Abstract
Adiponectin is adipocyte-secreted cytokine with potent insulin-sensitizing action in peripheral tissues. The heritability of plasma adiponectin is high in Han Chinese population.
To identify genetic loci influencing plasma adiponectin levels in Chinese population, we performed a genome-wide linkage scan in 1949 Chinese participants of the Stanford Asia-Pacific Program for Hypertension and Insulin Resistance family study and mapped a quantitative trail locus located on chromosome 15 at 31 cM (logarithm of odds = 3.04) with 1-logarithm of odds support interval at 24 to 34 cM. Within this mapped region, we further genotyped a total of 68 single-nucleotide polymorphisms in 12 genes. Association analysis revealed that haplotypes composed of single-nucleotide polymorphisms in the ryanodine receptor 3 (RYR3) gene had strongest association with plasma adiponectin. RYR3 haplotypes were also associated with systolic (P = 0.001) and diastolic (P = 7.1 × 10−4) blood pressure and high-density lipoprotein cholesterol (P = 1.4 × 10−4). Furthermore, an inverse relationship between expression of RYR3 and adiponectin was observed in human abdominal adipose tissue. In conclusion, a genome-wide linkage scan and regional association fine-mapping identified variants in the RYR3 gene as a quantitative trail locus for plasma adiponectin levels in Chinese population.
Keywords: adiponectin, Chinese, genome-wide, RYR3, SAPPHIRe
1. Introduction
Adiponectin is an adipocyte-secreted protein with potent insulin-sensitizing action in peripheral tissues such as skeletal muscle and liver. Plasma adiponectin levels are negatively associated with plasma triglycerides, measures of obesity, insulin resistance, and risk of type 2 diabetes but are positively associated with plasma high-density lipoprotein cholesterol (HDL-C) levels.[1–3] The heritability of plasma adiponectin level is high (30–70%) in European population.[4,5] A large-scale multiethnic meta-analysis of genome-wide association analysis (GWAS) involving 45,891 individuals identified variants in the LYPLAL1, GNL3, TSC22D2, ADIPOQ, VEGFA, TRIB1, PDE3A, GPR109A, DNAH10, CMIP, CDH13, ZNF664, and PEPD genes associated with plasma adiponectin.[6] A GWAS of participants in the Nurses’ Health Study confirmed the association of ADIPOQ variants and found a new locus in the FER gene associated with plasma adiponectin.[7] Another meta-analysis of GWAS involving 14,733 Europeans also confirmed the association of ADIPOQ variants and revealed a novel locus in the ARL15 gene associated with plasma adiponectin.[9] Another GWAS in Filipino women identified a novel signal near KNG1 gene.[8] A recent meta-analysis of GWAS in 7827 East Asians confirmed the association of variants at CDH13, ADIPOQ, PEPD, CMIP, ZNF664, and GPR109A genes and further identified a novel variant at WDR11-FGFR2 gene associated with plasma adiponectin.[9] These variants are also associated with triglycerides, HDL-C, blood pressure, measures of obesity, type 2 diabetes, and cardiometabolic outcomes.[6,7,10,11]
We have previously demonstrated a high heritability (h2 = 0.64) of plasma adiponectin in Chinese population.[12] However, no genetic association study for plasma adiponectin has been carried out in Han Chinese population. In a previous genome-wide linkage analysis for plasma adiponectin levels using 376 microsatellite markers in the Stanford Asia-Pacific Program for Hypertension and Insulin Resistance (SAPPHIRe) Chinese family cohort,[12] we identified a single peak with logarithm of odds (LOD) of 3.19 at 39 cM of chromosome 15. In this study, we sought to refine this linkage signal using additional microsatellite markers and performed regional association fine mapping using 68 single-nucleotide polymorphism (SNP) markers. We identified genetic variants in the ryanodine receptor 3 (RYR3) gene associated with plasma adiponectin in Han Chinese.
2. Methods
2.1. The SAPPHIRe study cohort
The SAPPHIRe was a collaborative study that was part of the Family Blood Pressure Program of the National Heart, Lung, and Blood Institute of the National Institutes of Health originally designed to investigate the genetic determinants of hypertension and insulin resistance in Chinese individuals. The study collected sibling pairs who were either concordant or discordant for high blood pressure. Detailed descriptions of the study cohort were published in our previous work.[12,13] Hypertension was defined as systolic blood pressure ≧140 mm Hg, diastolic blood pressure ≧90 mm Hg, or use of medications for high blood pressure. Individuals with pre-existing chronic illness such as diabetes, cancer, or diseases of the heart, liver, or kidney were excluded. A total of 1153 subjects of Han Chinese descent from 392 families were enrolled at baseline. The institutional review board of Tri-Service General Hospital in Taiwan, the National Taiwan University Hospital (NTUH) Research Ethics Committee, the institutional review board of Taipei Veterans General Hospital, and the institutional review board of Taichung Veterans General Hospital approved this study. Written informed consent was obtained from each participant. The baseline characteristics of participants are summarized in Table 1.
Table 1.
Characteristics of study participants.

2.2. Selection of tag SNPs and genotyping
Within the linkage region, we selected 68 SNPs in 12 genes including cholinergic receptor, nicotinic, alpha 7 (CHRNA7, HGCN ID: 1960), Rho GTPase activating protein 11A (ARHGAP11A, HGCN ID: 15783), secretogranin V (SCG5, HGCN ID: 10816), gremlin-1 precursor (GREM1, HGCN ID: 2001), RYR3 (HGCN ID: 10485), cholinergic receptor, muscarinic 5 (CHRM5, HGCN ID: 1954), solute carrier family 12, member 6 (SLC12A6, HGCN ID: 10914), nuclear protein in testis (NUT, HGCN ID: 29919), PLSC domain containing protein (AYTL3, HGCN ID: 30059), golgin subfamily a, 8A (GOLGA8A, HGCN ID: 31972), aquarius (AQR, HGCN ID: 29513), and RAS guanyl releasing protein 1 (RASGRP1, HGCN ID:9878) genes from the HapMap Chinese Beijing database (HapMap genome browser release #24) (http://www.hapmap.org) based on physical intervals (Table 2).[14] The average physical interval is 17.1 kb. The average call rate is 95.48%. The concordance rate of this system based on 160 genotyping duplications was 99.38%.
Table 2.
Single-nucleotide polymorphisms (SNPs) genotyped in genes within linkage region.

2.3. Subjects for measurement of gene expression in adipose tissue
We recruited 44 adult subjects undergoing bariatric surgery or elective abdominal surgery such as cholecystectomy or partial hepatectomy in Ming-Sheng General Hospital and Yunlin branch of NTUH in Taiwan. Abdominal omental adipose tissues were sampled in a fasting state during surgery and were placed in liquid nitrogen immediately until processing. The study was approved by the institutional review board of Ming-Sheng General Hospital and National Taiwan University Hospital. Written informed consent was obtained from each patient. Their baseline characteristics are summarized in Table 1.
2.4. Reverse transcription and quantitative real-time PCR
Total RNA was isolated using REzol C&T reagent (Protech, Taipei, Taiwan) and reverse transcribed with SuperScript III (Invitrogen, Carlsbad, CA) according to the manufacturer’s instructions. Polymerase chain reaction amplification was performed using LightCycler FastStart DNA Master Plus SYBR (Roche, Mannheim, Germany). Each sample was analyzed in duplicate and calibrated to the cyclophilin A housekeeping gene. The primer used for RYR3 was PPH20011A (BD Bioscience, New Jersey, USA). The primers used for PPLA (cyclophilin A) were forward (5′-GCATACGGGTCCTGGCATCTTGTCC-3′) and reverse (5′-ATGGTGATCTTCTTGCTGGTCTTGC-3′).
2.5. Statistical analysis
The genome-wide scans were performed for logarithm-transformed adiponectin with adjustments for age and gender. Variance component model was used for linkage analysis using the Sequential Oligogenic Linkage Analysis Routines computer package.[15] The detailed description of linkage analysis was published in our previous work.[12,13] Likelihood ratio tests were used to test for the null hypothesis of no linkage. LOD scores were calculated as logarithm to base 10 of the likelihood ratios. A LOD score exceeding 3.3 was considered to be of genome-wide significance for evidence of linkage, whereas a LOD score >1.9 was suggestive of evidence for linkage. One-unit LOD support intervals were obtained by identifying the peak for the maximum LOD score on the plot of the linkage results, dropping down 1 LOD unit and finding the chromosomal region defined by the shoulders of the curve. For regional fine mapping, we used family-based association tests for single-SNP and haplotype association analysis.[16] Hardy–Weinberg equilibrium test was performed before marker–trait analysis.
3. Results
3.1. Fine mapping of genome-wide linkage signal identified RYR3 as QTL for plasma adiponectin
In a previous genome-wide linkage scan in the SAPPHIRe Chinese family cohort, we identified a linkage signal on chromosome 15 at 39 cM (LOD score = 3.19 with 1-LOD support interval at 31–48 cM) with plasma adiponectin levels using microsatellite marker spanned at an interval of approximately 17 cM. To refine this signal, we placed additional 6 microsatellite markers and mapped a quantitative trail locus (QTL) located at 31 cM (LOD = 3.04 with 1-LOD support interval at 24–34 cM) (Fig. 1). Compared to original linkage signal, the additional markers resulted in a peak shifted by about 8 cM and the 1-LOD support interval shortened by 7 cM. Within this region, we further genotyped a total of 68 SNPs for the 12 genes including the CHRNA7, ARHGAP11A, SCG5, GREM1, RYR3, CHRM5, SLC12A6, NUT, AYTL3, GOLGA8A, AQR, and RASGRP1 genes.
Figure 1.

Genetic association of RYR3 with circulating adiponectin levels. Genome-wide multipoint linkage analysis of plasma adiponectin levels. Multipoint LOD scores using are depicted by solid lines (blue line indicates previous LOD scores using 12 microsatellite markers; black line indicates LOD scores using additional 6 microsatellite markers). LOD = logarithm of odds, RYR3 = ryanodine receptor 3.
Sliding window analysis using family-based association test found that haplotypes of the RYR3 gene had strongest association with plasma adiponectin levels (P = 0.001) (Fig. 2). RYR3 haplotypes were also associated with systolic (P = 0.001) and diastolic (P = 7.1 × 10−4) blood pressure and HDL-C (P = 1.4 × 10−4) (Fig. 3).
Figure 2.

Regional fine mapping of the linkage signal using 68 SNPs in 12 genes. Sliding window analysis for haplotype association with plasma adiponectin is shown. The global P values (shown as Y-axis) for single SNP or 2-, 3-, 4-, 5-, 6-, and 7-SNP haplotypes are shown as horizontal bar. Single-locus P values are shown as vertical bar. SNP = single-nucleotide polymorphism.
Figure 3.

Sliding window analysis for haplotype association with waist–hip ratio, high-density lipoprotein (HDL) cholesterol, and systolic and diastolic blood pressure (BP) is shown. The global P values (shown as Y-axis) for single SNP or 2-, 3-, 4-, 5-, 6-, and 7-SNP haplotypes are shown. Single-locus P values are shown as vertical bar. SNP = single-nucleotide polymorphism.
3.2. Correlation between RYR3 and adiponectin expression in human adipose tissue
Since adiponectin is expressed in adipose tissues, we examined the correlation between expression of RYR3 and adiponectin in 44 human adipose tissues. We found an inverse relationship between RYR3 and adiponectin expression in omental (r = −0.34, P = 0.02) adipose tissues (Fig. 4), suggesting that RYR3 is a negative regulator of adiponectin; this finding is consistent with a previous report that silencing of RYR3 both in vitro and in vitro elevated plasma insulin and improved insulin sensitivity.[17]
Figure 4.

Association between RYR3 and adiponectin expression in human (A) omental and (B) subcutaneous adipose tissues. RYR3 = ryanodine receptor 3.
4. Discussion
In this study, we refined the genome-wide linkage signal of our previous study using additional microsatellite marker and mapped a QTL located in chromosome 15 at 31 cM with LOD of 3.04. Within this linkage region, we identified variants in the RYR3 gene associated with plasma adiponectin. RYR3 genetic variants were also associated with HDL-C, waist–hip ratio, and blood pressures. In addition, RYR3 expression was inversely associated with adiponectin expression in human abdominal adipose tissues.
Adiponectin is an adipokine with potent insulin-sensitizing and antiatherosclerosis actions. Previous genome-wide association studies (GWAS) or meta-analyses of GWAS in European population have identified ∼10 SNPs associated with plasma adiponectin levels.[6,7,10,11] The SNPs are also associated with altered risk of type 2 diabetes, plasma triglycerides, HDL-C, blood pressure, and waist–hip ratio.[6,7,10,11]
Here we reported that novel variants in the RYR3 gene are associated with plasma adiponectin levels in Chinese population. Interestingly, a recent European GWAS also identified a variant in the RYR3 gene (rs6495001) associated with plasma adiponectin in 2097 men.[10] This variant is associated with plasma adiponectin level in both stage 1 (P = 8.8 × 10−6) and stage 1 + 2 (P = 0.039) analyses.[10] We tried to replicate this finding in our dataset. However, this SNP is not polymorphic in Chinese population (data not shown) and therefore a direct comparison is not possible. However, the appearance of similar signals from 2 independent studies suggests RYR3 as a genetic determinant of plasma adiponectin.
The RYR3 gene encodes for a cation channel that releases intracellular calcium storage pools. Three RyR isoforms were identified with tissue-specific expression pattern including RyR1, RyR2, and RyR3. RyR1 is expressed mainly in skeletal muscle while RyR2 is expressed in heart. RyR3 is ubiquitously expressed. Knockdown of RyR3 in preadipocytes has been shown to upregulate adiponectin promoter activity, enhance adiponectin expression, and cause more adiponectin secretion into the medium through activation of activating transcription factor 3.[18] Activating transcription factor 3 is an inducible transcriptional repressor controlling adiponectin expression.[18,19] Silencing of RyR3 with small interfering RNA in db/db mice and high-fat diet-induced obese mice also increased serum adiponectin level and improved insulin sensitivity.[17] Consistently, we observed an inverse relationship between RYR3 and adiponectin expression in human adipose tissue. Collectively, these data suggest that RyR3 is a suppressor of adiponectin expression.
Our data have some unique strengths. First, this is the first genome-wide linkage analysis and fine association mapping for plasma adiponectin in Chinese population. Second, the family-based design of this study prevented potential confounding by population stratification, a frequent cause for spurious association in case–control association studies.[9] Third, the metabolic phenotypes of the SAPPHIRe cohort are complete, making a comprehensive testing for metabolic phenotypes feasible. However, there are some limitations of our study. First, the significance level of most single-locus association did not pass the study-wide significance threshold. Based on the linkage method, a LOD score exceeding 3.3 was considered to be of genome-wide significance for evidence of linkage, whereas a LOD score >1.9 was suggestive of evidence for linkage. However, a LOD of 3.04 of our studies can still be considered as a highly suggestive signal almost reaching genome-wide significance. Second, although the association of RYR3 genetic variation with plasma adiponectin was also observed in another European GWAS in men,[10] further replication is still needed to confirm the association. Third, only 68 SNPs were used for association testing in our study across a large chromosomal region. A finer mapping would help to identify variants with best association within this region.
5. Conclusions
In summary, we demonstrated for the first time that genetic variants in the RYR3 gene are associated with plasma adiponectin in Chinese, which is consistent with the finding of a recent GWAS. [10] A reverse relationship between RYR3 and adiponectin expression in human adipose tissue was also observed. These data, together with previous functional investigations, support a role of RYR3 in the regulation of adiponectin expression.
Acknowledgments
The authors would like to thank all participants in the SAPPHIRe study. They also would like to thank Ms Kuo Shan-Shan, Ms Lee Kuan-Ching, and Ms Linda Huang for their excellent technical support and Ms Chun-Yi Lee for her efficient computational assistance.
Footnotes
Abbreviations: GWAS = genome-wide association studies, HDL-C = high-density lipoprotein cholesterol, LOD = logarithm of odds, QTL = quantitative trail locus, SAPPHIRe = Stanford Asia-Pacific Program for Hypertension and Insulin Resistance, SNP = single-nucleotide polymorphism.
This study was supported by the grants (NSC95-3112-B-002-002, NSC96-3112-B-002-002, NSC100-2321-B-002-040) from National Science Council, Executive Yuan, Taiwan.
The authors have no conflicts of interest to disclose.
References
- 1.Kadowaki T, Yamauchi T, Kubota N, et al. Adiponectin and adiponectin receptors in insulin resistance, diabetes, and the metabolic syndrome. J Clin Invest 2006; 116:1784–1792. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2.Yamamoto Y, Hirose H, Saito I, et al. Correlation of the adipocyte-derived protein adiponectin with insulin resistance index and serum high-density lipoprotein-cholesterol, independent of body mass index, in the Japanese population. Clin Sci (Lond) 2002; 103:137–142. [DOI] [PubMed] [Google Scholar]
- 3.Yang WS, Lee WJ, Funahashi T, et al. Weight reduction increases plasma levels of an adipose-derived anti-inflammatory protein, adiponectin. J Clin Endocrinol Metab 2001; 86:3815–3819. [DOI] [PubMed] [Google Scholar]
- 4.Cesari M, Narkiewicz K, De Toni R, et al. Heritability of plasma adiponectin levels and body mass index in twins. J Clin Endocrinol Metab 2007; 92:3082–3088. [DOI] [PubMed] [Google Scholar]
- 5.Comuzzie AG, Funahashi T, Sonnenberg G, et al. The genetic basis of plasma variation in adiponectin, a global endophenotype for obesity and the metabolic syndrome. J Clin Endocrinol Metab 2001; 86:4321–4325. [DOI] [PubMed] [Google Scholar]
- 6.Dastani Z, Hivert MF, Timpson N, et al. Novel loci for adiponectin levels and their influence on type 2 diabetes and metabolic traits: a multi-ethnic meta-analysis of 45,891 individuals. PLoS Genet 2012; 8:e1002607. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7.Qi L, Menzaghi C, Salvemini L, et al. Novel locus FER is associated with serum HMW adiponectin levels. Diabetes 2010; 60:2197–2201. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8.Wu Y, Li Y, Lange EM, et al. Genome-wide association study for adiponectin levels in Filipino women identifies CDH13 and a novel uncommon haplotype at KNG1-ADIPOQ. Hum Mol Genet 2010; 19:4955–4964. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9.Wu Y, Gao H, Li H, et al. A meta-analysis of genome-wide association studies for adiponectin levels in East Asians identifies a novel locus near WDR11-FGFR2. Hum Mol Genet 2013; 23:1108–1119. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10.Heid IM, Henneman P, Hicks A, et al. Clear detection of ADIPOQ locus as the major gene for plasma adiponectin: results of genome-wide association analyses including 4659 European individuals. Atherosclerosis 2010; 208:412–420. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11.Richards JB, Waterworth D, O’Rahilly S. A genome-wide association study reveals variants in ARL15 that influence adiponectin levels. PLoS Genet 2009; 5:e1000768. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12.Chuang LM, Chiu YF, Sheu WH, et al. Biethnic comparisons of autosomal genomic scan for loci linked to plasma adiponectin in populations of Chinese and Japanese origin. J Clin Endocrinol Metab 2004; 89:5772–5778. [DOI] [PubMed] [Google Scholar]
- 13.Ranade K, Wu KD, Risch N, et al. Genetic variation in aldosterone synthase predicts plasma glucose levels. Proc Natl Acad Sci U S A 2001; 98:13219–13224. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14.International HapMap Consortium. The International HapMap Project. Nature 2003; 426:789–796. [DOI] [PubMed] [Google Scholar]
- 15.Almasy L, Blangero J. Multipoint quantitative-trait linkage analysis in general pedigrees. Am J Hum Genet 1998; 62:1198–1211. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16.Lange C, Silverman EK, Xu X, et al. A multivariate family-based association test using generalized estimating equations: FBAT-GEE. Biostatistics 2003; 4:195–206. [DOI] [PubMed] [Google Scholar]
- 17.Tsai SH, Chang EY, Chang YC, et al. Knockdown of RyR3 enhances adiponectin expression through an atf3-dependent pathway. Endocrinology 2013; 154:1117–1129. [DOI] [PubMed] [Google Scholar]
- 18.Kim HB, Kong M, Kim TM, et al. NFATc4 and ATF3 negatively regulate adiponectin gene expression in 3T3-L1 adipocytes. Diabetes 2006; 55:1342–1352. [DOI] [PubMed] [Google Scholar]
- 19.Park HJ, Kang YM, Kim CH, et al. ATF3 negatively regulates adiponectin receptor 1 expression. Biochem Biophys Res Commun 2000; 400:72–77. [DOI] [PubMed] [Google Scholar]
