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Genotype based Risk Predictors for Polycystic Ovary Syndrome in Western Saudi Arabia



Sherin Bakhashab1, 2, * & Nada Ahmed1



1Biochemistry Department, King Abdulaziz University, Jeddah, P.O. Box 80218, Saudi Arabia; 2Centre of Innovation in Personalized Medicine, King Abdulaziz University, Jeddah, P.O. Box 80216, Saudi Arabia



Sherin Bakhashab - Phone: +966 12 6400000; E-mail:sbakhashab@kau.edu.sa; Nada Ahmad: E-mail: nahmed0028@stu.kau.edu.sa; *Corresponding author


Article Type

Research Article



Received November 14, 2019; Revised November 28, 2019; Accepted December 7, 2019; Published December 10, 2019



Polycystic ovary syndrome (PCOS) is the most common endocrine disease among premenopausal women. The genetic risk of PCOS in the Saudi population is still unclear. Therefore, it is of interest to study the genotype and allele frequency for six gene variants (THADA rs13429458, TOX3 rs4784165, FSHRrs2268361, YAP1 rs1894116, RAB5B rs705702, and HMGA2 rs2272046) in patients with PCOS in western Saudi population. The study included 95 PCOS patients and 94 normal ovulatory females as controls. Genotyping was performed using TaqManTM real-time polymerase chain reaction assays. There was significant link between the THADA rs13429458 variant and PCOS.
Homozygosity in allele A of the rs13429458 variant was correlated with hyperandrogenism (HA) risk. Homozygosity in the T allele of the FSHR rs2268361 variant was associated with normal levels of AMH among non-PCOS women. The THADA rs13429458 and TOX3 rs4784165 variants were significantly associated with the combined oligo/amenorrhea (OA) and polycystic ovarian morphology subgroups while the HMGA2 rs2272046 variant was significantly associated with the combined HA and OA subgroup. Thus, results show the genetic risk of the THADA rs13429458, TOX3 rs4784165, and HMGA2 rs2272046 variants on PCOS patients in the western Saudi population.



Polycystic ovary syndrome; THADA; TOX3; FSHR; YAP1; RAB5B; HMGA2



Bakhashab & Ahmed, Bioinformation 15(11): 812-819 (2019)


Edited by

P Kangueane






Biomedical Informatics



This is an Open Access article which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. This is distributed under the terms of the Creative Commons Attribution License.