Genetic factors of idiopathic central precocious puberty and their polygenic risk in early puberty

in European Journal of Endocrinology
Authors:
Wei-De LinGenetic Center, Department of Medical Research, China Medical University Hospital, Taichung, Taiwan
School of Post Baccalaureate Chinese Medicine, China Medical University, Taichung, Taiwan

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Chi-Fung ChengGenetic Center, Department of Medical Research, China Medical University Hospital, Taichung, Taiwan
Department of Health Services Administration, China Medical University, Taichung, Taiwan

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Chung-Hsing WangDivision of Medical Genetics, China Medical University Children’s Hospital, Taichung, Taiwan

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Wen-Miin LiangDepartment of Health Services Administration, China Medical University, Taichung, Taiwan

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Chien-Hsiun ChenInstitute of Biomedical Sciences, Academia Sinica, Taipei, Taiwan
School of Chinese Medicine, China Medical University, Taichung, Taiwan

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Ai-Ru HsiehDepartment of Statistics, Tamkang University, New Taipei City, Taiwan

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Mu-Lin ChiuSchool of Chinese Medicine, China Medical University, Taichung, Taiwan

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Ting-Hsu LinGenetic Center, Department of Medical Research, China Medical University Hospital, Taichung, Taiwan

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Chiu-Chu LiaoGenetic Center, Department of Medical Research, China Medical University Hospital, Taichung, Taiwan

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Shao-Mei HuangGenetic Center, Department of Medical Research, China Medical University Hospital, Taichung, Taiwan

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Chang-Hai TsaiDepartment of Biotechnology and Bioinformatics, Asia University, Taichung, Taiwan

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Cherry Yin-Yi ChangDivision of Minimal Invasive Endoscopy Surgery, Department of Obstetrics and Gynecology, China Medical University Hospital, Taichung, Taiwan
Department of Medicine, China Medical University, Taichung, Taiwan

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Ying-Ju LinGenetic Center, Department of Medical Research, China Medical University Hospital, Taichung, Taiwan
School of Chinese Medicine, China Medical University, Taichung, Taiwan

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Fuu-Jen TsaiGenetic Center, Department of Medical Research, China Medical University Hospital, Taichung, Taiwan
Division of Medical Genetics, China Medical University Children’s Hospital, Taichung, Taiwan
School of Chinese Medicine, China Medical University, Taichung, Taiwan
Department of Biotechnology and Bioinformatics, Asia University, Taichung, Taiwan

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Correspondence should be addressed to Y-J Lin or F-J Tsai; Email: yjlin.kath@gmail.com or d0704@mail.cmuh.org.tw

*(W-D Lin and C-F Cheng contributed equally to this work)

Free access

Objective,

To investigate the genetic characteristics of idiopathic central precocious puberty (ICPP) and validate its polygenic risk for early puberty.

Design and methods

A bootstrap subsampling and genome-wide association study were performed on Taiwanese Han Chinese girls comprising 321 ICPP patients and 148 controls. Using previous GWAS data on pubertal timing, a replication study was performed. A validation group was also investigated for the weighted polygenic risk score (wPRS) of the risk of early puberty.

Results

A total of 105 SNPs for the risk of ICPP were identified, of which 22 yielded an area under the receiver operating characteristic curve of 0.713 for the risk of early puberty in the validation group. A replication study showed that 33 SNPs from previous GWAS data of pubertal timing were associated with the risk of ICPP (training group: P-value < 0.05). In the validation group, a cumulative effect was observed between the wPRS and the risk of early puberty in a dose-dependent manner (validation group: Cochran–Armitage trend test: P-value < 1.00E−04; wPRS quartile 2 (Q2) (odds ratio (OR) = 5.00, 95% CI: 1.55–16.16), and wPRS Q3 (OR = 11.67, 95% CI: 2.44–55.83)).

Conclusions

This study reveals the ICPP genetic characteristics with 22 independent and 33 reported SNPs in the Han Chinese population from Taiwan. This study may contribute to understand the genetic features and underlying biological pathways that control pubertal timing and pathogenesis of ICPP and also to the identification of individuals with a potential genetic risk of early puberty.

Abstract

Objective,

To investigate the genetic characteristics of idiopathic central precocious puberty (ICPP) and validate its polygenic risk for early puberty.

Design and methods

A bootstrap subsampling and genome-wide association study were performed on Taiwanese Han Chinese girls comprising 321 ICPP patients and 148 controls. Using previous GWAS data on pubertal timing, a replication study was performed. A validation group was also investigated for the weighted polygenic risk score (wPRS) of the risk of early puberty.

Results

A total of 105 SNPs for the risk of ICPP were identified, of which 22 yielded an area under the receiver operating characteristic curve of 0.713 for the risk of early puberty in the validation group. A replication study showed that 33 SNPs from previous GWAS data of pubertal timing were associated with the risk of ICPP (training group: P-value < 0.05). In the validation group, a cumulative effect was observed between the wPRS and the risk of early puberty in a dose-dependent manner (validation group: Cochran–Armitage trend test: P-value < 1.00E−04; wPRS quartile 2 (Q2) (odds ratio (OR) = 5.00, 95% CI: 1.55–16.16), and wPRS Q3 (OR = 11.67, 95% CI: 2.44–55.83)).

Conclusions

This study reveals the ICPP genetic characteristics with 22 independent and 33 reported SNPs in the Han Chinese population from Taiwan. This study may contribute to understand the genetic features and underlying biological pathways that control pubertal timing and pathogenesis of ICPP and also to the identification of individuals with a potential genetic risk of early puberty.

Introduction

Puberty is a developmental process by which children become reproductively competent young adults (1). Pubertal timing is a consequence of both genetic and environmental factors, including diet, exercise, disease, socioeconomic status, psychosocial stress, and exposure to environmental endocrine disruptors (2, 3, 4, 5). However, 50–75% of the variance in pubertal timing can be explained by genetics (6, 7).

Genetic mutations have been reported in cases and families affected by rare genetic disorders of puberty (8). These genetic mutations are mainly located in the genes encoding the hypothalamic–pituitary–gonadal (HPG) axis, including hypothalamic neurotransmitter and receptor genes (KISS1, KISSR, TAC3, TACR3, GnRH1, MKRN3, and GnRHR) and pituitary development genes (HESX1, SOX2, FSH, FSHR, LH, and LHR). In addition, the discovery of age at menarche common genetic variants (SNPs) has been conducted using genome-wide association studies (GWAS) (9, 10, 11). These genetic mutations and common genetic SNPs highlight the polygenic regulation of the timing of human puberty.

Precocious puberty (PP) in girls is considered to occur when they experience breast budding (the beginning of puberty) before 8 years of age or when the age at menarche (a milestone for female sexual maturation) is less than 10 years (12). Growing evidence suggests that early onset of menarche is associated with a higher risk of subsequent adverse health outcomes, such as short stature, obesity, cardiovascular disease, type 2 diabetes, and breast cancer in adulthood (13, 14, 15, 16, 17).

Idiopathic central precocious puberty (ICPP), a phenotype of early puberty, accounts for 90% of central precocious puberty (CPP) cases worldwide (18). Like CPP, ICPP is also characterized by premature activation of the HPG axis, through the secretion of gonadotropin-releasing hormone (GnRH) in the hypothalamus and the luteinizing hormone (LH) and follicle-stimulating hormone (FSH) in the pituitary gland (19). However, these patients have normal structural morphologies of the HPG axis of the CNS, ovaries, testes, and adrenal glands (20, 21). Studies have shown that monogenic mutations in MKRN3, DLK1, KISS1, KISS1R, NOTCH2, HERC2, TNRC6B, AREL1, UGT2B4, and MKKS are reported in CPP (22, 23, 24). Common genetic SNPs in LIN28B and KISS1 are also associated with CPP (25, 26, 27).

In this study, we investigated the genetic characteristics of ICPP in Taiwanese Han Chinese girls using a bootstrap and genome-wide SNP analysis repeated 100 times (28). Analysis of the 100 lists revealed 105 potential ICPP genetic SNPs that were reproducible and were associated with susceptibility to ICPP. Among the 105 genetic SNPs, we identified 22 independent SNPs, established a weighted polygenic risk score (wPRS), and validated the risk of early puberty with 71% accuracy. We also identified 33 overlapping SNPs from a previous GWAS of pubertal timing. We also found that the 22 independent SNP-based wPRS was associated with the risk of early puberty in a dose-dependent manner. This study contributes to the understanding of the population-based genetic association between control of pubertal timing and pathogenesis of early puberty. This study also contributes to the identification of individuals with potential genetic risks of early puberty prior to the pubertal transition.

Subjects and methods

Study subjects and study design

This was a case–control study wherein the cases were defined as the girls with ICPP and those who met the ICPP diagnostic criteria were recruited (29, 30, 31). The ICPP diagnostic criteria included girls with breast budding before 8 years of age, the onset of menarche before 10 years of age, a difference of more than 2 years between bone age (BA) and chronological age (CA) (BA−CA ≥ 2 years), serum levels of basal LH > 0.3 IU/L, and GnRH-stimulated LH ≥ 10 IU/L. The exclusion criteria were as follows: (i) patients with identified pathological cause(s) of altered hypothalamic–pituitary–gonadal (HPG) axis, (ii) patients with identified ovarian and adrenal disorders, and (iii) patients with CNS abnormalities (20, 21). Finally, 321 patients with ICPP were included in this study (Fig. 1). The control group included 148 normal girls with breast budding after 8 years of age, onset of menarche after 10 years of age, and a difference of less than 1 year (BA−CA < 1 year) (Fig. 1). These ICPP cases and controls were girls sequentially enrolled between August 2004 and September 2017 and were from the Children’s Hospital of China Medical University in Taichung, Taiwan. They were of East Asian ancestry analysed using principal component analysis of genome-wide data (Supplementary Fig. 1, see section on supplementary materials given at the end of this article).

Figure 1
Figure 1

Flow diagram of the analysis process. A full colour version of this figure is available at https://doi.org/10.1530/EJE-21-0424.

Citation: European Journal of Endocrinology 185, 4; 10.1530/EJE-21-0424

The cases and controls were randomly assigned to the training and validation groups using a simple random sampling method (Fig. 1). The training group comprised 80% of the total population (256 ICPP cases and 118 controls) and was used to identify reproducible SNPs associated with ICPP using the bootstrap subsampling and GWAS which was repeated 100 times (28). Genetic SNPs associated with ICPP were analysed using regression analysis under the additive genetic model in PLINK (32). The top 20% significant SNPs were ranked from the genome-wide SNP analysis of each subsampling cohort to obtain 100 lists (Fig. 1). Analysis of the 100 lists revealed 105 common SNPs that were present in each list of the training group (Supplementary Table 1). The validation group comprised 20% of the total population (65 ICPP cases and 30 controls) and was used to validate the risk of early puberty. The Institutional Review Board at China Medical University Hospital in Taichung, Taiwan provided the ethical approval to this study (CMUH107-REC1-183). All participants and their parents or legal guardians signed the written informed consent according to the principles and institutional requirements of the Declaration of Helsinki.

Genotyping, imputation, and quality control

Genomic DNA was extracted from the blood samples using a Genomic DNA Isolation Kit (Qiagen) based on standard protocols and was genotyped using the Axiom genome-wide CHB 1 array plate according to the manufacturer’s instructions (Affymetrix Inc.). Genotyping was performed at the National Genotyping Centre of Academia Sinica in Taipei, Taiwan.

A two-step approach was applied for SNP genotype imputation to maximize the number of SNPs. First, the study genotypes were pre-phased into full haplotypes using SHAPEIT2 (33). Secondly, the imputation was performed by IMPUTE2 according to the Phase I 1000 Genomes Project reference panel released in June 2011 (34). This panel consists of 1094 phased individuals from various ancestry groups so that it can be used to improve imputation accuracy (35, 36, 37). The homogenization of strand annotation was performed using GTOOL software by merging the imputation results from each genotype dataset (http://www.well.ox.ac.uk/~cfreeman/software/gwas/gtool.html).

Genotypes and imputed genotypes were then subjected to quality control. Study subjects of non-Chinese ancestry, who had evidence of relatedness and of possible DNA contamination were excluded. SNPs were excluded when: (i) the total call rate was < 95% for both patients and controls, (ii) only one allele emerged in the patients and controls, (iii) the Hardy–Weinberg equilibrium (HWE) for the SNPs in the controls (P-value < 0.05), and (iv) the minor allele frequency was < 0.5% in the controls.

wPRS construction

A cumulative wPRS based on 22 independent SNPs was constructed (Supplementary Table 3). Each genetic SNP was weighted by the average effect size (log-odds ratio (OR)). The cumulative wPRS was calculated by multiplying each log-OR by the number of corresponding non-risk allele homozygotes (the genotype coding was '0'), risk allele heterozygotes (the genotype coding was '1'), and risk allele homozygotes (the genotype coding was '2') based on the additive inheritance model. The wPRS for each individual was then calculated from these 22 independent SNPs weighted by the estimation of effect sizes (log-OR).

Statistical analyses

Genotype and imputed genotype data of patients with ICPP and controls were used for GWAS using a regression model with the additive inheritance model in PLINK (32). For GWAS in the training group, a P-value < 5.00E−5 was considered significant for the additive test (Supplementary Table 1). For replication studies in the validation group, a P-value < 0.05 was considered significant for the additive test. HWE was evaluated for the SNPs in controls using the goodness-of-fit χ2 test. Logistical regression models were used to measure the difference in genotype frequency between the cases and controls under the additive model (Tables 1, 2 and Supplementary Tables 1, 2). Odds ratios (ORs) with 95% CIs were calculated. PLINK and SPSS (v12.0) for Windows were used to perform all statistical analyses.

Table 1

Twenty-two newly identified independent SNPs associated with idiopathic central precocious puberty (ICPP) in Taiwan. OR calculation was conducted according to the defined risk alleles. Data are presented as OR (95% CI).

No. rs ID Nearest gene CHR Position Risk allele Training group* Validation group Total population P-value
1 rs56698321 IL23R 1 67671378 A 5.41 (2.80–10.46) 6.03 (1.33–27.40) 5.49 (3.00–10.04) 3.32E−08
2 rs4654084 SMYD3 1 245923715 G 2.97 (1.83–4.81) 3.39 (1.14–10.14) 3.03 (1.95–4.71) 9.04E−07
3 rs118156955 2 222996269 T 3.46 (1.93–6.22) NA 4.25 (2.40–7.52) 7.04E−07
4 rs62245082 SRGAP3 3 9041193 T 2.89 (1.75–4.78) 1.97 (0.55–1.82) 2.73 (1.72–4.34) 2.09E−05
5 rs11130329 TMEM110-MUSTN1 3 52896855 C 6.31 (3.19–12.50) NA 7.65 (3.91–14.99) 3.02E−09
6 rs1024889 3 70463330 A 11.56 (6.73–19.85) 7.88 (2.69–23.12) 10.67 (6.59–17.28) 6.52E−22
7 rs9997440 LINC02497 4 31208611 A 3.58 (2.13–6.02) 1.38 (0.55–1.82) 2.83 (1.81–4.43) 4.87E−06
8 rs78767288 4 136455374 A 7.26 (2.98–17.71) NA 6.11 (2.63–14.24) 2.70E−05
9 rs4543136 4 146893952 A 13.53 (4.83–37.91) NA 15.54 (5.60–43.11) 1.36E−07
10 rs466002 MAST4 5 66197480 T 2.21 (1.55–3.15) 1.30 (0.64–1.56) 1.99 (1.45–2.73) 1.88E−05
11 rs12662085 LOC107983965 6 9894433 C 9.56 (4.00–22.85) 4.13 (0.92–1.09) 7.91 (3.75–16.68) 5.65E−08
12 rs2347637 ESR1 6 152028479 G 4.78 (2.40–9.55) 4.53 (1.04–19.65) 4.73 (2.53–8.84) 1.11E−06
13 rs74297783 7 55850376 C 7.51 (3.24–17.36) 0.86 (0.16–6.39) 4.81 (2.39–9.72) 1.14E−05
14 rs4738789 8 60871604 C 2.39 (1.62–3.53) 0.63 (0.81–1.23) 1.76 (1.27–2.43) 7.43E−04
15 rs76540613 8 126773759 A 5.58 (2.82–11.01) 2.26 (0.16–6.21) 4.43 (2.53–7.78) 2.12E−07
16 rs72695197 9 7721620 T 2.29 (1.55–3.38) 1.02 (0.46–2.19) 1.96 (1.38–2.77) 1.46E−04
17 rs7851008 LINC01507 9 82596501 G 2.11 (1.52–2.94) 1.67 (0.90–1.11) 2.00 (1.50–2.68) 3.01E−06
18 rs10512247 LOC105376157 9 98296403 C 3.18 (1.85–5.44) 1.42 (0.42–2.36) 2.77 (1.70–4.51) 4.09E−05
19 rs56810982 PRKCQ 10 6596332 A 2.21 (1.59–3.09) 1.20 (0.64–1.56) 1.94 (1.45–2.59) 8.37E−06
20 rs77000039 11 112679061 A 3.25 (1.98–5.32) 1.71 (0.69–1.45) 2.81 (1.82–4.32) 2.62E−06
21 rs117195585 12 45541319 G 4.42 (2.38–8.22) 10.14 (1.31–78.19) 4.83 (2.68–8.69) 1.49E−07
22 rs8056826 LOC107984851 16 22822411 T 5.46 (2.43–12.30) 3.05 (0.76–1.32) 4.74 (2.37–9.49) 1.11E−05

*256 cases and 118 controls; 65 cases and 30 controls; 321 cases and 148 controls.

CHR, chromosome; ICPP, idiopathic central precocious puberty; NA, not available; OR, odds ratio.

Table 2

Previous GWAS genetic SNPs of pubertal timing associated with idiopathic central precocious puberty (ICPP) in Taiwan. OR calculation was conducted according to the defined risk alleles.

No. rs ID Gene CHR Position Risk allele PMID number Population Total population*
OR (95% CI) P-value
1 rs9386427 HACE1 6 105166802 G 23599027 African; European; African American 1.494 (1.107–2.016) 8.72E−03
2 rs364663 LIN28B 6 105443189 A 23667675 East Asian 1.481 (1.130–1.943) 4.50E−03
3 rs369065 LIN28B 6 105444058 T 23133486; 22131368; 20734064; 30249230 European; East Asian 1.465 (1.127–1.904) 4.39E−03
4 rs11792861 9 111809295 C 25231870 European 1.501 (1.093–2.060) 1.21E−02
5 rs11031010 LOC105376607 11 30240178 C 20734064; 24101221; 22248077 European 3.036 (1.466–6.289) 2.79E−03
6 rs11216435 DSCAML1 11 117388932 T 23599027 African; European; African American 1.558 (1.130–2.147) 6.78E−03
7 rs6563739 13 40239785 T 25231870 European 1.397 (1.044–1.869) 2.44E−02
8 rs7359257 15 67702907 A 21102462 European 1.515 (1.050–2.186) 2.65E−02
9 rs12149832 FTO 16 53842908 A 23599027 African American 2.252 (1.413–3.589) 6.45E−04
10 rs12607903 DLGAP1 18 3817134 T 25231870 European 1.468 (1.103–1.953) 8.50E−03
11 rs2048523 18 44799515 G 27182965 European 1.507 (1.067–2.130) 2.00E−02

*321 cases and 148 controls.

CHR, chromosome; GWAS, genome-wide association study; ICPP, idiopathic central precocious puberty; NA, not available; No., number; OR, odds ratio.

The areas under the receiver operating characteristic (ROC) curves (AUCs) for the validation group (Fig. 2) were used to validate the accuracy of the selected genetic SNPs (38). The accuracy of the AUC value can range from 0.5 to 1.0, indicating a total lack of discrimination to perfect discrimination.

Figure 2
Figure 2

Receiver operating characteristic (ROC) curve analysis of genetic SNPs in the training and validation groups. (A) For the training group, ROC curve analysis for 22 independent genetic SNPs (Novel only) and 22 independent + 33 reported genetic SNPs (Novel + Report). (B) For the validation group, ROC curve analysis for 22 independent genetic SNPs (Novel only) and 22 independent + 33 reported genetic SNPs (Novel + Report). A full colour version of this figure is available at https://doi.org/10.1530/EJE-21-0424.

Citation: European Journal of Endocrinology 185, 4; 10.1530/EJE-21-0424

Lewontin’s D′ and R2 values were used to evaluate the intermarker coefficient of linkage disequilibrium (LD) for both cases and controls for haplotype block analysis (Supplementary Fig. 2) (39). The CI for LD was estimated to construct the haplotype blocks using a resampling procedure (40, 41).

Results

Identification of genetic SNPs associated with ICPP

In this study, we characterized the genetic predisposition to ICPP in girls of Han Chinese ancestry in Taiwan (Fig. 1). The bootstrap subsampling and GWAS identified 105 potential genetic SNPs that were susceptible and reproducible and were associated with ICPP in the training group (Supplementary Table 1). A stepwise logistic regression model was applied to the training group to select a few among the 105 genetic SNPs for validation. Furthermore, pair-wise linkage disequilibrium (LD) analysis was performed to exclude SNPs with strong LD (D′ > 0.8) (Supplementary Fig. 2) (39). This resulted in the identification of 22 independent genetic SNPs (Fig. 1 and Table 1).

The AUCs analyses were applied to the validation group using the ROC curve and AUC values (38). For the training group, the AUC of the 22 independent SNPs was 0.955 (95% CI: 0.933–0.978) (Fig. 2A). For individuals with wPRS > 22.6, the sensitivity was 0.925 (0.891–0.952), the specificity was 0.697 (0.615–0.770), and the accuracy was 0.854 (0.819–0.885) (Supplementary Table 7). For individuals with wPRS > 24.9, the sensitivity was 0.689 (0.636–0.740), the specificity was 0.910 (0.852–0.951), and the accuracy was 0.758 (0.717–0.796) (Supplementary Table 7).

For the validation group, the AUC of the 22 independent SNPs was 0.713 (0.602–0.823) (Fig. 2B). For individuals with wPRS > 22.6, the sensitivity was 0.800 (0.682–0.889), the specificity was 0.533 (0.343–0.717), and the accuracy was 0.716 (0.614–0.804) (Supplementary Table 7). For individuals with wPRS > 24.9, the sensitivity was 0.523 (0.395–0.649), the specificity was 0.767 (0.577–0.901), and the accuracy was 0.600 (0.494–0.699) (Supplementary Table 7). These results showed that the wPRS of 24.9 was with higher specificity, but lower sensitivity than the wPRS of 22.6 for ICPP. However, the wPRS of 22.6 was with higher sensitivity, but lower specificity than the wPRS of 24.9. The overall results suggested that the wPRS of 22.6 may be the better cut-off value for determining the risk of early puberty than the wPRS of 24.9 using ROC curve analysis.

Replication studies of previous GWAS genetic SNPs of pubertal timing in the risk of ICPP

Several studies have previously identified genetic SNPs of pubertal timing by GWAS (9, 10, 11, 26, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56). In the present study, a replication analysis of the reported GWAS genetic SNPs of pubertal timing was performed. There were 130 genetic SNPs reported in 19 previous GWASs on pubertal timing (Supplementary Table 2). Among them, 33 SNPs were identified as susceptible to ICPP (training group: call rates of genotyping for both cases and controls > 95%, genotypes in controls with their Hardy–Weinberg equilibrium (P-value > 0.05), and P-value for the additive model < 0.05). The 33 GWAS-determined SNPs in the six closest genes were associated with the risk of ICPP (Supplementary Table 2). To select fewer SNPs from these 33 genetic SNPs for validation, a stepwise logistic regression model was applied. Furthermore, pair-wise linkage disequilibrium (LD) analysis was performed to exclude SNPs with strong LD (D′ > 0.8) (Supplementary Fig. 3) (39). This resulted in 11 independent GWAS-determined SNPs (Table 2).

For the training group, the AUC of the 22 independent SNPs was 0.955 (0.933–0.978) (Fig. 2A). With the addition of 11 reported SNPs, the AUC of the 22 independent + 33 reported SNPs slightly increased to 0.973 (0.956–0.989) (Fig. 2A). For the validation group, the AUC of the 22 independent SNPs was 0.713 (0.602–0.823) (Fig. 2B). With the addition of 11 reported SNPs, the AUC of the 22 independent + 11 reported SNPs also increased to 0.725 (0.616–0.834) (Fig. 2B). Overall, with the addition of 11 reported SNPs, there was a slight increase in the AUC for the risk of early puberty.

Association between the wPRS and risk of early puberty in the validation group

The cumulative effect of these 22 independent SNPs on the risk of early puberty was also investigated (Table 3). The wPRS was calculated based on the sum of the risk alleles from the 22 independent SNPs by multiplying each log-odds ratio by the number of corresponding risk alleles (Fig. 3A: training group; Fig. 3B: validation group). For the training group, the frequency distribution of wPRS was observed between the cases and controls (Fig. 3A). In this group, a wPRS > 24.9 was observed among 36.7% of the patients and only 2.5% of the controls (Fig. 3A: the training group). For the validation group, the frequency distribution of wPRS was observed between the cases and controls (Fig. 3B). In this group, 32.3% of the patients had a weighted PRS >24.9, whereas none of the controls did (Fig. 3B: validation group).

Table 3

Association between the 22 independent SNP-based weighted PRS and risk of early puberty in the validation group. The 22 independent SNPs and their respective risk genotypes were shown in Table 1.

wPRS quartile Cases (n = 65) Controls (n = 30) OR (95% CI) P-value
Q1 6 (9.2%) 15 (50.0%) Ref. Ref.
Q2 24 (36.9%) 12 (40.0%) 5.00 (1.55–16.16) 7.20E−03
Q3 14 (21.5%) 3 (10.0%) 11.67 (2.44–55.83) 2.10E−03
Q4 21 (32.3%) 0 (0.0%) ND ND
Cochran–Armitage trend test <1.00E−04

WPRS quartile 1 (Q1): wPRS ≤ 20.8; wPRS quartile 2 (Q2): 20.8 < wPRS ≤22.6; wPRS quartile 3 (Q3): 22.6 < wPRS ≤ 24.9; wPRS quartile 4 (Q4): wPRS > 24.9.

ICPP, idiopathic central precocious puberty; ND, not determined; OR, odds ratio; PRS, polygenic risk score; Ref., reference; wPRS, weighted polygenic risk score. Bold P values denote statistical significance.

Figure 3
Figure 3

Frequency distribution of 22 independent SNP-based weighted polygenic risk score (wPRS) in the training and validation groups, respectively. (A) For the training group, frequency distribution of wPRS between cases and controls (cases: black squares; controls: white squares). (B) For the validation group, the frequency distribution of wPRS between cases and controls (cases: black squares; controls: white squares). A full colour version of this figure is available at https://doi.org/10.1530/EJE-21-0424.

Citation: European Journal of Endocrinology 185, 4; 10.1530/EJE-21-0424

The frequency distribution of the 22 SNPs wPRS was divided into quartiles (quartile 1 (Q1)–quartile 4 (Q4); Supplementary Table 3: the training group; Table 3: the validation group). Individuals in Q1 served as references. The wPRS was associated with an increased risk of ICPP in a dose-dependent manner in both the training and validation groups (Table 3 and Supplementary Table 3) (P-value < 1.00E−04; Cochran–Armitage trend test). In the validation group, in contrast to the individuals in wPRS Q1, an association with an increased risk of ICPP in a dose-dependent manner was evident in wPRS Q2 (OR = 5.00, 95% CI: 1.55–16.16) and wPRS Q3 (OR = 11.67, 95% CI: 2.44–55.83) (Table 3). Similar results were observed in the training group (Supplementary Table 3). The wPRS was also associated with an increased risk of ICPP in a dose-dependent manner (Supplementary Table 3; wPRS Q2 (OR = 13.92, 95% CI: 6.60–29.37), wPRS Q3 (OR = 120.19, 95% CI: 41.11–351.35), and wPRS Q4 (OR = 202.46, 95% CI: 55.76–735.05)). Our results also showed that individuals with higher wPRS had more pronounced features of ICPP, such as younger chronologic age, older bone age, higher LH/FSH ratio, and higher oestradiol concentrations (Supplementary Table 4). Taken together, these results suggest that there was an association between the 22 independent SNP-based wPRS and the risk of early puberty in a dose-dependent manner.

Discussion

For puberty genetics, monogenic mutations, and common genetic SNPs in genes mainly in the hypothalamic–pituitary–gonadal (HPG) axis have been reported in central precocious puberty (CPP) (22, 23, 24, 25, 26, 27). Till date, only common genetic SNPs in LIN28B have been reported in idiopathic CPP (57). In this study, we investigated the genetic characteristics of ICPP in Han Chinese ancestry using a bootstrap and genome-wide association analysis repeated 100 times (28). We identified 105 novels and 33 reported common genetic SNPs for ICPP in the Han Chinese population in Taiwan. This study may provide important information pertaining to ICPP genetics for the Han Chinese ancestry. This study may also contribute to the understanding of the population-based genetic association and the control of pubertal timing and pathogenesis of early puberty. The results discussed here may also contribute towards the identification of individuals with potential genetic risks of early puberty.

Rare genetic disorders of puberty show monogenic mutations in genes regulating the HPG axis including KISS1, KISSR, TAC3, TACR3, GnRH1, MKRN3, GnRHR, HESX1, SOX2, FSH, FSHR, LH, and LHR (8). For CPP, monogenic mutations in MKRN3, DLK1, KISS1, KISS1R, NOTCH2, HERC2, TNRC6B, AREL1, UGT2B4, and MKKS have been reported (22, 23, 24). For CPP, common genetic SNPs in LIN28B and KISS1 have also been reported (25, 26, 27).

In line with the results of the GWAS studies on pubertal timing (9, 10, 11, 26, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56), the 33 reported SNPs were associated with the risk of early puberty in our study. These 33 reported SNPs were located in six genes, including HACE1, LIN28B, LOC105376607, DSCAML1, FTO, and DLGAP1. Among the 33 reported SNPs, 23 were located near LIN28B on chromosome 6. In agreement with previous studies, we also observed that LIN28B genetic SNPs were associated with the risk of ICPP (57). LIN28B encodes Lin-28 homologue B (LIN28B), a member of the Lin-28 family. LIN28B and its homologue LIN28A are RNA-binding proteins that regulate let-7 microRNA biogenesis. The LIN28-let7 pathway plays a role in modulating mammalian growth and puberty (58, 59, 60). Transgenic mice overexpressing LIN28 exhibited increased body size and delayed puberty, suggesting that LIN28 may be involved in the timing of sexual maturation (60, 61).

In agreement with the previous GWAS studies on pubertal timing, we observed that there were 2 of the 33 reported SNPs located near FTO (FTO alpha-ketoglutarate dependent dioxygenase) on chromosome 16. We also observed that FTO genetic SNPs were associated with ICPP risk in this study. FTO is a fat mass and obesity-associated gene (62) and a DNA-binding protein that interacts with the AT-rich interaction domain 5B (ARID5B), Iroquois homeobox 3 (IRX3), and IRX5 proteins. The ARID5B-FTO-IRX3-IRX5 networks play a role in regulating thermogenesis, lipid storage, and adipogenesis (63, 64).

In this study, 22 independent genetic SNPs were associated with ICPP in the Han Chinese population of Taiwan. We confirmed the genotyping data (Supplementary Table 5) using the MALDI-TOF mass spectrometry-based SNP genotyping method (65, 66). The wPRS based on the 22 genetic SNPs was associated with early puberty in a dose-dependent manner, suggesting that individuals with higher wPRS had a higher risk of early puberty (Supplementary Table 3). Furthermore, individuals with higher wPRS had more pronounced features of ICPP, such as younger chronologic age, older bone age, higher LH/FSH ratio, and higher oestradiol concentrations (Supplementary Table 4). These 22 SNPs were located in 12 genes: IL23R, SMYD3, SRGAP3, TMEM110-MUSTN1, LINC02497, MAST4, LOC107983965, ESR1, LINC01507, LOC105376157, PRKCQ, and LOC107984851. Among these, SMYD3 protein may affect folliculogenesis by regulating histone H3K4me3 methylation status (67). Genetic SNPs in ESR1 are associated with age at menarche (68, 69). ESR1 encodes oestrogen receptor 1 and is involved in oestrogen biosynthesis and metabolism (70) and its mutations have been associated with delayed puberty (71). Genetic SNPs in SRGAP3, MAST4, and PRKCQ are associated with BMI and body fat (72, 73, 74). Adipose tissue is a key endocrine organ involved in many processes, including reproductive function (75). However, the role of adipose tissue content in the timing of sexual maturation remains to be elucidated. Further genetic investigations are required to elucidate the relationship between obesity and puberty.

This study had several limitations. First, this study was performed on a small sample cohort, which may have limited the ability to investigate gene–environment interaction effects. However, our study is the first comprehensive investigation of the ICPP genetic profile in Han Chinese ancestry in Taiwan, and further studies with larger sample sizes will be performed in the future. Secondly, the study subjects were all girls with Han Chinese ancestry in Taiwan, and the genetic profile for ICPP may not represent the genetic profile in boys or Caucasians. Further, the genetic profile for ICPP may not represent the genetic profile in age at menarche or age at menopause in Caucasians (Supplementary Table 6). Thirdly, this study was performed with individuals of Han Chinese ancestry in Taiwan and needs to be validated in other ethnic populations.

In conclusion, our study is the first comprehensive investigation of the genetic characteristics of ICPP in the Han Chinese population in Taiwan. We first reported 105 newly identified SNPs as new genetic risk factors for early puberty. We also report 33 SNPs from previous GWASs of pubertal timing to be associated with the risk of early puberty. The findings reveal genetic risk factors for early puberty and will advance the understanding of the genetics and biology that control pubertal timing and pathogenesis of ICPP. This study also contributes to the identification of individuals with potential genetic risks of early puberty.

Supplementary materials

This is linked to the online version of the paper at https://doi.org/10.1530/EJE-21-0424.

Declaration of interest

The authors declare that there is no conflict of interest that could be perceived as prejudicing the impartiality of this study.

Funding

This study was supported by grants from the China Medical University, Taiwan (CMU109-MF-41, CMU109-MF-126, CMU109-S-18, and CMU109-S-27), the China Medical University Hospital, Taiwan (DMR-109-145, DMR-109-188, DMR-109-192, DMR-110-134, and DMR-110-152), and the Ministry of Science and Technology, Taiwan (MOST 108-2314-B-039-044-MY3, MOST 109-2320-B-039-035-MY3, and MOST 109-2410-H-039-002). The funding entities for this study had no role in the study design, data collection, data analysis, interpretation, or authorship of this manuscript.

Acknowledgements

The authors are grateful to the Health Data Science Center at the China Medical University Hospital for providing administrative, technical, and funding support. The authors also thank the National Center for Genome Medicine of the National Core Facility Program for Biotechnology, and Ministry of Science and Technology for technical and bioinformatics support.

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    Figure 1

    Flow diagram of the analysis process. A full colour version of this figure is available at https://doi.org/10.1530/EJE-21-0424.

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    Figure 2

    Receiver operating characteristic (ROC) curve analysis of genetic SNPs in the training and validation groups. (A) For the training group, ROC curve analysis for 22 independent genetic SNPs (Novel only) and 22 independent + 33 reported genetic SNPs (Novel + Report). (B) For the validation group, ROC curve analysis for 22 independent genetic SNPs (Novel only) and 22 independent + 33 reported genetic SNPs (Novel + Report). A full colour version of this figure is available at https://doi.org/10.1530/EJE-21-0424.

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    Figure 3

    Frequency distribution of 22 independent SNP-based weighted polygenic risk score (wPRS) in the training and validation groups, respectively. (A) For the training group, frequency distribution of wPRS between cases and controls (cases: black squares; controls: white squares). (B) For the validation group, the frequency distribution of wPRS between cases and controls (cases: black squares; controls: white squares). A full colour version of this figure is available at https://doi.org/10.1530/EJE-21-0424.

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