Genetic and spatial determinants of poor ovarian response: an integrative computational study

Abstract Background Poor ovarian response (POR) is a major clinical challenge in assisted reproductive technologies (ART), characterized by suboptimal response to ovarian stimulation. While age and environmental factors contribute to POR, genetic determinants, especially within regulatory regions of key reproductive genes, remain underexplored. Objective This study aimed to investigate the association between single-nucleotide polymorphisms (SNPs) in the 3′-untranslated regions (3′-UTRs) of GATA4 and WT1 genes and POR, and to assess their spatial and demographic distribution using advanced computational and spatial genetics methods. Methods A total of 100 Iranian women undergoing IVF (50 POR and 50 controls) were genotyped for selected SNPs in GATA4 and WT1 . Logistic regression, Factor Analysis of Mixed Data (FAMD), redundancy analysis (RDA), canonical correspondence analysis (CCA), and spatial principal component analysis (sPCA) were used to explore associations between SNPs, demographic factors, and geographic variables. Global linkage disequilibrium (LD)-based SNP data from diverse populations were also analyzed to assess population differentiation and geographic structuring. All analyses were performed after bootstrapping to ensure robust results. Results SNP8 (rs3203358) in the 3′-UTR of GATA4 showed a significant association with POR, with individuals carrying the CG genotype having a substantially elevated risk (OR = 37.4, p = 0.0018). FAMD highlighted the number of oocytes, age, and weight as key demographic differentiators. RDA, CCA, and sPCA revealed significant spatial structuring of POR-associated SNPs across Iranian regions ( p = 0.001), and similar patterns were observed in global populations for SNPs linked to GATA4 and WT1 . Several SNPs showed strong linkage to latitude and longitude, supporting both global and local genetic differentiation. An isolation-by-distance pattern ( p = 0.01) was detected, while Moran’s I was non-significant ( p = 0.95), indicating limited fine-scale autocorrelation. Given the modest sample size and exploratory nature of the analysis, findings were interpreted cautiously. Conclusions This study identifies SNP8 as a potential genetic marker for poor ovarian response and shows that its distribution varies across geographic and demographic contexts. By integrating spatial analyses with genetic and clinical data, we demonstrate that regional heterogeneity may contribute to differences in ovarian response. These findings support the value of incorporating spatially informed genetic assessments into fertility planning to improve the precision of risk prediction and personalize ovarian stimulation strategies.