Phenome-wide Association Study of Male and Female Sex Chromosome Trisomies in 1.5 Million Participants of Million Veteran Program, FinnGen, and UK Biobank

Sex chromosome trisomies (SCTs), caused by the presence of an extra X or Y chromosome, are among the most common chromosomal abnormalities but remain substantially underrecognized. Klinefelter syndrome (47,XXY) is the most frequently diagnosed SCT and, as a result, has been the primary focus of research on associated comorbidities, while individuals with 47,XYY and 47,XXX are typically undiagnosed and understudied. This reliance on clinically identified cases introduces bias and limits understanding of the full spectrum of SCT-associated disease, particularly for physical health outcomes. Large-scale biobanks that link genetic data with medical records offer a unique opportunity to study both diagnosed and undiagnosed SCTs across subtypes. This retrospective cohort study uses biobank data to examine the risk of physical comorbidities across all three SCTs, including both diagnosed and undiagnosed individuals. Data were compiled from three large population-based biobanks: the Million Veteran Program (MVP), FinnGen, and the UK Biobank, each of which includes genotyped blood-derived DNA linked to detailed electronic health records. The MVP cohort was started in 2011 and includes ~650,000 veterans in the VA health care system, while FinnGen includes 500,000 Finns across varied national health registries, with records beginning at or before 1969. The UK Biobank is a prospective cohort of ~500,000 volunteers in the United Kingdom aged 40 to 70 that began recruitment in 2006. To determine the presence of an SCT, the researchers analyzed SNP array data for the relative signal strength of X and Y chromosomes present, from which the copy number was inferred. The number of these patients who were officially diagnosed was abstracted from ICD-9 or ICD-10 codes. Disease outcome rates were captured using phecodes, which are clinically meaningful groups of International Classification of Diseases (ICD) codes, and were grouped into system-based categories. Associations between SCT subtypes and lifetime disease risk were evaluated using a matched case-control design, with each SCT carrier matched to five control individuals by genetic sex, birth year, and genetic ancestry. Among the 1.5 million individuals included, 2769 (0.19%) were found to have an SCT, the large majority of which (86.2%) were undiagnosed. Diagnosis rates differed by subtype, with 26.2% of 47,XXY patients, 1.4% of those with 47,XYY and 6.4% of those with 47,XXX carrying a clinical diagnosis. Phenome-wide association analyses identified hundreds of disease associations spanning nearly all organ systems, with 43 conditions shared across all three SCTs, most of which were categorized as cardiovascular, respiratory, and metabolic diseases. Vascular conditions, including venous thromboembolism, atherosclerosis, and cerebrovascular disease, showed strong associations across SCT subtypes. Metabolic and respiratory conditions, such as obesity, type 2 diabetes, asthma, and sleep apnea, were also more prevalent across SCT subtypes. Subtype-specific associations were also identified, with reproductive and bone disorders concentrated in 47,XXY, select renal and infectious conditions concentrated in 47,XXX, and no distinct phenotype unique to 47,XYY. The findings show that most individuals with SCTs remain undiagnosed into adulthood and experience an increase in a wide range of chronic diseases that are shared across SCT subtypes. Despite the historical attribution of many of these phenotypes to hypogonadism, particularly in cases of 47,XXY, the commonalities found across karyotypes, genetic sexes, and cohorts suggests that increased sex chromosome gene dosage plays a central role in SCT pathophysiology. The vascular phenotypes that were frequently observed across all three SCTs, including venous thromboembolism and chronic venous disease, are consistent with previous studies, and future research should investigate possible underlying etiologies. Limitations of the study include reliance on EHR-based phenotyping, potential homogeneity between biobanks, and limited power to assess phenotype severity or differences across ancestral groups. (Abstracted from Am J Hum Genet. 2025;112:2088–2101. doi:10.1016/j.ajhg.2025.07.017)