways. Differential gene expression analysis showed that in peripheral blood samples from patients with ovarian EM, TOP3A, MKNK1, SIPA1L2, and NUCB1 were significantly upregulated, while HOXB2, GIMAP5, and MGMT were significantly downregulated compared with their expression levels in samples from the controls. Immunohistochemistry further confirmed the increased expression levels of MKNK1 and TOP3A in the ectopic and eutopic endometrium compared to normal endometrium, while HOBX2 was downregulated in the endometrium of women with ovarian EM. Finally, in ex vivo functional experiments, MKNK1 knockdown inhibited ectopic endometrial stromal cells (EESCs) migration and invasion. TOP3A knockdown inhibited EESCs proliferation, migration, and invasion, while promoting their apoptosis. Convergent lines of evidence suggested that MKNK1 and TOP3A are novel EM risk-related genes.

Abstract Background Immunoglobulin A nephropathy (IgAN) is a complex autoimmune disease, and the exact pathogenesis remains to be elucidated. This study aimed to explore genes underlying the pathogenesis of IgAN. Methods We conducted the summary data-based Mendelian randomization (SMR) analysis and performed functional mapping and annotation using FUMA to explore genetic loci that are potentially involved in the pathogenies of IgAN. Both analyses used summarized data of a recent genome-wide association study (GWAS) on IgANs, which included 477,784 Europeans (15,587 cases and 462,197 controls) and 175,359 East Asians (71 cases and 175,288 controls). We performed SMR analysis using Consortium for the Architecture of Gene Expression (CAGE) expression quantitative trait loci (eQTL) data and replicated the analysis using Genotype-Tissue Expression (GTEx) eQTL data. Results Using the CAGE eQTL data, our SMR analysis identified 32 probes tagging 25 unique genes whose expression were pleiotropically associated with IgAN, with the top three probes being ILMN_2150787 (tagging HLA-C, P SMR= 2.10 × 10–18), ILMN_1682717 (tagging IER3, P SMR= 1.07 × 10–16) and ILMN_1661439 (tagging FLOT1, P SMR=1.16 × 10–14). Using GTEx eQTL data, our SMR analysis identified 24 probes tagging 24 unique genes whose expressions were pleiotropically associated with IgAN, with the top three probes being ENSG00000271581.1 (tagging XXbac-BPG248L24.12, P SMR= 1.44 × 10–10), ENSG00000186470.9 (tagging BTN3A2, P SMR= 2.28 × 10–10), and ENSG00000224389.4 (tagging C4B, P SMR= 1.23 × 10 –9). FUMA analysis identified 3 independent, significant and lead SNPs, 2 genomic risk loci and 39 genes that are potentially involved in the pathogenesis of IgAN. Conclusion We identified many genetic variants/loci that are potentially involved in the pathogenesis of IgAN. More studies are needed to elucidate the exact mechanisms of the identified genetic variants/loci in the etiology of IgAN.

Transposable elements (TEs) are repetitive elements which make up around 45% of the human genome. A class of TEs, known as SINE-VNTR-Alu (SVA), demonstrate the capacity to mobilise throughout the genome, resulting in SVA polymorphisms for their presence or absence within the population. Although studies have previously highlighted the involvement of TEs within neurodegenerative diseases, such as Parkinson’s disease and amyotrophic lateral sclerosis (ALS), the exact mechanism has yet to be identified. In this study, we used whole-genome sequencing and RNA sequencing data of ALS patients and healthy controls from the New York Genome Centre ALS Consortium to elucidate the influence of reference SVA elements on gene expressions genome-wide within central nervous system (CNS) tissues. To investigate this, we applied a matrix expression quantitative trait loci analysis and demonstrate that reference SVA insertion polymorphisms can significantly modulate the expression of numerous genes, preferentially in the trans position and in a tissue-specific manner. We also highlight that SVAs significantly regulate mitochondrial genes as well as genes within the HLA and MAPT loci, previously associated within neurodegenerative diseases. In conclusion, this study continues to bring to light the effects of polymorphic SVAs on gene regulation and further highlights the importance of TEs within disease pathology.

IntroductionAsthma is a heterogeneous inflammatory disease often associated with other complex phenotypes. Identifying asthma-associated diseases and uncovering the molecular mechanisms mediating their interaction can help detangle the heterogeneity of asthma. Network analysis is a powerful approach for untangling such inter-disease relationships.MethodsHere, we integrated information on physical contacts between common single nucleotide polymorphisms (SNPs) and gene expression with expression quantitative trait loci (eQTL) data from the lung and whole blood to construct two tissue-specific spatial gene regulatory networks (GRN). We then located the asthma GRN (level 0) within each tissue-specific GRN by identifying the genes that are functionally affected by asthma-associated spatial eQTLs. Curated protein interaction partners were subsequently identified up to four edges or levels away from the asthma GRN. The eQTLs spatially regulating genes on levels 0–4 were queried against the GWAS Catalog to identify the traits enriched (hypergeometric test; FDR ≤ 0.05) in each level.ResultsWe identified 80 and 82 traits significantly enriched in the lung and blood GRNs, respectively. All identified traits were previously reported to be comorbid or associated (positively or negatively) with asthma (e.g., depressive symptoms and lung cancer), except 8 traits whose association with asthma is yet to be confirmed (e.g., reticulocyte count). Our analysis additionally pinpoints the variants and genes that link asthma to the identified asthma-associated traits, a subset of which was replicated in a comorbidity analysis using health records of 26,781 asthma patients in New Zealand.DiscussionOur discovery approach identifies enriched traits in the regulatory space proximal to asthma, in the tissue of interest, without a priori selection of the interacting traits. The predictions it makes expand our understanding of possible shared molecular interactions and therapeutic targets for asthma, where no cure is currently available.

Genome-wide association studies (GWAS) have revealed approximately 100 genomic signals associated with Hodgkin lymphoma (HL); however, their target genes and underlying mechanisms causing HL susceptibility remain unclear. In this study, transcriptome-wide analysis of expression quantitative trait loci (eQTL) was conducted to identify target genes associated with HL GWAS signals. A mixed model, which explains polygenic regulatory effects by the genomic covariance among individuals, was implemented to discover expression genes (eGenes) using genotype data from 462 European/African individuals. Overall, 80 eGenes were identified to be associated with 20 HL GWAS signals. Enrichment analysis identified apoptosis, immune responses, and cytoskeletal processes as functions of these eGenes. The eGene of rs27524 encodes ERAP1 that can cleave peptides attached to human leukocyte antigen in immune responses; its minor allele may help Reed–Sternberg cells to escape the immune response. The eGene of rs7745098 encodes ALDH8A1 that can oxidize the precursor of acetyl-CoA for the production of ATP; its minor allele may increase oxidization activity to evade apoptosis of pre-apoptotic germinal center B cells. Thus, these minor alleles may be genetic risk factors for HL susceptibility. Experimental studies on genetic risk factors are needed to elucidate the underlying mechanisms of HL susceptibility and improve the accuracy of precision oncology. [ABSTRACT FROM AUTHOR]

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]

Multiple integrative studies have been performed to identify the potential target genes of the non-coding schizophrenia (SCZ) risk variants. However, all the integrative studies used expression quantitative trait loci (eQTL) data from bulk tissues. Considering the cell type–specific regulatory effect of many genetic variants, it is important to conduct integrative studies using cell type–specific eQTL data. Here, we conduct a Mendelian randomization (MR) study by integrating genome-wide associations of SCZ (74,776 cases and 101,023 controls) and eQTL data (N = 215) from dopaminergic neurons, which were differentiated from human-induced pluripotent stem cell (iPSC) lines. For eQTL from young post-mitotic dopaminergic neurons (differentiation of iPSC for 30 days, D30), we identified 34 genes whose genetically regulated expression in dopaminergic neurons may have a causal role in SCZ. Among which, ARL3 showed the most significant associations with SCZ. For eQTL from more mature dopaminergic neurons (D52), we identified 37 potential SCZ causal genes, and ARL3 and GNL3 showed the most significant associations. Only 12 genes showed significant associations with SCZ in both D30 and D52 eQTL datasets, indicating the time point–specific genetic regulatory effects in young post-mitotic dopaminergic neurons and more mature dopaminergic neurons. Comparing the results from dopaminergic neurons with bulk brain tissues prioritized 2 high-confidence risk genes, including DDHD2 and GALNT10. Our study identifies multiple risk genes whose genetically regulated expression in dopaminergic neurons may have a causal role in SCZ. Further mechanistic investigation will provide pivotal insights into SCZ pathophysiology. [ABSTRACT FROM AUTHOR]

In this study, we attempted to find genetic variants affecting gene expression (eQTL = expression Quantitative Trait Loci) in the human placenta in normal and pathological situations. The analysis of gene expression in placental diseases (Pre-eclampsia and Intra-Uterine Growth Restriction) is hindered by the fact that diseased placental tissue samples are generally taken at earlier gestations compared to control samples. The difference in gestational age is considered a major confounding factor in the transcriptome regulation of the placenta. To alleviate this significant problem, we propose here a novel approach to pinpoint disease-specific cis-eQTLs. By statistical correction for gestational age at sampling as well as other confounding/surrogate variables systematically searched and identified, we found 43 e-genes for which proximal SNPs influence expression level. Then, we performed the analysis again, removing the disease status from the covariates, and we identified 54 e-genes, 16 of which are identified de novo and, thus, possibly related to placental disease. We found a highly significant overlap with previous studies for the list of 43 e-genes, validating our methodology and findings. Among the 16 disease-specific e-genes, several are intrinsic to trophoblast biology and, therefore, constitute novel targets of interest to better characterize placental pathology and its varied clinical consequences. The approach that we used may also be applied to the study of other human diseases where confounding factors have hampered a better understanding of the pathology. [ABSTRACT FROM AUTHOR]

Background: Immunoglobulin A nephropathy (IgAN) is a complex autoimmune disease, and the exact pathogenesis remains to be elucidated. This study aimed to explore genes underlying the pathogenesis of IgAN. Methods: We conducted the summary data-based Mendelian randomization (SMR) analysis and performed functional mapping and annotation using FUMA to explore genetic loci that are potentially involved in the pathogenies of IgAN. Both analyses used summarized data of a recent genome-wide association study (GWAS) on IgANs, which included 477,784 Europeans (15,587 cases and 462,197 controls) and 175,359 East Asians (71 cases and 175,288 controls). We performed SMR analysis using Consortium for the Architecture of Gene Expression (CAGE) expression quantitative trait loci (eQTL) data and replicated the analysis using Genotype-Tissue Expression (GTEx) eQTL data. Results: Using the CAGE eQTL data, our SMR analysis identified 32 probes tagging 25 unique genes whose expression were pleiotropically associated with IgAN, with the top three probes being ILMN_2150787 (tagging HLA-C, PSMR= 2.10 × 10–18), ILMN_1682717 (tagging IER3, PSMR= 1.07 × 10–16) and ILMN_1661439 (tagging FLOT1, PSMR=1.16 × 10–14). Using GTEx eQTL data, our SMR analysis identified 24 probes tagging 24 unique genes whose expressions were pleiotropically associated with IgAN, with the top three probes being ENSG00000271581.1 (tagging XXbac-BPG248L24.12, PSMR= 1.44 × 10–10), ENSG00000186470.9 (tagging BTN3A2, PSMR= 2.28 × 10–10), and ENSG00000224389.4 (tagging C4B, PSMR= 1.23 × 10 –9). FUMA analysis identified 3 independent, significant and lead SNPs, 2 genomic risk loci and 39 genes that are potentially involved in the pathogenesis of IgAN. Conclusion: We identified many genetic variants/loci that are potentially involved in the pathogenesis of IgAN. More studies are needed to elucidate the exact mechanisms of the identified genetic variants/loci in the etiology of IgAN. [ABSTRACT FROM AUTHOR]

3-O-caffeoylquinic acid, also known as chlorogenic acid (CGA), functions as an intermediate in lignin biosynthesis in the phenylpropanoid pathway. It is widely distributed among numerous plant species and acts as an antioxidant in both plants and animals. Using GC-MS, we discovered consistent and extreme variation in CGA content across a population of 739 4-yr-old Populus trichocarpa accessions. We performed genome-wide association studies (GWAS) from 917 P. trichocarpa accessions and expression-based quantitative trait loci (eQTL) analyses to identify key regulators. The GWAS and eQTL analyses resolved an overlapped interval encompassing a hydroxycinnamoyl-CoA:shikimate hydroxycinnamoyl transferase 2 (PtHCT2) that was significantly associated with CGA and partially characterized metabolite abundances. PtHCT2 leaf expression was significantly correlated with CGA abundance and it was regulated by cis-eQTLs containing W-box for WRKY binding. Among all nine PtHCT homologs, PtHCT2 is the only one that responds to infection by the fungal pathogen Sphaerulina musiva (a Populus pathogen). Validation using protoplast-based transient expression system suggests that PtHCT2 is regulated by the defense-responsive WRKY. These results are consistent with reports of CGA functioning as an antioxidant in response to biotic stress. This study provides insights into data-driven and omics-based inference of gene function in woody species.

The identification of pathogenically-relevant genes and tissues for complex traits can be a difficult task. We developed an approach named genome-wide imputed differential expression enrichment (GIDEE), to prioritise trait-relevant tissues by combining genome-wide association study (GWAS) summary statistic data with tissue-specific expression quantitative trait loci (eQTL) data from 49 GTEx tissues. Our GIDEE approach analyses robustly imputed gene expression and tests for enrichment of differentially expressed genes in each tissue. Two tests (mean squared z-score and empirical Brown’s method) utilise the full distribution of differential expression p-values across all genes, while two binomial tests assess the proportion of genes with tissue-wide significant differential expression. GIDEE was applied to nine training datasets with known trait-relevant tissues and ranked 49 GTEx tissues using the individual and combined enrichment tests. The best-performing enrichment test produced an average rank of 1.55 out of 49 for the known trait-relevant tissue across the nine training datasets—ranking the correct tissue first five times, second three times, and third once. Subsequent application of the GIDEE approach to 20 test datasets—whose pathogenic tissues or cell types are uncertain or unknown—provided important prioritisation of tissues relevant to the trait’s regulatory architecture. GIDEE prioritisation may thus help identify both pathogenic tissues and suitable proxy tissue/cell models (e.g., using enriched tissues/cells that are more easily accessible). The application of our GIDEE approach to GWAS datasets will facilitate follow-up in silico and in vitro research to determine the functional consequence(s) of their risk loci.

Background: Large‐scale genome‐wide association studies (GWASs) have reported multiple risk variants for Parkinson's disease (PD). However, little is known about how these reported risk variants confer risk of PD. Objective: To nominate genes whose genetically regulated expression in dopaminergic neurons may have a causal role in PD. Methods: We conducted a two‐sample Mendelian randomization (MR) study by integrating large‐scale genome‐wide associations and expression quantitative trait loci (eQTL) data from dopaminergic neurons. Results: MR analysis nominated 10 risk genes whose genetically regulated expression in dopaminergic neurons may have a causal role in PD. These MR significant genes include FAM200B, NDUFAF2, NUP42, SH3GL2, STX1B, CCDC189, KAT8, PRSS36, VAMP4, and ZSWIM7. Conclusions: We report the first MR study of PD by using dopaminergic neuron‐specific eQTL and nominate novel risk genes for PD. Further functional characterization of the nominated risk genes will provide mechanistic insights into PD pathogenesis and potential therapeutic targets. © 2022 International Parkinson and Movement Disorder Society. [ABSTRACT FROM AUTHOR]