Large-scale transcriptome-wide association study identifies new prostate cancer risk regions

Transcriptome-Wide Association Study Identified Novel Blood Tissue Gene Biomarkers for Prostate Cancer Risk

OBJECTIVE

A number of susceptibility genes in prostate tissue have been identified to be associated with prostate cancer (PCa) risk. However, the reported genes based on assessing prostate tissue could not fully explain PCa genetic susceptibility. It is believed that genes functioning in the immune system may fill in the gap of some missing heritability.

METHODS

To study potential susceptibility genes acting in such pathways, we performed a transcriptome-wide association study (TWAS) of 79,194 PCa cases and 61,112 control of European ancestry by using three sets of gene expression prediction models of blood tissue.

RESULTS

A total of 470 genes were associated at false discovery rates-corrected p-value < 0.05, of which 51 were implicated as likely causal genes based on fine-mapping analysis. Compared with previous literature, 133 novel genes were reported for the first time. Of the identified genes, five (CREB3L4, GSTP1, MAPK3, NKX3-1, and PIK3C2B) were enriched in a PCa signaling pathway, and 128 genes were enriched in five PCa categories. Importantly, 13 genes (SCP2, LMNA, ZNF148, H2AFV, TACC1, FLII, SUPT4H1, CD300LF, MYO9B, COX6B1, CTSA, EP300, and TSPO) showed consistent effect directions for the measured levels in circulating immune cells between PCa cases and controls, and 14 genes (SLC39A1, ZBTB7B, TRIM59, NCEH1, N4BP2, TAGAP, TACC1, TRAF1, AIP, SECTM1, C18orf54, ZNF793, YIF1B, and TSPO) showed consistency for levels in blood exosomes between PCa patients and controls.

CONCLUSION

The identified blood-based candidate susceptibility genes provide further insights into the genetic basis of PCa risk.

Transcriptome-wide association studies at cell state level using single-cell eQTL data

Transcriptome-wide association studies (TWAS) have been widely used to prioritize relevant genes for diseases. Current methods for TWAS test gene-disease associations at bulk tissue or cell-type-specific pseudobulk level, which do not account for the heterogeneity within cell types. We present TWiST, a statistical method for TWAS analysis at cell state resolution using single-cell expression quantitative trait loci (eQTL) data. Our method uses pseudotime to represent cell states and models the effect of gene expression on trait as a continuous pseudotemporal curve. Therefore, it allows flexible hypothesis testing of global, dynamic, and nonlinear effects. Through simulation studies and real data analysis, we demonstrated that TWiST leads to significantly improved power compared to pseudobulk methods that ignores heterogeneity due to cell states. Application to the OneK1K study identified hundreds of genes with dynamic effects on autoimmune diseases along the trajectory of immune cell differentiation. TWiST presents great promise to understand disease genetics using single-cell eQTL studies.

Genetic insights into idiopathic pulmonary fibrosis: a multi-omics approach to identify potential therapeutic targets

OBJECTIVE

To identify potential therapeutic targets and evaluate the safety profiles for Idiopathic Pulmonary Fibrosis (IPF) using a comprehensive multi-omics approach.

METHOD

We integrated genomic and transcriptomic data to identify therapeutic targets for IPF. First, we conducted a transcriptome-wide association study (TWAS) using the Omnibus Transcriptome Test using Expression Reference Summary data (OTTERS) framework, combining plasma expression quantitative trait loci (eQTL) data with IPF Genome-Wide Association Studies (GWAS) summary statistics from the Global Biobank (discovery) and Finngen (duplication). We then applied Mendelian randomization (MR) to explore causal relationships. RNA-seq co-expression analysis (bulk, single-cell and spatial transcriptomics) was used to identify critical genes, followed by molecular docking to evaluate their druggability. Finally, phenome-wide MR (PheW-MR) using GWAS data from 679 diseases in the UK Biobank assessed the potential adverse effects of the identified genes.

RESULT

We identified 696 genes associated with IPF in the discovery dataset and 986 genes in the duplication dataset, with 126 overlapping genes through TWAS. MR analysis revealed 29 causal genes in the discovery dataset, with 13 linked to increased and 16 to decreased IPF risk. Summary data-based MR (SMR) confirmed six essential genes: ANO9, BRCA1, CCDC200, EZH1, FAM13A, and SFR1. Bulk RNA-seq showed FAM13A upregulation and SFR1 and EZH1 downregulation in IPF. Single-cell RNA-seq revealed gene expression changes across cell types. Molecular docking identified binding solid affinities for essential genes with respiratory drugs, and PheW-MR highlighted potential side effects.

CONCLUSION

We identified six key genes-ANO9, BRCA1, CCDC200, EZH1, FAM13A, and SFR1-as potential drug targets for IPF. Molecular docking revealed strong drug affinities, while PheW-MR analysis highlighted therapeutic potential and associated risks. These findings offer new insights for IPF treatment and further investigation of potential side effects.

Identification of genes associated with testicular germ cell tumor susceptibility through a transcriptome-wide association study

Transcriptome-wide association studies (TWASs) have the potential to identify susceptibility genes associated with testicular germ cell tumors (TGCTs). We conducted a comprehensive TGCT TWAS by integrating genome-wide association study (GWAS) summary data with predicted expression models from normal testis, TGCT tissues, and a cross-tissue panel that encompasses shared regulatory features across 22 normal tissues, including the testis. Gene associations were evaluated while accounting for variant-level effects from GWASs, followed by fine-mapping analyses in regions exhibiting multiple TWAS signals, and finally supplemented by colocalization analysis. Expression and protein patterns of identified TWAS genes were further examined in relevant tissues. Our analysis tested 19,805 gene-disease links, revealing 165 TGCT-associated genes with a false discovery rate of less than 0.01. We prioritized 46 candidate genes by considering GWAS-inflated signals, correlations between neighboring genes, and evidence of colocalization. Among these, 23 genes overlap with 22 GWAS loci, with 7 being associations not previously implicated in TGCT risk. Additionally, 23 genes located within 21 loci are at least 1 Mb away from published GWAS index variants. The 46 prioritized genes display expression levels consistent with expected expression levels in human gonadal cell types and precursor tumor cells and significant enrichment in TGCTs. Additionally, immunohistochemistry revealed protein-level accumulation of two candidate genes, ARID3B and GINM1, in both precursor and tumor cells. These findings enhance our understanding of the genetic predisposition to TGCTs and underscore the importance of further functional investigations into these candidate genes.

Proteome-wide Mendelian randomization identifies causal plasma proteins in prostate cancer development

BACKGROUND

The etiology of prostate cancer remained elusive, whether plasma protein levels are associated with prostate cancer is still unknown.

METHODS

We have performed Mendelian randomization analyses to calculate the causal effects of plasma proteins on the risk of prostate cancer in the PRACTICAL consortium dataset using cis-protein quantitative trait loci (cis-pQTL) variants as instrumental variables for plasma proteins, and cis-expression quantitative trait locus (cis-eQTL) for the circulating gene expression. We also replicated the findings in the FinnGen consortium.

RESULTS

Genetically proxied levels of 4 plasma proteins (CREB3L4, HDGF, SERPINA3, GNPNAT1) were identified as positively correlated with an increased risk of prostate cancer, while an increase in genetically proxied levels of 5 plasma proteins (TNFRSF6B, GSK3A, EIF4B, CLIC1, SMAD2) were significantly associated with a decreased risk of prostate cancer in the PRACTICAL consortium. Among the identified proteins, the causal effects of six proteins including CREB3L4, HDGF, SERPINA3, TNFRSF6B, EIF4B, and SMAD2 remained significant in the replication analyses in the FinnGen consortium and when combined with meta-analyses (SMAD2: OR 0.710, 95% CI 0.578-0.873, p-value = 0.001; CREB3L4: OR 1.260, 95% CI 1.164-1.364, p-value < 0.0001; HDGF: OR 1.072, 95% CI 1.021-1.125, p-value = 0.005; SERPINA3: OR 1.138, 95% CI 1.091-1.187, p-value < 0.0001; TNFRSF6B: OR 0.656, 95% CI 0.496-0.869, p-value = 0.003; EIF4B: OR 0.701, 95% CI 0.618-0.796, p-value < 0.0001). SMAD2 and CREB3L4 gene expressions proxied with cis-expression quantitative trait loci are also significantly associated with the risk of prostate cancer in both consortiums and when combined with meta-analyses (SMAD2: OR 0.787, 95% CI 0.719-0.861, p-value = 1.00 × 10-4; CREB3L4: OR 1.219, 95% CI 1.033-1.438, p-value = 0.019).

CONCLUSIONS

Our consistent results highlighted the important roles of plasma SMAD2 and CREB3L4 in the risk of prostate cancer. Further investigations on these proteins may reveal their potential in the prevention and treatment of prostate cancer.

Enhancing disease risk gene discovery by integrating transcription factor-linked trans-variants into transcriptome-wide association analyses

Transcriptome-wide association studies (TWAS) have been successful in identifying disease susceptibility genes by integrating cis-variants predicted gene expression with genome-wide association studies (GWAS) data. However, trans-variants for predicting gene expression remain largely unexplored. Here, we introduce transTF-TWAS, which incorporates transcription factor (TF)-linked trans-variants to enhance model building for TF downstream target genes. Using data from the Genotype-Tissue Expression project, we predict gene expression and alternative splicing and applied these prediction models to large GWAS datasets for breast, prostate, lung cancers and other diseases. We demonstrate that transTF-TWAS outperforms other existing TWAS approaches in both constructing gene expression prediction models and identifying disease-associated genes, as shown by simulations and real data analysis. Our transTF-TWAS approach significantly contributes to the discovery of disease risk genes. Findings from this study shed new light on several genetically driven key TF regulators and their associated TF-gene regulatory networks underlying disease susceptibility.

Fine-mapping causal tissues and genes at disease-associated loci

Complex diseases often have distinct mechanisms spanning multiple tissues. We propose tissue-gene fine-mapping (TGFM), which infers the posterior inclusion probability (PIP) for each gene-tissue pair to mediate a disease locus by analyzing summary statistics and expression quantitative trait loci (eQTL) data; TGFM also assigns PIPs to non-mediated variants. TGFM accounts for co-regulation across genes and tissues and models uncertainty in cis-predicted expression models, enabling correct calibration. We applied TGFM to 45 UK Biobank diseases or traits using eQTL data from 38 Genotype-Tissue Expression (GTEx) tissues. TGFM identified an average of 147 PIP > 0.5 causal genetic elements per disease or trait, of which 11% were gene-tissue pairs. Causal gene-tissue pairs identified by TGFM reflected both known biology (for example, TPO-thyroid for hypothyroidism) and biologically plausible findings (for example, SLC20A2-artery aorta for diastolic blood pressure). Application of TGFM to single-cell eQTL data from nine cell types in peripheral blood mononuclear cells (PBMCs), analyzed jointly with GTEx tissues, identified 30 additional causal gene-PBMC cell type pairs.

A cross-tissue transcriptome-wide association study identifies WDPCP as a potential susceptibility gene for coronary atherosclerosis

Background

Coronary atherosclerosis (CAS) is a complex chronic inflammatory disease with significant genetic and environmental contributions. While genome-wide association studies (GWAS) have pinpointed many risk loci, over 75 % are in non-coding regions, complicating functional analysis and understanding gene-disease mechanisms.

Methods

We conducted a cross-tissue transcriptome-wide association study (TWAS) using data from the GWAS Catalog (16,041 cases, 440,307 controls) and the Genotype-Tissue Expression (GTEx) v8 eQTL dataset. Initially, we used the Unified Test for Molecular Signatures (UTMOST) for analysis, followed by validation with Functional Summary-based Imputation (FUSION) and conditional and joint (COJO) analyses. Candidate genes were further refined using Multi-marker Analysis of Genomic Annotation (MAGMA). Causal relationships were assessed through Summary Data-Based Mendelian Randomization (SMR), colocalization analysis (COLOC), and Mendelian Randomization (MR). GeneMANIA was used to identify interacting genes, and Phenome-Wide Association Study (PheWAS) was employed to enhance the results.

Results

UTMOST identified 33 susceptibility genes for CAS. Out of these, 17 met stringent criteria in both UTMOST and FUSION analyses. Combining results from UTMOST, FUSION, and MAGMA, we identified four critical candidate genes. WDPCP was the only gene to pass SMR, COLOC, and MR analyses, confirming its causal role in CAS. GeneMANIA revealed additional interacting genes, and PheWAS validated WDPCP's role as a susceptibility gene.

Conclusion

WDPCP is a potential novel susceptibility gene for CAS, influencing endothelial function, lipid metabolism, and coronary artery development. This study extends GWAS findings, highlighting WDPCP's potential as a therapeutic target and its consistent expression across different tissues. Further validation studies are warranted.

Isoform-level analyses of 6 cancers uncover extensive genetic risk mechanisms undetected at the gene-level

Integrating genome-wide association study (GWAS) and transcriptomic datasets can help identify potential mediators for germline genetic risk of cancer. However, traditional methods have been largely unsuccessful because of an overreliance on total gene expression. These approaches overlook alternative splicing, which can produce multiple isoforms from the same gene, each with potentially different effects on cancer risk. Here, we integrate genetic and multi-tissue isoform-level gene expression data from the Genotype Tissue-Expression Project (GTEx, N = 108-574) with publicly available European-ancestry GWAS summary statistics (all N > 20,000 cases) to identify both isoform- and gene-level risk associations with six cancers (breast, endometrial, colorectal, lung, ovarian, prostate) and six related cancer subtype classifications (N = 12 total). Compared to traditional methods leveraging total gene expression, directly modeling isoform expression through transcriptome-wide association studies (isoTWAS) substantially increases discovery of transcriptomic mechanisms underlying genetic associations. Using the same RNA-seq datasets, isoTWAS identified 164% more significant unique gene associations compared to TWAS (6,163 and 2,336, respectively), with isoTWAS-prioritized genes enriched 4-fold for evolutionarily-constrained genes (P = 6.1 × 10-13). isoTWAS tags transcriptomic associations at 52% more independent GWAS loci compared to TWAS across the six cancers. Additionally, isoform expression mediates an estimated 63% greater proportion of cancer risk SNP heritability compared to gene expression when evaluating cis-genetic influence on isoform expression. We highlight several notable isoTWAS associations that demonstrate GWAS colocalization at the isoform level but not at the gene level, including, CLPTM1L (lung cancer), LAMC1 (colorectal), and BABAM1 (breast). These results underscore the critical importance of modeling isoform-level expression to maximize discovery of genetic risk mechanisms for cancers.

Hierarchical joint analysis of marginal summary statistics-Part II: High-dimensional instrumental analysis of omics data

Instrumental variable (IV) analysis has been widely applied in epidemiology to infer causal relationships using observational data. Genetic variants can also be viewed as valid IVs in Mendelian randomization and transcriptome-wide association studies. However, most multivariate IV approaches cannot scale to high-throughput experimental data. Here, we leverage the flexibility of our previous work, a hierarchical model that jointly analyzes marginal summary statistics (hJAM), to a scalable framework (SHA-JAM) that can be applied to a large number of intermediates and a large number of correlated genetic variants-situations often encountered in modern experiments leveraging omic technologies. SHA-JAM aims to estimate the conditional effect for high-dimensional risk factors on an outcome by incorporating estimates from association analyses of single-nucleotide polymorphism (SNP)-intermediate or SNP-gene expression as prior information in a hierarchical model. Results from extensive simulation studies demonstrate that SHA-JAM yields a higher area under the receiver operating characteristics curve (AUC), a lower mean-squared error of the estimates, and a much faster computation speed, compared to an existing approach for similar analyses. In two applied examples for prostate cancer, we investigated metabolite and transcriptome associations, respectively, using summary statistics from a GWAS for prostate cancer with more than 140,000 men and high dimensional publicly available summary data for metabolites and transcriptomes.

Cross-population enhancement of PrediXcan predictions with a gnomAD-based east Asian reference framework

Over the past decade, genome-wide association studies have identified thousands of variants significantly associated with complex traits. For each locus, gene expression levels are needed to further explore its biological functions. To address this, the PrediXcan algorithm leverages large-scale reference data to impute the gene expression level from single nucleotide polymorphisms, and thus the gene-trait associations can be tested to identify the candidate causal genes. However, a challenge arises due to the fact that most reference data are from subjects of European ancestry, and the accuracy and robustness of predicted gene expression in subjects of East Asian (EAS) ancestry remains unclear. Here, we first simulated a variety of scenarios to explore the impact of the level of population diversity on gene expression. Population differentiated variants were estimated by using the allele frequency information from The Genome Aggregation Database. We found that the weights of a variants was the main factor that affected the gene expression predictions, and that ~70% of variants were significantly population differentiated based on proportion tests. To provide insights into this population effect on gene expression levels, we utilized the allele frequency information to develop a gene expression reference panel, Predict Asian-Population (PredictAP), for EAS ancestry. PredictAP can be viewed as an auxiliary tool for PrediXcan when using genotype data from EAS subjects.

Identification of novel drug targets for osteoarthritis by integrating genetics and proteomes from blood

BACKGROUND

Osteoarthritis (OA) is a degenerative osteoarticular disease, involving genetic predisposition. How the risk variants confer the risk of OA through their effects on proteins remains largely unknown. Therefore, we aimed to discover new and effective drug targets for OA and its subtypes.

METHODS

A proteome-wide association study (PWAS) was performed based on OA and its subtypes genome-wide association studies (GWAS) summary datasets and the protein quantitative trait loci (pQTL) data. Subsequently, Mendelian randomization (MR) and colocalization analysis was conducted to estimate the associations between protein and OA risk. The replication analysis was performed in an independent dataset of human plasma pQTL data.

RESULTS

The abundance of seven proteins was causally related to OA, two proteins to knee OA and six proteins to hip OA, respectively. We replicated 2 of these proteins using an independent pQTL dataset. With the further support of colocalization, and higher ECM1 level was causally associated with a higher risk of OA and hip OA. Higher PCSK1 level was causally associated with a lower risk of OA. And higher levels of ITIH1, EFEMP1, and ERLEC1 were associated with decreased risk of hip OA.

CONCLUSION

Our study provides new insights into the genetic component of protein abundance in OA and a promising therapeutic target for future drug development.

Large-Scale Alternative Polyadenylation-Wide Association Studies to Identify Putative Cancer Susceptibility Genes

Alternative polyadenylation (APA) modulates mRNA processing in the 3'-untranslated regions (3' UTR), affecting mRNA stability and translation efficiency. Research into genetically regulated APA has the potential to provide insights into cancer risk. In this study, we conducted large APA-wide association studies to investigate associations between APA levels and cancer risk. Genetic models were built to predict APA levels in multiple tissues using genotype and RNA sequencing data from 1,337 samples from the Genotype-Tissue Expression project. Associations of genetically predicted APA levels with cancer risk were assessed by applying the prediction models to data from large genome-wide association studies of six common cancers among European ancestry populations: breast, ovarian, prostate, colorectal, lung, and pancreatic cancers. A total of 58 risk genes (corresponding to 76 APA sites) were associated with at least one type of cancer, including 25 genes previously not linked to cancer susceptibility. Of the identified risk APAs, 97.4% and 26.3% were supported by 3'-UTR APA quantitative trait loci and colocalization analyses, respectively. Luciferase reporter assays for four selected putative regulatory 3'-UTR variants demonstrated that the risk alleles of 3'-UTR variants, rs324015 (STAT6), rs2280503 (DIP2B), rs1128450 (FBXO38), and rs145220637 (LDHA), significantly increased the posttranscriptional activities of their target genes compared with reference alleles. Furthermore, knockdown of the target genes confirmed their ability to promote proliferation and migration. Overall, this study provides insights into the role of APA in the genetic susceptibility to common cancers. Significance: Systematic evaluation of associations of alternative polyadenylation with cancer risk reveals 58 putative susceptibility genes, highlighting the contribution of genetically regulated alternative polyadenylation of 3'UTRs to genetic susceptibility to cancer.

Enhancing Disease Risk Gene Discovery by Integrating Transcription Factor-Linked Trans-located Variants into Transcriptome-Wide Association Analyses

Transcriptome-wide association studies (TWAS) have been successful in identifying disease susceptibility genes by integrating cis-variants predicted gene expression with genome-wide association studies (GWAS) data. However, trans-located variants for predicting gene expression remain largely unexplored. Here, we introduce transTF-TWAS, which incorporates transcription factor (TF)-linked trans-located variants to enhance model building. Using data from the Genotype-Tissue Expression project, we predict gene expression and alternative splicing and applied these models to large GWAS datasets for breast, prostate, and lung cancers. We demonstrate that transTF-TWAS outperforms other existing TWAS approaches in both constructing gene prediction models and identifying disease-associated genes, as evidenced by simulations and real data analysis. Our transTF-TWAS approach significantly contributes to the discovery of disease risk genes. Findings from this study have shed new light on several genetically driven key regulators and their associated regulatory networks underlying disease susceptibility.

Large-scale integration of omics and electronic health records to identify potential risk protein biomarkers and therapeutic drugs for cancer prevention and intervention

Identifying risk protein targets and their therapeutic drugs is crucial for effective cancer prevention. Here, we conduct integrative and fine-mapping analyses of large genome-wide association studies data for breast, colorectal, lung, ovarian, pancreatic, and prostate cancers, and characterize 710 lead variants independently associated with cancer risk. Through mapping protein quantitative trait loci (pQTL) for these variants using plasma proteomics data from over 75,000 participants, we identify 365 proteins associated with cancer risk. Subsequent colocalization analysis identifies 101 proteins, including 74 not reported in previous studies. We further characterize 36 potential druggable proteins for cancers or other disease indications. Analyzing >3.5 million electronic health records, we uncover five drugs (Haloperidol, Trazodone, Tranexamic Acid, Haloperidol, and Captopril) associated with increased cancer risk and two drugs (Caffeine and Acetazolamide) linked to reduced colorectal cancer risk. This study offers novel insights into therapeutic drugs targeting risk proteins for cancer prevention and intervention.

Improved multi-ancestry fine-mapping identifies cis-regulatory variants underlying molecular traits and disease risk

Multi-ancestry statistical fine-mapping of cis-molecular quantitative trait loci (cis-molQTL) aims to improve the precision of distinguishing causal cis-molQTLs from tagging variants. However, existing approaches fail to reflect shared genetic architectures. To solve this limitation, we present the Sum of Shared Single Effects (SuShiE) model, which leverages LD heterogeneity to improve fine-mapping precision, infer cross-ancestry effect size correlations, and estimate ancestry-specific expression prediction weights. We apply SuShiE to mRNA expression measured in PBMCs (n=956) and LCLs (n=814) together with plasma protein levels (n=854) from individuals of diverse ancestries in the TOPMed MESA and GENOA studies. We find SuShiE fine-maps cis-molQTLs for 16% more genes compared with baselines while prioritizing fewer variants with greater functional enrichment. SuShiE infers highly consistent cis-molQTL architectures across ancestries on average; however, we also find evidence of heterogeneity at genes with predicted loss-of-function intolerance, suggesting that environmental interactions may partially explain differences in cis-molQTL effect sizes across ancestries. Lastly, we leverage estimated cis-molQTL effect-sizes to perform individual-level TWAS and PWAS on six white blood cell-related traits in AOU Biobank individuals (n=86k), and identify 44 more genes compared with baselines, further highlighting its benefits in identifying genes relevant for complex disease risk. Overall, SuShiE provides new insights into the cis-genetic architecture of molecular traits.

Multi-tissue transcriptome-wide association study identifies novel candidate susceptibility genes for cataract

Introduction

Cataract is the leading cause of blindness among the elderly worldwide. Twin and family studies support an important role for genetic factors in cataract susceptibility with heritability estimates up to 58%. To date, 55 loci for cataract have been identified by genome-wide association studies (GWAS), however, much work remains to identify the causal genes. Here, we conducted a transcriptome-wide association study (TWAS) of cataract to prioritize causal genes and identify novel ones, and examine the impact of their expression.

Methods

We performed tissue-specific and multi-tissue TWAS analyses to assess associations between imputed gene expression from 54 tissues (including 49 from the Genotype Tissue Expression (GTEx) Project v8) with cataract using FUSION software. Meta-analyzed GWAS summary statistics from 59,944 cataract cases and 478,571 controls, all of European ancestry and from two cohorts (GERA and UK Biobank) were used. We then examined the expression of the novel genes in the lens tissue using the iSyTE database.

Results

Across tissue-specific and multi-tissue analyses, we identified 99 genes for which genetically predicted gene expression was associated with cataract after correcting for multiple testing. Of these 99 genes, 20 (AC007773.1, ANKH, ASIP, ATP13A2, CAPZB, CEP95, COQ6, CREB1, CROCC, DDX5, EFEMP1, EIF2S2, ESRRB, GOSR2, HERC4, INSRR, NIPSNAP2, PICALM, SENP3, and SH3YL1) did not overlap with previously reported cataract-associated loci. Tissue-specific analysis identified 202 significant gene-tissue associations for cataract, of which 166 (82.2%), representing 9 unique genes, were attributed to the previously reported 11q13.3 locus. Tissue-enrichment analysis revealed that gastrointestinal tissues represented one of the highest proportions of the Bonferroni-significant gene-tissue associations (21.3%). Moreover, this gastrointestinal tissue type was the only anatomical category significantly enriched in our results, after correcting for the number of tissue donors and imputable genes for each reference panel. Finally, most of the novel cataract genes (e.g., Capzb) were robustly expressed in iSyTE lens data.

Discussion

Our results provide evidence of the utility of imputation-based TWAS approaches to characterize known GWAS risk loci and identify novel candidate genes that may increase our understanding of cataract etiology. Our findings also highlight the fact that expression of genes associated with cataract susceptibility is not necessarily restricted to lens tissue.

Prioritizing susceptibility genes for the prognosis of male-pattern baldness with transcriptome-wide association study

BACKGROUND

Male-pattern baldness (MPB) is the most common cause of hair loss in men. It can be categorized into three types: type 2 (T2), type 3 (T3), and type 4 (T4), with type 1 (T1) being considered normal. Although various MPB-associated genetic variants have been suggested, a comprehensive study for linking these variants to gene expression regulation has not been performed to the best of our knowledge.

RESULTS

In this study, we prioritized MPB-related tissue panels using tissue-specific enrichment analysis and utilized single-tissue panels from genotype-tissue expression version 8, as well as cross-tissue panels from context-specific genetics. Through a transcriptome-wide association study and colocalization analysis, we identified 52, 75, and 144 MPB associations for T2, T3, and T4, respectively. To assess the causality of MPB genes, we performed a conditional and joint analysis, which revealed 10, 11, and 54 putative causality genes for T2, T3, and T4, respectively. Finally, we conducted drug repositioning and identified potential drug candidates that are connected to MPB-associated genes.

CONCLUSIONS

Overall, through an integrative analysis of gene expression and genotype data, we have identified robust MPB susceptibility genes that may help uncover the underlying molecular mechanisms and the novel drug candidates that may alleviate MPB.

Genetic imputation of kidney transcriptome, proteome and multi-omics illuminates new blood pressure and hypertension targets

Genetic mechanisms of blood pressure (BP) regulation remain poorly defined. Using kidney-specific epigenomic annotations and 3D genome information we generated and validated gene expression prediction models for the purpose of transcriptome-wide association studies in 700 human kidneys. We identified 889 kidney genes associated with BP of which 399 were prioritised as contributors to BP regulation. Imputation of kidney proteome and microRNAome uncovered 97 renal proteins and 11 miRNAs associated with BP. Integration with plasma proteomics and metabolomics illuminated circulating levels of myo-inositol, 4-guanidinobutanoate and angiotensinogen as downstream effectors of several kidney BP genes (SLC5A11, AGMAT, AGT, respectively). We showed that genetically determined reduction in renal expression may mimic the effects of rare loss-of-function variants on kidney mRNA/protein and lead to an increase in BP (e.g., ENPEP). We demonstrated a strong correlation (r = 0.81) in expression of protein-coding genes between cells harvested from urine and the kidney highlighting a diagnostic potential of urinary cell transcriptomics. We uncovered adenylyl cyclase activators as a repurposing opportunity for hypertension and illustrated examples of BP-elevating effects of anticancer drugs (e.g. tubulin polymerisation inhibitors). Collectively, our studies provide new biological insights into genetic regulation of BP with potential to drive clinical translation in hypertension.

Association between night work and prostate cancer: a systematic review and meta-analysis

OBJECTIVE

The aim was to conduct a systematic review and meta-analysis to study the association between night work and the development of prostate cancer.

METHODS

A systematic literature search was conducted in CINAHL, Embase, MEDLINE, and Web of Science. Studies were included based on a PECOS; the population included men in/above the working age, exposure defined as night work, outcome defined as prostate cancer, and study design restricted to cohort studies. The exclusion of articles, risk-of-bias assessment, and data extraction were performed by two reviewers. A meta-analysis was conducted using a random-effects model, including a sensitivity analysis stratified based on the risk-of-bias assessment. We evaluated publication bias using a funnel plot and Egger´s test, and the level of evidence was assessed using GRADE.

RESULTS

A total of 528 articles were identified, and eight cohort studies were included. Three studies had a moderate risk of bias, while five studies had a high risk of bias. The meta-analysis showed a pooled hazard ratio (HR) of 1.0 (95% CI 0.6-1.7). In the sensitivity analysis, moderate vs. high risk-of-bias studies showed a pooled HR of 1.2 (95% CI 0.3-4.1) and 0.9 (95% CI 0.6-1.3), respectively. Based on GRADE, the level of evidence was rated low.

CONCLUSION

We found no association between night work and the development of prostate cancer. The evidence was assessed as limited and inconsistent. Future studies encompassing consistent definitions of night work, including objective exposure data, are highly warranted.

Comparing feature selection and machine learning approaches for predicting CYP2D6 methylation from genetic variation

Introduction

Pharmacogenetics currently supports clinical decision-making on the basis of a limited number of variants in a few genes and may benefit paediatric prescribing where there is a need for more precise dosing. Integrating genomic information such as methylation into pharmacogenetic models holds the potential to improve their accuracy and consequently prescribing decisions. Cytochrome P450 2D6 (CYP2D6) is a highly polymorphic gene conventionally associated with the metabolism of commonly used drugs and endogenous substrates. We thus sought to predict epigenetic loci from single nucleotide polymorphisms (SNPs) related to CYP2D6 in children from the GUSTO cohort.

Methods

Buffy coat DNA methylation was quantified using the Illumina Infinium Methylation EPIC beadchip. CpG sites associated with CYP2D6 were used as outcome variables in Linear Regression, Elastic Net and XGBoost models. We compared feature selection of SNPs from GWAS mQTLs, GTEx eQTLs and SNPs within 2 MB of the CYP2D6 gene and the impact of adding demographic data. The samples were split into training (75%) sets and test (25%) sets for validation. In Elastic Net model and XGBoost models, optimal hyperparameter search was done using 10-fold cross validation. Root Mean Square Error and R-squared values were obtained to investigate each models' performance. When GWAS was performed to determine SNPs associated with CpG sites, a total of 15 SNPs were identified where several SNPs appeared to influence multiple CpG sites.

Results

Overall, Elastic Net models of genetic features appeared to perform marginally better than heritability estimates and substantially better than Linear Regression and XGBoost models. The addition of nongenetic features appeared to improve performance for some but not all feature sets and probes. The best feature set and Machine Learning (ML) approach differed substantially between CpG sites and a number of top variables were identified for each model.

Discussion

The development of SNP-based prediction models for CYP2D6 CpG methylation in Singaporean children of varying ethnicities in this study has clinical application. With further validation, they may add to the set of tools available to improve precision medicine and pharmacogenetics-based dosing.

Large-scale integrative analysis of juvenile idiopathic arthritis for new insight into its pathogenesis

BACKGROUND

Juvenile idiopathic arthritis (JIA) is one of the most prevalent rheumatic disorders in children and is classified as an autoimmune disease (AID). While a robust genetic contribution to JIA etiology has been established, the exact pathogenesis remains unclear.

METHODS

To prioritize biologically interpretable susceptibility genes and proteins for JIA, we conducted transcriptome-wide and proteome-wide association studies (TWAS/PWAS). Then, to understand the genetic architecture of JIA, we systematically analyzed single-nucleotide polymorphism (SNP)-based heritability, a signature of natural selection, and polygenicity. Next, we conducted HLA typing using multi-ethnicity RNA sequencing data. Additionally, we examined the T cell receptor (TCR) repertoire at a single-cell level to explore the potential links between immunity and JIA risk.

RESULTS

We have identified 19 TWAS genes and two PWAS proteins associated with JIA risks. Furthermore, we observe that the heritability and cell type enrichment analysis of JIA are enriched in T lymphocytes and HLA regions and that JIA shows higher polygenicity compared to other AIDs. In multi-ancestry HLA typing, B*45:01 is more prevalent in African JIA patients than in European JIA patients, whereas DQA1*01:01, DQA1*03:01, and DRB1*04:01 exhibit a higher frequency in European JIA patients. Using single-cell immune repertoire analysis, we identify clonally expanded T cell subpopulations in JIA patients, including CXCL13+BHLHE40+ TH cells which are significantly associated with JIA risks.

CONCLUSION

Our findings shed new light on the pathogenesis of JIA and provide a strong foundation for future mechanistic studies aimed at uncovering the molecular drivers of JIA.

A large-scale microRNA transcriptome-wide association study identifies two susceptibility microRNAs, miR-1307-5p and miR-192-3p, for colorectal cancer risk

Transcriptome-wide association studies (TWAS) have identified many putative susceptibility genes for colorectal cancer (CRC) risk. However, susceptibility miRNAs, critical dysregulators of gene expression, remain unexplored. We genotyped DNA samples from 313 CRC East Asian patients and performed small RNA sequencing in their normal colon tissues distant from tumors to build genetic models for predicting miRNA expression. We applied these models and data from genome-wide association studies (GWAS) including 23 942 cases and 217 267 controls of East Asian ancestry to investigate associations of predicted miRNA expression with CRC risk. Perturbation experiments separately by promoting and inhibiting miRNAs expressions and further in vitro assays in both SW480 and HCT116 cells were conducted. At a Bonferroni-corrected threshold of P < 4.5 × 10-4, we identified two putative susceptibility miRNAs, miR-1307-5p and miR-192-3p, located in regions more than 500 kb away from any GWAS-identified risk variants in CRC. We observed that a high predicted expression of miR-1307-5p was associated with increased CRC risk, while a low predicted expression of miR-192-3p was associated with increased CRC risk. Our experimental results further provide strong evidence of their susceptible roles by showing that miR-1307-5p and miR-192-3p play a regulatory role, respectively, in promoting and inhibiting CRC cell proliferation, migration, and invasion, which was consistently observed in both SW480 and HCT116 cells. Our study provides additional insights into the biological mechanisms underlying CRC development.

Novel insights into genetic susceptibility for colorectal cancer from transcriptome-wide association and functional investigation

BACKGROUND

Transcriptome-wide association studies have been successful in identifying candidate susceptibility genes for colorectal cancer (CRC). To strengthen susceptibility gene discovery, we conducted a large transcriptome-wide association study and an alternative splicing transcriptome-wide association study in CRC using improved genetic prediction models and performed in-depth functional investigations.

METHODS

We analyzed RNA-sequencing data from normal colon tissues and genotype data from 423 European descendants to build genetic prediction models of gene expression and alternative splicing and evaluated model performance using independent RNA-sequencing data from normal colon tissues of the Genotype-Tissue Expression Project. We applied the verified models to genome-wide association studies (GWAS) summary statistics among 58 131 CRC cases and 67 347 controls of European ancestry to evaluate associations of genetically predicted gene expression and alternative splicing with CRC risk. We performed in vitro functional assays for 3 selected genes in multiple CRC cell lines.

RESULTS

We identified 57 putative CRC susceptibility genes, which included the 48 genes from transcriptome-wide association studies and 15 genes from splicing transcriptome-wide association studies, at a Bonferroni-corrected P value less than .05. Of these, 16 genes were not previously implicated in CRC susceptibility, including a gene PDE7B (6q23.3) at locus previously not reported by CRC GWAS. Gene knockdown experiments confirmed the oncogenic roles for 2 unreported genes, TRPS1 and METRNL, and a recently reported gene, C14orf166.

CONCLUSION

This study discovered new putative susceptibility genes of CRC and provided novel insights into the biological mechanisms underlying CRC development.

Novel insight into the etiology of ischemic stroke gained by integrative multiome-wide association study

Stroke, characterized by sudden neurological deficits, is the second leading cause of death worldwide. Although genome-wide association studies (GWAS) have successfully identified many genomic regions associated with ischemic stroke (IS), the genes underlying risk and their regulatory mechanisms remain elusive. Here, we integrate a large-scale GWAS (N = 1 296 908) for IS together with molecular QTLs data, including mRNA, splicing, enhancer RNA (eRNA), and protein expression data from up to 50 tissues (total N = 11 588). We identify 136 genes/eRNA/proteins associated with IS risk across 60 independent genomic regions and find IS risk is most enriched for eQTLs in arterial and brain-related tissues. Focusing on IS-relevant tissues, we prioritize 9 genes/proteins using probabilistic fine-mapping TWAS analyses. In addition, we discover that blood cell traits, particularly reticulocyte cells, have shared genetic contributions with IS using TWAS-based pheWAS and genetic correlation analysis. Lastly, we integrate our findings with a large-scale pharmacological database and identify a secondary bile acid, deoxycholic acid, as a potential therapeutic component. Our work highlights IS risk genes/splicing-sites/enhancer activity/proteins with their phenotypic consequences using relevant tissues as well as identify potential therapeutic candidates for IS.

Splicing transcriptome-wide association study to identify splicing events for pancreatic cancer risk

A large proportion of the heritability of pancreatic cancer risk remains elusive, and the contribution of specific mRNA splicing events to pancreatic cancer susceptibility has not been systematically evaluated. In this study, we performed a large splicing transcriptome-wide association study (spTWAS) using three modeling strategies (Enet, LASSO and MCP) to develop alternative splicing genetic prediction models for identifying novel susceptibility loci and splicing introns for pancreatic cancer risk by assessing 8275 pancreatic cancer cases and 6723 controls of European ancestry. Data from 305 subjects of whom the majority are of European descent in the Genotype-Tissue Expression Project (GTEx) were used and both cis-acting and promoter-enhancer interaction regions were considered to build these models. We identified nine splicing events of seven genes (ABO, UQCRC1, STARD3, ETAA1, CELA3B, LGR4 and SFT2D1) that showed an association of genetically predicted expression with pancreatic cancer risk at a false discovery rate ≤0.05. Of these genes, UQCRC1 and LGR4 have not yet been reported to be associated with pancreatic cancer risk. Fine-mapping analyses supported likely causal associations corresponding to six splicing events of three genes (P4HTM, ABO and PGAP3). Our study identified novel genes and splicing events associated with pancreatic cancer risk, which can improve our understanding of the etiology of this deadly malignancy.

Characterizing prostate cancer risk through multi-ancestry genome-wide discovery of 187 novel risk variants

The transferability and clinical value of genetic risk scores (GRSs) across populations remain limited due to an imbalance in genetic studies across ancestrally diverse populations. Here we conducted a multi-ancestry genome-wide association study of 156,319 prostate cancer cases and 788,443 controls of European, African, Asian and Hispanic men, reflecting a 57% increase in the number of non-European cases over previous prostate cancer genome-wide association studies. We identified 187 novel risk variants for prostate cancer, increasing the total number of risk variants to 451. An externally replicated multi-ancestry GRS was associated with risk that ranged from 1.8 (per standard deviation) in African ancestry men to 2.2 in European ancestry men. The GRS was associated with a greater risk of aggressive versus non-aggressive disease in men of African ancestry (P = 0.03). Our study presents novel prostate cancer susceptibility loci and a GRS with effective risk stratification across ancestry groups.

Transcriptome-wide association analyses reveal the impact of regulatory variants on rice panicle architecture and causal gene regulatory networks

Panicle architecture is a key determinant of rice grain yield and is mainly determined at the 1-2 mm young panicle stage. Here, we investigated the transcriptome of the 1-2 mm young panicles from 275 rice varieties and identified thousands of genes whose expression levels were associated with panicle traits. Multimodel association studies suggested that many small-effect genetic loci determine spikelet per panicle (SPP) by regulating the expression of genes associated with panicle traits. We found that alleles at cis-expression quantitative trait loci of SPP-associated genes underwent positive selection, with a strong preference for alleles increasing SPP. We further developed a method that integrates the associations of cis- and trans-expression components of genes with traits to identify causal genes at even small-effect loci and construct regulatory networks. We identified 36 putative causal genes of SPP, including SDT (MIR156j) and OsMADS17, and inferred that OsMADS17 regulates SDT expression, which was experimentally validated. Our study reveals the impact of regulatory variants on rice panicle architecture and provides new insights into the gene regulatory networks of panicle traits.

Large-scale alternative polyadenylation (APA)-wide association studies to identify putative susceptibility genes in human common cancers

Alternative polyadenylation (APA) modulates mRNA processing in the 3' untranslated regions (3'UTR), which affect mRNA stability and translation efficiency. Here, we build genetic models to predict APA levels in multiple tissues using sequencing data of 1,337 samples from the Genotype-Tissue Expression, and apply these models to assess associations between genetically predicted APA levels and cancer risk with data from large genome-wide association studies of six common cancers, including breast, ovary, prostate, colorectum, lung, and pancreas among European-ancestry populations. At a Bonferroni-corrected P □<□0.05, we identify 58 risk genes, including seven in newly identified loci. Using luciferase reporter assays, we demonstrate that risk alleles of 3'UTR variants, rs324015 ( STAT6 ), rs2280503 ( DIP2B ), rs1128450 ( FBXO38 ) and rs145220637 ( LDAH ), could significantly increase post-transcriptional activities of their target genes compared to reference alleles. Further gene knockdown experiments confirm their oncogenic roles. Our study provides additional insight into the genetic susceptibility of these common cancers.

Identification of blood protein biomarkers associated with prostate cancer risk using genetic prediction models: analysis of over 140,000 subjects

Prostate cancer (PCa) brings huge public health burden in men. A growing number of conventional observational studies report associations of multiple circulating proteins with PCa risk. However, the existing findings may be subject to incoherent biases of conventional epidemiologic studies. To better characterize their associations, herein, we evaluated associations of genetically predicted concentrations of plasma proteins with PCa risk. We developed comprehensive genetic prediction models for protein levels in plasma. After testing 1308 proteins in 79 194 cases and 61 112 controls of European ancestry included in the consortia of BPC3, CAPS, CRUK, PEGASUS, and PRACTICAL, 24 proteins showed significant associations with PCa risk, including 16 previously reported proteins and eight novel proteins. Of them, 14 proteins showed negative associations and 10 showed positive associations with PCa risk. For 18 of the identified proteins, potential functional somatic changes of encoding genes were detected in PCa patients in The Cancer Genome Atlas (TCGA). Genes encoding these proteins were significantly involved in cancer-related pathways. We further identified drugs targeting the identified proteins, which may serve as candidates for drug repurposing for treating PCa. In conclusion, this study identifies novel protein biomarker candidates for PCa risk, which may provide new perspectives on the etiology of PCa and improve its therapeutic strategies.

Candidate pathway analysis of surfactant proteins identifies CTSH and SFTA2 that influences lung cancer risk

Pulmonary surfactant is a lipoprotein synthesized and secreted by alveolar type II cells in lung. We evaluated the associations between 200,139 single nucleotide polymorphisms (SNPs) of 40 surfactant-related genes and lung cancer risk using genotyped data from two independent lung cancer genome-wide association studies. Discovery data included 18,082 cases and 13,780 controls of European ancestry. Replication data included 1,914 cases and 3,065 controls of European descent. Using multivariate logistic regression, we found novel SNPs in surfactant-related genes CTSH [rs34577742 C > T, odds ratio (OR) = 0.90, 95% confidence interval (CI) = 0.89-0.93, P = 7.64 × 10-9] and SFTA2 (rs3095153 G > A, OR = 1.16, 95% CI = 1.10-1.21, P = 1.27 × 10-9) associated with overall lung cancer in the discovery data and validated in an independent replication data-CTSH (rs34577742 C > T, OR = 0.88, 95% CI = 0.80-0.96, P = 5.76 × 10-3) and SFTA2 (rs3095153 G > A, OR = 1.14, 95% CI = 1.01-1.28, P = 3.25 × 10-2). Among ever smokers, we found SNPs in CTSH (rs34577742 C > T, OR = 0.89, 95% CI = 0.85-0.92, P = 1.94 × 10-7) and SFTA2 (rs3095152 G > A, OR = 1.20, 95% CI = 1.14-1.27, P = 4.25 × 10-11) associated with overall lung cancer in the discovery data and validated in the replication data-CTSH (rs34577742 C > T, OR = 0.88, 95% CI = 0.79-0.97, P = 1.64 × 10-2) and SFTA2 (rs3095152 G > A, OR = 1.15, 95% CI = 1.01-1.30, P = 3.81 × 10-2). Subsequent transcriptome-wide association study using expression weights from a lung expression quantitative trait loci study revealed genes most strongly associated with lung cancer are CTSH (PTWAS = 2.44 × 10-4) and SFTA2 (PTWAS = 2.32 × 10-6).

Transcriptome-wide association study identifies new susceptibility genes and pathways for spondyloarthritis

BACKGROUND

Spondyloarthritis (SpA) is a group of multifactorial bone diseases influenced by genetic factors, the environment and lifestyle. However, current studies have found a limited number of SpA-related genes, and the genetic and pathogenic mechanisms of SpA are still unclear.

METHODS

A tissue-specific transcriptome-wide association study (TWAS) of SpA was performed using GWAS (including 3966 SpA patients and 448,298 controls) summary data and gene expression weights of whole blood and skeletal muscle. The SpA-associated genes identified by TWAS were further compared with the differentially expressed genes (DEGs) identified in the SpA gene expression profile acquired from the Gene Expression Omnibus database (GEO, GSE58667). Finally, functional enrichment and annotation analyses of the identified genes were performed.

RESULTS

The TWAS detected 499 suggestive genes associated with SpA in whole blood and skeletal muscle, such as CTNNAL1 (PSM = 3.04 × 10-2, PWB = 9.58 × 10-3). The gene expression profile of SpA identified 20 candidate genes that overlapped in the TWAS data, such as MCM4 (PTWAS = 1.32 × 10-2, PDEG = 2.75 × 10-2) and KIAA1109 (PTWAS = 3.71 × 10-2, PDEG = 4.67 × 10-2). Enrichment analysis of the genes identified by TWAS identified 93 significant GO terms and 33 KEGG pathways, such as mitochondrion organization (GO: 0007005) and axon guidance (hsa04360).

CONCLUSION

We identified multiple candidate genes that were genetically related to SpA. Our study may provide novel clues regarding the genetic mechanism, diagnosis, and treatment of SpA.

A Splicing Transcriptome-Wide Association Study Identifies Candidate Altered Splicing for Prostate Cancer Risk

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

Transcriptome-wide association study identifies novel candidate susceptibility genes for migraine

Genome-wide association studies (GWASs) have identified more than 130 genetic susceptibility loci for migraine; however, how most of these loci impact migraine development is unknown. To identify novel genes associated with migraine and interpret the transcriptional products of those genes, we conducted a transcriptome-wide association study (TWAS). We performed tissue-specific and multi-tissue TWAS analyses to assess associations between imputed gene expression from 53 tissues and migraine susceptibility using FUSION software. Meta-analyzed GWAS summary statistics from 26,052 migraine cases and 487,214 controls, all of European ancestry and from two cohorts (the Kaiser Permanente GERA and the UK Biobank), were used. We evaluated the associations for genes after conditioning on variant-level effects from GWAS, and we tested for colocalization of GWAS migraine-associated loci and expression quantitative trait loci (eQTLs). Across tissue-specific and multi-tissue analyses, we identified 53 genes for which genetically predicted gene expression was associated with migraine after correcting for multiple testing. Of these 53 genes, 10 (ATF5, CNTNAP1, KTN1-AS1, NEIL1, NEK4, NNT, PNKP, RUFY2, TUBG2, and VAT1) did not overlap known migraine-associated loci identified from GWAS. Tissue-specific analysis identified 45 gene-tissue pairs and cardiovascular tissues represented the highest proportion of the Bonferroni-significant gene-tissue pairs (n = 22 [49%]), followed by brain tissues (n = 6 [13%]), and gastrointestinal tissues (n = 4 [9%]). Colocalization analyses provided evidence of shared genetic variants underlying eQTL and GWAS signals in 18 of the gene-tissue pairs (40%). Our TWAS reports novel genes for migraine and highlights the important contribution of brain, cardiovascular, and gastrointestinal tissues in migraine susceptibility.

A splicing transcriptome-wide association study identifies novel altered splicing for Alzheimer's disease susceptibility

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

Prioritization of potential causative genes for schizophrenia in placenta

Our earlier work has shown that genomic risk for schizophrenia converges with early life complications in affecting risk for the disorder and sex-biased neurodevelopmental trajectories. Here, we identify specific genes and potential mechanisms that, in placenta, may mediate such outcomes. We performed TWAS in healthy term placentae (N = 147) to derive candidate placental causal genes that we confirmed with SMR; to search for placenta and schizophrenia-specific associations, we performed an analogous analysis in fetal brain (N = 166) and additional placenta TWAS for other disorders/traits. The analyses in the whole sample and stratifying by sex ultimately highlight 139 placenta and schizophrenia-specific risk genes, many being sex-biased; the candidate molecular mechanisms converge on the nutrient-sensing capabilities of placenta and trophoblast invasiveness. These genes also implicate the Coronavirus-pathogenesis pathway and showed increased expression in placentae from a small sample of SARS-CoV-2-positive pregnancies. Investigating placental risk genes for schizophrenia and candidate mechanisms may lead to opportunities for prevention that would not be suggested by study of the brain alone.

Novel insight into the etiology of ischemic stroke gained by integrative transcriptome-wide association study

Stroke, characterized by sudden neurological deficits, is the second leading cause of death worldwide. Although genome-wide association studies (GWAS) have successfully identified many genomic regions associated with ischemic stroke (IS), the genes underlying risk and their regulatory mechanisms remain elusive. Here, we integrate a large-scale GWAS (N=1,296,908) for IS together with mRNA, splicing, enhancer RNA (eRNA) and protein expression data (N=11,588) from 50 tissues. We identify 136 genes/eRNA/proteins associated with IS risk across 54 independent genomic regions and find IS risk is most enriched for eQTLs in arterial and brain-related tissues. Focusing on IS-relevant tissues, we prioritize 9 genes/proteins using probabilistic fine-mapping TWAS analyses. In addition, we discover that blood cell traits, particularly reticulocyte cells, have shared genetic contributions with IS using TWAS-based pheWAS and genetic correlation analysis. Lastly, we integrate our findings with a large-scale pharmacological database and identify a secondary bile acid, deoxycholic acid, as a potential therapeutic component. Our work highlights IS risk genes/splicing-sites/enhancer activity/proteins with their phenotypic consequences using relevant tissues as well as identify potential therapeutic candidates for IS.

OTTERS: a powerful TWAS framework leveraging summary-level reference data

Most existing TWAS tools require individual-level eQTL reference data and thus are not applicable to summary-level reference eQTL datasets. The development of TWAS methods that can harness summary-level reference data is valuable to enable TWAS in broader settings and enhance power due to increased reference sample size. Thus, we develop a TWAS framework called OTTERS (Omnibus Transcriptome Test using Expression Reference Summary data) that adapts multiple polygenic risk score (PRS) methods to estimate eQTL weights from summary-level eQTL reference data and conducts an omnibus TWAS. We show that OTTERS is a practical and powerful TWAS tool by both simulations and application studies.

Clonal hematopoiesis and risk of prostate cancer in large samples of European ancestry men

Little is known regarding the potential relationship between clonal hematopoiesis (CH) of indeterminate potential (CHIP), which is the expansion of hematopoietic stem cells with somatic mutations, and risk of prostate cancer, the fifth leading cause of cancer death of men worldwide. We evaluated the association of age-related CHIP with overall and aggressive prostate cancer risk in two large whole-exome sequencing studies of 75 047 European ancestry men, including 7663 prostate cancer cases, 2770 of which had aggressive disease, and 3266 men carrying CHIP variants. We found that CHIP, defined by over 50 CHIP genes individually and in aggregate, was not significantly associated with overall (aggregate HR = 0.93, 95% CI = 0.76-1.13, P = 0.46) or aggressive (aggregate OR = 1.14, 95% CI = 0.92-1.41, P = 0.22) prostate cancer risk. CHIP was weakly associated with genetic risk of overall prostate cancer, measured using a polygenic risk score (OR = 1.05 per unit increase, 95% CI = 1.01-1.10, P = 0.01). CHIP was not significantly associated with carrying pathogenic/likely pathogenic/deleterious variants in DNA repair genes, which have previously been found to be associated with aggressive prostate cancer. While findings from this study suggest that CHIP is likely not a risk factor for prostate cancer, it will be important to investigate other types of CH in association with prostate cancer risk.

Predicting Gene Regulatory Interactions Using Natural Genetic Variation

Genome-wide association studies (GWAS) are a powerful tool to elucidate the genotype-phenotype map. Although GWAS are usually used to assess simple univariate associations between genetic markers and traits of interest, it is also possible to infer the underlying genetic architecture and to predict gene regulatory interactions. In this chapter, we describe the latest methods and tools to perform GWAS by calculating permutation-based significance thresholds. For this purpose, we first provide guidelines on univariate GWAS analyses that are extended in the second part of this chapter to more complex models that enable the inference of gene regulatory networks and how these networks vary.

Prostate Cancer Transcriptomic Regulation by the Interplay of Germline Risk Alleles, Somatic Mutations, and 3D Genomic Architecture

Prostate cancer is one of the most heritable human cancers. Genome-wide association studies have identified at least 185 prostate cancer germline risk alleles, most noncoding. We used integrative three-dimensional (3D) spatial genomics to identify the chromatin interaction targets of 45 prostate cancer risk alleles, 31 of which were associated with the transcriptional regulation of target genes in 565 localized prostate tumors. To supplement these 31, we verified transcriptional targets for 56 additional risk alleles using linear proximity and linkage disequilibrium analysis in localized prostate tumors. Some individual risk alleles influenced multiple target genes; others specifically influenced only distal genes while leaving proximal ones unaffected. Several risk alleles exhibited widespread germline-somatic interactions in transcriptional regulation, having different effects in tumors with loss of PTEN or RB1 relative to those without. These data clarify functional prostate cancer risk alleles in large linkage blocks and outline a strategy to model multidimensional transcriptional regulation.

SIGNIFICANCE

Many prostate cancer germline risk alleles are enriched in the noncoding regions of the genome and are hypothesized to regulate transcription. We present a 3D genomics framework to unravel risk SNP function and describe the widespread germline-somatic interplay in transcription control. This article is highlighted in the In This Issue feature, p. 2711.

Investigating the prediction of CpG methylation levels from SNP genotype data to help elucidate relationships between methylation, gene expression and complex traits

As popularised by PrediXcan (and related methods), transcriptome-wide association studies (TWAS), in which gene expression is imputed from single-nucleotide polymorphism (SNP) genotypes and tested for association with a phenotype, are a popular approach for investigating the role of gene expression in complex traits. Like gene expression, DNA methylation is an important biological process and, being under genetic regulation, may be imputable from SNP genotypes. Here, we investigate prediction of CpG methylation levels from SNP genotype data to help elucidate relationships between methylation, gene expression and complex traits. We start by examining how well CpG methylation can be predicted from SNP genotypes, comparing three penalised regression approaches and examining whether changing the window size improves prediction accuracy. Although methylation at most CpG sites cannot be accurately predicted from SNP genotypes, for a subset it can be predicted well. We next apply our methylation prediction models (trained using the optimal method and window size) to carry out a methylome-wide association study (MWAS) of primary biliary cholangitis. We intersect the regions identified via MWAS with those identified via TWAS, providing insight into the interplay between CpG methylation, gene expression and disease status. We conclude that MWAS has the potential to improve understanding of biological mechanisms in complex traits.

Integrating transcription factor occupancy with transcriptome-wide association analysis identifies susceptibility genes in human cancers

Transcriptome-wide association studies (TWAS) have successfully discovered many putative disease susceptibility genes. However, TWAS may suffer from inaccuracy of gene expression predictions due to inclusion of non-regulatory variants. By integrating prior knowledge of susceptible transcription factor occupied elements, we develop sTF-TWAS and demonstrate that it outperforms existing TWAS approaches in both simulation and real data analyses. Under the sTF-TWAS framework, we build genetic models to predict alternative splicing and gene expression in normal breast, prostate and lung tissues from the Genotype-Tissue Expression project and apply these models to data from large genome-wide association studies (GWAS) conducted among European-ancestry populations. At Bonferroni-corrected P < 0.05, we identify 354 putative susceptibility genes for these cancers, including 189 previously unreported in GWAS loci and 45 in loci unreported by GWAS. These findings provide additional insight into the genetic susceptibility of human cancers. Additionally, we show the generalizability of the sTF-TWAS on non-cancer diseases.

Transcriptome-wide association study of HIV-1 acquisition identifies HERC1 as a susceptibility gene

The host genetic factors conferring protection against HIV type 1 (HIV-1) acquisition remain elusive, and in particular the contributions of common genetic variants. Here, we performed the largest genome-wide association meta-analysis of HIV-1 acquisition, which included 7,303 HIV-1-positive individuals and 587,343 population controls. We identified 25 independent genetic loci with suggestive association, of which one was genome-wide significant within the major histocompatibility complex (MHC) locus. After exclusion of the MHC signal, linkage disequilibrium score regression analyses revealed a SNP heritability of 21% and genetic correlations with behavioral factors. A transcriptome-wide association study identified 15 susceptibility genes, including HERC1, UEVLD, and HIST1H4K. Convergent evidence from conditional analyses and fine-mapping identified HERC1 downregulation in immune cells as a robust mechanism associated with HIV-1 acquisition. Functional studies on HERC1 and other identified candidates, as well as larger genetic studies, have the potential to further our understanding of the host mechanisms associated with protection against HIV-1.

eQTL Set-Based Association Analysis Identifies Novel Susceptibility Loci for Barrett Esophagus and Esophageal Adenocarcinoma

BACKGROUND

Over 20 susceptibility single-nucleotide polymorphisms (SNP) have been identified for esophageal adenocarcinoma (EAC) and its precursor, Barrett esophagus (BE), explaining a small portion of heritability.

METHODS

Using genetic data from 4,323 BE and 4,116 EAC patients aggregated by international consortia including the Barrett's and Esophageal Adenocarcinoma Consortium (BEACON), we conducted a comprehensive transcriptome-wide association study (TWAS) for BE/EAC, leveraging Genotype Tissue Expression (GTEx) gene-expression data from six tissue types of plausible relevance to EAC etiology: mucosa and muscularis from the esophagus, gastroesophageal (GE) junction, stomach, whole blood, and visceral adipose. Two analytical approaches were taken: standard TWAS using the predicted gene expression from local expression quantitative trait loci (eQTL), and set-based SKAT association using selected eQTLs that predict the gene expression.

RESULTS

Although the standard approach did not identify significant signals, the eQTL set-based approach identified eight novel associations, three of which were validated in independent external data (eQTL SNP sets for EXOC3, ZNF641, and HSP90AA1).

CONCLUSIONS

This study identified novel genetic susceptibility loci for EAC and BE using an eQTL set-based genetic association approach.

IMPACT

This study expanded the pool of genetic susceptibility loci for EAC and BE, suggesting the potential of the eQTL set-based genetic association approach as an alternative method for TWAS analysis.

Genetic determinants of chromatin reveal prostate cancer risk mediated by context-dependent gene regulation

Many genetic variants affect disease risk by altering context-dependent gene regulation. Such variants are difficult to study mechanistically using current methods that link genetic variation to steady-state gene expression levels, such as expression quantitative trait loci (eQTLs). To address this challenge, we developed the cistrome-wide association study (CWAS), a framework for identifying genotypic and allele-specific effects on chromatin that are also associated with disease. In prostate cancer, CWAS identified regulatory elements and androgen receptor-binding sites that explained the association at 52 of 98 known prostate cancer risk loci and discovered 17 additional risk loci. CWAS implicated key developmental transcription factors in prostate cancer risk that are overlooked by eQTL-based approaches due to context-dependent gene regulation. We experimentally validated associations and demonstrated the extensibility of CWAS to additional epigenomic datasets and phenotypes, including response to prostate cancer treatment. CWAS is a powerful and biologically interpretable paradigm for studying variants that influence traits by affecting transcriptional regulation.

Biological insights into systemic lupus erythematosus through an immune cell-specific transcriptome-wide association study

OBJECTIVE

Genome-wide association studies (GWAS) have identified >100 risk loci for systemic lupus erythematosus (SLE), but the disease genes at most loci remain unclear, hampering translation of these genetic discoveries. We aimed to prioritise genes underlying the 110 SLE loci that were identified in the latest East Asian GWAS meta-analysis.

METHODS

We built gene expression predictive models in blood B cells, CD4+ and CD8+ T cells, monocytes, natural killer cells and peripheral blood cells of 105 Japanese individuals. We performed a transcriptome-wide association study (TWAS) using data from the latest genome-wide association meta-analysis of 208 370 East Asians and searched for candidate genes using TWAS and three data-driven computational approaches.

RESULTS

TWAS identified 171 genes for SLE (p<1.0×10-5); 114 (66.7%) showed significance only in a single cell type; 127 (74.3%) were in SLE GWAS loci. TWAS identified a strong association between CD83 and SLE (p<7.7×10-8). Meta-analysis of genetic associations in the existing 208 370 East Asian and additional 1498 cases and 3330 controls found a novel single-variant association at rs72836542 (OR=1.11, p=4.5×10-9) around CD83. For the 110 SLE loci, we identified 276 gene candidates, including 104 genes at recently-identified SLE novel loci. We demonstrated in vitro that putative causal variant rs61759532 exhibited an allele-specific regulatory effect on ACAP1, and that presence of the SLE risk allele decreased ACAP1 expression.

CONCLUSIONS

Cell-level TWAS in six types of immune cells complemented SLE gene discovery and guided the identification of novel genetic associations. The gene findings shed biological insights into SLE genetic associations.

Multi-ancestry fine-mapping improves precision to identify causal genes in transcriptome-wide association studies

Transcriptome-wide association studies (TWASs) are a powerful approach to identify genes whose expression is associated with complex disease risk. However, non-causal genes can exhibit association signals due to confounding by linkage disequilibrium (LD) patterns and eQTL pleiotropy at genomic risk regions, which necessitates fine-mapping of TWAS signals. Here, we present MA-FOCUS, a multi-ancestry framework for the improved identification of genes underlying traits of interest. We demonstrate that by leveraging differences in ancestry-specific patterns of LD and eQTL signals, MA-FOCUS consistently outperforms single-ancestry fine-mapping approaches with equivalent total sample sizes across multiple metrics. We perform TWASs for 15 blood traits using genome-wide summary statistics (average nEA = 511 k, nAA = 13 k) and lymphoblastoid cell line eQTL data from cohorts of primarily European and African continental ancestries. We recapitulate evidence demonstrating shared genetic architectures for eQTL and blood traits between the two ancestry groups and observe that gene-level effects correlate 20% more strongly across ancestries than SNP-level effects. Lastly, we perform fine-mapping using MA-FOCUS and find evidence that genes at TWAS risk regions are more likely to be shared across ancestries than they are to be ancestry specific. Using multiple lines of evidence to validate our findings, we find that gene sets produced by MA-FOCUS are more enriched in hematopoietic categories than alternative approaches (p = 2.36 × 10-15). Our work demonstrates that including and appropriately accounting for genetic diversity can drive more profound insights into the genetic architecture of complex traits.

Transcriptome-wide association study identifies PSMB9 as a susceptibility gene for coal workers' pneumoconiosis

Coal workers' pneumoconiosis (CWP) is a type of typical occupational lung disease caused by prolonged inhalation of coal mine dust. The individuals' different genetic background may underlie their different susceptibility to develop pneumoconiosis, even under the same exposure level. This study aimed to identify susceptibility genes associated with CWP. Based on our previous genome-wide association study (GWAS, 202 CWP cases vs. 198 controls) and gene expression data obtained by analyzing human lungs and whole blood from the Genotype-Tissue Expression (GTEx) Portal, a transcriptome-wide association study (TWAS) was applied to identify CWP risk-related genes. Luciferase report gene assay, qRT-PCR, Western blot, immunofluorescence assay, and TUNEL assay were conducted to explore the potential role of the candidate gene in CWP. Proteasome 20S subunit beta 9 (PSMB9) was identified as a strong risk-related gene of CWP in both lungs and whole blood (Lungs: P_TWAS  = 4.22 × 10^-4 ; Whole blood: P_TWAS  = 2.11 × 10^-4 ). Single nucleotide polymorphisms (SNPs) rs2071480 and rs1351383, which locate in the promoter region and the first intron of the PSMB9 gene, were in high linkage disequilibrium (LD, r²  = 0.98) with the best GWAS SNP rs4713600 (G>T, OR = 0.55, 95% CI: 0.42-0.74, P = 6.86 × 10^-5 ). Both rs2071480 and rs1351383 significantly enhanced the transcriptional activity of PSMB9. Functional experiments revealed that silica exposure remarkably reduced the PSMB9 expression and caused cell apoptosis, while overexpression of PSMB9 markedly abolished silica-induced cell apoptosis. We here identified PSMB9 as a novel susceptibility gene for CWP and provided important insights into the further exploration of the CWP pathogenesis.

DeCAF: a novel method to identify cell-type specific regulatory variants and their role in cancer risk

Here, we propose DeCAF (DEconvoluted cell type Allele specific Function), a new method to identify cell-fraction (cf) QTLs in tumors by leveraging both allelic and total expression information. Applying DeCAF to RNA-seq data from TCGA, we identify 3664 genes with cfQTLs (at 10% FDR) in 14 cell types, a 5.63× increase in discovery over conventional interaction-eQTL mapping. cfQTLs replicated in external cell-type-specific eQTL data are more enriched for cancer risk than conventional eQTLs. Our new method, DeCAF, empowers the discovery of biologically meaningful cfQTLs from bulk RNA-seq data in moderately sized studies.