A large-scale multi-ancestry genome-wide association study of chronic prostatitis/chronic pelvic pain syndrome in men

Domain: biology
Published: 2026-01-09
Editorial outcome: major_revision

Abstract
Chronic prostatitis/chronic pelvic pain syndrome is common, and it impacts men’s health and quality of life. The genetic basis of this condition remains largely unknown. Here, we conduct a GWAS using data from the Million Veteran Program of over 590,000 men of European, African, and Hispanic ancestry, including 14,575 chronic prostatitis/chronic pelvic pain syndrome cases. The multi-ancestry analysis identifies eight novel loci associated with chronic prostatitis/chronic pelvic pain syndrome risk, an increase from three significant genome-wide loci found in the European participants alone. We also estimate the genetic correlations between chronic prostatitis/chronic pelvic pain syndrome and 12 phenotypes. Notably, the genetic correlation between chronic prostatitis/chronic pelvic pain syndrome and prostate cancer is not significant. Further, Mendelian randomization shows a significant, potentially bidirectional causal relationship between chronic prostatitis/chronic pelvic pain syndrome and benign prostatic hyperplasia, but not between chronic prostatitis/chronic pelvic pain syndrome and prostate cancer, suggesting a complex interplay between chronic prostatitis/chronic pelvic pain syndrome and benign prostatic hyperplasia. Results of bivariate causal mixture modeling indicate that some of the same genetic variants likely contribute to the development of chronic prostatitis/chronic pelvic pain syndrome, benign prostatic hyperplasia, and prostate cancer.

Introduction
Chronic Prostatitis/Chronic Pelvic Pain Syndrome (CP/CPPS) is a prevalent and debilitating condition affecting 2–10% of men1. Over 90% of all prostatitis diagnoses are CP/CPPS, making it the most prevalent prostatitis diagnosis2. Symptoms include persistent pelvic pain and discomfort, sexual dysfunction, and urinary symptoms for at least three months, which can severely impair quality of life3. CP/CPPS diagnosis relies on the presence of symptoms of prostatitis in the absence of detectable urinary tract infections and other identifiable disorders. This diagnostic approach serves to differentiate CP/CPPS from acute bacterial prostatitis, chronic bacterial prostatitis, and asymptomatic inflammatory prostatitis. Epidemiological studies have linked a history of prostatitis (unspecified, but most symptomatic cases are CP/CPPS) with increased risk for other prostate conditions, including benign prostatic hyperplasia (BPH) and prostate cancer (CaP)3.

CP/CPPS is characterized by heterogeneity and transient symptomology that can fluctuate over time in intensity and order of appearance. CP/CPPS often mirrors other urological conditions, such as BPH and urological cancers like CaP, as well as chronic overlapping pain conditions (COPCs), complicating diagnosis and leading to inconsistent treatment3,4. While the etiology of CP/CPPS is multifaceted and not fully understood, current research suggests a possible pathophysiological overlap with other urinary conditions5 and implicates prostatic inflammation potentially involving autoimmune responses against prostatic antigens6 and neurophysiological alternations. A better understanding of the etiology of CP/CPPS is essential to improve diagnosis and therapy.

The extensive comorbidity between CP/CPPS and other COPCs, such as fibromyalgia, cystitis, irritable bowel syndrome, myalgic encephalomyelitis/chronic fatigue syndrome, migraine, tension headache, temporomandibular disorder, and chronic back pain, underscores the nature of CP/CPPS as a chronic pain condition and the potential for shared underlying pathogenic mechanisms5. Furthermore, the complex relationship between CP/CPPS and psychiatric conditions, such as post-traumatic stress disorder (PTSD), anxiety, and depression, emphasizes the need for a better etiological model that integrates physiological and psychological aspects of CP/CPPS7,8. Understanding the multifactorial nature of CP/CPPS and integrating physiological and psychological factors is likely necessary for comprehensive treatment and management strategies.

Despite heritability estimates from twin studies indicating a substantial genetic component of 36%, the genetic basis of CP/CPPS remains largely uncharted, and the genetic architecture of this condition remains poorly understood5,9. Genetic research has not fully utilized genome-wide association study (GWAS) methodologies to evaluate genetic factors contributing to CP/CPPS, which is essential for developing a more complete etiological framework and advancing toward precision medicine. Previous research has shown that genetic factors may contribute to the comorbidity between CP/CPPS and other urinary, COPC, and psychiatric disorders5,9. Further clarifying these genetic associations may enhance our understanding of the etiology of CP/CPPS as well as the broader genetic factors involved in male urogenital disorders. The multifaceted etiology of CP/CPPS, documented heritability, and overlap with other conditions present a compelling case to use GWAS to identify genetic factors associated with these conditions.

Our aims were to (1) elucidate the genetic architecture of CP/CPPS by conducting a GWAS within the Million Veteran Program (MVP), a large population-based sample of US Veterans, and (2) explore the associations between CP/CPPS and comorbid urinary and psychiatric conditions. Using the rich genetic and clinical MVP sample, this study was uniquely positioned to explore the genetic nature of CP/CPPS10 and improve our understanding of its genetic architecture.

In this work, we conduct a GWAS using data from the MVP of over 590,000 Veterans of European, African, and Hispanic ancestry, including 14,575 CP/CPPS cases. In the multi-ancestry meta-analysis, we identify three additional novel loci associated with CP/CPPS risk, in addition to the three novel significant genome-wide loci found in the European cohort alone. We report significant genetic correlations between CP/CPPS and 12 phenotypes, including prostate cancer (CaP), benign prostatic hyperplasia (BPH), genitourinary disease, abdominal and pelvic pain, back pain, depression, and anxiety. Notably, the genetic correlation between CP/CPPS and CaP was insignificant. We identify a significant, potentially causal relationship between CP/CPPS and BPH, but not between CP/CPPS and CaP. This work lays the foundation for future studies to improve our understanding of the biological mechanisms underlying CP/CPPS and to develop new strategies for prevention and treatment.

Results
Phenotypic prevalence over the available observation period in MVP was 2.2% (22,290/962,151) for CP/CPPS, 3.3% (31,715/962,151) for at CaP, and 31.9% (306,799/962,151) for BPH. Among Veterans with CP/CPPS, 76.26% also had BPH, and 7.59% had CaP. Although many individuals with CP/CPPS also had BPH, most individuals with BPH did not have CP/CPPS. Specifically, among individuals with BPH, only 5.5% had CP/CPPS, and 4.2% had CaP. Among CaP cases, 5.3% had CP/CPPS, and 40.6% had BPH. Tetrachoric correlations showed positive associations, strongest between CP/CPPS and BPH (rtet = 0.47 [95% CI = 0.47–0.47, p < 0.001]), followed by CP/CPPS and CaP (rtet = 0.18 [95% CI = 0.18–0.18, p < 0.001]), and CaP and BPH (rtet = 0.11 [95% CI = 0.10–0.11, p < 0.001]).

The genetics sample included 583,395 participants with 14,575 CP/CPPS cases and 568,820 controls stratified by three HARE race/ancestry groups, with EUR comprising 73.8% of the sample, followed by AFR (18.2%), and HIS (8.1%). Table 1 shows the average age and CP/CPPS prevalence by HARE groups. CP/CPPS prevalence for the three groups was 2.5% and varied across HARE groups, with 2.3% of EUR (10,035 of 430,306 individuals), 3.4% of AFR (3553 of 106,081 individuals), and 2.1% of HIS (987 of 47,008 individuals). Average age varied across the HARE groups from 64.64 (SD = 13.35) years for EUR to 55.84 (15.63) years for HIS, and was higher for CP/CPPS cases than controls in all ancestries (p < 0.001). Prevalence rate did not differ between EUR and HIS ancestry (p = 0.002).

CP/CPPS genome-wide significant loci and genes
The CP/CPPS GWAS included 10,035 cases and 420,271 controls of EUR cohort. Three loci were identified with genome-wide significant associations with CP/CPPS (p < 5 × 10−8) in EUR, including one locus on chr10, a large intergenic region mapping to the gene FGFR2, and two loci on chr19 (Table 2, Supplementary Data 1) (Fig. 1A, B). One chr19 locus did not map to any nearby gene, but the other chr19 locus encompassed a large coding region that included 13 mapped genes (LRRC4B, GPR32, C19orf48, KLK15, KLK3, KLK2, AC037199.1, KLK4, KLK7, CTU1, IGLON5, CLDND2, NKG7). Of these genes, KLK2 and KLK3 were mapped by both physical proximity (by being near the lead SNP) and eQTL associations, KLK15 and AC037199.1 were mapped by physical proximity only, and LRRC4B, GPR32, C19orf48, KLK4, KLK7, CTU1, IGLON5, CLDND2, and NKG7 were mapped by eQTL association only (Fig. 1A, B and Table 1). MAGMA was also run on the summary statistics as an alternative gene-mapping strategy. The genes identified by MAGMA were consistent with FUMA (Supplementary Data 2). Of 118 SNPs significantly associated with CP/CPPS in the three GWS loci (p < 5 × 10−8), 26 had a regulomeDB score of 3a or lower, indicating possible regulatory relationships (Supplementary Data 3). Conducting the analyses with an age of MVP-enrollment covariate yielded the same three genome-wide significant loci and lead SNPs. The genetic correlation between the EUR CP/CPPS GWAS without age and the EUR CP/CPPS GWAS was 0.9922 (SE = 0.181).

Tissue-specific enrichment analyses with MAGMA revealed significant enrichment for the prostate (p = 2 × 10−4) and bladder (p = 0.02), indicating that genes associated with CP/CPPS are specifically expressed in relevant tissues (Supplementary Data 4). MAGMA pathway analysis reveals significant enrichment for brain-related pathways (Supplementary Data 5), including negative regulation of neurotransmitter secretion, Purkinje system development, brain morphogenesis, and midbrain development.

SNPs in the three genome-wide significant loci had prior associations with relevant phenotypes, including CaP and prostate-specific antigen (PSA) levels, through the GWAS catalog. The chr19 locus, which did not map to any nearby gene, had a known association with BPH, lower urinary tract symptoms, and PSA11,12. (Fig. 1D and Supplementary Data 6).

An analysis of marginally associated SNPs (p < 5 × 10−6) with FUMA revealed an additional 18 loci, on top of the 3 already found, in which 73 genes were mapped (Fig. 1A). These marginally mapped genes were strongly enriched for prostate (Supplementary Fig. 1), using the TSEA tool13. TSEA is a method similar to MAGMA’s tissue-specific enrichment analysis, but is designed for the input of lists of significant genes, as opposed to MAGMA’s strategy of collapsing the GWAS signal within the gene set of interest. We used TSEA to evaluate if the genes mapping from the marginal FUMA analysis likely contained true positives previously missed by the stringent threshold. Furthermore, SNPs within the marginal loci had previous associations with relevant phenotypes, including CaP, BPH, lower urinary tract symptoms, and PSA levels (Fig. 1E)14. This strong enrichment for relevant tissues and previous associations with relevant phenotypes implies that some marginally associated genes represent true positive associations with CP/CPPS.

SNP-based heritability
Our SNP-based heritability analysis of EUR CP/CPPS identified a mean C2 of 1.0815, and we observed a modest genomic control inflation factor GC λ = 1.0736, suggesting that our results are unlikely to be influenced by population stratification or other sources of bias. An LDSC intercept of 1.0235 (SE: 0.0074) and attenuation ratio of 0.2883 (SE: 0.0905) indicated that additive genetic effects primarily drive the heritability of CP/CPPS. The observed scale EUR h2 was estimated at 6.7% (SE: 0.12) with a corresponding Z-score of 5.43, indicating that genetic factors explain a portion of the variance in CP/CPPS.

Multi-ancestry meta-analysis
To increase power to detect smaller effect sizes, multi-ancestry meta-analysis was conducted on summary statistics from CP/CPPS GWAS on EUR (10,035 cases, 420,271 controls), AFR (3,553 cases, 102,528 controls), and HIS (987 cases, 46,021 controls). The multi-ancestry analyses (14,575 cases, 568,820 controls) yielded three additional autosomal genome-wide significant loci on chr5, chr10, and chr13 (Supplementary Fig. 2; Table 2 and Supplementary Data 1) and two loci on chr23 (X). The chr5 locus mapped to 3 genes: NKD2, TERT, and CLPTM1L. SNPs in this locus have previously been associated with PSA levels and BPH in GWAS studies11. The new chr10 locus mapped to 3 genes: PTCHD3, MKX, ARMC4. SNPs in this locus have previously been associated with PSA levels and BPH11,15. The chr13 locus mapped to 6 genes, including DLEU1, EBPL, and PHF11. SNPs in this locus have previously been associated with PSA levels and BPH11,12. Two out of the three GWS loci newly identified in the multi-ancestry meta-analysis were also identified in the marginal SNPs EUR analysis (p < 5 × 10−6), providing more evidence that some of the marginally significant loci do contain relevant information. The two chr23 loci mapped to 10 genes (Table 2), of which GRPR is involved in spinal itch signaling, REPS2 is associated with androgen receptor signaling, and RAI2 is linked to higher PSA levels16.

We also examined the results of individual ancestry GWAS. While the AFR cohort did not yield any significant loci, one genome-wide significant SNP (CHR2: 52764460; rs144707855) was found in the HIS cohort that was found neither in the EUR cohort nor in the multi-ancestry analysis, indicating that it may be a HIS-specific variant.

Relationship to other traits
To gain further insights into the genetic basis of CP/CPPS, we conducted an unbiased LDSC screen of genetic correlations with 1466 publicly available GWAS using the complex traits genetics virtual lab (CTG-VL)17. These data include phenotypes from the UK Biobank, GIANT consortium, Psychiatric Genomics Consortium, FinnGen, and CHARGE. These analyses allowed us to identify significant genetic correlations between CP/CPPS and a range of other phenotypes (with a stringent Bonferroni-corrected p < 0.05; Supplementary Data 7). We observed significant genetic correlations between CP/CPPS and 12 other phenotypes. Consistent with other studies5, among these were genitourinary and pain conditions, BPH, diseases of the genitourinary system, and abdominal and pelvic pain (from UKB) (Fig. 2A). Other significantly correlated phenotypes were related to depression, anxiety, and non-urinary pain, including back pain and multisite pain (from UKB, MVP, and PGC, among others; Fig. 2A). These results confirm previous work that suggested CP/CPPS and other COPCs may have a shared genetic component9,18,19. These findings support previous suggestions of the urological and psychiatric relationships of CP/CPPS18,20,21 and the interconnected nature of chronic pain and mental health.

The FUMA analysis of GWAS Catalog findings for CP/CPPS revealed associations between the lead SNPs in this study and a range of prostate-related traits, including CaP, BPH, serum PSA levels, and CaP aggressiveness. Full GWAS Catalog results are presented in Supplementary Data 6.

Relationship to BPH and CaP
Genetic correlation analysis with LDSC revealed that CP/CPPS and CaP had a moderate positive but not significant post Bonferroni-correction correlation (rg = 0.16, SE: 0.07). Interestingly, the magnitude of correlation was much lower than that observed for BPH (rg = 0.78, SE = 0.16) and depression and pain phenotypes (Fig. 2A).

As an alternative approach, we quantified the degree to which CP/CPPS and CaP and CP/CPPS and BPH share genetic origins using MiXeR22. From the bivariate MiXeR analysis (Supplementary Data 8), we observed polygenic overlap, defined as the ratio of causal variants associated with both traits divided by the causal variants associated with either trait, as 0.44 (Fig. 2B) for CP/CPPS and CaP and 0.43 for CP/CPPS and BPH, indicating that some of the same genetic variants contribute to the development of all three conditions. Between CP/CPPS and CaP we observe variation specific to each trait, while between CP/CPPS and BPH, there is much less variation specific to BPH (AIC > 0, Fig. 2B, C). In contrast to genetic correlation, which computes the proportion of variance two traits share, the polygenic overlap measures the fraction of predicted causal variants shared by both traits.

Mendelian randomization (MR) was used to evaluate the potential causal relationships between CP/CPPS, BPH, and CaP, since these phenotypes often appear together. We modeled CP/CPPS as the exposure variable and BPH and CaP as the outcome variables using MRbase23. All selected SNPs had F-statistics greater than 10, confirming the strength of the instrument.

When MR was applied to CP/CPPS and BPH, we found mixed results across different methods. The IVW method, weighted median method, and simple mode methods all indicated a significant positive relationship between CP/CPPS and BPH (IVW: b = 0.041, SE = 0.013, p = 0.002; Weighted median: b = 0.036, SE = 0.005, p < 0.001; Simple mode: b = 0.057, SE = 0.010, p = 0.032) (Supplementary Data 9a). Neither the weighted mode method nor the MR Egger method resulted in a significant association (p = 0.099, p = 0.427, respectively). The MR Egger intercept term was not significantly different from zero (−0.0074, p = 0.14), suggesting no evidence of directional pleiotropy. Thus, three out of five methods tested supported a potential causal relationship between CP/CPPS and an increased risk of BPH. Intriguingly, MR also yielded significant results when the exposure and outcome variables were reversed (with BPH causing CP/CPPS) in the analysis of CP/CPPS and BPH (Supplementary Data 9b), with all five methods tested yielding a significant positive association between BPH and CP/CPPS (p < 0.05). The MR Egger intercept term was not significantly different from zero (0.0863, p = 0.11), suggesting no evidence of directional pleiotropy. Because BPH primarily affects the prostate gland and is characterized by its enlargement, BPH may contribute to the development of CP/CPPS. These significant bidirectional results from MR suggest a complex interplay between CP/CPPS and BPH that should be explored further with better-powered samples and additional variants.

As expected, based on the non-significant correlation between CP/CPPS and CaP, when MR was applied to CP/CPPS and CaP, we did not observe a significant effect with any of the five methods (p > 0.05), suggesting that CP/CPPS is not a causative factor for CaP (Supplementary Data 9c).

CAUSE analyses yielded similar results when comparing causal and sharing models (Supplementary Data 10). For CP/CPPS against BPH, the causal model was a better fit than both the null (p = 0.02) and sharing models (p = 0.01), suggesting a causal relationship from CP/CPPS to BPH. Similarly, for BPH against CP/CPPS, the causal model was a better fit than the null (p = 0.01) and sharing models (p = 0.03), suggesting a causal relationship in the reversed direction. These bidirectional results likely indicate shared genetic architecture or pleiotropy rather than distinct causal mechanisms in both directions. When CAUSE was applied to CP/CPPS and CaP, neither the sharing nor the causal model was significant, suggesting that horizontal pleiotropy may explain the association between these two phenotypes. Various models of comorbidity can exist, and it is possible that causality operates in both directions, necessitating more research to understand these relationships fully.

Case-case GWAS
As an alternative approach to identify CP/CPPS-specific risk loci, we performed a case-case GWAS (CC-GWAS)24, comparing CP/CPPS cases to CaP cases. Case-case GWAS is a computational method that compares the allele frequencies between two different case-control cohorts and operates on pre-computed summary statistics. CC-GWAS identified 1113 GWS SNPs with significantly different allele frequencies in cases of CP/CPPS versus CaP cases. Of these, 1112 were also GWS SNPs in CaP, none were GWS in CP/CPPS, and 1 was uniquely identified by CC-GWAS (rs8113621). Additionally, two loci were identified within these 1113 SNPs, suggesting potential regions of interest that may be associated with the genetic distinction between CP/CPPS and CaP cases. The first locus on chromosome 11 (lead rs7126851) included SIGIRR, TOLLIP, and BRSK2, which are involved in immune response and cellular signaling pathways, as well as other genes (Supplementary Data 11). The second locus (lead rs11650165) on chromosome 17 included AC007461.1. The finding that most CC-GWAS-identified SNPs are also significant in CaP indicates that variants that distinguish CaP from controls also distinguish CaP from CP/CPPS and that the phenotypes are at least partially driven by different genetic origins. Consistent with this observation, CaP and CP/CPPS cases have a CC-GWAS genetic distance value (FST) of 0.50. This value is nearly equivalent to the genetic distance between CP/CPPS cases and controls (FST = 0.44) (Fig. 2D). A comparative analysis with BPH was not feasible due to the high correlation between CP/CPPS and BPH that limits our ability to distinguish between the genetic factors influencing each condition.

Proximal interaction network
Conducting network analyses of the potentially underpowered CP/CPPS GWAS enables the integration of diverse datasets, enhancing detection of marginal signals that may have been overlooked. Network analysis can uncover highly interacting genes, providing insights into potential molecular pathways and mechanisms that underlie CP/CPPS, ultimately contributing to a better understanding and improved treatment.

We built an integrated CP/CPPS molecular interaction network using the technique of network propagation25, a tool useful for integrating datasets and boosting marginal signals. Nineteen (19) high confidence CP/CPPS genes from the multi-ancestry analysis out of the original 28 were contained within the STRING background interactome (9 genes had no interaction information in STRING: AC037199.1, C19orf48, CLDND2, DLEU1, DLEU7, EBPL, PGLYRP2, PTCHD3, and VSIG10L). Following propagation seeded by the 19 high-confidence CP/CPPS genes in STRING, we identified 102 highly proximal genes in the network. These included the 19 high-confidence CP/CPPS genes themselves, two medium-confidence FUMA genes (KLK1, NKX3-1), and 81 network-proximal genes (Fig. 3).

Functional enrichment analyses across a wide array of curated pathways and gene sets of the 38 CP/CPPS-network genes revealed significant enrichment for genes downregulated in the aging prostate (adj. p = 1 × 10−8, from GTEx aging signatures, Fig. 3B). We also identified a significant association with androgen receptor signaling that has been linked to CaP (adj p = 5 × 10−9, Fig. 3B)26.

Patients with CP/CPPS are at a higher risk for other neurological conditions, including migraine headaches, internalizing psychopathology, depression, and anxiety9. Interestingly, the CP/CPPS network was significantly enriched for brain-related terms and pathways, including synapse interactions, organization, and neuronal system in general (Supplementary Data 12 and Fig. 3B). The genes driving this brain-specific enrichment include LRRC4B, PTPRF, NTNG1, DNER, and LRRC4. LRRC4B is of note, as it is a high-confidence CP/CPPS GWAS gene, is also involved in synaptic processes, and is predicted to play an important role in the postsynaptic density membrane. These brain-related pathways in the CP/CPPS network could underpin the observed neurological comorbidities.

Replication
As a source of replication, we queried the FinnGen trait called “Inflammatory disease of the prostate (prostatitis),” where the cases were coded using the presence of ICD-10 code N41, a non-specific parent code that includes various conditions involving the prostate (acute prostatitis, chronic prostatitis, abscess of prostate, prostatocystitis, granulomatous prostatitis, other inflammatory diseases of prostate, and inflammatory disease of prostate, unspecified) rather than specifically CP/CPPS. These data contained 4160 cases and 130,139 controls. In the summary statistics for this trait, the lead SNPs for two out of three genome-wide significant loci replicated at nominal significance (rs10886893: p = 0.03, and rs62113214: p = 0.001). The genome-wide significant SNPs from the muti-ancestry analysis did not replicate in FinnGen (p > 0.05).

A search of publicly available transcriptomics data on CP/CPPS within GEO yielded limited results. We identified one study, GSE11842, that generated gene expression data from the prostate epithelium and ventral prostate tissue from control and vinclozolin-treated rats27. After multiple generations, the treated animals presented symptoms that model prostate disease. While standard differential expression analysis comparing control and treated rats identified 0 genes with adjusted p value < 0.05, a targeted approach focusing on the members of the CP/CPPS-proximal interaction network identified 5 genes with a nominally significant p value < 0.01. FGFR2 is decreased with treatment in both tissue types and was identified above as significantly associated with CP/CPPS. KRT5 is increased with treatment and is a marker for the basal epithelium in prostate tissue28. IGHMBP2, PPFIBP1, and CCDC59 are increased in both tissues with treatment, but no known evidence for their role in the prostate. IGHMBP2 is an immunoglobulin mu DNA-binding protein, CCDC59 is a coiled-coil domain-containing protein indirectly involved in RNA binding, and PPFIBP1 has been associated with various non-prostate-related tumors. These results support the GWAS results as they relate to FGFR2 and suggest additional genes like KRT5 for further investigation.

Methods
Ethics
The VA Central Institutional Review Board (cIRB) approved the MVP protocol in 2010, and enrollment began in 2011. The VA cIRB and the Research and Development Committee at the VA San Diego Healthcare System and VA Puget Sound Health Care System approved these analyses (under project “MVP033”).

Study participants and design
Observational data were from the MVP, a national research project to determine how genetic traits, health habits, and environmental factors affect Veteran health and illness. MVP’s design and recruitment procedures have been previously documented10. In brief, MVP data include self-reported surveys, electronic health records (EHR), and genetic data. MVP Veterans were included if they had complete data. At the time of analysis (May 2023), 819,417 Veterans were enrolled in the MVP.

Race, ethnicity, and ancestry
We used Harmonizing Genetic Ancestry and Self-identified Race/Ethnicity (HARE) groups to define race/ethnicity36. Briefly, HARE enhances classification by integrating self-identified race/ethnicity (SIRE) and genetically inferred ancestry (GIA). HARE improves the accuracy of classification using GIA to refine and, if necessary, impute SIRE, improving reliability of race/ethnicity assignment in genetic research. Less than 2% of individuals are not assigned a HARE group when participant-identified and genetically inferred ancestry data produce discordant results. Here, we use the term “Hispanic” (HIS) for the HARE race and ethnicity groups comprised of individuals who are Latino or Hispanic, the term “European” (EUR) for individuals who are White but not Hispanic, and “African” (AFR) for individuals who are Black but not Hispanic. People of East Asian and South Asian ancestry were not analyzed due to the low numbers in MVP. Hereafter, we refer to the HARE groups used in analyses as HIS, EUR, and AFR.

GWAS cohort and phenotype
Of 819,417 enrolled Veterans, individuals were excluded if they did not have sufficient EHR records (n = 121), were identified as women (n = 75,911), missing gender (n = 131), were not genotyped (n = 144,119), had missing HARE race/ancestry estimates (n = 8516), or had a small HARE race/ancestry group (n = 7355). Overall, we analyzed data from 583,395 individuals.

At a total of 583,395 participants had available phenotype and genotype information and were used for GWAS analysis (Table 1). The reference population groups (EUR, AFR, HIS) in the 1000 Genomes samples were used to define EUR, AFR, HIS ancestries for CP/CPPS, and EUR for CaP. SNPs with an imputation INFO score > 0.3, minor allele frequency (MAF) ≥ 0.01, and HWE > 1 × 10−15 were reported in the analysis. For the primary analysis, a genome-wide significance was set as a p value ≤ 5.0 × 10−8.

Chronic prostatitis/chronic pelvic pain syndrome (CP/CPPS)
CP/CPPS cases and controls were identified using EHR ICD9/10 codes to generate a binary variable. ICD codes 601.1 and N41.1 were used to identify CP/CPPS cases, defined as Veterans with one or more of the ICD codes. Controls were defined as Veterans who did not have either ICD code.

Genotyping, imputation, and quality control
Genotyping, imputation, and quality control within MVP have been described previously and were conducted by MVP Core10. Briefly, MVP samples were genotyped using a 723,305-SNP Affymetrix Axiom Biobank array, customized to include variants of interest in multiple diverse ancestries10. Imputation was performed with Minimac4 using data from the TopMed reference panel. Analyses were performed using MVP Release 4 data (GRCh38). Final genotype data consisted of 96 million variants. Principal components were calculated for each ancestry using PLINK 2.0 alpha37.

Computation and statistical analyses
GWAS regression and meta-analysis. GWAS analysis was carried out separately for CP/CPPS in EUR, AFR, and HIS. Logistic regression analyses were performed to test association between phenotypes and imputed dosages using REGENIE v3.1.3. GWAS was performed on populations stratified by HARE ancestry. The model included the first 10 principal components of genotype as covariates. A sensitivity analysis was carried out with an additional covariate of age of MVP enrollment. SNPs with imputation INFO scores > 0.3, minor allele frequency (MAF) ≥ 0.01, and HWE > 1 × 10−15 were reported. A genome-wide significance (GWS) was set for the primary analysis as P ≤ 5.0 × 10−8. Post-GWAS analyses were conducted utilizing autosomal SNPs, as most methods rely on autosomal data, and public summary statistics frequently do not include the X chromosome.

REGENIE is a two-step machine learning method that adjusts for relatedness. Step One analyzed MVP genotype array data only, dividing SNPs into blocks and using ridge regression to generate predictions. These predictions were combined in second ridge regression and decomposed by chromosome for leave-one-chromosome-out analysis, which served as covariates in Step Two. Step Two used Release 4 imputed data for cross-validation and applied Firth logistic regression and saddle point approximation for the binary trait analysis.

Meta-analyses were conducted across summary statistics from 3 ancestries (EUR, AFR, and HIS) using Plink37 and MR-MEGA38, with default parameters. Results were consistent across both platforms. The 1000 Genomes phase 3 reference data39 were used to calculate LD, relying on ancestry-specific reference genotypes.

Functional annotation with FUMA. SNPs were annotated to nearby and relevant genes using the FUMA tool, positional mapping, and eQTL mapping from all current eQTL databases available on FUMA. We excluded old versions of genotype-tissue expression (GTEx) still available on the platform40. Default settings were used in all FUMA analyses unless specified. The SNP2Gene module was used to identify independent genomic risk loci and variants in LD with lead SNPs (r2 > 0.6, calculated using ancestry-appropriate 1000 Genomes reference: EUR for European Ancestry, AFR for African ancestry, and AMR for Hispanic Ancestry). FUMA results are reported in hg38. Both positional and eQTL information were used for gene mapping. FUMA was run twice, once with stringent parameters (high confidence genes; p < 5 × 10−8) and once with relaxed parameters (medium confidence genes; p < 5 × 10−6).

Gene-based and gene set, and tissue-enrichment analyses with MAGMA. We used the MAGMA41 tool, analyzed through the FUMA web tool, for gene-based, gene-pathway, and tissue enrichment analyses.

Tissue-specific enrichment analysis (TSEA). Tissue-specific enrichment analysis on the genes mapped to marginally significant EUR loci (p < 5 × 10−6) was run using the TSEA tool13.

Additional regulatory information. SNPs with potential regulatory relationships were further annotated through regulomeDB42, to identify genes known to bind to focal sites through ChIP assays.

Linkage Disequilibrium Score Regression and SNP-Based Heritability. Linkage disequilibrium score regression (LDSC)43 was used to estimate SNP-based heritability of CP/CPPS. A CP/CPPS prevalence of 8.2% was used1,44,45. The 1000 Genomes phase 3 reference data (1KGPp3)39 were used to calculate LD. The extent to which test statistic (GC λ) was due to polygenic signal (rather than population stratification) was calculated with LDSC as 1 - (LDSC intercept - 1) / (mean observed χ2 - 1)43.

Genetic correlations with traits. We evaluated the genetic relationship of CP/CPPS with BPH and CaP. BPH summary statistics from the UK BioBank (UKB)46 were used. BPH was defined by UKB-based self-report (data field 20002_1516: Non-cancer illness code, self-reported: bph / benign prostatic hypertrophy). CaP summary statistics were derived from the MVP based on an EHR ICD9/10 codes (185, V10.46, C61, Z85.45, Z85.46, Z85.47, Z85.49, and Z85.9) with patients considered cases with at least four ICD codes for CaP to account for routine billing of diagnostic biopsies. Controls were defined as having no ICD codes for CaP.

To further explore the relationship between CP/CPPS and other phenotypes, we leveraged publicly available GWAS data within the Complex Trait Genetics Virtual Lab17 (https://vl.genoma.io/). Cross-trait LDSC regression was performed on various conditions to assess genetic correlation with CP/CPPS. To ensure robust findings, we restricted our analyses from the 1437 available phenotypes to those displaying a SNP heritability z-score greater than 4, yielding 823 phenotypes for evaluation. Bonferroni adjustment was applied to control for multiple comparisons, setting the threshold for significance at p < 0.05/823 = 6.08 × 10−5.

Mendelian randomization. Mendelian randomization (MR) was performed using the R package associated with MRbase, TwoSampleMR23. We modeled CP/CPPS as the exposure variable and BPH and CaP as the outcome variables. To evaluate potential bidirectional relationships, we also conducted MR analyses using BPH and CaP as the exposure and CP/CPPS as the outcome. Before running the MR, we ran clumping and pruned for LD as recommended by MRbase. MR was performed using 5 common methods (MR Egger, Weighted median, Inverse variance weighted [IVW], Simple mode, and Weighted mode). To ensure the strength of our instrumental variables (IVs), we calculated the F-statistic using the formula F = β2 / (SE)2 and considered SNPs with F-statistic greater than 10. The variance explained by each SNP was calculated using β2 × 2f × (1 − f) where f is MAF. SNPs in high LD (r2 ≥ 0.001) with a more significant SNP within a 1Mb window were pruned. Harmonization of exposure and outcome data was performed to ensure allele alignment and consistency using the “harmonize_data” function. To ensure independence, SNPs were screened for associations with potential confounders using the GWAS Catalog and by evaluating the MR Egger intercept to assess the presence of directional pleiotropy. MR-Egger intercept test was used to assess the robustness of results and address potential pleiotropy. Finally, we used CAUSE, an MR approach using full GWAS summary statistics to evaluate the causal relationship of CP/CPPS on BPH and CP/CPPS on CaP47. To avoid false positives that can occur using other methods, CAUSE models correlated and uncorrelated horizontal pleiotropy.

Mixed effects score regression (MiXeR). We employed the mixed effects score regression (MiXeR) framework22 to estimate genetic overlap between CP/CPPS and CaP and between CP/CPPS and BPH. MiXeR utilizes a Bayesian approach to provide posterior probabilities for two key estimates: the number of shared and trait-specific loci. This approach facilitates unbiased estimation of genetic overlap, independent of the power of individual GWAS. Analyses were conducted using MiXeR v1.3.

Case-case GWAS (CC-GWAS). To further investigate shared genetic architecture between CP/CPS, CaP, and BPH, we utilized a case-case GWAS (CC-GWAS) approach24. This method integrates summary statistics from GWAS summary results for each condition, leveraging the combined power across results to identify genetic loci contributing to more than one trait and to detect cross-phenotypic associations. The implementation of CC-GWAS focused on identifying loci that exhibit pleiotropic effects that are common between CP/CPPS and CaP and BPH.

Integrative network analysis. We employed a network propagation algorithm48 to connect the multi-ancestry high-confidence genes (genes annotated to p < 5 × 10−8 loci) to any interacting medium-confidence genes (genes annotated to p < 5 × 10−6 multi-ancestry loci), thus, boosting the signal from any marginal results. The network propagation was seeded with the CP/CPPS high-confidence FUMA-mapped genes using the STRING high-confidence interactome (v12)49. Highly significantly interacting genes (z > 4) were retained for further analysis. Functional enrichment analysis was calculated with enrichR50.

Replication. A search of the Gene Expression Omnibus (GEO) using the term “prostatitis” identified one gene expression study (GSE11842) that compared control and treated prostate epithelium and ventral prostate tissue in a transgenerational model of vinclozolin-induced prostate disease27. Differential gene expression was performed using limma with the model (~tissue type + treatment)51. Other search results identified studies that were excluded due to tissue type (i.e., colonic epithelium, cecal bacteria) or measurement target (i.e., miRNA, methylated DNA).

FinnGen (https://www.finngen.fi/en) trait called “Inflammatory disease of the prostate (prostatitis)” (Release 10, 4,160 cases, 130,139 controls) was also compared with the EUR results from the current study.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "Review of:\nThe Genetic Basis of Chronic Prostatitis/Chronic Pelvic Pain Syndrome: Insights from a Large-Scale Multi-ancestry Study\nThis study addresses an important biomedical question, specifically the common genetics between Chronic\nProstatitis/Chronic Pelvic Pain Syndrome and other conditions affecting the prostate and other conditions generally. The\nstudy is based on the Million Veteran Program study including 590,000 veterans of European, African and Hispanic\nancestry from the United States, of which 14,575 have CP/CPPS. The most interesting finding are lines of evidence supporting\ncommon genetics between CP/CPPS and benign prostate hyperplasia but not between CP/CPPS and prostate cancer.\nOverlapping genetics between CP/CPPS and other traits are also examined highlighting neurological function, matching\nsome well-known findings of overlapping genetics across pain phenotypes and neurological function.\nWhile the overall idea of the study is one that merits strong consideration in terms of a prominent venue for publication, the\nmanuscript in its present form suffers from poor or incomplete execution in several key ways that weaken the whole to the\npoint where a thorough re-working of the manuscript would be necessary. These impact on how convincing or well-\nsupported the conclusions of the study are.\n--Combined CP/CPPS phenotype\nThe numbers of study subjects classified as cases who were attributed an ICD code of one or the other out of CP and/or\nCPPS phenotypes is not detailed. How many study subjects were attributed codes for both phenotypes? Based on\ninformation provided in the first para of the introduction, most who have CP also have CPPS type symptoms. However, what\nis not detailed is the proportion of individuals with CPPS who have CP. Both sides would need to be examined carefully in a\nstudy that now puts the two together presenting these as a single phenotype, and in two ways – via the literature, and via the\ndataset being used. Basic univariate analyses of the phenotypes considered in the study should be performed. E.g. the\nreader should be presented with the proportion of CPPS individuals who have benign prostate hyperplasia. What about\nthose who have prostate cancer? What about the distribution of the 12 phenotypes according to CP and CPPS status? It is\nimpossible to tell from the information provided whether the genetic overlap with phenotypes like backpain may be due to a\nhigher proportion of CPPS compared to CP in the combined phenotype. The second introduction paragraph evokes\n“unpredictable symptomatology” – what is unpredictable: the order of symptoms over time, the magnitude of importance of\neach element; the number of possible symptoms that must be present for a diagnosis to be positive? Are the authors saying\nthat the diagnosis process for identifying this condition is highly problematic and therefore it isn’t clear what CP and or\nCPPS is anyway? How is the diagnosis made in the clinic? Would there be a lot of disagreement across physicians on\nattributing a CP or CPPS diagnosis? The starting point of the present study is so fuzzy that none of the results can be\ninterpreted in any meaningful way, it seems.\nMendelian Randomization:\nBasic reporting guidelines were not followed. Examination of the four assumptions was not undertaken. 5 methods were\ntried and one reported in the results section. Going to the table S8, only two methods give a nominally statistically significant\nresult. The robust Egger method is not statistically significant. There is no process for screening the instrumental variables.\nWhat were the instrumental variables used? What about sensitivity analyses? Logic in the paragraph ll. 453-465 does not\nhold up: the sentence “we modeled…” suggests a uni-directional approach was used. Later it is suggested a bi-directional\napproach was used. This paragraph leaves an impression of confusion. These results simply cannot be interpreted or\ntrusted.\nParagraph ll. 610-623 goes into lots of detail about a well know truism relating to common complex disease: twin and family-\nbased study estimates of heritability are always higher than SNP chip heritability. This paragraph adds nothing that is\nspecific to prostate conditions.\nLines 698 etc. Are there no other biobanks that have recorded prostate condition data? If the authors tried to go to other\nbiobanks and searched for other resources with both prostate condition data and genotyping data but the data was not\navailable, this should be detailed explicitly. What needs to be done to remedy this situation so that prostate conditions get\nadequate representation from biobanks?\nTable 1: Why aren’t tests presented showing differences in case proportions by ancestry group? Why aren’t percentages of\nthe different ancestries as a part of the whole MVP presented?\nLine 356: the authors seem to be confused on how to interpret RegulomeDB scores. Lower scores show greatest evidence\nof regulatory involvement.\nGene symbols are sometimes in italics, sometimes not. Check your manuscript before submitting it.", "reviewer_2": "1. In the GWAS Cohort and Phenotype section, the term “ancestry” is used. Based on the context, I believe the authors are\nlikely referring to the categorical HARE race variable for MVP participants. I think that moving the Race, Ethnicity, and\nAncestry section before the GWAS Cohort and Phenotype section, and stating that the groupings of dropped individuals\nwere based on the HARE race/ancestry categories would perhaps avoid misunderstandings with readers who are not\nfamiliar with the HARE approach. In subsequent mentions elsewhere in the paper, I also think it would be prudent to adjust\nthe terminology to specify that you are using the HARE race/ancestry categories to stratify participants into analysis groups.\nThis is clearly presented in Table 1, but I feel the precise language should be used throughout the paper. For example, then\nsentence: “The CP/CPPS GWAS included 10,035 cases and 420,271 controls of European ancestry.” On line 343, would be\nmore accurately stated: “The CP/CPPS GWAS included 10,035 cases and 420,271 controls from the HARE race/ancestry\ncategory.” This is because those individuals are predominantly European and self-identify as “non-Hispanic white” but are\nnot necessarily entirely European.\n2. The abbreviations EUR, AFR, HIS are established on line 205 without defining them. I think it would be fine to state that\nthese abbreviations refer to the HARE race/ancestry categories and use them on subsequent mentions. In general I see\nmany instances of switching between using these abbreviations and XX ancestry. I think it is important to clean this up and\nuse a consistent naming convention based on the approach.\n3. The lGC should be changed to the Greek lambda (line 394)\n4. Were all the statistics presented in the SNP-based heritability section derived from analysis of EUR data? Perhaps the\nHIS was too small, but perhaps the AFR data could provide an estimate.\n5. Fst is usually presented with a subscript\n6. GTEx should be spelled out and cited on first mention\n7. Is there any measure of how well a single instance of an ICD code classifies either of these outcomes (i.e. a PPV\ncompared to expert review of records)? If not, this should be acknowledged as a limitation and an opportunity for\nimprovement in future studies in the Discussion.", "reviewer_3": "Rosenthal S.B. et al performed the first GWAS of Chronic Prostatitis/Chronic Pelvic Pain Syndrome (CP/CPPS) in men with\n14,575 cases and 568,820 controls without CP/CPPS. They identified a total of six loci across ancestry groups, including a\nHispanic ethnicity specific locus. The majority of these loci have been associated with related phenotypes including benign\nprostatic hyperplasia (BPH) and prostate-specific antigen (PSA), supporting the hypothesis of the multifaceted etiology of\nCP/CPPS. They follow the GWAS with appropriate methods to further identify pleiotropy between CP/CPPS and other\nrelated phenotypes (CaP and BPH). They further demonstrate an interconnection between CP/CPPS and neurological\ndisorders. The work presented supports the knowledge of the field and provides routes of further investigation into the\npleiotropy of CP/CPPS and BPH. Overall, the authors performed the study well, they utilized appropriate methods, and the\nresults are novel and clearly communicated.\nI have a few comments the authors may wish to consider:\nComments:\n1. It would be beneficial to include the X chromosome in the analyses.\n2. To further the authors’ point of the shared genetic architecture between CP/CPPS and other diseases, it could be\nbeneficial to conduct a PheWAS of the identified loci (potentially just the clumped and pruned variants utilized in the MR) to\ninvestigate the other associated phenotypes.\nMinor Comments:\n1. While age was not included in the model, it would be worthwhile to conduct a sensitivity analysis including age as a\ncovariate.\n2. As the authors use the CC-GWAS method for CaP and BPH, it would be worthwhile to include the MR intercepts as\nfurther support of potential pleiotropy between the phenotypes.\n3. While informative, Figure 1B is difficult to read at the presented font size.\n4. Line 432: Authors state 1,466 traits while the methods state 823 traits.\n5. Line 455 – 458: If the authors could clarify this section. Based on the authors description of the exposure (CP/CPPS) and\noutcome (BPH) and result, it would seem that CP/CPPS may be causative of BPH, however, it is written as BPH may be\ncausative of CP/CPPS.\n6. While the title includes prostatitis suggesting a study of men, the title should include the denotation of including only men\nin the study following the Nature Reporting Summary."}, "author_response": ""}, {"round": 2, "reviewer_comments": {"reviewer_1": "Abstract :\nMiXer is mentioned as a jargony black box rather than in language that can be interpreted by a scientist who has not used\nthe software, especially for the general audience targeted by Nature Communications. The authors should interpret what\nMiXer contributed without using the acronym MiXer.\nIntroduction:\nSome clarity is still missing from the explanation of the CP/CPPS diagnosis. A sentence like “CP/CPPS is a diagnostic label\nattributed to chronic prostatitis cases for which a concomitant infection is not detected” is needed here as the provided text\ndoes not explain that a diagnostic algorithm is followed by physicians with this label being one possible classification.\n“In the multi-ancestry meta-analysis, we identify six novel loci associated with CP/CPPS\n158 risk, an increase from three significant genome-wide loci found in the European cohort.”\n  Are you saying the three loci identified among Europeans are not novel? Does this mean they have been reported\nelsewhere? Or do you mean there are three that are also found in other ethnicities, showing validation across different\nancestries? If they are not novel and have been reported elsewhere, references should be provided.\nUnder reviewer 1, comment 1, the authors did not take the comment into consideration.\nReviewer 1 comment 3\n3) A basic statistical analysis of the phenotypes available to researchers in the MVP cohort should be provided. The authors\nhave chosen to ignore this reviewer comment.\nThe interpretation of genetic correlations is much more meaningful if this can be done in the light of phenotypic correlations.\nNature communications readers should benefit from an analysis that is easily accessible to the researchers and that is\nstraightforward to perform and that would help in understanding the results.\n5) There are interpretation mistakes in the presentation of MR results. In a rewrite of this piece, the authors would do well to\ninclude an MR statistical expert on their team to guide the interpretation of their results.\nl. 511-512\n…used to evaluate if CP/CPPS causally affects BPH or CaP since these phenotypes often appear together\nIn this first sentence to introduce the MR results, the authors mention one direction of association: CP/CPPS causally\naffecting BPH. However, the results actually show more evidence for the opposite direction.\nSpecifically:\nThe MR results in the paragraph starting line 516 contain the following statements:\n1.“Mixed results” line 516\n2.“consistent inverse associations” line 523\n3.“MR Egger did not show a significant association.” L. 521.\nLooking at results in ST9a that these comments refer to, I am seeing a negative z for all methods presented except Egger\nregression. Also, three out of five methods are nominally significant if we consider a p of 0.05 as the significance cut-off.\nEgger regression is not significant. Given that Egger supports absence of pleiotropy, and has lower power, we can still\nconsider the three out of five methods tested to show statistical significance as showing evidence of causality. Thus I agree\nwith statement 1 and 3, but statement 2 is not true since it would imply all five methods give a negative z.\nL. 524-525:\nThe authors suggest “The findings indicate that BPH may be causative of CP/CPPS.” The evidence for this statement is\nTable S9a.\nThis is a misunderstanding by the authors. The findings in Table S9a indicate a causal relationship where CP/CPPS might\nreduce the risk of BPH. This is very different from BPH causing CP/CPPS. In other words, a reduction in risk in one direction\nexamined by MR does not imply an opposite direction for the causal arrow like the authors assert.\nEvidence from Table S9b does seem to suggest that BPH may indeed be causative of CP/CPPS (with BPH potentially\nreducing the risk of CP/CPPS). All MR methods are statistically significant, and the direction of association is negative.\nHowever, the reduction of risk is not mentioned by the authors.\nThe authors summarize these results as reflecting a “complex interplay.” Unfortunately, these results are difficult to interpret.\nThe strongest evidence supports BPH as protective against CP/CPPS – this does not seem intuitive. How would a pathway\nthat seems to decrease risk of CP-CPPS but increase risk of BPH be helpful in devising a therapeutic strategy? In the\nabstract, the authors suggest that these MR results “could inform therapeutic strategies.”", "reviewer_2": "Reviewer 2: All comments have been Addressed", "reviewer_3": "REVIEWER COMMENT: Rosenthal S.B. et al performed the first GWAS of Chronic Prostatitis/Chronic Pelvic Pain\nSyndrome (CP/CPPS) in men with 14,575 cases and 568,820 controls without CP/CPPS. They identified a total of six loci\nacross ancestry groups, including a Hispanic ethnicity specific locus. The majority of these loci have been associated with\nrelated phenotypes including benign prostatic hyperplasia (BPH) and prostate-specific antigen (PSA), supporting the\nhypothesis of the multifaceted etiology of CP/CPPS. They follow the GWAS with appropriate methods to further identify\npleiotropy between CP/CPPS and other related phenotypes (CaP and BPH). They further demonstrate an interconnection between\nCP/CPPS and neurological disorders. The work presented supports the knowledge of the field and provides routes\nof further investigation into the pleiotropy of CP/CPPS and BPH. Overall, the authors performed the study well, they utilized\nappropriate methods, and the results are novel and clearly communicated.\nREVIEWER RESPONSE: X chromosome data is available in MVP.\n2) REVIEWER COMMENT: To further the authors’ point of the shared genetic architecture between CP/CPPS and other\ndiseases, it could be beneficial to conduct a PheWAS of the identified loci (potentially just the clumped and pruned variants\nutilized in the MR) to investigate the other associated phenotypes.\nREVIEWER RESPONSE: Based on written results and Supplemental Table 6, it would be more apt to remove the term\nPheWAS and continue with GWAS catalog trait lookup.\n3) REVIEWER COMMENT: While age was not included in the model, it would be worthwhile to conduct a sensitivity\nanalysis including age as a covariate.\nREVIEWER RESPONSE: While I appreciate the sensitivity analysis; I am curious why they used age and MVP\nenrollment and not age at first CP/CPPS diagnosis.\n4) REVIEWER COMMENT: As the authors use the CC-GWAS method for CaP and BPH, it would be worthwhile to include\nthe MR intercepts as further support of potential pleiotropy between the phenotypes.\nREVIEWER RESPONSE: I am satisfied with the inclusion of the MR Egger intercepts as well as the language added in the\nmanuscript.\n5) REVIEWER COMMENT: While informative, Figure 1B is difficult to read at the presented font size.\nREVIEWER RESPONSE: I am satisfied with the updated title.\n6) REVIEWER COMMENT: Line 432: Authors state 1,466 traits while the methods state 823 traits.\nREVIEWER RESPONSE: I am satisfied with the edited wording.\n7) REVIEWER COMMENT: Line 455 – 458: If the authors could clarify this section. Based on the authors description of the exposure (CP/CPPS) and outcome (BPH) and result, it would seem that CP/CPPS may be causative of BPH, however, it is written as BPH may be causative of CP/CPPS.\nREVIEWER RESPONSE: I would recommend adding both the term ‘bidirectional’ as well as a sentence on using BPH as\nthe exposure and CP/CPPS as the outcome into the mendelian randomization section of the Methods. Otherwise, I am\nhappy with the clarification.\n8) REVIEWER COMMENT: While the title includes prostatitis, suggesting a study of men, it should include the denotation of\nincluding only men in the study following the Nature Reporting Summary.\nREVIEWER RESPONSE: I am satisfied with the updated title.\nReviewer #3\n(Remarks to the Author)\nI co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature\nCommunications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career\nResearchers who co-review manuscripts.", "reviewer_author_response": "We thank the reviewer for their comments. We address specific\nrequests and clarify our approach in the response below.\n2) REVIEWER COMMENT: The numbers of study subjects classified as cases who were\nattributed an ICD code of one or the other out of CP and/or CPPS phenotypes is not detailed.\nHow many study subjects were attributed codes for both phenotypes? Based on information\nprovided in the first para of the introduction, most who have CP also have CPPS type\nsymptoms. However, what is not detailed is the proportion of individuals with CPPS who have\nCP. Both sides would need to be examined carefully in a study that now puts the two together\npresenting these as a single phenotype, and in two ways – via the literature, and via the\ndataset being used.\nREVIEWER 2\n1) REVIEWER COMMENT: In the GWAS Cohort and Phenotype section, the term “ancestry” is\nused. Based on the context, I believe the authors are likely referring to the categorical HARE\nrace variable for MVP participants. I think that moving the Race, Ethnicity, and Ancestry section\nbefore the GWAS Cohort and Phenotype section, and stating that the groupings of dropped\nindividuals were based on the HARE race/ancestry categories would perhaps avoid\nmisunderstandings with readers who are not familiar with the HARE approach.\nIn subsequent mentions elsewhere in the paper, I also think it would be prudent to adjust the\nterminology to specify that you are using the HARE race/ancestry categories to stratify\nparticipants into analysis groups.\nThis is clearly presented in Table 1, but I feel the precise language should be used throughout\nthe paper. For example, then sentence: “The CP/CPPS GWAS included 10,035 cases and\n420,271 controls of European ancestry.” On line 343, would be more accurately stated: “The\nCP/CPPS GWAS included 10,035 cases and 420,271 controls from the HARE race/ancestry\ncategory.” This is because those individuals are predominantly European and self-identify as\n“non-Hispanic white” but are not necessarily entirely European.\nREVIEWER 3\n1) REVIEWER COMMENT: Rosenthal S.B. et al performed the first GWAS of Chronic\nProstatitis/Chronic Pelvic Pain Syndrome (CP/CPPS) in men with 14,575 cases and 568,820\ncontrols without CP/CPPS. They identified a total of six loci across ancestry groups, including a\nHispanic ethnicity specific locus. The majority of these loci have been associated with related\nphenotypes including benign prostatic hyperplasia (BPH) and prostate-specific antigen (PSA),\nsupporting the hypothesis of the multifaceted etiology of CP/CPPS. They follow the GWAS with\nappropriate methods to further identify pleiotropy between CP/CPPS and other related\nphenotypes (CaP and BPH). They further demonstrate an interconnection between CP/CPPS\nand neurological disorders. The work presented supports the knowledge of the field and\nprovides routes of further investigation into the pleiotropy of CP/CPPS and BPH. Overall, the\nauthors performed the study well, they utilized appropriate methods, and the results are novel\nand clearly communicated.\nReviewer 1\n1) REVIEWER COMMENT: MiXer is mentioned as a jargony black box rather than in language\nthat can be interpreted by a scientist who has not used the software, especially for the\ngeneral audience targeted by Nature Communications. The authors should interpret\nwhat MiXer contributed without using the acronym MiXer.\nreviewer comment.\nThe interpretation of genetic correlations is much more meaningful if this can be done in the\nlight of phenotypic correlations. Nature communications readers should benefit from an\nanalysis that is easily accessible to the researchers and that is straightforward to perform\nand that would help in understanding the results.\nReviewer 2\n1) REVIEWER COMMENT: All comments have been Addressed\nReviewer 3\n1) REVIEWER COMMENT: It would be beneficial to include the X chromosome in\nthe analyses.\nREVIEWER RESPONSE: Based on written results and Supplemental Table 6, it would be\nmore apt to remove the term PheWAS and continue with GWAS catalog trait lookup.\nREVIEWER RESPONSE: While I appreciate the sensitivity analysis; I am curious why they\nused age and MVP enrollment and not age at first CP/CPPS diagnosis.\nREVIEWER RESPONSE: I am satisfied with the inclusion of the MR Egger intercepts as well\nas the language added in the manuscript.\nREVIEWER RESPONSE: I am satisfied with the updated title.\nREVIEWER RESPONSE: I am satisfied with the edited wording.\nREVIEWER RESPONSE: I would recommend adding both the term ‘bidirectional’ as well as\na sentence on using BPH as the exposure and CP/CPPS as the outcome into the mendelian\nrandomization section of the Methods. Otherwise, I am happy with the clarification.\nREVIEWER RESPONSE: I am satisfied with the updated title."}, "author_response": ""}, {"round": 3, "reviewer_comments": {"reviewer_1": "Abstract:\nWe identified the genetic correlations…\nWord choice problem: the genetic correlations were estimated. Identified gives the impression they have a fixed value and\nthat the study was able to find those values. That is misleading.\nReviewer 1 responses:\nResponses to points 1 and 2 seem OK.\nNew text on p. 8:\nDid all the authors read this?\nSNPs on high LD (r2≥0.001) with a more significant SNP\nwith a 1Mb window would be pruned.\nOn? Would – under what circumstances?\nNew text results first line: lifetime phenotypic prevalence\nThis seems to imply the veterans were all followed until death. Is this true? If not, then the wording used here is quite\nconfusing.\nReviewer comment 5:\nResults section: “Among Veterans with CP/CPPS, 76.26% had BPH”\nGiven that over 75% percent of those with CP/CPPS are the same individuals as those with BPH, it seems it is not surprising\nthat they share causal variants (Figure 2)? It is not surprising that genetic correlation is high also. While the results section\nhas been updated to present data, this new data has not been used to provide interpretation in relation to this new finding.\nDiscussion:\nThe strong genetic correlation between CP/CPPS and BPH, and the GWAS catalog associations, suggest that the genetic\ncauses may be very similar for these conditions.\nIsn’t high sample overlap a more straightforward explanation for the high genetic correlation?\nAlso… “suggesting that these traits are deeply linked.”\nMany who have CP/CPPS also have BPH.\nNew text; p.9: MVP analyses showed lifetime phenotypic prevalence of CP/CPPS at 2.23% (22,290/962,151), CaP at 3.30%\n(31,715/962,151), and BPH at 2.32% (22,290/962,151).\nThere is an issue since the percent and count of CP/CPPS and BPH are identical. The expectation is that the number of\nindividuals with BPH would be higher than the number with CP/CPPS.\nAn error present in a previous version of the manuscript seems like it was not corrected on p.11:\nGWAS of Chronic Prostatitis/Chronic Pelvic Pain Syndrome\nhad a regulomeDB score of 3a or higher, indicating possible regulatory relationships. (Table S3).\n lower regulomeDB scores are indicative of regulatory relationships.\nFigure 1 legend: I don’t see an indication of what S1, S2 and S3 mean. It seems M1-25 are the genes but this is not explicit\nin the legend.\nThe phrase \"collapsing the GWAS signal within the gene set of interest\" is not the clearest way to describe what MAGMA\ndoes.\nBetter: to draw from phrases such as: aggregating GWAS signals across all SNPs within each gene; MAGMA incorporates\nall genes regardless of their statistical significance\nUnder Relationship to BPH and CaP\nBetter not to use the term: causal interplay. This is vague and suggests some kind of network of causal relationships.\nInstrumental variables are strictly defined. Each causal arrow is evaluated explicitly. The authors should say what arrows\nwere tested, and then what arrows have associated causal evidence.\nNew text page 16:\n“For CP/CPPS against BPH, … suggesting a causal relationship between CP/CPPS and BPH”. Similarly,\nFor BPH against CP/CPPS, the causal model was a better fit than the null (p=.01) and sharing\nmodels (p=.03), suggesting a causal relationship between CP/CPPS and BPH”.\nHere opposite directions of causality are tested (green), and the conclusion by the authors is the same (blue). I am not\nseeing an interpretation that considers the meaning of the directionality.", "reviewer_3": "1. It seems the authors could have performed a GWA scan of the X chromosome among men and provided a Manhattan plot\nwith those findings? The X chromosome test statistics could simply be appended to those for the autosomes. It is\nunderstandable that downstream analyses would not be conducted for now on X chromosome data, but the Manhattan plot\nwould at least provide basic findings.", "reviewer_2": "Reviewer #2\n(Remarks to the Author)", "reviewer_4": "I was invited to review this manuscript specifically to comment on the MR analyses. It appears that there was an error in\npreviously submitted analyses, but this has now been resolved. Overall, the MR analyses are performed somewhat\nalgorithmically and uncritically, but also clearly. When I see bidirectional results from an MR study, I think this is likely to\nrepresent shared aetiology (i.e. common causes of both traits) rather than literal causal effects in both directions. However,\nthis is a matter of interpretation. Overall, the MR analyses are not a barrier to the publication of this manuscript.\nAs for the rest of the work, these sort of untargeted \"throw everything at the wall\" analyses aren't my cup of tea if I'm being\nhonest, but I'm sure some people like them. But again, that is a matter of preference.\nMinor comment:\n\"HARE ensures accurate classification using GIA...\" - I doubt any statistical method ensures anything - 'ensure' seems too\nstrong here."}, "author_response": "1 REVIEWER COMMENT: Abstract:\nWe identified the genetic correlations…\nWord choice problem: the genetic correlations were estimated. Identified gives the\nimpression they have a fixed value and that the study was able to find those values.\nThat is misleading.\nAUTHOR RESPONSE: Thank you, we have adjusted our text. The abstract now reads\n\"We estimated the genetic correlations between CP/CPPS and 12 phenotypes,\nincluding prostate cancer (CaP), benign prostatic hyperplasia (BPH), genitourinary\ndisease, abdominal and pelvic pain, back pain, depression, and anxiety.\"\nDid all the authors read this? SNPs on high LD (r2≥0.001) with a more significant SNP\nwith a 1Mb window would be pruned. On? Would – under what circumstances?\nAUTHOR RESPONSE: The phrase now reads \" SNPs in high LD (r2≥0.001) with a more\nsignificant SNP within a 1Mb window were pruned. \"\nThis seems to imply the veterans were all followed until death. Is this true? If not, then\nthe wording used here is quite confusing.\nAUTHOR RESPONSE: We have rephrased this sentence to read \"Phenotypic\nprevalence over the available observation period in MVP was 2.23% (22,290/962,151)\nfor CP/CPPS…..\"\nResults section: “Among Veterans with CP/CPPS, 76.26% had BPH”\nGiven that over 75% percent of those with CP/CPPS are the same individuals as those\nwith BPH, it seems it is not surprising that they share causal variants (Figure 2)? It is not\nsurprising that genetic correlation is high also. While the results section has been\nupdated to present data, this new data has not been used to provide interpretation in\nrelation to this new finding.\nDiscussion:\nThe strong genetic correlation between CP/CPPS and BPH, and the GWAS catalog associations,\nsuggest that the genetic causes may be very similar for these conditions.\nIsn’t high sample overlap a more straightforward explanation for the high genetic correlation?\nAlso… “suggesting that these traits are deeply linked.”\nMany who have CP/CPPS also have BPH.\nAUTHOR RESPONSE: We thank the reviewer for this comment regarding the\ninterpretation of the relationship between CP/CPPS and BPH. To address this, we have\nrevised the Results section to more explicitly highlight that, although there is substantial\ncomorbidity between CP/CPPS and BPH, the phenotypic overlap is far from complete.\nThe revised Results paragraph now reads:\n1\n“Phenotypic prevalence over the available observation period in MVP was 2.23%\n(22,290/962,151) for CP/CPPS, 3.30% (31,715/962,151) for at CaP, and 31.8%\n(306,799 / 962,151) for BPH. Among Veterans with CP/CPPS, 76.26% also had BPH,\nand 7.59% had CaP. Although many individuals with CP/CPPS also had BPH,\nmost\nindividuals with BPH did not have CP/CPPS. Specifically, among individuals with BPH,\nonly 5.5% had CP/CPPS, and 4.2% had CaP. Among CaP cases, 5.3% had CP/CPPS,\nand 40.6% had BPH. Tetrachoric correlations showed positive associations, strongest\nbetween CP/CPPS and BPH (rtet = 0.47 [95% CI = 0.47 – 0.47, p<.001]), followed by\nCP/CPPS and CaP (rtet = 0.18 [95% CI = 0.18 – 0.18, p<.001]), and CaP and BPH (rtet\n= 0.11 [95% CI = 0.10 – 0.11, p<.001]).”\nThis revision quantifies that, while 76% of individuals with CP/CPPS also have BPH,\nonly 5.5% of individuals with BPH have CP/CPPS, and the phenotypic correlation\nbetween these conditions is moderate rather than strong (r=0.47). We have also revised\nthe Discussion section to more clearly integrate this point regarding the interplay\nbetween phenotypic comorbidity and genetic architecture. The updated Discussion\nparagraph reads:\n“Additionally, MR showed a bidirectional relationship between CP/CPPS and BPH.\nHowever, this pattern may reflect shared genetic etiology or pleiotropy rather than\nstrictly causal effects in both directions. Several possible scenarios may underlie these\nresults, including common genetic factors influencing both conditions, pleiotropy where\nthe variants influence both CP/CPPS and BHP through distinct biological pathways,\nmethodological factors like phenotype heterogeneity, true bidirectional causality, or\nsome combination. Consistent with our other results, the finding suggests that factors\ncontributing to the development of one condition might also increase the risk of the other\nand vice versa, underscoring the need to improve our understanding of the biological\nmechanisms and pathways shared between these conditions. While clinical comorbidity\nbetween CP/CPPS and BPH may contribute to the observed relationship, the moderate\nphenotypic correlation in our sample, together with genetic findings from two\nindependent approaches (LDSC-derived correlations and twin biometric modeling)\nsupport the presence of shared genetic architecture between these two conditions,\npotentially involving inflammatory processes and endocrine changes. ”\nWe believe these revisions distinguish the contributions of clinical comorbidity from the\nevidence supporting shared genetic factors.\nprevalence of CP/CPPS at 2.23% (22,290/962,151), CaP at 3.30% (31,715/962,151),\nand BPH at 2.32% (22,290/962,151). There is an issue since the percent and count of\nCP/CPPS and BPH are identical. The expectation is that the number of individuals with\nBPH would be higher than the number with CP/CPPS.\nAUTHOR RESPONSE: We thank the reviewer for noting this issue. We have clarified\nthe reported prevalence of BPH in the revised manuscript. The prevalence of BPH is\n2\nnow correctly stated as 31.8% (306,799/962,151), consistent with expectations based\non the study population. The Results section reads \"Phenotypic prevalence over the\navailable observation period in MVP was 2.23% (22,290/962,151) for CP/CPPS, 3.30%\n(31,715/962,151) for at CaP, and 31.8% (306,799 / 962,151) for BPH. \"\nseems like it was not corrected on p.11:\nGWAS of Chronic Prostatitis/Chronic Pelvic Pain Syndrome\nhad a regulomeDB score of 3a or higher, indicating possible regulatory relationships.\n(Table S3).\nàlower regulomeDB scores are indicative of regulatory relationships.\nAUTHOR RESPONSE: We have corrected this to read “… had a regulomeDB score of\n3a or lower, indicating possible regulatory relationships (Table S3).”\nS3 mean. It seems M1-25 are the genes but this is not explicit in the legend.\nAUTHOR RESPONSE: We have modified the legend to read \"Figure 1: Summary of\nsignificant loci (EUR HARE race/ancestry only). A) Manhattan plot showing negative log\np-value of the European-only CP/CPPS GWAS. Mapped genes from FUMA are\nannotated. S1-S3 refer to the genome-wide significant loci (p< 5E-8 = red dotted line).\nM1-M27 refer to the marginal loci (p< 5E-6 = yellow dotted line). \"\nof interest\" is not the clearest way to describe what MAGMA does.\nBetter: to draw from phrases such as: aggregating GWAS signals across all SNPs within\neach gene; MAGMA incorporates all genes regardless of their statistical significance\nAUTHOR RESPONSE: The phrase now reads \"TSEA is a method similar to MAGMA’s\ntissue-specific enrichment analysis but is designed for the input of lists of significant\ngenes, whereas MAGMA aggregates GWAS signals across all SNPs within each gene\nand includes all genes regardless of their statistical significance. \"\nBetter not to use the term: causal interplay. This is vague and suggests some kind of\nnetwork of causal relationships. Instrumental variables are strictly defined. Each causal\narrow is evaluated explicitly. The authors should say what arrows were tested, and then\nwhat arrows have associated causal evidence.\nAUTHOR RESPONSE: We have replaced \"causal interplay\" with \"causal relationships\"\n(Results, p.15). Specific relationships tested are outlined in the methods (p. Mendelian\nRandomization) as \"We modeled CP/CPPS as the exposure variable and BPH and CaP\nas the outcome variables. To evaluate potential bidirectional relationships, we also\nconducted MR analyses using BPH and CaP as the exposure and CP/CPPS as the\noutcome. \" We also report the specific associations in the results and the supplemental\ntable ST9 and ST10\n3\n“For CP/CPPS against BPH, … suggesting a causal relationship between CP/CPPS and\nBPH”. Similarly,\nFor BPH against CP/CPPS, the causal model was a better fit than the null (p=.01) and sharing\nmodels (p=.03), suggesting a causal relationship between CP/CPPS and BPH”.\nHere opposite directions of causality are tested (green), and the conclusion by the authors is the same (blue). I am not\nseeing an interpretation that considers the meaning of the directionality.\nAUTHOR RESPONSE: We thank the reviewer for this clarification. While we had\nacknowledged the bidirectional nature of the MR and CAUSE findings, we have now\nrevised the interpretation to more explicitly consider the implications of directionality. In\nparticular, we clarify that these results may reflect shared etiology or biological feedback\nrather than independent causal effects in both directions. We have adjusted the text to\nread:\n\"CAUSE analyses yielded similar results when comparing causal and sharing models\n(Table S10). For CP/CPPS against BPH, the causal model was a better fit than both the\nnull (p=.02) and sharing models (p=.01), suggesting a causal relationship from\nCP/CPPS to BPH. Similarly, For BPH against CP/CPPS, the causal model was a better\nfit than the null (p=.01) and sharing models (p=.03), suggesting a causal relationship in\nthe reverse direction. These bidirectional results likely indicate shared genetic\narchitecture or pleiotropy rather than distinct causal mechanisms in both directions.\nWhen CAUSE was applied to CP/CPPS and CaP, neither the sharing nor the causal\nmodel was significant, suggesting that horizontal pleiotropy may explain the association\nbetween these two phenotypes. Various models of comorbidity can exist, and it is\npossible that causality operates in both directions, necessitating more research to\nunderstand these relationships fully.”\nREVIEWER 3\nREVIEWER COMMENT: Reviewer 3\n1. It seems the authors could have performed a GWA scan of the X chromosome\namong men and provided a Manhattan plot with those findings? The X chromosome test\nstatistics could simply be appended to those for the autosomes. It is understandable that\ndownstream analyses would not be conducted for now on X chromosome data, but the\nManhattan plot would at least provide basic findings.\nAUTHOR RESPONSE: We have conducted a GWAS of the X chromosome among men\nand now include these results as Figure 1A, which presents a Manhattan plot displaying\nassociation statistics for both the autosomes and the X chromosome. We included\ngenome-wide significant X-linked loci in Table 2. We also revised the Methods and\nLimitations sections to clarify that post-GWAS analyses were limited to autosomal\nSNPs, as most tools and external summary statistics currently exclude the X\nchromosome. Additionally, we updated the Discussion to describe the X-linked findings\nfrom the multi-ancestry meta-analysis and highlight the potential relevance of genes\nsuch as GRPR, REPS2, and RAI2. These revisions incorporate the X chromosome into\n4\nour primary analyses while maintaining transparency about its exclusion from\ndownstream analyses.\nREVIEWER 4\non the MR analyses. It appears that there was an error in previously submitted\nanalyses, but this has now been resolved. Overall, the MR analyses are performed\nsomewhat algorithmically and uncritically, but also clearly. When I see bidirectional\nresults from an MR study, I think this is likely to represent shared aetiology (i.e. common\ncauses of both traits) rather than literal causal effects in both directions. However,\nthis is a matter of interpretation. Overall, the MR analyses are not a barrier to the publication of\nthis manuscript. As for the rest of the work, these sort of untargeted \"throw everything at\nthe wall\" analyses aren't my cup of tea if I'm being honest, but I'm sure some people like\nthem. But again, that is a matter of preference.\nAUTHOR RESPONSE: We thank the reviewer for their careful review of the MR\nanalyses. We agree that interpreting bidirectional MR findings is complex and that\nshared etiology is a plausible explanation for the associations observed, in addition to\npotential causal relationships. In response, we have revised the Discussion to\nacknowledge this interpretation more explicitly and to present a more cautious\ninterpretation of the bidirectional MR results.\nGIA...\" - I doubt any statistical method ensures anything - 'ensure' seems too\nstrong here.\nAUTHOR RESPONSE: We have adjusted our text in line with reviewer's suggestion.\nThe phrase now reads \"HARE improves the accuracy of classification using GIA to\nrefine and, if necessary, impute SIRE, improving reliability of race/ethnicity assignment\nin genetic research.\"\nraised. However, some of the issues, such as the incorrect use of verb tenses or the data\nreporting error noted under point 5, suggest a need for greater attention to detail. These\ntypes of errors may occur elsewhere in the manuscript and ideally the authors should\nconduct a more comprehensive check to ensure the overall consistency and accuracy of\ntheir work.\nAUTHOR RESPONSE: We thank the reviewer for their feedback, which has improved\nthe manuscript.\n1"}, {"round": 4, "reviewer_comments": {"reviewer_1": "The authors have provided adequate responses to the points raised. However, some of the issues, such as the incorrect use of verb tenses or the data reporting error noted under point 5, suggest a need for greater attention to detail. These types of errors may occur elsewhere in the manuscript and ideally the authors should conduct a more comprehensive check to ensure the overall consistency and accuracy of their work."}, "author_response": ""}]

Decision letter