Genome-wide Expression Quantitative Trait Loci Research Articles

Insights into preeclampsia: a bioinformatics approach to deciphering genetic and immune contributions.

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. 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. 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. 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.

Mendelian randomization based on genome-wide association studies and expression quantitative trait loci, predicting gene targets for the complexity of osteoarthritis as well as the clinical prognosis of the condition.

Osteoarthritis (OA) entails a prevalent chronic ailment, marked by the widespread involvement of entire joints. Prolonged low-grade synovial inflammation serves as the key instigator for a cascade of pathological alterations in the joints. The study seeks to explore potential therapeutic targets for OA and investigate the associated mechanistic pathways. Summary-level data for OA were downloaded from the genome-wide association studies (GWAS) database, expression quantitative trait loci (eQTL) data were acquired from the eQTLGen consortium, and synovial chip data for OA were obtained from the GEO database. Following the integration of data and subsequent Mendelian randomization analysis, differential analysis, and weighted gene co-expression network analysis (WGCNA) analysis, core genes that exhibit a significant causal relationship with OA traits were pinpointed. Subsequently, by employing three machine learning algorithms, additional identification of gene targets for the complexity of OA was achieved. Additionally, corresponding ROC curves and nomogram models were established for the assessment of clinical prognosis in patients. Finally, western blotting analysis and ELISA methodology were employed for the initial validation of marker genes and their linked pathways. Twenty-two core genes with a significant causal relationship to OA traits were obtained. Through the application of distinct machine learning algorithms, MAT2A and RBM6 emerged as diagnostic marker genes. ROC curves and nomogram models were utilized for evaluating both the effectiveness of the two identified marker genes associated with OA in diagnosis. MAT2A governs the synthesis of SAM within synovial cells, thereby thwarting synovial fibrosis induced by the TGF-β1-activated Smad3/4 signaling pathway. The first evidence that MAT2A and RBM6 serve as robust diagnostic for OA is presented in this study. MAT2A, through its involvement in regulating the synthesis of SAM, inhibits the activation of the TGF-β1-induced Smad3/4 signaling pathway, thereby effectively averting the possibility of synovial fibrosis. Concurrently, the development of a prognostic risk model facilitates early OA diagnosis, functional recovery evaluation, and offers direction for further therapy.

Identify Regulatory eQTLs by Multiome Sequencing in Prostate Single Cells.

While genome-wide association studies and expression quantitative trait loci (eQTL) analysis have made significant progress in identifying noncoding variants associated with prostate cancer risk and bulk tissue transcriptome changes, the regulatory effect of these genetic elements on gene expression remains largely unknown. Recent developments in single-cell sequencing have made it possible to perform ATAC-seq and RNA-seq profiling simultaneously to capture functional associations between chromatin accessibility and gene expression. In this study, we tested our hypothesis that this multiome single-cell approach allows for mapping regulatory elements and their target genes at prostate cancer risk loci. We applied a 10X Multiome ATAC + Gene Expression platform to encapsulate Tn5 transposase-tagged nuclei from multiple prostate cell lines for a total of 65,501 high quality single cells from RWPE1, RWPE2, PrEC, BPH1, DU145, PC3, 22Rv1 and LNCaP cell lines. To address data sparsity commonly seen in the single-cell sequencing, we performed targeted sequencing to enrich sequencing data at prostate cancer risk loci involving 2,730 candidate germline variants and 273 associated genes. Although not increasing the number of captured cells, the targeted multiome data did improve eQTL gene expression abundance by about 20% and chromatin accessibility abundance by about 5%. Based on this multiomic profiling, we further associated RNA expression alterations with chromatin accessibility of germline variants at single cell levels. Cross validation analysis showed high overlaps between the multiome associations and the bulk eQTL findings from GTEx prostate cohort. We found that about 20% of GTEx eQTLs were covered within the significant multiome associations (p-value ≤ 0.05, gene abundance percentage ≥ 5%), and roughly 10% of the multiome associations could be identified by significant GTEx eQTLs. We also analyzed accessible regions with available heterozygous SNP reads and observed more frequent association in genomic regions with allelically accessible variants (p = 0.0055). Among these findings were previously reported regulatory variants including rs60464856-RUVBL1 (multiome p-value = 0.0099 in BPH1) and rs7247241-SPINT2 (multiome p-value = 0.0002- 0.0004 in 22Rv1). We also functionally validated a new regulatory SNP and its target gene rs2474694-VPS53 (multiome p-value = 0.00956 in BPH1 and 0.00625 in DU145) by reporter assay and SILAC proteomics sequencing. Taken together, our data demonstrated the feasibility of the multiome single-cell approach for identifying regulatory SNPs and their regulated genes.

Genome-wide association and expression quantitative trait loci in cattle reveals common genes regulating mammalian fertility

Most genetic variants associated with fertility in mammals fall in non-coding regions of the genome and it is unclear how these variants affect fertility. Here we use genome-wide association summary statistics for Heifer puberty (pubertal or not at 600 days) from 27,707 Bos indicus, Bos taurus and crossbred cattle; multi-trait GWAS signals from 2119 indicine cattle for four fertility traits, including days to calving, age at first calving, pregnancy status, and foetus age in weeks (assessed by rectal palpation of the foetus); and expression quantitative trait locus for whole blood from 489 indicine cattle, to identify 87 putatively functional genes affecting cattle fertility. Our analysis reveals a significant overlap between the set of cattle and previously reported human fertility-related genes, impling the existence of a shared pool of genes that regulate fertility in mammals. These findings are crucial for developing approaches to improve fertility in cattle and potentially other mammals.

MR-GGI: accurate inference of gene–gene interactions using Mendelian randomization

BackgroundResearchers have long studied the regulatory processes of genes to uncover their functions. Gene regulatory network analysis is one of the popular approaches for understanding these processes, requiring accurate identification of interactions among the genes to establish the gene regulatory network. Advances in genome-wide association studies and expression quantitative trait loci studies have led to a wealth of genomic data, facilitating more accurate inference of gene–gene interactions. However, unknown confounding factors may influence these interactions, making their interpretation complicated. Mendelian randomization (MR) has emerged as a valuable tool for causal inference in genetics, addressing confounding effects by estimating causal relationships using instrumental variables. In this paper, we propose a new statistical method, MR-GGI, for accurately inferring gene–gene interactions using Mendelian randomization.ResultsMR-GGI applies one gene as the exposure and another as the outcome, using causal cis-single-nucleotide polymorphisms as instrumental variables in the inverse-variance weighted MR model. Through simulations, we have demonstrated MR-GGI's ability to control type 1 error and maintain statistical power despite confounding effects. MR-GGI performed the best when compared to other methods using the F1 score on the DREAM5 dataset. Additionally, when applied to yeast genomic data, MR-GGI successfully identified six clusters. Through gene ontology analysis, we have confirmed that each cluster in our study performs distinct functional roles by gathering genes with specific functions.ConclusionThese findings demonstrate that MR-GGI accurately inferences gene–gene interactions despite the confounding effects in real biological environments.

Examining the Causal Connection between Lipid-lowering Medications and Malignant Meningiomas through Drug-target Mendelian Randomization Analysis

Objectives: This study aims to investigate the causal link between the use of statins, a type of 3-hydroxy-3-methylglutaryl-coenzyme A (HMG-CoA) reductase inhibitor, and the risk of developing malignant meningiomas, which are aggressive and recurrent tumors of the central nervous system with limited treatment options. Methods: Using Mendelian Randomization (MR) analysis, the study explored the relationship between genetic variants related to the expression of lipid-lowering drug targets (HMGCR, PCSK9, NPC1L1, and APOB) and malignant meningiomas. The analysis utilized data from Genome-Wide Association Studies (GWAS) and expression quantitative trait loci (eQTL) databases, with a focus on the genetic homogeneity of the Finnish population. Instrumental variables for the MR analysis were derived from significant eQTLs for the mentioned drug targets. Results: The MR analysis found a significant association between genetic variants linked to HMGCR inhibitor (statin) exposure and a reduced risk of malignant meningiomas. Specifically, an increased expression of the HMGCR gene in the blood was associated with lower susceptibility to malignant meningiomas (Odds Ratio [OR] = 2.57, 95% Confidence Interval [CI] = 1.05 - 6.31; p = 0.039). No significant associations were observed for other lipid-lowering drug targets. Conclusion: Preliminary evidence suggests that statin use may lower the risk of developing malignant meningiomas, indicating a potential therapeutic benefit for managing this type of cancer. However, further research, including clinical trials, is necessary to confirm these findings and understand the mechanisms behind the protective effect of statins against malignant meningiomas.

Genome-wide expression quantitative trait locus analysis reveals silk-preferential gene regulatory network in maize.

Silk of maize (Zea mays L.) contains diverse metabolites with complicated structures and functions, making it a great challenge to explore the mechanisms of metabolic regulation. Genome-wide identification of silk-preferential genes and investigation of their expression regulation provide an opportunity to reveal the regulatory networks of metabolism. Here, we applied the expression quantitative trait locus (eQTL) mapping on a maize natural population to explore the regulation of gene expression in unpollinated silk of maize. We obtained 3,985 silk-preferential genes that were specifically or preferentially expressed in silk using our population. Silk-preferential genes showed more obvious expression variations compared with broadly expressed genes that were ubiquitously expressed in most tissues. We found that trans-eQTL regulation played a more important role for silk-preferential genes compared to the broadly expressed genes. The relationship between 38 transcription factors and 85 target genes, including silk-preferential genes, were detected. Finally, we constructed a transcriptional regulatory network around the silk-preferential gene Bx10, which was proposed to be associated with response to abiotic stress and biotic stress. Taken together, this study deepened our understanding of transcriptome variation in maize silk and the expression regulation of silk-preferential genes, enhancing the investigation of regulatory networks on metabolic pathways.

Bayesian colocalization of GWAS and eQTL signals reveals cell type-specific genes and regulatory variants for susceptibility to subtypes of ischemic stroke

A colocalization analysis of genome-wide association study (GWAS) signals and expression quantitative trait loci (eQTL) was conducted to pinpoint target genes and their regulatory nucleotide variants for subtypes of ischemic stroke. We utilized GWAS data from prominent meta-analysis consortia (MEGASTROKE and GIGASTROKE) and single-cell eQTL data in brain and blood tissues to enhance accuracy and minimize noise inherent in bulk RNA-seq. Employing Bayesian colocalization methods, we identified ten shared loci between GWAS and eQTL signals, targeting five eGenes. Specifically, RAPH1 and ICA1L were discovered for small vessel stroke (SVS), whereas SCYL3, CAV1, and CAV2 were for cardioembolic stroke (CS). However, no findings have been made for large artery stroke. The exploration and subsequent functional analysis of causal variants within the colocalized regions revealed their regulatory roles, particularly as enhancer variants (e.g., rs144505847 and rs72932755 targeting ICA1L; rs629234 targeting SCYL3; rs3807989 targeting CAV1 and CAV2). Notably, our study unveiled that all eQTL for CS were identified in oligodendrocytes, while those for SVS were across excitatory neurons, astrocytes, and oligodendrocyte precursor cells. This underscores the heterogeneous tissue-specific genetic factors by subtypes of ischemic stroke. The study emphasizes the need for intensive research efforts to discover causative genes and variants, unravelling the cell type-specific genetic architecture of ischemic stroke subtypes. This knowledge is crucial for advancing our understanding of the underlying pathophysiology and paving the way for precision neurology applications.

Genome-wide QTL and eQTL mapping reveal genes associated with growth rate trait of the Pacific white shrimp (Litopenaeus vannamei)

BackgroundGrowth rate is a crucial economic trait for farmed animals, but the genetic regulation of this trait is largely unknown in non-model organisms such as shrimp.ResultsIn this study, we performed genome-wide phenotypic quantitative trait loci (QTL) and expression quantitative trait loci (eQTL) mapping analyses to identify genes affecting the growth rate of Pacific white shrimp (Litopenaeus vannamei), which is the most commercially-farmed crustacean worldwide. We used RNA-sequencing of 268 individuals in a mapping population, and subsequently validated our findings through gene silencing and shrimp growth experiments. We constructed a high-density genetic linkage map comprising 5533 markers spanning 44 linkage groups, with a total distance of 6205.75 cM and an average marker interval of 1.12 cM. Our analyses identified 11 QTLs significantly correlated with growth rate, and 117,525 eQTLs. By integrating QTL and eQTL data, we identified a gene (metalloreductase STEAP4) highly associated with shrimp growth rate. RNA interference (RNAi) analysis and growth experiments confirmed that STEAP4 was significantly correlated with growth rate in L. vannamei.ConclusionsOur results indicate that the comprehensive analysis of QTL and eQTL can effectively identify genes involved in complex animal traits. This is important for marker-assisted selection (MAS) of animals. Our work contributes to the development of shrimp breeding and available genetic resources.

Mycobacterium tuberculosis-dependent monocyte expression quantitative trait loci, cytokine production, and TB pathogenesis.

The heterogeneity of outcomes after Mycobacterium tuberculosis (Mtb) exposure is a conundrum associated with millennia of host-pathogen co-evolution. We hypothesized that human myeloid cells contain genetically encoded, Mtb-specific responses that regulate critical steps in tuberculosis (TB) pathogenesis. 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-γ release assay (IGRA) conversion. 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 Mtb-induced 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. 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.

Five genes identified as prognostic markers for colorectal cancer through the integration of genome-wide association study and expression quantitative trait locidata.

Background: Colorectal cancer (CRC) is a prominent form of cancer globally, ranking second in terms of prevalence and serving as a leading cause of cancer-related deaths, but the underlying biological interpretation remains largely unknown. Methods: We used the summary data-based Mendelian randomizationmethod to integrate CRC genome-wide association studies(ncase=7062;ncontrol=195,745) and expression quantitative trait loci summary data in peripheral whole blood (Consortium for Architecture of Gene Expression: n = 2765; Genotype-Tissue Expression [v8]: n = 755) and colon tissue (colon-transverse: n = 406;colon-sigmoid: n = 373) and identified related genes. Results: Genes ABTB1, CYP21A2, NLRP1, PHKG1 and PIP5K1C have emerged as significant prognostic markers for CRC patient survival. Functional analysis revealed their involvement in cancer cell migration and invasion mechanisms, providing valuable insights for the development of future anti-CRC drugs. Conclusion: We successfully identified five CRC risk genes, providing new insights and research directions for the effective mechanisms of CRC.

Genetic association of lipid-lowering drugs with aortic aneurysms: a Mendelian randomization study.

The lack of effective pharmacotherapies for aortic aneurysms (AA) is a persistent clinical challenge. Lipid metabolism plays an essential role in AA. However, the impact of lipid-lowering drugs on AA remains controversial. The study aimed to investigate the genetic association between lipid-lowering drugs and AA. Our research used publicly available data on genome-wide association studies (GWASs) and expression quantitative trait loci (eQTL) studies. Genetic instruments, specifically eQTLs related to drug-target genes and SNPs (single nucleotide polymorphisms) located near or within the drug-target loci associated with low-density lipoprotein cholesterol (LDL-C), have been served as proxies for lipid-lowering medications. Drug-Target Mendelian Randomization (MR) study is used to determine the causal association between lipid-lowering drugs and different types of AA. The MR analysis revealed that higher expression of HMGCR (3-hydroxy-3-methylglutaryl coenzyme A reductase) was associated with increased risk of AA (OR = 1.58, 95% CI = 1.20-2.09, P = 1.20 × 10-03) and larger lumen size (aortic maximum area: OR = 1.28, 95% CI = 1.13-1.46, P = 1.48 × 10-04; aortic minimum area: OR = 1.26, 95% CI = 1.21-1.42, P = 1.78 × 10-04). PCSK9 (proprotein convertase subtilisin/kexin type 9) and CETP (cholesteryl ester transfer protein) show a suggestive relationship with AA (PCSK9: OR = 1.34, 95% CI = 1.10-1.63, P = 3.07 × 10-03; CETP: OR = 1.38, 95% CI = 1.06-1.80, P = 1.47 × 10-02). No evidence to support genetically mediated NPC1L1 (Niemann-Pick C1-Like 1) and LDLR (low-density lipoprotein cholesterol receptor) are associated with AA. This study provides causal evidence for the genetic association between lipid-lowering drugs and AA. Higher gene expression of HMGCR, PCSK9, and CETP increases AA risk. Furthermore, HMGCR inhibitors may link with smaller aortic lumen size.

Comprehensive Deciphering the Complexity of the Deep Bite: Insight from Animal Model to Human Subjects.

Deep bite is a malocclusion phenotype, defined as the misalignment in the vertical dimension of teeth and jaws and characterized by excessive overlap of the upper front teeth over the lower front teeth. Numerous factors, including genetics, environmental factors, and behavioral ones, might contribute to deep bite. In this study, we discuss the current clinical treatment strategies for deep bite, summarize the already published findings of genetic analysis associated with this complex phenotype, and their constraints. Finally, we propose a comprehensive roadmap to facilitate investigations for determining the genetic bases of this complex phenotype development. Initially, human deep bite phenotype, genetics of human deep bite, the prevalence of human deep bite, diagnosis, and treatment of human deep bite were the search terms for published publications. Here, we discuss these findings and their limitations and our view on future strategies for studying the genetic bases of this complex phenotype. New preventative and treatment methods for this widespread dental issue can be developed with the help of an understanding of the genetic and epigenetic variables that influence malocclusion. Additionally, malocclusion treatment may benefit from technological developments like 3D printing and computer-aided design and manufacture (CAD/CAM). These technologies enable the development of personalized surgical and orthodontic guidelines, enhancing the accuracy and effectiveness of treatment. Overall, the most significant results for the patient can only be achieved with a customized treatment plan created by an experienced orthodontic professional. To design a plan that meets the patient's specific requirements and expectations, open communication between the patient and the orthodontist is essential. Here, we propose to conduct a genome-wide association study (GWAS), RNAseq analysis, integrating GWAS and expression quantitative trait loci (eQTL), micro and small RNA, and long noncoding RNA analysis in tissues associated with deep bite malocclusion in human, and complement it by the same approaches in the collaborative cross (CC) mouse model which offer a novel platform for identifying genetic factors as a cause of deep bite in mice, and subsequently can then be translated to humans. An additional direct outcome of this study is discovering novel genetic elements to advance our knowledge of how this malocclusion phenotype develops and open the venue for early identification of patients carrying the susceptible genetic factors so that we can offer early prevention and treatment strategies, a step towards applying a personalized medicine approach.

Transcriptional risk scores in Alzheimer's disease: From pathology to cognition.

Our previously developed blood-based transcriptional risk scores (TRS) showed associations with diagnosis and neuroimaging biomarkers for Alzheimer's disease (AD). Here, we developed brain-based TRS. We integrated AD genome-wide association study summary and expression quantitative trait locus data to prioritize target genes using Mendelian randomization. We calculated TRS using brain transcriptome data of two independent cohorts (N=878) and performed association analysis of TRS with diagnosis, amyloidopathy, tauopathy, and cognition. We compared AD classification performance of TRS with polygenic risk scores (PRS). Higher TRS values were significantly associated with AD, amyloidopathy, tauopathy, worse cognition, and faster cognitive decline, which were replicated in an independent cohort. The AD classification performance of PRS was increased with the inclusion of TRS up to 16% with the area under the curve value of 0.850. Our results suggest brain-based TRS improves the AD classification of PRS and may be a potential AD biomarker. Transcriptional risk score (TRS) is developed using brain RNA-Seq data. Higher TRS values are shown in Alzheimer's disease (AD). TRS improves the AD classification power of PRS up to 16%. TRS is associated with AD pathology presence. TRS is associated with worse cognitive performance and faster cognitive decline.

Combined genome-wide association studies and expression quantitative trait locus analysis uncovers a genetic regulatory network of floral organ number in a tree peony (Paeonia suffruticosa Andrews) breeding population.

Great progress has been made in our understanding of floral organ identity determination and its regulatory network in many species; however, the quantitative genetic basis of floral organ number variation is far less well understood for species-specific traits from the perspective of population variation. Here, using a tree peony (Paeonia suffruticosa Andrews, Paeoniaceae) cultivar population as a model, the phenotypic polymorphism and genetic variation based on genome-wide association studies (GWAS) and expression quantitative trait locus (eQTL) analysis were analyzed. Based on 24 phenotypic traits of 271 representative cultivars, the transcript profiles of 119 cultivars were obtained, which indicated abundant genetic variation in tree peony. In total, 86 GWAS-related cis-eQTLs and 3188 trans-eQTL gene pairs were found to be associated with the numbers of petals, stamens, and carpels. In addition, 19 floral organ number-related hub genes with 121 cis-eQTLs were obtained by weighted gene co-expression network analysis, among which five hub genes belonging to the ABCE genes of the MADS-box family and their spatial-temporal co-expression and regulatory network were constructed. These results not only help our understanding of the genetic basis of floral organ number variation during domestication, but also pave the way to studying the quantitative genetics and evolution of flower organ number and their regulatory network within populations.

CoNet: Efficient Network Regression for Survival Analysis in Transcriptome-Wide Association Studies—With Applications to Studies of Breast Cancer

Transcriptome-wide association studies (TWASs) aim to detect associations between genetically predicted gene expression and complex diseases or traits through integrating genome-wide association studies (GWASs) and expression quantitative trait loci (eQTL) mapping studies. Most current TWAS methods analyze one gene at a time, ignoring the correlations between multiple genes. Few of the existing TWAS methods focus on survival outcomes. Here, we propose a novel method, namely a COx proportional hazards model for NEtwork regression in TWAS (CoNet), that is applicable for identifying the association between one given network and the survival time. CoNet considers the general relationship among the predicted gene expression as edges of the network and quantifies it through pointwise mutual information (PMI), which is under a two-stage TWAS. Extensive simulation studies illustrate that CoNet can not only achieve type I error calibration control in testing both the node effect and edge effect, but it can also gain more power compared with currently available methods. In addition, it demonstrates superior performance in real data application, namely utilizing the breast cancer survival data of UK Biobank. CoNet effectively accounts for network structure and can simultaneously identify the potential effecting nodes and edges that are related to survival outcomes in TWAS.

Deciphering the chromatin spatial organization landscapes during BMMSC differentiation

The differentiation imbalance in bone marrow mesenchymal stem cells (BMMSCs) is critical for the development of bone density diseases as the population ages. BMMSCs are precursor cells for osteoblasts and adipocytes; however, the chromatin organization landscapes during BMMSC differentiation remain elusive. In this study, we systematically delineate the four-dimensional genome and dynamic epigenetic atlas of BMMSCs by RNA sequencing, assay for transposase-accessible chromatin sequencing, and high-throughput chromosome conformation capture. The structure analyses reveal 17.5% common and 28.5%–30% specific loops among BMMSCs, osteoblasts, and adipocytes. The subsequent correlation of genome-wide association studies and expression quantitative trait locus (eQTL) data with multi-omics analysis reveal 274 genes and 3634 single nucleotide polymorphisms (SNPs) associated with bone degeneration and osteoporosis (OP). We hypothesize that SNP mutations affect transcription factor (TF) binding sites, thereby affecting changes in gene expression. Furthermore, 26 motifs, 260 TFs, and 291 SNPs are identified to affect the eQTL. Among these genes, DAAM2, TIMP2, and TMEM241 are found to be essential for diseases such as bone degeneration and OP and may serve as potential drug targets.

Multi-ancestry transcriptome-wide association analyses yield insights into tobacco use biology and drug repurposing

Most transcriptome-wide association studies (TWASs) so far focus on European ancestry and lack diversity. To overcome this limitation, we aggregated genome-wide association study (GWAS) summary statistics, whole-genome sequences and expression quantitative trait locus (eQTL) data from diverse ancestries. We developed a new approach, TESLA (multi-ancestry integrative study using an optimal linear combination of association statistics), to integrate an eQTL dataset with a multi-ancestry GWAS. By exploiting shared phenotypic effects between ancestries and accommodating potential effect heterogeneities, TESLA improves power over other TWAS methods. When applied to tobacco use phenotypes, TESLA identified 273 new genes, up to 55% more compared with alternative TWAS methods. These hits and subsequent fine mapping using TESLA point to target genes with biological relevance. In silico drug-repurposing analyses highlight several drugs with known efficacy, including dextromethorphan and galantamine, and new drugs such as muscle relaxants that may be repurposed for treating nicotine addiction.

Massively parallel reporter assays and variant scoring identified functional variants and target genes for melanoma loci and highlighted cell-type specificity

The most recent genome-wide association study (GWAS) of cutaneous melanoma identified 54 risk-associated loci, but functional variants and their target genes for most have not been established. Here, we performed massively parallel reporter assays (MPRAs) by using malignant melanoma and normal melanocyte cells and further integrated multi-layer annotation to systematically prioritize functional variants and susceptibility genes from these GWAS loci. Of 1,992 risk-associated variants tested in MPRAs, we identified 285 from 42 loci (78% of the known loci) displaying significant allelic transcriptional activities in either cell type (FDR<1%). We further characterized MPRA-significant variants by motif prediction, epigenomic annotation, and statistical/functional fine-mapping to create integrative variant scores, which prioritized one to six plausible candidate variants per locus for the 42 loci and nominated a single variant for 43% of these loci. Overlaying the MPRA-significant variants with genome-wide significant expression or methylation quantitative trait loci (eQTLs or meQTLs, respectively) from melanocytes or melanomas identified candidate susceptibility genes for 60% of variants (172 of 285 variants). CRISPRi of top-scoring variants validated their cis-regulatory effect on the eQTL target genes, MAFF (22q13.1) and GPRC5A (12p13.1). Finally, we identified 36 melanoma-specific and 45 melanocyte-specific MPRA-significant variants, a subset of which are linked to cell-type-specific target genes. Analyses of transcription factor availability in MPRA datasets and variant-transcription-factor interaction in eQTL datasets highlighted the roles of transcription factors in cell-type-specific variant functionality. In conclusion, MPRAs along with variant scoring effectively prioritized plausible candidates for most melanoma GWAS loci and highlighted cellular contexts where the susceptibility variants are functional.

Network regression analysis for binary and ordinal categorical phenotypes in transcriptome-wide association studies.

Transcriptome-wide association studies aim to integrate genome-wide association studies and expression quantitative trait loci mapping studies for exploring the gene regulatory mechanisms underlying diseases. Existing transcriptome-wide association study methods primarily focus on 1 gene at a time. However, complex diseases are seldom resulted from the abnormality of a single gene, but from the biological network involving multiple genes. In addition, binary or ordinal categorical phenotypes are commonly encountered in biomedicine. We develop a proportional odds logistic model for network regression in transcriptome-wide association study, Proportional Odds LOgistic model for NEtwork regression in Transcriptome-wide association study, to detect the association between a network and binary or ordinal categorical phenotype. Proportional Odds LOgistic model for NEtwork regression in Transcriptome-wide association study relies on 2-stage transcriptome-wide association study framework. It first adopts the distribution-robust nonparametric Dirichlet process regression model in expression quantitative trait loci study to obtain the SNP effect estimate on each gene within the network. Then, Proportional Odds LOgistic model for NEtwork regression in Transcriptome-wide association study uses pointwise mutual information to represent the general relationship among the network nodes of predicted gene expression in genome-wide association study, followed by the association analysis with all nodes and edges involved in proportional odds logistic model. A key feature of Proportional Odds LOgistic model for NEtwork regression in Transcriptome-wide association study is its ability to simultaneously identify the disease-related network nodes or edges. With extensive realistic simulations including those under various between-node correlation patterns, we show Proportional Odds LOgistic model for NEtwork regression in Transcriptome-wide association study can provide calibrated type I error control and yield higher power than other existing methods. We finally apply Proportional Odds LOgistic model for NEtwork regression in Transcriptome-wide association study to analyze bipolar and major depression status and blood pressure from UK Biobank to illustrate its benefits in real data analysis.