- Research article
- Open Access
Heritability and genome-wide association analyses of fasting plasma glucose in Chinese adult twins
BMC Genomics volume 21, Article number: 491 (2020)
Currently, diabetes has become one of the leading causes of death worldwide. Fasting plasma glucose (FPG) levels that are higher than optimal, even if below the diagnostic threshold of diabetes, can also lead to increased morbidity and mortality. Here we intend to study the magnitude of the genetic influence on FPG variation by conducting structural equation modelling analysis and to further identify specific genetic variants potentially related to FPG levels by performing a genome-wide association study (GWAS) in Chinese twins.
The final sample included 382 twin pairs: 139 dizygotic (DZ) pairs and 243 monozygotic (MZ) pairs. The DZ twin correlation for the FPG level (rDZ = 0.20, 95% CI: 0.04–0.36) was much lower than half that of the MZ twin correlation (rMZ = 0.68, 95% CI: 0.62–0.74). For the variation in FPG level, the AE model was the better fitting model, with additive genetic parameters (A) accounting for 67.66% (95% CI: 60.50–73.62%) and unique environmental or residual parameters (E) accounting for 32.34% (95% CI: 26.38–39.55%), respectively. In the GWAS, although no genetic variants reached the genome-wide significance level (P < 5 × 10− 8), 28 SNPs exceeded the level of a suggestive association (P < 1 × 10− 5). One promising genetic region (2q33.1) around rs10931893 (P = 1.53 × 10− 7) was found. After imputing untyped SNPs, we found that rs60106404 (P = 2.38 × 10− 8) located at SPATS2L reached the genome-wide significance level, and 216 SNPs exceeded the level of a suggestive association. We found 1007 genes nominally associated with the FPG level (P < 0.05), including SPATS2L, KCNK5, ADCY5, PCSK1, PTPRA, and SLC26A11. Moreover, C1orf74 (P = 0.014) and SLC26A11 (P = 0.021) were differentially expressed between patients with impaired fasting glucose and healthy controls. Some important enriched biological pathways, such as β-alanine metabolism, regulation of insulin secretion, glucagon signaling in metabolic regulation, IL-1 receptor pathway, signaling by platelet derived growth factor, cysteine and methionine metabolism pathway, were identified.
The FPG level is highly heritable in the Chinese population, and genetic variants are significantly involved in regulatory domains, functional genes and biological pathways that mediate FPG levels. This study provides important clues for further elucidating the molecular mechanism of glucose homeostasis and discovering new diagnostic biomarkers and therapeutic targets for diabetes.
Diabetes, as a chronic and metabolic disease, can cause serious damage to the blood vessels, heart, kidneys, nerves and eyes. This condition is one of the leading causes of death worldwide, and higher fasting plasma glucose (FPG) levels, even if below the diagnostic threshold of diabetes, can also lead to increased morbidity and mortality. Diabetes and higher-than-optimal FPG level together leaded to 3.7 million deaths from 1980 to 2014 worldwide . Therefore, it is important to elucidate the underlying pathogenesis of increased FPG levels.
The FPG level is affected by both genetic and environmental factors. Currently, the magnitude of genetic impact on FPG variation has been researched in some studies. And the heritability of the FPG level varied, with 0–0.77 in Europeans [2,3,4,5,6,7,8], 0.16–0.51 in Americans [9,10,11,12,13,14,15] and 0.17–0.71 in Asians [16,17,18,19,20,21,22]. For the African population, two family studies found heritability values of 0.47 and 0.07 [23, 24]. Currently, genome-wide association studies (GWASs) are a promising approach to discover susceptibility genetic loci or genes associated with a phenotype. Several GWASs performed in Western countries found some genetic loci located at ADCY5, G6PC2, MADD, TCF7L2, GCK, XIRP2, VPS16, PTPRA, etc. [25,26,27]. However, few studies have explored the genetic effects on FPG levels in the Chinese population.
Chinese population are different from other ethnic populations in the aspect of genetic constitutions. Genetically related individuals (e.g. twins) will greatly increase the power of genetic association analysis and effectively identify the genetic variants potentially associated with complex traits . Here, we performed this twin-based genetic epidemiological study to evaluate the magnitude of the genetic influence on FPG variation and further conducted a GWAS to identify specific genetic variants related to the FPG level in a sample of 382 Chinese twin pairs.
The final sample consisted of 382 twin pairs: 139 dizygotic (DZ) pairs and 243 monozygotic (MZ) pairs. The median (interquartile range) age for all twins was 50 (45–57) years, and the median (interquartile range) FPG level was 5.10 (4.59–5.80) mmol/L (Additional file 1).
After adjustment for the effect of covariates, the DZ twin correlation for the FPG level (rDZ = 0.20, 95% CI: 0.04–0.36) was much lower than half of the MZ twin correlation (rMZ = 0.68, 95% CI: 0.62–0.74), suggesting the genetic effect on the FPG level (Additional file 2).
As Table 1 shows, for the variation in FPG level, the AE model provided the better fit (AIC = 420.6, P > 0.05), with additive genetic parameters (A) accounting for 67.66% (95% CI: 60.50–73.62%) and unique environmental or residual parameters (E) accounting for 32.34% (95% CI: 26.38–39.55%), respectively.
The median age of 139 DZ twin pairs was 49 years (interquartile range: 45–56 years), and the median FPG level was 5.14 mmol/L (interquartile range: 4.60–5.90 mmol/L) (Additional file 1).
The quantile-quantile (Q-Q) plot is shown in Fig. 1.a; there was no evidence of genomic inflation of test statistics or bias caused by population stratification (λ-statistic = 1.001). The slight deviation in the upper right tail from the null distribution indicated evidence of a weak association. None of the SNPs reached the genome-wide significance level (P < 5 × 10− 8), as illustrated by the Manhattan plot (Fig. 2.a). However, 28 SNPs were suggestive of association (P < 1 × 10− 5), with 17, 1, 4, 1, 4 and 1 SNPs located on chromosomes 2, 5, 6, 8, 10, and 13, respectively (Table 2). The strongest association was found with the SNP rs10931893 (P = 1.53 × 10− 7) on chromosome 2q33.1 at SPATS2L.
As shown in the regional association plot (Fig. 3), one promising chromosomal locus (2q33.1) around rs10931893 showed a potential association with FPG levels. In this region, 17 SNPs (P = 1.53 × 10− 7- 6.94 × 10− 6) were located at or close to SPATS2L which could moderate the protein expression of β2-adrenergic receptors . Additionally, SPATS2L was nominally associated with FPG level (P < 0.05) in the subsequent gene-based analysis.
After performing imputation analysis, a total of 7,405,822 SNPs were identified for analysis. The Q-Q plot indicated evidence of moderate association (Fig. 1.b). One SNP, rs60106404 (P = 2.38 × 10− 8), located at SPATS2L reached the genome-wide significance level (P < 5 × 10− 8), as illustrated by the Manhattan plot (Fig. 2.b). A total of 216 SNPs showed suggestive evidence of an association (P < 1 × 10− 5) with the FPG level (Additional file 3).
When comparing our post-imputation results (P < 0.05) with previously reported FPG-associated SNPs found in relevant GWASs, we found that 8 SNPs rs7684538, rs2367204, rs7186570, rs861085, rs1402837, rs2302593, rs4869272 and rs492594 could be replicated (Additional file 4).
Although none of the genes reached the genome-wide significance level (P < 2.63 × 10− 6), a total of 1007 genes were nominally associated with the FPG level (P < 0.05). The top 20 genes ranked by P-values are shown in Table 3. Several genes potentially related to FPG levels, including BRAT1, TSPO, SLC2A12, KCNK5, PTPRA, ADCY5, PCSK1, and VPS16, were found.
Pathway enrichment analysis
A total of 719 biological pathways were nominally associated with the FPG level (emp-P < 0.05) were found, and the top 30 pathways are shown in Table 4. The important pathways were mainly involved in β-alanine metabolism, regulation of insulin secretion, glucagon signaling in metabolic regulation, IL-1 receptor pathway, signaling by platelet derived growth factor (PDGF), cysteine and methionine metabolism, etc.
The gene expression levels of 25 genes in patients with impaired fasting glucose (IFG) and healthy controls (Additional file 5) were tested by the Wilcoxon rank sum method, and C1orf74 (P = 0.014) and SLC26A11 (P = 0.021) were differentially expressed between the two independent groups.
In this study, we evaluated the genetic contributions to FPG variation by twin modelling analyses and further identified the genetic variants associated with FPG levels by GWAS. We found that the heritability of FPG was 0.68, which was consistent with the previously reported range (0.22–0.71) in mainland China [16, 30,31,32,33,34].
Even no SNPs reached the genome-wide significance level, 28 SNPs showed suggestive evidence of an association with the FPG level. We found one promising genetic region (2q33.1) where 17 suggestive SNPs were linked to SPATS2L. SPATS2L might indirectly affect FPG levels by regulating the protein expression of β2-adrenergic receptors  that could increase glucose uptake [35, 36]. In addition, SPATS2L was the topmost gene in the gene-based analysis. Thus, SPATS2L may serve as candidate gene to be further validated and a potential biomarker for diabetes.
Post-imputation analysis revealed that one SNP, rs60106404, was significantly associated with the FPG level. This SNP is located at an important gene, SPATS2L, that has been discussed above. Furthermore, more than 200 SNPs were found to reach the level of a suggestive association. We compared our results with previously reported SNPs [25,26,27, 37,38,39,40] and found that 8 SNPs could be replicated, indicating our results were credible.
In the gene-based analysis, 1007 genes were nominally associated with FPG levels. Several interesting genes might influence FPG levels through the following mechanisms: (1) BRAT1 deficiency could lead to increased glucose consumption ; (2) TSPO expression plays an important role in maintaining healthy adipocyte functions, and the activation of TSPO in adipocytes could improve glucose uptake ; (3) SLC2A12, a member of the solute carrier family, catalyzes the uptake of sugars through facilitated diffusion ; (4) the proteins encoded by KCNK5 could influence the homeostasis of glucose by regulating insulin secretion ; (5) the protein encoded by the PTPRA gene is a member of the protein tyrosine phosphatase (PTP) family. PTPRA might play a role in insulin signaling as a negative regulator and further influence glucose homeostasis . Moreover, the association between PTPRA and FPG levels has previously been reported ; (6) ADCY5 plays a role in the normal regulation of insulin secretion , which might influence FPG levels. In addition, ADCY5 has been previously reported to be associated with FPG levels [25, 27]; (7) the protein encoded by PCSK1 is prohormone convertase 1/3 (PC1/3), which is essential to activate some peptide hormone precursors involved in regulating glucose homeostasis , and its association with FPG levels has also been previously reported ; (8) although the association of VPS16 with FPG levels has been previously reported , its function in glucose metabolism is still unclear. However, other genes, especially the top 20 genes, were currently have unknown functions in glucose metabolism, and they may be potential candidate genes that need to be researched and validated in the future.
In addition, we tested the gene expression levels of several top genes in IFG cases and healthy controls, and found that C1orf74 and SLC26A11 were differentially expressed. SLC26A11 was involved in the transport of glucose and other sugars, bile salts and organic acids, metal ions and amine compounds, as indicated by the GeneCards database, while the mechanism of C1orf74 involved in blood glucose metabolism still needs to be explored.
The pathway enrichment analysis identified some important FPG-associated biological pathways: (1) β-alanine could significantly decrease glycolytic metabolism and change glycolytic-related gene expression ; (2) glucagon binding to its receptor could activate adenylate cyclase and improve cyclic adenosine monophosphate (cAMP) levels, which could promote insulin secretion [49,50,51]; (3) the IL-1R signaling system can regulate glucose homeostasis by sustaining the health and function of islet β-cells. When pancreatic IL-1R signaling is absent, the whole-body glucose homeostasis is disrupted ; (4) in the presence of sufficient PDGF receptor, PDGF can activate protein kinase B and result in the transportation of glucose transporter 4 (GLUT 4) to the surface of the cell, which finally promotes the absorption of glucose and produces an insulin-like effect [53,54,55]; (5) experimental and clinical studies have indicated that cysteine affects the regulation of insulin secretion and glucose levels. In addition, methionine could improve insulin sensitivity ; (6) PIPs can be phosphorylated by phosphatidylinositol 3-kinase to produce PIP3, which is involved in the insulin secretion signaling system by activating a PH-containing signaling protein such as protein kinase B [57, 58].
The strength of twin samples in our study was observed. The variation of human phenotype may be due to effects of genetic structure, gender, age and certain environmental exposures. Twin samples, as genetically related individuals, will highly increase the power of genetic association analysis and effectively find the genetic variants potentially associated with complex traits . Hence, our results would be more credible.
Nevertheless, this study also has some limitations. This study was with a relatively small sample size because of the difficulties of recruiting and identifying qualified twin pairs. However, our results could still provide useful clues for hypotheses to be further replicated and validated while exploring the molecular mechanism of diabetes. Considering that the genetic influence on FPG variation is expected to be comprised of a lot of SNPs, a meta-analysis with a larger number of samples will be an ideal and desirable method.
Our study has confirmed the significant contribution of genetic effects on FPG variation. The FPG level is highly heritable in the Chinese population, and some genetic variants are involved in regulatory domains, functional genes and biological pathways that mediate FPG levels. The results provide important clues for further elucidating the molecular mechanism of glucose homeostasis. The potential candidate biomarkers of FPG level presented here merit further verification.
Materials and methods
Briefly, we collected samples through the Qingdao Twin Registry (QTR) [61, 62]. All twins took a questionnaire (Additional file 6) and underwent a health examination. We tested the FPG level by using the semiautomatic analyzer (Hitachi 7600, Japan). Twins who were pregnant or lactating, took hypoglycaemic agents, or used insulin were eliminated. We also dropped incomplete twin pairs. Finally, a total of 382 twin pairs (139 DZ pairs and 243 MZ pairs) aged 18 years or older were included in the heritability analysis and the subset of 139 DZ twin pairs was further included in the GWAS. All participants signed the written informed consent.
Genotyping, quality control and imputation
Briefly, we firstly genotyped DNA samples, and then conducted quality control analysis . 1,365,181 SNPs were obtained for subsequent SNP-based analysis. IMPUTE2 software  was used to impute untyped SNPs , and 7,405,822 SNPs were finally obtained.
Genetic analysis were conducted by using Mx programme . The FPG level was transformed following Blom’s formula for normality. Pearson’s product-moment correlation coefficient was calculated to measure intraclass phenotypic correlations per zygosity. When the correlation of DZ twins (rDZ) was much lower than half of that of MZ twins (rMZ), the ADE model was taken into account.
In the classical twin design, the phenotypic variation was decomposed into that due to additive genetic (A), dominant genetic (D), and unique environmental or residual (E) influences. Standard structural equation modeling (SEM) methods were used to estimate the A, D, and E components while adjusting for age, sex, and BMI. We performed a likelihood ratio test to compare the performances of the full ADE model and its nested model, i.e., AE model. And the better fitting model was chosen according to the parsimonious principle and a lower Akaike’s information criterion (AIC) value . The power of this classical twin design was above 90%, which was computed based on the sample size combining significance level α and degree of freedom by the Mx programme.
GEMMA  was adopted to evaluate the association of FPG levels with SNP genotypes with adjusting for age, sex, and BMI. The conventional Bonferroni-corrected threshold (5 × 10− 8) was set as genome-wide statistical significance, and the commonly used threshold (1 × 10− 5) was adopted for suggestive hits [68,69,70].
We performed this analysis by using VEGAS2 tool [71, 72]. The genes showing more signal or strength of association than expected by chance were found. P < 2.63 × 10− 6 was set as genome-wide statistical significance.
Pathway enrichment analysis
We used the PASCAL tool to compute the gene and pathway scores . Individual SNPs from GWAS were firstly mapped to genes involved in each pathway. The default parameter values were employed, including all markers inside the gene ±50 flanking kb and the maximum number of 3000 SNPs per gene. Then the gene scores for all genes in one pathway were computed and a joint score was estimated. At last, the pathway scores were computed, and the pathway enrichment of high-scoring genes was evaluated through two parameter-free procedures, e.g., chi-square and empirical scores. BioCarta, KEGG and Reactome were selected in the MSigDB database to obtain pathways and corresponding gene annotation .
The blood samples of 8 subjects (4 cases and 4 healthy controls) were sequenced to obtain the gene expression data. The cases were defined as those with FPG ≥ 6.1 mmol/L, i.e., IFG status, and the healthy controls were defined as those with FPG ≤ 4.7 mmol/L. Then, the expression levels of 25 genes, i.e., the genes where the top SNPs were located and the top 20 genes in the VEGAS2 analysis, between the two independent groups were compared by the Wilcoxon rank sum test. We set the P-value < 0.05 as statistically significant.
Availability of data and materials
The dataset supporting the conclusions of this article is available in the European Variation Archive (EVA) repository (Accession No. PRJEB23749).
Fasting plasma glucose
Genome-wide association study
World Heath Organization: Global report on diabetes. http://www.who.int/diabetes/global-report/en/. Accessed 10 Apr 2020.
Jermendy G, Horvath T, Littvay L, Steinbach R, Jermendy AL, Tarnoki AD, Tarnoki DL, Metneki J, Osztovits J. Effect of genetic and environmental influences on cardiometabolic risk factors: a twin study. Cardiovasc Diabetol. 2011;10:96.
Mills GW, Avery PJ, McCarthy MI, Hattersley AT, Levy JC, Hitman GA, Sampson M, Walker M. Heritability estimates for beta cell function and features of the insulin resistance syndrome in UK families with an increased susceptibility to type 2 diabetes. Diabetologia. 2004;47:732–8.
Poveda A, Chen Y, Brandstrom A, Engberg E, Hallmans G, Johansson I, Renstrom F, Kurbasic A, Franks PW. The heritable basis of gene-environment interactions in cardiometabolic traits. Diabetologia. 2017;60:442–52.
Rahman I, Bennet AM, Pedersen NL, de Faire U, Svensson P, Magnusson PK. Genetic dominance influences blood biomarker levels in a sample of 12,000 Swedish elderly twins. Twin Res Hum Genet. 2009;12:286–94.
Simonis-Bik AM, Eekhoff EM, Diamant M, Boomsma DI, Heine RJ, Dekker JM, Willemsen G, van Leeuwen M, de Geus EJ. The heritability of HbA1c and fasting blood glucose in different measurement settings. Twin Res Hum Genet. 2008;11:597–602.
Snieder H, Sawtell PA, Ross L, Walker J, Spector TD, Leslie RD. HbA(1c) levels are genetically determined even in type 1 diabetes: evidence from healthy and diabetic twins. Diabetes. 2001;50:2858–63.
Van Dongen J, Willemsen G, Chen WM, de Geus EJ, Boomsma DI. Heritability of metabolic syndrome traits in a large population-based sample. J Lipid Res. 2013;54:2914–23.
Edwards KL, Wan JY, Hutter CM, Fong PY, Santorico SA. Multivariate linkage scan for metabolic syndrome traits in families with type 2 diabetes. Obesity (Silver Spring). 2011;19:1235–43.
Fowler SP, Puppala S, Arya R, Chittoor G, Farook VS, Schneider J, Resendez RG, Upadhayay RP, Vandeberg J, Hunt KJ, et al. Genetic epidemiology of cardiometabolic risk factors and their clustering patterns in Mexican American children and adolescents: the SAFARI study. Hum Genet. 2013;132:1059–71.
Henkin L, Bergman RN, Bowden DW, Ellsworth DL, Haffner SM, Langefeld CD, Mitchell BD, Norris JM, Rewers M, Saad MF, et al. Genetic epidemiology of insulin resistance and visceral adiposity. The IRAS family study design and methods. Ann Epidemiol. 2003;13:211–7.
Lin HF, Boden-Albala B, Juo SH, Park N, Rundek T, Sacco RL. Heritabilities of the metabolic syndrome and its components in the northern Manhattan family study. Diabetologia. 2005;48:2006–12.
Pena GG, Dutra MS, Gazzinelli A, Correa-Oliveira R, Velasquez-Melendez G. Heritability of phenotypes associated with glucose homeostasis and adiposity in a rural area of Brazil. Ann Hum Genet. 2014;78:40–9.
Reding-Bernal A, Sanchez-Pedraza V, Moreno-Macias H, Sobrino-Cossio S, Tejero-Barrera ME, Burguete-Garcia AI, Leon-Hernandez M, Serratos-Canales MF, Duggirala R, Lopez-Alvarenga JC. Heritability and genetic correlation between GERD symptoms severity, metabolic syndrome, and inflammation markers in families living in Mexico City. PLoS One. 2017;12:e0178815.
Vattikuti S, Guo J, Chow CC. Heritability and genetic correlations explained by common SNPs for metabolic syndrome traits. PLoS Genet. 2012;8:e1002637.
Duan H, Pang Z, Zhang D, Li S, Kruse TA, Kyvik KO, Christensen K, Tan Q. Genetic and environmental dissections of sub-phenotypes of metabolic syndrome in the Chinese population: a twin-based heritability study. Obes Facts. 2011;4:99–104.
Li JK, Ng MC, So WY, Chiu CK, Ozaki R, Tong PC, Cockram CS, Chan JC. Phenotypic and genetic clustering of diabetes and metabolic syndrome in Chinese families with type 2 diabetes mellitus. Diabetes Metab Res Rev. 2006;22:46–52.
Lin CC, Peyser PA, Kardia SL, Li CI, Liu CS, Chu JS, Lin WY, Li TC. Heritability of cardiovascular risk factors in a Chinese population--Taichung community health study and family cohort. Atherosclerosis. 2014;235:488–95.
Mathias RA, Deepa M, Deepa R, Wilson AF, Mohan V. Heritability of quantitative traits associated with type 2 diabetes mellitus in large multiplex families from South India. Metabolism. 2009;58:1439–45.
Sung J, Lee K, Song YM. Heritabilities of the metabolic syndrome phenotypes and related factors in Korean twins. J Clin Endocrinol Metab. 2009;94:4946–52.
Wu KD, Hsiao CF, Ho LT, Sheu WH, Pei D, Chuang LM, Curb D, Chen YD, Tsai HJ, Dzau VJ, et al. Clustering and heritability of insulin resistance in Chinese and Japanese hypertensive families: a Stanford-Asian Pacific program in hypertension and insulin resistance sibling study. Hypertens Res. 2002;25:529–36.
Zarkesh M, Daneshpour MS, Faam B, Fallah MS, Hosseinzadeh N, Guity K, Hosseinpanah F, Momenan AA, Azizi F. Heritability of the metabolic syndrome and its components in the Tehran lipid and glucose study (TLGS). Genet Res (Camb). 2012;94:331–7.
Bayoumi RA, Al-Yahyaee SA, Albarwani SA, Rizvi SG, Al-Hadabi S, Al-Ubaidi FF, Al-Hinai AT, Al-Kindi MN, Adnan HT, Al-Barwany HS, et al. Heritability of determinants of the metabolic syndrome among healthy Arabs of the Oman family study. Obesity (Silver Spring). 2007;15:551–6.
Jowett JB, Diego VP, Kotea N, Kowlessur S, Chitson P, Dyer TD, Zimmet P, Blangero J. Genetic influences on type 2 diabetes and metabolic syndrome related quantitative traits in Mauritius. Twin Res Hum Genet. 2009;12:44–52.
DeMenna J, Puppala S, Chittoor G, Schneider J, Kim JY, Shaibi GQ, Mandarino LJ, Duggirala R, Coletta DK. Association of common genetic variants with diabetes and metabolic syndrome related traits in the Arizona insulin resistance registry: a focus on Mexican American families in the southwest. Hum Hered. 2014;78:47–58.
Suh YJ, Kim S, Kim SH, Park J, Lim HA, Park HJ, Choi H, Ng D, Lee MK, Nam M. Combined genome-wide linkage and association analyses of fasting glucose level in healthy twins and families of Korea. J Korean Med Sci. 2013;28:415–23.
Ivarsdottir EV, Steinthorsdottir V, Daneshpour MS, Thorleifsson G, Sulem P. Effect of sequence variants on variance in glucose levels predicts type 2 diabetes risk and accounts for heritability. Nat Genet. 2017;49:1398–402.
Tan Q, Li W, Vandin F. Disease-concordant twins empower genetic association studies. Ann Hum Genet. 2017;81:20–6.
Himes BE, Jiang X, Hu R, Wu AC, Lasky-Su JA, Klanderman BJ, Ziniti J, Senter-Sylvia J, Lima JJ, Irvin CG, et al. Genome-wide association analysis in asthma subjects identifies SPATS2L as a novel bronchodilator response gene. PLoS Genet. 2012;8:e1002824.
Zhang S, Liu X, Yu Y, Hong X, Christoffel KK, Wang B, Tsai HJ, Li Z, Liu X, Tang G, et al. Genetic and environmental contributions to phenotypic components of metabolic syndrome: a population-based twin study. Obesity (Silver Spring). 2009;17:1581–7.
Luo BF, Du L, Li JX, Pan BY, Xu JM, Chen J, Yin XY, Ren Y, Zhang F. Heritability of metabolic syndrome traits among healthy younger adults: a population based study in China. J Med Genet. 2010;47:415–20.
Jian WX, Su Q, Luo M. Genetic determination of fast plasma glucose concentration and correlation with anthropometric indices. Zhonghua Yi Xue Yi Chuan Xue Za Zhi. 2009;26:161–4.
Tao R, Dingfeng W, Yonghua H, Weihua C, Siyan Z, Jun L, Ying Q, Tao W, Liming L. Heritability analysis of related indicators of metabolic syndrome in twins. Zhongguo Man Xing Bing Yu Fang Yu Kong Zhi. 2003;13:15–21.
Li S, Kyvik KO, Duan H, Zhang D, Pang Z, Hjelmborg J, Tan Q, Kruse T, Dalgård C. Longitudinal Investigation into Genetics in the Conservation of Metabolic Phenotypes in Danish and Chinese Twins. PLoS One. 2016;11:e0162805.
Nevzorova J, Bengtsson T, Evans BA, Summers RJ. Characterization of the beta-adrenoceptor subtype involved in mediation of glucose transport in L6 cells. Br J Pharmacol. 2002;137:9–18.
Hutchinson DS, Chernogubova E, Dallner OS, Cannon B, Bengtsson T. Beta-adrenoceptors, but not alpha-adrenoceptors, stimulate AMP-activated protein kinase in brown adipocytes independently of uncoupling protein-1. Diabetologia. 2005;48:2386–95.
Chambers JC, Zhang W, Zabaneh D, Sehmi J, Jain P, McCarthy MI, Froguel P, Ruokonen A, Balding D, Jarvelin MR, et al. Common genetic variation near melatonin receptor MTNR1B contributes to raised plasma glucose and increased risk of type 2 diabetes among Indian Asians and European Caucasians. Diabetes. 2009;58:2703–8.
Freedman BI, Rich SS, Sale MM, Heiss G, Djousse L, Pankow JS, Province MA, Rao DC, Lewis CE, Chen YD, et al. Genome-wide scans for heritability of fasting serum insulin and glucose concentrations in hypertensive families. Diabetologia. 2005;48:661–8.
Meigs JB, Manning AK, Fox CS, Florez JC, Liu C, Cupples LA, Dupuis J. Genome-wide association with diabetes-related traits in the Framingham Heart Study. BMC Med Genet. 2007;8(Suppl 1):S16.
Spracklen CN. Identification and functional analysis of glycemic trait loci in the China health and nutrition survey. Diabetes. 2018;14:e1007275.
So EY, Ouchi T. BRAT1 deficiency causes increased glucose metabolism and mitochondrial malfunction. BMC Cancer. 2014;14:548.
Li J, Papadopoulos V. Translocator protein (18 kDa) as a pharmacological target in adipocytes to regulate glucose homeostasis. Biochem Pharmacol. 2015;97:99–110.
Rogers S, Macheda ML, Docherty SE, Carty MD, Henderson MA, Soeller WC, Gibbs EM, James DE, Best JD. Identification of a novel glucose transporter-like protein-GLUT-12. Am J Physiol Endocrinol Metab. 2002;282:E733–8.
Matsuo M. ATP-binding cassette proteins involved in glucose and lipid homeostasis. Biosci Biotechnol Biochem. 2010;74:899–907.
Kapp K, Metzinger E, Kellerer M, Haring HU, Lammers R. The protein tyrosine phosphatase alpha modifies insulin secretion in INS-1E cells. Biochem Biophys Res Commun. 2003;311:361–4.
Hodson DJ, Mitchell RK, Marselli L, Pullen TJ, Gimeno Brias S, Semplici F, Everett KL, Cooper DM, Bugliani M, Marchetti P, et al. ADCY5 couples glucose to insulin secretion in human islets. Diabetes. 2014;63:3009–21.
Ramos-Molina B, Martin MG, Lindberg I. PCSK1 variants and human obesity. Prog Mol Biol Transl Sci. 2016;140:47–74.
Vaughan RA, Gannon NP, Garcia-Smith R, Licon-Munoz Y, Barberena MA, Bisoffi M, Trujillo KA. beta-alanine suppresses malignant breast epithelial cell aggressiveness through alterations in metabolism and cellular acidity in vitro. Mol Cancer. 2014;13:14.
Drucker DJ, Philippe J, Mojsov S, Chick WL, Habener JF. Glucagon-like peptide I stimulates insulin gene expression and increases cyclic AMP levels in a rat islet cell line. Proc Natl Acad Sci U S A. 1987;84:3434–8.
Kashima Y, Miki T, Shibasaki T, Ozaki N, Miyazaki M, Yano H, Seino S. Critical role of cAMP-GEFII--Rim2 complex in incretin-potentiated insulin secretion. J Biol Chem. 2001;276:46046–53.
Thorens B. Expression cloning of the pancreatic beta cell receptor for the gluco-incretin hormone glucagon-like peptide 1. Proc Natl Acad Sci U S A. 1992;89:8641–5.
Burke SJ, Batdorf HM, Burk DH, Martin TM, Mendoza T, Stadler K, Alami W, Karlstad MD, Robson MJ, Blakely RD, et al. Pancreatic deletion of the interleukin-1 receptor disrupts whole body glucose homeostasis and promotes islet beta-cell de-differentiation. Mol Metab. 2018;14:95–107.
Simpson F, Whitehead JP, James DE. GLUT4--at the cross roads between membrane trafficking and signal transduction. Traffic. 2001;2:2–11.
Whiteman EL, Chen JJ, Birnbaum MJ. Platelet-derived growth factor (PDGF) stimulates glucose transport in 3T3-L1 adipocytes overexpressing PDGF receptor by a pathway independent of insulin receptor substrates. Endocrinology. 2003;144:3811–20.
Whiteman EL, Cho H, Birnbaum MJ. Role of Akt/protein kinase B in metabolism. Trends Endocrinol Metab. 2002;13:444–51.
Manna P, Das J, Sil PC. Role of sulfur containing amino acids as an adjuvant therapy in the prevention of diabetes and its associated complications. Curr Diabetes Rev. 2013;9:237–48.
Saltiel AR, Kahn CR. Insulin signalling and the regulation of glucose and lipid metabolism. Nature. 2001;414:799–806.
Taniguchi CM, Emanuelli B, Kahn CR. Critical nodes in signalling pathways: insights into insulin action. Nat Rev Mol Cell Biol. 2006;7:85–96.
Liu H, Wang W, Zhang C, Xu C, Duan H, Tian X, Zhang D. Heritability and Genome-Wide Association Study of Plasma Cholesterol in Chinese Adult Twins. Front Endocrinol (Lausanne). 2018;9:677.
Wang W, Zhang D, Xu C, Wu Y, Duan H, Li S, Tan Q. Heritability and Genome-Wide Association Analyses of Serum Uric Acid in Middle and Old-Aged Chinese Twins. Front Endocrinol (Lausanne). 2018;9:75.
Xu C, Zhang D, Tian X, Duan H, Wu Y, Pang Z, Li S, Tan Q. Genetic and environmental influences on correlations between hearing and cognitive functions in middle and older Chinese twins. Twin Res Hum Genet. 2017;20:374–9.
Xu C, Zhang D, Tian X, Wu Y, Pang Z, Li S, Tan Q. Genetic and environmental basis in phenotype correlation between physical function and cognition in aging Chinese twins. Twin Res Hum Genet. 2017;20:60–5.
Zhou X, Stephens M. Genome-wide efficient mixed-model analysis for association studies. Nat Genet. 2012;44:821–4.
Marchini J, Howie B, Myers S, McVean G, Donnelly P. A new multipoint method for genome-wide association studies by imputation of genotypes. Nat Genet. 2007;39:906–13.
Auton A, Brooks LD, Durbin RM, Garrison EP, Kang HM, Korbel JO, Marchini JL, McCarthy S, McVean GA, Abecasis GR. A global reference for human genetic variation. Nature. 2015;526:68–74.
Mx. http://www.vcu.edu/mx. Accessed 19 Apr 2020.
Akaike H. Factor-Analysis and Aic Psychometrika. 1987;52:317–32.
Dudbridge F, Gusnanto A. Estimation of significance thresholds for genomewide association scans. Genet Epidemiol. 2008;32:227–34.
Charmet R, Duffy S, Keshavarzi S, Gyorgy B, Marre M, Rossing P, McKnight AJ, Maxwell AP, Ahluwalia TVS, Paterson AD, et al. Novel risk genes identified in a genome-wide association study for coronary artery disease in patients with type 1 diabetes. Cardiovasc Diabetol. 2018;17:61.
Rautanen A, Mills TC, Gordon AC, Hutton P, Steffens M, Nuamah R, Chiche JD, Parks T, Chapman SJ, Davenport EE, et al. Genome-wide association study of survival from sepsis due to pneumonia: an observational cohort study. Lancet Respir Med. 2015;3:53–60.
Liu JZ, McRae AF, Nyholt DR, Medland SE, Wray NR, Brown KM, Hayward NK, Montgomery GW, Visscher PM, Martin NG, et al. A versatile gene-based test for genome-wide association studies. Am J Hum Genet. 2010;87:139–45.
Mishra A, Macgregor S. VEGAS2: software for more flexible gene-based testing. Twin Res Hum Genet. 2015;18:86–91.
Lamparter D, Marbach D, Rueedi R, Kutalik Z, Bergmann S. Fast and rigorous computation of gene and Pathway scores from SNP-based summary statistics. PLoS Comput Biol. 2016;12:e1004714.
Molecular Signatures Database (MSigDB). http://software.broadinstitute.org/gsea/msigdb. Accessed 18 Apr 2020.
We thank Gu Zhu for providing technical guidance in the data analysis.
This study was supported by the National Natural Science Foundation of China  and the EFSD/CDS/Lilly Programme award (2013).
Ethics approval and consent to participate
This study was approved by the local ethics committee at Qingdao CDC, Qingdao, China. All participants provided written informed consent for participating in the study. We have acquired administrative permission to access the data which was used in the research.
Consent for publication
The authors declared that they had no competing interests.
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Descriptive statistics for twins.
Phenotypic correlation coefficients (95% confidence intervals) with effects of covariates in twin pairs.
Summary of the imputed SNPs with a P-value < 1 × 10− 5 for association of the FPG level in GWAS.
Comparison between our imputed SNPs (P-value < 0.05) and other previously reported SNPs in GWAS.
The characteristics of subjects in validation analysis.
The questionare of survey on health status of elderly twins in Qingdao city.
About this article
Cite this article
Wang, W., Zhang, C., Liu, H. et al. Heritability and genome-wide association analyses of fasting plasma glucose in Chinese adult twins. BMC Genomics 21, 491 (2020). https://doi.org/10.1186/s12864-020-06898-z
- Fasting plasma glucose
- Genome-wide association study