Association of GHRL and CYP19A1 Variants with Uterine Leiomyoma: An Exploratory Case–Control Study with In Silico Annotation

Uterine leiomyomas (ULMs) are common benign gynecological tumors associated with estrogen signaling and metabolic–endocrine pathways. This study investigated the associations of CYP19A1 (cytochrome P450 family 19 subfamily A member 1; rs2414096) and GHRL (ghrelin and obestatin prepropeptide; rs34911341, rs696217) polymorphisms with leiomyoma risk and evaluated their potential functional effects in silico. This case–control study included 65 women with uterine leiomyoma and 72 healthy controls. Genotyping was performed using polymerase chain reaction–restriction fragment length polymorphism (PCR-RFLP). Associations were assessed using Firth’s penalized-likelihood logistic regression under additive, dominant, and recessive models, with adjustment for age, body mass index (BMI), and parity. Functional predictions employed CADD, PolyPhen-2, SIFT, SpliceAI, and I-Mutant v2.0. Cases and controls were comparable in age, BMI, and parity (all p > 0.05). CYP19A1 rs2414096 was not associated with leiomyoma risk. Conversely, GHRL rs34911341 showed a significant association under the adjusted additive model (OR = 2.85, 95% CI: 1.59–5.45, p = 0.0003; FDR q = 0.001) and remained independently associated in the joint multivariable model (OR = 2.75, 95% CI: 1.52–5.30, p = 0.0006). GHRL rs696217 showed a nominal additive association (adjusted OR = 2.11, p = 0.037), which did not remain significant after false discovery rate (FDR) correction or in the joint model. Recessive associations for both GHRL variants were affected by complete separation. In silico analyses classified rs2414096 as benign, whereas both GHRL variants were computationally predicted to be potentially deleterious and to reduce protein stability. These findings suggest a candidate association between GHRL rs34911341 and leiomyoma susceptibility. Given the modest sample size, control-group Hardy–Weinberg equilibrium deviations, and absence of biochemical peptide measurements, larger multicenter studies are needed for validation.