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

Background Height is an important anthropometric measurement and is associated with many health-related outcomes. Genome-wide association studies (GWASs) have identified hundreds of genetic loci associated with height, mainly in individuals of European ancestry. Methods We performed genome-wide association analyses and replicated previously reported GWAS-determined single nucleotide polymorphisms (SNPs) in the Taiwanese Han population (Taiwan Biobank; n = 67,452). A genetic instrument composed of 251 SNPs was selected from our GWAS, based on height and replication results as the best-fit polygenic risk score (PRS), in accordance with the clumping and p-value threshold method. We also examined the association between genetically determined height (PRS251) and measured height (phenotype). We performed observational (phenotype) and genetic PRS251 association analyses of height and health-related outcomes. Results GWAS identified 6843 SNPs in 89 genomic regions with genome-wide significance, including 18 novel loci. These were the most strongly associated genetic loci (EFEMP1, DIS3L2, ZBTB38, LCORL, HMGA1, CS, and GDF5) previously reported to play a role in height. There was a positive association between PRS251 and measured height (p < 0.001). Of the 14 traits and 49 diseases analyzed, we observed significant associations of measured and genetically determined height with only eight traits (p < 0.05/[14 + 49]). Height was positively associated with body weight, waist circumference, and hip circumference but negatively associated with body mass index, waist-hip ratio, body fat, total cholesterol, and low-density lipoprotein cholesterol (p < 0.05/[14 + 49]). Conclusions This study contributes to the understanding of the genetic features of height and health-related outcomes in individuals of Han Chinese ancestry in Taiwan. Supplementary Information The online version contains supplementary material available at 10.1186/s12916-022-02450-w.

Height is associated with several health-related outcomes later in life [27][28][29][30][31][32][33][34]. For instance, taller people tend to have a higher risk of cancer [28] and cancerrelated 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][31][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][40][41][42][43][44] and cancer-related mortality [45,46] but a reduced risk of CVD [42,[47][48][49][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.

Taiwan Biobank
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. twbio bank. 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.

Taiwan Biobank phenotypes
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.

Study population
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.
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 meancentered 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).
We also ensured that our GWAS findings of the training group replicated previously identified height-associated genetic variants (https:// www. ebi. ac. uk/ gwas/ efotr aits/ EFO_ 00043 39). The reported GWAS heightassociated genetic variants were mainly from individuals Fig. 1 Flowchart of the study design and analysis process 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).
Genotypic data were then subjected to QC procedures (individual QC and SNP QC) in the Taiwan Biobank (https:// www. bioba nk. 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%.

QC for this study
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.

Statistical analyses
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 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 used to construct haplotype blocks by resampling [61,62]. LocusZoom was used to plot the resulting significant locus [63].
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 ( Table S2; Fig. 3C).

Replication of previously reported GWAS-determined SNPs in the Taiwan Han population
The previously reported GWAS-determined SNPs for height were obtained from the GWAS catalog (https:// www. ebi. ac. uk/ gwas/ efotr aits/ EFO_ 00043 39) 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.

Association between the genetically determined height (PRS 251 ) and the measured height (phenotype)
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).

Discussion
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 communitybased 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][16][17][18][19][20][21][22][23][24][25][26]. Accordingly, our findings support the validity of height in observational (phenotype) studies Table 1 Newly identified SNPs associated with height in Taiwan   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  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 and are consistent with the health-related outcomes of this phenotype [73][74][75][76][77][78][79][80][81].
In this study, we identified 6843 SNPs with genomewide 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][65][66][67][68][69][70][71][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 BMIadjusted WHR [23,[64][65][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 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) 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][74][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][79][80][81]. There was a negative correlation between genetically determined height and BMI [78][79][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. Table 3 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 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.

Conclusions
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.