Skip to main content
NCBI home page

This is a preprint.

It has not yet been peer reviewed by a journal.

The National Library of Medicine is running a pilot to include preprints that result from research funded by NIH in PMC and PubMed.

medRxiv logoLink to medRxiv
[Preprint]. 2023 May 5:2023.05.04.23289526. [Version 1] doi: 10.1101/2023.05.04.23289526

Transcriptome-Wide Association Analysis Identifies Novel Candidate Susceptibility Genes for Prostate-Specific Antigen Levels in Men Without Prostate Cancer

Dorothy M Chen 1,*, Ruocheng Dong 2,*, Linda Kachuri 2,3, Thomas Hoffmann 1,4, Yu Jiang 1, Sonja I Berndt 5, John P Shelley 6, Kerry R Schaffer 7,12, Mitchell J Machiela 5, Neal D Freedman 5, Wen-Yi Huang 5, Shengchao A Li 5, Hans Lilja 8,9, Stephen K Van Den Eeden 10, Stephen Chanock 5, Christopher A Haiman 11,12, David V Conti 11,12, Robert J Klein 13, Jonathan D Mosley 14, John S Witte 1,2,15,†, Rebecca E Graff 1,†
PMCID: PMC10187439  PMID: 37205487

Abstract

Deciphering the genetic basis of prostate-specific antigen (PSA) levels may improve their utility to screen for prostate cancer (PCa). We thus conducted a transcriptome-wide association study (TWAS) of PSA levels using genome-wide summary statistics from 95,768 PCa-free men, the MetaXcan framework, and gene prediction models trained in Genotype-Tissue Expression (GTEx) project data. Tissue-specific analyses identified 41 statistically significant (p < 0.05/12,192 = 4.10e-6) associations in whole blood and 39 statistically significant (p < 0.05/13,844 = 3.61e-6) associations in prostate tissue, with 18 genes associated in both tissues. Cross-tissue analyses that combined associations across 45 tissues identified 155 genes that were statistically significantly (p < 0.05/22,249 = 2.25e-6) associated with PSA levels. Based on conditional analyses that assessed whether TWAS associations were attributable to a lead GWAS variant, we found 20 novel genes (11 single-tissue, 9 cross-tissue) that were associated with PSA levels in the TWAS. Of these novel genes, five showed evidence of colocalization (colocalization probability > 0.5): EXOSC9, CCNA2, HIST1H2BN, RP11-182L21.6, and RP11-327J17.2. Six of the 20 novel genes are not known to impact PCa risk. These findings yield new hypotheses for genetic factors underlying PSA levels that should be further explored toward improving our understanding of PSA biology.

Introduction

Prostate-specific antigen (PSA) is a serine protease of the human tissue kallikrein-related (KLK) peptidase family. Serum levels are commonly used as a biomarker for detection, monitoring, and risk stratification of prostate cancer (PCa).1-3 The process by which a small portion of the highly abundant PSA in the prostate is released into the blood and elevated in men prostate cancer is incompletely understood, but disruption of the prostate gland architecture by neoplastic transformation has been speculated as a possible mechanism.1,3,4 PSA levels can additionally be influenced by age, race, body mass index (BMI), infection, prostate volume, benign prostate hyperplasia (BPH), and germline genetics.5,6

PSA screening has been used for over 25 years for the detection of PCa, which is the second leading cause of cancer death among men in the United States.1,7-10 However, low test specificity and discrimination have complicated the use and interpretation of PSA as a screening tool.9,11 Long-term outcomes data from large population-based randomized PSA screening trials show that it significantly reduces death from prostate cancer but also results in considerable overdiagnosis of low risk disease.12,13 Screening might be improved were it to account for variation in PSA levels that is attributable to germline genetics rather than PCa. Twin studies and genome-wide association studies (GWAS) have estimated that 30-45% of serum PSA variation is influenced by hereditary factors.14,15 A recent GWAS meta-analysis from our group identified 128 independent variants associated with serum PSA levels, which explained approximately 9% of genetic variation in PSA.16,17

Additional genetic variation in PSA levels may be determined by analyses of genes, as opposed to individual variants. Such work could lead to the identification of regions or biological pathways that affect PSA levels and provide clarity on mechanisms underlying constitutional increases in PSA levels in the absence of carcinogenesis. Transcriptome-wide association studies (TWAS) using expression quantitative trait loci (eQTL) allow for identifying genes whose cis-regulated expression is associated with complex polygenic traits.18-21 We thus performed a TWAS of PSA levels based on summary statistics from a GWAS meta-analysis of 95,768 men without PCa to identify genes associated with PSA levels and prioritize them for functional investigation.15,22

Methods

Discovery Populations

A total of 95,768 PCa-free men from the following five study populations were included: UK Biobank, Kaiser Permanente’s Genetic Epidemiology Research on Adult Health and Aging, Prostate, Lung, Colorectal and Ovarian (PLCO) Cancer Screening Trial, Vanderbilt University Medical Center’s BioVU, and Malmö Diet and Cancer Study. The men included in these analyses were restricted to PCa-free individuals (i.e., without a PCa diagnosis or history of prostate resection, where information was available). Additionally, analyses were restricted to men with PSA values between 0.01 ng/mL and 10 ng/mL. Additional details about the study cohorts are described in detail in Kachuri et al. and Table S1. Median PSA levels were used for individuals with multiple PSA measurements available, with the exception of PLCO, which used PSA levels at the start of follow-up of the trial.

Quality Control and GWAS Meta-analysis

Genotyping, imputation, study-specific quality control of genetic data, and the GWAS meta-analysis have been previously described for Kachuri et al.17 Briefly, ancestry and study-specific GWAS analyses used linear regression of log(PSA) on genetic variants, age, and the first 10 genetic ancestry principal components. After results across studies were meta-analyzed within ancestral groups, ancestry-specific summary statistics from individuals of European (n=85,824), African (n=3,509), East Asian (n=3,337), and Hispanic/Latino (n= 3,098) ancestry were meta-analyzed to generate multi-ancestry summary statistics.

MetaXcan Transcriptome-wide Gene-based Analysis

We undertook a TWAS using the MetaXcan approach, which directly estimates Z-scores for associations between gene expression levels and PSA levels using meta-analyzed GWAS summary statistics with tissue-specific prediction models. Multivariate adaptive shrinkage (mashr) prediction models were trained on GTEx (version 8) eQTL data for whole blood and, separately, prostate tissue. The covariances and weights for models trained on individual tissue types were from the PredictDB Data Repository (https://predictdb.org/).23 We also undertook a cross-tissue analysis across 45 GTEx tissues that excluded tissues found primarily or exclusively in women to identify potential associations with genes that may have been missed in single-tissue analyses. Tissue types found primarily or exclusively in females (mammary breast, ovary, uterus, vagina) were excluded from the multiple tissue analysis. A Bonferroni correction was implemented based on the number of genes tested (whole blood: 0.05/12,192 =4.10e-6; prostate: 0.05/13,844 =3.61e-6; cross-tissue: 0.05/22,249 =2.25e-6).

Conditional Analysis Ascertaining Independence of TWAS and Previous GWAS Findings

To identify the set of TWAS genes for PSA that were novel in comparison to previous GWAS results, we first limited consideration to significant TWAS genes that do not contain genome-wide significant (p < 5e-8) variants from the Kachuri et al GWAS (i.e., within exact gene boundaries).17 Second, for the remaining genes, we performed conditional analyses using COJO24 that simultaneously modeled eQTLs for a given TWAS-identified gene and GWAS results. For individual tissue analyses, eQTLs used in the prediction models of TWAS-significant genes were selected from the tissue-specific PredictDB TWAS eQTL weight files for each gene. For the cross-tissue analyses, the maximum noncollinear eQTLs (variant inflation factor < 8 within a window size of 50kb) were extracted from all 45 GTEx tissues (i.e., to remove variants in linkage disequilibrium) . COJO was performed using LD reference panels from 10,000 European ancestry UK Biobank participants (as 89.6% of our population was European ancestry).24 GWAS summary statistics were then conditioned on the eQTLs for one gene at a time. If the set of genome-wide significant variants was reduced after conditioning on eQTLs used to predict gene expression, then the eQTLs and GWAS hits were assumed to be in LD and thus not independent of one another. (Methods that assess the conditional fit of eQTLs on GWAS hits are lacking.) However, if the set of genome-wide significant variants remained unchanged after conditional analyses, then we considered the TWAS gene to be independent of GWAS variants from Kachuri et al.17

Colocalization Analyses

To further investigate whether genes and variants exhibited shared genetic signals, we used fastENLOC software (version 2) to undertake colocalization analyses of eQTLs for all genes with prediction models across TWAS analyses and all variants investigated in the Kachuri et al. GWAS.17,25,26 For these analyses, LD blocks were based on 1000 Genome Project European ancestry individuals.27 Z-scores were calculated by dividing the betas from GWAS summary statistics by their standard errors and converting them to posterior inclusion probabilities.25 Finally, the GWAS summary statistics were colocalized for 49 GTEx version 8 tissues using pre-computed GTEx multiple-tissue annotations. Signal-level results returned regional-level colocalization probabilities (RCP) between eQTL and GWAS signals, which sum up the variant-level colocalization probabilities of correlated variants within an LD block that harbors a single GWAS association signal. Gene-results were based on gene-level regional colocalization probabilities (GRCP), which represent the probability that a candidate gene contains at least one colocalized variant.26 RCP and GRCP values > 0.5 indicate strong evidence of shared genetic signals between eQTLs and GWAS variants. 26

Pathway Enrichment Analysis

To explore the potential biological relevance of PSA-associated genes, we applied the Enrichr tool to all significant genes identified in the whole blood, prostate, and cross-tissue analyses to assess enrichment against three gene set libraries: Kyoto Encyclopedia of Genes and Genomes (KEGG) 2021 human; gene ontology (GO) biological process 2021; and GO molecular function 2021. Enrichment was assessed by multiplying the p-value from a Fisher’s exact test with the z-score of the deviation from the expected rank.28,29 Pathways with a Benjamini-Hochberg-corrected p-value < 0.05 were considered significantly enriched.

Results

Using MetaXcan and GWAS summary statistics based on 95,769 individuals (Table S1), TWAS analyses were conducted for whole blood, prostate, and cross-tissue matrices. The sample was primarily European (n=85,824), although individuals of African (n=3,509), East Asian (n=3,337), and Hispanic/Latino (n= 3,098) ancestry were also included in analyses. The median PSA value across all possible values across different cohorts was 2.35 ng/mL.

Whole Blood TWAS

In the TWAS based on whole blood gene expression models, 41 out of 12,192 genes were associated with PSA levels at the Bonferroni corrected threshold (Figure 1, Table S2). Expression of 20 genes was positively associated with elevated PSA, and expression of 21 genes was inversely associated. Two of the significant genes are located at 19q13.33, which contains KLK3, the gene encoding serum PSA.30 While a prediction model for KLK3 was not available in whole blood, a member of the same gene family at 19q13.33, KLK2, was the gene most strongly associated with PSA levels (p = 1.28e-62). Twenty-four of the 41 genes did not contain genome-wide significant variants and had not been annotated in previous GWAS. Among them, 7 genes were independent of previously published GWAS findings (Table 1).17 Of these, increased expression of 6 genes was associated with elevated PSA levels in PCa-free men: GPBP1L1 (1p34.1, p = 2.21e-7); TMEM69 (1p34.1, p = 2.21e-7); UQCRH (1p33, p = 4.76e-7); ACTRT3 (3q26.2, p = 5.90e-7); EXOSC9 (4q27, p = 1.80e-6); and CCNA2 (4q27, p = 7.80e-7). Decreased expression of ITH4 was associated with increased PSA levels (3p21.1, p = 3.65e-6).

Figures 1-3.

Figures 1-3.

Figures 1-3.

Figures 1-3.

Open in a new tab

TWAS results by chromosome, as depicted by (1) Miami plot for whole blood analyses, (2) Miami plot for prostate tissue analyses, and (3) Manhattan plot for cross-tissue analyses. Each dot represents a gene, and the red dashed lines represent the significance thresholds after Bonferroni correction (whole blood: 4.10e-6, prostate: 3.61e-6, cross-tissue: 2.25e-6). For the Miami plots in figures 1 and 2, all statistically significant genes are annotated, and the genes independent of results from the largest prior GWAS are highlighted in yellow; the genes in the upper half of the plots were positively associated with PSA levels, and the genes in the lower half of the plots were inversely associated with PSA levels. For the Manhattan plot in figure 3, the 30 genes with the smallest association p-values are annotated and colored blue, and significant genes from the whole blood and prostate tissue analyses are annotated and colored yellow. Directions of association between genes and PSA levels are not available for cross-tissue analyses due to the joint tissue inference methodology used in S-MultiXcan.

Table 1.

Novel genes associated with prostate-specific antigen (PSA) levels in transcriptome-wide association studies (TWAS) based on whole blood, prostate, and cross-tissue predicted gene expression*

Ensembl gene ID Gene name Chr Cytoband† Analysis~ Z-score Beta p p-bonf
ENSG00000132128 LRRC41 1 p33 Prostate 5.04 0.29 4.76E-07 6.59E-03
ENSG00000159592 GPBP1L1 1 p34.1 Whole Blood 5.18 17.92 2.21E-07 2.69E-03
Cross-Tissue −1.92 - 2.08E-08 4.59E-04
ENSG00000159596 TMEM69 1 p34.1 Whole Blood 5.18 0.28 2.21E-07 2.69E-03
ENSG00000173660 UQCRH 1 p33 Whole Blood 5.04 8.04 4.76E-07 5.80E-03
ENSG00000070831 CDC42 1 p36.12 Cross-Tissue −1.55 - 6.24E-07 1.37E-02
ENSG00000184378 ACTRT3 3 q26.2 Whole Blood 4.99 0.25 5.90E-07 7.19E-03
ENSG00000055955 ITIH4 3 p21.1 Whole Blood −4.63 −0.03 3.65E-06 4.45E-02
Prostate −4.69 −0.03 2.69E-06 3.72E-02
ENSG00000163950 SLBP 4 p16.3 Cross-Tissue −2.15 - 8.40E-08 1.85E-03
ENSG00000145386 CCNA2 4 q27 Whole Blood 4.94 0.61 7.80E-07 9.51E-03
Cross-Tissue −1.81 - 1.02E-06 2.25E-02
ENSG00000123737 EXOSC9 4 q27 Whole Blood 4.77 0.09 1.80E-06 2.19E-02
Prostate −4.86 −0.42 1.18E-06 1.63E-02
Cross-Tissue 0.36 - 8.60E-07 1.89E-02
ENSG00000233822 HIST1H2BN 6 p22.1 Prostate 5.35 0.24 8.86E-08 1.23E-03
ENSG00000272447 RP11-182L21.6 10 q22.3 Cross-Tissue 1.25 - 1.54E-07 3.40E-03
ENSG00000264230 ANXA8L1 10 q11.22 Cross-Tissue −0.46 - 2.17E-07 4.77E-03
ENSG00000165891 E2F7 12 q21.2 Cross-Tissue 1.45 - 8.54E-09 1.88E-04
ENSG00000188277 C15orf62 15 q15.1 Cross-Tissue −0.85 - 2.01E-07 4.42E-03
ENSG00000259359 RP11- 327J17.2 15 q26.2 Cross-Tissue −0.16 - 5.82E-07 1.28E-02
ENSG00000155719 OTOA 16 p12.2 Prostate −4.82 −5.36 1.44E-06 1.99E-02
ENSG00000197445 C16orf47 16 q22.3 Cross-Tissue 0.44 - 1.11E-07 2.47E-03
ENSG00000141052 MYOCD 17 p12 Cross-Tissue −3.03 - 1.20E-07 2.66E-03
ENSG00000181513 ACBD4 17 q21.31 Cross-Tissue −1.05 - 2.17E-06 4.77E-02
ENSG00000186834 HEXIM1 17 q21.31 Prostate 5.03 0.12 4.95E-07 6.85E-03
Open in a new tab

Abbreviations: Chr – chromosome; p-bonf – Bonferroni-adjusted p-value

*

The genes included in this table were significant (Bonferroni-adjusted p < 0.05) in at least one TWAS and, based on conditional analyses, independent from the largest prior genome-wide association study of PSA levels.

†

GRCh38

~

Indicates the tissue(s) in which predicted gene expression was significantly associated with PSA levels.

Prostate Tissue TWAS

In TWAS based on prostate tissue gene expression models, 39 out of 13,884 genes were statistically significantly associated with PSA levels (Figure 2, Table S3). Among them, increased expression of 18 and decreased expression of 21 was associated with elevated PSA levels. No prediction models were available for genes in the KLK family in prostate tissue. Instead, MMP7 on 11q22 was most strongly associated with PSA levels (p= 2.78e-18). MMP7 was among 18 PSA-associated TWAS genes containing known genome-wide significant variants. Of the remaining 21 significant genes, six were conditionally independent of previous GWAS results after conditioning on the eQTL variants used in gene prediction models (Table 1). Of these, increased expression of 3 genes was associated with elevated PSA levels: LRRC41 (1p33, p = 4.76e-7), HIST1H2BN (6p22.1, p = 8.86e-8), and HEXIM1 (17q21.31, p = 4.95e-7). Decreased expression of the remaining 3 genes was associated with increased PSA levels: ITIH4 (3p21.1, p = 2.69e-6), EXOSC9 (4q27 p = 1.18e-6), and OTOA (16p12.2, p = 1.44e-6).

Cross-Tissue TWAS

Using S-MultiXcan to integrate signals from 45 tissues excluding tissues primarily or exclusively present in women, we identified 155 out of 22,249 genes whose predicted expression was associated with elevated PSA levels in cross-tissue matrices (Figure 3). Among the 155 statistically significant genes, 8 are located on 19q13.33 and part of the expanded human KLK gene family. We found 71 genes that do not contain genome-wide significant loci within their gene boundaries. Conditional analyses determined that 13 genes from this set were independent of known genome-wide significant variants: CDC42 (1p36.12, p = 6.24e-07), GPBP1L1 (1p34.1, p = 2.08e-08), SLBP (4p16.3, p = 8.40e-08), EXOSC9 (4q27, p = 8.60e-07), CCNA2 (4q27, p = 1.02e-06), ANXA8L1 (10q11.22, p = 2.17e-07), RP11-182L21.6 (10q22.3, p = 1.54e-07), E2F7 (12q21.2, p = 8.54e-09), C15orf62 (15q15.1, p = 2.01e-07), RP11-327J17.2 (15q26.2, p = 5.82e-07), C16orf47 (16q22.3, p = 1.11e-07), MYOCD (17p12, p=1.20E-07), and ABCD4 (17q21.31, p = 2.17e-06) (Table 1).

Significant Genes Overlapping Across TWAS

Out of the 41 genes that were significantly associated with PSA levels in the whole blood TWAS, 34 were also imputed in the prostate tissue TWAS. Thirty (30) out of 39 significantly associated genes from the prostate tissue TWAS were also evaluated in whole blood analyses. Of the 155 significantly associated genes detected in the cross-tissue analysis, 89 genes and 105 genes had transcriptome prediction models available for whole blood and prostate tissues, respectively.

Across all TWAS in whole blood, prostate tissue, and cross-tissue models, we identified 13 genes whose predicted expression was associated with elevated PSA levels: RAB29 (1q32.1), LDAH (2p24.1), ZFP36L2 (2p21), SIDT1 (3q13.2), TRIM59 (3q25.33), EXOSC9 (4q27), HIBADH (7p15.2), TAX1BP1 (7p15.2), MEGF9 (9q33.2), ADD3 (10q25.1), APIP (11p13), LINC01569 (16p13.3), and EIF2S2 (20q11.22) (Figure 4). Of these genes, only EXOSC9 was conditionally independent after accounting for prior loci and demonstrated evidence of colocalization (described below). Five (5) associations were not statistically significant in the cross-tissue analysis, but had significant tissue-specific signals in whole blood and/or prostate. Seventeen significant genes were shared by only the whole blood and cross-tissue TWAS, six of which did not have prediction models in prostate tissue. Similarly, the prostate tissue and cross-tissue TWAS shared 14 significant genes; of these, seven genes did not have prediction models in whole blood.

Figure 4. Statistically significant genes overlapping across TWAS analyses.

Figure 4.

Open in a new tab

Of the significant gene sets from whole blood (41 genes), prostate tissue (39 genes), and cross-tissue analyses (155 genes), 13 genes were significant across all three analyses, 36 genes were significant in exactly two analyses, and 124 genes were significant in a single analysis.

Colocalization Analysis

Colocalization identified 372 signals with RCP > 0.5 at the variant level (Table S6) and 307 genes with GRCP > 0.5 at the gene level (Table S7). The top 10 genes with the largest GRCP were OTX1 (2p15), MAFF (22q13.1), FUT2 (19q13.33), EMSY (11q13.5), IFT80 (3q25.33), EXOSC9 (4q27), SMC4 (3q25.33), RCAN3 (1p36.11), PBXIP1 (1q21.3), and PMVK (1q21.3). Of these genes, only EXOSC9 was determined to be independent of previously discovered GWAS loci.17

Among 155 significant genes from the cross-tissue TWAS, locus-level results identified 50 genes with strong evidence of colocalization (Table 2). Variant-level results identified 66 regions in 51 genes (Table S5). When comparing the overlap between transcriptome-wide significant genes and results from colocalization analyses, we identified 5 novel genes (EXOSC9, CCNA2, HIST1H2BN, RP11-182L21.6, and RP11-327J17.2) with GRCP/RCP > 0.5. Two of these genes are long non-coding RNA while the remaining three are protein-coding genes.

Table 2.

Gene level colocalization results for significant genes from the cross-tissue transcriptome-wide association study*

Ensembl gene ID Gene name Chr Cytoband† GRCP GLCP
ENSG00000069275 NUCKS1 1 q32.1 0.97 2.55
ENSG00000117280 RAB29 1 q32.1 1.13 0.52
ENSG00000117602 RCAN3 1 p36.11 2.13 −24.54
ENSG00000143603 KCNN3 1 q21.3 1.42 −136.50
ENSG00000158715 SLC45A3 1 q32.1 0.59 15.17
ENSG00000163344 PMVK 1 q21.3 1.97 −50.77
ENSG00000163346 PBXIP1 1 q21.3 1.99 −14.21
ENSG00000270361 RP11-307C12.13 1 q21.3 1.08 −0.32
ENSG00000115507 OTX1 2 p15 4.91 −7.33
ENSG00000143869 GDF7 2 p24.1 1.00 620.30
ENSG00000068885 IFT80 3 q25.33 2.41 24.05
ENSG00000072858 SIDT1 3 q13.2 0.62 −211.30
ENSG00000113810 SMC4 3 q25.33 2.18 −52.64
ENSG00000132394 EEFSEC 3 q21.3 1.34 −160.40
ENSG00000175792 RUVBL1 3 q21.3 1.24 −72.12
ENSG00000177311 ZBTB38 3 q23 1.22 −39.66
ENSG00000186432 KPNA4 3 q25.33 1.27 −19.18
ENSG00000213186 TRIM59 3 q25.33 1.54 −38.26
ENSG00000123737 EXOSC9 4 q27 2.24 −108.20
ENSG00000145386 CCNA2 4 q27 1.39 −54.29
ENSG00000049656 CLPTM1L 5 p15.33 0.71 −27.67
ENSG00000164362 TERT 5 p15.33 0.72 4.54
ENSG00000184357 HIST1H1B 6 p22.1 0.92 2.70
ENSG00000189298 ZKSCAN3 6 p22.1 0.84 1.28
ENSG00000204713 TRIM27 6 p22.1 0.76 −12.84
ENSG00000233822 HIST1H2BN 6 p22.1 1.08 −9.88
ENSG00000281831 HCP5B 6 p22.1 0.88 1.25
ENSG00000164684 ZNF704 8 q21.13 1.00 −94.45
ENSG00000167034 NKX3-1 8 p21.2 0.96 −293.10
ENSG00000056558 TRAF1 9 q33.2 0.78 5,137.00
ENSG00000095261 PSMD5 9 q33.2 0.61 −14.25
ENSG00000119403 PHF19 9 q33.2 0.97 −23.73
ENSG00000130956 HABP4 9 q22.32 0.82 30.20
ENSG00000130958 SLC35D2 9 q22.32 1.22 −11.44
ENSG00000165244 ZNF367 9 q22.32 0.60 −10.13
ENSG00000148700 ADD3 10 q25.1 1.21 −118.60
ENSG00000272447 RP11-182L21.6 10 q22.3 0.99 5.94
ENSG00000137673 MMP7 11 q22.2 0.96 183.70
ENSG00000158636 EMSY 11 q13.5 2.64 −9.56
ENSG00000177951 BET1L 11 p15.5 0.78 −63.92
ENSG00000111707 SUDS3 12 q24.23 1.33 −225.60
ENSG00000175899 A2M 12 p13.31 0.64 −156.60
ENSG00000172766 NAA16 13 q14.11 1.25 −14.31
ENSG00000259359 RP11-327J17.2 15 q26.2 1.10 2.93
ENSG00000141052 MYOCD 17 p12 0.62 −1.94
ENSG00000275410 HNF1B 17 q12 0.81 2.32
ENSG00000176920 FUT2 19 q13.33 3.04 320.20
ENSG00000177045 SIX5 19 q13.32 1.87 −15.39
ENSG00000185800 DMWD 19 q13.32 1.00 461.40
ENSG00000185022 MAFF 22 q13.1 3.63 3.06
Open in a new tab

Abbreviations: Chr – chromosome; GLCP – gene-level colocalization probability; GRCP - gene-level variant colocalization probability

*

The genes in bold were, based on COJO analyses, independent from the largest prior genome-wide association study of prostate-specific antigen levels.

†

GRCh38

Pathway Enrichment Analysis

TWAS genes were significantly enriched in 23 pathways underlying molecular activities and biological processes present in KEGG and GO gene set databases (Table 3, Figure 5). Enrichment analyses in the whole blood gene set identified the renin-angiotensin-aldosterone system in KEGG, which works to regulate arterial blood pressure. No pathways were significantly enriched by prostate and cross-tissue gene sets in the KEGG catalog.

Table 3.

Results from pathway analyses based on all significant genes identified in the whole blood, prostate tissue, and cross-tissue transcriptome-wide association studies

Database Pathway Overlap p Score Genes
Whole Blood
KEGG renin-angiotensin system 2/23 0.047 335.87 KLK1;KLK2
Cross-Tissue
GO Molecular Function RNA polymerase II transcription regulatory region sequence-specific DNA binding (GO:0000977) 26/1359 6.82E-04 30.85 EHF;ZBTB4;ETS2;GLIS2;HOXA10;POU5F1B;PGBD1;HOXA9;SIX5;ZNF704;ZKSCAN3;OTX1;ZBTB7B;ZNF367;E2F7;NKX3-1;NKX2-6;BCL11A;ZBTB38;HNF1B;TBX3;DMRTA2;TFAP4;MAFF;MKX;TP53
GO Molecular Function sequence-specific double-stranded DNA binding (GO:1990837) 14/712 0.024 18.07 ETS2;TBX3;GLIS2;HOXA10;DMRTA2;HOXA9;TFAP4;ZNF704;MAFF;OTX1;ZBTB7B;TP53;E2F7;NKX3-1
GO Molecular Function double-stranded DNA binding (GO:0003690) 14/651 0.013 22.45 NUCKS1;ETS2;TBX3;GLIS2;HOXA10;DMRTA2;HOXA9;TFAP4;ZNF704;MAFF;OTX1;ZBTB7B;E2F7;NKX3-1
GO Molecular Function RNA polymerase II cis-regulatory region sequence-specific DNA binding (GO:0000978) 26/1149 6.54E-05 47.24 EHF;ZBTB4;ETS2;GLIS2;HOXA10;POU5F1B;PGBD1;HOXA9;SIX5;ZNF704;ZKSCAN3;OTX1;ZBTB7B;ZNF367;E2F7;NKX3-1;NKX2-6;BCL11A;ZBTB38;HNF1B;TBX3;DMRTA2;TFAP4;MAFF;MKX;TP53
GO Molecular Function cis-regulatory region sequence-specific DNA binding (GO:0000987) 27/1149 3.69E-05 53.95 EHF;ZBTB4;ETS2;GLIS2;HOXA10;POU5F1B;PGBD1;HOXA9;SIX5;ZNF704;ZKSCAN3;OTX1;ZBTB7B;ZNF367;E2F7;NKX3-1;NKX2-6;BCL11A;ZBTB38;HMGA1;HNF1B;TBX3;DMRTA2;TFAP4;MAFF;MKX;TP53
GO Molecular Function sequence-specific DNA binding (GO:0043565) 17/707 0.0012 35.01 ZBTB38;ZBTB4;ETS2;TBX3;GLIS2;HOXA10;DMRTA2;HOXA9;TFAP4;TERT;ZNF704;MAFF;ZKSCAN3;OTX1;ZBTB7B;E2F7;NKX3-1
GO Molecular Function endopeptidase activity (GO:0004175) 8/315 0.049 19.86 MMP7;KLK1;ADAMTS18;KLK5;KLK3;KLK15;KLK2;KLK8
GO Molecular Function histone deacetylase binding (GO:0042826) 5/1993 0.016 53.49 HOXA10;TFAP4;SUDS3;TP53;NKX3-1
GO Molecular Function serine-type endopeptidase activity (GO:0004252) 6/105 0.0047 69.79 KLK1;KLK5;KLK3;KLK15;KLK2;KLK8
GO Molecular Function serine-type peptidase activity (GO:0008236) 8/125 3.30E-04 110.44 KLK1;KLK5;KLK4;KLK3;KLK15;KLK2;KLK8;KLK11
GO Molecular Function thioesterase binding (GO:0031996) 2/11 0.049 166.1 CDC42;TRAF1
GO Biological Process regulation of transcription by RNA polymerase II (GO:0006357) 36/2206 0.0064 28.19 EHF;NUCKS1;ZBTB4;ETS2;GLIS2;HOXA10;POU5F1B;PGBD1;SDR16C5;HOXA9;ZNF827;SIX5;ZNF704;RUVBL1;ZKSCAN3;TRIM27;OTX1;SUDS3;ZBTB7B;ZNF367;E2F7;NKX3-1;MYOCD;NKX2-6;BCL11A;ZBTB38;HMGA1;HNF1B;TBX3;DMRTA2;TFAP4;MAFF;MKX;TP53;TLR4;FGFR2
GO Biological Process regulation of transcription, DNA-templated (GO:0006355) 38/2244 0.0028 33.66 EHF;NUCKS1;PBXIP1;ZBTB4;ETS2;GLIS2;HOXA10;POU5F1B;PGBD1;HOXA9;SIX5;ZNF704;RUVBL1;ZKSCAN3;GPBP1L1;OTX1;SUDS3;ZBTB7B;JARID2;ZNF367;E2F7;NKX3-1;MYOCD;NKX2-6;BCL11A;ZBTB38;SETDB2;HMGA1;HNF1B;TBX3;GDF7;DMRTA2;TFAP4;MAFF;NAA16;MKX;TP53;PHF19
GO Biological Process positive regulation of transcription, DNA-templated (GO:0045893) 22/1183 0.027 24.05 EHF;MYOCD;NKX2-6;ZBTB38;HMGA1;HNF1B;ETS2;TBX3;GLIS2;GDF7;HOXA10;TFAP4;ZNF827;MAFF;ZKSCAN3;NAA16;OTX1;TP53;TLR4;FGFR2;E2F7;NKX3-1
GO Biological Process positive regulation of transcription by RNA polymerase II (GO:0045944) 18/908 0.032 23.3 EHF;MYOCD;NKX2-6;ZBTB38;HMGA1;HNF1B;TBX3;GLIS2;HOXA10;TERT;ZNF827;MAFF;OTX1;TP53;TLR4;FGFR2;E2F7;NKX3-1
GO Biological Process negative regulation of transcription, DNA-templated (GO:0045892) 20/948 0.018 30.19 BCL11A;ZBTB38;SETDB2;HMGA1;PBXIP1;ZBTB4;ETS2;TBX3;GLIS2;SDR16C5;TFAP4;ZKSCAN3;TRIM27;SUDS3;ZBTB7B;JARID2;TP53;FGFR2;E2F7;NKX3-1
GO Biological Process negative regulation of cellular macromolecule biosynthetic process (GO:2000113) 13/547 0.041 26.39 ZBTB38;SETDB2;HMGA1;PBXIP1;ZBTB4;TBX3;GLIS2;TFAP4;ZKSCAN3;SUDS3;JARID2;TP53;NKX3-1
GO Biological Process negative regulation of nucleic acid-templated transcription (GO:1903507) 13/464 0.021 37.74 ZBTB38;SETDB2;HMGA1;PBXIP1;ZBTB4;TBX3;GLIS2;TFAP4;ZKSCAN3;SUDS3;JARID2;TP53;NKX3-1
GO Biological Process regulation of cyclin-dependent protein serine/threonine kinase activity (GO:0000079) 5/82 0.047 66.09 CCNA2;MYOCD;CDKN2C;TFAP4;CDK5RAP1
GO Biological Process cellular component disassembly (GO:0022411) 5/66 0.027 94.49 MMP7;KLK5;KLK4;KLK2;A2M
GO Biological Process extracellular matrix disassembly (GO:0022617) 5/66 0.027 94.49 MMP7;KLK5;KLK4;KLK2;A2M
GO Biological Process negative regulation of cyclin-dependent protein serine/threonine kinase activity (GO:0045736) 3/20 0.047 176.28 MYOCD;TFAP4;CDK5RAP1
GO Biological Process regulation of systemic arterial blood pressure (GO:0003073) 3/16 0.032 251.29 KLK1;KLK3;KLK2
GO Molecular Function RNA polymerase II transcription regulatory region sequence-specific DNA binding (GO:0000977) 26/1359 6.82E-04 30.85 EHF;ZBTB4;ETS2;GLIS2;HOXA10;POU5F1B;PGBD1;HOXA9;SIX5;ZNF704;ZKSCAN3;OTX1;ZBTB7B;ZNF367;E2F7;NKX3-1;NKX2-6;BCL11A;ZBTB38;HNF1B;TBX3;DMRTA2;TFAP4;MAFF;MKX;TP53
Open in a new tab

Abbreviations: p – adjusted p-value; Score – combined score

Figure 5. GO term enrichment results from the cross-tissue TWAS.

Figure 5.

Open in a new tab

The GO molecular function and biological process data repositories were queried for pathway enrichment analyses. The color gradient represents the magnitude of p-values, with darker colors corresponding to smaller values. The size of the circles represents the number of genes in the pathway.

Enrichment analyses within the GO biological process and molecular function gene set libraries identified 23 pathways that were significantly enriched in the cross-tissue gene set. No pathways were significantly enriched in GO for whole blood nor prostate tissue TWAS sets. Of note, the biological process underlying the regulation of cyclin-dependent protein serine/threonine kinase (CDK) activity in cell cycle progression is particularly relevant for the function of CCNA2. We also observed enrichment in 13 pathways involving both single and double stranded DNA binding mechanisms and regulation by transcription factors, including RNA polymerase II (pol II), a core component of the DNA transcription machinery. Moreover, two pathways underlying extracellular matrix and cellular component disassembly were enriched in the cross-tissue gene set. Analogous to the findings in KEGG for the whole blood TWAS, biological pathways underlying the regulation of systemic arterial blood pressure were also identified in the GO biological process catalog.

Discussion

This TWAS of 95,768 PCa-free men identified 173 genes whose predicted expression levels were significantly associated with PSA. Conditional analyses identified 20 novel candidate susceptibility genes for PSA, and colocalization analyses highlighted five of them: EXOSC9 and CCNA2 at 4q27, HIST1H2BN at 6p22.1, RP11-182L21.6 at 10q22.3, and RP11-327J17.2 at 15q26.2. Pathway enrichment analyses across three gene ontology catalogs implicated regulatory pathways related to transcription, cell signaling, and disassembly of cellular and noncellular components such as the extracellular matrix.

The five colocalized genes introduce new hypotheses and insights regarding the genetic mechanisms regulating PSA production. Of particular interest are the signals at the 4q27 locus (EXOSC9 and CCNA2), a genomic region that was not detected in previous GWAS of PSA.16,17 Researchers have previously investigated an autoimmune related block on the 4q27 locus and did not find an association with PCa risk. 32-35 EXOSC9, which encodes a core protein involved in the RNA degradation machinery in humans, was identified across all three TWAS analyses. Currently, there is no literature supporting an association between EXCOSC9 and PSA levels or PCa risk. Genetically predicted expression of EXOSC9 in whole blood was positively associated with PSA levels, while the opposite direction of effect was observed for expression in prostate tissue. There exists limited tissue-specific knowledge of the physiological and pathological function of EXOSC9 in non-cancer cell lines.34,35 Literature documenting the relationship between CCNA2 and PSA is also limited, though CCNA2 was previously tested as a candidate autoantibody signature marker for distinguishing PCa from BPH in patients with elevated serum PSA;36 it was not determined to be a top antibody signature of any specific PCa targets. Gene co-expression network analyses have linked CCNA2 to biochemical recurrence and survival in men with PCa.37,38 Further exploration of CCNA2 expression in individuals without PCa may unveil biological pathways that influence PSA levels.

Pathway enrichment results also highlighted a number of interesting pathways involving Pol II, which plays a critical role in the regulated synthesis of both protein-coding and noncoding messenger RNA in eukaryotic genomes.39-41 In addition to its numerous roles in DNA transcription, Pol II is known to associate with androgen receptor binding regions of the PSA enhancer and promoter to initiate androgen-dependent transcription.42 The two colocalized lncRNA genes, RP11-182L21.6 and RP11-327J17.2, could feasibly regulate epigenetic modification through histone or DNA acetylation or methylation.43 That histone deacetylase binding was enriched in our pathway analyses suggests a possible mechanism through which lncRNAs could affect constitutive PSA levels. Aberrant expression of various lncRNA products has also been observed for various cancers, including PCa.44

Because PSA is used for both PCa detection and monitoring of PCa progression, it is difficult to disentangle the mechanisms underlying our observed associations. PSA-related screening bias may account for a portion of the observed relationship between gene expression and PSA levels. Genes may also exert pleiotropic effects on PCa and PSA through overlapping biological mechanisms.1,9 Nevertheless, our restriction to PCa-free men for all analyses minimizes the potential for reverse causation and bolsters confidence that the observed genetic signals inform constitutive PSA levels. Fourteen of the 20 novel PSA-related genes identified from conditional analyses, spanning 12 genomic regions not implicated by prior PSA GWAS, have been associated with prostatic malignancies: CDC42 (1p36.12), GPBP1L1 and TMEM69 ( 1p34.1), LRRC41 (1p33), ITIH4 (3p21.1), SLBP (4p16.3), CCNA2 (4q27), HIST1H2BN (6p22.1), ANXA8L1 (10q11.2), E2F7 (12q21.2), C15orf62 (15q15.1), OTOA (16p12.2), C16orf47 (16q22.3), and HEXIM1 (17q21.31).45-55 Colocalized gene HIST1H2BN, which encodes a component of a core nucleosome histone, has been linked to PCa cell growth and epithelial-mesenchymal transition through upregulated NF-kB/Rel expression.45 Activation of the NF-kB pathway can induce activation of the PSA promoter-enhancer, even in the absence of androgens, and NF-kB can directly bind to the PSA enhancer in prostate cancer cell lines.56 To our knowledge, no experiments have been conducted to investigate the relationship between NF-kB and PSA in PCa-free populations.

Conditional analyses further identified 6 genes that have not been implicated in PCa susceptibility in gene function, experimental, or human population research: UQCRH (1p33), ACTRT3 (3q26.2), EXOSC9 (4q27), RP11-182L21.6 (10q22.3), RP11-327J17.2 (15q26.2), and ACBD4 (17q21.31). ACTRT3 is of particular interest, as it is critical in regulating sperm nucleus cytomorphology upstream of the processing of spermatid into mature motile sperm.57 Increased expression of ACTRT3, which forms a testis-specific profilin III-ACTRT3 complex that facilitates male germ cell head cytomorphology and maintains sperm motility in animal models, was associated with elevated PSA levels in the TWAS based on whole blood.57,58 Though there is no documented link between ACTRT3 and PSA levels or PCa risk, the pronounced role of ACTRT3 in mediating conformational changes in sperm nuclei suggests possible shared biological pathways between PSA and the production and processing of male germ cells.5,57 Loci that map to the multigenic region that contains ACTRT3 have been linked to melanoma, colorectal cancer, and lung cancer susceptibility.59-61 The functional role of ACTRT3 is poorly characterized and warrants further analysis.

Our study had several limitations. First, nearly 90% of men in the study population were of primarily European ancestry, and 85% of the tissue samples used to train the TWAS models were derived from European populations.62 Therefore, our TWAS may have missed ancestry-specific signals, and our findings may not be generalizable to broader ancestral populations. To better characterize the genetic mechanisms underlying circulating PSA levels, it will be imperative for future analyses to expand to multi-ethnic populations; diversity in genetic studies of PSA levels is critical for equitably improving PSA screening.63 Second, our study assessed only cis-eQTLs, so any trans-eQTL effects are not incorporated. Third, colocalization analyses have a high type 2 error rate and may be underpowered to detect shared association signals.26 Fourth, although we restricted our analyses to men who had not been diagnosed with PCa, we cannot rule out the possibility of latent, undiagnosed disease or disease diagnosed at a later timepoint. However, the prevalence of undiagnosed PCa in our population was likely to be low on account of low prevalence at the time of the first PSA measurement or increased monitoring and surveillance in longitudinal cohorts.7,9 Finally, many of the novel genes that we identified were clustered at multi-gene loci, in part due to co-regulation by a shared set of eQTLs. Sentinel genes at these loci should be interpreted with caution, as there may be correlated predicted expression.64

Our study also had several key strengths. The use of GWAS summary statistics from a cohort of 95,768 men provided us with high statistical power to quantify PSA-associated genes. In addition to conducting tissue-specific association analyses in tissues that are biologically meaningful for PSA, we integrated association signals across 45 GTEx tissues to improve power for genes with similar regulatory mechanisms across tissues. This method allowed us to capture PSA-related genes with expression patterns that were significant for specific tissues, as well as genes with expression that was similar across multiple tissues. Our study also used COJO analysis to find novel genes conditionally independent from known GWAS variants.

In summary, our TWAS identified gene expression profiles associated with PSA levels in men without PCa. These findings provide several novel hypotheses for genes that affect constitutive PSA. Further exploration of these results, including functional analyses of these genes in in-vivo settings, will augment our understanding of the genetic etiology of PSA variation. Transcriptomic analyses might also be vertically integrated with downstream -omic approaches to uncover complete mechanisms through which genetics influence circulating PSA levels. In addition, TWAS findings may be used to develop polygenic transcriptome risk scores65 for PSA levels, which could be leveraged for improving PSA as a tool for PCa screening.

Supplementary Material

Supplement 1
media-1.xlsx (10.5MB, xlsx)

Funding sources:

The Precision PSA study is supported by funding from the National Institutes of Health (NIH) and National Cancer Institute (NCI) under award number R01CA241410 (PI: JSW). REG is supported by a Prostate Cancer Foundation Young Investigator Award. LK is supported by funding from National Cancer Institute (R00CA246076). JPS is supported by funding from the National Institute of General Medical Sciences (T32GM007347). HL is supported in part by funding from NIH/NCI (P30-CA008748, U01-CA199338, R01CA244948) and the Swedish Cancer Society (Cancerfonden 20 1354 PjF). RK is supported by funding from the NIH (R01 CA244948 and R01 CA175491).

The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH.

Footnotes

Declaration of Interests:

JSW is a non-employee, cofounder of Avail Bio. HL is named on a patent for assays to measure intact prostate-specific antigen and a patent for a statistical method to detect prostate cancer commercialized by OPKO Health (4KScore). HL receives royalties from sales of the assay and has stock in OPKO Health. HL serves on the Scientific Advisory Board for Fujirebio Diagnostics Inc and owns stock in Diaprost AB and Acousort AB.

Data Availability:

GWAS summary statistics used in this analysis are available for download from Kachuri et al are available from the following Zenodo repository: https://doi.org/10.5281/zenodo.7460134. Transcriptome prediction weights and models used for MetaXcan analyses are available from: https://predictdb.org.

References

  • 1.Lilja H, Ulmert D, Vickers AJ. Prostate-specific antigen and prostate cancer: prediction, detection and monitoring. Nat Rev Cancer. 2008;8(4):268–278. doi: 10.1038/nrc2351 [DOI] [PubMed] [Google Scholar]
  • 2.Cucchiara V, Cooperberg MR, Dall’Era M, et al. Genomic Markers in Prostate Cancer Decision Making. Eur Urol. 2018;73(4):572–582. doi: 10.1016/j.eururo.2017.10.036 [DOI] [PubMed] [Google Scholar]
  • 3.LeBeau AM, Kostova M, Craik CS, Denmeade SR. Prostate-specific antigen: an overlooked candidate for the targeted treatment and selective imaging of prostate cancer. Biol Chem. 2010;391(4):333–343. doi: 10.1515/BC.2010.044 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 4.Qiu SD, Young CY, Bilhartz DL, et al. In situ hybridization of prostate-specific antigen mRNA in human prostate. J Urol. 1990;144(6):1550–1556. doi: 10.1016/s0022-5347(17)39797-5 [DOI] [PubMed] [Google Scholar]
  • 5.Lilja H, Oldbring J, Rannevik G, Laurell CB. Seminal vesicle-secreted proteins and their reactions during gelation and liquefaction of human semen. J Clin Invest. 1987;80(2):281–285. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 6.Pinsky PF, Kramer BS, Crawford ED, et al. Prostate volume and prostate-specific antigen levels in men enrolled in a large screening trial. Urology. 2006;68(2):352–356. doi: 10.1016/j.urology.2006.02.026 [DOI] [PubMed] [Google Scholar]
  • 7.Ilic D, Neuberger MM, Djulbegovic M, Dahm P. Screening for prostate cancer. Cochrane Database Syst Rev. 2013;2013(1):CD004720. doi: 10.1002/14651858.CD004720.pub3. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 8.Fenton JJ, Weyrich MS, Durbin S, Liu Y, Bang H, Melnikow J. Prostate-Specific Antigen-Based Screening for Prostate Cancer: Evidence Report and Systematic Review for the US Preventive Services Task Force. JAMA. 2018;319(18):1914–1931. doi: 10.1001/jama.2018.3712 [DOI] [PubMed] [Google Scholar]
  • 9.Ilic D, Djulbegovic M, Jung JH, et al. Prostate cancer screening with prostate-specific antigen (PSA) test: a systematic review and meta-analysis. The BMJ. 2018;362:k3519. doi: 10.1136/bmj.k3519 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 10.Potosky AL, Feuer EJ, Levin DL. Impact of screening on incidence and mortality of prostate cancer in the United States. Epidemiol Rev. 2001;23(1):181–186. doi: 10.1093/oxfordjournals.epirev.a000787 [DOI] [PubMed] [Google Scholar]
  • 11.Han PKJ, Kobrin S, Breen N, et al. National Evidence on the Use of Shared Decision Making in Prostate-Specific Antigen Screening. Ann Fam Med. 2013;11(4):306–314. doi: 10.1370/afm.1539 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 12.Hugosson J, Roobol MJ, Månsson M, et al. A 16-yr Follow-up of the European Randomized study of Screening for Prostate Cancer. Eur Urol. 2019;76(1):43–51. doi: 10.1016/j.eururo.2019.02.009 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 13.de Koning HJ, Gulati R, Moss SM, et al. The efficacy of prostate-specific antigen screening: Impact of key components in the ERSPC and PLCO trials. Cancer. 2018;124(6):1197–1206. doi: 10.1002/cncr.31178 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 14.Bansal A, Murray DK, Wu JT, Stephenson RA, Middleton RG, Meikle AW. Heritability of Prostate-Specific Antigen and Relationship with Zonal Prostate Volumes in Aging Twins*. J Clin Endocrinol Metab. 2000;85(3):1272–1276. doi: 10.1210/jcem.85.3.6399 [DOI] [PubMed] [Google Scholar]
  • 15.Pilia G, Chen WM, Scuteri A, et al. Heritability of Cardiovascular and Personality Traits in 6,148 Sardinians. PLoS Genet. 2006;2(8):e132. doi: 10.1371/journal.pgen.0020132 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 16.Hoffmann TJ, Passarelli MN, Graff RE, et al. Genome-wide association study of prostate-specific antigen levels identifies novel loci independent of prostate cancer. Nat Commun. 2017;8:14248. doi: 10.1038/ncomms14248 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 17.Kachuri L, Hoffmann TJ, Jiang Y, et al. Incorporating Genetic Determinants of Prostate-Specific Antigen Levels Improves Prostate Cancer Screening. MedRvix. doi: 10.1101/2022.04.18.22273850 [DOI] [Google Scholar]
  • 18.Nicolae DL, Gamazon E, Zhang W, Duan S, Dolan ME, Cox NJ. Trait-associated SNPs are more likely to be eQTLs: annotation to enhance discovery from GWAS. PLoS Genet. 2010;6(4):e1000888. doi: 10.1371/journal.pgen.1000888 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 19.Gamazon ER, Wheeler HE, Shah KP, et al. A gene-based association method for mapping traits using reference transcriptome data. Nat Genet. 2015;47(9):1091–1098. doi: 10.1038/ng.3367 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 20.Gusev A, Ko A, Shi H, et al. Integrative approaches for large-scale transcriptome-wide association studies. Nat Genet. 2016;48(3):245–252. doi: 10.1038/ng.3506 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 21.Pasaniuc B, Price AL. Dissecting the genetics of complex traits using summary association statistics. Nat Rev Genet. 2017;18(2):117–127. doi: 10.1038/nrg.2016.142 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 22.Barbeira AN, Dickinson SP, Bonazzola R, et al. Exploring the phenotypic consequences of tissue specific gene expression variation inferred from GWAS summary statistics. Nat Commun. 2018;9(1):1825. doi: 10.1038/s41467-018-03621-1 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 23.Barbeira A.N., Bonazzola R., Gamazon E.R. et al. Exploiting the GTEx resources to decipher the mechanisms at GWAS loci. Genome Biol. 2021;22(49). doi: 10.1186/s13059-020-02252-4 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 24.Yang J, Ferreira T, Morris AP, et al. Conditional and joint multiple-SNP analysis of GWAS summary statistics identifies additional variants influencing complex traits. Nat Genet. 2012;44(4):369–S3. doi: 10.1038/ng.2213 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 25.Wen X, Pique-Regi R, Luca F. Integrating molecular QTL data into genome-wide genetic association analysis: Probabilistic assessment of enrichment and colocalization. PLOS Genet. 2017;13(3):e1006646. doi: 10.1371/journal.pgen.1006646 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 26.Hukku A, Sampson MG, Luca F, Pique-Regi R, Wen X. Analyzing and reconciling colocalization and transcriptome-wide association studies from the perspective of inferential reproducibility. Am J Hum Genet. 2022;109(5):825–837. doi: 10.1016/j.ajhg.2022.04.005 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 27.Berisa T, Pickrell JK. Approximately independent linkage disequilibrium blocks in human populations. Bioinformatics. 2016;32(2):283–285. doi: 10.1093/bioinformatics/btv546 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 28.Chen EY, Tan CM, Kou Y, et al. Enrichr: interactive and collaborative HTML5 gene list enrichment analysis tool. BMC Bioinformatics. 2013;14(1):128. doi: 10.1186/1471-2105-14-128 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 29.Xie Z, Bailey A, Kuleshov MV, et al. Gene Set Knowledge Discovery with Enrichr. Curr Protoc. 2021;1(3):e90. doi: 10.1002/cpz1.90 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 30.Balk SP, Ko YJ, Bubley GJ. Biology of Prostate-Specific Antigen. J Clin Oncol. 2003;21(2):383–391. doi: 10.1200/JCO.2003.02.083 [DOI] [PubMed] [Google Scholar]
  • 31.Barbeira AN, Pividori M, Zheng J, Wheeler HE, Nicolae DL, Im HK. Integrating predicted transcriptome from multiple tissues improves association detection. PLOS Genet. 2019;15(1):e1007889. doi: 10.1371/journal.pgen.1007889 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 32.Tindall EA, Hoang HN, Southey MC, et al. The 4q27 locus and prostate cancer risk. BMC Cancer. 2010;10(1):69. doi: 10.1186/1471-2407-10-69 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 33.Yoshino S, Hara T, Weng JS, Takahashi Y, Seiki M, Sakamoto T. Genetic Screening of New Genes Responsible for Cellular Adaptation to Hypoxia Using a Genome-Wide shRNA Library. PLoS ONE. 2012;7(4):e35590. doi: 10.1371/journal.pone.0035590 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 34.Yoshino S, Matsui Y, Fukui Y, et al. EXOSC9 depletion attenuates P-body formation, stress resistance, and tumorigenicity of cancer cells. Sci Rep. 2020;10(1):9275. doi: 10.1038/s41598-020-66455-2 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 35.Burns DT, Donkervoort S, Müller JS, et al. Variants in EXOSC9 Disrupt the RNA Exosome and Result in Cerebellar Atrophy with Spinal Motor Neuronopathy. Am J Hum Genet. 2018;102(5):858–873. doi: 10.1016/j.ajhg.2018.03.011 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 36.O’Rourke DJ, DiJohnson DA, Caiazzo RJ, et al. Autoantibody Signatures as Biomarkers to Distinguish Prostate Cancer from Benign Prostatic Hyperplasia in Patients with Increased Serum Prostate Specific Antigen. Clin Chim Acta Int J Clin Chem. 2012;413(5-6):561–567. doi: 10.1016/j.cca.2011.11.027 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 37.Feng T, Wei D, Li Q, et al. Four Novel Prognostic Genes Related to Prostate Cancer Identified Using Co-expression Structure Network Analysis. Front Genet. 2021;12. doi: 10.3389/fgene.2021.584164 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 38.Yang R, Du Y, Wang L, Chen Z, Liu X. Weighted gene co-expression network analysis identifies CCNA2 as a treatment target of prostate cancer through inhibiting cell cycle. J Cancer. 2020;11(5):1203–1211. doi: 10.7150/jca.38173 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 39.Smale ST, Kadonaga JT. The RNA polymerase II core promoter. Annu Rev Biochem. 2003;72:449–479. doi: 10.1146/annurev.biochem.72.121801.161520 [DOI] [PubMed] [Google Scholar]
  • 40.Wagner SD, Yakovchuk P, Gilman B, et al. RNA polymerase II acts as an RNA-dependent RNA polymerase to extend and destabilize a non-coding RNA. EMBO J. 2013;32(6):781–790. doi: 10.1038/emboj.2013.18 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 41.Schier AC, Taatjes DJ. Structure and mechanism of the RNA polymerase II transcription machinery. Genes Dev. 2020;34(7-8):465–488. doi: 10.1101/gad.335679.119 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 42.Louie MC, Yang HQ, Ma AH, et al. Androgen-induced recruitment of RNA polymerase II to a nuclear receptor–p160 coactivator complex. Proc Natl Acad Sci. 2003;100(5):2226–2230. doi: 10.1073/pnas.0437824100 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 43.Zhang X, Wang W, Zhu W, et al. Mechanisms and Functions of Long Non-Coding RNAs at Multiple Regulatory Levels. Int J Mol Sci. 2019;20(22):5573. doi: 10.3390/ijms20225573 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 44.Misawa A, Takayama K, Inoue S. Long non-coding RNAs and prostate cancer. Cancer Sci. 2017;108(11):2107–2114. doi: 10.1111/cas.13352 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 45.Zhang J., Chang Y., Xia H. et al. HIST1H2BN induced cell proliferation and EMT phenotype in prostate cancer via NF-κB signal pathway. Genes Genom. 2021;43:1361–1369. doi: 10.1007/s13258-021-01164-2 [DOI] [PubMed] [Google Scholar]
  • 46.Liu Q, Reed M, Zhu H, et al. Epigenome-wide DNA methylation and transcriptome profiling of localized and locally advanced prostate cancer: Uncovering new molecular markers. Genomics. 2022;114(5):110474. doi: 10.1016/j.ygeno.2022.110474 [DOI] [PubMed] [Google Scholar]
  • 47.Pressinotti NC, Klocker H, Schäfer G, et al. Differential expression of apoptotic genes PDIA3 and MAP3K5 distinguishes between low- and high-risk prostate cancer. Mol Cancer. 2009;8:130. doi: 10.1186/1476-4598-8-130 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 48.Mahajan NP, Liu Y, Majumder S, et al. Activated Cdc42-associated kinase Ack1 promotes prostate cancer progression via androgen receptor tyrosine phosphorylation. Proc Natl Acad Sci U S A. 2007;104(20):8438–8443. doi: 10.1073/pnas.0700420104 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 49.Davalieva K, Kiprijanovska S, Komina S, Petrusevska G, Zografska NC, Polenakovic M. Proteomics analysis of urine reveals acute phase response proteins as candidate diagnostic biomarkers for prostate cancer. Proteome Sci. 2015;13:2. doi: 10.1186/s12953-014-0059-9 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 50.Krishnan N, Titus MA, Thapar R. The Prolyl Isomerase Pin1 Regulates mRNA Levels of Genes with Short Half-Lives by Targeting Specific RNA Binding Proteins. PLOS ONE. 2014;9(1):e85427. doi: 10.1371/journal.pone.0085427 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 51.Wang Y, Pei X, Xu P, et al. E2F7, regulated by miR 30c, inhibits apoptosis and promotes cell cycle of prostate cancer cells. Oncol Rep. 2020;44(3):849–862. doi: 10.3892/or.2020.7659 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 52.Sheeba JDJ, Hegde S, Tamboli N, Nadig N, Keshavamurthy R, Ranganathan P. Gene expression signature of castrate resistant prostate cancer. BioRxiv. 2022.03.16.484397. doi: 10.1101/2022.03.16.484397 [DOI] [PubMed] [Google Scholar]
  • 53.Grasso CS, Wu YM, Robinson DR, et al. The mutational landscape of lethal castration-resistant prostate cancer. Nature. 2012;487(7406):239–243. doi: 10.1038/nature11125 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 54.Lew QJ, Chu KL, Chia YL, Cheong N, Chao SH. HEXIM1, a New Player in the p53 Pathway. Cancers. 2013;5(3):838–856. doi: 10.3390/cancers5030838 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 55.Chen R, Yik JHN, Lew QJ, Chao SH. Brd4 and HEXIM1: Multiple Roles in P-TEFb Regulation and Cancer. BioMed Res Int. 2014;2014:e232870. doi: 10.1155/2014/232870 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 56.Chen CD, Sawyers CL. NF-κB Activates Prostate-Specific Antigen Expression and Is Upregulated in Androgen-Independent Prostate Cancer. Mol Cell Biol. 2002;22(8):2862–2870. doi: 10.1128/MCB.22.8.2862-2870.2002 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 57.Hara Y, Yamagata K, Oguchi K, Baba T. Nuclear localization of profilin III-ArpM1 complex in mouse spermiogenesis. FEBS Lett. 2008;582(20):2998–3004. doi: 10.1016/j.febslet.2008.07.058 [DOI] [PubMed] [Google Scholar]
  • 58.Umer N, Arévalo L, Phadke S, et al. Loss of Profilin3 Impairs Spermiogenesis by Affecting Acrosome Biogenesis, Autophagy, Manchette Development and Mitochondrial Organization. Front Cell Dev Biol. 2021;9 9:749559. doi: 10.3389/fcell.2021.749559 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 59.Song F, Amos CI, Lee JE, et al. Identification of a melanoma susceptibility locus and somatic mutation in TET2. Carcinogenesis. 2014;35(9):2097–2101. doi: 10.1093/carcin/bgu140 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 60.Law PJ, Timofeeva M, Fernandez-Rozadilla C, et al. Association analyses identify 31 new risk loci for colorectal cancer susceptibility. Nat Commun. 2019;10(1):2154. doi: 10.1038/s41467-019-09775-w [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 61.McKay JD, Hung RJ, Han Y, et al. Large-scale association analysis identifies new lung cancer susceptibility loci and heterogeneity in genetic susceptibility across histological subtypes. Nat Genet. 2017;49(7):1126–1132. doi: 10.1038/ng.3892 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 62.THE GTEX CONSORTIUM. The GTEx Consortium atlas of genetic regulatory effects across human tissues. Science. 2020;369(6509):1318–1330. doi: 10.1126/science.aaz1776 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 63.Martin AR, Kanai M, Kamatani Y, Okada Y, Neale BM, Daly MJ. Clinical use of current polygenic risk scores may exacerbate health disparities. Nat Genet. 2019;51(4):584–591. doi: 10.1038/s41588-019-0379-x [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 64.Wainberg M, Sinnott-Armstrong N, Mancuso N, et al. Opportunities and challenges for transcriptome-wide association studies. Nat Genet. 2019;51(4):592–599. doi: 10.1038/s41588-019-0385-z [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 65.Liang Y., Pividori M., Manichaikul A. et al. Polygenic transcriptome risk scores (PTRS) can improve portability of polygenic risk scores across ancestries. Genome Biol 2022;23(23). doi: 10.1186/s13059-021-02591-w [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplement 1
media-1.xlsx (10.5MB, xlsx)

Data Availability Statement

GWAS summary statistics used in this analysis are available for download from Kachuri et al are available from the following Zenodo repository: https://doi.org/10.5281/zenodo.7460134. Transcriptome prediction weights and models used for MetaXcan analyses are available from: https://predictdb.org.

Articles from medRxiv are provided here courtesy of Cold Spring Harbor Laboratory Preprints

RESOURCES