New genomic evidence unmasks the biological pathways driving erectile dysfunction, linking cardiometabolic disease and addiction traits to ED risk through shared genetic roots.
Study: Multi-ancestry investigation of the genomics of erectile dysfunction. Image Credit: anastasy.a / Shutterstock
In a recent study published in the journal Nature Communications, researchers investigated the genetic architecture of erectile dysfunction (ED). ED is the inability of males to gain and sufficiently maintain an erection for sexual activity.
Its prevalence increases with age: 30% of males aged 40 or older report ED, compared with less than 10% of males under 40. The most common cause of organic ED is arterial insufficiency and reduced blood flow, which might result from vascular disease.
Phosphodiesterase type 5 (PDE5) inhibitors are the most common treatment for ED, are considered safe, and have a success rate of 65% to 70%. However, many individuals do not respond, highlighting the need for a deeper understanding of ED biology and new therapeutic targets.
All of Us Biobank Used to Define Clinical ED Phenotype
In the present study, researchers investigated the genetic architecture of ED using data from individuals of African and European ancestries in the All of Us (AoU) biobank. ED was defined using electronic health records (EHRs) or the prescription of ED medications (PDE5 inhibitors).
Because the definition relied on EHR data, the phenotype was labeled EHR-ED to distinguish it from population estimates and to reflect clinically presenting ED. Individuals with pulmonary hypertension were excluded to avoid confounding because PDE5 inhibitors are prescribed for this condition.
Genome-Wide Association Analyses Across Ancestries
A genome-wide association study (GWAS) of EHR-ED was performed using logistic regression, followed by meta-analysis with published ED GWAS datasets. Analyses were stratified by ancestry and also conducted cross-ancestry. Linkage disequilibrium score (LDSC) regression was used to estimate SNP-based heritability.
The team also calculated inter-cohort genetic correlations and correlations between EHR-ED and 50 additional traits, including personality, substance use, psychiatric disorders, and general health characteristics. Correlations between EHR-ED and brain function or structure measures were evaluated, and local genetic correlations were examined between the European EHR-ED meta-analysis and those 50 traits.
Mendelian Randomization and Polygenic Risk Score Evaluation
Mendelian randomization (MR) analyses assessed whether genetically driven variation in correlated traits exerted causal effects on EHR-ED or vice versa, focusing on shared genetic liability rather than clinical causation.
Polygenic risk scores (PRSs) for ED were estimated for each ancestry group. A phenome-wide association study (PheWAS) of the European lead SNP evaluated associations with other clinical traits. A transcriptome-wide association study (TWAS) tested predicted differential gene expression linked to EHR-ED in European subjects.
Multi-Ancestry GWAS Identifies Novel and Ancestry-Specific ED Loci
The study included 30,448 African and 88,722 European participants from the AoU biobank. In Europeans, one genome-wide significant locus (rs17185536) in a non-coding region on chromosome 6 was detected.
No significant variants were found in African individuals in the initial AoU GWAS. European meta-analysis identified 40 lead SNPs across 27 loci, including nine novel variants, with the strongest signal from rs78677597. African ancestry meta-analysis identified two significant variants, rs17185536-T and a novel variant, rs55659406 (intronic to RABGAP1L).
A cross-ancestry meta-analysis identified 51 lead SNPs, with rs17185536 again the most significant. SNP-based heritability for the meta-analyzed dataset was 0.062. PRSs significantly predicted EHR-ED in Europeans, but not in Africans, and overall predictive performance remained modest.
Genetic Correlations Link ED With Psychiatric, Cardiometabolic, and Behavioral Traits
LDSC regression found positive genetic correlations of EHR-ED with depression, cannabis use disorder, attention-deficit/hyperactivity disorder (ADHD), type 2 diabetes, heart failure, post-traumatic stress disorder (PTSD), number of lifetime sexual partners, and number of children.
Negative correlations were observed with age of smoking initiation and age at first sexual intercourse. Many correlations likely reflect ascertainment bias because EHR-based ED captures men who seek clinical care. No correlations were detected with brain imaging phenotypes.
Seventy-six local genetic correlations emerged, particularly with substance-use traits; cannabis lifetime use had 12 genetically correlated regions with EHR-ED.
Bidirectional Causal Relationships Identified Through MR
MR analyses showed 14 traits with bidirectional causal relationships with EHR-ED. The strongest causal effects of EHR-ED on other traits were observed for type 2 diabetes and obesity. The strongest causal effects on EHR-ED came from opioid use disorder and cannabis use disorder, suggesting that clinically significant substance-use disorders, not casual use, shape ED liability. Genomic structural equation modeling placed EHR-ED on two latent factors:
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One linked to substance use and risk-taking traits.
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Another is associated with cardiometabolic pathways.
Functional Analyses Highlight SIM1 Pathway and β-Catenin Biology
PheWAS of rs78677597 identified associations with 15 traits, including nine related to cardiovascular or metabolic function, along with dermatological infections, respiratory issues, and pain. TWAS identified nine independent genes, with CTNNB1 (catenin beta 1) showing the strongest association, with negative enrichment in the amygdala.
A complementary drug-repurposing analysis identified several candidate compounds, including sulindac, a PDE5 inhibitor and β-catenin suppressor, suggesting mechanistic therapeutic potential.
ED Genetics Implicate SIM1 Regulatory Region and Multi-Pathway Biology
Taken together, the GWAS identified one genome-wide significant locus in Europeans and none in the initial African cohort. Meta-analyses yielded 40, 2, and 51 lead SNPs in European, African, and cross-ancestry analyses, respectively, with moderate genetic correlations across health and psychiatric traits.
The strongest variant effects mapped to a non-coding region regulating SIM1, a transcription factor involved in melanocortin signaling, energy balance, obesity, and sexual behavior, providing biological context for ED’s multifactorial nature.
The authors caution that EHR-based definitions may miss milder or unreported ED, and that prostate cancer and its treatments may influence associations.
Overall, the findings illustrate ED as a complex, polygenic trait shaped by cardiometabolic, neurobehavioral, and substance-use pathways, emphasizing the importance of genetic research to clarify mechanisms and identify therapeutic targets.
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