Your height affects your health: genetic determinants and health-related outcomes in Taiwan.

The Taiwan Biobank is a database for phenotypic and genomic measurements of the Taiwanese population that was established in 2012. The study recruited volunteers aged 30–70 years with no history of malignancy at enrollment (Twbiobank; https://www.twbiobank.org.tw/new_web/ ) [ 51 , 52 ]. All volunteers were residents of Taiwan and provided informed consent. Participants completed questionnaires and underwent interviews, anthropometric measurements, and blood and urine tests to collect demographic, lifestyle, and genomic data. Anthropometric measurements, including height, body weight, waist circumference, hip circumference, and body fat percentage, were obtained from participants in the Taiwan Biobank (Additional file 1 : Table S1). Body mass index (BMI) was calculated as BMI = body weight/body height 2 . The waist-hip ratio (WHR) was calculated as WHR = waist circumference/ hip circumference. Anthropometric measurements were stratified by sex and analyzed using the mean and standard deviation (SD), where data were normalized to one SD before further analysis. Blood pressure and lipid and glucose levels were quantitatively measured in participants in the Taiwan Biobank. Systolic blood pressure (SBP), diastolic blood pressure (DBP), total cholesterol (TC), triglyceride (TG), low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), fasting glucose, and hemoglobin (Hb) A1c levels were obtained (Additional file 1 : Table S1). Participants were asked to report their health status using questionnaires and interviews. According to participants’ self-reported health status (comorbidities) in the Taiwan Biobank, 10 broad categories of 49 diseases were investigated in our study as follows (Additional file 1 : Table S1): (1) orthopedic or joint disorders: osteoporosis, arthritis, rheumatoid arthritis, osteoarthritis, and gout; (2) lung and respiratory diseases: asthma and emphysema or chronic bronchitis; (3) cardiovascular diseases: valvular heart disease, coronary artery disease, heart arrhythmia, cardiomyopathy, congenital heart defect, other type of heart disease, hyperlipidemia, hypertension, and stroke; (4) diabetes: type 1 diabetes and type 2 diabetes; (5) digestive diseases: peptic ulcer disease, gastroesophageal reflux disease, and irritable bowel syndrome; (6) Mental or emotional disorders: depression, bipolar disorder, postpartum depression, obsessive-compulsive disorder, alcohol addiction or drug abuse, and schizophrenia; (7) nervous system disorders: epilepsy, migraine, multiple sclerosis, Parkinson’s disorder, and dementia; (8) other types of disease: gallstones, kidney stones, kidney failure, and vertigo; (9) eye diseases: cataract, glaucoma, dry eye syndrome, retinal detachment, floaters, blindness, color blindness, and others; and (10) female diseases: severe menstrual cramps, uterine fibroids, ovarian cysts, endometriosis, and uterine/cervical polyps. A total of 132,720 participants were selected from the Taiwan Biobank (Fig. 1 ). The exclusion criteria were as follows: (1) individuals who did not have GWAS data ( N = 16,654), (2) individuals who did not pass the quality control (QC) and principal component analysis (PCA) of GWAS data ( N = 19,555), (3) individuals who did not have height information ( N = 25), (4) individuals with their height more than ±4 SD ( N = 23), (5) individuals without drinking information ( N = 52), (6) individuals without smoking information ( N = 12), and (7) individuals without regular exercise information ( N = 38). The criteria for drinking included current drinkers for at least 6 months; smoking criteria included current smokers for at least 6 months; finally, regular exercise criteria included participants performing regular exercise currently, for at least 6 months. Fig. 1 Flowchart of the study design and analysis process Flowchart of the study design and analysis process Finally, 96,361 participants of Han Chinese ancestry were included in this study (Fig. 1 ) and assigned to the training, testing, and validation groups using a simple random sampling method (7:1.5:1.5 ratio). The training group ( N = 67,452 participants) comprised 70% of the total study population and underwent GWAS based on height (Additional file 2 : Table S2; Figs. 2 and 3 ). Before the height GWAS analysis, the measured height (phenotype) was stratified by sex and subsequently mean-centered and normalized to one SD. GWAS for height was then performed using a linear regression model with the assumption of additive allelic effects of SNP dosages, with adjusted covariates including age, sex, education, drinking, smoking, regular exercise, and the first 10 PCAs (Additional file 4 : Fig. S2), using the PLINK software (version 1.9, 2.0) [ 3 – 9 , 20 , 53 – 56 ]. A genome-wide significance value was used ( p < 5.00E−8 for the additive test). Fig. 2 Manhattan plot for height with Han Chinese ancestry Fig. 3 Regional plots for the independent signals at seven genetic loci for height in individuals with Han Chinese ancestry. A rs3791675 in EGF containing fibulin extracellular matrix protein 1 ( EFEMP1 ). B rs76803230 in DIS3 like 3′-5′ exoribonuclease 2 ( DIS3L2 ). C rs57345461 in zinc finger and BTB domain containing 38 ( ZBTB38 ). D rs16895971 in ligand dependent nuclear receptor corepressor like ( LCORL ). E rs2780226 in high mobility group AT-hook 1 ( HMGA1 ). F rs3816804 in citrate synthase ( CS ). G rs143384 in growth differentiation factor 5 ( GDF5 ). Each plot shows the –log10 p -value on the y -axis for each SNP and the SNP position in the genome region on the x -axis. The top significant SNP is shown by a purple diamond; genes in its proximity are shown below each plot. LD with nearby SNPs is measured using R 2 values, according to the 1000 Genomes Project Phase 3 East Asia Summit data, and is indicated by the color of each circle Manhattan plot for height with Han Chinese ancestry Regional plots for the independent signals at seven genetic loci for height in individuals with Han Chinese ancestry. A rs3791675 in EGF containing fibulin extracellular matrix protein 1 ( EFEMP1 ). B rs76803230 in DIS3 like 3′-5′ exoribonuclease 2 ( DIS3L2 ). C rs57345461 in zinc finger and BTB domain containing 38 ( ZBTB38 ). D rs16895971 in ligand dependent nuclear receptor corepressor like ( LCORL ). E rs2780226 in high mobility group AT-hook 1 ( HMGA1 ). F rs3816804 in citrate synthase ( CS ). G rs143384 in growth differentiation factor 5 ( GDF5 ). Each plot shows the –log10 p -value on the y -axis for each SNP and the SNP position in the genome region on the x -axis. The top significant SNP is shown by a purple diamond; genes in its proximity are shown below each plot. LD with nearby SNPs is measured using R 2 values, according to the 1000 Genomes Project Phase 3 East Asia Summit data, and is indicated by the color of each circle We also ensured that our GWAS findings of the training group replicated previously identified height-associated genetic variants ( https://www.ebi.ac.uk/gwas/efotraits/EFO_0004339 ). The reported GWAS height-associated genetic variants were mainly from individuals of European ancestry (excluding the genetic variants of infant or child height traits) and were downloaded from the GWAS catalog website. After removal of the repeated SNPs, 1722 reported GWAS body height-related SNPs were obtained from the GWAS catalog (Additional file 3 : Table S3). These SNPs were replicated in our cohort, and we further identified 313 GWAS SNPs associated with height in our cohort ( p < 0.05/1722 SNPs) (Additional file 3 : Table S3). The testing group ( N = 14,454 participants) comprised 15% of the total study population and was used to select the best-fit PRS, to investigate the association between genetically determined height (PRS 251 ) and measured height (phenotype) using linear regression analysis (Fig. 4 ). The validation group ( n = 14,455 participants) comprised 15% of the total population and was used to determine the association between genetically determined height (PRS 251 ) and measured height (phenotype) using linear regression analysis (Fig. 4 ). This study was approved by the Human Studies Committee of the China Medical University Hospital, Taichung, Taiwan (approval number: CMUH107-REC3-074). Fig. 4 Association between genetically determined height (PRS 251 ) and measured height (phenotype). Measured height (cm) and calculated polygenic risk score (PRS) for height in the testing and validation groups were stratified by sex, mean-centered, and normalized to one standard deviation (SD), respectively (males, N = 10,919; females, N = 17,990). The normalized measured height is shown on the y -axis and normalized genetically determined height (PRS 251 ) is shown on the x -axis Association between genetically determined height (PRS 251 ) and measured height (phenotype). Measured height (cm) and calculated polygenic risk score (PRS) for height in the testing and validation groups were stratified by sex, mean-centered, and normalized to one standard deviation (SD), respectively (males, N = 10,919; females, N = 17,990). The normalized measured height is shown on the y -axis and normalized genetically determined height (PRS 251 ) is shown on the x -axis Genomic DNA from the Taiwan Biobank was genotyped using Axiom genome-wide TWB1 (653,291 SNPs) or TWB2 (752,921 SNPs) array plates based on the Axiom genome-wide array plate system, according to the manufacturer’s instructions (Affymetrix Inc., Santa Clara, CA, USA). Genotyping was performed at the National Genotyping Center of Academia Sinica, Taipei, Taiwan ( http://ncgm.sinica.edu.tw/affymetrix_tech_01.html ) ( https://www.biobank.org.tw/fd.php ). Genotypic data were then subjected to QC procedures (individual QC and SNP QC) in the Taiwan Biobank ( https://www.biobank.org.tw/fd.php ). The exclusion criteria for individual QC were as follows: (1) individuals with a missing call rate of > 5%, (2) a heterozygosity rate of >±5 SD, (3) individual identity by descent (IBD) score of ≥ 0.125, and (4) individuals who did not fit the East Asia Summit ancestry PCA. Similar to the results of a previous study [ 51 ], most individuals from the Taiwan Biobank were of Han Chinese ancestry. The exclusion criteria for SNP QC were as follows: (1) SNPs with a missing call rate of >5%, (2) SNPs with Hardy–Weinberg equilibrium (HWE) p -value of <1 × 10 −5 , and (3) SNPs with a minor allele frequency (MAF) of <5%. Qualified genotype data were then subjected to an imputation procedure to maximize the number of SNPs in the Taiwan Biobank ( https://www.biobank.org.tw/fd.php ). First, the SNPs of the qualified genotype data were excluded based on the following criteria: (1) SNPs with MAF of <1%, (2) SNPs with a HWE p -value of 5% using the PLINK software (versions 1.9 and 2.0, http://zzz.bwh.harvard.edu/plink/ ). SHAPEIT2 (v2.r790) was used to phase the genotypes into full haplotypes ( https://mathgen.stats.ox.ac.uk/genetics_software/shapeit/shapeit.html ). Third, imputation was performed using IMPUTE2 (v2.3.1, https://mathgen.stats.ox.ac.uk/impute/impute_v2.html ), according to the pooled reference panel [Taiwan Biobank (TWB) + East Asian (EAS)]. The pooled reference panel comprised 973 phased individuals with the TWB panel from the Taiwan Biobank [ 57 ] and 504 phased individuals from the EAS panel [ 57 , 58 ] (The Phase 3 1000 Genomes Project reference panel; The 1000 Genomes Project Consortium, 2010). The pooled reference panel with TWB and EAS ancestry groups was used to improve imputation accuracy [ 57 , 58 ]. The following imputed SNPs were excluded: (1) SNP with a missing call rate of > 5%, (2) SNPs with MAF < 0.01%, and (3) SNPs with IMPUTE2 information score of < 0.3. Imputed GWAS data were obtained from the Taiwan Biobank. In our study, SNP QC and individual QC procedures were applied before GWAS of height (Fig. 1 ). SNPs were excluded from the SNP QC based on the following criteria: (1) SNP with a missing call rate of > 5%, (2) SNPs with HWE p -value of < 1 × 10 −6 ; and (3) SNPs with MAF < 0.01%. After SNP QC, the remaining SNPs were used to perform ancestry PCA for the population structure analysis. The exclusion criteria for individual QC were as follows: (1) individual with a missing call rate > 5%, (2) heterozygosity rate > ±5 SD, (3) individual IBD score ≥0.125, and (4) individuals who did not fit the East Asia Summit ancestry PCA. Participants of non-Chinese ancestry, with evidence of relatedness, or with DNA contamination were excluded. The PRS was calculated in the testing group using PLINK software (versions 1.9 and 2.0) [ 35 , 53 , 59 ], based on the statistical results of the 6,941 SNPs in the training group (Fig. 1 ). The 6941 SNPs comprised SNPs with genome-wide significance ( p < 5 × 10 −8 ) and SNPs that were replicated from previously reported body-height GWAS SNPs ( p 0.1), according to the estimated linkage disequilibrium (LD) among the SNPs in the testing group (Additional file 4 : Fig. S1A). After clumping, 251 SNPs were obtained. These 251 SNPs were used to select the “best-fit” PRS according to a series of cutoff values for height-associated p -value thresholds (including 5 × 10 −15 , 5 × 10 −14 , 5 × 10 −13 , 5 × 10 −12 , 5 × 10 −11 , 5 × 10 −10 , 5 × 10 −9 , 5 × 10 −8 , 5 × 10 −7 , 5 × 10 −6 , and 5 × 10 −5 ) in the testing group. The p -value cutoff (5 × 10 −5 ) was adopted by the “best-fit” PRS with the largest explicable phenotype r 2 using only the PRS (PRS r 2 = 0.0712, SNP number = 251; Additional file 4 : Fig. S1A). In total, 251 SNPs were obtained for the best-fit PRS calculations for all participants. For each participant, the genetically determined height (PRS value) was calculated [ 35 , 53 , 59 ] using 251 SNPs obtained after the clumping protocol. Data centering and standardization were also performed for the PRS height data. Genotype and imputed genotype data were used for GWAS analysis, as previously described. The HWE for the SNPs in the controls was evaluated using chi-square ( χ 2 ) tests. Lewontin’s D and R 2 values were used to evaluate the inter-marker coefficient of LD for haplotype block analysis [ 60 ]. The confidence interval (CI) for LD was used to construct haplotype blocks by resampling [ 61 , 62 ]. LocusZoom was used to plot the resulting significant locus [ 63 ]. Measured height (phenotype) served as the exposure variables. Sixty-three health-related outcomes, including 14 traits and 49 diseases, were used as outcome variables. A multivariate linear regression model was made for continuous outcome variables (14 traits), with adjustments for age, sex, education, drinking, smoking, regular exercise, and 10 PCAs [ 3 – 9 , 20 ]. Multivariate logistic regression analysis was performed for binary outcome variables (49 diseases), with adjustments for age, sex, education, drinking, smoking, regular exercise, and 10 PCAs [ 3 – 9 , 20 ]. The genetically determined height (PRS 251 ) also served as the exposure variable. Sixty-three health-related outcomes, including 14 traits and 49 diseases, were used as outcome variables. A multivariate linear regression model was performed for continuous outcome variables (14 traits), with adjustments for age, sex, education, drinking, smoking, regular exercise, and 10 PCAs [ 3 – 9 , 20 ]. Multivariate logistic regression analysis was performed for binary outcome variables (49 diseases), with adjustments for age, sex, education, drinking, smoking, regular exercise, and 10 PCAs [ 3 – 9 , 20 ]. PLINK software (versions 1.9 and 2.0) and R packages for Windows were used for all statistical analyses.

The Manhattan and QQ plots for the adult-height GWAS results are shown in Fig. 2 . GWAS association analysis identified 6843 SNPs in 89 genomic regions with genome-wide significance ( p < 5.00E−08 [5 × 10 −8 ], not shown). The top lead SNPs were selected in 89 genomic regions with significant associations ( p < 5 × 10 −8 ) using an LD of < 0.2 (Additional file 2 : Table S2). Among these, 18 novel lead SNPs within 18 novel regions, 48 novel lead SNPs within 48 reported regions, and 23 lead SNPs within 23 reported regions were found (Additional file 2 : Table S2). Moreover, among these 89 genomic regions, the seven lead SNPs were located within seven genetic loci (Fig. 2 ). These seven genetic loci were located near the following genes: EGF-containing fibulin extracellular matrix protein 1 ( EFEMP1 ), DIS3 like 3′-5′ exoribonuclease 2 ( DIS3L2 ), zinc finger and BTB domain containing 38 ( ZBTB38 ), ligand-dependent nuclear receptor corepressor like ( LCORL ), high-mobility group AT-hook 1 ( HMGA1 ), citrate synthase ( CS ), and growth differentiation factor 5 ( GDF5 ). The seven lead SNPs from these seven genetic loci are shown in Additional file 2 : Table S2. Regional plots of the lead SNPs and their neighboring SNPs from these seven genetic loci are shown in Fig. 3 . Among them, two lead SNPs were novel (Fig. 3 B, C; chromosome 2, rs76803230 in DIS3L2 ; chromosome 3, rs57345461 in ZBTB38 ), whereas the remaining five lead SNPs were previously reported (Fig. 3 A, D–G). On chromosome 2, the lead SNP rs76803230 was located in the intronic region of DIS3L2 (risk allele: T, beta = 0.0681, [95% CI:0.0583–0.0780], p = 7.47E−42 [7.47 × 10 −42 ]; Additional file 2 : Table S2; Fig. 3 B). On chromosome 3, the lead SNP rs57345461 was located in the intronic region of ZBTB38 (risk allele: T, beta = 0.0723, [95% CI: 0.0619–0.0827], p = 2.54E−42 [2.54 × 10 −42 ]; Additional file 2 : Table S2; Fig. 3 C). In the previously reported SNPs on chromosome 2, only a handful reached genome-wide significance associated with height, where the lead SNP rs3791675 was located in the intronic region of EFEMP1 (risk allele: C; training group: beta = 0.0753, [95% CI: 0.0638–0.0869], p = 2.78E-37 [2.78 × 10 −37 ]; Additional file 2 : Table S2; Fig. 3 A). This SNP has been associated with body height, BMI-adjusted waist circumference, pelvic organ prolapse, and BMI-adjusted WHR [ 23 , 64 – 66 ]. On chromosome 4, the lead SNP rs16895971 was located in the 3-untranslated region (3UTR) of LCORL (risk allele: T, beta = 0.1018, [95% CI: 0.0911–0.1125], p = 3.69E−77 [3.69 × 10 −77 ]; Additional file 2 : Table S2; Fig. 3 D). This SNP has been associated with body height in East Asians [ 67 ]. On chromosome 6, the lead SNP rs2780226 was located in the 5 untranslated region (UTR) of HMGA1 (risk allele: C, beta = 0.0948, [95% CI: 0.0791–0.1104], p = 1.75E−32 [1.75 × 10 −32 ]; Additional file 2 : Table S2; Fig. 3 E). This SNP has been associated with body height, BMI-adjusted waist circumference, and birth weight [ 15 , 68 , 69 ]. On chromosome 12, the lead SNP rs3816804 was located in the intronic region of CS (risk allele: C, beta = 0.1124, [95% CI: 0.0993–0.1255], p = 6.35E−63 [6.35 × 10 −63 ]; Additional file 2 : Table S2; Fig. 3 F). This SNP has also been associated with body height in East Asians [ 70 ]. On chromosome 20, the lead SNP rs143384 was located in the 5-UTR of GDF5 (risk allele: G, beta = 0.0738, [95% CI: 0.0629–0.0847], p = 3.61E−40 [3.61 × 10 −40 ]; Additional file 2 : Table S2; Fig. 3 G). This SNP has been associated with body height, BMI-adjusted hip circumference, BMI-adjusted WHR, and body fat [ 15 , 64 , 71 , 72 ]. The previously reported GWAS-determined SNPs for height were obtained from the GWAS catalog ( https://www.ebi.ac.uk/gwas/efotraits/EFO_0004339 ) and used to replicate the reported SNPs in the training group using the linear regression model, as described previously. In this study, an association analysis identified 313 SNPs that were significantly associated with height ( p < 0.05/1722 SNPs; Additional file 3 : Table S3). In this study, GWAS-identified 6843 SNPs, and 313 of the reported SNPs were combined. After removing duplicate SNPs, 6941 SNPs were associated with height (Fig. 1 ). These 6941 SNPs were then applied to exclude SNPs with strong LD and to select the best SNP combination for the best-fit PRS calculation in the testing group, using PLINK software (versions 1.9 and 2.0) [ 53 ]. This resulted in the identification of independent genetic signals for the best-fit PRS with 251 SNPs. These 251 SNPs included 168 GWAS-identified SNPs (Table 1 ) and 83 previously reported GWAS-determined SNPs (Table 2 ). These results show that 168 novel GWAS-identified SNPs and 83 reported SNPs were associated with height in individuals of Han Chinese ancestry in Taiwan. Table 1 Newly identified SNPs associated with height in Taiwan No. rs ID Nearest Gene Chr. Position Minor allele Major allele Risk allele Training group ( N = 67,452) ( p < 5 ×10 −8 ) Testing group ( N = 14,454) Beta 95% CI P -value Beta 95% CI P -value 1 rs56265117 MFAP2 1 16980428 C T C 0.040 0.030 0.050 4.66E−15 0.042 0.021 0.064 1.31E−04 2 rs76910682 1 50408548 A G A 0.037 0.025 0.050 7.45E−09 0.010 −0.018 0.037 4.97E−01 3 rs12098132 FAF1 1 50661852 A C A 0.060 0.044 0.077 8.05E−13 0.049 0.013 0.086 8.04E−03 4 rs61115731 FAF1 1 50932429 C G C 0.061 0.044 0.077 2.48E−13 0.054 0.018 0.090 2.96E−03 5 rs140830175 LINC01562 1 51197120 T C T 0.058 0.041 0.075 3.85E−11 0.054 0.016 0.092 5.26E−03 6 rs4926705 1 55953858 A G A 0.039 0.025 0.053 4.55E−08 0.045 0.015 0.075 3.12E−03 7 rs3806340 PKN2-AS1 1 88683110 G T T 0.036 0.026 0.045 9.75E−13 0.015 −0.006 0.037 1.59E−01 8 rs7530513 KYAT3 1 88944228 A G G 0.033 0.023 0.044 7.58E−11 0.027 0.005 0.049 1.64E−02 9 rs7513580 1 118307286 A G G 0.046 0.036 0.057 1.01E−18 0.041 0.019 0.064 2.98E−04 10 rs10489289 DNM3 1 172254949 C T C 0.043 0.032 0.054 6.19E−14 0.028 0.003 0.052 2.68E−02 11 rs12047271 1 184044357 C T C 0.036 0.026 0.046 8.73E−13 0.052 0.030 0.073 2.52E−06 12 rs1046017 TGFB2, TGFB2-OT1 1 218443793 C G G 0.039 0.028 0.050 2.75E−12 0.023 −0.001 0.047 5.71E−02 13 rs7538503 1 219615188 G A G 0.037 0.025 0.049 1.13E−09 0.029 0.003 0.054 2.92E−02 14 rs2367623 LTBP1 2 33202983 A C A 0.035 0.024 0.045 2.80E−11 0.021 −0.001 0.043 6.47E−02 15 rs143098957 LTBP1 2 33263857 T G G 0.043 0.030 0.056 1.02E−10 0.039 0.011 0.068 6.66E−03 16 rs17019115 FEZ2 2 36575874 C G G 0.029 0.019 0.039 2.49E−08 0.009 −0.013 0.031 4.11E−01 17 rs4670703 2 37385957 C A C 0.036 0.026 0.046 6.39E−13 0.012 −0.010 0.033 2.84E−01 18 rs79121675 2 55724807 A C A 0.085 0.056 0.114 6.55E−09 0.077 0.016 0.139 1.41E−02 19 rs146446706 LOC112268416, EFEMP1 2 55870880 T C T 0.151 0.111 0.191 2.34E−13 0.030 −0.059 0.119 5.04E−01 20 rs1824305 2 71179325 T C C 0.051 0.041 0.061 4.96E−23 0.047 0.025 0.068 2.55E−05 21 rs57092473 2 71440419 A G G 0.044 0.034 0.054 4.47E−18 0.045 0.024 0.067 4.20E−05 22 rs1913671 EIF2AK3 2 88600365 T C C 0.035 0.025 0.044 5.04E−12 0.027 0.006 0.048 1.22E−02 23 rs1118150 DIRC3 2 217415545 A C A 0.034 0.022 0.045 5.54E−09 0.013 −0.011 0.038 2.90E−01 24 rs484085 USP37 2 218531961 C T T 0.036 0.024 0.047 1.19E−09 0.020 −0.005 0.045 1.10E−01 25 rs422702 CFAP65 2 219037931 C T C 0.036 0.026 0.046 5.30E−13 0.045 0.023 0.066 4.45E−05 26 rs374935766 2 231850100 C G G 0.175 0.117 0.234 4.07E−09 0.102 −0.019 0.222 9.82E−02 27 rs33994242 2 231915948 G A A 0.043 0.028 0.058 2.01E−08 0.059 0.027 0.091 3.61E−04 28 rs76803230 DIS3L2 2 232063990 G T T 0.068 0.058 0.078 7.47E−42 0.078 0.057 0.099 7.90E−13 29 rs146229392 DIS3L2 2 232064573 C G G 0.092 0.061 0.123 6.79E−09 0.079 0.013 0.144 1.84E−02 30 rs3748967 DIS3L2 2 232333663 A G G 0.055 0.045 0.065 6.35E−27 0.061 0.040 0.083 2.85E−08 31 rs894857163 GIGYF2 2 232726985 T C C 0.280 0.191 0.369 6.79E−10 0.248 0.056 0.440 1.15E−02 32 rs11130111 CCDC12 3 46968315 C T C 0.028 0.018 0.038 2.58E−08 0.019 −0.002 0.040 7.92E−02 33 rs12495173 KIF9-AS1, KIF9 3 47241409 T C T 0.040 0.026 0.054 1.12E−08 0.042 0.012 0.072 6.08E−03 34 rs1209842003 3 52186981 T G G 0.263 0.177 0.350 2.53E−09 0.387 0.194 0.580 8.58E−05 35 rs754871503 NT5DC2 3 52525012 C T T 0.268 0.181 0.355 1.38E−09 0.464 0.269 0.660 3.22E−06 36 rs1328122506 SFMBT1 3 52913981 A C C 0.260 0.173 0.346 3.59E−09 0.471 0.280 0.662 1.34E−06 37 rs13086339 RYBP 3 72428668 C A C 0.033 0.023 0.043 6.55E−11 0.013 −0.008 0.035 2.22E−01 38 rs11710894 BOC 3 113273112 T C C 0.029 0.019 0.039 9.04E−09 0.012 −0.010 0.033 2.98E−01 39 rs4073154 H1FX-AS1 3 129316642 A G G 0.038 0.027 0.049 2.71E−12 0.028 0.005 0.051 1.56E−02 40 rs7632556 3 134087102 A G G 0.030 0.020 0.041 1.37E−08 0.047 0.024 0.070 5.71E−05 41 rs57345461 ZBTB38 3 141407983 T A T 0.072 0.062 0.083 2.54E−42 0.070 0.047 0.092 1.04E−09 42 rs1104288 RSRC1 3 158249730 A C C 0.029 0.019 0.039 4.04E−08 0.008 −0.014 0.031 4.67E−01 43 rs12639337 FNDC3B 3 172279149 G C C 0.038 0.028 0.048 8.28E−14 0.038 0.016 0.060 5.45E−04 44 rs9790124 RTP2, LOC100131635 3 187712899 G A G 0.041 0.029 0.052 3.34E−12 0.023 −0.002 0.048 7.31E−02 45 rs116972792 FAM184B 4 17648794 A G G 0.080 0.052 0.108 1.53E−08 0.036 −0.027 0.099 2.62E−01 46 rs16895971 LCORL 4 17883363 C T T 0.102 0.091 0.113 3.69E−77 0.080 0.057 0.104 1.42E−11 47 rs16896140 LCORL 4 17957655 C T C 0.085 0.060 0.111 6.25E−11 0.063 0.008 0.118 2.41E−02 48 rs2724485 LCORL 4 17968075 C T T 0.028 0.018 0.038 3.41E−08 0.035 0.013 0.056 1.64E−03 49 rs148309730 4 18085973 G A A 0.093 0.064 0.122 3.64E−10 0.047 −0.014 0.108 1.31E−01 50 rs76924442 4 18118006 A G G 0.072 0.053 0.090 2.18E−14 0.031 −0.009 0.071 1.32E−01 51 rs4698216 4 18128100 T C T 0.045 0.035 0.055 2.43E−18 0.034 0.012 0.055 2.62E−03 52 rs56281640 4 56899650 A G A 0.030 0.020 0.040 3.41E−09 0.036 0.014 0.058 1.20E−03 53 rs10027494 ADAMTS3 4 72541929 A T A 0.033 0.022 0.045 1.94E−08 0.009 −0.016 0.035 4.72E−01 54 rs1662840 4 81235255 T C T 0.052 0.039 0.065 1.40E−15 0.060 0.032 0.088 2.06E−05 55 rs117072351 HHIP 4 144676679 T C T 0.091 0.060 0.121 4.51E−09 0.028 −0.038 0.095 4.04E−01 56 rs12654242 5 42365278 G A G 0.051 0.035 0.067 4.54E−10 0.062 0.027 0.097 4.64E−04 57 rs4273617 GHR 5 42695369 G A G 0.051 0.038 0.065 1.23E−13 0.035 0.006 0.064 1.80E−02 58 rs6453386 SCAMP1 5 78408512 C G C 0.033 0.022 0.044 3.80E−09 0.004 −0.020 0.028 7.24E−01 59 rs985296 MEF2C-AS1 5 89081827 A G G 0.034 0.024 0.044 8.81E−12 0.036 0.014 0.057 1.09E−03 60 rs184923695 ARHGAP26 5 143097754 A G G 0.054 0.036 0.072 4.62E−09 0.071 0.033 0.110 2.97E−04 61 rs186405009 SLC17A1 6 25823049 A G A 0.180 0.124 0.236 3.59E−10 0.099 −0.030 0.228 1.31E−01 62 rs811041 6 26225804 C G C 0.047 0.035 0.059 2.28E−15 0.050 0.025 0.075 1.05E−04 63 rs181680390 BTN3A3 6 26452046 A T A 0.126 0.081 0.171 4.06E−08 0.099 −0.003 0.201 5.67E−02 64 rs185780403 6 26745946 A C A 0.132 0.086 0.177 1.26E−08 0.100 −0.003 0.202 5.62E−02 65 rs192632187 6 27113326 C T C 0.137 0.091 0.182 3.75E−09 0.116 0.015 0.217 2.50E−02 66 rs183108303 ZNF204P 6 27370053 T G T 0.126 0.082 0.170 2.53E−08 0.117 0.017 0.218 2.15E−02 67 rs182819650 6 27823957 C T C 0.129 0.085 0.174 1.31E−08 0.108 0.008 0.208 3.48E−02 68 rs182706663 6 28116420 T C T 0.127 0.083 0.172 2.34E−08 0.094 −0.006 0.193 6.47E−02 69 rs2299870 PPARD 6 35417160 G C G 0.068 0.044 0.091 1.66E−08 0.033 −0.019 0.084 2.17E−01 70 rs1564926 CD2AP 6 47502410 C T C 0.030 0.020 0.041 2.70E−08 0.034 0.011 0.058 4.20E−03 71 rs145101575 BCKDHB 6 80344040 A G A 0.062 0.042 0.082 1.18E−09 0.053 0.009 0.096 1.79E−02 72 rs62424499 6 80678133 T A A 0.035 0.024 0.046 3.66E−10 0.042 0.018 0.065 5.78E−04 73 rs1145861 6 80940193 C G G 0.037 0.026 0.048 1.15E−10 0.041 0.016 0.065 1.15E−03 74 rs13197753 6 104924810 G C G 0.037 0.026 0.047 3.82E−12 0.047 0.024 0.069 4.91E−05 75 rs78638402 6 129995451 G C C 0.069 0.048 0.089 6.24E−11 0.029 −0.016 0.074 2.09E−01 76 rs6926186 L3MBTL3 6 130029149 G A G 0.053 0.034 0.072 2.11E−08 0.029 −0.011 0.070 1.51E−01 77 rs1040525 ADGRG6 6 142382532 T C C 0.044 0.034 0.054 7.17E−18 0.046 0.024 0.067 4.12E−05 78 rs73780873 ESR1 6 151829789 A G A 0.045 0.034 0.056 1.87E−15 0.018 −0.006 0.042 1.36E−01 79 rs1182176 GNA12 7 2834967 G A A 0.046 0.033 0.059 3.16E−12 0.063 0.035 0.090 1.18E−05 80 rs185053690 KBTBD2 7 32868716 T G G 0.158 0.111 0.206 6.20E−11 0.205 0.104 0.307 7.61E−05 81 rs2960429 LOC102723446, LOC105375264 7 46008459 G C C 0.035 0.025 0.045 4.87E−12 0.020 −0.001 0.042 6.40E−02 82 rs62452707 7 46530573 T A T 0.029 0.019 0.038 1.25E−08 0.027 0.006 0.049 1.20E−02 83 rs6557667 LOXL2 8 23390501 C T C 0.035 0.023 0.046 5.71E−09 0.013 −0.012 0.039 3.14E−01 84 rs74476179 EXTL3 8 28740152 A G G 0.111 0.078 0.145 9.56E−11 0.134 0.063 0.205 2.17E−04 85 rs10957084 LOC105375821 8 48444248 G A G 0.038 0.027 0.049 3.92E−11 0.022 −0.002 0.046 7.47E−02 86 rs181231559 PLAG1 8 56188663 C T T 0.185 0.123 0.246 3.55E−09 0.152 0.023 0.282 2.10E−02 87 rs6984782 8 56223330 C T T 0.081 0.062 0.099 2.42E−17 0.071 0.030 0.111 6.13E−04 88 rs112083368 8 56438778 G C C 0.061 0.042 0.081 7.16E−10 0.024 −0.018 0.066 2.68E−01 89 rs3886938 GSDMC 8 129725300 T G T 0.043 0.032 0.054 2.01E−14 0.046 0.022 0.070 1.33E−04 90 rs566313810 8 134043298 T C T 0.272 0.191 0.352 4.62E−11 0.305 0.114 0.496 1.73E−03 91 rs1213791479 8 134354346 C T C 0.283 0.206 0.359 5.41E−13 0.245 0.063 0.427 8.42E−03 92 rs368372931 ZFAT 8 134609000 G A G 0.139 0.094 0.184 1.51E−09 0.146 0.043 0.250 5.36E−03 93 rs1246647183 ZFAT 8 134627377 A T A 0.345 0.267 0.423 6.19E−18 0.307 0.119 0.494 1.38E−03 94 rs12541381 ZFAT 8 134637605 A G G 0.034 0.023 0.045 4.95E−10 0.059 0.036 0.082 6.14E−07 95 rs56119276 9 95518374 C T C 0.037 0.027 0.047 1.05E−12 0.023 0.001 0.045 4.50E−02 96 rs4743291 9 95758524 T A A 0.036 0.024 0.048 6.27E−09 0.024 −0.003 0.050 7.86E−02 97 rs10985794 9 122844338 C T T 0.035 0.023 0.048 2.68E−08 0.024 −0.003 0.051 8.51E−02 98 rs10901208 FUBP3 9 130587253 T C T 0.035 0.025 0.045 1.91E−12 0.031 0.010 0.052 4.40E−03 99 rs35859988 CCDC3 10 12902646 T C C 0.046 0.033 0.059 2.66E−12 0.033 0.005 0.061 2.28E−02 100 rs10998375 TET1 10 68665362 A G G 0.044 0.029 0.058 5.85E−09 0.042 0.010 0.073 9.99E−03 101 rs4979861 10 79371839 A G A 0.030 0.020 0.040 1.18E−08 0.011 −0.011 0.034 3.33E−01 102 rs291979 GRK5 10 119370285 A G A 0.035 0.023 0.047 1.43E−08 0.012 −0.014 0.039 3.66E−01 103 rs1003484 IGF2, INS-IGF2 11 2146388 G A G 0.036 0.026 0.046 7.41E−13 0.058 0.037 0.080 1.00E−07 104 rs78899385 PSMA1 11 14538479 T C C 0.081 0.062 0.101 2.17E−16 0.067 0.025 0.109 1.60E−03 105 rs76778262 PDE3B 11 14815707 C A A 0.083 0.057 0.108 1.67E−10 0.074 0.019 0.130 8.79E−03 106 rs4752839 CELF1 11 47473650 A G A 0.033 0.022 0.044 2.43E−09 0.058 0.034 0.081 1.72E−06 107 rs763648441 LTBP3 11 65546523 C A C 0.365 0.245 0.484 2.16E−09 0.299 0.051 0.548 1.82E−02 108 rs594318 FOXRED1 11 126277818 C G C 0.030 0.019 0.040 2.13E−08 0.028 0.006 0.051 1.37E−02 109 rs4763719 ETV6 12 11724486 G A A 0.030 0.020 0.040 3.28E−09 0.021 −0.001 0.042 5.78E−02 110 rs57454081 12 27950661 T C T 0.048 0.033 0.062 6.68E−11 0.027 −0.005 0.058 9.50E−02 111 rs76467375 12 46655632 T C T 0.040 0.026 0.054 1.43E−08 0.024 −0.006 0.054 1.20E−01 112 rs10444558 12 53661701 T A T 0.042 0.031 0.054 3.36E−13 0.054 0.029 0.078 1.84E−05 113 rs139121417 RAB5B 12 55986276 T C C 0.045 0.031 0.059 5.68E−10 0.055 0.024 0.086 4.71E−04 114 rs11834895 HMGA2, HMGA2-AS1 12 65853230 G C C 0.033 0.021 0.044 4.84E−08 0.018 −0.007 0.044 1.58E−01 115 rs151174669 12 65980466 T C C 0.069 0.046 0.092 7.72E−09 −0.004 −0.056 0.048 8.76E−01 116 rs7971647 SOCS2 12 93590078 C T C 0.046 0.036 0.056 1.10E−18 0.041 0.019 0.063 3.33E−04 117 rs80328976 WASHC3 12 102022590 C G G 0.058 0.047 0.069 3.78E−26 0.075 0.052 0.098 1.98E−10 118 rs1986854 WASHC3 12 102023697 C T C 0.043 0.027 0.058 3.00E−08 0.050 0.018 0.082 2.45E−03 119 rs12424129 LINC02456 12 102281461 C T T 0.060 0.049 0.070 9.80E−28 0.072 0.049 0.095 8.79E−10 120 rs12228148 LINC02456 12 102313697 A G A 0.050 0.037 0.063 5.00E−14 0.059 0.030 0.087 4.74E−05 121 rs17032833 LOC105369944 12 102561342 C T T 0.039 0.029 0.049 1.40E−14 0.025 0.003 0.046 2.42E−02 122 rs117988169 HVCN1 12 110672222 T G G 0.059 0.041 0.078 2.96E−10 0.011 −0.029 0.051 5.90E−01 123 rs3782886 BRAP 12 111672685 C T T 0.045 0.034 0.056 4.75E−16 0.019 −0.005 0.043 1.21E−01 124 rs116873087 NAA25 12 112074109 C G G 0.046 0.035 0.057 2.94E−16 0.016 −0.008 0.040 2.00E−01 125 rs11066359 RPH3A 12 112607850 T C C 0.029 0.019 0.039 1.39E−08 0.015 −0.007 0.037 1.70E−01 126 rs2072134 OAS3 12 112971371 A G G 0.039 0.026 0.052 2.44E−09 0.012 −0.016 0.040 4.01E−01 127 rs12590263 TC2N 14 91852154 A G A 0.033 0.023 0.042 7.92E−11 0.012 −0.010 0.033 2.80E−01 128 rs7143616 ATXN3 14 92065114 C A A 0.053 0.043 0.063 4.03E−24 0.041 0.019 0.063 3.09E−04 129 rs3759556 14 100725962 G A A 0.071 0.049 0.094 6.89E−10 0.073 0.024 0.123 3.66E−03 130 rs35443927 14 103383378 T C C 0.029 0.019 0.039 4.28E−08 0.026 0.004 0.049 2.07E−02 131 rs2663534 15 50883261 C T T 0.030 0.020 0.040 4.54E−09 0.027 0.005 0.049 1.41E−02 132 rs8041967 MIR4713HG, CYP19A1 15 51252127 A G A 0.039 0.029 0.049 1.75E−14 0.040 0.019 0.062 2.43E−04 133 rs28723025 CYP19A1 15 51304114 A C C 0.054 0.043 0.064 3.03E−22 0.054 0.031 0.078 5.60E−06 134 rs2162062 VPS13C 15 61987738 A G G 0.038 0.028 0.049 1.95E−13 0.014 −0.008 0.037 2.09E−01 135 rs2415130 MYO9A 15 71950213 A G A 0.028 0.018 0.038 2.08E−08 0.020 −0.002 0.041 7.45E−02 136 rs55763892 PARP6 15 72247839 T C C 0.032 0.021 0.043 3.00E−09 0.029 0.006 0.052 1.40E−02 137 rs6495171 SIN3A 15 75373282 A G A 0.031 0.020 0.042 2.70E−08 0.024 0.000 0.048 4.67E−02 138 rs1526080 ADAMTSL3 15 83921168 G A A 0.045 0.033 0.056 4.54E−15 0.024 −0.001 0.048 5.58E−02 139 rs938608 ACAN 15 88855374 T G G 0.041 0.029 0.053 5.26E−11 0.064 0.038 0.091 1.83E−06 140 rs138351276 ACAN 15 88859943 G A A 0.102 0.069 0.134 6.62E−10 0.169 0.096 0.241 5.54E−06 141 rs28456063 15 98637993 T C C 0.078 0.062 0.095 2.07E−20 0.102 0.066 0.138 3.09E−08 142 rs897377828 IGF1R, IRAIN 15 98649099 A G G 0.260 0.185 0.335 1.20E−11 0.157 0.001 0.313 4.80E−02 143 rs2573650 ADAMTS17 15 99973892 G A A 0.037 0.027 0.047 1.38E−13 0.047 0.025 0.068 1.87E−05 144 rs4619391 WWP2 16 69780860 T A T 0.030 0.019 0.040 9.78E−09 0.023 0.001 0.045 3.75E−02 145 rs114509338 SF3B3 16 70574733 C T T 0.033 0.021 0.044 1.72E−08 0.000 −0.024 0.025 9.74E−01 146 rs116560331 POLR2A 17 7488366 A G G 0.062 0.040 0.084 2.80E−08 0.049 0.002 0.095 4.04E−02 147 rs113934718 ATAD5 17 30887862 A C C 0.048 0.033 0.063 2.75E−10 0.024 −0.008 0.057 1.44E−01 148 rs67474242 LRRC37A2, WNT3 17 46777685 A G G 0.031 0.021 0.041 1.36E−09 0.000 −0.022 0.021 9.86E−01 149 rs2411374 17 48945636 C T C 0.037 0.026 0.047 1.49E−11 0.021 −0.002 0.044 7.40E−02 150 rs6504608 ZNF652 17 49347319 A C C 0.030 0.020 0.041 1.41E−08 0.014 −0.009 0.037 2.30E−01 151 rs9905385 17 61420889 A G A 0.053 0.042 0.064 1.08E−21 0.046 0.022 0.069 1.67E−04 152 rs2320125 CD79B 17 63930958 C T C 0.046 0.036 0.056 4.63E−20 0.040 0.019 0.061 2.11E−04 153 rs11651289 17 63936114 T C T 0.070 0.047 0.094 3.45E−09 0.073 0.024 0.121 3.41E−03 154 rs4239437 CABLES1 18 23152260 T C C 0.074 0.061 0.087 5.92E−29 0.053 0.025 0.082 2.24E−04 155 rs9807648 TMEM241 18 23344958 G A G 0.033 0.022 0.045 3.28E−08 0.030 0.004 0.056 2.16E−02 156 rs4349223 FHOD3 18 36541412 A C C 0.029 0.019 0.039 6.93E−09 0.021 −0.001 0.042 5.91E−02 157 rs12606199 DYM, LOC100129878 18 49045546 A G G 0.046 0.034 0.057 3.49E−15 0.023 −0.002 0.048 7.04E−02 158 rs201707253 DYM 18 49342419 T G G 0.042 0.031 0.052 6.17E−14 0.017 −0.006 0.041 1.53E−01 159 rs3843750 SLC44A2 19 10637397 C G C 0.039 0.029 0.050 1.55E−13 0.039 0.016 0.061 8.68E−04 160 rs754332 KIZ, KIZ-AS1 20 21197259 A G A 0.029 0.019 0.039 1.28E−08 0.018 −0.004 0.040 1.04E−01 161 rs61016611 20 35624229 A G A 0.062 0.043 0.081 1.23E−10 0.058 0.017 0.099 5.31E−03 162 rs3827030 PHF20 20 35887025 G A G 0.060 0.047 0.074 1.87E−19 0.042 0.014 0.071 3.47E−03 163 rs8183892 20 36980155 T C T 0.041 0.031 0.051 2.79E−16 0.028 0.006 0.049 1.18E−02 164 rs4608 RPN2 20 37236651 T C T 0.044 0.034 0.054 2.23E−18 0.026 0.005 0.048 1.61E−02 165 rs8121252 GNAS 20 58901754 T C C 0.048 0.037 0.059 2.30E−17 0.053 0.029 0.077 1.42E−05 166 rs5754190 SYN3, LOC105373002 22 32654480 C T C 0.052 0.042 0.063 4.32E−22 0.050 0.027 0.073 1.90E−05 167 rs4821086 SYN3 22 32687867 A C A 0.077 0.052 0.102 2.10E−09 0.039 −0.015 0.093 1.59E−01 168 rs7290267 MIRLET7BHG 22 46088855 G A G 0.052 0.037 0.068 1.75E−11 0.062 0.029 0.095 2.30E−04 SNP , single nucleotide polymorphism; No. , number; Chr. , chromosome; 95% CI , 95% confidence interval; GWAS , genome-wide association study This analysis was performed under the additive inheritance model. These SNPs were ordered by chromosome and position. Positions were based on the NCBI GRCh38 version. Genes were identified based on the gene containing the SNP or the nearest gene (within 100 kb up- or downstream) to the SNP The measured (phenotypic) heights (cm) were stratified by sex, mean-centered, and normalized to one standard deviation (SD) before height GWAS analysis Beta-value calculation was conducted according to the defined risk alleles Table 2 Association of previously reported GWAS height SNPs with height in Taiwan No. rs ID Gene Chr. Position Minor allele Major allele Risk allele Training group ( N = 67,452) ( p < 0.05/1722) Testing group ( N = 14,454) Beta 95% CI P -value Beta 95% CI P -value 1 rs2300092 MTOR 1 11206407 T C T 0.027 0.014 0.039 2.62E−05 0.024 −0.003 0.050 8.51E−02 2 rs3014240 CCDC17 1 45623553 C G C 0.025 0.015 0.036 9.06E−07 0.032 0.010 0.054 4.17E−03 3 rs2666504 1 62169409 C T C 0.024 0.013 0.034 5.80E−06 0.015 −0.007 0.037 1.84E−01 4 rs11205303 MTMR11 1 149934520 C T C 0.057 0.047 0.067 5.69E−28 0.033 0.011 0.055 3.26E−03 5 rs6587515 1 150636412 A G A 0.026 0.014 0.037 2.27E−05 0.018 −0.008 0.043 1.71E−01 6 rs1325596 PAPPA2 1 176824930 G A A 0.032 0.021 0.044 6.10E−08 0.043 0.018 0.068 9.05E−04 7 rs10911212 LAMC1 1 183055334 C T C 0.024 0.014 0.034 2.83E−06 0.007 −0.015 0.029 5.30E−01 8 rs4472734 PTPN14 1 214444842 C T C 0.028 0.018 0.037 4.96E−08 0.013 −0.009 0.034 2.48E−01 9 rs10165255 CYS1 2 10059474 A G A 0.030 0.016 0.044 1.70E−05 0.037 0.008 0.066 1.30E−02 10 rs6735681 2 15983051 T C T 0.023 0.013 0.033 5.34E−06 0.014 −0.007 0.036 1.98E−01 11 rs780094 GCKR 2 27518370 T C C 0.022 0.012 0.031 1.69E−05 0.025 0.004 0.046 2.22E−02 12 rs3755206 CRIM1 2 36456285 G T T 0.054 0.042 0.067 7.94E−17 0.054 0.026 0.081 1.35E−04 13 rs6544743 LOC102723904 2 44163230 T G T 0.024 0.013 0.035 1.52E−05 0.005 −0.018 0.029 6.55E−01 14 rs3791675 EFEMP1 2 55884174 C T C 0.075 0.064 0.087 2.78E−37 0.072 0.047 0.097 2.06E−08 15 rs1432559 LOC105374690 2 55962483 G T G 0.055 0.030 0.080 1.32E−05 0.073 0.019 0.126 8.21E−03 16 rs4241349 ANTXR1 2 69103152 G A G 0.026 0.014 0.037 9.55E−06 0.039 0.015 0.064 1.78E−03 17 rs76709099 IHH 2 219055182 A C C 0.064 0.047 0.080 8.72E−14 0.061 0.025 0.097 9.16E−04 18 rs2564923 3 53069246 A G A 0.027 0.016 0.038 1.49E−06 0.034 0.011 0.058 4.25E−03 19 rs9841212 3 134473096 C T T 0.024 0.013 0.036 2.79E−05 0.003 −0.021 0.027 8.10E−01 20 rs1055153 WWTR1 3 149657086 T G G 0.046 0.030 0.062 1.33E−08 0.013 −0.022 0.047 4.69E−01 21 rs6774762 GHSR 3 172447200 G A A 0.037 0.024 0.050 1.80E−08 0.032 0.005 0.060 2.16E−02 22 rs7697556 4 72649596 C T T 0.037 0.027 0.047 1.77E−13 0.030 0.009 0.051 5.37E−03 23 rs17017911 GUSBP5 4 143559481 G A A 0.022 0.012 0.032 2.19E−05 0.016 −0.005 0.038 1.40E−01 24 rs6845999 HHIP-AS1 4 144644674 T C T 0.058 0.046 0.070 7.29E−22 0.062 0.037 0.088 1.52E−06 25 rs4240326 4 144918112 A G A 0.028 0.017 0.039 7.17E−07 0.018 −0.006 0.042 1.50E−01 26 rs301901 NIPBL 5 37046524 A G A 0.024 0.014 0.034 2.04E−06 0.016 −0.005 0.038 1.32E−01 27 rs4865956 5 55586677 T A T 0.024 0.014 0.035 5.07E−06 0.018 −0.005 0.040 1.22E−01 28 rs7706662 CEP120 5 123419868 T C C 0.023 0.013 0.032 7.16E−06 −0.014 −0.035 0.008 2.09E−01 29 rs2908532 5 142242319 A C A 0.027 0.016 0.037 1.23E−06 0.010 −0.013 0.033 3.93E−01 30 rs2974438 SLIT3 5 168823898 A G G 0.030 0.017 0.043 3.53E−06 0.020 −0.008 0.047 1.61E−01 31 rs12153391 SMIM23 5 171776434 A C C 0.029 0.019 0.039 1.22E−08 0.016 −0.006 0.038 1.46E−01 32 rs4868126 5 171856465 T G G 0.031 0.020 0.041 4.13E−09 0.041 0.019 0.063 2.39E−04 33 rs722585 GMDS, HCG17 6 1775629 A G G 0.023 0.013 0.034 1.61E−05 0.060 0.037 0.083 3.26E−07 34 rs78566116 6 32428369 T G G 0.040 0.022 0.057 5.70E−06 0.039 0.002 0.075 3.87E−02 35 rs2780226 6 34231315 C T C 0.095 0.079 0.110 1.75E−32 0.072 0.038 0.106 2.87E−05 36 rs12209223 FILIP1, LOC101928540 6 75454873 A C A 0.050 0.033 0.067 1.02E−08 0.057 0.020 0.095 2.76E−03 37 rs648831 BCKDHB 6 80246491 C T T 0.024 0.014 0.034 1.53E−06 0.017 −0.004 0.038 1.22E−01 38 rs3805859 BCKDHB 6 80339229 C A C 0.025 0.015 0.035 5.62E−07 0.017 −0.005 0.038 1.23E−01 39 rs113898003 L3MBTL3 6 130020090 C T T 0.042 0.032 0.052 4.72E−16 0.045 0.023 0.068 5.45E−05 40 rs7765757 EPB41L2 6 131050608 C T T 0.038 0.022 0.053 1.29E−06 0.038 0.006 0.070 2.12E−02 41 rs3020359 ESR1 6 152044128 T C C 0.023 0.013 0.033 1.11E−05 0.035 0.013 0.057 1.78E−03 42 rs73029259 LOC107986666 6 163690316 A T A 0.037 0.020 0.054 1.99E−05 0.033 −0.004 0.069 7.95E−02 43 rs57246313 7 25850077 A G A 0.024 0.014 0.034 3.01E−06 0.026 0.004 0.048 1.93E−02 44 rs1007358 7 46161757 G A G 0.036 0.023 0.049 1.12E−07 0.041 0.012 0.069 5.43E−03 45 rs42377 CDK6 7 92614358 A G A 0.035 0.019 0.052 2.44E−05 0.007 −0.029 0.042 7.15E−01 46 rs445 CDK6 7 92779056 T C C 0.025 0.015 0.035 1.86E−06 0.029 0.007 0.050 1.08E−02 47 rs76364830 DLC1 8 13514611 A G G 0.041 0.023 0.059 9.41E−06 0.059 0.019 0.098 3.93E−03 48 rs10958476 PLAG1 8 56183249 C T C 0.030 0.017 0.042 2.60E−06 0.014 −0.013 0.041 3.04E−01 49 rs7842996 8 77194904 A T A 0.031 0.019 0.043 6.50E−07 0.033 0.007 0.060 1.47E−02 50 rs7817087 8 116552698 A G G 0.026 0.016 0.035 4.15E−07 0.010 −0.012 0.031 3.81E−01 51 rs6992491 8 128185657 G C G 0.022 0.012 0.032 1.68E−05 0.011 −0.011 0.033 3.22E−01 52 rs10120219 LOC105376158 9 95602265 C T T 0.036 0.026 0.046 7.29E−13 0.040 0.018 0.061 2.77E−04 53 rs34575265 9 106181520 T C C 0.022 0.012 0.032 2.66E−05 0.011 −0.011 0.033 3.16E−01 54 rs7858562 ZNF483, PTGR1 9 111562668 G A A 0.025 0.014 0.037 2.33E−05 0.035 0.009 0.060 7.19E−03 55 rs12344818 9 115728289 T C C 0.031 0.019 0.043 1.73E−07 −0.006 −0.031 0.020 6.65E−01 56 rs3789280 PAPPA 9 116191093 A T A 0.036 0.020 0.053 1.23E−05 0.018 −0.018 0.053 3.30E−01 57 rs12338076 QSOX2 9 136229894 C A C 0.040 0.029 0.050 3.63E−13 0.045 0.022 0.068 1.50E−04 58 rs779933 ZMIZ1 10 79158760 A G G 0.028 0.017 0.039 8.37E−07 0.038 0.014 0.062 2.22E−03 59 rs2648725 PCGF5 10 91255322 A T A 0.044 0.024 0.064 1.61E−05 0.052 0.009 0.095 1.77E−02 60 rs1938679 11 69457328 T C C 0.038 0.029 0.048 2.98E−14 0.032 0.010 0.053 3.84E−03 61 rs645935 SERPINH1 11 75568245 C T T 0.042 0.032 0.051 9.90E−17 0.034 0.012 0.055 2.03E−03 62 rs59917308 12 56264924 T C T 0.069 0.039 0.099 5.37E−06 −0.005 −0.069 0.059 8.81E−01 63 rs3816804 CS 12 56286961 T C C 0.112 0.099 0.126 6.35E−63 0.114 0.085 0.142 3.14E−15 64 rs2277339 PRIM1 12 56752285 G T T 0.037 0.025 0.049 1.81E−09 0.040 0.014 0.066 2.91E−03 65 rs10747784 12 57857579 G A G 0.027 0.015 0.038 4.71E−06 0.019 −0.005 0.044 1.21E−01 66 rs10878984 12 69434754 C T T 0.044 0.034 0.054 5.99E−17 0.025 0.003 0.047 2.84E−02 67 rs3847787 CRADD 12 93813756 G A G 0.022 0.012 0.033 1.91E−05 0.031 0.009 0.053 6.20E−03 68 rs2093210 C14orf39 14 60490561 T C C 0.043 0.031 0.055 2.54E−12 0.027 0.001 0.053 4.52E−02 69 rs910316 TMED10 14 75159339 A C A 0.029 0.016 0.042 1.20E−05 0.030 0.002 0.058 3.67E−02 70 rs7156335 ITPK1 14 92939887 C T C 0.049 0.028 0.070 4.46E−06 0.033 −0.013 0.080 1.56E−01 71 rs12592845 15 48392761 T C C 0.028 0.015 0.041 1.99E−05 0.065 0.037 0.093 5.11E−06 72 rs975210 TLE3 15 70072013 A G A 0.033 0.019 0.048 7.70E−06 0.004 −0.028 0.036 8.25E−01 73 rs750460 LOXL1 15 73949165 A G G 0.047 0.031 0.063 1.16E−08 0.048 0.012 0.083 8.87E−03 74 rs8025068 ARID3B 15 74577704 G T G 0.023 0.013 0.032 6.66E−06 0.028 0.006 0.049 1.10E−02 75 rs4467054 ADAMTS17 15 100255167 G T G 0.029 0.018 0.039 9.87E−08 0.038 0.014 0.061 1.45E−03 76 rs258324 CDK10 16 89687847 T G T 0.043 0.032 0.054 1.97E−15 0.046 0.023 0.069 1.13E−04 77 rs74494415 GALR1 18 77260182 T C C 0.040 0.022 0.058 8.97E−06 0.026 −0.013 0.064 1.88E−01 78 rs1741344 20 4121153 C T C 0.032 0.019 0.044 3.37E−07 0.040 0.014 0.067 2.99E−03 79 rs967417 20 6640246 G A G 0.033 0.020 0.046 1.10E−06 0.040 0.011 0.068 5.95E−03 80 rs3213180 E2F1 20 33675818 C G G 0.032 0.022 0.043 1.83E−09 0.043 0.020 0.066 2.71E−04 81 rs143384 GDF5 20 35437976 G A G 0.074 0.063 0.085 3.61E−40 0.056 0.032 0.079 4.19E−06 82 rs2235363 ZHX3 20 41179129 G A G 0.023 0.013 0.032 7.22E−06 0.011 −0.010 0.032 3.10E−01 83 rs11537645 UBE2C 20 45812764 G C C 0.044 0.026 0.061 1.45E−06 0.008 −0.031 0.046 7.01E−01 SNP , single nucleotide polymorphism; GWAS , genome-wide association study; No ., number; Chr ., chromosome; 95% CI , 95% confidence interval The measured (phenotypic) heights (cm) were stratified by sex, mean-centered, and normalized to one standard deviation (SD) before height GWAS analysis Beta-value calculation was performed in agreement with the defined risk alleles Newly identified SNPs associated with height in Taiwan SNP , single nucleotide polymorphism; No. , number; Chr. , chromosome; 95% CI , 95% confidence interval; GWAS , genome-wide association study This analysis was performed under the additive inheritance model. These SNPs were ordered by chromosome and position. Positions were based on the NCBI GRCh38 version. Genes were identified based on the gene containing the SNP or the nearest gene (within 100 kb up- or downstream) to the SNP The measured (phenotypic) heights (cm) were stratified by sex, mean-centered, and normalized to one standard deviation (SD) before height GWAS analysis Beta-value calculation was conducted according to the defined risk alleles Association of previously reported GWAS height SNPs with height in Taiwan SNP , single nucleotide polymorphism; GWAS , genome-wide association study; No ., number; Chr ., chromosome; 95% CI , 95% confidence interval The measured (phenotypic) heights (cm) were stratified by sex, mean-centered, and normalized to one standard deviation (SD) before height GWAS analysis Beta-value calculation was performed in agreement with the defined risk alleles The association between genetically determined height (PRS 251 ) and measured height (phenotype) was investigated in the testing and validation groups, where height was stratified by sex (male: N = 10,919; female: N = 17,990; Fig. 4 ). For males, the regression line indicated that a 1-SD increase in PRS 251 was associated with a 0.257-SD increase in normalized measured height (slope = 0.257; p < 0.001; green line). For females, the regression line indicated that a 1-SD increase in PRS 251 was associated with a 0.274-SD increase in normalized measured height (slope = 0.274; p < 0.001; red line). Furthermore, to assess the validity of our findings, we replicated the association between genetically determined height (PRS 237 ) and measured heights (phenotype) in another cohort, kindly provided by the Big Data Center in China Medical University Hospital (CMUH), Taichung, Taiwan (Additional file 4 : Fig. S3). As shown, only 237 of the 251 SNPs were available from the independent cohort of the Big Data Center at CMUH (Additional file 5 : Table S4). The measured height (phenotype) and genetically determined height (PRS 237 ) were normalized (standardized) by sex. For males, the regression line indicated that a 1-SD increase in PRS 237 was associated with a 0.0972-SD increase in normalized measured height (slope = 0.0972; p < 0.001; green line; Additional file 4 : Fig. S3). For females, the regression line indicated that a 1-SD increase in (PRS 237 ) was associated with a 0.104-SD increase in normalized measured height (slope = 0.104; p < 0.001; red line; Additional file 4 : Fig. S3). In this study, we performed both observational (phenotype) and genetic PRS association analyses of height with 63 health-related outcomes using the Taiwan Biobank (Fig. 5 ). We examined the association between observational (phenotype) height with 63 health-related outcomes, including 14 traits and 49 diseases, in 67,452 individuals of Han Chinese ancestry (Fig. 5 ). Similar analyses of the association between genetic PRS of height and height were performed (Fig. 5 ). The genetically determined height of PRS 251 (251 SNPs) applied in this analysis was calculated from our GWAS results, consisting of 168 GWAS-identified SNPs (Table 1 ) and 83 previously reported GWAS-determined SNPs (Table 2 ). The estimated beta values (95% CI) for the 14 traits are shown in Fig. 5 A–C. The estimated odds ratios (95% CI) for the 49 diseases are also shown in Fig. 5 D–M. After adjusting for age, sex, education, drinking, smoking, regular exercise, and 10 PCA results, our analyses showed that observational (phenotype) height was associated with eight of the 14 traits ( p < 0.05/[14 + 49]; Table 3 ). Further analyses confirmed that genetic (PRS 251 ) height was also associated with these eight traits (Table 3 ). No significant associations were observed between the measured and genetic PRS height with the 49 diseases ( p > 0.05/[14 + 49]; Fig. 5 D–M). Among anthropometric traits, observational height was positively associated with body weight, waist circumference, and hip circumference but negatively associated with BMI, WHR, and body fat (Table 3 ). Genetic PRS height was associated with increased body weight (beta = 1.2182, 95% CI = 1.1405–1.2959), waist circumference (beta = 0.4462, 95% CI = 0.3754–0.5171), and hip circumference (beta = 0.6006, 95% CI = 0.5488–0.6523), and a decreased BMI (beta = −0.0837, 95% CI = (−0.1110)–(−0.0563)), WHR (beta = −0.0008, 95% CI = (−0.0012)–(−0.0003)), and body fat (beta = −0.1401, 95% CI = (−0.1856)–(−0.0946)). Fig. 5 Observational (phenotype) and genetic PRS 251 associations of height with 63 health-related outcomes. Beta value and 95% confidence interval (CI) per standard deviation (SD) increase in height are shown for A anthropometric trait 1 (hip circumference, waist circumference, and body weight), B anthropometric trait 2 (body fat, waist-hip ratio, and body mass index), and C blood pressure, blood lipid level, and blood glucose level (including systolic blood pressure (SBP), diastolic blood pressure (DBP), total cholesterol (TC), triglyceride (TG), low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), fasting glucose, and HbA1c). Odds ratio and 95% CI per SD increase in height are shown for D orthopedic or joint disorders (osteoporosis, arthritis, rheumatoid arthritis, osteoarthritis, and gout), E lung and respiratory diseases (asthma and emphysema or chronic bronchitis), F cardiovascular diseases (valvular heart disease, coronary artery disease, heart arrhythmia, cardiomyopathy, congenital heart defect, other type of heart disease, hyperlipidemia, hypertension, and stroke), G diabetes (type 1 diabetes and type 2 diabetes), H mental or emotional disorders (depression, bipolar disorder, postpartum depression, obsessive-compulsive disorder, alcohol addiction or drug abuse, and schizophrenia), I digestive diseases (peptic ulcer disease, gastroesophageal reflux disease, and irritable bowel syndrome), J nervous system disorders (epilepsy, migraine, multiple sclerosis, Parkinson’s disorder, and dementia), K other types of disease (gallstones, kidney stones, kidney failure, and vertigo), L eye diseases (cataract, glaucoma, dry eye syndrome, retinal detachment, floaters, blindness, color blindness, and others), and M female diseases (severe menstrual cramps, uterine fibroids, ovarian cysts, endometriosis, and Uterine/cervical polyps) Table 3 Significant association between phenotypic and genetically determined height with eight traits Height (exposure) 8 traits (outcome) Beta 95% confidence interval P -value Anthropometric trait 1 Height (Phenotype) Body weight (Phenotype) 4.162 4.083 4.241 0.00E+00 Height (PRS) Body weight (Phenotype) 1.218 1.141 1.296 7.81E−206 Height (Phenotype) Waist circumference (Phenotype) 1.363 1.287 1.440 1.56E−264 Height (PRS) Waist circumference (Phenotype) 0.446 0.375 0.517 5.81E−35 Height (Phenotype) Hip circumference (Phenotype) 1.845 1.790 1.900 0.00E+00 Height (PRS) Hip circumference (Phenotype) 0.601 0.549 0.652 4.53E−114 Anthropometric trait 2 Height (Phenotype) Body mass index (Phenotype) −0.192 −0.222 −0.162 1.36E−36 Height (PRS) Body mass index (Phenotype) −0.084 −0.111 -0.056 2.01E−09 Height (Phenotype) Waist-hip ratio (Phenotype) −0.003 −0.003 −0.002 1.75E−24 Height (PRS) Waist-hip ratio (Phenotype) −0.001 −0.001 0.000 7.18E−04 Height (Phenotype) Body fat (Phenotype) −0.335 −0.385 −0.286 3.82E−40 Height (PRS) Body fat (Phenotype) −0.140 −0.186 −0.095 1.58E−09 Blood pressure, lipids, and glucose trait Height (Phenotype) Total cholesterol (Phenotype) −1.117 −1.407 −0.828 4.13E−14 Height (PRS) Total cholesterol (Phenotype) −0.587 −0.853 −0.321 1.55E−05 Height (Phenotype) Low-density lipoprotein cholesterol (Phenotype) −1.180 −1.439 −0.921 4.68E−19 Height (PRS) Low-density lipoprotein cholesterol (Phenotype) −0.629 −0.867 −0.391 2.25E−07 PRS polygenic risk score Height phenotype indicates the measured height (normalized using Z -scores) Height polygenic risk score (PRS) indicates the height calculated from 251 SNPs (Tables 1 and 2 ) Multivariate linear regression analysis was used with adjustment factors (age, sex, educational attainment, drinking, smoking, regular exercise, and 10 principal components analysis data). P -value ( p < 0.05/(14+49)) was highlighted in bold italic. P -value less than 2.23E−308 were expressed as 0.00E+00 Observational (phenotype) and genetic PRS 251 associations of height with 63 health-related outcomes. Beta value and 95% confidence interval (CI) per standard deviation (SD) increase in height are shown for A anthropometric trait 1 (hip circumference, waist circumference, and body weight), B anthropometric trait 2 (body fat, waist-hip ratio, and body mass index), and C blood pressure, blood lipid level, and blood glucose level (including systolic blood pressure (SBP), diastolic blood pressure (DBP), total cholesterol (TC), triglyceride (TG), low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), fasting glucose, and HbA1c). Odds ratio and 95% CI per SD increase in height are shown for D orthopedic or joint disorders (osteoporosis, arthritis, rheumatoid arthritis, osteoarthritis, and gout), E lung and respiratory diseases (asthma and emphysema or chronic bronchitis), F cardiovascular diseases (valvular heart disease, coronary artery disease, heart arrhythmia, cardiomyopathy, congenital heart defect, other type of heart disease, hyperlipidemia, hypertension, and stroke), G diabetes (type 1 diabetes and type 2 diabetes), H mental or emotional disorders (depression, bipolar disorder, postpartum depression, obsessive-compulsive disorder, alcohol addiction or drug abuse, and schizophrenia), I digestive diseases (peptic ulcer disease, gastroesophageal reflux disease, and irritable bowel syndrome), J nervous system disorders (epilepsy, migraine, multiple sclerosis, Parkinson’s disorder, and dementia), K other types of disease (gallstones, kidney stones, kidney failure, and vertigo), L eye diseases (cataract, glaucoma, dry eye syndrome, retinal detachment, floaters, blindness, color blindness, and others), and M female diseases (severe menstrual cramps, uterine fibroids, ovarian cysts, endometriosis, and Uterine/cervical polyps) Significant association between phenotypic and genetically determined height with eight traits PRS polygenic risk score Height phenotype indicates the measured height (normalized using Z -scores) Height polygenic risk score (PRS) indicates the height calculated from 251 SNPs (Tables 1 and 2 ) Multivariate linear regression analysis was used with adjustment factors (age, sex, educational attainment, drinking, smoking, regular exercise, and 10 principal components analysis data). P -value ( p < 0.05/(14+49)) was highlighted in bold italic. P -value less than 2.23E−308 were expressed as 0.00E+00 Regarding blood pressure, and lipid and glucose levels, observational height was negatively associated with TC and LDL-C (Table 3 ). Genetic PRS height was associated with decreased TC (beta = −0.5869, 95% CI = (−0.8530)–(−0.3207)) and LDL-C levels (beta = −0.6291, 95% CI = (−0.8672)–(−0.3910)).

Height is the growth phenotype during the entire developmental period from infancy to adulthood and becomes relatively stable in adulthood [ 1 – 3 ]. Previous studies have reported that social and environmental factors can influence height. Some of these factors include educational attainment, smoking, alcohol consumption, and regular exercise [ 3 – 9 ]. Higher levels of education [ 4 , 5 ] and regular exercise [ 9 ] are associated with increased growth and height. In contrast, exposure to smoking or drinking may cause bone mass loss, reduced growth, and reduced height [ 10 – 12 ]. Height is determined by polygenic inheritance under complex and multi-locus genetic regulation [ 13 – 15 ]. Genome-wide association studies (GWAS) of height have identified hundreds of genetic loci (or single nucleotide polymorphisms [SNPs]) with genome-wide significance, especially in individuals of European ancestry [ 15 – 26 ]. These identified genetic loci are associated with proteins of the tyrosine phosphatase family, insulin-like growth factors, proteins involved in skeletal development and mitosis, fibroblast growth factors, the Wnt/β-catenin pathway, Hedgehog signaling, and cancer-associated pathways. These findings highlight the polygenic, complex, and multilocus genetic regulation of height. Height is associated with several health-related outcomes later in life [ 27 – 34 ]. For instance, taller people tend to have a higher risk of cancer [ 28 ] and cancer-related mortality [ 27 ] but have a reduced risk of CVD [ 27 , 29 ], CVD-related mortality [ 27 , 29 ], type 2 diabetes [ 34 ], better retention of cognitive function [ 30 – 32 ], and healthy aging [ 33 ]. Height can be measured as a genetic component using a polygenic risk score (PRS). PRS is the sum of the weighted risk alleles from a combination of independent SNPs, usually with genome-wide significance, derived from GWAS results [ 13 , 35 , 36 ]. PRS serves as a genetic instrument variable and can be used to assess associations with health-related outcomes without confounding [ 37 , 38 ]. Genetically determined taller height (in those with European ancestry) is also associated with an increased risk of cancers [ 39 – 44 ] and cancer-related mortality [ 45 , 46 ] but a reduced risk of CVD [ 42 , 47 – 50 ]. The precise shared genetic loci between height and health-related outcomes are yet to be elucidated, especially in individuals of Han Chinese ancestry. In addition, the mechanisms on how shared genetic loci contribute to both height and health-related outcomes remain unclear. Therefore, this study aimed to identify the genetic architecture for height in individuals from the Taiwan Biobank—a community-based biobank in Taiwan. We also performed observational and genetic PRS analyses of height and health-related outcomes.

We reported a genetic profile for height in the Han Chinese population using genome-wide SNP analysis and a replication study in the Taiwan Biobank—a community-based database in Taiwan. This is the first large-scale finding on the genetic basis for height and health-related outcomes in individuals of Han Chinese ancestry in Taiwan. Our study results are consistent with the genetic profile of height observed mainly in individuals of European ancestry [ 15 – 26 ]. Accordingly, our findings support the validity of height in observational (phenotype) studies and are consistent with the health-related outcomes of this phenotype [ 73 – 81 ]. In this study, we identified 6843 SNPs with genome-wide significance in 89 genomic regions, including 18 novel loci. Among these, we identified seven independent lead SNPs at seven genetic loci (two of these lead SNPs were novel: chromosome 2, rs76803230 in DIS3L2 ; chromosome 3, rs57345461 in ZBTB38 ) with genome-wide significance. DIS3L2 encodes one of the subunits of the RNA exosome, and its genetic variants are associated with height in individuals of European ancestry [ 21 , 82 ] and East Asian ancestry [ 83 , 84 ]. ZBTB38 is a zinc finger transcriptional activator that binds methylated DNA and is associated with apoptosis. ZBTB38 genetic variants are associated with height in individuals of European ancestry [ 17 , 18 ] and East Asian ancestry [ 20 , 23 ]. The remaining five lead SNPs were reported previously [ 15 , 23 , 64 – 72 ]. The lead SNP rs3791675 in EFEMP1 encodes an extracellular matrix glycoprotein of the fibulin family and has been associated with body height, BMI-adjusted waist circumference, pelvic organ prolapse, and BMI-adjusted WHR [ 23 , 64 – 66 ]. The lead SNP rs16895971 in LCORL , a transcription factor involved in spermatogenesis, has been associated with height in East Asians [ 67 ]. The lead SNP rs2780226 in HMGA1 encodes a chromatin-associated protein that regulates gene transcription and metastatic progression of cancer cells and has been associated with body height, BMI-adjusted waist circumference, and birth weight [ 15 , 68 , 69 ]. The lead SNP rs3816804 in CS has also been associated with height in East Asians [ 70 ]. The lead SNP rs143384 in GDF5 , which encodes a secreted ligand of the transforming growth factor-beta superfamily, regulates the development of numerous tissue and cell types and has been associated with body height, BMI-adjusted hip circumference, BMI-adjusted WHR, and body fat [ 15 , 64 , 71 , 72 ]. Our observations report novel lead SNPs in the Han Chinese population that share an overlapping genetic architecture for height, mainly discovered in individuals of European ancestry. Our observational (phenotype) analyses showed that height was associated with eight traits. Furthermore, our PRS 251 analyses confirmed that genetic height was also associated with these eight traits. Taller height was associated with decreased BMI, WHR, body fat, TC, and LDL-C, but with increased body weight, waist circumference, and hip circumference. For anthropometric traits, we observed that both observational (phenotype) and genetically determined height (PRS 251 ) were associated with BMI, WHR, body fat, body weight, waist circumference, and hip circumference. Taller height was associated with decreased BMI, WHR, and body fat, but increased body weight, waist circumference, and hip circumference. Our findings are consistent with previous observational (phenotype) studies that reported an inverse association between height and obesity-related traits, including BMI, WHR, and body fat [ 73 , 74 ]. As expected, taller adults had lower rates of obesity [ 73 , 74 ]. Our findings also support previous observational (phenotype) studies that reported positive associations between height and body weight [ 73 – 75 ], waist circumference [ 74 , 75 ], and hip circumference [ 75 ]. Furthermore, taller adults had increased body weight and waist and hip circumferences. The results of our genetic PRS of height association were also in agreement with previous genetic correlation studies, mainly conducted in individuals of European ancestry [ 78 – 81 ]. There was a negative correlation between genetically determined height and BMI [ 78 – 80 ], and positive correlations between genetically determined height with waist and hip circumference [ 80 , 81 ]. Our findings may reflect a partial genetic overlap between height and anthropometric traits including BMI, WHR, body fat, body weight, waist circumference, and hip circumference. However, genetic correlations in individuals of Han Chinese ancestry remain to be elucidated. Regarding blood pressure and lipids and glucose levels, both observational (phenotype) and genetically determined height (PRS 251 ) were inversely associated with TC and LDL-C. Our findings are consistent with those of previous observational (phenotype) studies that reported an inverse association between height and blood lipid levels [ 76 , 77 , 85 ]. Taller adults have lower levels of TC and LDL-C [ 76 , 77 , 85 ]. The results of our genetic PRS of height are also in agreement with previous studies, mainly conducted in individuals of European ancestry [ 50 , 80 , 81 ]. Negative genetic correlations between height and TC were found [ 80 , 81 ]. A taller genetic PRS was associated with lower LDL-C levels in individuals of European ancestry [ 50 ]. Our results also suggest that genetically taller individuals of Han Chinese ancestry have lower levels of TC and LDL-C.

This large-scale assessment of the genetic architecture of height in the Han Chinese population of Taiwan quantified the extent of the shared genetic basis with individuals of European ancestry. Our observational and genetic study supports the relevance of height to the etiology of various health-related outcomes, especially those regarding anthropometric traits and blood lipids.

Additional file 1: Table S1 . Basic characteristics of the study participants at enrollment. Additional file 2: Table S2 . The top lead SNPs in 89 genomic regions that were significantly associated with height ( p < 5 × 10 −8 ), using LD (r 2 < 0.2), in individuals of Han Chinese ancestry. Additional file 3: Table S3 . Replication of a previous GWAS of body height in the SNPs of the training group (313 of 1722 SNPs). Additional file 4: Figure S1 . The clumping and p -value threshold method identifies the “best-fit” SNP number for the polygenic risk score (PRS) calculation, according to the largest explainable phenotype correlation r 2 using only PRS (PRS r 2 and SNP number). The x-axis shows the p -value thresholds from the height of GWAS results. The y-axis represents the explainable phenotypic correlation r 2 using only the PRS (PRS r 2 ). The p -values above the bars show the statistical significance of the associations between genetically determined height (PRS) and measured height (phenotype). (A) 251 SNPs were obtained from 6,941 SNPs (novel and reported SNPs; PRS r 2 = 0.0712, SNP number = 251). (B) 194 SNPs were obtained from 6,843 SNPs (novel SNPs; PRS r 2 = 0.0622, SNP number = 194). (C) 154 SNPs were obtained from 313 SNPs (reported SNPs; PRS r 2 = 0.0706, SNP number = 154). Figure S2 . Scree plot identifying the number of principal component analyses (PCA) needed for the correction of population structure in the height GWAS study, using pcadapt (an R package used to determine the number of principal components). Figure S3 . Association between genetically determined height (PRS 237 ) and measured height (phenotype) in an independent cohort of the Big Data Center in China Medical University Hospital in Taiwan. The measured height (cm) and calculated polygenic risk score (PRS) for height were stratified by sex, mean-centered, and normalized to one standard deviation (SD; males, N = 46,310; females, N = 54,728). The normalized measured height is represented on the y-axis, and normalized genetically determined height (PRS 237 ) is represented on the x-axis. Additional file 5: Table S4 . Characteristics of 237 out of the 251 SNPs associated with height in the independent cohort of the Big Data Center at China Medical University Hospital in Taiwan. Additional file 1: Table S1 . Basic characteristics of the study participants at enrollment. Additional file 2: Table S2 . The top lead SNPs in 89 genomic regions that were significantly associated with height ( p < 5 × 10 −8 ), using LD (r 2 < 0.2), in individuals of Han Chinese ancestry. Additional file 3: Table S3 . Replication of a previous GWAS of body height in the SNPs of the training group (313 of 1722 SNPs). Additional file 4: Figure S1 . The clumping and p -value threshold method identifies the “best-fit” SNP number for the polygenic risk score (PRS) calculation, according to the largest explainable phenotype correlation r 2 using only PRS (PRS r 2 and SNP number). The x-axis shows the p -value thresholds from the height of GWAS results. The y-axis represents the explainable phenotypic correlation r 2 using only the PRS (PRS r 2 ). The p -values above the bars show the statistical significance of the associations between genetically determined height (PRS) and measured height (phenotype). (A) 251 SNPs were obtained from 6,941 SNPs (novel and reported SNPs; PRS r 2 = 0.0712, SNP number = 251). (B) 194 SNPs were obtained from 6,843 SNPs (novel SNPs; PRS r 2 = 0.0622, SNP number = 194). (C) 154 SNPs were obtained from 313 SNPs (reported SNPs; PRS r 2 = 0.0706, SNP number = 154). Figure S2 . Scree plot identifying the number of principal component analyses (PCA) needed for the correction of population structure in the height GWAS study, using pcadapt (an R package used to determine the number of principal components). Figure S3 . Association between genetically determined height (PRS 237 ) and measured height (phenotype) in an independent cohort of the Big Data Center in China Medical University Hospital in Taiwan. The measured height (cm) and calculated polygenic risk score (PRS) for height were stratified by sex, mean-centered, and normalized to one standard deviation (SD; males, N = 46,310; females, N = 54,728). The normalized measured height is represented on the y-axis, and normalized genetically determined height (PRS 237 ) is represented on the x-axis. Additional file 5: Table S4 . Characteristics of 237 out of the 251 SNPs associated with height in the independent cohort of the Big Data Center at China Medical University Hospital in Taiwan.