Abstract
Yoshiji Yamada1,2, Jun Sakuma2,3,4, Ichiro Takeuchi2,4,5, Yoshiki Yasukochi1,2, Kimihiko Kato1,6, Mitsutoshi Oguri1,7, Tetsuo Fujimaki8, Hideki Horibe9, Masaaki Muramatsu10, Motoji Sawabe11, Yoshinori Fujiwara12, Yu Taniguchi12, Shuichi Obuchi13, Hisashi Kawai13, Shoji Shinkai14, Seijiro Mori15, Tomio Arai16 and Masashi Tanaka17
1Department of Human Functional Genomics, Advanced Science Research Promotion Center, Mie University, Tsu, Japan
2CREST, Japan Science and Technology Agency, Kawaguchi, Japan
3Computer Science Department, College of Information Science, University of Tsukuba, Tsukuba, Japan
4RIKEN Center for Advanced Intelligence Project, Tokyo, Japan
5Department of Computer Science, Nagoya Institute of Technology, Nagoya, Japan
6Department of Internal Medicine, Meitoh Hospital, Nagoya, Japan
7Department of Cardiology, Kasugai Municipal Hospital, Kasugai, Japan
8Department of Cardiovascular Medicine, Inabe General Hospital, Inabe, Japan
9Department of Cardiovascular Medicine, Gifu Prefectural Tajimi Hospital, Tajimi, Japan
10Department of Molecular Epidemiology, Medical Research Institute, Tokyo Medical and Dental University, Tokyo, Japan
11Section of Molecular Pathology, Graduate School of Health Care Sciences, Tokyo Medical and Dental University, Tokyo, Japan
12Research Team for Social Participation and Community Health, Tokyo Metropolitan Institute of Gerontology, Tokyo, Japan
13Research Team for Promoting Support System for Home Care, Tokyo Metropolitan Institute of Gerontology, Tokyo, Japan
14Research Team for Social Participation and Health Promotion, Tokyo Metropolitan Institute of Gerontology, Tokyo, Japan
15Center for Promotion of Clinical Investigation, Tokyo Metropolitan Geriatric Hospital, Tokyo, Japan
16Department of Pathology, Tokyo Metropolitan Geriatric Hospital, Tokyo, Japan
17Department of Clinical Laboratory, Tokyo Metropolitan Geriatric Hospital, Tokyo, Japan
Correspondence to:
Yoshiji Yamada, email: [email protected]
Keywords: body mass index, obesity, metabolic syndrome, genetics, exome-wide association study
Received: February 15, 2017 Accepted: March 14, 2017 Published: April 07, 2017
ABSTRACT
We have performed exome-wide association studies to identify genetic variants that influence body mass index or confer susceptibility to obesity or metabolic syndrome in Japanese. The exome-wide association study for body mass index included 12,890 subjects, and those for obesity and metabolic syndrome included 12,968 subjects (3954 individuals with obesity, 9014 controls) and 6817 subjects (3998 individuals with MetS, 2819 controls), respectively. Exome-wide association studies were performed with Illumina HumanExome-12 DNA Analysis BeadChip or Infinium Exome-24 BeadChip arrays. The relation of genotypes of single nucleotide polymorphisms to body mass index was examined by linear regression analysis, and that of allele frequencies of single nucleotide polymorphisms to obesity or metabolic syndrome was evaluated with Fisher’s exact test. The exome-wide association studies identified six, 11, and 40 single nucleotide polymorphisms as being significantly associated with body mass index, obesity (P <1.21 × 10–6), or metabolic syndrome (P <1.20 × 10–6), respectively. Subsequent multivariable logistic regression analysis with adjustment for age and sex revealed that three and five single nucleotide polymorphisms were related (P < 0.05) to obesity or metabolic syndrome, respectively, with one of these latter polymorphisms—rs7350481 (C/T) at chromosome 11q23.3—also being significantly (P < 3.13 × 10–4) associated with metabolic syndrome. The polymorphism rs7350481 may thus be a novel susceptibility locus for metabolic syndrome in Japanese. In addition, single nucleotide polymorphisms in three genes (CROT, TSC1, RIN3) and at four loci (ANKK1, ZNF804B, CSRNP3, 17p11.2) were implicated as candidate determinants of obesity and metabolic syndrome, respectively.
INTRODUCTION
Obesity is a major risk factor for coronary artery disease, hypertension, diabetes mellitus, dyslipidemia, and several types of cancer [1, 2]. Epidemiological studies in different populations have recognized a contribution of genetic factors to individual susceptibility to obesity [1, 3], with the heritability of this condition having been estimated at 40% to 70% [4]. Genome-wide association studies (GWASs) and meta-analyses of such studies—most performed with individuals of European descent [5–14] or in East Asian populations [15–17]—have identified >100 loci or genes that influence body mass index (BMI) or confer susceptibility to obesity or adiposity.
Metabolic syndrome (MetS) is also a crucial risk factor for cardiovascular disease, diabetes mellitus [18, 19], and cancer [20, 21]. The heritability of MetS has been estimated to be ~30% [22, 23]. GWASs have implicated various loci or genes in predisposition to MetS or to traits of this syndrome in individuals of European [22, 23] or African [24] ancestry or in Asian Indians [25].
Genetic variants identified in previous GWASs typically have a minor allele frequency (MAF) of ≥5% and a small individual effect size. Given that these common variants explain only a fraction of the heritability, it is expected that low-frequency (0.5% ≤ MAF < 5%) or rare (MAF < 0.5%) variants with larger effect sizes also contribute to the genetic architecture of obesity or MetS [26]. Although several polymorphisms have been found to be significantly associated with BMI in East Asian populations [15–17], genetic variants, including low-frequency and rare variants, that influence BMI or contribute to predisposition to obesity or MetS in Japanese remain to be identified definitively.
We have now performed exome-wide association studies (EWASs) with the use of exome array–based genotyping methods to identify SNPs—in particular, low-frequency or rare coding variants with moderate to large effect sizes—that influence BMI or confer susceptibility to obesity or MetS in the Japanese population. We used Illumina arrays that provide coverage of functional single nucleotide polymorphisms (SNPs) in entire exons including low-frequency and rare variants.
RESULTS
EWAS for BMI
We examined the relation of genotypes of 41,327 SNPs that passed quality control to BMI in 12,890 subjects by linear regression analysis. A Manhattan plot of the EWAS is shown in Supplementary Figure 1A. After Bonferroni’s correction, six SNPs were significantly [P < 1.21 × 10–6 (0.05/41,327)] associated with BMI (Table 1).
Table 1: The six SNPs significantly (P < 1.21 × 10–6) associated with BMI in the EWAS
Gene | dbSNP | Nucleotide (amino acid) substitutiona | Chromosome: position | MAF (%) | P (genotype) |
|---|---|---|---|---|---|
rs633715 | T/C | 1: 177883445 | 22.0 | 8.33 × 10–8 | |
rs543874 | A/G | 1: 177920345 | 21.9 | 1.36 × 10–7 | |
FTO | rs1421085 | T/C | 16: 53767042 | 19.1 | 5.37 × 10–7 |
FTO | rs1558902 | T/A | 16: 53769662 | 19.1 | 6.67 × 10–7 |
FTO | rs8050136 | C/A | 16: 53782363 | 19.2 | 1.08 × 10–6 |
FTO | rs9939609 | T/A | 16: 53786615 | 19.3 | 1.13 × 10–6 |
The relation of genotypes of SNPs to BMI was evaluated by linear regression analysis. aMajor allele/minor allele.
EWAS for obesity
We performed an EWAS for obesity with 12,968 subjects [3954 individuals with obesity (BMI of ≥25 kg/m2), 9014 controls (BMI of <25 kg/m2)], the characteristics of whom are shown in Table 2. The frequency of men and the prevalence of smoking, hypertension, diabetes mellitus, dyslipidemia, and hyperuricemia as well as BMI, waist circumference, systolic and diastolic blood pressure, fasting plasma glucose level, blood glycosylated hemoglobin (hemoglobin A1c) content, and the serum concentrations of triglycerides, low density lipoprotein (LDL)–cholesterol, and uric acid were greater, whereas age and the serum concentration of high density lipoprotein (HDL)-cholesterol were lower, in subjects with obesity than in controls.
Table 2: Characteristics of the 12,968 subjects in the EWAS of obesity
Characteristic | Obesity | Controls | P |
|---|---|---|---|
No. of subjects | 3954 | 9014 | 9014 |
Age (years) | 59.3 ± 12.5 | 60.8 ± 13.6 | <0.0001 |
Sex (male/female, %) | 62.2/37.8 | 54.3/45.7 | <0.0001 |
BMI (kg/m2) | 27.2 ± 2.7 | 21.5 ± 2.1 | <0.0001 |
Waist circumference (cm) | 90.3 ± 7.1 | 77.2 ± 6.2 | <0.0001 |
Current or former smoker (%) | 41.0 | 34.1 | <0.0001 |
Hypertension (%) | 64.4 | 47.9 | <0.0001 |
Systolic blood pressure (mmHg) | 135 ± 23 | 129 ± 24 | <0.0001 |
Diastolic blood pressure (mmHg) | 79 ± 13 | 75 ± 13 | <0.0001 |
Diabetes mellitus (%) | 30.5 | 20.1 | <0.0001 |
Fasting plasma glucose (mmol/L) | 6.49 ± 2.56 | 6.09 ± 2.35 | <0.0001 |
Blood hemoglobin A1c (%) | 6.15 ± 1.28 | 5.80 ± 1.11 | <0.0001 |
Dyslipidemia (%) | 75.2 | 57.5 | <0.0001 |
Serum triglycerides (mmol/L) | 1.70 ± 1.14 | 1.35 ± 0.95 | <0.0001 |
Serum HDL-cholesterol (mmol/L) | 1.37 ± 0.38 | 1.58 ± 0.47 | <0.0001 |
Serum LDL-cholesterol (mmol/L) | 3.22 ± 0.87 | 3.07 ± 0.83 | <0.0001 |
Chronic kidney disease (%) | 25.0 | 23.2 | 0.0312 |
Serum creatinine (µmol/L) | 77.6 ± 72.6 | 78.8 ± 87.6 | 0.4543 |
Estimated GFR (mL min–1 1.73 m–2) | 71.8 ± 18.7 | 72.5 ± 21.2 | 0.0510 |
Hyperuricemia (%) | 23.7 | 15.6 | <0.0001 |
Serum uric acid (µmol/L) | 348 ± 93 | 319 ± 94 | <0.0001 |
Quantitative data are means ± SD and were compared between subjects with obesity and controls with the unpaired Student’s t test. Categorical data were compared between the two groups with Fisher’s exact test. Based on Bonferroni’s correction, a P value of <0.0025 (0.05/20) was considered statistically significant. GFR, glomerular filtration rate.
We examined the relation of allele frequencies of 41,327 SNPs to obesity with Fisher’s exact test. A Manhattan plot for the EWAS is shown in Supplementary Figure 1B. After Bonferroni’s correction, 11 SNPs were significantly (P < 1.21 × 10–6) associated with obesity (Table 3). The genotype distributions of these SNPs were in Hardy-Weinberg equilibrium (P > 0.001) among both subjects with obesity and controls (Supplementary Table 1).
Table 3: The 11 SNPs significantly (P < 1.21 × 10–6) associated with obesity in the EWAS
Gene | dbSNP | Nucleotide (amino acid) substitutiona | Chromosome: position | MAF (%) | P (genotype) | Allele OR |
|---|---|---|---|---|---|---|
TRIM36 | rs3749745 | G/C (E725Q) | 5: 115126517 | 1.9 | 5.01 × 10–38 | 0.88 |
PDIA5 | rs2292661 | C/T (T391M) | 3: 123150263 | 0.7 | 2.05 × 10–35 | 0.95 |
OR10P1 | rs7970885 | G/A (V200M) | 12: 55637489 | 19.0 | 6.82 × 10–14 | 1.01 |
CROT | rs7808249 | G/A | 7: 87354399 | 32.0 | 1.30 × 10–12 | 1.06 |
rs2068204 | C/T | 6: 33090941 | 37.1 | 5.02 × 10–11 | 1.02 | |
DAW1 | rs10191097 | T/G | 2: 227911955 | 29.1 | 7.17 × 10–11 | 0.98 |
TRIM40 | rs757259 | G/A (E244K) | 6: 30147765 | 16.0 | 7.21 × 10–11 | 0.98 |
TSC1 | rs1076160 | A/G | 9: 132900647 | 45.9 | 4.89 × 10–10 | 0.95 |
RIN3 | rs8018360 | C/T | 14: 92521014 | 19.9 | 6.79 × 10–9 | 1.04 |
HYOU1 | rs144079825 | G/C (E258Q) | 11: 119054143 | 0.1 | 3.96 × 10–7 | 0.98 |
ECE2 | rs145491613 | A/G (N388S) | 3: 184278550 | 0.4 | 9.91 × 10–7 | 0.69 |
Allele frequencies were analyzed with Fisher’s exact test. aMajor allele/minor allele. OR, odds ratio.
The relation of the 11 SNPs to obesity was further examined by multivariable logistic regression analysis with adjustment for age and sex (Supplementary Table 2). Three SNPs [rs7808249 (G/A) of CROT, rs1076160 (A/G) of TSC1, rs8018360 (C/T) of RIN3] were related (P < 0.05 in at least one genetic model) to obesity, although there was no SNP significantly [P < 0.0011 (0.05/44)] associated with this condition (Table 4). The minor A and T alleles of rs7808249 and rs8018360, respectively, were risk factors for obesity, whereas the minor G allele of rs1076160 was protective against this condition.
Table 4: Relation of SNPs to obesity as determined by multivariable logistic regression analysis
SNP | Dominant | Recessive | Additive 1 | Additive 2 | |||||
|---|---|---|---|---|---|---|---|---|---|
P | OR (95% CI) | P | OR (95% CI) | P | OR (95% CI) | P | OR (95% CI) | ||
rs7808249 | G/A | 0.0241 | 1.09 (1.01–1.18) | 0.5172 | 0.0307 | 1.09 (1.01–1.18) | 0.2039 | ||
rs1076160 | A/G | 0.2661 | 0.0340 | 0.90 (0.82–0.99) | 0.6676 | 0.0410 | 0.89 (0.80–0.99) | ||
rs8018360 | C/T | 0.6700 | 0.0339 | 1.23 (1.02–1.47) | 0.8658 | 0.0378 | 1.22 (1.01–1.47) | ||
Multivariable logistic regression analysis was performed with adjustment for age and sex. Based on Bonferroni’s correction, a P value of <0.0011 (0.05/44) was considered statistically significant. OR, odds ratio; CI, confidence interval.
Relation of nine SNPs to BMI
We examined the relation of genotypes for nine SNPs to BMI by one-way analysis of variance (ANOVA). The six SNPs (rs633715, rs543874, rs1421085, rs1558902, rs8050136, rs9939609) identified in the EWAS of BMI were all significantly [P < 0.0056 (0.05/9)] associated with BMI. Among the three SNPs found to be related to obesity, rs7808249 and rs1076160 were related (P < 0.05) to BMI, whereas rs8018360 was not (Table 5).
Table 5: Relation of SNPs identified in the present study to BMI
SNP | BMI (kg/m2) | P | |||
|---|---|---|---|---|---|
Associated with BMI | |||||
rs633715 | T/C | TT | TC | CC | |
23.1 ± 3.4 | 23.5 ± 3.6 | 23.5 ± 3.6 | 5.38 × 10–8 | ||
rs543874 | A/G | AA | AG | GG | |
23.1 ± 3.4 | 23.5 ± 3.5 | 23.5 ± 3.6 | 9.52 × 10–8 | ||
rs1421085 | T/C | TT | TC | CC | |
23.2 ± 3.4 | 23.4 ± 3.5 | 23.7 ± 3.8 | 3.49 × 10–6 | ||
rs1558902 | T/A | TT | TA | AA | |
23.2 ± 3.4 | 23.4 ± 3.5 | 23.7 ± 3.8 | 4.28 × 10–6 | ||
rs8050136 | C/A | CC | CA | AA | |
23.2 ± 3.4 | 23.4 ± 3.5 | 23.7 ± 3.8 | 6.85 × 10–6 | ||
rs9939609 | T/A | TT | TA | AA | |
23.2 ± 3.4 | 23.4 ± 3.5 | 23.7 ± 3.8 | 7.16 × 10–6 | ||
Associated with obesity | |||||
rs7808249 | G/A | GG | GA | AA | |
23.2 ± 3.4 | 23.3 ± 3.5 | 23.3 ± 3.5 | 0.0266 | ||
rs1076160 | A/G | AA | AG | GG | |
23.3 ± 3.5 | 23.3 ± 3.5 | 23.1 ± 3.4 | 0.0424 | ||
rs8018360 | C/T | CC | CT | TT | |
23.3 ± 3.4 | 23.2 ± 3.5 | 23.4 ± 3.4 | 0.6303 | ||
Data were compared among genotypes by one-way ANOVA. Based on Bonferroni’s correction, P values of <0.0056 (0.05/9) were considered statistically significant and are shown in bold.
EWAS for MetS
We performed an EWAS of MetS with 6817 subjects [3998 individuals with MetS (three or more of the five components of MetS), 2819 controls (none of the five components of MetS)], the characteristics of whom are shown in Table 6. Age, the frequency of men, BMI, waist circumference, systolic and diastolic blood pressure, fasting plasma glucose level, blood hemoglobin A1c content, and the serum concentrations of triglycerides, LDL-cholesterol, creatinine, and uric acid were greater, whereas the serum concentration of HDL-cholesterol and estimated glomerular filtration rate were lower, in subjects with MetS than in controls.
Table 6: Characteristics of the 6817 subjects in the EWAS for MetS
Characteristic | MetS | Controls | P |
|---|---|---|---|
No. of subjects | 3998 | 2819 | |
Age (years) | 62.2 ± 11.5 | 54.1 ± 14.1 | <0.0001 |
Sex (male/female, %) | 60.0/40.0 | 44.7/55.3 | <0.0001 |
BMI (kg/m2) | 25.5 ± 3.6 | 21.0 ± 2.3 | <0.0001 |
Waist circumference (cm) | 87.0 ± 8.7 | 74.6 ± 6.2 | <0.0001 |
Current or former smoker (%) | 38.2 | 35.1 | 0.0111 |
Systolic blood pressure (mmHg) | 141 ± 23 | 111 ± 11 | <0.0001 |
Diastolic blood pressure (mmHg) | 81 ± 14 | 68 ± 9 | <0.0001 |
Fasting plasma glucose (mmol/L) | 7.14 ± 2.93 | 4.97 ± 0.40 | <0.0001 |
Blood hemoglobin A1c (%) | 6.38 ± 1.41 | 5.41 ± 0.41 | <0.0001 |
Serum triglycerides (mmol/L) | 2.08 ± 1.32 | 0.89 ± 0.32 | <0.0001 |
Serum HDL-cholesterol (mmol/L) | 1.27 ± 0.37 | 1.81 ± 0.43 | <0.0001 |
Serum LDL-cholesterol (mmol/L) | 3.24 ± 0.93 | 3.00 ± 0.74 | <0.0001 |
Serum creatinine (µmol/L) | 83.5 ± 95.7 | 69.0 ± 64.0 | <0.0001 |
Estimated GFR (mL min–1 1.73 m–2) | 67.0 ± 22.1 | 77.7 ± 17.7 | <0.0001 |
Serum uric acid (µmol/L) | 350 ± 95 | 297 ± 83 | <0.0001 |
Quantitative data are means ± SD and were compared between subjects with MetS and controls with the unpaired Student’s t test. Categorical data were compared between the two groups with Fisher’s exact test. Based on Bonferroni’s correction, a P value of <0.0033 (0.05/15) was considered statistically significant.
We examined the relation of allele frequencies of 41,675 SNPs that passed quality control to MetS with Fisher’s exact test. A Manhattan plot of the EWAS is shown in Supplementary Figure 1C. After Bonferroni’s correction, 40 SNPs were significantly [P < 1.20 × 10–6 (0.05/41,675)] associated with MetS (Table 7). The genotype distributions of these SNPs were in Hardy-Weinberg equilibrium (P > 0.001) among both subjects with MetS and controls (Supplementary Table 3).
Table 7: The 40 SNPs significantly (P < 1.20 × 10–6) associated with MetS in the EWAS
Gene | dbSNP | Nucleotide (amino acid) substitutiona | Chromosome: position | MAF (%) | P (allele) | Allele OR |
|---|---|---|---|---|---|---|
SLC4A5 | rs10177833 | A/C | 2: 74230591 | 46.8 | 2.51 × 10–89 | 0.98 |
TAP2 | rs2071544 | A/G | 6: 32838344 | 48.7 | 2.85 × 10–76 | 1.02 |
MYPN | rs7916821 | G/A (S707N) | 10: 68174212 | 28.4 | 2.78 × 10–68 | 0.96 |
PSMD9 | rs14259 | A/G (E92G) | 12: 121915890 | 46.3 | 8.72 × 10–63 | 1.00 |
ANKK1 | rs1800497 | G/A (E713K) | 11: 113400106 | 37.1 | 9.27 × 10–61 | 1.02 |
ZNF700 | rs75607624 | T/G (F290C) | 19: 11948884 | 3.7 | 1.32 × 10–53 | 0.97 |
PLEKHG1 | rs17348890 | A/G | 6: 150839882 | 6.6 | 1.42 × 10–52 | 1.02 |
DEPDC7 | rs34161108 | G/A (A192T) | 11: 33027795 | 6.2 | 3.94 × 10–51 | 1.02 |
ARHGEF28 | rs536568 | A/C | 5: 73935841 | 45.8 | 9.67 × 10–44 | 0.99 |
IGSF22 | rs7125943 | C/T (V559M) | 11: 18714400 | 18.6 | 7.53 × 10–40 | 1.03 |
PKD1L1 | rs147417448 | T/A (N1607K) | 7: 47854920 | 0.4 | 1.87 × 10–35 | 0.83 |
TICRR | rs79501973 | G/A (V1373I) | 15: 89624427 | 14.7 | 8.41 × 10–33 | 0.98 |
GBF1 | rs143872476 | T/C (M240T) | 10: 102358118 | 0.2 | 2.59 × 10–31 | 1.85 |
ZNF804B | rs1406503 | C/G | 7: 88931564 | 5.2 | 3.38 × 10–24 | 0.84 |
GON4L | rs183379906 | C/G (D1943E) | 1: 155753217 | 0.3 | 6.69 × 10–23 | 1.00 |
rs962040 | A/G | 8: 15454368 | 30.1 | 5.33 × 10–21 | 0.98 | |
rs365488 | C/T | 6: 29550181 | 48.8 | 7.69 × 10–21 | 1.01 | |
CATSPERD | rs73544757 | T/C (V95A) | 19: 5733863 | 0.3 | 2.11 × 10–20 | 0.96 |
rs1507493 | A/G | 4: 147108517 | 26.7 | 2.03 × 10–17 | 1.03 | |
MCEE | rs6748672 | C/A (R104L) | 2: 71124273 | 24.2 | 4.15 × 10–17 | 0.98 |
MAP1A | rs3803335 | G/A (R1185H) | 15: 43525027 | 1.6 | 8.61 × 10–17 | 1.08 |
NXPE2 | rs11215158 | T/C (V103A) | 11: 114698220 | 16.0 | 1.82 × 10–16 | 1.06 |
TRAT1 | rs79029897 | G/A (R179H) | 3: 108853852 | 0.7 | 1.88 × 10–16 | 1.18 |
CCDC13 | rs75893579 | C/G (R153G) | 3: 42752631 | 3.9 | 3.13 × 10–16 | 0.87 |
OR10W1 | rs56302613 | C/G (S245C) | 11: 58267125 | 3.1 | 9.91 × 10–15 | 0.85 |
rs3135365 | T/G | 6: 32421478 | 18.9 | 2.71 × 10–14 | 0.93 | |
ACAD10 | rs192237004 | A/G (Y578C) | 12: 111744661 | 0.4 | 5.83 × 10–12 | 1.07 |
CSRNP3 | rs1007732 | G/T | 2: 165602788 | 34.0 | 8.13 × 10–12 | 0.92 |
TEFM | rs2433 | T/C (I348V) | 17: 30899210 | 18.1 | 4.20 × 10–8 | 1.03 |
B4GALNT2 | rs7224888 | T/C (C380R) | 17: 49168801 | 3.5 | 6.94 × 10–8 | 1.05 |
BCAS3 | rs2643103 | G/A (S87N) | 17: 60709264 | 12.5 | 8.49 × 10–8 | 1.02 |
PPARGC1B | rs143268818 | C/T | 5: 149845772 | 0.6 | 8.50 × 10–8 | 0.84 |
rs56150213 | G/A | 17: 19761005 | 49.4 | 1.43 × 10–7 | 1.08 | |
ZNF597 | rs140727539 | C/T (E315K) | 16: 3436756 | 0.1 | 1.45 × 10–7 | 1.01 |
GPR179 | rs201149338 | C/G (Q1461E) | 17: 38329188 | 3.1 | 1.51 × 10–7 | 0.89 |
TENM2 | rs9313396 | T/G | 5: 168072827 | 50.0 | 2.45 × 10–7 | 1.02 |
PDP2 | rs141108875 | C/T (R473W) | 16: 66885701 | 0.5 | 2.69 × 10–7 | 1.50 |
SLC15A1 | rs12853441 | C/A | 13: 98725694 | 49.1 | 2.99 × 10–7 | 0.96 |
rs7350481 | C/T | 11: 116715567 | 27.7 | 7.43 × 10–7 | 1.21 | |
rs9500989 | C/G | 6: 29802381 | 0.8 | 7.76 × 10–7 | 1.06 |
Allele frequencies were analyzed with Fisher’s exact test. aMajor allele/minor allele. OR, odds ratio.
The relation of the 40 SNPs to MetS was further examined by multivariable logistic regression analysis with adjustment for age and sex (Supplementary Table 4). Five SNPs (rs1800497, rs1406503, rs1007732, rs56150213, rs7350481) were related (P < 0.05 in at least one genetic model) to MetS. Among these SNPs, rs7350481 (C/T) at chromosomal region 11q23.3 was also significantly [P < 3.13 × 10-4 (0.05/160)] associated with MetS, with the minor T allele being a risk factor for this condition (Table 8).
Table 8: Relation of SNPs to MetS as determined by multivariable logistic regression analysis
SNP | Dominant | Recessive | Additive 1 | Additive 2 | |||||
|---|---|---|---|---|---|---|---|---|---|
P | OR (95% CI) | P | OR (95% CI) | P | OR (95% CI) | P | OR (95% CI) | ||
rs1800497 | G/A (E713K) | 0.8813 | 0.0227 | 1.19 (1.02–1.38) | 0.5289 | 0.0627 | |||
rs1406503 | C/G | 0.0212 | 0.82 (0.69–0.97) | 0.0597 | 0.0477 | 0.84 (0.70–0.99) | 0.0545 | ||
rs1007732 | G/T | 0.0344 | 0.89 (0.81–0.99) | 0.1245 | 0.0861 | 0.0435 | 0.84 (0.71–0.99) | ||
rs56150213 | G/A | 0.0094 | 1.17 (1.04–1.31) | 0.4890 | 0.0121 | 1.17 (1.04–1.33) | 0.0478 | 1.16 (1.00–1.34) | |
rs7350481 | C/T | 7.99 × 10–5 | 1.23 (1.11–1.36) | 0.0018 | 1.35 (1.12–1.63) | 0.0018 | 1.19 (1.07–1.32) | 0.0001 | 1.45 (1.20–1.77) |
Multivariable logistic regression analysis was performed with adjustment for age and sex. Based on Bonferroni’s correction, P values of <3.13 × 10–4 (0.05/160) were considered statistically significant and are shown in bold.
Relation of five SNPs to components of MetS
We examined the relation of the five identified SNPs to the components of MetS—waist circumference, serum concentrations of triglycerides and HDL-cholesterol, blood pressure, and fasting plasma glucose level—in the 6817 subjects by one-way ANOVA (Table 9). The SNP rs7350481 (C/T) was significantly [P < 0.0013 (0.05/40)] associated with the serum concentrations of triglycerides and HDL-cholesterol (for men), whereas the other four SNPs were not associated with any of these parameters.
Table 9: Relation of SNPs identified in the present study to each component of MetS
SNP | Waist circumference | Serum triglycerides | Serum HDL-cholesterol | Systolic BP | Diastolic BP | Fasting plasma glucose | |||
|---|---|---|---|---|---|---|---|---|---|
Men | Women | Men | Women | ||||||
rs1800497 | G/A (E713K) | 0.6493 | 0.0334 | 0.0662 | 0.0142 | 0.3710 | 0.0874 | 0.4210 | 0.0667 |
rs1406503 | C/G | 0.6225 | 0.7892 | 0.4958 | 0.3470 | 0.9441 | 0.3309 | 0.3962 | 0.4527 |
rs1007732 | G/T | 0.9507 | 0.0375 | 0.2333 | 0.0145 | 0.0321 | 0.1330 | 0.1602 | 0.3483 |
rs56150213 | G/A | 0.9135 | 0.1495 | 0.1220 | 0.5452 | 0.8970 | 0.7663 | 0.4566 | 0.4820 |
rs7350481 | C/T | 0.9403 | 0.7694 | 1.72 × 10–18 | 0.0002 | 0.0054 | 0.1548 | 0.3894 | 0.0060 |
Data are P values for comparisons among genotypes by one-way ANOVA. Based on Bonferroni’s correction, P values of <0.0013 (0.05/40) were considered statistically significant and are shown in bold. BP, blood pressure.
Relation of SNPs identified in the present study to phenotypes examined in previous GWASs
We examined the genes, chromosomal loci, and SNPs identified in the present study to obesity- or MetS-related phenotypes previously investigated in GWASs deposited in a public database [GWAS Catalog (http://www.ebi.ac.uk/gwas)]. In the case of our BMI and obesity studies, chromosomal region 1q25 was previously shown to be related to BMI [7, 8, 12], obesity [7], and body fat percentage [14], whereas FTO was previously identified as a genetic determinant of BMI [6, 12, 27], obesity [7, 28], body fat percentage [14], adiposity [29], and circulating leptin level [30]. The remaining three genes (CROT, TSC1, RIN3) have not been previously associated with BMI or obesity (Supplementary Table 5). In the case of our MetS study, the five identified genes or loci (ANKK1, ZNF804B, CSRNP3, 17p11.2, 11q23.3) have not been previously identified as susceptibility loci for MetS, although ZNF804B and chromosomal region 11q23.3 were found to be related to BMI in women [31] or serum concentrations of triglycerides and HDL-cholesterol [32, 33], respectively (Supplementary Table 5).
DISCUSSION
We have now shown that two SNPs [rs633715 (T/C), rs543874 (A/G)] at chromosomal region 1q25 and four SNPs [rs1421085 (T/C), rs1558902 (T/A), rs8050136 (C/A), rs9939609 (T/A)] of FTO were significantly associated with BMI. Chromosomal region 1q25 and FTO were previously identified as susceptibility loci for BMI and obesity [6–8, 12, 27, 28]. We have also identified three obesity-related genes (CROT, TSC1, RIN3) that have not previously been implicated as determinants of BMI or obesity. In addition, we identified rs7350481 (C/T) at 11q23.3 as a new susceptibility locus for MetS, with SNPs in ANKK1, ZNF804B, and CSRNP3 as well as at 17p11.2 also being implicated as candidate susceptibility loci for MetS.
SNPs associated with obesity
The carnitine O-octanoyltransferase gene (CROT) is located at chromosome 7q21.12 (NCBI Gene, https://www.ncbi.nlm.nih.gov/gene) and is expressed in various tissues and organs including adipose tissue (The Human Protein Atlas, http://www.proteinatlas.org). The CROT protein is a member of the carnitine acyltransferase family and converts 4,8-dimethylnonanoyl-CoA to its corresponding carnitine ester. CROT activates lipid metabolism by promoting the β-oxidation of fatty acids [34]. We have now shown that rs7808249 (G/A) of CROT was related to obesity with the minor A allele representing a risk factor for this condition. This association of CROT with obesity may be attributable to the role of this gene in lipid metabolism, although the molecular mechanism remains unclear.
The tuberous sclerosis 1 gene (TSC1) is located at chromosome 9q34.13 (NCBI Gene) and is widely expressed including in adipose tissue (The Human Protein Atlas). TSC1 and TSC2 encode proteins that form a complex with each other that functions as an inhibitor of mechanistic target of rapamycin (mTOR) signaling [35, 36]. Deletion, nonsense, or missense mutations of TSC1 and TSC2 lead to loss of function of the TSC1-TSC2 complex and constitutive activation of mTOR signaling. Such mutations of TSC1 or TSC2 cause tuberous sclerosis, a multisystem disorder associated with tumor formation in the brain, heart, lung, kidney, or eye [37, 38]. We have now shown that rs1076160 (A/G) of TSC1 was related to obesity, with the minor G allele being protective against this condition. The association of TSC1 with obesity might be attributable to the effect of this gene on protein synthesis and cell growth, although the underlying mechanism remains unknown.
The Ras and Rab interactor 3 gene (RIN3) is located at chromosome 14q32.12 (NCBI Gene) and is expressed in many tissues including adipose tissue (The Human Protein Atlas). The RIN3 protein is a member of the RIN family of Ras effectors [39] and activates Rab5 in human mast cells, leading to pathological conditions associated with mast cell–mediated chronic inflammation and mast cell hyperproliferation [40]. A GWAS showed that rs10498635 of RIN3 was associated with Paget’s disease of bone [41]. Obesity is closely linked to chronic inflammation in adipose tissue [42–45]. We have now shown that rs8018360 (C/T) of RIN3 was related to obesity, with the minor T allele representing a risk factor for this condition. Although the mechanism underpinning this association remains to be determined, it may be attributable to an effect of RIN3 on inflammation in adipose tissue.
SNPs associated with MetS
We have now identified rs7350481 (C/T) at chromosomal region 11q23.3 as a new susceptibility locus for MetS in Japanese. Chromosome 11q23.3 has previously been shown to be related to serum concentrations of triglycerides and HDL-cholesterol in Japanese and Mexican populations [32, 33]. In our study, rs7350481 was also significantly associated with the serum concentrations of triglycerides (P = 1.72 × 10–18) and of HDL-cholesterol in men (P = 0.0002). It was also related to the serum HDL-cholesterol concentration in women (P = 0.0054) and to the fasting plasma glucose level (P = 0.0060). The association of rs7350481 (C/T) at 11q23.3 with MetS may thus be attributable to the effect of this SNP on lipid and glucose metabolism, although the underlying mechanism remains to be elucidated.
The ankyrin repeat and kinase domain containing 1 gene (ANKK1) is located at chromosome 11q23.2 (NCBI Gene) and is expressed ubiquitously (The Human Protein Atlas). ANKK1 is closely linked to the D2 dopamine receptor gene, and rs1800497 of ANKK1 has previously been associated with both addictive disorders [46] and obesity [47, 48]. Moreover, obese individuals manifest fewer D2 receptors in the striatum compared with lean ones [48, 49]. We have now shown that rs1800497 [G/A (E713K)] of ANKK1 was related to MetS, with the minor A allele representing a risk factor for this condition. This association of ANKK1 with MetS may be attributable to an effect of this gene on obesity.
The zinc finger protein 804B gene (ZNF804B) is located at chromosome 7q21.13 (NCBI Gene) and is expressed at a high level in the thyroid gland (The Human Protein Atlas). The rs1406503 (C/G) SNP of ZNF804B has previously been shown to be related to BMI in Filipino women [31]. We have shown that this SNP was related to MetS in Japanese, with the minor G allele being protective against this condition. It is possible that the association ZNF804B with MetS is attributable to the effect of this gene on BMI.
The cysteine and serine rich nuclear protein 3 gene (CSRNP3) is located at chromosome 2q24.3 (NCBI Gene) and is expressed ubiquitously (The Human Protein Atlas). The three members of the CSRNP family of nuclear proteins (CSRNP1, -2, and -3) share conserved regions including cysteine- and serine-rich regions and a basic domain, possess a transcriptional activation domain, and bind to the DNA sequence motif AGAGTG [50]. We have now shown that rs1007732 (G/T) of CSRNP3 was related to MetS, with the minor T allele being protective against this condition, although the molecular mechanism underlying this association remains unclear.
Chromosome 17p11.2 has been shown to be related to longevity [51] and glomerular filtration rate [52], but it has not been previously found to be related to MetS. We have now shown that rs56150213 (G/A) at 17p11.2 was related to MetS, with the minor A allele representing a risk factor for this condition, although the functional relevance of this association remains unknown.
General considerations
In a previous GWAS [15] and meta-analyses of GWASs [16, 17] for BMI in East Asian populations, the MAF and effect size (explained variance in BMI) of identified SNPs ranged from 3% to 50% and from 0.02% to 0.20%, respectively. In the case of SNPs of FTO, the MAF and effect size of rs12149832 were 20% and 0.20%, respectively [15]; those of rs17817449 were 17% and 0.18%, respectively [16]; and those of rs1558902 were 15% and 0.15%, respectively [17]. In our study, we identified six SNPs associated with BMI, with the MAF and effect size (percentage difference in BMI among genotypes) of rs633715 and rs543874 at 1q25 as well as of rs1421085, rs1558902, rs8050136, and rs9939609 of FTO being 22.0% and 1.7%, 21.9% and 1.7%, 19.1% and 2.1%, 19.1% and 2.1%, 19.2% and 2.1%, and 19.3% and 2.1%, respectively. These SNPs were thus common variants with a moderate effect size. For SNPs related to obesity in the present study, the MAF and allele odds ratio for obesity of rs7808249, rs1076160, and rs8018360 were 32.0% and 1.06, 45.9% and 0.95, and 19.9% and 1.04, respectively. These SNPs were thus common variants with a small effect size. The MAF and allele odds ratio for MetS for rs7350481 identified in the present study were 27.7% and 1.21, respectively, revealing this SNP to be a common variant with a moderate effect size. For the four additional SNPs related to MetS in our study, the MAF and allele odds ratio for MetS of rs1800497, rs1406503, rs1007732, and rs56150213 were 37.1% and 1.02, 5.2% and 0.84, 34.0% and 0.92, and 49.4% and 1.08, respectively. These SNPs were thus common variants with a small effect size.
There are several limitations to the present study. (i) There were significant differences in age and sex between subjects with obesity or MetS and corresponding controls, which may be caused by selection bias of study subjects. (ii) Given that our results were not replicated, they will require validation in other populations. (iii) It is possible that SNPs identified in the present study are in linkage disequilibrium with other polymorphisms in nearby genes that are actually responsible for the observed associations. (iv) Three SNPs associated with obesity were not significantly related to BMI, and four SNPs associated with MetS were not related to components of this syndrome. The discrepancy in the obesity study might be attributable to substantial percentages of subjects with BMI close to 25 kg/m2. The discrepancy in the MetS study might be attributable to the effects of medical treatment for dyslipidemia, hypertension or diabetes mellitus in most subjects with MetS. A lack of relation to waist circumference might be due to substantial percentages of subjects with waist circumference close to cut-off values. (v) Information of treatment such as medication and duration of treatment was not available in the present study. (vi) The functional relevance of the identified SNPs to BMI or the pathogenesis of obesity or MetS remains to be determined.
In conclusion, we have identified rs7350481 (C/T) at 11q23.3 as a novel susceptibility locus for MetS. We also identified three genes (CROT, TSC1, RIN3) as new candidate susceptibility loci for obesity as well as three genes (ANKK1, ZNF804B, CSRNP3) and chromosome 17p11.2 as new candidate loci for MetS. Examination of genotypes for the identified SNPs may prove informative for assessment of the genetic risk for obesity or MetS in Japanese.
MATERIALS AND METHODS
Study subjects
For the BMI, obesity, and MetS studies, 12,890, 12,968, or 6817 subjects, respectively, were examined. The subjects were recruited from individuals as previously described [53].
On the basis of the recent recognition of a need to revise BMI criteria for obesity in Japanese and other Asian populations [54], obesity was defined as a BMI of ≥25 kg/m2. According to this definition, we examined 3954 subjects with obesity and 9014 controls for the obesity study, with the control individuals having a BMI of <25 kg/m2. Individuals with obesity associated with single gene disorders or with metabolic or endocrinologic diseases were excluded from the study, as were those taking medications that may cause secondary obesity. These subjects were largely the same as those for the BMI study.
Diagnosis of MetS was based on a modified version of the definition proposed by the International Diabetes Federation Task Force on Epidemiology and Prevention; National Heart, Lung, and Blood Institute; American Heart Association; World Heart Federation; International Atherosclerosis Society; and International Association for the Study of Obesity [18]. We used cutoff values for waist circumference of ≥90 cm in men or ≥80 cm in women on the basis of a recommendation of the International Diabetes Association [18]. A total of 3998 subjects with MetS thus had three or more of the following five components: (i) a waist circumference of ≥90 cm for men or ≥80 cm for women; (ii) a serum triglyceride concentration of ≥1.65 mmol/L (150 mg/dL) or drug treatment for elevated triglycerides; (iii) a serum HDL-cholesterol concentration of <1.04 mmol/L (40 mg/dL) for men or <1.30 mmol/L (50 mg/dL) for women; (iv) a systolic blood pressure of ≥130 mmHg, diastolic blood pressure of ≥85 mmHg, or drug treatment for hypertension; and (v) a fasting plasma glucose level of ≥5.50 mmol/L (100 mg/dL) or drug treatment for elevated glucose. History of obesity, dyslipidemia, hypertension, or diabetes mellitus was evaluated with a detailed questionnaire. The control subjects comprised 2819 individuals who had none of the five components of MetS. Autopsy cases were excluded from controls for both the obesity and MetS studies.
The study protocol complied with the Declaration of Helsinki and was approved by the Committees on the Ethics of Human Research of Mie University Graduate School of Medicine, Hirosaki University Graduate School of Medicine, Tokyo Metropolitan Institute of Gerontology, and participating hospitals. Written informed consent was obtained from each participant or families of the deceased subjects.
EWASs for BMI, obesity, and MetS
Methods for collection and extraction of genomic DNA samples were described previously [53]. The EWASs were performed with the use of the HumanExome-12 v1.1 or v1.2 DNA Analysis BeadChip or Infinium Exome-24 v1.0 BeadChip (Illumina, San Diego, CA, USA). Detailed information of these exome arrays and methods of quality control were described previously [53]. Totals of 41,327 or 41,675 SNPs passed quality control for the BMI and obesity studies and for the MetS study, respectively, and were subjected to analysis.
Statistical analysis
For analysis of the characteristics of study subjects, quantitative data were compared between subjects with obesity or MetS and controls with the unpaired Student’s t test. Categorical data were compared between two groups with Fisher’s exact test. The relation of genotypes of SNPs to BMI in the EWAS was examined by linear regression analysis. Allele frequencies were estimated by the gene counting method, and Fisher’s exact test was used to identify departure from Hardy-Weinberg equilibrium. The relation of allele frequencies of SNPs to obesity or MetS in the EWASs was examined with Fisher’s exact test. To compensate for multiple comparisons of genotypes or allele frequencies with BMI, obesity, or MetS, we applied Bonferroni’s correction for statistical significance of association. Given that 41,327 or 41,675 SNPs were analyzed in the BMI or obesity and the MetS studies, respectively, the significance level was set at P < 1.21 × 10–6 (0.05/41,327) or P < 1.20 × 10–6 (0.05/41,657), respectively. Quantile-quantile plots for P values of genotypes in the EWAS for BMI or for those of allele frequencies in the EWASs for obesity or MetS are shown in Supplementary Figure 2. The inflation factor (λ) was 1.03 for BMI, 1.22 for obesity, and 1.13 for MetS. Multivariable logistic regression analysis was performed with obesity or MetS as a dependent variable and independent variables including age, sex (0, woman; 1, man), and genotype of each SNP. A detailed method of analysis was described previously [53]. The relation of genotypes of SNPs identified in the EWASs to BMI or the five components of MetS was examined by one-way ANOVA. Bonferroni’s correction was also applied to other statistical analysis as indicated. Statistical tests were performed with JMP Genomics version 6.0 software (SAS Institute, Cary, NC, USA).
Author contributions
Y. Yamada contributed to conception and design of the study; to acquisition, analysis, and interpretation of the data; and to drafting of the manuscript. J. Sakuma, I. Takeuchi, and Y. Yasukochi contributed to analysis and interpretation of the data as well as to revision of the manuscript. K. Kato, M. Oguri, T. Fujimaki, H. Horibe, M. Muramatsu, M. Sawabe, Y. Fujiwara, Y. Taniguchi, S. Obuchi, H. Kawai, S. Shinkai, S. Mori, and T. Arai contributed to acquisition of the data and to revision of the manuscript. M. Tanaka contributed to acquisition, analysis, and interpretation of the data as well as to revision of the manuscript. All authors approved submission of the final version of the article for publication.
CONFLICTS OF INTEREST
The authors declare no conflicts of interest.
FUNDING
This work was supported by CREST (H25-H30), Japan Science and Technology Agency (to Y. Yamada, J. Sakuma, and I. Takeuchi) and by Japan Society for the Promotion of Science KAKENHI grants JP15H04772 (to Y. Yamada), JP25242062 (to M. Tanaka), and JP16H01872 (to M. Tanaka).
REFERENCES
1. Walley AJ, Blakemore AI, Froguel P. Genetics of obesity and the prediction of risk for health. Hum Mol Genet. 2006; 15: R124–R130.
2. De Pergola G, Silvestris F. Obesity as a major risk factor for cancer. J Obes. 2013; 2013: 291546.
3. Waalen J. The genetics of human obesity. Transl Res. 2014; 164: 293–301.
4. Barsh GS, Farooqi IS, O’Rahilly S. Genetics of body-weight regulation. Nature. 2000; 404: 644–651.
5. Speliotes EK, Willer CJ, Berndt SI, Monda KL, Thorleifsson G, Jackson AU, Lango Allen H, Lindgren CM, Luan J, Mägi R, Randall JC, Vedantam S, Winkler TW, et al. Association analyses of 249,796 individuals reveal 18 new loci associated with body mass index. Nat Genet. 2010; 42: 937–948.
6. Yang J, Loos RJ, Powell JE, Medland SE, Speliotes EK, Chasman DI, Rose LM, Thorleifsson G, Steinthorsdottir V, Mägi R, Waite L, Smith AV, Yerges-Armstrong LM, et al. FTO genotype is associated with phenotypic variability of body mass index. Nature. 2012; 490: 267–272.
7. Berndt SI, Gustafsson S, Mägi R, Ganna A, Wheeler E, Feitosa MF, Justice AE, Monda KL, Croteau-Chonka DC, Day FR, Esko T, Fall T, Ferreira T, et al. Genome-wide meta-analysis identifies 11 new loci for anthropometric traits and provides insights into genetic architecture. Nat Genet. 2013; 45: 501–512.
8. Graff M, Ngwa JS, Workalemahu T, Homuth G, Schipf S, Teumer A, Völzke H, Wallaschofski H, Abecasis GR, Edward L, Francesco C, Sanna S, Scheet P, et al. Genome-wide analysis of BMI in adolescents and young adults reveals additional insight into the effects of genetic loci over the life course. Hum Mol Genet. 2013; 22: 3597–3607.
9. Pei YF, Zhang L, Liu Y, Li J, Shen H, Liu YZ, Tian Q, He H, Wu S, Ran S, Han Y, Hai R, Lin Y, et al. Meta-analysis of genome-wide association data identifies novel susceptibility loci for obesity. Hum Mol Genet. 2014; 23: 820–830.
10. Shungin D, Winkler TW, Croteau-Chonka DC, Ferreira T, Locke AE, Mägi R, Strawbridge RJ, Pers TH, Fischer K, Justice AE, Workalemahu T, Wu JM, Buchkovich ML, et al. New genetic loci link adipose and insulin biology to body fat distribution. Nature. 2015; 518: 187–196.
11. Winkler TW, Justice AE, Graff M, Barata L, Feitosa MF, Chu S, Czajkowski J, Esko T, Fall T, Kilpeläinen TO, Lu Y, Mägi R, Mihailov E, et al. The influence of age and sex on genetic associations with adult body size and shape: a large-scale genome-wide interaction study. PLoS Genet. 2015; 11: e1005378.
12. Locke AE, Kahali B, Berndt SI, Justice AE, Pers TH, Day FR, Powell C, Vedantam S, Buchkovich ML, Yang J, Croteau-Chonka DC, Esko T, Fall T, et al. Genetic studies of body mass index yield new insights for obesity biology. Nature. 2015; 518: 197–206.
13. Hägg S, Ganna A, Van Der Laan SW, Esko T, Pers TH, Locke AE, Berndt SI, Justice AE, Kahali B, Siemelink MA, Pasterkamp G, GIANT Consortium, Strachan DP, et al. Gene-based meta-analysis of genome-wide association studies implicates new loci involved in obesity. Hum Mol Genet. 2015; 24: 6849–6860.
14. Lu Y, Day FR, Gustafsson S, Buchkovich ML, Na J, Bataille V, Cousminer DL, Dastani Z, Drong AW, Esko T, Evans DM, Falchi M, Feitosa MF, et al. New loci for body fat percentage reveal link between adiposity and cardiometabolic disease risk. Nat Commun. 2016; 7: 10495.
15. Okada Y, Kubo M, Ohmiya H, Takahashi A, Kumasaka N, Hosono N, Maeda S, Wen W, Dorajoo R, Go MJ, Zheng W, Kato N, Wu JY, et al. Common variants at CDKAL1 and KLF9 are associated with body mass index in east Asian populations. Nat Genet. 2012; 44: 302–306.
16. Wen W, Cho YS, Zheng W, Dorajoo R, Kato N, Qi L, Chen CH, Delahanty RJ, Okada Y, Tabara Y, Gu D, Zhu D, Haiman CA, et al. Meta-analysis identifies common variants associated with body mass index in east Asians. Nat Genet. 2012; 44: 307–311.
17. Wen W, Zheng W, Okada Y, Takeuchi F, Tabara Y, Hwang JY, Dorajoo R, Li H, Tsai FJ, Yang X, He J, Wu Y, He M, et al. Meta-analysis of genome-wide association studies in East Asian-ancestry populations identifies four new loci for body mass index. Hum Mol Genet. 2014; 23: 5492–5504.
18. Alberti KG, Eckel RH, Grundy SM, Zimmet PZ, Cleeman JI, Donato KA, Fruchart JC, James WP, Loria CM, Smith SC Jr, International Diabetes Federation Task Force on Epidemiology and Prevention, National Heart, Lung, and Blood Institute, American Heart Association, et al. Harmonizing the metabolic syndrome: a joint interim statement of the International Diabetes Federation Task Force on Epidemiology and Prevention; National Heart, Lung, and Blood Institute; American Heart Association; World Heart Federation; International Atherosclerosis Society; and International Association for the Study of Obesity. Circulation. 2009; 120: 1640–1645.
19. Alberti KG, Zimmet P, Shawn J, IDF Epidemiology Task Force Consensus Group. The metabolic syndrome—a new worldwide definition. Lancet. 2005; 366: 1059–1062.
20. Esposito K, Chiodini P, Colao A, Lenzi A, Giugliano D. Metabolic syndrome and risk of cancer: a systematic review and meta-analysis. Diabetes Care. 2012; 35: 2402–2411.
21. Mendonça FM, de Sousa FR, Barbosa AL, Martins SC, Araújo RL, Soares R, Abreu C. Metabolic syndrome and risk of cancer: Which link? Metabolism. 2015; 64: 182–189.
22. Kraja AT, Vaidya D, Pankow JS, Goodarzi MO, Assimes TL, Kullo IJ, Sovio U, Mathias RA, Sun YV, Franceschini N, Absher D, Li G, Zhang Q, et al. A bivariate genome-wide approach to metabolic syndrome: STAMPEED consortium. Diabetes. 2011; 60: 1329–1339.
23. Kristiansson K, Perola M, Tikkanen E, Kettunen J, Surakka I, Havulinna AS, Stancáková A, Barnes C, Widen E, Kajantie E, Eriksson JG, Viikari J, Kähönen M, et al. Genome-wide screen for metabolic syndrome susceptibility loci reveals strong lipid gene contribution but no evidence for common genetic basis for clustering of metabolic syndrome traits. Circ Cardiovasc Genet. 2012; 5: 242–249.
24. Tekola-Ayele F, Doumatey AP, Shriner D, Bentley AR, Chen G, Zhou J, Fasanmade O, Johnson T, Oli J, Okafor G, Eghan BA Jr, Agyenim-Boateng K, Adebamowo C, et al. Genome-wide association study identifies African-ancestry specific variants for metabolic syndrome. Mol Genet Metab. 2015; 116: 305–313.
25. Zabaneh D, Balding DJ. A genome-wide association study of the metabolic syndrome in Indian Asian men. PLoS One. 2010; 5: e11961.
26. Manolio TA, Collins FS, Cox NJ, Goldstein DB, Hindorff LA, Hunter DJ, McCarthy MI, Ramos EM, Cardon LR, Chakravarti A, Cho JH, Guttmacher AE, Kong A, et al. Finding the missing heritability of complex diseases. Nature. 2009; 461: 747–753.
27. Willer CJ, Speliotes EK, Loos RJ, Li S, Lindgren CM, Heid IM, Berndt SI, Elliott AL, Jackson AU, Lamina C, Lettre G, Lim N, Lyon HN, et al. Six new loci associated with body mass index highlight a neuronal influence on body weight regulation. Nat Genet. 2009; 41: 25–34.
28. Wheeler E, Huang N, Bochukova EG, Keogh JM, Lindsay S, Garg S, Henning E, Blackburn H, Loos RJ, Wareham NJ, O’Rahilly S, Hurles ME, Barroso I, et al. Genome-wide SNP and CNV analysis identifies common and low-frequency variants associated with severe early-onset obesity. Nat Genet. 2013; 45: 513–517.
29. Kilpeläinen TO, Zillikens MC, Stančákova A, Finucane FM, Ried JS, Langenberg C, Zhang W, Beckmann JS, Luan J, Vandenput L, Styrkarsdottir U, Zhou Y, Smith AV, et al. Genetic variation near IRS1 associates with reduced adiposity and an impaired metabolic profile. Nat Genet. 2011; 43: 753–760.
30. Kilpeläinen TO, Carli JF, Skowronski AA, Sun Q, Kriebel J, Feitosa MF, Hedman ÅK, Drong AW, Hayes JE, Zhao J, Pers TH, Schick U, Grarup N, et al. Genome-wide meta-analysis uncovers novel loci influencing circulating leptin levels. Nat Commun. 2016; 7: 10494.
31. Croteau-Chonka DC, Marvelle AF, Lange EM, Lee NR, Adair LS, Lange LA, Mohlke KL. Genome-wide association study of anthropometric traits and evidence of interactions with age and study year in Filipino women. Obesity. 2011; 19: 1019–1027.
32. Kamatani Y, Matsuda K, Okada Y, Kubo M, Hosono N, Daigo Y, Nakamura Y, Kamatani N. Genome-wide association study of hematological and biochemical traits in a Japanese population. Nat Genet. 2010; 42: 210–215.
33. Below JE, Parra EJ, Gamazon ER, Torres J, Krithika S, Candille S, Lu Y, Manichakul A, Peralta-Romero J, Duan Q, Li Y, Morris AP, Gottesman O, et al. Meta-analysis of lipid-traits in Hispanics identifies novel loci, population-specific effects, and tissue-specific enrichment of eQTLs. Sci Rep. 2016; 6: 19429.
34. Jogl G, Hsiao YS, Tong L. Structure and function of carnitine acyltransferases. Ann NY Acad Sci. 2004; 1033: 17–29.
35. Menon S, Manning BD. Common corruption of the mTOR signaling network in human tumors. Oncogene. 2008; 27: S43–S51.
36. Napolioni V, Curatolo P. Genetics and molecular biology of tuberous sclerosis complex. Curr Genomics. 2008; 9: 475–487.
37. Orlova KA, Crino PB. The tuberous sclerosis complex. Ann NY Acad Sci. 2010; 1184: 87–105.
38. Caban C, Khan N, Hasbani DM, Crino PB. Genetics of tuberous sclerosis complex: implications for clinical practice. Appl Clin Genet. 2016; 10: 1–8.
39. Kajiho H, Sakurai K, Minoda T, Yoshikawa M, Nakagawa S, Fukushima S, Kontani K, Katada T. Characterization of RIN3 as a guanine nucleotide exchange factor for the Rab5 subfamily GTPase Rab31. J Biol Chem. 2011; 286: 24364–24373.
40. Janson C, Kasahara N, Prendergast GC, Colicelli J. RIN3 is a negative regulator of mast cell responses to SCF. PLoS One. 2012; 7: e49615.
41. Albagha OM, Wani SE, Visconti MR, Alonso N, Goodman K, Brandi ML, Cundy T, Chung PY, Dargie R, Devogelaer JP, Falchetti A, Fraser WD, Gennari L, et al. Genome-wide association identifies three new susceptibility loci for Paget’s disease of bone. Nat Genet. 2011; 43: 685–689.
42. Nappo A, Iacoviello L, Fraterman A, Gonzalez-Gil EM, Hadjigeorgiou C, Marild S, Molnar D, Moreno LA, Peplies J, Sioen I, Veidebaum T, Siani A, Russo P. High-sensitivity C-reactive protein is a predictive factor of adiposity in children: results of the Identification and Prevention of Dietary- and Lifestyle-Induced Health Effects in Children and Infants (IDEFICS) study. J Am Heart Assoc. 2013; 2: e000101.
43. Hribal ML, Fiorentino TV, Sesti G. Role of C reactive protein (CRP) in leptin resistance. Curr Pharm Des. 2014; 20: 609–615.
44. Simons PJ, van den Pangaart PS, Aerts JM, Boon L. Pro-inflammatory delipidizing cytokines reduce adiponectin secretion from human adipocytes without affecting adiponectin oligomerization. J Endocrinol. 2007; 192: 289–299.
45. Mandal P, Pratt BT, Barnes M, McMullen MR, Nagy LE. Molecular mechanism for adiponectin-dependent M2 macrophage polarization: link between the metabolic and innate immune activity of full-length adiponectin. J Biol Chem. 2011; 286: 13460–13469.
46. Munafo MR, Matheson IJ, Flint J. Association of the DRD2 gene Taq1A polymorphism and alcoholism: a meta-analysis of case-control studies and evidence of publication bias. Mol Psychiatry. 2007; 12: 454–461.
47. Spitz MR, Detry MA, Pillow P, Hu Y, Amos CI, Hong WK, Wu X. Variant alleles of the D2 dopamine receptor gene and obesity. Nutr Res. 2000; 20: 371–380.
48. Wang G, Volkow ND, Logan J, Pappas NR, Wong CT, Zhu W, Netusil N, Fowler JS. Brain dopamine and obesity. Lancet. 2001; 357: 354–357.
49. Stice E, Spoor S, Bohon C, Small DM. Relation between obesity and blunted striatal response to food is moderated by TaqIA A1 allele. Science. 2008; 322: 449–452.
50. Gingras S, Pelletier S, Boyd K, Ihle JN. Characterization of a family of novel cysteine- serine-rich nuclear proteins (CSRNP). PLoS One. 2007; 2: e808.
51. Pilling LC, Atkins JL, Bowman K, Jones SE, Tyrrell J, Beaumont RN, Ruth KS, Tuke MA, Yaghootkar H, Wood AR, Freathy RM, Murray A, Weedon MN, et al. Human longevity is influenced by many genetic variants: evidence from 75,000 UK Biobank participants. Aging (Albany NY). 2016; 8: 547–560. doi: 10.18632/aging.100930.
52. Pattaro C, Teumer A, Gorski M, Chu AY, Li M, Mijatovic V, Garnaas M, Tin A, Sorice R, Li Y, Taliun D, Olden M, Foster M, et al. Genetic associations at 53 loci highlight cell types and biological pathways relevant for kidney function. Nat Commun. 2016; 7: 10023.
53. Yamada Y, Sakuma J, Takeuchi I, Yasukochi Y, Kato K, Oguri M, Fujimaki T, Horibe H, Muramatsu M, Sawabe M, Fujiwara Y, Taniguchi Y, Obuchi S, et al. Identification of STXBP2 as a novel susceptibility locus for myocardial infarction in Japanese individuals by an exome-wide association study. Oncotarget. 2017; 8:33527-33535. doi: 10.18632/oncotarget.16536.
54. Examination Committee of Criteria for ‘Obesity Disease’ in Japan; Japan Society for the Study of Obesity. New criteria for ‘obesity disease’ in Japan. Circ J. 2002; 66: 987–992.
