Genetic Predisposition Analysis of the Fshr Gene in Pcos: Insights From a South Indian Population

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Despite being one of the most populated and diverse countries in the world, there is an absence of polymorphisms study on promoter region and a paucity of data on the association of common exonic variations of FSHR gene with PCOS in a homogenous group in India. Materials and Methods: In our case-control study, we recruited 1018 women (438 PCOS and 580 Controls). We carefully selected 121 participants from the 438 PCOS patients based on their maternal or paternal lineage and the severity of their symptoms from menarche onwards with fulfilling all the three Rotterdam criteria. From 580 controls, to reduce maximum genetic propensity, 121 age-matched individuals who did not have PCOS in either maternal or paternal relatives up to the second degree were enrolled as experimental controls. The proximal promoter region of the FSHR gene was analyzed in PCOS and control samples by PCR-Sanger sequencing. Further, significantly observed 5’UTR variant (rs1394205) in sanger sequencing and two common exon 10 SNPs [Ala307Thr A>G (rs6165) and Ser680Asn A>G (rs6166)] were analyzed by PCR-RFLP in 121 PCOS patients and 121 control subjects. Finally, the pathogenic evaluation of Ala307Thr A>G (rs6165) and Ser680Asn A>G (rs6166) was performed by applying various bioinformatics tools. Results: In our study, a notable significance were observed in the FSHR rs1394205 and rs6165 polymorphisms with the PCOS predisposition. Apart from this, rs6165 has a notable variance in genotype frequency between individuals with the normal BMI group. However, the in-silico pathogenicity prediction tools predicted that this variation was non-pathogenic. Conclusion: Our finding suggests that the FSHR rs1394205, −29G>A and rs6165 polymorphisms are significantly associated with PCOS predisposition in South Indian PCOS patients. Molecular Genetics Obstetrics & Gynecology Polycystic ovary syndrome (PCOS) 5’UTR variant -29 G > A (rs1394205) Ala307Thr A>G (rs6165) Ser680Asn A>G (rs6166) and In-silico prediction of FSHR. Figures Figure 1 Figure 2 Figure 3 INTRODUCTION Polycystic Ovary Syndrome (PCOS) is a polygenic, multifactorial, systemic, inflammatory, and hormonal syndrome. It is a chronic and multifaceted illness that affects 15–20% of women globally who are of reproductive age [1]. The hallmarks of PCOS are hyperandrogenism, irregular menstrual cycles, and the presence of polycystic ovaries. Obesity, glucose intolerance, insulin resistance, elevated lipid profiles, altered secretion of pituitary gonadotropins and various anomalies in the expenditure of energy[2–5] are the common metabolic syndromes found in PCOS. PCOS is associated with modifications in the Hypothalamic-Pituitary-Gonadal (HPG) axis function, possibly due to a higher frequency of hypothalamic gonadotropin-releasing hormone (GnRH) pulses. In healthy females, higher frequencies of GnRH pulses favour the secretion of Luteinizing Hormone (LH), while lower pulses favour Follicle Stimulating Hormone (FSH). This altered gonadotropin physiology, characterized by elevated serum LH levels and LH to FSH ratio, plays a pivotal role in PCOS [6]. Pathogenesis of PCOS involves intricate genetic and hormonal factors, and emerging research seeks to unravel the underlying mechanisms. Investigating the promoter and genetic polymorphisms linked to PCOS, specifically the function of the Follicle Stimulating Hormone Receptor (FSHR) gene, is a promising field of study. FSH is integral to ovarian follicle maturation, dominant follicle selection, and the aromatization of androgens. It exerts its effects through the Follicle Stimulating Hormone Receptor (FSHR), expressed on Sertoli cells in the testis and granulosa cells in the ovary [7]. Animal studies involving Fshr gene knockout mice have indicated the importance of FSHR in ovarian physiology, particularly in folliculogenesis [8]. FSHR may contribute to a person’s genetic susceptibility to PCOS [9]. Variants of the FSHR were found to be more closely associated with the degree of clinical characteristics of PCOS, such as gonadotrophic hormone levels and hyperandrogenism [10]. Various studies of FSHR polymorphisms in different ethnic variant populations are conflicting. Studies conducted on the Thai and Chinese populations did not provide substantial evidence of the role of these FSHR polymorphisms in PCOS [11,12]. However, studies done in the Pakistani and Korean populations have shown a significant involvement of these SNPs with PCOS [13,14]. The results of the meta-analysis are even contradictory [15]. The proximal promoter region of the FSHR gene has not been explored so far in PCOS patients. Ala307Thr (rs6165) and Ser680Asn (rs6166), are two well-characterised polymorphisms located in the 10th exon of the FSHR gene. A sole study regarding the involvement of both rs6165 and rs6166 polymorphisms in Indian PCOS patients was reported in Punjab Populations [16]. India is a country with various ethnicities. In genetic studies, ethnicity and the heterogeneous nature of PCOS are assumed to play a pivotal role in inconclusive results. Hence in the current study, we focused on the selection criteria for this particular study for a better understanding of the involvement of the FSHR in the manifestation of PCOS. MATERIALS AND METHODS Selection of subjects In this case-control study, a total of 1018 women aged between 15 to 35 years participated, all having provided proper consent for inclusion in the study. Among them, 438 were PCOS cases and the remaining were non-PCOS based on the Rotterdam criteria (Rotterdam ESHRE/ASRM-Sponsored PCOS consensus workshop group, 2004). Based on the BMI, the study subjects were stratified into four categories: lean (30 kg/m 2 ). From January 2021 to January 2024, all patients were consecutively recruited from the Department of Obstetrics and Gynaecology, Jubilee Mission Medical College and Research Institute (JMMC & RI), Thrissur, Kerala, India. The methods opted for the selection of study subjects were approved by the Institutional Ethics Committee of JMMC & RI, (IEC Study Ref: 47/20/IEC/JMMC &RI). Inclusion criteria After enrolling 438 participants who had been diagnosed with PCOS, we carefully chose 121 participants from them based on their maternal or paternal lineage and the severity of their symptoms as determined by a questionnaire. The purpose of this selection was to increase the identification of genetic predisposition in PCOS participants who had symptoms that were either severe or manifested from menarche onwards fulfilling all three Rotterdam criteria. This type of selection is assumed to reduce the heterogeneity seen in PCOS patients to some extent. As a control, 580 consenting, normoandrogenic, post-pubertal, premenopausal women with regular menstrual cycles who were determined to not have PCOS by clinical examination were enrolled. To reduce maximum genetic propensity, 121 age-matched individuals who did not have PCOS in either maternal or paternal relatives up to the second degree were enrolled as experimental controls. Selection of the control samples was the limiting criterion of our study. Exclusion criteria for the experimental studies We excluded all cases with a history of hormonal treatment and contraception within the past 6 months, pregnancy and the first year of delivery as well as patients with an androgen-secreting ovarian/adrenal tumour, Congenital Adrenal Hyperplasia (CAH) or those who are taking antiepileptics, antipsychotics or corticosteroids. Table 1 displays the clinical and anthropometric characteristics of the PCOS and Control study subjects. Promoter analysis of the FSHR gene A literature search showed a lack of genetic studies in the FSHR promoter region of PCOS patients. This prompted us to screen the proximal promoter region (-1to-481) of the FSHR gene in PCOS patients. Peripheral blood samples from the study subjects were drawn into EDTA vacutainers after getting informed consent and kept at -20°C until processed for molecular analysis. Genomic DNA extraction was carried out by using the QIAamp DNA Blood Mini Kit (Qiagen, Germany), and DNA concentration was determined using a Qubit fluorometer (Life Technologies, CA, USA). Further, the proximal promoter region of the FSHR gene in 25 samples from PCOS patients and a same number of control subjects were screened for genetic variations analysis by Sanger sequencing. The primers were designed by using PRIMER3 software. The following primer pair set was used for the amplification of the proximal promoter region of the FSHR gene; F-5’AGA GCA GTG ACA GAT CCG ATG-3’ and R-5’-ACC TCC CAC CTA CGG AAA TC-3’. Briefly, 100 ng of genomic DNA, 1x EmeraldAmp GT PCR Master Mix (Takara Bio, USA Inc.), and 0.3 picomoles of each oligonucleotide primer were used for PCR, which was carried out in a total reaction volume of 25μL. The PCR cycle conditions included a 5-minute initial denaturation step at 95°C, 35 cycles of 45 seconds at 95°C, 30 seconds at 52°C, 45 seconds at 72°C, and a 5-minute final extension at 72°C. The amplified PCR products were subjected to Sanger sequencing. SNP genotyping SNP genotyping for 5’UTR (rs1394205) and common exonic (rs6165 and rs6166) polymorphisms of the FSHR gene were performed in 121 PCOS and a same number of control samples by Polymerase Chain Reaction- Fragment Length Polymorphism (PCR-RFLP) and analysed in the 10% Polyacrylamide Gel Electrophoresis. Primer details and PCR conditions are shown in Table 2. Random samples from each of the three genotypes from rs6165 and rs6166 were subjected to Sanger sequencing to re-confirm our PCR-RFLP results. Evaluation of the pathogenic nature of the exonic polymorphisms Researchers employed a range of sequence-based analysis tools to assess the pathogenic nature of the Ala307Thr (rs6165) and Ser680Asn (rs6166) variations in the FSHR gene. These tools, including Polyphen-2 [17], Mut Pred [18], SNPs & GO [19], PON-P2 [20], SIFT [21], MAPP [22], PhD-SNP [23], SNAP [24], PANTHER [25], and Predict SNP1,[26], utilize diverse methodologies such as cellular localization, aggregation tendencies, structural disorder, functional effects, and stability analysis [27]. PolyPhen-2 utilizes machine learning and multiple sequence alignment to compare the physical properties of wild-type and mutant variants for pathogenicity prediction. MutPred2 employs a machine-learning approach to estimate the molecular mechanisms underlying pathogenicity. SNPs & GO is an SVM-based server that integrates GO annotations and SwissProt code. PON-P2 incorporates GO annotations, evolutionary sequence conservation, and biochemical and physical attributes of the protein. SIFT considers sequence similarity and physical properties to calculate damaging probability. MAPP leverages physicochemical traits and evolutionary data to predict the impact of missense variants. PhD-SNPs employ Support Vector Machines (SVMs) to forecast if nonsynonymous SNPs are linked to human genetic diseases based on protein sequence data. SNAP utilizes a neural network method to predict the functional effects of nonsynonymous SNPs. PANTHER compares evolutionary analyses using amino acid substitution scores, hidden Markov models, and Ka/Ks ratios for Mendelian and complex disease-associated cSNPs. PredictSNP is a consensus tool that considers various parameters derived from the protein's evolutionary, physicochemical, or structural characteristics. Finally, a consensus-based approach was implemented to predict the pathogenicity of the identified mutations. A random cutoff value was also implemented to select pathogenic/non-pathogenic variants. If eight or more tools predicted the variations as pathogenic, we considered it pathogenic, and vice versa. We also collected other variants on the same positions to understand whether the genetic variation site is a hot spot or not. The variations were collected from mutation databases like dbSNP [28], HGMD [29], Ensemble [30], and gnomAD [31]. STATISTICS We performed statistical analyses like mean, standard deviation, chi-square test and odds ratios (OR) with 95% confidence intervals (CI) to investigate the association between genotype frequencies, allele frequencies, and disease occurrence in both the patient and control groups as well as in the BMI classifications. Hardy-Weinberg equilibrium (HWE) was assessed by using Microsoft Excel. SPSS version 25 and online tools like SNPStats (http://bioinfo.iconcologia.net/SNP stats), SISA and MedCalc were used to analyze the data. SNPStats and the Haploview version 4.2 were used for haplotype analysis and linkage disequilibrium. A statistical significance threshold of p < 0.05 was applied to ascertain the results. RESULTS The anthropometric characteristics of PCOS and the control subjects are summarised in Table 1. In our study, we observed a significance in menarche age, weight, height, BMI, normal and overweight classification of BMI in PCOS patients compared to control subjects. In proximal promoter region analysis of the FSHR gene, we observed two SNPs i.e., rs1317756940 (χ 2 =0.002, p=0.9640) and rs1394205 ( χ 2 =6.852, p=0.033 ) (Table 3). The significantly obtained 5’UTR variant (rs1394205) was further analysed by PCR-RFLP in 121 PCOS cases and control study subjects. Along with the 5’UTR variant, two common exonic SNPs [Ala307Thr A>G (rs6165) and Ser680Asn A>G (rs6166)] were also analysed by PCR-RFLP in PCOS patients and control subjects (Figure 1 & 2). We calculated Hardy-Weinberg equilibrium (HWE) and confirmed that the selected samples were representing the population (Table 4). Genetic association of the FSHR gene polymorphisms and PCOS The frequency distribution and genetic association of polymorphic genotypes of FSHR –29 G>A (rs1394205), 307 A>G (rs6165), and 680 A>G (rs6166) with PCOS is given in Table 5. The frequencies of FSHR –29 G>A genotypes G/G, G/A, and A/A in the patients were 41.32%, 41.32%, 17.35%, and 33.88%, 55.37%, and 10.74%, respectively in controls (p = 0.0727). The frequencies of A and G alleles in patients were 61.98% and 38.02%, and in controls were 61.57% and 38.43%, respectively (p=0.9254). The frequencies of FSHR 307 A>G (rs6165) genotypes A/A, A/G, and G/G in the patients were 32.23%, 45.45%, and 22.32%, and in the controls, were 57.02%, 37.19%, and 5.78% respectively (p = 0.00002 ). The frequencies of A and G alleles in patients were 54.96% and 45.04%, and in controls, they were 75.62% and 24.38% respectively (p = 0.00001 ). The frequencies of FSHR 680 A>G (rs6166) genotypes A/A, A//G, and GG in the patients were 29.75%, 49.59%, and 20.66%, and in the controls, were 33.06%, 49.59%, and 17.35% respectively (p = 0.7564). The frequencies of A and G alleles in patients were (54.54% and 45.46%, and in controls, they were 57.85% and 42.15% respectively (p = 0.4636). The PCOS and the control groups of the gene polymorphism FSHR –29 G>A (rs1394205) and 307 A>G (rs6165) have a statistically substantial association. Based on the genotypes of the FSHR polymorphisms, the haplotype frequencies were calculated. HWE, Chi-Square and p-value were noted in Table 4 in which the rs1394205, rs6165 and rs6166 have not shown any deviation from HWE. In the haplotype analysis among PCOS patients and controls, we found no significant association or linkage disequilibrium (D¢ = 0.006; r 2 = 0.0). Genetic association of the FSHR gene polymorphisms And PCOS based on BMI One of the most prominent physiological attributions of PCOS is obesity. The frequency distribution and genetic association of polymorphic genotypes of FSHR –29 G>A (rs1394205), 307 A>G (rs6165), and 680 A>G (rs6166) between PCOS patients and control subjects based on BMI status were also determined, and the results are shown in Table 5. In our study, we found that the lean and normal BMI were distributed to 57 (47.11%) PCOS and 98 (80.99%) control subjects whereas, overweight and obesity (BMI ≥25) were found in 64 PCOS patients (52.89%), but only in 23 (19.01%) of the control group and the results are shown in Table 6. Genotype and allele frequencies showed no statistically significant difference between lean, overweight, and obese groups for both FSHR –29 G>A (rs1394205), and 680 A>G (rs6166). However, concerning 307 A>G (rs6165), we found a notable variance in genotype frequency between individuals in the Normal BMI group. The frequencies of AA, AG and GG genotypes were 34.8%, 39.1% and 26.1% respectively. For normal BMI controls these frequencies were 39.4%, 52.1% and 8.5% respectively ( p=0.033 ). In obese BMI PCOS subjects the distribution of frequencies was 26.9%, 53.9% and 19.2%, while in obese BMI controls, they were 80%, and 20%, for AA and AG genotypes ( p= 0.047). The allelic distribution of A and G in obese PCOS patients was 46.7% and 53.3% respectively, whereas in obese Control it was 90% and 10%, respectively ( p=0.012 ). In the normal BMI group, individuals carrying the GG genotype of the FSHR 307 A>G (rs6165) polymorphism have a 3.5-fold increased risk of PCOS compared to those with the AA genotype (OR 3.5000, 95% CI = 1.1009 - 11.1268 and p=0.0338 ). However, among obese BMI individuals, while the GG genotype has a significant chi-square value (χ 2 =6.09, p=0.047), it does not show a significant Odds ratio (OR). Nevertheless, the presence of the G allele confers a 10-fold increased risk of PCOS compared with the A allele (OR 10.2857, 95% CI = 1.2012 - 88.0745 and p=0.0334 ). Bioinformatics analysis of the exonic polymorphisms The pathogenic evaluation of Ala307Thr A>G (rs6165) and Ser680Asn A>G (rs6166) was performed by applying various bioinformatics tools. Although structure-based pathogenicity prediction is more accurate than sequence-based prediction, we are forced to adopt the sequence-based pathogenic prediction, due to the lack of appropriate crystal structure of human FSHR . Only five three crystal structures of human FSHR were deposited (PDB IDs: 1XWD, 4AY9, and 4MQW,8I2G and 8I2H) with Protein Data Bank (PDB), in which the structure 1XWD has amino acids from 17-268, and 4AY9 and 4MQW has amino acids 16-366 and 8I2G and 8I2H has amino acids 18-695. However, due to the poor electron density, the loop regions (residues 296-330) are missing in these structures. Similarly, the position 680 is also missing in all the structures. So, due to the lack of exact structure of these two positions, we performed sequence-based pathogenic prediction for rs6165 and rs6166 using 10 different tools. In our extensive analysis, we failed to identify any significant pathogenicity of these variations (Figure 3). DISCUSSION The Rotterdam criteria serve as a prevalent diagnostic criterion to diagnose PCOS among clinicians. However, the diverse range of symptom heterogeneity poses a significant challenge to understanding PCOS. Numerous associated comorbidities like obesity, insulin resistance, diabetes, and cardiovascular diseases are often observed in patients with PCOS, which cannot be logically considered as a single research sample group. Therefore, it is crucial to categorize the patients into subgroups based on their symptoms for a more nuanced understanding of PCOS. The failure of the sample selection into distinct multiple groups could contribute to the challenge of replicating genetic analysis results despite obtaining significant results. Ethnicity further complicates these inconsistencies in results. Hence, in our study, we meticulously selected the subjects based on strict criteria to mitigate the impact of PCOS symptom heterogeneity. All the enrolled patients met all three Rotterdam criteria and exhibited PCOS from the menarche onwards with high severity of symptoms along with a strong familial predisposition of PCOS. Furthermore, the control population was carefully chosen to exclude the history of PCOS among their paternal and maternal relatives, thus reducing the genetic predisposition. In the present study, we performed the promoter analysis of the FSHR gene along with FSHR 5’UTR variant − 29 G > A (rs1394205) and exon 10 gene variants Ala307Thr A > G (rs6165) and Ser680Asn A > G (rs6166) in the south Indian population. Further, we also analysed the pathogenicity of rs6165 and rs6166 polymorphisms by applying various bioinformatics tools. Compared to the well-studied core promoter regions of mice, rats and sheep, the human FSHR is fundamentally different. It is devoid of the usual CAAT and TATA box sequences and the CpG dinucleotide methylation loci that are known to influence the expression of rat FSHR [32]. In our study, we observed statistically significant FSHR rs1394205 polymorphisms in PCOS patients compared to control subjects. The SNP rs1394205, located at the 5’ untranslated region, has been identified within the transcription factor binding site of the viral E26 transformation-specific sequence (c-ETS-1) [33]. So, this SNP may modulate the FSHR expression by altering the transcription factor binding sites. Previous studies show that the AA genotype at this SNP required a higher amount of exogenous FSH hormone level for ovulation induction [34,35]. The involvement of this SNP has been reported in primary and secondary amenorrhea cases [36]. The rs1394205 AA genotype has been associated with reduced FSHR gene expression [37]. As Conforti et al stated in their work, FSHR rs1394205, − 29G > A can affect the duration of gonadotropin stimulation[38] and Liu et al reported that the A allele of rs1394205 associated with Premature Ovarian insufficiency [39]. Even though the mechanism of this SNP has been well studied, there is no study concerning PCOS. A significant association of rs1394205 polymorphisms in PCOS patients in our study pointing further exploration of this variant in PCOS pathogenicity. Thus, an unknown intrinsic promoter mechanism is involved in PCOS genetic susceptibility. However, the lack of expression of FSHR protein in the blood limits further investigation. Numerous polymorphisms have been identified within the FSHR gene, among them, two closely linked polymorphisms, rs6165 (A/G) and rs6166 (A/G) in exon 10[34] were well studied in various disease conditions including PCOS. The Ala307Thr A > G (rs6165) polymorphism is a non-synonymous, well-studied even in PCOS but, the ambiguity and reproducibility of the results raise serious concerns in the research and clinical aspects. Here ethnicity plays a crucial role. It can be assumed that this particular variation may have a significant effect on people with reduced ovarian response [40,41]. In the Controlled Ovarian Stimulation, the GG genotype-carrying individuals produced a greater number of oocytes compared to other AG and AA genotypes [42]. In addition, the ovarian stimulation was remarkably lesser for the GG genotype than the others [34]. However, in the in-vitro studies, this variation in a transient transfection resulted in no significant effect on the hormone binding [43]. It can be proposed that this particular variation which resulted in the amino acid change may reduce the downstream conduction of the signals and slow down the normal process of receptor-ligand binding. This effect is not universal and can vary based on ethnicity. In our study, we got a significant association of this variation in PCOS patients compared to control. Most previous studies have shown an absence of association of rs6165 with PCOS. A sole study in Indian Punjabi populations also showed the same result [16]. However, our observation aligns with the study conducted by Kim et al and Dolfin et al in the Korean and Caucasian populations, respectively [13,44]. Apart from the ethnic variations, we assumed that our stringent genetic selection criteria applied both in PCOS and control subjects might be the reason behind our observed result. The Ser680Asn A > G (rs6166) (G/G) genotype in PCOS women has been linked to conditions such as amenorrhea or anovulation, longer menstrual cycles [43], and hyperandrogenism. Patients with the rs6166 (G/G) genotype in PCOS have been found to exhibit higher serum FSH levels, suggesting reduced sensitivity to FSH [10]. In our study, we could not find any significant association between this variation in our PCOS subjects and BMI either. Although most of the previous studies in European and Pakistani populations showed an increased risk of rs6166 with PCOS, our result is in concordance with the majority of Chinese and Caucasian reports. A similar observation was also reported by Kaur et al in Indian Punjabi populations [16]. Since rs6165 has a noteworthy association, we assessed the pathogenic potential of the A307T variation. Even though in our study we could not find any significant association of rs6166 in PCOS condition, there are many studies of this particular variation that have shown significant association with PCOS even in different Asian populations [40,45]. Thus, we performed the sequence-based pathogenicity of this variation. All the tools predicted these variations as non-pathogenic. We further identified other variations reported on the same positions, from various databases, and found three, of which 2 belong to the 307th positions (A307V and A307P) and one belongs to the 680th position (S680R). Our analysis revealed that these positions are also not exhibiting any pathogenicity (Fig. 3 ). Studies regarding the relationship between FSHR polymorphisms and PCOS susceptibility are conflicting [46]. Reports suggest the variation of A307T is particularly prevalent in PCOS patients [15]. The Ala307Thr (rs6165) variation resides within the extracellular topological domain of the FSHR gene, specifically in the loop region near its hinge area. This elongated hinge loop of the FSHR plays a crucial role in direct interaction with follicle-stimulating hormone (FSH) and contributes to the receptor's flexibility during ligand binding [47,48]. In the case of the A307T variation, the substitution of the lower molecular weight non-polar, aliphatic alanine (89Da) with comparatively higher weight polar threonine (119Da) may potentially impact the flexibility or proper functioning of the receptor. However, to confirm this assumption, additional experiments are necessary. Apart from this, the selection criteria employed by us might be the factor behind the significance of rs1394205 and rs6165 polymorphisms in PCOS patients. Based on this we proposed to screen FSHR polymorphisms in maximum genetically predisposed PCOS patients for conclusive results. CONCLUSION We analysed FSHR promoter (rs1394205) and exonic polymorphisms (rs6165 and rs6166) in patients who met all three Rotterdam criteria and exhibited PCOS from the menarche onwards along with a strong familial predisposition of PCOS. In our study, we observed a significant association of FSHR 5’UTR variant − 29 G > A (rs1394205) and exon 10 gene variant Ala307Thr A > G (rs6165) with South Indian PCOS patients. In-silico prediction based on the sequence failed to identify the pathogenicity of Ala307Thr. In addition, in the normal BMI group, individuals carrying the GG genotype of the FSHR 307 A > G (rs6165) polymorphism showed a 3.5-fold increased risk of PCOS compared to those with the AA genotype. To conclude, FSHR 5’UTR variant − 29 G > A (rs1394205) and exon 10 gene variant Ala307Thr A > G (rs6165) were associated with PCOS in the south Indian population. Declarations The authors declare that they have no conflict of interest. ACKNOWLEDGEMENT S.F. and D.K.V. acknowledge the financial assistance (R12015/01/2021/HR) received as a fellowship from the Department of Health Research, Govt. of India. References Achrekar SK, Modi DN, Desai SK, Mangoli VS, Mangoli R V., Mahale SD. Poor ovarian response to gonadotropin stimulation is associated with FSH receptor polymorphism. Reprod Biomed Online 2009;18:509–15. https://doi.org/10.1016/S1472-6483(10)60127-7. Achrekar SK, Modi DN, Meherji PK, Patel ZM, Mahale SD. Follicle stimulating hormone receptor gene variants in women with primary and secondary amenorrhea. J Assist Reprod Genet 2010;27:317–26. https://doi.org/10.1007/s10815-010-9404-9. Adzhubei IA, Schmidt S, Peshkin L, Ramensky VE, Gerasimova A, Bork P, et al. 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Tables Table 1: Clinical Parameters of PCOS and the Controls Clinical parameters PCOS Mean±SD (n=121) Control Mean±SD (n=121) p-Value Age at Menarche (years) 12.42±1.258 12.88±1.196 0.0039* Weight (kg) 62.44±12.818 53.79±12.069 <0.0001* Height (m) 155.76±5.276 157.47±7.45 0.0404* BMI (kg/m2) 25.80±5.265 21.91±4.494 <0.0001* PCOM, Oligomenorrhea and Hyperandrogenism 121 (100%) 0 (0%) - Lean (<18.5) 11 (9.09%), 17.04±1.005 27 (22.31%), 17.31±0.707 0.3523 Normal (18.6 – 24.9) 46 (38.02%), 22.30± 1.753 71 (58.68%), 21.41±1.719 0.0077* Over Weight (25 – 29.9) 38 (31.40%), 24.15±1.231 18 (14.88%), 27.14±1.623 30) 26 (21.49%), 33.141±2.963 5 (4.13%), 36.18±4.449 0.0623 Table 2: PCR and RFLP Conditions Restriction enzymes Primer sequence Product size SNP Sequence variation PCR Condition RFLP Condition MboII Primer F 5´ -GGA GCT TCT GAG ATC TGT GG-3` Primer R 5`AAA TGC CAG CCA TGC AGT TG-3` 266 rs1394205 -29 G>A 95°C, 5 min, 1 cycle; 95°C, 20 sec; 52°C 20 sec and 72°C 20 sec, 40 cycles; 72°C, 5 min, 1 cycle; 12°C, ∞ 37°C for 10 min then inactivate 65°C 20 min CviKI Primer F 5´ - TCT GAG CTT CAT CCA ATT TGC A-3` Primer R 5` ACG TCA ACC ACT TCA TTG CA-3` 176 rs6165 307 A>G 95°C, 5 min, 1 cycle; 95°C, 20 sec; 53°C 20 sec and 72°C 20 sec, 40 cycles; 72°C, 5 min, 1 cycle; 12°C, ∞ 37°C for 60 min BsrI Primer F 5´ - CCC AAA TTT ATA GGA CAG-3` Primer R 5` GAG GGA CAA GTA TGT AAG TG-3` 114 rs6166 680 A>G 95°C, 5 min, 1 cycle; 95°C, 20 sec; 45°C 20 sec and 72°C 20 sec, 40 cycles; 72°C, 5 min, 1 cycle; 12°C, ∞ 65°C for 10 min then inactivate 80°C for 20 min Table 3: Frequency distribution and genetic association of polymorphic genotypes of FSHR- -40 T>C (rs1317756940) and –29 G>A (rs1394205) with PCOS susceptibility under different genetic models after getting promoter Sanger sequence. FSHR genetic variation Patient n=25(%) Control n=25(%) χ 2 p-value OR (CI 95%) p-value -40 T>C (rs1317756940) Codominant model T/T 3 (12%) 2 (8%) 0.002 0.9640 1 (Reference) --- T/C 11 (44%) 7 (28%) 1.0476 (0.1383 - 7.9344) 0.9641 C/C 0 (0%) 0 (0%) 0.7143 (0.0103 – 49.7119) 0.8765 Allele T 17 (60.71%) 11 (61.11%) 0.001 0.9785 1 (Reference) --- C 11 (39.29%) 7 (38.89%) 1.0168 (0.3021 - 3.4227) 0.9785 FSHR genetic variation Patient n=25(%) Control n=25(%) χ 2 p-value OR (CI 95%) p-value -29 G>A (rs1394205) Codominant model G/G 14 (56%) 18 (72%) 8.077 0.0176 1 (Reference) --- G/A 6 (24%) 7 (28%) 1.1020 (0.3019 - 4.0233) 0.8831 A/A 5 (20%) 0 (0%) 14.0345 (0.7160 - 275.09) 0.0819 Allele G 34 (68%) 43 (86%) 3.069 0.0797 1 (Reference) --- A 16 (32%) 7 (14%) 2.4202 (0.8865 – 6.6070) 0.0845 Table 4: HWE analysis on rs1394205, rs6165 and rs6166 FSHR genetic variation PCOS Control -29 G>A (rs1394205) HWE Analysis 0.38 0.38 χ 2 1.8362 3.5007 p-value 0.1753 0.0613 307 A>G (rs6165) HWE Analysis 0.45 0.24 χ 2 0.8112 0.0089 p-value 0.3678 0.9245 680 A>G (rs6166) HWE Analysis 0.45 0.42 χ 2 2.7487 0.0341 p-value 1.0000 0.8533 Table 5: Frequency distribution and genetic association of polymorphic genotypes of FSHR –29 G>A (rs1394205), 307 A>G (rs6165) and 680 A>G (rs6166) with PCOS susceptibility under different genetic models. FSHR genetic variation Patient n=121(%) Control n=121(%) χ 2 p-value OR (CI 95%) p-value -29 G>A (rs1394205) Codominant model G/G 50 (41.32%) 41 (33.88%) 5.2425 0.07271 1 (Reference) --- G/A 50 (41.32%) 67 (55.37%) 0.6119 (0.3524 - 1.0628) 0.0812 A/A 21 (17.35%) 13 (10.74%) 1.3246 (0.5919 - 2.9645) 0.4940 Dominant model G/G 50 (41.32%) 41 (33.88%) 1.4265 0.23233 1 (Reference) --- G/A - A/A 71 (58.68%) 80 (66.12%) 0.7278 (0.4317 - 1.2267) 0.2329 Recessive model G/G - G/A 100 (82.65%) 108 (89.26%) 2.1900 0.13891 1 (Reference) --- A/A 21 (17.35%) 13 (10.74%) 1.7446 (0.8297 - 3.6685) 0.1422 Over dominant model G/G-A/A 71 (58.68%) 54 (44.63%) 4.7820 0.02875 1 (Reference) --- G/A 50 (41.32%) 67 (55.37%) 0.5676 (0.3411 - 0.9446) 0.0293 Allele G 150 (61.98%) 149 (61.57%) 0.0087 0.92547 1 (Reference) --- A 92 (38.02%) 93 (38.43%) 0.9827 (0.6810 - 1.4179) 0.9255 307 A>G (rs6165) Codominant model A/A 39 (32.23%) 69 (57.02%) 21.098 0.00002 1 (Reference) --- A/G 55 (45.45%) 45 (37.19%) 2.1624 (1.2398 - 3.7714) 0.0066 G/G 27 (22.32%) 7 (5.78%) 6.8242 (2.7213 - 17.113) 0.0001 Dominant model A/A 39 (32.23%) 69 (57.02%) 15.049 0.00010 1 (Reference) --- A/G - G/G 82 (67.77%) 52 (42.98%) 2.7899 (1.6515 - 4.7130) 0.0001 Recessive model A/A - A/G 94 (77.69%) 114 (94.21%) 13.687 0.00021 1 (Reference) --- G/G 27 (22.31%) 7 (5.79%) 4.6778 (1.9500 - 11.2216) 0.0005 Over dominant model A/A-G/G 66 (54.55%) 76 (62.81%) 1.7042 0.19173 1 (Reference) --- A/G 55 (45.45%) 45 (37.19%) 1.4074 (0.8420 - 2.3526) 0.1923 Allele A 133 (54.96%) 183 (75.62%) 22.792 0.00001 1 (Reference) --- G 109 (45.04%) 59 (24.38%) 2.5420 (1.7252 to 3.7455) 0.0001 680 A>G (rs6166) Codominant model A/A 36 (29.75%) 40 (33.06%) 0.558 0.75641 1 (Reference) --- A/G 60 (49.59%) 60 (49.59%) 1.1111 (0.6251 - 1.9749) 0.7196 G/G 25 (20.66%) 21 (17.35%) 1.3228 (0.6346 - 2.7569) 0.4554 Dominant model A/A 36 (29.75%) 40 (33.05%) 0.307 0.57958 1 (Reference) --- A/G - G/G 85 (70.25%) 81 (66.94%) 1.1660 (0.6771 - 2.0078) 0.5797 Recessive model A/A - A/G 96 (79.34%) 100 (82.64%) 0.429 0.51225 1 (Reference) --- G/G 25 (20.66%) 21 (17.35%) 1.2401 (0.6511 - 2.3618) 0.5127 Over dominant model A/A-G/G 61 (50.41%) 61 (50.41%) 0.004 0.948 1 (Reference) --- A/G 60 (49.59%) 60 (49.59%) 1.0000 (0.6041 - 1.6553) 1.0000 Allele A 132 (54.54%) 140 (57.85%) 0.537 0.4636 1 (Reference) --- G 110 (45.46%) 102 (42.15%) 1.1438 (0.7985 - 1.6384) 0.4637 Table 6: Frequency distribution and genetic association of polymorphic genotypes of FSHR –29 G>A (rs1394205), 307 A>G (rs6165) and 680 A>G (rs6166) with PCOS susceptibility based on BMI. FSHR genetic variation Patient n=121(%) Control n=121(%) χ 2 p-value OR (CI 95%) p-value FSHR –29 G>A (rs1394205) Genotype Lean n=11 (9.1%) Lean n=27 (22.3%) G/G 7 (63.6%) 9 (33.33) 5.351 0.069 1 (Reference) --- G/A 2 (18.2%) 16 (59.26) 0.1607 (0.0273 - 0.9445) 0.0431 A/A 2 (18.2%) 2 (7.41) 1.2857 (0.1432 - 11.5437) 0.8224 Allele G 16 (72.72%) 34 (62.96%) 0.662 0.416 1 (Reference) --- A 6 (27.27%) 20 (37.04%) 0.6375 (0.2146 - 1.8938) 0.4177 Genotype Normal n=46 (38%) Normal n=71 (58.7%) G/G 16 (34.78%) 26 (36.62%) 2.292 0.318 1 (Reference) --- G/A 21 (45.65%) 38 (53.52%) 0.8980 (0.3956 - 2.0383) 0.7970 A/A 9 (19.57%) 7 (9.86%) 2.0893 (0.6499 - 6.7161) 0.2162 Allele G 53 (57.61%) 90 (63.38%) 0.783 0.376 1 (Reference) --- A 39 (42.39%) 52 (36.62%) 1.2736 (0.7450 - 2.1773) 0.3767 Genotype Overweight n=38 (31.4%) Overweight n=18 (14.9%) G/G 15 (39.47%) 2 (11.11%) 4.965 0.084 1 (Reference) --- G/A 17 (44.74%) 13 (72.22%) 0.1744 (0.0337 - 0.9013) 0.0372 A/A 6 (15.79) 3 (16.67) 0.2667 (0.0352 - 2.0188) 0.2006 Allele G 47 (61.84%) 17 (47.22%) 2.132 0.144 1 (Reference) --- A 29 (38.16%) 19 (52.78%) 0.5521 (0.2477 – 1.2305) 0.1463 Genotype Obese n=26 (21.5%) Obese n=5 (4.1%) G/G 12 (46.16%) 1 (20%) 1.203 0.548 1 (Reference) --- G/A 10 (38.46%) 3 (60%) 0.2778 (0.0249 - 3.1045) 0.2983 A/A 4 (15.38%) 1 (20%) 0.3333 (0.0167 - 6.6548) 0.4720 Allele G 34 (65.38%) 5 (50%) 0.851 0.356 1 (Reference) --- A 18 (34.62%) 5 (50%) 0.5294 (0.1352 - 2.0729) 0.3611 FSHR 307 A>G (rs6165) Genotype Lean n=11 (9.1%) Lean n=27 (22.3%) A/A 3 (27.27%) 7 (25.93%) 3.29 0.193 1 (Reference) --- A/G 7 (63.64%) 20 (74.07%) 0.8167 (0.1644 - 4.0579) 0.8044 G/G 1 (9.09%) 0 (0%) 6.4286 (0.2055 - 201.0850) 0.2895 Allele A 13 (59.09%) 44 (68.75%) 0.683 0.408 1 (Reference) --- G 9 (49.91%) 20 (31.25%) 1.5231 (0.5598 - 4.1438) 0.4100 Genotype Normal n=46 (38.0%) Normal n=71 (58.7%) A/A 16 (34.78%) 28 (39.44%) 6.805 0.033 1 (Reference) --- A/G 18 (39.13%) 37 (52.11%) 0.8514 (0.3700 - 1.9591) 0.7051 G/G 12 (26.09%) 6 (8.45%) 3.5000 (1.1009 - 11.1268) 0.0338 Allele A 50 (54.35%) 93 (65.49%) 2.918 0.088 1 (Reference) --- G 42 (45.65%) 49 (35.51%) 1.5943 (0.9322 - 2.7267) 0.0885 Genotype Overweight n=38 (31.4%) Overweight n=18 (14.9%) A/A 13 (34.21%) 6 (33.33%) 3.166 0.205 1 (Reference) --- A/G 16 (42.11%) 11 (61.11%) 0.6713 (0.1953 - 2.3082) 0.5271 G/G 9 (23.68%) 1 (5.56%) 4.1538 (0.4243 - 40.6627) 0.2211 Allele A 42 (55.26%) 23 (63.89%) 0.746 0.388 1 (Reference) --- G 34 (44.74%) 13 (36.11%) 1.4322 (0.6329 - 3.2412) 0.3886 Genotype Obese n=26 (21.5%) Obese n=5 (4.1%) A/A 7 (26.92%) 4 (80%) 6.09 0.047 1 (Reference) --- A/G 14 (53.85%) 1 (20%) 8.0000 (0.7465 - 85.7291) 0.0857 G/G 5 (19.23%) 0 (%) 6.6000 (0.2908 - 149.7819) 0.2361 Allele A 21 (46.67%) 9 (90%) 6.197 0.012 1 (Reference) --- G 24 (53.33%) 1 (10%) 10.2857 (1.2012 - 88.0745) 0.0334 FSHR 680 A>G (rs6166) Genotype Lean n=11 (9.1%) Lean n=27 (22.3%) A/A 4 (36.36%) 6 (22.22%) 3.244 0.197 1 (Reference) --- A/G 3 (27.27%) 16 (59.26%) 0.2813 (0.0481 - 1.6458) 0.1593 G/G 4 (36.36%) 5 (18.52%) 1.2000 (0.1935 - 7.4408) 0.8447 Allele A 11 (50%) 28 (51.85%) 0.021 0.883 1 (Reference) --- G 11 (50%) 26 (48.15%) 1.0769 (0.3995 - 2.9031) 0.8835 Genotype Normal n=46 (38.0%) Normal n=71 (58.7%) A/A 13 (28.26%) 27 (38.03%) 1.189 0.551 1 (Reference) --- A/G 23 (50%) 31 (43.66%) 1.5409 (0.6562 - 3.6185) 0.3208 G/G 10 (21.74%) 13 (18.31%) 1.5976 (0.5551 - 4.5980) 0.3850 Allele A 49 (59.76%) 85 (59.86%) 0 0.988 1 (Reference) --- G 33 (40.24%) 57 (40.14%) 1.0043 (0.5769 - 1.7484) 0.9879 Genotype Overweight n=38 (31.4%) Overweight n=18 (14.9%) A/A 10 (26.32%) 4 (22.22%) 0.188 0.910 1 (Reference) --- A/G 23 (60.53%) 11 (61.11%) 0.8364 (0.2138 - 3.2721) 0.7974 G/G 5 (13.16%) 3 (16.67%) 0.6667 (0.1057 - 4.2066) 0.6662 Allele A 43 (56.58%) 19 (52.78%) 0.143 0.705 1 (Reference) --- G 33 (43.42%) 17 (47.22%) 0.8577 (0.3869 - 1.9017) 0.7056 Genotype Obese n=26 (21.5%) Obese n=5 (4.1%) A/A 9 (34.62%) 3 (60%) 2.825 0.244 1 (Reference) --- A/G 11 (42.31%) 2 (40%) 1.8333 (0.2495 - 13.4702) 0.5514 G/G 6 (23.08%) 0 (%) 4.7895 (0.2101 - 109.1924) 0.3261 Allele A 29 (55.77%) 8 (80%) 2.046 0.153 1 (Reference) --- G 23 (44.23%) 2 (20%) 3.1724 (0.6133 - 16.4087) 0.1685 Additional Declarations The authors declare no competing interests. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5236464","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":364379744,"identity":"25592bfe-c578-4896-b927-438a749e07a1","order_by":0,"name":"Jijo Francis","email":"","orcid":"https://orcid.org/0000-0003-1172-0049","institution":"Christ College (Autonomous), Irinjalakuda (Affiliated to University of Calicut), Kerala, India-680125","correspondingAuthor":false,"prefix":"","firstName":"Jijo","middleName":"","lastName":"Francis","suffix":""},{"id":364379745,"identity":"5814c3d6-55f4-4123-af2a-27f844e5f454","order_by":1,"name":"Honey Sebastian","email":"","orcid":"","institution":"Vimala College (Autonomous), Thrissur, Kerala, India- 680009","correspondingAuthor":false,"prefix":"","firstName":"Honey","middleName":"","lastName":"Sebastian","suffix":""},{"id":364379746,"identity":"abfc107a-9ce4-488e-aeed-1011ce904b9e","order_by":2,"name":"Neetha George","email":"","orcid":"","institution":"Department of Gynaecology, JMMC\u0026 RI, Thrissur, Kerala, India- 680005","correspondingAuthor":false,"prefix":"","firstName":"Neetha","middleName":"","lastName":"George","suffix":""},{"id":364379747,"identity":"269415d4-e865-4825-b687-6cb293a69c59","order_by":3,"name":"Saritha, F.","email":"","orcid":"","institution":"Laboratory for Computational and Structural Biology, Jubilee Centre for Medical Research,\tJMMC\u0026RI, Thrissur, Kerala, India- 680005","correspondingAuthor":false,"prefix":"","firstName":"F.","middleName":"","lastName":"Saritha","suffix":""},{"id":364379748,"identity":"8e57be73-0119-4e68-9efb-2f34c2446968","order_by":4,"name":"Sareena Gilvaz","email":"","orcid":"https://orcid.org/0009-0005-2917-3663","institution":"Department of Gynaecology, JMMC\u0026 RI, Thrissur, Kerala, India- 680005","correspondingAuthor":false,"prefix":"","firstName":"Sareena","middleName":"","lastName":"Gilvaz","suffix":""},{"id":364379749,"identity":"4f5848d4-f055-4951-86e6-2befde552944","order_by":5,"name":"Dileep, K.V.","email":"","orcid":"","institution":"Laboratory for Computational and Structural Biology, Jubilee Centre for Medical Research,\tJMMC\u0026RI, Thrissur, Kerala, India- 680005","correspondingAuthor":false,"prefix":"","firstName":"K.V.","middleName":"","lastName":"Dileep","suffix":""},{"id":364379750,"identity":"dc4de46b-bd6a-4cfa-bc7d-9a47849bac52","order_by":6,"name":"Ragitha T.S.","email":"","orcid":"","institution":"Cytogenetics and Genomics Laboratory, Jubilee Centre for Medical Research, JMMC\u0026RI,\tThrissur, Kerala, India- 680005","correspondingAuthor":false,"prefix":"","firstName":"Ragitha","middleName":"","lastName":"T.S.","suffix":""},{"id":364379751,"identity":"02c5d893-fca1-4a6c-86b6-dd7b52be9636","order_by":7,"name":"Siji Susan George","email":"","orcid":"https://orcid.org/0009-0002-7773-9498","institution":"Northern Ontario School of Medicine, Ontario, Canada","correspondingAuthor":false,"prefix":"","firstName":"Siji","middleName":"Susan","lastName":"George","suffix":""},{"id":364379752,"identity":"bf66f97e-e127-4acb-851c-a6333a5f44a4","order_by":8,"name":"Roger Francis","email":"","orcid":"https://orcid.org/0000-0002-3093-8150","institution":"Cytogenetics and Genomics Laboratory, Jubilee Centre for Medical Research, JMMC\u0026RI,\tThrissur, Kerala, India- 680005","correspondingAuthor":false,"prefix":"","firstName":"Roger","middleName":"","lastName":"Francis","suffix":""},{"id":364379753,"identity":"764a8e1e-b70f-4c9a-874b-508ed3677b2f","order_by":9,"name":"Mary Martin","email":"","orcid":"https://orcid.org/0000-0003-4471-2205","institution":"Cytogenetics and Genomics Laboratory, Jubilee Centre for Medical Research, JMMC\u0026RI,\tThrissur, Kerala, India- 680005","correspondingAuthor":false,"prefix":"","firstName":"Mary","middleName":"","lastName":"Martin","suffix":""},{"id":364379754,"identity":"b53a9c21-386d-4575-a502-4efb2cf5cc9f","order_by":10,"name":"Smriti Menon","email":"","orcid":"","institution":"Cytogenetics and Genomics Laboratory, Jubilee Centre for Medical Research, JMMC\u0026RI,\tThrissur, Kerala, India- 680005","correspondingAuthor":false,"prefix":"","firstName":"Smriti","middleName":"","lastName":"Menon","suffix":""},{"id":364379755,"identity":"a24f7ad3-97d9-46db-abed-0353f4465760","order_by":11,"name":"Suresh Kumar Raveendran","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA8UlEQVRIiWNgGAWjYDCCAxAqQQJMVbDBxCWI1XKGZC2MbUS4i+/28WfSFRV1eZIzshM/V87ji9ZtP8D44QeDRR4uLZLncswkz5w5XCwtkbtZ8uw2ttxtZxKYJXsYJIpxaTE4w8Mm2dh2IHGeRO4GyUaQlhsMDNJAvyQ24NTC/kyy8V8dSMvmn41zwFqYf+PXwmAm2djAnDhbIncbkAHWwobXFskzPMaWDccOJ87sebsNyAD5JbHNsscAtxa+M+wPbzbU1CXOOJ67Gcg4lrvt+OHDN35U1OHUgg6OATEjULEBkeqBoIZ4paNgFIyCUTBiAAAzm1sPaX7pcQAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0001-8996-3951","institution":"Cytogenetics and Genomics Laboratory, Jubilee Centre for Medical Research, JMMC\u0026RI,\tThrissur, Kerala, India- 680005","correspondingAuthor":true,"prefix":"","firstName":"Suresh","middleName":"Kumar","lastName":"Raveendran","suffix":""}],"badges":[],"createdAt":"2024-10-10 05:05:54","currentVersionCode":1,"declarations":{"humanSubjects":true,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":true,"humanSubjectConsent":true,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-5236464/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5236464/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":66550977,"identity":"b04f6496-69ce-4742-852f-25f54dc81797","added_by":"auto","created_at":"2024-10-14 09:05:24","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":82670,"visible":true,"origin":"","legend":"\u003cp\u003eRepresentative polyacrylamide gel image of A) rs1394205: M- 50 bp DNA marker (Himedia), lane 3\u0026amp;4- GG genotype, lane 1,5\u0026amp;6- GA genotype, and lane 2- AA genotype; B) rs6165: M- 50 bp DNA marker (Himedia), lane 1\u0026amp;3 - AA genotype lane 4- AG genotype, lane 2- GG genotype. C) rs6166: M- 50 bp DNA marker (Himedia), lane 2,3\u0026amp;7 - AA genotype, lane 5\u0026amp;6- AG genotype and lane 1\u0026amp;4- GG genotype\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-5236464/v1/125c347e1f813d29cf1d34a6.png"},{"id":66550978,"identity":"100a0474-eb19-4442-8e44-00bb25b28168","added_by":"auto","created_at":"2024-10-14 09:05:24","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":171395,"visible":true,"origin":"","legend":"\u003cp\u003eRepresentative electropherograms showing \u003cem\u003eFSHR \u003c/em\u003e1)\u003cem\u003e \u003c/em\u003ers1394205 (G\u0026gt;A), a\u003cstrong\u003e) \u003c/strong\u003eHomozygous GG, b\u003cstrong\u003e) \u003c/strong\u003eHeterozygous GA, c\u003cstrong\u003e) \u003c/strong\u003eHomozygous AA, 2) rs6165 (A\u0026gt;G) a) Homozygous AA, b\u003cstrong\u003e) \u003c/strong\u003eHeterozygous AG c) Homozygous GG, and 3) rs6166 (A\u0026gt;G), a) Homozygous AA, b) Heterozygous AG, c\u003cstrong\u003e) \u003c/strong\u003eHomozygous\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-5236464/v1/4c74186c9859e32a70ff7c41.png"},{"id":66550979,"identity":"7473ef8c-3adf-476b-9f95-8f0e210dd064","added_by":"auto","created_at":"2024-10-14 09:05:24","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":123342,"visible":true,"origin":"","legend":"\u003cp\u003eOverview of the pathogenicity prediction of different mutations represented by a heatmap.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-5236464/v1/9656edaf9c364a2ae65f4d80.png"},{"id":66553588,"identity":"850aa82b-23ca-4d95-863e-7048424449e7","added_by":"auto","created_at":"2024-10-14 09:13:25","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1947855,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5236464/v1/27f501a7-6123-4f99-b524-9c76f7405f0c.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eGenetic Predisposition Analysis of the Fshr Gene in Pcos: Insights From a South Indian Population\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003ePolycystic Ovary Syndrome (PCOS) is a polygenic, multifactorial, systemic, inflammatory, and hormonal syndrome. It is a chronic and multifaceted illness that affects 15\u0026ndash;20% of women globally who are of reproductive age [1]. The hallmarks of PCOS are hyperandrogenism, irregular menstrual cycles, and the presence of polycystic ovaries. Obesity, glucose intolerance, insulin resistance, elevated lipid profiles, altered secretion of pituitary gonadotropins and various anomalies in the expenditure of energy[2\u0026ndash;5] are the common metabolic syndromes found in PCOS.\u003c/p\u003e \u003cp\u003ePCOS is associated with modifications in the Hypothalamic-Pituitary-Gonadal (HPG) axis function, possibly due to a higher frequency of hypothalamic gonadotropin-releasing hormone (GnRH) pulses. In healthy females, higher frequencies of GnRH pulses favour the secretion of Luteinizing Hormone (LH), while lower pulses favour Follicle Stimulating Hormone (FSH). This altered gonadotropin physiology, characterized by elevated serum LH levels and LH to FSH ratio, plays a pivotal role in PCOS [6].\u003c/p\u003e \u003cp\u003ePathogenesis of PCOS involves intricate genetic and hormonal factors, and emerging research seeks to unravel the underlying mechanisms. Investigating the promoter and genetic polymorphisms linked to PCOS, specifically the function of the \u003cem\u003eFollicle Stimulating Hormone Receptor\u003c/em\u003e (FSHR) gene, is a promising field of study. FSH is integral to ovarian follicle maturation, dominant follicle selection, and the aromatization of androgens. It exerts its effects through the Follicle Stimulating Hormone Receptor (FSHR), expressed on Sertoli cells in the testis and granulosa cells in the ovary [7]. Animal studies involving \u003cem\u003eFshr\u003c/em\u003e gene knockout mice have indicated the importance of \u003cem\u003eFSHR\u003c/em\u003e in ovarian physiology, particularly in folliculogenesis [8]. \u003cem\u003eFSHR\u003c/em\u003e may contribute to a person\u0026rsquo;s genetic susceptibility to PCOS [9]. Variants of the \u003cem\u003eFSHR\u003c/em\u003e were found to be more closely associated with the degree of clinical characteristics of PCOS, such as gonadotrophic hormone levels and hyperandrogenism [10].\u003c/p\u003e \u003cp\u003eVarious studies of \u003cem\u003eFSHR\u003c/em\u003e polymorphisms in different ethnic variant populations are conflicting. Studies conducted on the Thai and Chinese populations did not provide substantial evidence of the role of these \u003cem\u003eFSHR\u003c/em\u003e polymorphisms in PCOS [11,12]. However, studies done in the Pakistani and Korean populations have shown a significant involvement of these SNPs with PCOS [13,14]. The results of the meta-analysis are even contradictory [15].\u003c/p\u003e \u003cp\u003eThe proximal promoter region of the \u003cem\u003eFSHR\u003c/em\u003e gene has not been explored so far in PCOS patients.\u003c/p\u003e \u003cp\u003eAla307Thr (rs6165) and Ser680Asn (rs6166), are two well-characterised polymorphisms located in the 10th exon of the \u003cem\u003eFSHR\u003c/em\u003e gene. A sole study regarding the involvement of both rs6165 and rs6166 polymorphisms in Indian PCOS patients was reported in Punjab Populations [16].\u003c/p\u003e \u003cp\u003eIndia is a country with various ethnicities. In genetic studies, ethnicity and the heterogeneous nature of PCOS are assumed to play a pivotal role in inconclusive results. Hence in the current study, we focused on the selection criteria for this particular study for a better understanding of the involvement of the \u003cem\u003eFSHR\u003c/em\u003e in the manifestation of PCOS.\u003c/p\u003e"},{"header":"MATERIALS AND METHODS","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003eSelection of subjects\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn this case-control study, a total of 1018 women aged between 15 to 35 years participated, all having provided proper consent for inclusion in the study. Among them, 438 were PCOS cases and the remaining were non-PCOS based on the Rotterdam criteria (Rotterdam ESHRE/ASRM-Sponsored PCOS consensus workshop group, 2004). Based on the BMI, the study subjects were stratified into four categories: lean (\u0026lt;18.5 kg/m\u003csup\u003e2\u003c/sup\u003e), normal (18.6 - 24.9 kg/m\u003csup\u003e2\u003c/sup\u003e), overweight (25 - 29.9 kg/m\u003csup\u003e2\u003c/sup\u003e) and obese (\u0026gt;30 kg/m\u003csup\u003e2\u003c/sup\u003e). From January 2021 to January 2024, all patients were consecutively recruited from the Department of Obstetrics and Gynaecology, Jubilee Mission Medical College and Research Institute (JMMC \u0026amp; RI), Thrissur, Kerala, India. The methods opted for the selection of study subjects were\u0026nbsp;approved\u0026nbsp;by the Institutional Ethics Committee of JMMC \u0026amp; RI, (IEC Study Ref: 47/20/IEC/JMMC \u0026amp;RI).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eInclusion criteria\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAfter enrolling 438 participants who had been diagnosed with PCOS, we carefully chose 121 participants from them based on their maternal or paternal lineage and the severity of their symptoms as determined by a questionnaire. The purpose of this selection was to increase the identification of genetic predisposition in PCOS participants who had symptoms that were either severe or manifested from menarche onwards fulfilling all three Rotterdam criteria. This type of selection is assumed to reduce the heterogeneity seen in PCOS patients to some extent.\u003c/p\u003e\n\u003cp\u003eAs a control, 580 consenting, normoandrogenic, post-pubertal, premenopausal women with regular menstrual cycles who were determined to not have PCOS by clinical examination were enrolled. To reduce maximum genetic propensity, 121 age-matched individuals who did not have PCOS in either maternal or paternal relatives up to the second degree were enrolled as experimental controls. Selection of the control samples was the limiting criterion of our study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eExclusion criteria for the experimental studies\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe excluded all cases with a history of hormonal treatment and contraception within the past 6 months, pregnancy and the first year of delivery as well as patients with an androgen-secreting ovarian/adrenal tumour, Congenital Adrenal Hyperplasia (CAH) or those who are taking antiepileptics, antipsychotics or corticosteroids. Table 1 displays the clinical and anthropometric characteristics of the PCOS and Control study subjects.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003ePromoter analysis of the FSHR gene\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA literature search showed a lack of genetic studies in the \u003cem\u003eFSHR\u003c/em\u003e promoter region of PCOS patients. This prompted us to screen the proximal promoter region (-1to-481) of the \u003cem\u003eFSHR\u003c/em\u003e gene in PCOS patients. Peripheral blood samples from the study subjects were drawn into EDTA vacutainers after getting informed consent and kept at -20°C until processed for molecular analysis. Genomic DNA extraction was carried out by using the QIAamp DNA Blood Mini Kit (Qiagen, Germany), and DNA concentration was determined using a Qubit fluorometer (Life Technologies, CA, USA). Further, the proximal promoter region of the \u003cem\u003eFSHR\u003c/em\u003e gene in 25 samples from PCOS patients and a same number of control subjects were screened for genetic variations analysis by Sanger sequencing. The primers were designed by using PRIMER3 software. The following primer pair set was used for the amplification of the proximal promoter region of the \u003cem\u003eFSHR\u003c/em\u003e gene; F-5’AGA GCA GTG ACA GAT CCG ATG-3’ and R-5’-ACC TCC CAC CTA CGG AAA TC-3’. Briefly, 100 ng of genomic DNA, 1x EmeraldAmp GT PCR Master Mix (Takara Bio, USA Inc.), and 0.3 picomoles of each oligonucleotide primer were used for PCR, which was carried out in a total reaction volume of 25μL. The PCR cycle conditions included a 5-minute initial denaturation step at 95°C, 35 cycles of 45 seconds at 95°C, 30 seconds at 52°C, 45 seconds at 72°C, and a 5-minute final extension at 72°C. The amplified PCR products were subjected to Sanger sequencing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eSNP genotyping\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSNP genotyping for 5’UTR (rs1394205) and common exonic (rs6165 and rs6166) polymorphisms of the \u003cem\u003eFSHR\u003c/em\u003e gene were performed in 121 PCOS and a same number of control samples by Polymerase Chain Reaction- Fragment Length Polymorphism (PCR-RFLP) and analysed in the 10% Polyacrylamide Gel Electrophoresis. Primer details and PCR conditions are shown in Table 2. Random samples from each of the three genotypes from rs6165 and rs6166 were subjected to Sanger sequencing to re-confirm our PCR-RFLP results.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eEvaluation of the pathogenic nature of the exonic polymorphisms\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eResearchers employed a range of sequence-based analysis tools to assess the pathogenic nature of the Ala307Thr (rs6165) and Ser680Asn (rs6166) variations in the \u003cem\u003eFSHR\u003c/em\u003e gene. These tools, including Polyphen-2 [17], Mut Pred [18], SNPs \u0026amp; GO [19], PON-P2 [20], SIFT [21], MAPP [22], PhD-SNP [23], SNAP [24], PANTHER [25], and Predict SNP1,[26], utilize diverse methodologies such as cellular localization, aggregation tendencies, structural disorder, functional effects, and stability analysis [27].\u003c/p\u003e\n\u003cp\u003ePolyPhen-2 utilizes machine learning and multiple sequence alignment to compare the physical properties of wild-type and mutant variants for pathogenicity prediction. MutPred2 employs a machine-learning approach to estimate the molecular mechanisms underlying pathogenicity. SNPs \u0026amp; GO is an SVM-based server that integrates GO annotations and SwissProt code. PON-P2 incorporates GO annotations, evolutionary sequence conservation, and biochemical and physical attributes of the protein. SIFT considers sequence similarity and physical properties to calculate damaging probability. MAPP leverages physicochemical traits and evolutionary data to predict the impact of missense variants. PhD-SNPs employ Support Vector Machines (SVMs) to forecast if nonsynonymous SNPs are linked to human genetic diseases based on protein sequence data. SNAP utilizes a neural network method to predict the functional effects of nonsynonymous SNPs. PANTHER compares evolutionary analyses using amino acid substitution scores, hidden Markov models, and Ka/Ks ratios for Mendelian and complex disease-associated cSNPs. PredictSNP is a consensus tool that considers various parameters derived from the protein's evolutionary, physicochemical, or structural characteristics. Finally, a consensus-based approach was implemented to predict the pathogenicity of the identified mutations. A random cutoff value was also implemented to select pathogenic/non-pathogenic variants. If eight or more tools predicted the variations as pathogenic, we considered it pathogenic, and vice versa. We also collected other variants on the same positions to understand whether the genetic variation site is a hot spot or not. The variations were collected from mutation databases like dbSNP [28], HGMD [29], Ensemble [30], and gnomAD [31].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSTATISTICS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe performed statistical analyses like mean, standard deviation, chi-square test and odds ratios (OR) with 95% confidence intervals (CI) to investigate the association between genotype frequencies, allele frequencies, and disease occurrence in both the patient and control groups as well as in the BMI classifications. Hardy-Weinberg equilibrium (HWE) was assessed by using Microsoft Excel. SPSS version 25 and online tools like SNPStats (http://bioinfo.iconcologia.net/SNP stats), SISA and MedCalc were used to analyze the data. SNPStats and the Haploview version 4.2 were used for haplotype analysis and linkage disequilibrium. A statistical significance threshold of p \u0026lt; 0.05 was applied to ascertain the results.\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cp\u003eThe anthropometric characteristics of PCOS and the control subjects are summarised in Table 1. In our study, we observed a significance in menarche age, weight, height, BMI, normal and overweight classification of BMI in PCOS patients compared to control subjects. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn proximal promoter region analysis of the \u003cem\u003eFSHR\u003c/em\u003e gene, we observed two SNPs i.e., rs1317756940 (χ\u003csup\u003e2\u003c/sup\u003e=0.002, p=0.9640) and rs1394205 (\u003cstrong\u003eχ\u003csup\u003e2\u003c/sup\u003e=6.852, p=0.033\u003c/strong\u003e) (Table 3). The significantly obtained 5’UTR variant (rs1394205) was further analysed by PCR-RFLP in 121 PCOS cases and control study subjects. Along with the 5’UTR variant, two common exonic SNPs [Ala307Thr A\u0026gt;G (rs6165) and Ser680Asn A\u0026gt;G (rs6166)] were also analysed by PCR-RFLP in PCOS patients and control subjects (Figure 1 \u0026amp; 2). We calculated Hardy-Weinberg equilibrium (HWE) and confirmed that the selected samples were representing the population (Table 4).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eGenetic association of the FSHR gene polymorphisms and PCOS\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe frequency distribution and genetic association of polymorphic genotypes of \u003cem\u003eFSHR\u0026nbsp;\u003c/em\u003e–29 G\u0026gt;A (rs1394205), 307 A\u0026gt;G (rs6165), and 680 A\u0026gt;G (rs6166) with PCOS is given in Table 5. The frequencies of \u003cem\u003eFSHR\u0026nbsp;\u003c/em\u003e–29 G\u0026gt;A genotypes G/G, G/A, and A/A in the patients were 41.32%, 41.32%, 17.35%, and 33.88%,\u0026nbsp;55.37%, and 10.74%, respectively in controls (p =\u0026nbsp;0.0727). The frequencies of A and G alleles in patients were 61.98% and 38.02%, and in controls were\u0026nbsp;61.57% and 38.43%, respectively (p=0.9254). The frequencies of \u003cem\u003eFSHR\u003c/em\u003e 307 A\u0026gt;G (rs6165) genotypes A/A, A/G, and G/G in the patients were 32.23%, 45.45%, and 22.32%, and in the controls, were 57.02%, 37.19%, and 5.78% respectively (p = \u003cstrong\u003e0.00002\u003c/strong\u003e). The frequencies of A and G alleles in patients were 54.96% and 45.04%, and in controls, they were 75.62% and 24.38% respectively (p = \u003cstrong\u003e0.00001\u003c/strong\u003e). The frequencies of \u003cem\u003eFSHR\u003c/em\u003e 680 A\u0026gt;G (rs6166) genotypes A/A, A//G, and GG in the patients were 29.75%, 49.59%, and 20.66%, and in the controls, were 33.06%, 49.59%, and 17.35% respectively (p =\u0026nbsp;0.7564). The frequencies of A and G alleles in patients were (54.54% and 45.46%, and in controls, they were 57.85% and 42.15% respectively (p = 0.4636). The PCOS and the control groups of the gene polymorphism \u003cem\u003eFSHR\u0026nbsp;\u003c/em\u003e–29 G\u0026gt;A (rs1394205) and 307 A\u0026gt;G (rs6165) have a statistically substantial association. Based on the genotypes of the \u003cem\u003eFSHR\u003c/em\u003e polymorphisms, the haplotype frequencies were calculated. HWE, Chi-Square and p-value were noted in Table 4 in which the rs1394205, rs6165 and rs6166 have not shown any deviation from HWE. In the haplotype analysis among PCOS patients and controls, we found no significant association or linkage disequilibrium (D¢ = 0.006; r\u003csup\u003e2\u003c/sup\u003e = 0.0).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eGenetic association of the FSHR gene polymorphisms And PCOS based on BMI\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOne of the most prominent physiological attributions of PCOS is obesity. The frequency distribution and genetic association of polymorphic genotypes of FSHR –29 G\u0026gt;A (rs1394205), 307 A\u0026gt;G (rs6165), and 680 A\u0026gt;G (rs6166) between PCOS patients and control subjects based on BMI status were also determined, and the results are shown in Table 5.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn our study, we found that the lean and normal BMI were distributed to 57 (47.11%) PCOS and 98 (80.99%) control subjects whereas, overweight and obesity (BMI ≥25) were found in 64 PCOS patients (52.89%), but only in 23 (19.01%) of the control group and the results are shown in Table 6. Genotype and allele frequencies showed no statistically significant difference between lean, overweight, and obese groups for both FSHR –29 G\u0026gt;A (rs1394205), and 680 A\u0026gt;G (rs6166). However, concerning 307 A\u0026gt;G (rs6165), we found a notable variance in genotype frequency between individuals in the Normal BMI group. The frequencies of AA, AG and GG genotypes were 34.8%, 39.1% and 26.1% respectively. For normal\u0026nbsp;BMI controls these frequencies were 39.4%, 52.1% and 8.5% respectively (\u003cstrong\u003ep=0.033\u003c/strong\u003e). In obese BMI PCOS subjects the distribution of frequencies was 26.9%, 53.9% and 19.2%, while in obese BMI controls, they were 80%, and 20%, for AA and AG genotypes (\u003cstrong\u003ep=\u003c/strong\u003e0.047). The allelic distribution of A and G in obese PCOS patients was 46.7% and 53.3% respectively, whereas in obese Control it was 90% and 10%, respectively (\u003cstrong\u003ep=0.012\u003c/strong\u003e). In the normal BMI group, individuals carrying the GG genotype of the \u003cem\u003eFSHR\u003c/em\u003e 307 A\u0026gt;G (rs6165) polymorphism have a 3.5-fold increased risk of PCOS compared to those with the AA genotype (OR 3.5000, 95% CI = 1.1009 - 11.1268 and \u003cstrong\u003ep=0.0338\u003c/strong\u003e). However, among obese BMI individuals, while the GG genotype has a significant chi-square value (χ\u003csup\u003e2\u003c/sup\u003e=6.09, p=0.047), it does not show a significant Odds ratio (OR). Nevertheless, the presence of the G allele confers a 10-fold increased risk of PCOS compared with the A allele (OR 10.2857, 95% CI = 1.2012 - 88.0745 and \u003cstrong\u003ep=0.0334\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eBioinformatics analysis of the exonic polymorphisms\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe pathogenic evaluation of Ala307Thr A\u0026gt;G (rs6165) and Ser680Asn A\u0026gt;G (rs6166) was performed by applying various bioinformatics tools. Although structure-based pathogenicity prediction is more accurate than sequence-based prediction, we are forced to adopt the sequence-based pathogenic prediction, due to the lack of appropriate crystal structure of human \u003cem\u003eFSHR\u003c/em\u003e. Only five three crystal structures of human \u003cem\u003eFSHR\u003c/em\u003e were deposited (PDB IDs: 1XWD, 4AY9, and 4MQW,8I2G and 8I2H) with Protein Data Bank (PDB), in which the structure 1XWD has amino acids from 17-268, and 4AY9 and 4MQW has amino acids 16-366 and 8I2G and 8I2H has amino acids 18-695. However, due to the poor electron density, the loop regions (residues 296-330) are missing in these structures. Similarly, the position 680 is also missing in all the structures. So, due to the lack of exact structure of these two positions, we performed sequence-based pathogenic prediction for rs6165 and rs6166 using 10 different tools. In our extensive analysis, we failed to identify any significant pathogenicity of these variations (Figure 3).\u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eThe Rotterdam criteria serve as a prevalent diagnostic criterion to diagnose PCOS among clinicians. However, the diverse range of symptom heterogeneity poses a significant challenge to understanding PCOS. Numerous associated comorbidities like obesity, insulin resistance, diabetes, and cardiovascular diseases are often observed in patients with PCOS, which cannot be logically considered as a single research sample group. Therefore, it is crucial to categorize the patients into subgroups based on their symptoms for a more nuanced understanding of PCOS. The failure of the sample selection into distinct multiple groups could contribute to the challenge of replicating genetic analysis results despite obtaining significant results.\u003c/p\u003e \u003cp\u003eEthnicity further complicates these inconsistencies in results. Hence, in our study, we meticulously selected the subjects based on strict criteria to mitigate the impact of PCOS symptom heterogeneity. All the enrolled patients met all three Rotterdam criteria and exhibited PCOS from the menarche onwards with high severity of symptoms along with a strong familial predisposition of PCOS. Furthermore, the control population was carefully chosen to exclude the history of PCOS among their paternal and maternal relatives, thus reducing the genetic predisposition.\u003c/p\u003e \u003cp\u003eIn the present study, we performed the promoter analysis of the \u003cem\u003eFSHR\u003c/em\u003e gene along with \u003cem\u003eFSHR\u003c/em\u003e 5\u0026rsquo;UTR variant \u0026minus;\u0026thinsp;29 G\u0026thinsp;\u0026gt;\u0026thinsp;A (rs1394205) and exon 10 gene variants Ala307Thr A\u0026thinsp;\u0026gt;\u0026thinsp;G (rs6165) and Ser680Asn A\u0026thinsp;\u0026gt;\u0026thinsp;G (rs6166) in the south Indian population. Further, we also analysed the pathogenicity of rs6165 and rs6166 polymorphisms by applying various bioinformatics tools. Compared to the well-studied core promoter regions of mice, rats and sheep, the human \u003cem\u003eFSHR\u003c/em\u003e is fundamentally different. It is devoid of the usual CAAT and TATA box sequences and the CpG dinucleotide methylation loci that are known to influence the expression of rat \u003cem\u003eFSHR\u003c/em\u003e [32].\u003c/p\u003e \u003cp\u003eIn our study, we observed statistically significant \u003cem\u003eFSHR\u003c/em\u003e rs1394205 polymorphisms in PCOS patients compared to control subjects. The SNP rs1394205, located at the 5\u0026rsquo; untranslated region, has been identified within the transcription factor binding site of the viral E26 transformation-specific sequence (c-ETS-1) [33]. So, this SNP may modulate the \u003cem\u003eFSHR\u003c/em\u003e expression by altering the transcription factor binding sites. Previous studies show that the AA genotype at this SNP required a higher amount of exogenous FSH hormone level for ovulation induction [34,35]. The involvement of this SNP has been reported in primary and secondary amenorrhea cases [36]. The rs1394205 AA genotype has been associated with reduced \u003cem\u003eFSHR\u003c/em\u003e gene expression [37]. As Conforti et al stated in their work, \u003cem\u003eFSHR\u003c/em\u003e rs1394205, \u0026minus;\u0026thinsp;29G\u0026thinsp;\u0026gt;\u0026thinsp;A can affect the duration of gonadotropin stimulation[38] and Liu et al reported that the A allele of rs1394205 associated with Premature Ovarian insufficiency [39]. Even though the mechanism of this SNP has been well studied, there is no study concerning PCOS. A significant association of rs1394205 polymorphisms in PCOS patients in our study pointing further exploration of this variant in PCOS pathogenicity. Thus, an unknown intrinsic promoter mechanism is involved in PCOS genetic susceptibility. However, the lack of expression of FSHR protein in the blood limits further investigation.\u003c/p\u003e \u003cp\u003eNumerous polymorphisms have been identified within the \u003cem\u003eFSHR\u003c/em\u003e gene, among them, two closely linked polymorphisms, rs6165 (A/G) and rs6166 (A/G) in exon 10[34] were well studied in various disease conditions including PCOS. The Ala307Thr A\u0026thinsp;\u0026gt;\u0026thinsp;G (rs6165) polymorphism is a non-synonymous, well-studied even in PCOS but, the ambiguity and reproducibility of the results raise serious concerns in the research and clinical aspects. Here ethnicity plays a crucial role. It can be assumed that this particular variation may have a significant effect on people with reduced ovarian response [40,41]. In the Controlled Ovarian Stimulation, the GG genotype-carrying individuals produced a greater number of oocytes compared to other AG and AA genotypes [42]. In addition, the ovarian stimulation was remarkably lesser for the GG genotype than the others [34]. However, in the in-vitro studies, this variation in a transient transfection resulted in no significant effect on the hormone binding [43]. It can be proposed that this particular variation which resulted in the amino acid change may reduce the downstream conduction of the signals and slow down the normal process of receptor-ligand binding. This effect is not universal and can vary based on ethnicity.\u003c/p\u003e \u003cp\u003eIn our study, we got a significant association of this variation in PCOS patients compared to control. Most previous studies have shown an absence of association of rs6165 with PCOS. A sole study in Indian Punjabi populations also showed the same result [16]. However, our observation aligns with the study conducted by Kim et al and Dolfin et al in the Korean and Caucasian populations, respectively [13,44]. Apart from the ethnic variations, we assumed that our stringent genetic selection criteria applied both in PCOS and control subjects might be the reason behind our observed result.\u003c/p\u003e \u003cp\u003eThe Ser680Asn A\u0026thinsp;\u0026gt;\u0026thinsp;G (rs6166) (G/G) genotype in PCOS women has been linked to conditions such as amenorrhea or anovulation, longer menstrual cycles [43], and hyperandrogenism. Patients with the rs6166 (G/G) genotype in PCOS have been found to exhibit higher serum FSH levels, suggesting reduced sensitivity to FSH [10]. In our study, we could not find any significant association between this variation in our PCOS subjects and BMI either. Although most of the previous studies in European and Pakistani populations showed an increased risk of rs6166 with PCOS, our result is in concordance with the majority of Chinese and Caucasian reports. A similar observation was also reported by Kaur et al in Indian Punjabi populations [16].\u003c/p\u003e \u003cp\u003eSince rs6165 has a noteworthy association, we assessed the pathogenic potential of the A307T variation. Even though in our study we could not find any significant association of rs6166 in PCOS condition, there are many studies of this particular variation that have shown significant association with PCOS even in different Asian populations [40,45]. Thus, we performed the sequence-based pathogenicity of this variation.\u003c/p\u003e \u003cp\u003eAll the tools predicted these variations as non-pathogenic. We further identified other variations reported on the same positions, from various databases, and found three, of which 2 belong to the 307th positions (A307V and A307P) and one belongs to the 680th position (S680R). Our analysis revealed that these positions are also not exhibiting any pathogenicity (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eStudies regarding the relationship between \u003cem\u003eFSHR\u003c/em\u003e polymorphisms and PCOS susceptibility are conflicting [46]. Reports suggest the variation of A307T is particularly prevalent in PCOS patients [15]. The Ala307Thr (rs6165) variation resides within the extracellular topological domain of the \u003cem\u003eFSHR\u003c/em\u003e gene, specifically in the loop region near its hinge area. This elongated hinge loop of the \u003cem\u003eFSHR\u003c/em\u003e plays a crucial role in direct interaction with follicle-stimulating hormone (FSH) and contributes to the receptor's flexibility during ligand binding [47,48]. In the case of the A307T variation, the substitution of the lower molecular weight non-polar, aliphatic alanine (89Da) with comparatively higher weight polar threonine (119Da) may potentially impact the flexibility or proper functioning of the receptor. However, to confirm this assumption, additional experiments are necessary.\u003c/p\u003e \u003cp\u003eApart from this, the selection criteria employed by us might be the factor behind the significance of rs1394205 and rs6165 polymorphisms in PCOS patients. Based on this we proposed to screen \u003cem\u003eFSHR\u003c/em\u003e polymorphisms in maximum genetically predisposed PCOS patients for conclusive results.\u003c/p\u003e"},{"header":"CONCLUSION","content":"\u003cp\u003eWe analysed \u003cem\u003eFSHR\u003c/em\u003e promoter (rs1394205) and exonic polymorphisms (rs6165 and rs6166) in patients who met all three Rotterdam criteria and exhibited PCOS from the menarche onwards along with a strong familial predisposition of PCOS. In our study, we observed a significant association of \u003cem\u003eFSHR\u003c/em\u003e 5\u0026rsquo;UTR variant \u0026minus;\u0026thinsp;29 G\u0026thinsp;\u0026gt;\u0026thinsp;A (rs1394205) and exon 10 gene variant Ala307Thr A\u0026thinsp;\u0026gt;\u0026thinsp;G (rs6165) with South Indian PCOS patients. In-silico prediction based on the sequence failed to identify the pathogenicity of Ala307Thr. In addition, in the normal BMI group, individuals carrying the GG genotype of the \u003cem\u003eFSHR\u003c/em\u003e 307 A\u0026thinsp;\u0026gt;\u0026thinsp;G (rs6165) polymorphism showed a 3.5-fold increased risk of PCOS compared to those with the AA genotype. To conclude, \u003cem\u003eFSHR\u003c/em\u003e 5\u0026rsquo;UTR variant \u0026minus;\u0026thinsp;29 G\u0026thinsp;\u0026gt;\u0026thinsp;A (rs1394205) and exon 10 gene variant Ala307Thr A\u0026thinsp;\u0026gt;\u0026thinsp;G (rs6165) were associated with PCOS in the south Indian population.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eThe authors declare that they have no conflict of interest.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eACKNOWLEDGEMENT\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eS.F. and D.K.V. acknowledge the financial assistance (R12015/01/2021/HR) received as a fellowship from the Department of Health Research, Govt. of India. \u0026nbsp;\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAchrekar SK, Modi DN, Desai SK, Mangoli VS, Mangoli R V., Mahale SD. Poor ovarian response to gonadotropin stimulation is associated with FSH receptor polymorphism. 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Bioinformatics 2006;22:2729\u0026ndash;34. https://doi.org/10.1093/bioinformatics/btl423.\u003c/li\u003e\n \u003cli\u003eCapriotti E, Calabrese R, Fariselli P, Martelli P, Altman RB, Casadio R. WS-SNPs\u0026amp;amp;GO: a web server for predicting the deleterious effect of human protein variants using functional annotation. BMC Genomics 2013;14:S6. https://doi.org/10.1186/1471-2164-14-S3-S6.\u003c/li\u003e\n \u003cli\u003eConforti A, T\u0026uuml;ttelmann F, Alviggi C, Behre HM, Fischer R, Hu L, et al. Effect of Genetic Variants of Gonadotropins and Their Receptors on Ovarian Stimulation Outcomes: A Delphi Consensus. Front Endocrinol (Lausanne) 2022;12. https://doi.org/10.3389/fendo.2021.797365.\u003c/li\u003e\n \u003cli\u003eCooper DN, Stenson PD, Chuzhanova NA. The Human Gene Mutation Database (HGMD) and Its Exploitation in the Study of Mutational Mechanisms. Curr Protoc Bioinformatics 2005;12. https://doi.org/10.1002/0471250953.bi0113s12.\u003c/li\u003e\n \u003cli\u003eDesai SS, Achrekar SK, Paranjape SR, Desai SK, Mangoli VS, Mahale SD. Association of allelic combinations of FSHR gene polymorphisms with ovarian response. Reprod Biomed Online 2013;27:400\u0026ndash;6. https://doi.org/10.1016/j.rbmo.2013.07.007.\u003c/li\u003e\n \u003cli\u003eDesai SS, Achrekar SK, Pathak BR, Desai SK, Mangoli VS, Mangoli R V., et al. Follicle-stimulating hormone receptor polymorphism (G -29A) is associated with altered level of receptor expression in granulosa cells. Journal of Clinical Endocrinology and Metabolism 2011;96:2805\u0026ndash;12. https://doi.org/10.1210/jc.2011-1064.\u003c/li\u003e\n \u003cli\u003eDierich A, Ram Sairam \u0026dagger; \u0026Dagger; \u0026sect; M, Monaco L, Fimia GM, Gansmuller A, Lemeur M, et al. Impairing follicle-stimulating hormone (FSH) signaling in vivo: Targeted disruption of the FSH receptor leads to aberrant gametogenesis and hormonal imbalance. vol. 95. 1998.\u003c/li\u003e\n \u003cli\u003eDolfin E, Guani B, Lussiana C, Mari C, Restagno G, Revelli A. FSH-receptor Ala307Thr polymorphism is associated to polycystic ovary syndrome and to a higher responsiveness to exogenous FSH in Italian women. J Assist Reprod Genet 2011;28:925\u0026ndash;30. https://doi.org/10.1007/s10815-011-9619-4.\u003c/li\u003e\n \u003cli\u003eDu J, Zhang W, Guo L, Zhang Z, Shi H, Wang J, et al. Two FSHR variants, haplotypes and meta-analysis in Chinese women with premature ovarian failure and polycystic ovary syndrome. Mol Genet Metab 2010;100:292\u0026ndash;5. https://doi.org/10.1016/j.ymgme.2010.03.018.\u003c/li\u003e\n \u003cli\u003eDuan J, Xu P, Zhang H, Luan X, Yang J, He X, et al. Mechanism of hormone and allosteric agonist mediated activation of follicle stimulating hormone receptor. Nat Commun 2023;14:519. https://doi.org/10.1038/s41467-023-36170-3.\u003c/li\u003e\n \u003cli\u003eGlintborg D, Kolster ND, Ravn P, Andersen MS. Prospective Risk of Type 2 Diabetes in Normal Weight Women with Polycystic Ovary Syndrome. Biomedicines 2022;10. https://doi.org/10.3390/biomedicines10061455.\u003c/li\u003e\n \u003cli\u003eGreb RR, Grieshaber K, Gromoll J, Sonntag B, Nieschlag E, Kiesel L, et al. A common single nucleotide polymorphism in exon 10 of the human follicle stimulating hormone receptor is a major determinant of length and hormonal dynamics of the menstrual cycle. Journal of Clinical Endocrinology and Metabolism 2005;90:4866\u0026ndash;72. https://doi.org/10.1210/jc.2004-2268.\u003c/li\u003e\n \u003cli\u003eGriswold MD, Kim J-S. Site-Specific Methylation of the Promoter Alters Deoxyribonucleic Acid-Protein Interactions and Prevents Follicle-Stimulating Hormone Receptor Gene Transcription1. Biol Reprod 2001;64:602\u0026ndash;10. https://doi.org/10.1095/biolreprod64.2.602.\u003c/li\u003e\n \u003cli\u003eHubbard T, Barker D, Birney E, Cameron G, Chen Y, Clark L, et al. The Ensembl genome database project. vol. 30. 2002. https://doi.org/10.1093/nar/30.1.38.\u003c/li\u003e\n \u003cli\u003eJiang X, Liu H, Chen X, Chen P-H, Fischer D, Sriraman V, et al. Structure of follicle-stimulating hormone in complex with the entire ectodomain of its receptor. Proceedings of the National Academy of Sciences 2012;109:12491\u0026ndash;6. https://doi.org/10.1073/pnas.1206643109.\u003c/li\u003e\n \u003cli\u003eKarczewski KJ, Francioli LC, Tiao G, Cummings BB, Alf\u0026ouml;ldi J, Wang Q, et al. The mutational constraint spectrum quantified from variation in 141,456 humans. Nature 2020;581:434\u0026ndash;43. https://doi.org/10.1038/s41586-020-2308-7.\u003c/li\u003e\n \u003cli\u003eKim JJ, Choi YM, Hong MA, Chae SJ, Hwang K, Yoon SH, et al. FSH receptor gene p. Thr307Ala and p. Asn680Ser polymorphisms are associated with the risk of polycystic ovary syndrome. J Assist Reprod Genet 2017;34:1087\u0026ndash;93. https://doi.org/10.1007/s10815-017-0953-z.\u003c/li\u003e\n \u003cli\u003eKim JW, Lee MH, Park JE, Yoon TK, Lee WS, Shim SH. Association of IL-18 genotype with impaired glucose regulation in Korean women with polycystic ovary syndrome. European Journal of Obstetrics and Gynecology and Reproductive Biology 2012;161:51\u0026ndash;5. https://doi.org/10.1016/j.ejogrb.2011.12.008.\u003c/li\u003e\n \u003cli\u003eLaven JSE. Follicle stimulating hormone receptor (FSHR) polymorphisms and polycystic ovary syndrome (PCOS). Front Endocrinol (Lausanne) 2019;10. https://doi.org/10.3389/fendo.2019.00023.\u003c/li\u003e\n \u003cli\u003eLiaqat I, Jahan N, Krikun G, Taylor HS. Genetic Polymorphisms in Pakistani Women With Polycystic Ovary Syndrome. Reproductive Sciences 2015;22:347\u0026ndash;57. https://doi.org/10.1177/1933719114542015.\u003c/li\u003e\n \u003cli\u003eLidaka L, Bekere L, Rota A, Isakova J, Lazdane G, Kivite-Urtane A, et al. Role of single nucleotide variants in fshr, gnrhr, esr2 and lhcgr genes in adolescents with polycystic ovary syndrome. Diagnostics 2021;11. https://doi.org/10.3390/diagnostics11122327.\u003c/li\u003e\n \u003cli\u003eLiu H, Guo T, Gong Z, Yu Y, Zhang Y, Zhao S, et al. Novel FSHR mutations in Han Chinese women with sporadic premature ovarian insufficiency. Mol Cell Endocrinol 2019;492. https://doi.org/10.1016/j.mce.2019.05.005.\u003c/li\u003e\n \u003cli\u003eMandeep Kaur, Sukhjashanpreet Singh, Ratneev Kaur, Archana Beri, Anupam Kaur. Analyzing the Impact of FSHR Variants on Polycystic Ovary Syndrome-a Case-Control Study in Punjab. Reproductive Sciences 2023;30:2563-2572. https://doi.org/10.1007/s43032-023-01194-z.\u003c/li\u003e\n \u003cli\u003eMoran L, Teede H. Metabolic features of the reproductive phenotypes of polycystic ovary syndrome. Hum Reprod Update 2009;15:477\u0026ndash;88. https://doi.org/10.1093/humupd/dmp008.\u003c/li\u003e\n \u003cli\u003eNg PC. SIFT: predicting amino acid changes that affect protein function. Nucleic Acids Res 2003;31:3812\u0026ndash;4. https://doi.org/10.1093/nar/gkg509.\u003c/li\u003e\n \u003cli\u003eNiroula A, Urolagin S, Vihinen M. PON-P2: Prediction Method for Fast and Reliable Identification of Harmful Variants. PLoS One 2015;10:e0117380. https://doi.org/10.1371/journal.pone.0117380.\u003c/li\u003e\n \u003cli\u003ePejaver V, Urresti J, Lugo-Martinez J, Pagel KA, Lin GN, Nam H-J, et al. Inferring the molecular and phenotypic impact of amino acid variants with MutPred2. Nat Commun 2020;11:5918. https://doi.org/10.1038/s41467-020-19669-x.\u003c/li\u003e\n \u003cli\u003ePolyzos NP, Neves AR, Drakopoulos P, Spits C, Alvaro Mercadal B, Garcia S, et al. The effect of polymorphisms in \u003cem\u003eFSHR\u003c/em\u003e and \u003cem\u003eFSHB\u003c/em\u003e genes on ovarian response: a prospective multicenter multinational study in Europe and Asia. Human Reproduction 2021;36:1711\u0026ndash;21. https://doi.org/10.1093/humrep/deab068.\u003c/li\u003e\n \u003cli\u003eQiu L, Liu J, Hei Q. Association between Two Polymorphisms of Follicle Stimulating Hormone Receptor Gene and Susceptibility to Polycystic Ovary Syndrome: a Meta-analysis. Chinese Medical Sciences Journal 2015;30:44\u0026ndash;50. https://doi.org/10.1016/S1001-9294(15)30008-0.\u003c/li\u003e\n \u003cli\u003eRotterdam ESHRE/ASRM-Sponsored PCOS consensus workshop group. Revised 2003 consensus on diagnostic criteria and long-term health risks related to polycystic ovary syndrome (PCOS). Human Reproduction 2004;19:41\u0026ndash;7. https://doi.org/10.1093/humrep/deh098.\u003c/li\u003e\n \u003cli\u003eSherry ST, Ward M-H, Kholodov M, Baker J, Phan L, Smigielski EM, et al. dbSNP: the NCBI database of genetic variation. vol. 29. 2001. https://doi.org/10.1093/nar/29.1.308.\u003c/li\u003e\n \u003cli\u003eSinghasena W, Pantasri T, Piromlertamorn W, Samchimchom S, Vutyavanich T. Follicle-stimulating hormone receptor gene polymorphism in chronic anovulatory women, with or without polycystic ovary syndrome: a cross-sectional study. Reproductive Biology and Endocrinology 2014;12:86. https://doi.org/10.1186/1477-7827-12-86.\u003c/li\u003e\n \u003cli\u003eStone EA, Sidow A. Physicochemical constraint violation by missense substitutions mediates impairment of protein function and disease severity. Genome Res 2005;15:978\u0026ndash;86. https://doi.org/10.1101/gr.3804205.\u003c/li\u003e\n \u003cli\u003eTaylor AE. Gonadotropin dysfunction in women with polycystic ovary syndrome. Fertil Steril 2006;86. https://doi.org/10.1016/j.fertnstert.2006.05.001.\u003c/li\u003e\n \u003cli\u003eThomas PD, Kejariwal A. Coding single-nucleotide polymorphisms associated with complex vs. Mendelian disease: Evolutionary evidence for differences in molecular effects. Proceedings of the National Academy of Sciences 2004;101:15398\u0026ndash;403. https://doi.org/10.1073/pnas.0404380101.\u003c/li\u003e\n \u003cli\u003eThusberg J, Vihinen M. Pathogenic or not? And if so, then how? Studying the effects of missense mutations using bioinformatics methods. Hum Mutat 2009;30:703\u0026ndash;14. https://doi.org/10.1002/humu.20938.\u003c/li\u003e\n \u003cli\u003eToulis KA, Goulis DG, Mintziori G, Kintiraki E, Eukarpidis E, Mouratoglou SA, et al. Meta-analysis of cardiovascular disease risk markers in women with polycystic ovary syndrome. Hum Reprod Update 2011;17:741\u0026ndash;60. https://doi.org/10.1093/humupd/dmr025.\u003c/li\u003e\n \u003cli\u003eValkenburg O, Uitterlinden AG, Piersma D, Hofman A, Themmen APN, De Jong FH, et al. Genetic polymorphisms of GnRH and gonadotrophic hormone receptors affect the phenotype of polycystic ovary syndrome. Human Reproduction 2009;24:2014\u0026ndash;22. https://doi.org/10.1093/humrep/dep113.\u003c/li\u003e\n \u003cli\u003eWan P, Meng L, Huang C, Dai B, Jin Y, Chai L, et al. Replication study and meta-analysis of selected genetic variants and polycystic ovary syndrome susceptibility in Asian population. J Assist Reprod Genet 2021;38:2781\u0026ndash;9. https://doi.org/10.1007/s10815-021-02291-1.\u003c/li\u003e\n \u003cli\u003eWu X, Xu S, Liu Jun-fen, Bi X, Wu Y, Liu Jing. Association between FSHR polymorphisms and polycystic ovary syndrome among Chinese women in north China. J Assist Reprod Genet 2014;31:371\u0026ndash;7. https://doi.org/10.1007/s10815-013-0166-z.\u003c/li\u003e\n \u003cli\u003eWunsch A, Ahda Y, Banaz-Yaşar F, Sonntag B, Nieschlag E, Simoni M, et al. Single-nucleotide polymorphisms in the promoter region influence the expression of the human follicle-stimulating hormone receptor. Fertil Steril 2005a;84:446\u0026ndash;53. https://doi.org/10.1016/j.fertnstert.2005.02.031.\u003c/li\u003e\n \u003cli\u003eWunsch A, Ahda Y, Banaz-Yaşar F, Sonntag B, Nieschlag E, Simoni M, et al. Single-nucleotide polymorphisms in the promoter region influence the expression of the human follicle-stimulating hormone receptor. Fertil Steril 2005b;84:446\u0026ndash;53. https://doi.org/10.1016/j.fertnstert.2005.02.031.\u003cstrong\u003e\u003c/strong\u003e\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1:\u0026nbsp;\u003c/strong\u003eClinical Parameters of PCOS and the Controls\u003c/p\u003e\n\u003cdiv align=\"Left\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"678\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 30.6785%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eClinical parameters\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 28.7611%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePCOS Mean\u0026plusmn;SD\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(n=121)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 27.8761%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eControl Mean\u0026plusmn;SD\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(n=121)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.6844%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-Value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 30.6785%;\"\u003e\n \u003cp\u003eAge at Menarche (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 28.7611%;\"\u003e\n \u003cp\u003e12.42\u0026plusmn;1.258\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 27.8761%;\"\u003e\n \u003cp\u003e12.88\u0026plusmn;1.196\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.6844%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.0039*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 30.6785%;\"\u003e\n \u003cp\u003eWeight (kg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 28.7611%;\"\u003e\n \u003cp\u003e62.44\u0026plusmn;12.818\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 27.8761%;\"\u003e\n \u003cp\u003e53.79\u0026plusmn;12.069\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.6844%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.0001*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 30.6785%;\"\u003e\n \u003cp\u003eHeight (m)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 28.7611%;\"\u003e\n \u003cp\u003e155.76\u0026plusmn;5.276\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 27.8761%;\"\u003e\n \u003cp\u003e157.47\u0026plusmn;7.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.6844%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.0404*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 30.6785%;\"\u003e\n \u003cp\u003eBMI (kg/m2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 28.7611%;\"\u003e\n \u003cp\u003e25.80\u0026plusmn;5.265\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 27.8761%;\"\u003e\n \u003cp\u003e21.91\u0026plusmn;4.494\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.6844%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.0001*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 30.6785%;\"\u003e\n \u003cp\u003ePCOM,\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Oligomenorrhea and Hyperandrogenism\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 28.7611%;\"\u003e\n \u003cp\u003e121 (100%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 27.8761%;\"\u003e\n \u003cp\u003e0 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.6844%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 30.6785%;\"\u003e\n \u003cp\u003eLean (\u0026lt;18.5)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 28.7611%;\"\u003e\n \u003cp\u003e11 (9.09%), 17.04\u0026plusmn;1.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 27.8761%;\"\u003e\n \u003cp\u003e27 (22.31%), 17.31\u0026plusmn;0.707\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.6844%;\"\u003e\n \u003cp\u003e0.3523\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 30.6785%;\"\u003e\n \u003cp\u003eNormal (18.6 \u0026ndash; 24.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 28.7611%;\"\u003e\n \u003cp\u003e46 (38.02%), 22.30\u0026plusmn; 1.753\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 27.8761%;\"\u003e\n \u003cp\u003e71 (58.68%), 21.41\u0026plusmn;1.719\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.6844%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.0077*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 30.6785%;\"\u003e\n \u003cp\u003eOver Weight (25 \u0026ndash; 29.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 28.7611%;\"\u003e\n \u003cp\u003e38 (31.40%), 24.15\u0026plusmn;1.231\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 27.8761%;\"\u003e\n \u003cp\u003e18 (14.88%), 27.14\u0026plusmn;1.623\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.6844%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt; 0.0001*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 30.6785%;\"\u003e\n \u003cp\u003eObese (\u0026gt;30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 28.7611%;\"\u003e\n \u003cp\u003e26 (21.49%), 33.141\u0026plusmn;2.963\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 27.8761%;\"\u003e\n \u003cp\u003e5 (4.13%), 36.18\u0026plusmn;4.449\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.6844%;\"\u003e\n \u003cp\u003e0.0623\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2:\u0026nbsp;\u003c/strong\u003ePCR and RFLP Conditions\u003c/p\u003e\n\u003cdiv align=\"Left\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"737\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 13.9946%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRestriction enzymes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.7989%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePrimer sequence\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.5978%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eProduct size\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.6359%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSNP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.0924%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSequence variation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.1087%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePCR Condition\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.7717%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRFLP Condition\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 13.9946%;\"\u003e\n \u003cp\u003eMboII\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.7989%;\"\u003e\n \u003cp\u003ePrimer F 5\u0026acute; -GGA GCT TCT GAG ATC TGT GG-3`\u003c/p\u003e\n \u003cp\u003ePrimer R 5`AAA TGC CAG CCA TGC AGT TG-3`\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.5978%;\"\u003e\n \u003cp\u003e266\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.6359%;\"\u003e\n \u003cp\u003ers1394205\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.0924%;\"\u003e\n \u003cp\u003e-29 G\u0026gt;A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.1087%;\"\u003e\n \u003cp\u003e95\u0026deg;C, 5 min, 1 cycle; 95\u0026deg;C, 20 sec; 52\u0026deg;C 20 sec and 72\u0026deg;C 20 sec, 40 cycles; 72\u0026deg;C, 5 min, 1 cycle; 12\u0026deg;C, \u0026infin;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.7717%;\"\u003e\n \u003cp\u003e37\u0026deg;C for 10 min then inactivate 65\u0026deg;C 20 min\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 13.9946%;\"\u003e\n \u003cp\u003eCviKI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.7989%;\"\u003e\n \u003cp\u003ePrimer F 5\u0026acute; - TCT GAG CTT CAT CCA ATT TGC A-3`\u003c/p\u003e\n \u003cp\u003ePrimer R 5` ACG TCA ACC ACT TCA TTG CA-3`\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.5978%;\"\u003e\n \u003cp\u003e176\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.6359%;\"\u003e\n \u003cp\u003ers6165\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.0924%;\"\u003e\n \u003cp\u003e307 A\u0026gt;G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.1087%;\"\u003e\n \u003cp\u003e95\u0026deg;C, 5 min, 1 cycle; 95\u0026deg;C, 20 sec; 53\u0026deg;C 20 sec and 72\u0026deg;C 20 sec, 40 cycles; 72\u0026deg;C, 5 min, 1 cycle; 12\u0026deg;C, \u0026infin;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.7717%;\"\u003e\n \u003cp\u003e37\u0026deg;C for 60 min\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 13.9946%;\"\u003e\n \u003cp\u003eBsrI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.7989%;\"\u003e\n \u003cp\u003ePrimer F 5\u0026acute; - CCC AAA TTT ATA GGA CAG-3`\u003c/p\u003e\n \u003cp\u003ePrimer R 5` GAG GGA CAA GTA TGT AAG TG-3`\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.5978%;\"\u003e\n \u003cp\u003e114\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.6359%;\"\u003e\n \u003cp\u003ers6166\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.0924%;\"\u003e\n \u003cp\u003e680 A\u0026gt;G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.1087%;\"\u003e\n \u003cp\u003e95\u0026deg;C, 5 min, 1 cycle; 95\u0026deg;C, 20 sec; 45\u0026deg;C 20 sec and 72\u0026deg;C 20 sec, 40 cycles; 72\u0026deg;C, 5 min, 1 cycle; 12\u0026deg;C, \u0026infin;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.7717%;\"\u003e\n \u003cp\u003e65\u0026deg;C for 10 min then\u003c/p\u003e\n \u003cp\u003einactivate 80\u0026deg;C for 20 min\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003cstrong\u003eTable 3:\u0026nbsp;\u003c/strong\u003eFrequency distribution and genetic association of polymorphic genotypes of \u003cem\u003eFSHR-\u0026nbsp;\u003c/em\u003e-40 T\u0026gt;C (rs1317756940) and \u0026ndash;29 G\u0026gt;A (rs1394205) with PCOS susceptibility under different genetic models after getting promoter Sanger sequence.\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv align=\"Left\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"656\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cem\u003eFSHR\u0026nbsp;\u003c/em\u003egenetic variation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003ePatient n=25(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003eControl n=25(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026chi;\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 174px;\"\u003e\n \u003cp\u003eOR (CI 95%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 189px;\"\u003e\n \u003cp\u003e-40 T\u0026gt;C (rs1317756940)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 174px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 189px;\"\u003e\n \u003cp\u003eCodominant model\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 174px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003eT/T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e3 (12%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e2 (8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.9640\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 174px;\"\u003e\n \u003cp\u003e1 (Reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e---\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003eT/C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e11 (44%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e7 (28%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 174px;\"\u003e\n \u003cp\u003e1.0476 (0.1383 - 7.9344)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.9641\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003eC/C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e0 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e0 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 174px;\"\u003e\n \u003cp\u003e0.7143 (0.0103 \u0026ndash; 49.7119)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.8765\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 189px;\"\u003e\n \u003cp\u003eAllele\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 174px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003eT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e17 (60.71%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e11 (61.11%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.9785\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 174px;\"\u003e\n \u003cp\u003e1 (Reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e---\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e11 (39.29%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e7 (38.89%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 174px;\"\u003e\n \u003cp\u003e1.0168 (0.3021 - 3.4227)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.9785\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 189px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 174px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cdiv align=\"Left\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"652\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cem\u003eFSHR\u0026nbsp;\u003c/em\u003egenetic variation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003ePatient n=25(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003eControl n=25(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026chi;\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003eOR (CI 95%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 189px;\"\u003e\n \u003cp\u003e-29 G\u0026gt;A (rs1394205)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 189px;\"\u003e\n \u003cp\u003eCodominant model\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003eG/G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e14 (56%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e18 (72%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width: 57px;\"\u003e\n \u003cp\u003e8.077\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.0176\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e1 (Reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e---\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003eG/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e6 (24%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e7 (28%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e1.1020 (0.3019 - 4.0233)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.8831\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003eA/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e5 (20%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e0 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e14.0345 (0.7160 - 275.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.0819\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 189px;\"\u003e\n \u003cp\u003eAllele\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003eG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e34 (68%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e43 (86%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 57px;\"\u003e\n \u003cp\u003e3.069\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.0797\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e1 (Reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e---\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003eA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e16 (32%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e7 (14%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e2.4202 (0.8865 \u0026ndash; 6.6070)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.0845\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4: HWE analysis on rs1394205,\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003ers6165 and rs6166\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv align=\"Left\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 48.538%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFSHR genetic variation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.9006%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePCOS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24.5614%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eControl\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" style=\"width: 100%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-29 G\u0026gt;A (rs1394205)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 48.538%;\"\u003e\n \u003cp\u003eHWE Analysis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.9006%;\"\u003e\n \u003cp\u003e0.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24.5614%;\"\u003e\n \u003cp\u003e0.38\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 48.538%;\"\u003e\n \u003cp\u003e\u0026chi;\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.9006%;\"\u003e\n \u003cp\u003e1.8362\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24.5614%;\"\u003e\n \u003cp\u003e3.5007\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 48.538%;\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.9006%;\"\u003e\n \u003cp\u003e0.1753\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24.5614%;\"\u003e\n \u003cp\u003e0.0613\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" style=\"width: 100%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e307 A\u0026gt;G (rs6165)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 48.538%;\"\u003e\n \u003cp\u003eHWE Analysis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.9006%;\"\u003e\n \u003cp\u003e0.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24.5614%;\"\u003e\n \u003cp\u003e0.24\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 48.538%;\"\u003e\n \u003cp\u003e\u0026chi;\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.9006%;\"\u003e\n \u003cp\u003e0.8112\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24.5614%;\"\u003e\n \u003cp\u003e0.0089\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 48.538%;\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.9006%;\"\u003e\n \u003cp\u003e0.3678\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24.5614%;\"\u003e\n \u003cp\u003e0.9245\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" style=\"width: 100%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e680 A\u0026gt;G (rs6166)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 48.538%;\"\u003e\n \u003cp\u003eHWE Analysis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.9006%;\"\u003e\n \u003cp\u003e0.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24.5614%;\"\u003e\n \u003cp\u003e0.42\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 48.538%;\"\u003e\n \u003cp\u003e\u0026chi;\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.9006%;\"\u003e\n \u003cp\u003e2.7487\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24.5614%;\"\u003e\n \u003cp\u003e0.0341\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 48.538%;\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.9006%;\"\u003e\n \u003cp\u003e1.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24.5614%;\"\u003e\n \u003cp\u003e0.8533\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eTable 5:\u0026nbsp;\u003c/strong\u003eFrequency distribution and genetic association of polymorphic genotypes of \u003cem\u003eFSHR\u003c/em\u003e \u0026ndash;29 G\u0026gt;A (rs1394205), 307 A\u0026gt;G (rs6165) and 680 A\u0026gt;G (rs6166) with PCOS susceptibility under different genetic models.\u003c/p\u003e\n\u003cdiv align=\"Left\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"652\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eFSHR\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003egenetic variation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePatient n=121(%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eControl n=121(%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026chi;\u003csup\u003e2\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOR (CI 95%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 189px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-29\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eG\u0026gt;A\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e(rs1394205)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 189px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCodominant model\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003eG/G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e50 (41.32%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e41 (33.88%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width: 57px;\"\u003e\n \u003cp\u003e5.2425\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.07271\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e1 (Reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e---\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003eG/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e50 (41.32%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e67 (55.37%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e0.6119 (0.3524\u0026nbsp;-\u0026nbsp;1.0628)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.0812\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003eA/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e21 (17.35%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e13 (10.74%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e1.3246 (0.5919\u0026nbsp;-\u0026nbsp;2.9645)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.4940\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 189px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDominant model\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003eG/G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e50 (41.32%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e41 (33.88%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e1.4265\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.23233\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e1 (Reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e---\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003eG/A - A/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e71 (58.68%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e80 (66.12%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e0.7278 (0.4317\u0026nbsp;-\u0026nbsp;1.2267)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.2329\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 189px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRecessive model\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003eG/G - G/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e100 (82.65%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e108 (89.26%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e2.1900\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.13891\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e1 (Reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e---\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003eA/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e21 (17.35%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e13 (10.74%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e1.7446 (0.8297 - 3.6685)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.1422\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 189px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOver dominant model\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003eG/G-A/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e71 (58.68%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e54 (44.63%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e4.7820\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.02875\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e1 (Reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e---\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003eG/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e50 (41.32%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e67 (55.37%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e0.5676 (0.3411 - 0.9446)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.0293\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAllele\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003eG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e150 (61.98%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e149 (61.57%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.0087\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.92547\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e1 (Reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e---\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003eA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e92 (38.02%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e93 (38.43%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e0.9827 (0.6810\u0026nbsp;-\u0026nbsp;1.4179)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.9255\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 189px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e307 A\u0026gt;G (rs6165)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 189px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCodominant model\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003eA/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;39 (32.23%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e69 (57.02%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width: 57px;\"\u003e\n \u003cp\u003e21.098\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.00002\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e1 (Reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e---\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003eA/G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;55 (45.45%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e45 (37.19%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e2.1624 (1.2398\u0026nbsp;-\u0026nbsp;3.7714)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.0066\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003eG/G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;27 (22.32%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e7 (5.78%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e6.8242 (2.7213\u0026nbsp;-\u0026nbsp;17.113)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.0001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 189px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDominant model\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003eA/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e39 (32.23%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e69 (57.02%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e15.049\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.00010\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e1 (Reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e---\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003eA/G - G/G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e82 (67.77%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e52 (42.98%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e2.7899 (1.6515\u0026nbsp;-\u0026nbsp;4.7130)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 189px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRecessive model\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003eA/A - A/G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e94 (77.69%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e114 (94.21%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e13.687\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.00021\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e1 (Reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e---\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003eG/G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e27 (22.31%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e7 (5.79%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e4.6778 (1.9500 - 11.2216)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.0005\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 189px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOver dominant model\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003eA/A-G/G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e66 (54.55%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e76 (62.81%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e1.7042\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.19173\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e1 (Reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e---\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003eA/G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e55 (45.45%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e45 (37.19%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e1.4074 (0.8420\u0026nbsp;-\u0026nbsp;2.3526)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.1923\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAllele\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003eA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e133 (54.96%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e183 (75.62%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e22.792\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.00001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e1 (Reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e---\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003eG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e109 (45.04%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e59 (24.38%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e2.5420 (1.7252\u0026nbsp;to\u0026nbsp;3.7455)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.0001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 189px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e680 A\u0026gt;G (rs6166)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 187px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCodominant model\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003eA/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e36 (29.75%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e40 (33.06%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.558\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.75641\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e1 (Reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e---\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003eA/G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e60 (49.59%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e60 (49.59%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e1.1111 (0.6251 - 1.9749)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.7196\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003eG/G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e25 (20.66%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e21 (17.35%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e1.3228 (0.6346 - 2.7569)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.4554\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 187px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDominant model\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003eA/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e36 (29.75%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e40 (33.05%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.307\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.57958\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e1 (Reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e---\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003eA/G - G/G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e85 (70.25%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e81 (66.94%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e1.1660 (0.6771 - 2.0078)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.5797\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 187px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRecessive model\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003eA/A - A/G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e96 (79.34%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e100 (82.64%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.429\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.51225\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e1 (Reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e---\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003eG/G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e25 (20.66%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e21 (17.35%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e1.2401 (0.6511 - 2.3618)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.5127\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 187px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOver dominant model\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003eA/A-G/G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e61 (50.41%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e61 (50.41%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.948\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e1 (Reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e---\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003eA/G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e60 (49.59%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e60 (49.59%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e1.0000 (0.6041 - 1.6553)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e1.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAllele\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003eA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e132 (54.54%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e140 (57.85%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.537\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.4636\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e1 (Reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e---\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003eG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e110 (45.46%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e102 (42.15%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e1.1438 (0.7985 - 1.6384)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.4637\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eTable 6:\u003c/strong\u003e Frequency distribution and genetic association of polymorphic genotypes of \u003cem\u003eFSHR\u003c/em\u003e \u0026ndash;29 G\u0026gt;A (rs1394205), 307 A\u0026gt;G (rs6165) and 680 A\u0026gt;G (rs6166) with PCOS susceptibility based on BMI.\u003c/p\u003e\n\u003cdiv align=\"Left\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"741\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 90px;\"\u003e\n \u003cp\u003e\u003cem\u003eFSHR\u003c/em\u003e genetic variation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 108px;\"\u003e\n \u003cp\u003ePatient n=121(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 108px;\"\u003e\n \u003cp\u003eControl n=121(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026chi;\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 59px;\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 217px;\"\u003e\n \u003cp\u003eOR (CI 95%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 87px;\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 6px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"9\" style=\"width: 312px;\"\u003e\n \u003cp\u003e\u003cem\u003eFSHR\u0026nbsp;\u003c/em\u003e\u0026ndash;29 G\u0026gt;A (rs1394205)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 222px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 4px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" style=\"width: 96px;\"\u003e\n \u003cp\u003eGenotype\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLean\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003en=11 (9.1%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 120px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLean\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003en=27 (22.3%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 222px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" style=\"width: 96px;\"\u003e\n \u003cp\u003eG/G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 108px;\"\u003e\n \u003cp\u003e7 (63.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 120px;\"\u003e\n \u003cp\u003e9 (33.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" rowspan=\"3\" style=\"width: 60px;\"\u003e\n \u003cp\u003e5.351\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" rowspan=\"3\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.069\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 222px;\"\u003e\n \u003cp\u003e1 (Reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 76px;\"\u003e\n \u003cp\u003e---\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" style=\"width: 96px;\"\u003e\n \u003cp\u003eG/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 108px;\"\u003e\n \u003cp\u003e2 (18.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 120px;\"\u003e\n \u003cp\u003e16 (59.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 222px;\"\u003e\n \u003cp\u003e0.1607 (0.0273 - 0.9445)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.0431\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" style=\"width: 96px;\"\u003e\n \u003cp\u003eA/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 108px;\"\u003e\n \u003cp\u003e2 (18.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 120px;\"\u003e\n \u003cp\u003e2 (7.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 222px;\"\u003e\n \u003cp\u003e1.2857 (0.1432 - 11.5437)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.8224\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" style=\"width: 96px;\"\u003e\n \u003cp\u003eAllele\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 120px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 222px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" style=\"width: 96px;\"\u003e\n \u003cp\u003eG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 108px;\"\u003e\n \u003cp\u003e16 (72.72%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 120px;\"\u003e\n \u003cp\u003e34 (62.96%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" rowspan=\"2\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.662\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" rowspan=\"2\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.416\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 222px;\"\u003e\n \u003cp\u003e1 (Reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 76px;\"\u003e\n \u003cp\u003e---\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" style=\"width: 96px;\"\u003e\n \u003cp\u003eA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 108px;\"\u003e\n \u003cp\u003e6 (27.27%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 120px;\"\u003e\n \u003cp\u003e20 (37.04%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 222px;\"\u003e\n \u003cp\u003e0.6375 (0.2146 - 1.8938)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.4177\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" style=\"width: 96px;\"\u003e\n \u003cp\u003eGenotype\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNormal\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003en=46 (38%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 120px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNormal\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003en=71 (58.7%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 222px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" style=\"width: 96px;\"\u003e\n \u003cp\u003eG/G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 108px;\"\u003e\n \u003cp\u003e16 (34.78%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 120px;\"\u003e\n \u003cp\u003e26 (36.62%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" rowspan=\"3\" style=\"width: 60px;\"\u003e\n \u003cp\u003e2.292\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" rowspan=\"3\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.318\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 222px;\"\u003e\n \u003cp\u003e1 (Reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 76px;\"\u003e\n \u003cp\u003e---\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" style=\"width: 96px;\"\u003e\n \u003cp\u003eG/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 108px;\"\u003e\n \u003cp\u003e21 (45.65%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 120px;\"\u003e\n \u003cp\u003e38 (53.52%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 222px;\"\u003e\n \u003cp\u003e0.8980 (0.3956 - 2.0383)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.7970\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" style=\"width: 96px;\"\u003e\n \u003cp\u003eA/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 108px;\"\u003e\n \u003cp\u003e9 (19.57%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 120px;\"\u003e\n \u003cp\u003e7 (9.86%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 222px;\"\u003e\n \u003cp\u003e2.0893 (0.6499 - 6.7161)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.2162\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" style=\"width: 96px;\"\u003e\n \u003cp\u003eAllele\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 120px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 222px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" style=\"width: 96px;\"\u003e\n \u003cp\u003eG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 108px;\"\u003e\n \u003cp\u003e53 (57.61%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 120px;\"\u003e\n \u003cp\u003e90 (63.38%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" rowspan=\"2\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.783\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" rowspan=\"2\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.376\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 222px;\"\u003e\n \u003cp\u003e1 (Reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 76px;\"\u003e\n \u003cp\u003e---\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" style=\"width: 96px;\"\u003e\n \u003cp\u003eA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 108px;\"\u003e\n \u003cp\u003e39 (42.39%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 120px;\"\u003e\n \u003cp\u003e52 (36.62%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 222px;\"\u003e\n \u003cp\u003e\u0026nbsp;1.2736 (0.7450 - 2.1773)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.3767\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" style=\"width: 96px;\"\u003e\n \u003cp\u003eGenotype\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOverweight\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003en=38 (31.4%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 120px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOverweight\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003en=18 (14.9%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 222px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" style=\"width: 96px;\"\u003e\n \u003cp\u003eG/G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 108px;\"\u003e\n \u003cp\u003e15 (39.47%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 120px;\"\u003e\n \u003cp\u003e2 (11.11%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" rowspan=\"3\" style=\"width: 60px;\"\u003e\n \u003cp\u003e4.965\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" rowspan=\"3\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.084\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 222px;\"\u003e\n \u003cp\u003e1 (Reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 76px;\"\u003e\n \u003cp\u003e---\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" style=\"width: 96px;\"\u003e\n \u003cp\u003eG/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 108px;\"\u003e\n \u003cp\u003e17 (44.74%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 120px;\"\u003e\n \u003cp\u003e13 (72.22%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 222px;\"\u003e\n \u003cp\u003e0.1744 (0.0337 - 0.9013)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.0372\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" style=\"width: 96px;\"\u003e\n \u003cp\u003eA/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 108px;\"\u003e\n \u003cp\u003e6 (15.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 120px;\"\u003e\n \u003cp\u003e3 (16.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 222px;\"\u003e\n \u003cp\u003e0.2667 (0.0352 - 2.0188)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.2006\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" style=\"width: 96px;\"\u003e\n \u003cp\u003eAllele\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 120px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 222px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" style=\"width: 96px;\"\u003e\n \u003cp\u003eG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 108px;\"\u003e\n \u003cp\u003e47 (61.84%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 120px;\"\u003e\n \u003cp\u003e17 (47.22%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" rowspan=\"2\" style=\"width: 60px;\"\u003e\n \u003cp\u003e2.132\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" rowspan=\"2\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.144\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 222px;\"\u003e\n \u003cp\u003e1 (Reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 76px;\"\u003e\n \u003cp\u003e---\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" style=\"width: 96px;\"\u003e\n \u003cp\u003eA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 108px;\"\u003e\n \u003cp\u003e29 (38.16%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 120px;\"\u003e\n \u003cp\u003e19 (52.78%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 222px;\"\u003e\n \u003cp\u003e0.5521 (0.2477 \u0026ndash; 1.2305)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.1463\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" style=\"width: 96px;\"\u003e\n \u003cp\u003eGenotype\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eObese\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003en=26 (21.5%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 120px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eObese\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003en=5 (4.1%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 222px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" style=\"width: 96px;\"\u003e\n \u003cp\u003eG/G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 108px;\"\u003e\n \u003cp\u003e12 (46.16%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 120px;\"\u003e\n \u003cp\u003e1 (20%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" rowspan=\"3\" style=\"width: 60px;\"\u003e\n \u003cp\u003e1.203\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" rowspan=\"3\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.548\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 222px;\"\u003e\n \u003cp\u003e1 (Reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 76px;\"\u003e\n \u003cp\u003e---\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" style=\"width: 96px;\"\u003e\n \u003cp\u003eG/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 108px;\"\u003e\n \u003cp\u003e10 (38.46%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 120px;\"\u003e\n \u003cp\u003e3 (60%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 222px;\"\u003e\n \u003cp\u003e0.2778 (0.0249 - 3.1045)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.2983\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" style=\"width: 96px;\"\u003e\n \u003cp\u003eA/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 108px;\"\u003e\n \u003cp\u003e4 (15.38%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 120px;\"\u003e\n \u003cp\u003e1 (20%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 222px;\"\u003e\n \u003cp\u003e0.3333 (0.0167 - 6.6548)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.4720\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" style=\"width: 96px;\"\u003e\n \u003cp\u003eAllele\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 120px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 222px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" style=\"width: 96px;\"\u003e\n \u003cp\u003eG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 108px;\"\u003e\n \u003cp\u003e34 (65.38%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 120px;\"\u003e\n \u003cp\u003e5 (50%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" rowspan=\"2\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.851\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" rowspan=\"2\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.356\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 222px;\"\u003e\n \u003cp\u003e1 (Reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 76px;\"\u003e\n \u003cp\u003e---\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" style=\"width: 96px;\"\u003e\n \u003cp\u003eA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 108px;\"\u003e\n \u003cp\u003e18 (34.62%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 120px;\"\u003e\n \u003cp\u003e5 (50%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 222px;\"\u003e\n \u003cp\u003e0.5294 (0.1352 - 2.0729)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.3611\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"9\" style=\"width: 312px;\"\u003e\n \u003cp\u003e\u003cem\u003eFSHR\u003c/em\u003e 307 A\u0026gt;G (rs6165)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 222px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 4px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" style=\"width: 96px;\"\u003e\n \u003cp\u003eGenotype\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLean\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003en=11 (9.1%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLean\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003en=27 (22.3%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 222px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" style=\"width: 96px;\"\u003e\n \u003cp\u003eA/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 108px;\"\u003e\n \u003cp\u003e3 (27.27%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 108px;\"\u003e\n \u003cp\u003e7 (25.93%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" rowspan=\"4\" style=\"width: 60px;\"\u003e\n \u003cp\u003e3.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" rowspan=\"4\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.193\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 222px;\"\u003e\n \u003cp\u003e1 (Reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 87px;\"\u003e\n \u003cp\u003e---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" style=\"width: 96px;\"\u003e\n \u003cp\u003eA/G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 108px;\"\u003e\n \u003cp\u003e7 (63.64%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 108px;\"\u003e\n \u003cp\u003e20 (74.07%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 222px;\"\u003e\n \u003cp\u003e0.8167 (0.1644 - 4.0579)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 87px;\"\u003e\n \u003cp\u003e0.8044\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" style=\"width: 96px;\"\u003e\n \u003cp\u003eG/G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 108px;\"\u003e\n \u003cp\u003e1 (9.09%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 108px;\"\u003e\n \u003cp\u003e0 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 222px;\"\u003e\n \u003cp\u003e6.4286 (0.2055 - 201.0850)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 87px;\"\u003e\n \u003cp\u003e0.2895\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" style=\"width: 96px;\"\u003e\n \u003cp\u003eAllele\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 222px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" style=\"width: 96px;\"\u003e\n \u003cp\u003eA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 108px;\"\u003e\n \u003cp\u003e13 (59.09%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 108px;\"\u003e\n \u003cp\u003e44 (68.75%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" rowspan=\"2\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.683\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" rowspan=\"2\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.408\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 222px;\"\u003e\n \u003cp\u003e1 (Reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 87px;\"\u003e\n \u003cp\u003e---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" style=\"width: 96px;\"\u003e\n \u003cp\u003eG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 108px;\"\u003e\n \u003cp\u003e9 (49.91%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 108px;\"\u003e\n \u003cp\u003e20 (31.25%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 222px;\"\u003e\n \u003cp\u003e1.5231 (0.5598 - 4.1438)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 87px;\"\u003e\n \u003cp\u003e0.4100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" style=\"width: 96px;\"\u003e\n \u003cp\u003eGenotype\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNormal\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003en=46 (38.0%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNormal\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003en=71 (58.7%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 222px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" style=\"width: 96px;\"\u003e\n \u003cp\u003eA/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 108px;\"\u003e\n \u003cp\u003e16 (34.78%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 108px;\"\u003e\n \u003cp\u003e28 (39.44%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" rowspan=\"3\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e6.805\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" rowspan=\"3\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.033\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 222px;\"\u003e\n \u003cp\u003e1 (Reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 87px;\"\u003e\n \u003cp\u003e---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" style=\"width: 96px;\"\u003e\n \u003cp\u003eA/G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 108px;\"\u003e\n \u003cp\u003e18 (39.13%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 108px;\"\u003e\n \u003cp\u003e37 (52.11%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 222px;\"\u003e\n \u003cp\u003e0.8514 (0.3700 - 1.9591)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 87px;\"\u003e\n \u003cp\u003e0.7051\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" style=\"width: 96px;\"\u003e\n \u003cp\u003eG/G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 108px;\"\u003e\n \u003cp\u003e12 (26.09%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 108px;\"\u003e\n \u003cp\u003e6 (8.45%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 222px;\"\u003e\n \u003cp\u003e3.5000 (1.1009 - 11.1268)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.0338\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" style=\"width: 96px;\"\u003e\n \u003cp\u003eAllele\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 222px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" style=\"width: 96px;\"\u003e\n \u003cp\u003eA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 108px;\"\u003e\n \u003cp\u003e50 (54.35%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 108px;\"\u003e\n \u003cp\u003e93 (65.49%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" rowspan=\"2\" style=\"width: 60px;\"\u003e\n \u003cp\u003e2.918\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" rowspan=\"2\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.088\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 222px;\"\u003e\n \u003cp\u003e1 (Reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 87px;\"\u003e\n \u003cp\u003e---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" style=\"width: 96px;\"\u003e\n \u003cp\u003eG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 108px;\"\u003e\n \u003cp\u003e42 (45.65%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 108px;\"\u003e\n \u003cp\u003e49 (35.51%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 222px;\"\u003e\n \u003cp\u003e1.5943 (0.9322 - 2.7267)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 87px;\"\u003e\n \u003cp\u003e0.0885\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" style=\"width: 96px;\"\u003e\n \u003cp\u003eGenotype\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOverweight\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003en=38 (31.4%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOverweight\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003en=18 (14.9%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 222px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" style=\"width: 96px;\"\u003e\n \u003cp\u003eA/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 108px;\"\u003e\n \u003cp\u003e13 (34.21%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 108px;\"\u003e\n \u003cp\u003e6 (33.33%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" rowspan=\"3\" style=\"width: 60px;\"\u003e\n \u003cp\u003e3.166\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" rowspan=\"3\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.205\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 222px;\"\u003e\n \u003cp\u003e1 (Reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 87px;\"\u003e\n \u003cp\u003e---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" style=\"width: 96px;\"\u003e\n \u003cp\u003eA/G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 108px;\"\u003e\n \u003cp\u003e16 (42.11%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 108px;\"\u003e\n \u003cp\u003e11 (61.11%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 222px;\"\u003e\n \u003cp\u003e0.6713 (0.1953 - 2.3082)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 87px;\"\u003e\n \u003cp\u003e0.5271\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" style=\"width: 96px;\"\u003e\n \u003cp\u003eG/G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 108px;\"\u003e\n \u003cp\u003e9 (23.68%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 108px;\"\u003e\n \u003cp\u003e1 (5.56%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 222px;\"\u003e\n \u003cp\u003e4.1538 (0.4243 - 40.6627)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 87px;\"\u003e\n \u003cp\u003e0.2211\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" style=\"width: 96px;\"\u003e\n \u003cp\u003eAllele\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 222px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" style=\"width: 96px;\"\u003e\n \u003cp\u003eA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 108px;\"\u003e\n \u003cp\u003e42 (55.26%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 108px;\"\u003e\n \u003cp\u003e23 (63.89%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" rowspan=\"2\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.746\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" rowspan=\"2\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.388\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 222px;\"\u003e\n \u003cp\u003e1 (Reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 87px;\"\u003e\n \u003cp\u003e---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" style=\"width: 96px;\"\u003e\n \u003cp\u003eG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 108px;\"\u003e\n \u003cp\u003e34 (44.74%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 108px;\"\u003e\n \u003cp\u003e13 (36.11%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 222px;\"\u003e\n \u003cp\u003e1.4322 (0.6329 - 3.2412)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 87px;\"\u003e\n \u003cp\u003e0.3886\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" style=\"width: 96px;\"\u003e\n \u003cp\u003eGenotype\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eObese\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003en=26 (21.5%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eObese\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003en=5 (4.1%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 222px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" style=\"width: 96px;\"\u003e\n \u003cp\u003eA/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 108px;\"\u003e\n \u003cp\u003e7 (26.92%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 108px;\"\u003e\n \u003cp\u003e4 (80%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" rowspan=\"3\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e6.09\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" rowspan=\"3\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.047\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 222px;\"\u003e\n \u003cp\u003e1 (Reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 87px;\"\u003e\n \u003cp\u003e---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" style=\"width: 96px;\"\u003e\n \u003cp\u003eA/G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e14 (53.85%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e1 (20%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 222px;\"\u003e\n \u003cp\u003e8.0000 (0.7465 - 85.7291)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 87px;\"\u003e\n \u003cp\u003e0.0857\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" style=\"width: 96px;\"\u003e\n \u003cp\u003eG/G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e5 (19.23%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e0 (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 222px;\"\u003e\n \u003cp\u003e6.6000 (0.2908 - 149.7819)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 87px;\"\u003e\n \u003cp\u003e0.2361\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" style=\"width: 96px;\"\u003e\n \u003cp\u003eAllele\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 222px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" style=\"width: 96px;\"\u003e\n \u003cp\u003eA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 108px;\"\u003e\n \u003cp\u003e21 (46.67%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 108px;\"\u003e\n \u003cp\u003e9 (90%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" rowspan=\"2\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e6.197\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" rowspan=\"2\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.012\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 222px;\"\u003e\n \u003cp\u003e1 (Reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 87px;\"\u003e\n \u003cp\u003e---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" style=\"width: 96px;\"\u003e\n \u003cp\u003eG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 108px;\"\u003e\n \u003cp\u003e24 (53.33%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 108px;\"\u003e\n \u003cp\u003e1 (10%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 222px;\"\u003e\n \u003cp\u003e10.2857 (1.2012 - 88.0745)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 87px;\"\u003e\n \u003cp\u003e0.0334\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"8\" style=\"width: 311px;\"\u003e\n \u003cp\u003e\u003cem\u003eFSHR\u0026nbsp;\u003c/em\u003e680 A\u0026gt;G (rs6166)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 61px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 222px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" style=\"width: 96px;\"\u003e\n \u003cp\u003eGenotype\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLean\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003en=11 (9.1%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLean\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003en=27 (22.3%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 222px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" style=\"width: 96px;\"\u003e\n \u003cp\u003eA/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 108px;\"\u003e\n \u003cp\u003e4 (36.36%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 108px;\"\u003e\n \u003cp\u003e6 (22.22%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" rowspan=\"3\" style=\"width: 60px;\"\u003e\n \u003cp\u003e3.244\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" rowspan=\"3\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.197\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 222px;\"\u003e\n \u003cp\u003e1 (Reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 87px;\"\u003e\n \u003cp\u003e---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" style=\"width: 96px;\"\u003e\n \u003cp\u003eA/G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 108px;\"\u003e\n \u003cp\u003e3 (27.27%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 108px;\"\u003e\n \u003cp\u003e16 (59.26%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 222px;\"\u003e\n \u003cp\u003e0.2813 (0.0481 - 1.6458)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 87px;\"\u003e\n \u003cp\u003e0.1593\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" style=\"width: 96px;\"\u003e\n \u003cp\u003eG/G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 108px;\"\u003e\n \u003cp\u003e4 (36.36%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 108px;\"\u003e\n \u003cp\u003e5 (18.52%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 222px;\"\u003e\n \u003cp\u003e1.2000 (0.1935 - 7.4408)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 87px;\"\u003e\n \u003cp\u003e0.8447\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" style=\"width: 96px;\"\u003e\n \u003cp\u003eAllele\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 222px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" style=\"width: 96px;\"\u003e\n \u003cp\u003eA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 108px;\"\u003e\n \u003cp\u003e11 (50%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 108px;\"\u003e\n \u003cp\u003e28 (51.85%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" rowspan=\"2\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" rowspan=\"2\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.883\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 222px;\"\u003e\n \u003cp\u003e1 (Reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 87px;\"\u003e\n \u003cp\u003e---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" style=\"width: 96px;\"\u003e\n \u003cp\u003eG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 108px;\"\u003e\n \u003cp\u003e11 (50%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 108px;\"\u003e\n \u003cp\u003e26 (48.15%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 222px;\"\u003e\n \u003cp\u003e1.0769 (0.3995 - 2.9031)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 87px;\"\u003e\n \u003cp\u003e0.8835\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" style=\"width: 96px;\"\u003e\n \u003cp\u003eGenotype\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNormal\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003en=46 (38.0%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNormal\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003en=71 (58.7%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 222px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" style=\"width: 96px;\"\u003e\n \u003cp\u003eA/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 108px;\"\u003e\n \u003cp\u003e13 (28.26%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 108px;\"\u003e\n \u003cp\u003e27 (38.03%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" rowspan=\"3\" style=\"width: 60px;\"\u003e\n \u003cp\u003e1.189\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" rowspan=\"3\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.551\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 222px;\"\u003e\n \u003cp\u003e1 (Reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 87px;\"\u003e\n \u003cp\u003e---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" style=\"width: 96px;\"\u003e\n \u003cp\u003eA/G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 108px;\"\u003e\n \u003cp\u003e23 (50%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 108px;\"\u003e\n \u003cp\u003e31 (43.66%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 222px;\"\u003e\n \u003cp\u003e1.5409 (0.6562 - 3.6185)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 87px;\"\u003e\n \u003cp\u003e0.3208\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" style=\"width: 96px;\"\u003e\n \u003cp\u003eG/G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 108px;\"\u003e\n \u003cp\u003e10 (21.74%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 108px;\"\u003e\n \u003cp\u003e13 (18.31%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 222px;\"\u003e\n \u003cp\u003e1.5976 (0.5551 - 4.5980)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 87px;\"\u003e\n \u003cp\u003e0.3850\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" style=\"width: 96px;\"\u003e\n \u003cp\u003eAllele\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 222px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" style=\"width: 96px;\"\u003e\n \u003cp\u003eA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 108px;\"\u003e\n \u003cp\u003e49 (59.76%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 108px;\"\u003e\n \u003cp\u003e85 (59.86%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" rowspan=\"2\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" rowspan=\"2\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.988\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 222px;\"\u003e\n \u003cp\u003e1 (Reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 87px;\"\u003e\n \u003cp\u003e---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" style=\"width: 96px;\"\u003e\n \u003cp\u003eG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 108px;\"\u003e\n \u003cp\u003e33 (40.24%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 108px;\"\u003e\n \u003cp\u003e57 (40.14%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 222px;\"\u003e\n \u003cp\u003e1.0043 (0.5769 - 1.7484)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 87px;\"\u003e\n \u003cp\u003e0.9879\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" style=\"width: 96px;\"\u003e\n \u003cp\u003eGenotype\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOverweight\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003en=38 (31.4%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOverweight\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003en=18 (14.9%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 222px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" style=\"width: 96px;\"\u003e\n \u003cp\u003eA/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 108px;\"\u003e\n \u003cp\u003e10 (26.32%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 108px;\"\u003e\n \u003cp\u003e4 (22.22%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" rowspan=\"3\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.188\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" rowspan=\"3\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.910\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 222px;\"\u003e\n \u003cp\u003e1 (Reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 87px;\"\u003e\n \u003cp\u003e---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" style=\"width: 96px;\"\u003e\n \u003cp\u003eA/G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 108px;\"\u003e\n \u003cp\u003e23 (60.53%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 108px;\"\u003e\n \u003cp\u003e11 (61.11%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 222px;\"\u003e\n \u003cp\u003e0.8364 (0.2138 - 3.2721)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 87px;\"\u003e\n \u003cp\u003e0.7974\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" style=\"width: 96px;\"\u003e\n \u003cp\u003eG/G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 108px;\"\u003e\n \u003cp\u003e5 (13.16%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 108px;\"\u003e\n \u003cp\u003e3 (16.67%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 222px;\"\u003e\n \u003cp\u003e0.6667 (0.1057 - 4.2066)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 87px;\"\u003e\n \u003cp\u003e0.6662\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" style=\"width: 96px;\"\u003e\n \u003cp\u003eAllele\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 222px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" style=\"width: 96px;\"\u003e\n \u003cp\u003eA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 108px;\"\u003e\n \u003cp\u003e43 (56.58%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 108px;\"\u003e\n \u003cp\u003e19 (52.78%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" rowspan=\"2\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.143\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" rowspan=\"2\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.705\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 222px;\"\u003e\n \u003cp\u003e1 (Reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 87px;\"\u003e\n \u003cp\u003e---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" style=\"width: 96px;\"\u003e\n \u003cp\u003eG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 108px;\"\u003e\n \u003cp\u003e33 (43.42%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 108px;\"\u003e\n \u003cp\u003e17 (47.22%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 222px;\"\u003e\n \u003cp\u003e0.8577 (0.3869 - 1.9017)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 87px;\"\u003e\n \u003cp\u003e0.7056\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" style=\"width: 96px;\"\u003e\n \u003cp\u003eGenotype\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eObese\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003en=26 (21.5%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eObese\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003en=5 (4.1%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 222px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" style=\"width: 96px;\"\u003e\n \u003cp\u003eA/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 108px;\"\u003e\n \u003cp\u003e9 (34.62%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 108px;\"\u003e\n \u003cp\u003e3 (60%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" rowspan=\"3\" style=\"width: 60px;\"\u003e\n \u003cp\u003e2.825\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" rowspan=\"3\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.244\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 222px;\"\u003e\n \u003cp\u003e1 (Reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 87px;\"\u003e\n \u003cp\u003e---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" style=\"width: 96px;\"\u003e\n \u003cp\u003eA/G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 108px;\"\u003e\n \u003cp\u003e11 (42.31%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 108px;\"\u003e\n \u003cp\u003e2 (40%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 222px;\"\u003e\n \u003cp\u003e1.8333 (0.2495 - 13.4702)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 87px;\"\u003e\n \u003cp\u003e0.5514\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" style=\"width: 96px;\"\u003e\n \u003cp\u003eG/G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 108px;\"\u003e\n \u003cp\u003e6 (23.08%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 108px;\"\u003e\n \u003cp\u003e0 (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 222px;\"\u003e\n \u003cp\u003e4.7895 (0.2101 - 109.1924)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 87px;\"\u003e\n \u003cp\u003e0.3261\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" style=\"width: 96px;\"\u003e\n \u003cp\u003eAllele\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 222px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" style=\"width: 96px;\"\u003e\n \u003cp\u003eA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 108px;\"\u003e\n \u003cp\u003e29 (55.77%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 108px;\"\u003e\n \u003cp\u003e8 (80%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" rowspan=\"2\" style=\"width: 60px;\"\u003e\n \u003cp\u003e2.046\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" rowspan=\"2\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.153\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 222px;\"\u003e\n \u003cp\u003e1 (Reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 87px;\"\u003e\n \u003cp\u003e---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" style=\"width: 96px;\"\u003e\n \u003cp\u003eG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 108px;\"\u003e\n \u003cp\u003e23 (44.23%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 108px;\"\u003e\n \u003cp\u003e2 (20%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 222px;\"\u003e\n \u003cp\u003e3.1724 (0.6133 - 16.4087)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 87px;\"\u003e\n \u003cp\u003e0.1685\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Jubilee Mission Medical College and Research Institute","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Polycystic ovary syndrome (PCOS), 5’UTR variant -29 G \u003e A (rs1394205), Ala307Thr A\u003eG (rs6165), Ser680Asn A\u003eG (rs6166), and In-silico prediction of FSHR.","lastPublishedDoi":"10.21203/rs.3.rs-5236464/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5236464/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003ePurpose: \u003c/strong\u003eIn genetic studies, ethnic variations and the heterogeneous nature of PCOS attributed to inconclusive results. Despite being one of the most populated and diverse countries in the world, there is an absence of polymorphisms study on promoter region and a paucity of data on the association of common exonic variations of \u003cem\u003eFSHR\u003c/em\u003egene with PCOS in a homogenous group in India.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMaterials and Methods:\u003c/strong\u003e In our case-control study, we recruited 1018 women (438 PCOS and 580 Controls). We carefully selected 121 participants from the 438 PCOS patients based on their maternal or paternal lineage and the severity of their symptoms from menarche onwards with fulfilling all the three Rotterdam criteria. From 580 controls, to reduce maximum genetic propensity, 121 age-matched individuals who did not have PCOS in either maternal or paternal relatives up to the second degree were enrolled as experimental controls. The proximal promoter region of the \u003cem\u003eFSHR\u003c/em\u003e gene was analyzed in PCOS and control samples by PCR-Sanger sequencing. Further, significantly observed 5’UTR variant (rs1394205) in sanger sequencing and two common exon 10 SNPs [Ala307Thr A\u0026gt;G (rs6165) and Ser680Asn A\u0026gt;G (rs6166)] were analyzed by PCR-RFLP in 121 PCOS patients and 121 control subjects. Finally, the pathogenic evaluation of Ala307Thr A\u0026gt;G (rs6165) and Ser680Asn A\u0026gt;G (rs6166) was performed by applying various bioinformatics tools.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eIn our study, a notable significance were observed in the \u003cem\u003eFSHR\u003c/em\u003e rs1394205 and rs6165 polymorphisms with the PCOS predisposition. Apart from this, rs6165 has a notable variance in genotype frequency between individuals with the normal BMI group. However, the in-silico pathogenicity prediction tools predicted that this variation was non-pathogenic.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion: \u003c/strong\u003eOur finding suggests that the FSHR rs1394205, −29G\u0026gt;A and rs6165 polymorphisms are significantly associated with PCOS predisposition in South Indian PCOS patients.\u003c/p\u003e","manuscriptTitle":"Genetic Predisposition Analysis of the Fshr Gene in Pcos: Insights From a South Indian Population","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-10-14 09:05:19","doi":"10.21203/rs.3.rs-5236464/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"88cea12c-b153-4ddb-9d0f-11f04942f96f","owner":[],"postedDate":"October 14th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":38824853,"name":"Molecular Genetics"},{"id":38824854,"name":"Obstetrics \u0026 Gynecology"}],"tags":[],"updatedAt":"2024-10-14T09:05:19+00:00","versionOfRecord":[],"versionCreatedAt":"2024-10-14 09:05:19","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5236464","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5236464","identity":"rs-5236464","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}