Polycystic Ovary Syndrome Physiologic Pathways Implicated Through Clustering of Genetic Loci

JOURNAL OF CLINICAL ENDOCRINOLOGY & METABOLISM(2024)

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摘要
Context Polycystic ovary syndrome (PCOS) is a heterogeneous disorder, with disease loci identified from genome-wide association studies (GWAS) having largely unknown relationships to disease pathogenesis.Objective This work aimed to group PCOS GWAS loci into genetic clusters associated with disease pathophysiology.Methods Cluster analysis was performed for 60 PCOS-associated genetic variants and 49 traits using GWAS summary statistics. Cluster-specific PCOS partitioned polygenic scores (pPS) were generated and tested for association with clinical phenotypes in the Mass General Brigham Biobank (MGBB, N = 62 252). Associations with clinical outcomes (type 2 diabetes [T2D], coronary artery disease [CAD], and female reproductive traits) were assessed using both GWAS-based pPS (DIAMANTE, N = 898,130, CARDIOGRAM/UKBB, N = 547 261) and individual-level pPS in MGBB.Results Four PCOS genetic clusters were identified with top loci indicated as following: (i) cluster 1/obesity/insulin resistance (FTO); (ii) cluster 2/hormonal/menstrual cycle changes (FSHB); (iii) cluster 3/blood markers/inflammation (ATXN2/SH2B3); (iv) cluster 4/metabolic changes (MAF, SLC38A11). Cluster pPS were associated with distinct clinical traits: Cluster 1 with increased body mass index (P = 6.6 x 10-29); cluster 2 with increased age of menarche (P = 1.5 x 10-4); cluster 3 with multiple decreased blood markers, including mean platelet volume (P = 3.1 x10-5); and cluster 4 with increased alkaline phosphatase (P = .007). PCOS genetic clusters GWAS-pPSs were also associated with disease outcomes: cluster 1 pPS with increased T2D (odds ratio [OR] 1.07; P = 7.3 x 10-50), with replication in MGBB all participants (OR 1.09, P = 2.7 x 10-7) and females only (OR 1.11, 4.8 x 10-5).Conclusion Distinct genetic backgrounds in individuals with PCOS may underlie clinical heterogeneity and disease outcomes.
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关键词
PCOS,genetics,clustering,obesity,hormones,metabolic
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