Identification of Novel Susceptibility Loci and Genes for Prostate Cancer Risk: A Transcriptome-Wide Association Study in Over 140,000 European Descendants

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.

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.

Comparative analysis of genetic risk scores for predicting biochemical recurrence in prostate cancer patients after radical prostatectomy

BACKGROUND

In recent years, Genome-Wide Association Studies (GWAS) has identified risk variants related to complex diseases, but most genetic variants have less impact on phenotypes. To solve the above problems, methods that can use variants with low genetic effects, such as genetic risk score (GRS), have been developed to predict disease risk.

METHODS

As the GRS model with the most incredible prediction power for complex diseases has not been determined, our study used simulation data and prostate cancer data to explore the disease prediction power of three GRS models, including the simple count genetic risk score (SC-GRS), the direct logistic regression genetic risk score (DL-GRS), and the explained variance weighted GRS based on directed logistic regression (EVDL-GRS).

RESULTS AND CONCLUSIONS

We used 26 SNPs to establish GRS models to predict the risk of biochemical recurrence (BCR) after radical prostatectomy. Combining clinical variables such as age at diagnosis, body mass index, prostate-specific antigen, Gleason score, pathologic T stage, and surgical margin and GRS models has better predictive power for BCR. The results of simulation data (statistical power = 0.707) and prostate cancer data (area under curve = 0.8462) show that DL-GRS has the best prediction performance. The rs455192 was the most relevant locus for BCR (p = 2.496 × 10-6) in our study.

rvTWAS: identifying gene-trait association using sequences by utilizing transcriptome-directed feature selection

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

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.

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.

Systematic analyses of GWAS summary statistics from UK Biobank identified novel susceptibility loci and genes for upper gastrointestinal diseases

In recent decades, upper gastrointestinal (GI) diseases have been highly prevalent worldwide. Although genome-wide association studies (GWASs) have identified thousands of susceptibility loci, only a few of them were conducted for chronic upper GI disorders, and most of them were underpowered and with small sample sizes. Additionally, for the known loci, only a tiny fraction of heritability can be explained and the underlying mechanisms and related genes remain unclear. In this study, we conducted a multi-trait analysis by the MTAG software and a two-stage transcriptome-wide association study (TWAS) with UTMOST and FUSION for seven upper GI diseases (oesophagitis, gastro-oesophageal reflux disease, other diseases of oesophagus, gastric ulcer, duodenal ulcer, gastritis and duodenitis and other diseases of stomach and duodenum) based on summary GWAS statistics from UK Biobank. In the MTAG analysis, we identified 7 loci associated with these upper GI diseases, including 3 novel ones at 4p12 (rs10029980), 12q13.13 (rs4759317) and 18p11.32 (rs4797954). In the TWAS analysis, we revealed 5 susceptibility genes in known loci and identified 12 novel potential susceptibility genes, including HOXC9 at 12q13.13. Further functional annotations and colocalization analysis indicated that rs4759317 (A>G) driven the association for GWAS signals and expression quantitative trait loci (eQTL) simultaneously at 12q13.13. The identified variant acted by decreasing the expression of HOXC9 to affect the risk of gastro-oesophageal reflux disease. This study provided insights into the genetic nature of upper GI diseases.

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.

Integrating eQTL and GWAS data characterises established and identifies novel migraine risk loci

Migraine-a painful, throbbing headache disorder-is the most common complex brain disorder, yet its molecular mechanisms remain unclear. Genome-wide association studies (GWAS) have proven successful in identifying migraine risk loci; however, much work remains to identify the causal variants and genes. In this paper, we compared three transcriptome-wide association study (TWAS) imputation models-MASHR, elastic net, and SMultiXcan-to characterise established genome-wide significant (GWS) migraine GWAS risk loci, and to identify putative novel migraine risk gene loci. We compared the standard TWAS approach of analysing 49 GTEx tissues with Bonferroni correction for testing all genes present across all tissues (Bonferroni), to TWAS in five tissues estimated to be relevant to migraine, and TWAS with Bonferroni correction that took into account the correlation between eQTLs within each tissue (Bonferroni-matSpD). Elastic net models performed in all 49 GTEx tissues using Bonferroni-matSpD characterised the highest number of established migraine GWAS risk loci (n = 20) with GWS TWAS genes having colocalisation (PP4 > 0.5) with an eQTL. SMultiXcan in all 49 GTEx tissues identified the highest number of putative novel migraine risk genes (n = 28) with GWS differential expression at 20 non-GWS GWAS loci. Nine of these putative novel migraine risk genes were later found to be at and in linkage disequilibrium with true (GWS) migraine risk loci in a recent, more powerful migraine GWAS. Across all TWAS approaches, a total of 62 putative novel migraine risk genes were identified at 32 independent genomic loci. Of these 32 loci, 21 were true risk loci in the recent, more powerful migraine GWAS. Our results provide important guidance on the selection, use, and utility of imputation-based TWAS approaches to characterise established GWAS risk loci and identify novel risk gene loci.

Genetic predisposition to metabolically unfavourable adiposity and prostate cancer risk: A Mendelian randomization analysis

BACKGROUND

The associations of adiposity with aggressive prostate cancer risk are unclear. Using two-sample Mendelian randomization, we assessed the association of metabolically unfavourable adiposity (UFA), favourable adiposity (FA) and for comparison body mass index (BMI), with prostate cancer, including aggressive prostate cancer.

METHODS

We examined the association of these genetically predicted adiposity-related traits with risk of prostate cancer overall, aggressive and early onset disease using outcome summary statistics from the PRACTICAL consortium (including 15,167 aggressive cases).

RESULTS

In inverse-variance weighted models, there was little evidence that genetically predicted one standard deviation higher UFA, FA and BMI were associated with aggressive prostate cancer [OR: 0.85 (95% CI:0.61-1.19), 0.80 (0.53-1.23) and 0.97 (0.88-1.08), respectively]; these associations were largely consistent in sensitivity analyses accounting for horizontal pleiotropy. There was no strong evidence that genetically determined UFA, FA or BMI were associated with overall prostate cancer or early age of onset prostate cancer.

CONCLUSIONS

We did not find differences in the associations of UFA and FA with prostate cancer risk, which suggest that adiposity is unlikely to influence prostate cancer via the metabolic factors assessed; however, these did not cover some aspects related to metabolic health that may link obesity with aggressive prostate cancer, which should be explored in future studies.

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.

Learning from the nexus of autoimmunity and cancer

The immune system plays critical roles in both autoimmunity and cancer, diseases at opposite ends of the immune spectrum. Autoimmunity arises from loss of T cell tolerance against self, while in cancer, poor immunity against transformed self fails to control tumor growth. Blockade of pathways that preserve self-tolerance is being leveraged to unleash immunity against many tumors; however, widespread success is hindered by the autoimmune-like toxicities that arise in treated patients. Knowledge gained from the treatment of autoimmunity can be leveraged to treat these toxicities in patients. Further, the understanding of how T cell dysfunction arises in cancer can be leveraged to induce a similar state in autoreactive T cells. Here, we review what is known about the T cell response in autoimmunity and cancer and highlight ways in which we can learn from the nexus of these two diseases to improve the application, efficacy, and management of immunotherapies.

Circulating insulin-like growth factors and risks of overall, aggressive and early-onset prostate cancer: a collaborative analysis of 20 prospective studies and Mendelian randomization analysis

BACKGROUND

Previous studies had limited power to assess the associations of circulating insulin-like growth factors (IGFs) and IGF-binding proteins (IGFBPs) with clinically relevant prostate cancer as a primary endpoint, and the association of genetically predicted IGF-I with aggressive prostate cancer is not known. We aimed to investigate the associations of IGF-I, IGF-II, IGFBP-1, IGFBP-2 and IGFBP-3 concentrations with overall, aggressive and early-onset prostate cancer.

METHODS

Prospective analysis of biomarkers using the Endogenous Hormones, Nutritional Biomarkers and Prostate Cancer Collaborative Group dataset (up to 20 studies, 17 009 prostate cancer cases, including 2332 aggressive cases). Odds ratios (OR) and 95% confidence intervals (CI) for prostate cancer were estimated using conditional logistic regression. For IGF-I, two-sample Mendelian randomization (MR) analysis was undertaken using instruments identified using UK Biobank (158 444 men) and outcome data from PRACTICAL (up to 85 554 cases, including 15 167 aggressive cases). Additionally, we used colocalization to rule out confounding by linkage disequilibrium.

RESULTS

In observational analyses, IGF-I was positively associated with risks of overall (OR per 1 SD = 1.09: 95% CI 1.07, 1.11), aggressive (1.09: 1.03, 1.16) and possibly early-onset disease (1.11: 1.00, 1.24); associations were similar in MR analyses (OR per 1 SD = 1.07: 1.00, 1.15; 1.10: 1.01, 1.20; and 1.13; 0.98, 1.30, respectively). Colocalization also indicated a shared signal for IGF-I and prostate cancer (PP4: 99%). Men with higher IGF-II (1.06: 1.02, 1.11) and IGFBP-3 (1.08: 1.04, 1.11) had higher risks of overall prostate cancer, whereas higher IGFBP-1 was associated with a lower risk (0.95: 0.91, 0.99); these associations were attenuated following adjustment for IGF-I.

CONCLUSIONS

These findings support the role of IGF-I in the development of prostate cancer, including for aggressive disease.

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.

Circulating free testosterone and risk of aggressive prostate cancer: Prospective and Mendelian randomisation analyses in international consortia

Previous studies had limited power to assess the associations of testosterone with aggressive disease as a primary endpoint. Further, the association of genetically predicted testosterone with aggressive disease is not known. We investigated the associations of calculated free and measured total testosterone and sex hormone-binding globulin (SHBG) with aggressive, overall and early-onset prostate cancer. In blood-based analyses, odds ratios (OR) and 95% confidence intervals (CI) for prostate cancer were estimated using conditional logistic regression from prospective analysis of biomarker concentrations in the Endogenous Hormones, Nutritional Biomarkers and Prostate Cancer Collaborative Group (up to 25 studies, 14 944 cases and 36 752 controls, including 1870 aggressive prostate cancers). In Mendelian randomisation (MR) analyses, using instruments identified using UK Biobank (up to 194 453 men) and outcome data from PRACTICAL (up to 79 148 cases and 61 106 controls, including 15 167 aggressive cancers), ORs were estimated using the inverse-variance weighted method. Free testosterone was associated with aggressive disease in MR analyses (OR per 1 SD = 1.23, 95% CI = 1.08-1.40). In blood-based analyses there was no association with aggressive disease overall, but there was heterogeneity by age at blood collection (OR for men aged <60 years 1.14, CI = 1.02-1.28; Phet  = .0003: inverse association for older ages). Associations for free testosterone were positive for overall prostate cancer (MR: 1.20, 1.08-1.34; blood-based: 1.03, 1.01-1.05) and early-onset prostate cancer (MR: 1.37, 1.09-1.73; blood-based: 1.08, 0.98-1.19). SHBG and total testosterone were inversely associated with overall prostate cancer in blood-based analyses, with null associations in MR analysis. Our results support free testosterone, rather than total testosterone, in the development of prostate cancer, including aggressive subgroups.

Large-scale Integrated Analysis of Genetics and Metabolomic Data Reveals Potential Links Between Lipids and Colorectal Cancer Risk

BACKGROUND

The etiology of colorectal cancer is not fully understood.

METHODS

Using genetic variants and metabolomics data including 217 metabolites from the Framingham Heart Study (n = 1,357), we built genetic prediction models for circulating metabolites. Models with prediction R2 > 0.01 (Nmetabolite = 58) were applied to predict levels of metabolites in two large consortia with a combined sample size of approximately 46,300 cases and 59,200 controls of European and approximately 21,700 cases and 47,400 controls of East Asian (EA) descent. Genetically predicted levels of metabolites were evaluated for their associations with colorectal cancer risk in logistic regressions within each racial group, after which the results were combined by meta-analysis.

RESULTS

Of the 58 metabolites tested, 24 metabolites were significantly associated with colorectal cancer risk [Benjamini-Hochberg FDR (BH-FDR) < 0.05] in the European population (ORs ranged from 0.91 to 1.06; P values ranged from 0.02 to 6.4 × 10-8). Twenty one of the 24 associations were replicated in the EA population (ORs ranged from 0.26 to 1.69, BH-FDR < 0.05). In addition, the genetically predicted levels of C16:0 cholesteryl ester was significantly associated with colorectal cancer risk in the EA population only (OREA: 1.94, 95% CI, 1.60-2.36, P = 2.6 × 10-11; OREUR: 1.01, 95% CI, 0.99-1.04, P = 0.3). Nineteen of the 25 metabolites were glycerophospholipids and triacylglycerols (TAG). Eighteen associations exhibited significant heterogeneity between the two racial groups (PEUR-EA-Het < 0.005), which were more strongly associated in the EA population. This integrative study suggested a potential role of lipids, especially certain glycerophospholipids and TAGs, in the etiology of colorectal cancer.

CONCLUSIONS

This study identified potential novel risk biomarkers for colorectal cancer by integrating genetics and circulating metabolomics data.

IMPACT

The identified metabolites could be developed into new tools for risk assessment of colorectal cancer in both European and EA populations.

The Effects of Anesthetics on Glioma Progression: A Narrative Review

There are many established factors that influence glioma progression, including patient age, grade of tumor, genetic mutations, extent of surgical resection, and chemoradiotherapy. Although the exposure time to anesthetics during glioma resection surgery is relatively brief, the hemodynamic changes involved and medications used, as well as the stress response throughout the perioperative period, may also influence postoperative outcomes in glioma patients. There are numerous studies that have demonstrated that choice of anesthesia influences non-brain cancer outcomes; of particular interest are those describing that the use of total intravenous anesthesia may yield superior outcomes compared with volatile agents in in vitro and human studies. Much remains to be discovered on the topic of anesthesia's effect on glioma progression.

TBX1 functions as a putative oncogene of breast cancer through promoting cell cycle progression

We have previously identified a genetic variant, rs34331122 in the 22q11.21 locus, as being associated with breast cancer risk in a genome-wide association study. This novel variant is located in the intronic region of the T-box transcription factor 1 (TBX1) gene. Cis-expression quantitative trait loci analysis showed that expression of TBX1 was regulated by the rs34331122 variant. In the current study, we investigated biological functions and potential molecular mechanisms of TBX1 in breast cancer. We found that TBX1 expression was significantly higher in breast cancer tumor tissues than adjacent normal breast tissues and increased with tumor stage (P < 0.05). We further knocked-down TBX1 gene expression in three breast cancer cell lines, MDA-MB-231, MCF-7 and T47D, using small interfering RNAs and examined consequential changes on cell oncogenicity and gene expression. TBX1 knock-down significantly inhibited breast cancer cell proliferation, colony formation, migration and invasion. RNA sequencing and flow cytometry analysis revealed that TBX1 knock-down in breast cancer cells induced cell cycle arrest in the G1 phase through disrupting expression of genes involved in the cell cycle pathway. Furthermore, survival analysis using the online Kaplan-Meier Plotter suggested that higher TBX1 expression was associated with worse outcomes in breast cancer patients, especially for estrogen receptor-positive breast cancer, with HRs (95% CIs) for overall survival (OS) and distant metastasis free survival (DMFS) of 1.5 (1.05-2.15) and 1.55 (1.10-2.18), respectively. In conclusion, our results suggest that the TBX1 gene may act as a putative oncogene of breast cancer through regulating expressions of cell cycle-related genes.

A transcriptome-wide association study identifies novel candidate susceptibility genes for prostate cancer risk

A large proportion of heritability for prostate cancer risk remains unknown. Transcriptome-wide association study combined with validation comparing overall levels will help to identify candidate genes potentially playing a role in prostate cancer development. Using data from the Genotype-Tissue Expression Project, we built genetic models to predict normal prostate tissue gene expression using the statistical framework PrediXcan, a modified version of the unified test for molecular signatures and Joint-Tissue Imputation. We applied these prediction models to the genetic data of 79 194 prostate cancer cases and 61 112 controls to investigate the associations of genetically determined gene expression with prostate cancer risk. Focusing on associated genes, we compared their expression in prostate tumor vs normal prostate tissue, compared methylation of CpG sites located at these loci in prostate tumor vs normal tissue, and assessed the correlations between the differentiated genes' expression and the methylation of corresponding CpG sites, by analyzing The Cancer Genome Atlas (TCGA) data. We identified 573 genes showing an association with prostate cancer risk at a false discovery rate (FDR) ≤ 0.05, including 451 novel genes and 122 previously reported genes. Of the 573 genes, 152 showed differential expression in prostate tumor vs normal tissue samples. At loci of 57 genes, 151 CpG sites showed differential methylation in prostate tumor vs normal tissue samples. Of these, 20 CpG sites were correlated with expression of 11 corresponding genes. In this TWAS, we identified novel candidate susceptibility genes for prostate cancer risk, providing new insights into prostate cancer genetics and biology.

A transcriptome-wide association study identifies novel blood-based gene biomarker candidates for Alzheimer's disease risk

Alzheimer's disease (ad) adversely affects the health, quality of life and independence of patients. There is a critical need to identify novel blood gene biomarkers for ad risk assessment. We performed a transcriptome-wide association study to identify biomarker candidates for ad risk. We leveraged two sets of gene expression prediction models of blood developed using different reference panels and modeling strategies. By applying the prediction models to a meta-GWAS including 71 880 (proxy) cases and 383 378 (proxy) controls, we identified significant associations of genetically determined expression of 108 genes in blood with ad risk. Of these, 15 genes were differentially expressed between ad patients and controls with concordant directions in measured expression data. With evidence from the analyses based on both genetic instruments and directly measured expression levels, this study identifies 15 genes with strong support as biomarkers in blood for ad risk, which may enhance ad risk assessment and mechanism-focused studies.

Novel strategy for disease risk prediction incorporating predicted gene expression and DNA methylation data: a multi-phased study of prostate cancer

BACKGROUND

DNA methylation and gene expression are known to play important roles in the etiology of human diseases such as prostate cancer (PCa). However, it has not yet been possible to incorporate information of DNA methylation and gene expression into polygenic risk scores (PRSs). Here, we aimed to develop and validate an improved PRS for PCa risk by incorporating genetically predicted gene expression and DNA methylation, and other genomic information using an integrative method.

METHODS

Using data from the PRACTICAL consortium, we derived multiple sets of genetic scores, including those based on available single-nucleotide polymorphisms through widely used methods of pruning and thresholding, LDpred, LDpred-funt, AnnoPred, and EBPRS, as well as PRS constructed using the genetically predicted gene expression and DNA methylation through a revised pruning and thresholding strategy. In the tuning step, using the UK Biobank data (1458 prevalent cases and 1467 controls), we selected PRSs with the best performance. Using an independent set of data from the UK Biobank, we developed an integrative PRS combining information from individual scores. Furthermore, in the testing step, we tested the performance of the integrative PRS in another independent set of UK Biobank data of incident cases and controls.

RESULTS

Our constructed PRS had improved performance (C statistics: 76.1%) over PRSs constructed by individual benchmark methods (from 69.6% to 74.7%). Furthermore, our new PRS had much higher risk assessment power than family history. The overall net reclassification improvement was 69.0% by adding PRS to the baseline model compared with 12.5% by adding family history.

CONCLUSIONS

We developed and validated a new PRS which may improve the utility in predicting the risk of developing PCa. Our innovative method can also be applied to other human diseases to improve risk prediction across multiple outcomes.

Integrating Genome and Methylome Data to Identify Candidate DNA Methylation Biomarkers for Pancreatic Cancer Risk

BACKGROUND

The role of methylation in pancreatic cancer risk remains unclear. We integrated genome and methylome data to identify CpG sites (CpG) with the genetically predicted methylation to be associated with pancreatic cancer risk. We also studied gene expression to understand the identified associations.

METHODS

Using genetic data and white blood cell methylation data from 1,595 subjects of European descent, we built genetic models to predict DNA methylation levels. After internal and external validation, we applied prediction models with satisfactory performance to the genetic data of 8,280 pancreatic cancer cases and 6,728 controls of European ancestry to investigate the associations of predicted methylation with pancreatic cancer risk. For associated CpGs, we compared their measured levels in pancreatic tumor versus benign tissue.

RESULTS

We identified 45 CpGs at nine loci showing an association with pancreatic cancer risk, including 15 CpGs showing an association independent from identified risk variants. We observed significant correlations between predicted methylation of 16 of the 45 CpGs and predicted expression of eight adjacent genes, of which six genes showed associations with pancreatic cancer risk. Of the 45 CpGs, we were able to compare measured methylation of 16 in pancreatic tumor versus benign pancreatic tissue. Of them, six showed differentiated methylation.

CONCLUSIONS

We identified methylation biomarker candidates associated with pancreatic cancer using genetic instruments and added additional insights into the role of methylation in regulating gene expression in pancreatic cancer development.

IMPACT

A comprehensive study using genetic instruments identifies 45 CpG sites at nine genomic loci for pancreatic cancer risk.

Associations Between Genetically Predicted Plasma N-Glycans and Prostate Cancer Risk: Analysis of Over 140,000 European Descendants

Background

Previous studies suggest a potential link between glycosylation and prostate cancer. To better characterize the relationship between the two, we performed a study to comprehensively evaluate the associations between genetically predicted blood plasma N-glycan levels and prostate cancer risk.

Methods

Using genetic variants associated with N-glycan levels as instruments, we evaluated the associations between levels of 138 plasma N-glycans and prostate cancer risk. We analyzed data of 79,194 cases and 61,112 controls of European ancestry included in the consortia of BPC3, CAPS, CRUK, PEGASUS, and PRACTICAL.

Results

We identified three N-glycans with genetically predicted levels in plasma to be associated with prostate cancer risk after Bonferroni correction. The estimated odds ratios (95% confidence intervals) were 1.29 (1.20–1.40), 0.80 (0.74–0.88), and 0.79 (0.72–0.87) for PGP18, PGP33, and PGP109, respectively, per every one standard deviation increase in genetically predicted levels of N-glycan. However, the instruments for these N-glycans only involved one to two variants. The proportions of variations that can be explained by the instruments range from 1.58% to 2.95% for these three N-glycans.

Conclusion

We observed associations between genetically predicted levels of three N-glycans PGP18, PGP33, and PGP109 and prostate cancer risk. Given the correlated nature of the N-glycans and that many N-glycans share genetic loci, pleiotropy is a major concern. Future work is warranted to better characterize the relationship between N-glycans and prostate cancer.

DDX52 knockdown inhibits the growth of prostate cancer cells by regulating c-Myc signaling

BACKGROUND

DDX52 is a type of DEAD/H box RNA helicase that was identified as a novel prostate cancer (PCa) genetic locus and possible causal gene in a European large-scale transcriptome-wide association study. However, the functions of DDX52 in PCa remain undetermined. The c-Myc oncogene plays a crucial role in the development of PCa, but the factors that regulate the activity of c-Myc in PCa are still unknown.

METHODS

We determined DDX52 protein levels in PCa tissues using immunohistochemistry (IHC). DDX52 expression and survival outcomes in other PCa cohorts were examined using bioinformatics analysis. The inhibition of DDX52 via RNA interference with shRNA was used to clarify the effects of DDX52 on PCa cell growth in vitro and in vivo. Gene set enrichment analysis and RNA sequencing were used to explore the signaling regulated by DDX52 in PCa. Western blotting and IHC were used to determine the possible DDX52 signaling mechanism in PCa.

RESULTS

DDX52 expression was upregulated in PCa tissues. Bioinformatics analysis showed that the level of DDX52 further increased in advanced PCa, with a high DDX52 level indicating a poor outcome. In vitro and in vivo experiments showed that downregulating DDX52 impeded the growth of PCa cells. High DDX52 levels contributed to activating c-Myc signaling in PCa patients and PCa cells. Furthermore, DDX52 expression was regulated by c-Myc and positively correlated with c-Myc expression in PCa.

CONCLUSION

DDX52 was overexpressed in PCa tissues in contrast to normal prostate tissues. DDX52 knockdown repressed the growth of PCa cells in vitro and in vivo. Deleting c-Myc inhibited DDX52 expression, which affected the activation of c-Myc signaling.

An integrative multiomics analysis identifies putative causal genes for COVID-19 severity

Purpose

It is critical to identify putative causal targets for SARS coronavirus 2, which may guide drug repurposing options to reduce the public health burden of COVID-19.

Methods

We applied complementary methods and multiphased design to pinpoint the most likely causal genes for COVID-19 severity. First, we applied cross-methylome omnibus (CMO) test and leveraged data from the COVID-19 Host Genetics Initiative (HGI) comparing 9,986 hospitalized COVID-19 patients and 1,877,672 population controls. Second, we evaluated associations using the complementary S-PrediXcan method and leveraging blood and lung tissue gene expression prediction models. Third, we assessed associations of the identified genes with another COVID-19 phenotype, comparing very severe respiratory confirmed COVID versus population controls. Finally, we applied a fine-mapping method, fine-mapping of gene sets (FOGS), to prioritize putative causal genes.

Results

Through analyses of the COVID-19 HGI using complementary CMO and S-PrediXcan methods along with fine-mapping, XCR1, CCR2, SACM1L, OAS3, NSF, WNT3, NAPSA, and IFNAR2 are identified as putative causal genes for COVID-19 severity.

Conclusion

We identified eight genes at five genomic loci as putative causal genes for COVID-19 severity.

Polygenic Risk Scores in Prostate Cancer Risk Assessment and Screening

More than 40% of the risk of developing prostate cancer (PCa) is from genetic factors. Genome-wide association studies have led to the discovery of more than 140 variants associated with PCa risk. Polygenic risk scores (PRS) generated using these variants show promise in identifying individuals at much higher (and lower) lifetime risk than the average man. PCa PRS also improve the predictive value of prostate-specific antigen screening, may inform the age for starting PCa screening, and are informative for development of more aggressive tumors. Despite the promise, few clinical trials have evaluated the benefit of PCa PRS for clinical care.

A Hierarchical Approach Using Marginal Summary Statistics for Multiple Intermediates in a Mendelian Randomization or Transcriptome Analysis

Previous research has demonstrated the usefulness of hierarchical modeling for incorporating a flexible array of prior information in genetic association studies. When this prior information consists of estimates from association analyses of single-nucleotide polymorphisms (SNP)-intermediate or SNP-gene expression, a hierarchical model is equivalent to a 2-stage instrumental or transcriptome-wide association study (TWAS) analysis, respectively. We propose to extend our previous approach for the joint analysis of marginal summary statistics to incorporate prior information via a hierarchical model (hJAM). In this framework, the use of appropriate estimates as prior information yields an analysis similar to Mendelian randomization (MR) and TWAS approaches. hJAM is applicable to multiple correlated SNPs and intermediates to yield conditional estimates for the intermediates on the outcome, thus providing advantages over alternative approaches. We investigated the performance of hJAM in comparison with existing MR and TWAS approaches and demonstrated that hJAM yields an unbiased estimate, maintains correct type-I error, and has increased power across extensive simulations. We applied hJAM to 2 examples: estimating the causal effects of body mass index (GIANT Consortium) and type 2 diabetes (DIAGRAM data set, GERA Cohort, and UK Biobank) on myocardial infarction (UK Biobank) and estimating the causal effects of the expressions of the genes for nuclear casein kinase and cyclin dependent kinase substrate 1 and peptidase M20 domain containing 1 on the risk of prostate cancer (PRACTICAL and GTEx).

Circulating insulin-like growth factor-I, total and free testosterone concentrations and prostate cancer risk in 200 000 men in UK Biobank

Insulin-like growth factor-I (IGF-I) and testosterone have been implicated in prostate cancer aetiology. Using data from a large prospective full-cohort with standardised assays and repeat blood measurements, and genetic data from an international consortium, we investigated the associations of circulating IGF-I, sex hormone-binding globulin (SHBG), and total and calculated free testosterone concentrations with prostate cancer incidence and mortality. For prospective analyses, risk was estimated using multivariable-adjusted Cox regression in 199 698 male UK Biobank participants. Hazard ratios (HRs) were corrected for regression dilution bias using repeat hormone measurements from a subsample. Two-sample Mendelian randomisation (MR) analysis of IGF-I and risk used genetic instruments identified from UK Biobank men and genetic outcome data from the PRACTICAL consortium (79 148 cases and 61 106 controls). We used cis- and all (cis and trans) SNP MR approaches. A total of 5402 men were diagnosed with and 295 died from prostate cancer (mean follow-up 6.9 years). Higher circulating IGF-I was associated with elevated prostate cancer diagnosis (HR per 5 nmol/L increment = 1.09, 95% CI 1.05-1.12) and mortality (HR per 5 nmol/L increment = 1.15, 1.02-1.29). MR analyses also supported the role of IGF-I in prostate cancer diagnosis (cis-MR odds ratio per 5 nmol/L increment = 1.34, 1.07-1.68). In observational analyses, higher free testosterone was associated with a higher risk of prostate cancer (HR per 50 pmol/L increment = 1.10, 1.05-1.15). Higher SHBG was associated with a lower risk (HR per 10 nmol/L increment = 0.95, 0.94-0.97), neither was associated with prostate cancer mortality. Total testosterone was not associated with prostate cancer. These findings implicate IGF-I and free testosterone in prostate cancer development and/or progression.

A cross-tissue transcriptome-wide association study identifies novel susceptibility genes for lung cancer in Chinese populations

Although dozens of susceptibility loci have been identified for lung cancer in genome-wide association studies (GWASs), the susceptibility genes and underlying mechanisms remain unclear. In this study, we conducted a cross-tissue transcriptome-wide association study (TWAS) with UTMOST based on summary statistics from 13 327 lung cancer cases and 13 328 controls and the genetic-expression matrix over 44 human tissues in the Genotype-Tissue Expression (GTEx) project. After further evaluating the associations in each tissue, we revealed 6 susceptibility genes in known loci and identified 12 novel ones. Among those, five novel genes, including DCAF16 (Pcross-tissue = 2.57 × 10-5, PLung = 2.89 × 10-5), CBL (Pcross-tissue = 5.08 × 10-7, PLung = 1.82 × 10-4), ATR (Pcross-tissue = 1.45 × 10-5, PLung = 9.68 × 10-5), GYPE (Pcross-tissue = 1.45 × 10-5, PLung = 2.17 × 10-3) and PARD3 (Pcross-tissue = 5.79 × 10-6, PLung = 4.05 × 10-3), were significantly associated with the risk of lung cancer in both cross-tissue and lung tissue models. Further colocalization analysis indicated that rs7667864 (C > A) and rs2298650 (G > T) drove the GWAS association signals at 4p15.31-32 (OR = 1.09, 95%CI: 1.04-1.12, PGWAS = 5.54 × 10-5) and 11q23.3 (OR = 1.08, 95%CI: 1.04-1.13, PGWAS = 5.55 × 10-5), as well as the expression of DCAF16 (βGTEx = 0.24, PGTEx = 9.81 × 10-15; βNJLCC = 0.29, PNJLCC = 3.84 × 10-8) and CBL (βGTEx = -0.17, PGTEx = 2.82 × 10-8; βNJLCC = -0.32, PNJLCC = 2.61 × 10-7) in lung tissue. Functional annotations and phenotype assays supported the carcinogenic effect of these novel susceptibility genes in lung carcinogenesis.

Association between lincRNA expression and overall survival for patients with triple-negative breast cancer

PURPOSE

Long intergenic non-coding RNAs (lincRNAs) are increasingly recognized as important regulators for pathogenesis and/or prognosis of breast cancer, including triple-negative breast cancer (TNBC) subtype. However, few previous studies used RNA-sequencing (RNA-Seq) technology, and none included an independent replication.

METHODS

To systematically evaluate the association between expression of lincRNAs and TNBC survival, we examined lincRNA expression profiles in TNBC tissues using RNA-Seq data for 200 TNBC patients from the Shanghai Breast Cancer Survival Study (SBCSS) and Southern Community Cohort Study (SCCS).

RESULTS

Twenty-five lincRNAs were found to be associated with overall survival (P < 0.05 and no significant heterogeneity across studies at Q statistic P > 0.1), and 61 lincRNAs were associated with disease-free survival (DFS). Among these, two lincRNAs (LINC01270 and LINC00449) were significantly associated with both worse overall survival and DFS and were expressed at significantly higher levels in tumor tissues compared with adjacent normal breast tissues (log2[Fold Change] > 0.5 and FDR < 0.05). We further evaluated the potential functions of LINC01270 and LINC00449 using in vitro functional experiments and found that siRNA-mediated knockdown of LINC01270 and LINC00449 expression significantly decreased cell viability, colony formation and cell migration ability in TNBC cells (P < 0.05).

CONCLUSIONS

Evidence from observational studies and in vitro experiments indicates that LINC00449 and LINC01270 may be prognostic biomarkers for TNBC.

A Large-Scale Association Study Detects Novel Rare Variants, Risk Genes, Functional Elements, and Polygenic Architecture of Prostate Cancer Susceptibility

To identify rare variants associated with prostate cancer susceptibility and better characterize the mechanisms and cumulative disease risk associated with common risk variants, we conducted an integrated study of prostate cancer genetic etiology in two cohorts using custom genotyping microarrays, large imputation reference panels, and functional annotation approaches. Specifically, 11,984 men (6,196 prostate cancer cases and 5,788 controls) of European ancestry from Northern California Kaiser Permanente were genotyped and meta-analyzed with 196,269 men of European ancestry (7,917 prostate cancer cases and 188,352 controls) from the UK Biobank. Three novel loci, including two rare variants (European ancestry minor allele frequency < 0.01, at 3p21.31 and 8p12), were significant genome wide in a meta-analysis. Gene-based rare variant tests implicated a known prostate cancer gene (HOXB13), as well as a novel candidate gene (ILDR1), which encodes a receptor highly expressed in prostate tissue and is related to the B7/CD28 family of T-cell immune checkpoint markers. Haplotypic patterns of long-range linkage disequilibrium were observed for rare genetic variants at HOXB13 and other loci, reflecting their evolutionary history. In addition, a polygenic risk score (PRS) of 188 prostate cancer variants was strongly associated with risk (90th vs. 40th-60th percentile OR = 2.62, P = 2.55 × 10-191). Many of the 188 variants exhibited functional signatures of gene expression regulation or transcription factor binding, including a 6-fold difference in log-probability of androgen receptor binding at the variant rs2680708 (17q22). Rare variant and PRS associations, with concomitant functional interpretation of risk mechanisms, can help clarify the full genetic architecture of prostate cancer and other complex traits. SIGNIFICANCE: This study maps the biological relationships between diverse risk factors for prostate cancer, integrating different functional datasets to interpret and model genome-wide data from over 200,000 men with and without prostate cancer.See related commentary by Lachance, p. 1637.

Aggregating multiple expression prediction models improves the power of transcriptome-wide association studies

Transcriptome-wide association study (TWAS) is an important integrative method for identifying genes that are causally associated with phenotypes. A key step of TWAS involves the construction of expression prediction models for every gene in turn using its cis-SNPs as predictors. Different TWAS methods rely on different models for gene expression prediction, and each such model makes a distinct modeling assumption that is often suitable for a particular genetic architecture underlying expression. However, the genetic architectures underlying gene expression vary across genes throughout the transcriptome. Consequently, different TWAS methods may be beneficial in detecting genes with distinct genetic architectures. Here, we develop a new method, HMAT, which aggregates TWAS association evidence obtained across multiple gene expression prediction models by leveraging the harmonic mean P-value combination strategy. Because each expression prediction model is suited to capture a particular genetic architecture, aggregating TWAS associations across prediction models as in HMAT improves accurate expression prediction and enables subsequent powerful TWAS analysis across the transcriptome. A key feature of HMAT is its ability to accommodate the correlations among different TWAS test statistics and produce calibrated P-values after aggregation. Through numerical simulations, we illustrated the advantage of HMAT over commonly used TWAS methods as well as ad hoc P-value combination rules such as Fisher's method. We also applied HMAT to analyze summary statistics of nine common diseases. In the real data applications, HMAT was on average 30.6% more powerful compared to the next best method, detecting many new disease-associated genes that were otherwise not identified by existing TWAS approaches. In conclusion, HMAT represents a flexible and powerful TWAS method that enjoys robust performance across a range of genetic architectures underlying gene expression.

Identification of Germline Genetic Variants that Increase Prostate Cancer Risk and Influence Development of Aggressive Disease

Prostate cancer (PrCa) is a heterogeneous disease, which presents in individual patients across a diverse phenotypic spectrum ranging from indolent to fatal forms. No robust biomarkers are currently available to enable routine screening for PrCa or to distinguish clinically significant forms, therefore late stage identification of advanced disease and overdiagnosis plus overtreatment of insignificant disease both remain areas of concern in healthcare provision. PrCa has a substantial heritable component, and technological advances since the completion of the Human Genome Project have facilitated improved identification of inherited genetic factors influencing susceptibility to development of the disease within families and populations. These genetic markers hold promise to enable improved understanding of the biological mechanisms underpinning PrCa development, facilitate genetically informed PrCa screening programmes and guide appropriate treatment provision. However, insight remains largely lacking regarding many aspects of their manifestation; especially in relation to genes associated with aggressive phenotypes, risk factors in non-European populations and appropriate approaches to enable accurate stratification of higher and lower risk individuals. This review discusses the methodology used in the elucidation of genetic loci, genes and individual causal variants responsible for modulating PrCa susceptibility; the current state of understanding of the allelic spectrum contributing to PrCa risk; and prospective future translational applications of these discoveries in the developing eras of genomics and personalised medicine.

Heritability Enrichment of Immunoglobulin G N-Glycosylation in Specific Tissues

Genome-wide association studies (GWAS) have identified over 60 genetic loci associated with immunoglobulin G (IgG) N-glycosylation; however, the causal genes and their abundance in relevant tissues are uncertain. Leveraging data from GWAS summary statistics for 8,090 Europeans, and large-scale expression quantitative trait loci (eQTL) data from the genotype-tissue expression of 53 types of tissues (GTEx v7), we derived a linkage disequilibrium score for the specific expression of genes (LDSC-SEG) and conducted a transcriptome-wide association study (TWAS). We identified 55 gene associations whose predicted levels of expression were significantly associated with IgG N-glycosylation in 14 tissues. Three working scenarios, i.e., tissue-specific, pleiotropic, and coassociated, were observed for candidate genetic predisposition affecting IgG N-glycosylation traits. Furthermore, pathway enrichment showed several IgG N-glycosylation-related pathways, such as asparagine N-linked glycosylation, N-glycan biosynthesis and transport to the Golgi and subsequent modification. Through phenome-wide association studies (PheWAS), most genetic variants underlying TWAS hits were found to be correlated with health measures (height, waist-hip ratio, systolic blood pressure) and diseases, such as systemic lupus erythematosus, inflammatory bowel disease, and Parkinson's disease, which are related to IgG N-glycosylation. Our study provides an atlas of genetic regulatory loci and their target genes within functionally relevant tissues, for further studies on the mechanisms of IgG N-glycosylation and its related diseases.

Multiple-Tissue Integrative Transcriptome-Wide Association Studies Discovered New Genes Associated With Amyotrophic Lateral Sclerosis

Genome-wide association studies (GWAS) have identified multiple causal genes associated with amyotrophic lateral sclerosis (ALS); however, the genetic architecture of ALS remains completely unknown and a large number of causal genes have yet been discovered. To full such gap in part, we implemented an integrative analysis of transcriptome-wide association study (TWAS) for ALS to prioritize causal genes with summary statistics from 80,610 European individuals and employed 13 GTEx brain tissues as reference transcriptome panels. The summary-level TWAS analysis with single brain tissue was first undertaken and then a flexible p-value combination strategy, called summary data-based Cauchy Aggregation TWAS (SCAT), was proposed to pool association signals from single-tissue TWAS analysis while protecting against highly positive correlation among tests. Extensive simulations demonstrated SCAT can produce well-calibrated p-value for the control of type I error and was often much more powerful to identify association signals across various scenarios compared with single-tissue TWAS analysis. Using SCAT, we replicated three ALS-associated genes (i.e., ATXN3, SCFD1, and C9orf72) identified in previous GWASs and discovered additional five genes (i.e., SLC9A8, FAM66D, TRIP11, JUP, and RP11-529H20.6) which were not reported before. Furthermore, we discovered the five associations were largely driven by genes themselves and thus might be new genes which were likely related to the risk of ALS. However, further investigations are warranted to verify these results and untangle the pathophysiological function of the genes in developing ALS.

A Transcriptome-Wide Association Study Identifies Candidate Susceptibility Genes for Pancreatic Cancer Risk

Pancreatic cancer is among the most well-characterized cancer types, yet a large proportion of the heritability of pancreatic cancer risk remains unclear. Here, we performed a large transcriptome-wide association study to systematically investigate associations between genetically predicted gene expression in normal pancreas tissue and pancreatic cancer risk. Using data from 305 subjects of mostly European descent in the Genotype-Tissue Expression Project, we built comprehensive genetic models to predict normal pancreas tissue gene expression, modifying the UTMOST (unified test for molecular signatures). These prediction models were applied to the genetic data of 8,275 pancreatic cancer cases and 6,723 controls of European ancestry. Thirteen genes showed an association of genetically predicted expression with pancreatic cancer risk at an FDR ≤ 0.05, including seven previously reported genes (INHBA, SMC2, ABO, PDX1, RCCD1, CFDP1, and PGAP3) and six novel genes not yet reported for pancreatic cancer risk [6q27: SFT2D1 OR (95% confidence interval (CI), 1.54 (1.25-1.89); 13q12.13: MTMR6 OR (95% CI), 0.78 (0.70-0.88); 14q24.3: ACOT2 OR (95% CI), 1.35 (1.17-1.56); 17q12: STARD3 OR (95% CI), 6.49 (2.96-14.27); 17q21.1: GSDMB OR (95% CI), 1.94 (1.45-2.58); and 20p13: ADAM33 OR (95% CI): 1.41 (1.20-1.66)]. The associations for 10 of these genes (SFT2D1, MTMR6, ACOT2, STARD3, GSDMB, ADAM33, SMC2, RCCD1, CFDP1, and PGAP3) remained statistically significant even after adjusting for risk SNPs identified in previous genome-wide association study. Collectively, this analysis identified novel candidate susceptibility genes for pancreatic cancer that warrant further investigation. SIGNIFICANCE: A transcriptome-wide association analysis identified seven previously reported and six novel candidate susceptibility genes for pancreatic cancer risk.

Multi-ethnic transcriptome-wide association study of prostate cancer

The genetic risk for prostate cancer has been governed by a few rare variants with high penetrance and over 150 commonly occurring variants with lower impact on risk; however, most of these variants have been identified in studies containing exclusively European individuals. People of non-European ancestries make up less than 15% of prostate cancer GWAS subjects. Across the globe, incidence of prostate cancer varies with population due to environmental and genetic factors. The discrepancy between disease incidence and representation in genetics highlights the need for more studies of the genetic risk for prostate cancer across diverse populations. To better understand the genetic risk for prostate cancer across diverse populations, we performed PrediXcan and GWAS in a case-control study of 4,769 self-identified African American (2,463 cases and 2,306 controls), 2,199 Japanese American (1,106 cases and 1,093 controls), and 2,147 Latin American (1,081 cases and 1,066 controls) individuals from the Multiethnic Genome-wide Scan of Prostate Cancer. We used prediction models from 46 tissues in GTEx version 8 and five models from monocyte transcriptomes in the Multi-Ethnic Study of Atherosclerosis. Across the three populations, we predicted 19 gene-tissue pairs, including five unique genes, to be significantly (lfsr < 0.05) associated with prostate cancer. One of these genes, NKX3-1, replicated in a larger European study. At the SNP level, 110 SNPs met genome-wide significance in the African American study while 123 SNPs met significance in the Japanese American study. Fine mapping revealed three significant independent loci in the African American study and two significant independent loci in the Japanese American study. These identified loci confirm findings from previous GWAS of prostate cancer in diverse populations while PrediXcan-identified genes suggest potential new directions for prostate cancer research in populations across the globe.

An integrative multi-omics analysis to identify candidate DNA methylation biomarkers related to prostate cancer risk

It remains elusive whether some of the associations identified in genome-wide association studies of prostate cancer (PrCa) may be due to regulatory effects of genetic variants on CpG sites, which may further influence expression of PrCa target genes. To search for CpG sites associated with PrCa risk, here we establish genetic models to predict methylation (N = 1,595) and conduct association analyses with PrCa risk (79,194 cases and 61,112 controls). We identify 759 CpG sites showing an association, including 15 located at novel loci. Among those 759 CpG sites, methylation of 42 is associated with expression of 28 adjacent genes. Among 22 genes, 18 show an association with PrCa risk. Overall, 25 CpG sites show consistent association directions for the methylation-gene expression-PrCa pathway. We identify DNA methylation biomarkers associated with PrCa, and our findings suggest that specific CpG sites may influence PrCa via regulating expression of candidate PrCa target genes.

Data-driven translational prostate cancer research: from biomarker discovery to clinical decision

Prostate cancer (PCa) is a common malignant tumor with increasing incidence and high heterogeneity among males worldwide. In the era of big data and artificial intelligence, the paradigm of biomarker discovery is shifting from traditional experimental and small data-based identification toward big data-driven and systems-level screening. Complex interactions between genetic factors and environmental effects provide opportunities for systems modeling of PCa genesis and evolution. We hereby review the current research frontiers in informatics for PCa clinical translation. First, the heterogeneity and complexity in PCa development and clinical theranostics are introduced to raise the concern for PCa systems biology studies. Then biomarkers and risk factors ranging from molecular alternations to clinical phenotype and lifestyle changes are explicated for PCa personalized management. Methodologies and applications for multi-dimensional data integration and computational modeling are discussed. The future perspectives and challenges for PCa systems medicine and holistic healthcare are finally provided.

Analysis of Over 140,000 European Descendants Identifies Genetically Predicted Blood Protein Biomarkers Associated with Prostate Cancer Risk

Several blood protein biomarkers have been associated with prostate cancer risk. However, most studies assessed only a small number of biomarkers and/or included a small sample size. To identify novel protein biomarkers of prostate cancer risk, we studied 79,194 cases and 61,112 controls of European ancestry, included in the PRACTICAL/ELLIPSE consortia, using genetic instruments of protein quantitative trait loci for 1,478 plasma proteins. A total of 31 proteins were associated with prostate cancer risk including proteins encoded by GSTP1, whose methylation level was shown previously to be associated with prostate cancer risk, and MSMB, SPINT2, IGF2R, and CTSS, which were previously implicated as potential target genes of prostate cancer risk variants identified in genome-wide association studies. A total of 18 proteins inversely correlated and 13 positively correlated with prostate cancer risk. For 28 of the identified proteins, gene somatic changes of short indels, splice site, nonsense, or missense mutations were detected in patients with prostate cancer in The Cancer Genome Atlas. Pathway enrichment analysis showed that relevant genes were significantly enriched in cancer-related pathways. In conclusion, this study identifies 31 candidates of protein biomarkers for prostate cancer risk and provides new insights into the biology and genetics of prostate tumorigenesis. SIGNIFICANCE: Integration of genomics and proteomics data identifies biomarkers associated with prostate cancer risk.