We conducted autosomal genome scans to map loci for metabolic syndrome (MES) and related traits in the Hong Kong Family Diabetes Study. We selected 55 families with 137 affected members (121 affected relative pairs) for nonparametric linkage analysis on MES. We also selected 179 families with 897 members (2,127 relative pairs) for variance component-based linkage analyses on seven MES-related traits: waist circumference, systolic and diastolic blood pressure (BP), triglyceride, HDL cholesterol, fasting plasma glucose, and insulin resistance index (insulin resistance index by homeostasis model assessment [HOMA%IR]). Analyses revealed three regions that showed suggestive linkage for MES and also showed overlapping signals for metabolic traits: chromosome 1 at 169.5–181.5 cM (logarithm of odds [LOD] = 4.50 for MES, 3.71 for waist circumference, and 1.24 for diastolic BP), chromosome 2 at 44.1–57.3 cM (LOD = 2.22 for MES, 2.07 for fasting plasma glucose, and 1.29 for diastolic BP), and chromosome 16 at 45.2–65.4 cM (LOD = 1.75 for MES, 1.61 for HOMA%IR, and 1.25 for HDL cholesterol). Other regions that showed suggestive linkages included chromosome 5q for diastolic BP; 2q, 3q, 6q, 9q, 10q, and 17q for triglyceride; 12p, 12q, and 22q for HDL-C; and 6q for HOMA%IR. Simulation studies demonstrated genome-wide significant linkage of the chromosome 1 region to both MES and waist circumference (Pgenome-wide = 0.002 and 0.019, respectively). In summary, we have found a susceptibility locus on chromosome 1q21-q25 involved in the pathogenesis of multiple metabolic abnormalities, in particular obesity. Our results confirm the findings of previous studies on diabetes and related phenotypes. We also suggest the locations of other loci that may contribute to the development of MES in Hong Kong Chinese.

The clustering of multiple metabolic abnormalities, including insulin resistance, glucose intolerance, hypertension, dyslipidemia, and obesity, in particular central obesity, is known as syndrome X, or metabolic syndrome (MES) (13). MES is a major health problem characterized by increased morbidity and mortality. The prevalence of MES in the general adult population varies from 10–13% in mainland Chinese (4,5) to 20–30% in U.S. whites and Mexican Americans (6,7). However, direct comparison of these rates is difficult because of the use of different diagnostic criteria (2,3).

The underlying pathogenic mechanism for the manifestation of these related diseases is unclear but likely involves multiple pathways influenced by interacting genetic and environmental factors. It has been hypothesized that insulin resistance (8) and abdominal obesity (9) may be the key factors linking the components of MES. Several lines of evidence suggest that pleiotropy or shared genetic factors contribute to the clustering of these traits. Twin studies show a higher concordance rate for MES in monozygotic than in dizygotic twins. A common latent genetic factor may influence the clustering of traits (10,11). Type 2 diabetes and hypertension show familial aggregation of MES (12). Bivariate genetic analyses in families suggest a pleiotropic effect between insulin level and obesity indexes or lipids (13,14) and between obesity indexes and lipids (15).

Most linkage mapping studies for metabolic diseases focus on dichotomous traits, partly because the ascertained families include only individuals with disease. Quantitative trait linkage analysis may be a powerful alternative for mapping genes for common diseases (16). Several genome scan studies have attempted to map quantitative trait loci (QTL) for MES traits, on either individual traits or composite factors derived from principle component or factor analyses (1720). A recent genome scan for coronary heart disease used both qualitative and quantitative linkage approaches to study multiple metabolic diseases, and three suggestive loci are found to be linked to more than one trait, consistent with pleiotropy, but not all traits are studied both qualitatively and quantitatively (21).

The Hong Kong Family Diabetes Study (HKFDS) was begun in 1998 to examine the genetic and environmental factors and their interactions in the development of type 2 diabetes and related traits. The age-standardized prevalence of diabetes (8.6%), hypertension (15.9%), hypercholesterolemia (11.6%), and central obesity (23.6%) in Hong Kong is high (22) as a result of the affluent westernized lifestyle. Our family study shows higher prevalences of diabetes, hypertension, and central obesity in siblings of diabetic patients compared with the general population (sibling recurrence risk, 2.0–4.3), with an age-standardized prevalence rate of 23% of MES in these siblings in the age range 25–54 years (J.K.Y.L. et al., unpublished data). In this article, we report results of autosomal genome scans for MES and related quantitative traits in our extensively phenotyped families.

The details of ascertainment, exclusion criteria, and phenotyping for the HKFDS are described elsewhere (23). In brief, 179 families who were ascertained through a diabetic proband with available first-degree relatives are included in the present study, after exclusion of patients with clinical or autoimmune type 1 diabetes and families with known maturity-onset diabetes of the young or mitochondrial DNA nucleotide 3243 A>G mutations. Of these 179 families, 55 (137 affected subjects) with at least two MES-affected relatives (nonparent-offspring) are appropriate for linkage study of MES. Sixty-seven (49%) of these subjects in 36 families are included in our previous genome scan for type 2 diabetes genes (23). For assessing the relative contribution of families involved in our type 2 diabetes study on the linkage signal for MES, subset linkage analyses were performed in 36 families (82 affected relative pairs) involved in both the type 2 diabetes and MES studies and 19 families (48 affected relative pairs) involved in only the MES study. Studies on quantitative traits were performed in all 179 families (897 subjects) excluding subjects aged <16 years. The family relationships and the clinical characteristics of subjects involved in MES and QTL linkage studies are summarized in Tables 1 and 2, respectively. The average family size in the QTL studies is 5.0 ± 2.3. The average number of affected subjects per family in the MES study is 2.5 ± 1.0. Informed consent was obtained for each participating subject. This study was approved by the Clinical Research Ethics Committee of the Chinese University of Hong Kong.

Clinical studies.

All available family members were assessed for blood pressure (BP) and standard anthropometric parameters and completed a questionnaire on demographic data, family and medical histories of cardiovascular risk factors and complications, and lifestyle. Fasting blood samples were collected for measurement of plasma glucose (FPG), insulin, C-peptide, lipid profile, liver and renal function, and DNA. Subjects with no history of diabetes were tested with a 75-g oral glucose tolerance test. Insulin resistance was assessed by the homeostasis model assessment (HOMA%IR) as fasting insulin (μU/ml) × FPG (mmol/l)/22.5. MES was defined in accordance with the National Cholesterol Education Program Adult Treatment Panel III (NCEP III) guidelines (3). Patients who have at least three of the following five risk factors were classified as having MES: 1) hyperglycemia with known diabetes or FPG ≥6.1 mmol/l, 2) known hypertension or BP ≥130/85 mmHg, 3) hypertriglyceridemia with triglyceride (TG) ≥1.7 mmol/l, 4) HDL cholesterol <1.0 mmol/l in men or <1.3 mmol/l in women, and 5) central obesity with waist circumference (WC) >90 cm in men or >80 cm in women. The definition of central obesity was modified for Asian populations (24).

Genotyping.

Detailed information on the genotype data and quality control have been described previously (23). In brief, 355 autosomal microsatellite markers (Research Genetics, Huntsville, AL) with an average intermarker distance of 10 cM and average heterozygosity of 71% were typed. Genetic relationships among family members were checked by RELPAIR (25) and PREST (26) using genome-scan data to assess consistency of allele-sharing pattern with the specified relationship. Misclassification of half siblings as full siblings and mislabeled DNA samples were identified and resolved. No families were removed as a result of relationship inconsistency. However, we removed nine subjects with no or unresolved relationship with other family members. Mendelian errors and potential genotyping errors were checked by PEDCHECK (v. 1.1) (27) and MERLIN (v. 0.9.12b) (28), respectively, and removed.

Linkage analyses.

Marker allele frequencies were estimated from all subjects in the 179 families. Marker position was based on the sex-average maps from Marshfield Medical Research Foundation (http://research.marshfieldclinic.org/genetics). For the MES study, nonparametric multipoint linkage analysis was performed with MERLIN (v. 0.9.12b) using the score-pairs statistic (28). In chromosomal regions that showed suggestive linkage with MES, linkage analyses of the five respective factors (hyperglycemia, hypertension, hypertriglyceridemia, low HDL cholesterol, and central obesity) were also performed in the 55 MES families that consisted of family members with various degrees of MES factors.

For QTL studies, seven quantitative traits related to MES, including WC, systolic and diastolic BP, TGs, HDL cholesterol, FPG, and HOMA%IR, were examined. Data on BP were removed for subjects who were taking antihypertensive medications. Similarly, data on FPG and HOMA%IR were removed for the 68% of diabetic subjects who were taking oral antidiabetic drugs or insulin. Data were transformed using natural logarithm (WC, systolic BP, TGs, HDL cholesterol, and HOMA%IR), square root (diastolic BP), or double natural logarithm (FPG) to reduce skewness and kurtosis. Extreme values >4 SD from the mean were removed. The number of individuals who were removed for quantitative trait analyses ranged from zero to eight. No whole families were removed for analyses of any traits. Data were standardized to mean zero and unit variance and analyzed by the variance component method implemented in MERLIN with simultaneous adjustment of covariates including age and sex. Type 2 diabetes was also used as a covariate in regions that showed significant linkage. MERLIN-REGRESS (v. 0.9.12b) (29) was performed on selected traits and chromosomes for comparison with variance component-based results. Sex-specific linear regression of each trait for age was obtained in a Chinese cohort (573 subjects) recruited from a community-based health screening program in Hong Kong. Data of each family member were regressed on the linear model. Standardized residuals (using population mean and SD) were then analyzed with MERLIN-REGRESS using a population mean of zero and a variance of one and estimated trait heritability from the families. Logarithm of odds (LOD) scores with associated pointwise P values are reported (30). Two linked regions were defined as independent when their maximum LOD score positions differed by ≥40 cM.

Simulation studies were conducted to assess the significance of the observed linkage signals in both the MES and QTL studies. One thousand simulated datasets were generated by gene dropping with MERLIN under the null hypothesis of no linkage. The maps, marker allele frequencies, pedigree structures, and missing genotype patterns used in these simulations were identical to those in the actual data. Linkage analyses on MES and each of the seven quantitative traits were then conducted on the simulated datasets. In each simulation, the number of independent linked regions (ILRs) greater than a particular LOD score threshold was tallied. Using a locus-counting method (31), Pgenome-wide for n-ILRs at a LOD score threshold was calculated as the proportion of simulations with ≥n ILRs at that particular LOD score. A significant excess of ILRs in the observed data set was defined as Pgenome-wide < 0.05. Suggestive linkage was indicated by the LOD score that corresponds to an average of one ILR per genome scan expected by chance through simulation (32). Because of a priori hypothesis and the high correlations among the examined quantitative traits and MES, no adjustment for multiple comparisons was made on the Pgenome-wide values.

Correlation analyses.

The phenotypic correlation (ρP) between the pairwise combination of traits was calculated on the basis of genetic (ρG) and environmental (ρE) correlations and of heritabilities of the two traits (h12 and h22), where

\({\rho}_{P}{=}{\rho}_{G}\sqrt{h1^{2}}\ \sqrt{h2^{2}}{+}{\rho}_{E}\sqrt{h1^{2}}\ \sqrt{h2^{2}}\)
⁠. These parameters were estimated by a maximum-likelihood variance component method implemented in SOLAR (version 2.1.2) (33).

Characteristics of study populations.

The MES study includes 55 families with 137 affected subjects (121 affected relative pairs; Table 1). The prevalence of metabolic abnormalities including hyperglycemia, hypertension, hypertriglyceridemia, low HDL cholesterol, and central obesity range from 64 to 87% (Table 2). The variance component-based QTL studies include 179 families with 897 subjects (2,127 relative pairs). These families also have a high prevalence of metabolic abnormalities (25–43%; Tables 1 and 2). The selection of quantitative traits for the QTL studies was based on their use in the definition of MES using the NCEP III criteria (3). Although families are selected through a diabetic proband, a large number of family members are unaffected. Thus, the distributions of transformed quantitative traits are approximately normal, and the mean values are similar to the general population except for FPG (Table 2). Heritabilities for WC, systolic and diastolic BP, TGs, and HDL cholesterol were moderate (h2 = 0.45–0.63) but relatively low for FPG (h2 = 0.28; J.K.Y.L. et al., unpublished data). We also assessed insulin resistance index (HOMA%IR; h2 = 0.60) because it is a key factor linking various MES traits.

MES linkage analyses.

Table 3 summarizes regions with LOD scores >1.18 (Ppointwise < 0.01). The region that showed the strongest evidence for linkage is on chromosome 1q21–25 at 169.5 cM (LOD = 4.50, one LOD confidence interval = 163–185 cM, Pgenome-wide = 0.002). Two other regions, chromosome 2 at 44.1 cM (LOD = 2.22) and chromosome 16 at 65.4 cM (LOD = 1.75), have suggestive evidence for linkage based on observing an average of one ILR per genome scan (32) at a LOD score of 1.57 (Tables 3 and 4) in our simulation. ILRs are significantly more frequent in the actual data than expected at LOD >2.08 (Table 4). Analyses on individual components of MES in these 55 families show clustering of linkage signals for most traits at the MES loci (Fig. 1). The strongest signals are for central obesity (LOD = 4.97) on chromosome 1, hypertension on chromosome 2 (LOD = 3.16), and low HDL cholesterol on chromosome 16 (LOD = 1.91). In a subset analyses of chromosome 1, the respective LOD scores for MES at 169.5 cM are 3.82 for the 36 families involved in the previous type 2 diabetes study and 0.90 for 19 families involved in only the MES study. The obesity indexes of diabetic subjects in the former study (BMI, 27.0 ± 4.6 kg/m2; WC, 93 ± 10 cm for men and 83 ± 10 cm for women, respectively) were similar to those in the present study (Table 2).

QTL linkage analyses.

In the QTL analyses, we observed genome-wide evidence of linkage for WC on chromosome 1q21–25 at 180.3 cM (LOD = 3.71, one LOD confidence interval = 172–190 cM, Pgenome-wide = 0.019; Tables 3 and 4, Fig. 2). The respective LOD score decreases to 2.87 after adjustment for type 2 diabetes status. We also observed suggestive evidence of linkage (LOD = 1.69–2.99) for several metabolic traits. These include diastolic BP on chromosome 5q; TGs on chromosomes 2q, 3q, 6q, 9q, 10q, and 17q; HDL cholesterol on chromosomes 12p, 12q, and 22q; FPG on chromosome 2p; and HOMA%IR on chromosome 6q (Tables 3 and 4, Fig. 2). Most traits except for BP show more ILRs than expected at several LOD score thresholds, especially for TGs and HDL cholesterol (Table 4). We also analyzed two regions using a regression-based method. These include WC on chromosome 1, showing significant linkage, and FPG on chromosome 2, showing suggestive linkage and with different mean values in family versus population data. We observed a higher LOD score for WC (5.97 vs. 3.71) but a similar LOD score for FPG (1.90 vs. 2.07) using the latter method.

Overlap of linkage for MES and related traits.

Several regions show nominal or suggestive linkages with two or more MES-related traits: chromosome 1 (169.5–181.5 cM) for MES, WC, and diastolic BP; chromosomes 2 (44.1–57.3 cM) for MES, FPG, and diastolic BP; and chromosome 16 (45.2–65.4 cM) for MES, HOMA%IR, and HDL cholesterol (Table 3).

We have carried out both qualitative and quantitative trait linkage analyses to localize genes that contribute to susceptibility to MES. We used simulation to assess the significance of linkage results, which took into account the incomplete marker information content, sample size, and ascertainment scheme of our families. We found significant linkage on chromosome 1 (169.5–181.5 cM) for MES and WC and suggestive linkages on chromosomes 2 (44.1–57.3 cM) and 16 (45.2–65.4 cM) for MES and various metabolic traits. Because only a small proportion of subjects (n = 137) are included in both studies and two different analytical methods are used, the overlapping linkage signals for MES and quantitative traits in these chromosomal regions provide complementary evidence for susceptibility gene(s) for multiple metabolic abnormalities and/or multiple genes at the same loci for different metabolic abnormalities.

We and others previously reported significant evidence of linkage of chromosome 1q21–25 to type 2 diabetes (24,34). Here, we show that chromosome 1q21–25 is also significantly linked to MES and central obesity (LOD = 4.50 at 169.5 cM and LOD = 3.71 at 180.3 cM, respectively). Sixty-seven (49%) individuals from 36 families in our MES study were part of the previous type 2 diabetes study. Subset analyses show that most but not all of the linkage signal for MES is derived from families involved in the previous type 2 diabetes study rather than from the 19 families involved only in the MES study. Of the analyses of individual phenotypes and quantitative traits, the highest LOD scores are observed for obesity indexes. Significant linkage signals for WC are observed in both variance-component and regression-based analyses; the latter are robust to ascertainment bias but require correct specification of population mean, variance, and heritability (29). This signal is partly explained by type 2 diabetes status. In bivariate analyses of our families, we observed higher correlations between obesity indexes and other metabolic indexes (BP, lipid and glucose profiles; phenotypic correlation, 0.28–0.48; genetic correlation, 0.16–0.60) compared with correlations among other metabolic indexes (phenotypic correlation, 0.09–0.41; genetic correlation, 0.01–0.43; J.K.Y.L. et al., unpublished data). Collectively, the present and previous results strongly suggest the presence of susceptibility gene(s) on chromosome 1q21–25 for clustered phenotypes of MES and type 2 diabetes. Obesity may be the key factor linking the pathogenesis of various metabolic abnormalities. These results also support our previous structural equation modeling analysis that demonstrated that obesity, rather than insulin resistance, was the key factor linking MES traits in our population (9). However, the weaker signal of hypertension in the MES linkage study (this study) and the weaker phenotypic correlations between BP and other metabolic traits (0.09–0.27) suggest that other genetic and environmental factors play a more important role in the pathogenesis of hypertension in these families.

Our linkage results in this region confirm the findings of studies in other populations on disease phenotypes and quantitative traits related to MES (18,35), obesity (36,37), type 2 diabetes or glucose intolerance (23,34), and dyslipidemia or familial combined hyperlipidemia (FCHL) (3842). To our knowledge, no linkage of chromosome 1q21–25 with hypertension has been reported so far, consistent with our observations that hypertension is less correlated with other MES traits. Recently, the upstream transcription factor 1 gene located on chromosome 1q21 was reported to be linked to and associated with FCHL (43). The contributions of this and other candidate genes associated with type 2 diabetes, MES, and related traits remain to be determined.

We observed suggestive linkage with MES on chromosomes 2 (LOD = 2.07) and 16 (LOD = 1.75). These regions also show nominal or suggestive linkage with MES-related traits. On chromosome 2, the strongest signals are for hypertension, FPG, and diastolic BP. On chromosome 16, the strongest signals are for low HDL cholesterol, HDL cholesterol level, and HOMA%IR. The correspondence in linkage signals for MES and QTLs for BP on chromosome 2 and HDL cholesterol on chromosome 16 in two largely independent datasets using different analytical methods suggest that they may be true susceptibility loci. In contrast to the chromosome 1 signal, the linkage signals on these chromosomes were not observed in our scan for type 2 diabetes genes. This suggests that these loci on chromosomes 2 and 16 may have a primary effect on individual traits rather than on MES as a whole. The chromosome 2 region was previously reported to be linked to MES (19); hypertension (44); obesity (45); and TG (46), leptin (47), and adiponectin levels (48). The HDL cholesterol linkage result on chromosome 16 replicates the finding of a coronary heart disease genome scan (21).

In the QTL analyses, nominal evidence for linkage (P < 0.01) for various metabolic traits is reached for many chromosomal regions. Many of these are expected to be false positive but cannot be distinguished from true signals. Several metabolic traits show linkage signals in more ILRs than expected, and some regions reach suggestive linkage (e.g., TG with LOD score of 2.58 on chromosome 6q), suggesting the presence of susceptibility loci, which may warrant further investigation.

The collection of extensively phenotyped families allows us to analyze complementary disease and quantitative phenotypes. We demonstrate that using different analytical methods on two largely independent datasets is a powerful approach to mapping loci for diseases with complex phenotypes. The identification of the genes that affect risk to MES will lead to a better understanding of the pathogenesis and improve diagnosis of MES in Chinese and other populations.

FIG. 1.

Multipoint linkage analyses on chromosomes 1, 2, and 16 for MES and its components in 55 families. The number of affected relative pairs included in each analysis is indicated in parentheses. The horizontal axis is cM from p-terminus.

FIG. 1.

Multipoint linkage analyses on chromosomes 1, 2, and 16 for MES and its components in 55 families. The number of affected relative pairs included in each analysis is indicated in parentheses. The horizontal axis is cM from p-terminus.

FIG. 2.

Multipoint linkage analyses for MES-related quantitative traits in 179 families. The horizontal axis is cM from p-terminus.

FIG. 2.

Multipoint linkage analyses for MES-related quantitative traits in 179 families. The horizontal axis is cM from p-terminus.

TABLE 1

Pairwise relationships of family members involved in the MES study (n = 137) and QTL studies (n = 897)*

MES study(55 families)QTL studies (179 families)
Parent-offspring — 630 
Full siblings 107 1,072 
Grandparent-grandchild 64 
Avuncular 290 
Half siblings 10 
Half avuncular 
First cousins 58 
Total 121 2,127 
MES study(55 families)QTL studies (179 families)
Parent-offspring — 630 
Full siblings 107 1,072 
Grandparent-grandchild 64 
Avuncular 290 
Half siblings 10 
Half avuncular 
First cousins 58 
Total 121 2,127 
*

Only affected subjects used in linkage analyses were counted for the MES study, whereas all family members were counted for QTL studies.

The number of parent-offspring pairs for the MES study was not reported because they are not informative for nonparametric linkage analysis.

TABLE 2

The clinical and metabolic characteristics of family members in the MES and QTL studies*

MES study (55 families)QTL studies (179 families)
n 137 897 
Age (years) 43 ± 9 42 ± 15 
Male/female 58/79 364/533 
BMI (kg/m228.6 ± 4.2 24.9 ± 4.4 
WC (cm)   
    Men 95 ± 8 86 ± 10 
    Women 88 ± 10 78 ± 11 
Systolic BP (mmHg) 135 ± 16 126 ± 21 
Diastolic BP (mmHg) 82 ± 11 75 ± 13 
TG (mmol/l) 2.1 (1.8–2.3) 1.2 (1.2–1.3) 
HDL cholesterol (mmol/l) 1.1 ± 0.2 1.4 ± 0.4 
FPG (mmol/l) 7.3 ± 2.8 6.1 ± 2.4 
FINS (pmol/l) 76 (68–85) 52 (50–55) 
HOMA%IR 3.9 (3.4–4.3) 2.2 (2.1–2.3) 
Central obesity (%) 87 37 
Hypertension (%) 76 43 
Hypertriglyceridemia (%) 64 25 
Low HDL cholesterol (%) 67 31 
Hyperglycemia (%) 69 37 
MES (%) 100 30 
MES study (55 families)QTL studies (179 families)
n 137 897 
Age (years) 43 ± 9 42 ± 15 
Male/female 58/79 364/533 
BMI (kg/m228.6 ± 4.2 24.9 ± 4.4 
WC (cm)   
    Men 95 ± 8 86 ± 10 
    Women 88 ± 10 78 ± 11 
Systolic BP (mmHg) 135 ± 16 126 ± 21 
Diastolic BP (mmHg) 82 ± 11 75 ± 13 
TG (mmol/l) 2.1 (1.8–2.3) 1.2 (1.2–1.3) 
HDL cholesterol (mmol/l) 1.1 ± 0.2 1.4 ± 0.4 
FPG (mmol/l) 7.3 ± 2.8 6.1 ± 2.4 
FINS (pmol/l) 76 (68–85) 52 (50–55) 
HOMA%IR 3.9 (3.4–4.3) 2.2 (2.1–2.3) 
Central obesity (%) 87 37 
Hypertension (%) 76 43 
Hypertriglyceridemia (%) 64 25 
Low HDL cholesterol (%) 67 31 
Hyperglycemia (%) 69 37 
MES (%) 100 30 

Data are n, percentage, mean ± SD, or geometric mean (95% CI). Hypertension, hypertriglyceridemia, low HDL cholesterol, hyperglycemia, central obesity, and MES were defined according to NCEP III criteria (3). The definition of central obesity was modified as WC >90 cm in men or >80 cm in women using the Asian criteria (25). FINS, fasting plasma insulin.

*

Only affected subjects used in linkage analyses were included in assessments for the MES study, whereas all family members were included in assessments for QTL studies. The mean values for metabolic indices in the 25- to 74-year-old general Hong Kong Chinese population are BMI, 24.1 kg/m2; WC (men), 83 cm; WC (women), 75 cm; systolic BP, 119 mmHg; diastolic BP, 75 mmHg; TG, 1.2 mmol/l; HDL cholesterol, 1.3 mmol/l; and FPG, 5.4 mmol/l (22).

TABLE 3

Regions showing nominal evidence of linkage (LOD >1.18, Ppointwise < 0.01) to MES and related traits in Hong Kong Chinese

Position (cM)*Flanking markersTraitLODPpointwise
Chromosome 1     
    169.5 D1S1653-APOA2 MES 4.50 <0.00001 
    180.3 D1S194-D1S196 WC 3.71 0.00002 
    181.5 D1S196 Diastolic BP 1.24 0.008 
    245.7 D1S549-D1S3462 FPG 1.42 0.005 
Chromosome 2     
    44.1 D2S1360-D2S405 MES 2.22 0.0007 
    55.5 D2S1788 Diastolic BP 1.29 0.007 
    57.3 D2S1788-D2S1356 FPG 2.07 0.001 
    186.2 D2S1391 TG 1.69 0.003 
Chromosome 3     
    112.4 D3S4529 TG 1.31 0.007 
    112.4 D3S4529 FPG 1.20 0.009 
    154.3 D3S1764-D3S1744 TG 1.69 0.003 
Chromosome 5     
    189.2 ATA52D02 Diastolic BP 1.88 0.002 
Chromosome 6     
    82.4 D6S2410-D6S1031 FPG 1.56 0.004 
    118.6 D6S474 HOMA%IR 2.99 0.0001 
    137.7 D6S1009 TG 2.58 0.0003 
Chromosome 7     
    54.8 D7S817-D7S2846 HDL cholesterol 1.31 0.007 
Chromosome 8     
    146.4 D8S1179-D8S1108 WC 1.22 0.009 
    154.0 D8S1108 Diastolic BP 1.57 0.004 
Chromosome 9     
    110.7 D9S910-D9S930 TG 1.19 0.01 
    157.5 D9S164-D9S1838 TG 1.90 0.002 
Chromosome 10     
    70.2 D10S1220 TG 1.87 0.002 
Chromosome 11     
    43.2 D11S1392 HOMA%IR 2.02 0.0012 
    109.1 D11S1995-D11S1998 Systolic BP 1.44 0.005 
Chromosome 12     
    6.4 D12S372 WC 1.26 0.008 
    17.7 GATA49D12 HDL cholesterol 1.76 0.002 
    102.2 D12S1064-D12S1300 WC 1.31 0.007 
    129.9 D12S2070-D12S395 HDL cholesterol 2.05 0.0011 
Chromosome 13     
    33.0 D13S894 HDL cholesterol 1.33 0.007 
Chromosome 16     
    45.2 D16S403-D16S769 HDL cholesterol 1.25 0.008 
    53.5 D16S769-D16S753 HOMA%IR 1.61 0.003 
    65.4 D16S3396-D16S3253 MES 1.75 0.002 
Chromosome 17     
    91.5 D17S2193-D17S1301 TG 1.87 0.002 
Chromosome 18     
    2.8 GATA178F11 FPG 1.56 0.004 
Chromosome 19     
    78.1 D19S246 HDL cholesterol 1.29 0.007 
Chromosome 22     
    28.6 D22S689 Diastolic BP 1.35 0.006 
    60.6 D22S1169 HDL cholesterol 2.49 0.0004 
Position (cM)*Flanking markersTraitLODPpointwise
Chromosome 1     
    169.5 D1S1653-APOA2 MES 4.50 <0.00001 
    180.3 D1S194-D1S196 WC 3.71 0.00002 
    181.5 D1S196 Diastolic BP 1.24 0.008 
    245.7 D1S549-D1S3462 FPG 1.42 0.005 
Chromosome 2     
    44.1 D2S1360-D2S405 MES 2.22 0.0007 
    55.5 D2S1788 Diastolic BP 1.29 0.007 
    57.3 D2S1788-D2S1356 FPG 2.07 0.001 
    186.2 D2S1391 TG 1.69 0.003 
Chromosome 3     
    112.4 D3S4529 TG 1.31 0.007 
    112.4 D3S4529 FPG 1.20 0.009 
    154.3 D3S1764-D3S1744 TG 1.69 0.003 
Chromosome 5     
    189.2 ATA52D02 Diastolic BP 1.88 0.002 
Chromosome 6     
    82.4 D6S2410-D6S1031 FPG 1.56 0.004 
    118.6 D6S474 HOMA%IR 2.99 0.0001 
    137.7 D6S1009 TG 2.58 0.0003 
Chromosome 7     
    54.8 D7S817-D7S2846 HDL cholesterol 1.31 0.007 
Chromosome 8     
    146.4 D8S1179-D8S1108 WC 1.22 0.009 
    154.0 D8S1108 Diastolic BP 1.57 0.004 
Chromosome 9     
    110.7 D9S910-D9S930 TG 1.19 0.01 
    157.5 D9S164-D9S1838 TG 1.90 0.002 
Chromosome 10     
    70.2 D10S1220 TG 1.87 0.002 
Chromosome 11     
    43.2 D11S1392 HOMA%IR 2.02 0.0012 
    109.1 D11S1995-D11S1998 Systolic BP 1.44 0.005 
Chromosome 12     
    6.4 D12S372 WC 1.26 0.008 
    17.7 GATA49D12 HDL cholesterol 1.76 0.002 
    102.2 D12S1064-D12S1300 WC 1.31 0.007 
    129.9 D12S2070-D12S395 HDL cholesterol 2.05 0.0011 
Chromosome 13     
    33.0 D13S894 HDL cholesterol 1.33 0.007 
Chromosome 16     
    45.2 D16S403-D16S769 HDL cholesterol 1.25 0.008 
    53.5 D16S769-D16S753 HOMA%IR 1.61 0.003 
    65.4 D16S3396-D16S3253 MES 1.75 0.002 
Chromosome 17     
    91.5 D17S2193-D17S1301 TG 1.87 0.002 
Chromosome 18     
    2.8 GATA178F11 FPG 1.56 0.004 
Chromosome 19     
    78.1 D19S246 HDL cholesterol 1.29 0.007 
Chromosome 22     
    28.6 D22S689 Diastolic BP 1.35 0.006 
    60.6 D22S1169 HDL cholesterol 2.49 0.0004 
*

Marker positions are indicated in Haldane cM from pter obtained from Marshfield Medical Research Foundation.

Significant linkage at Pgenome-wide < 0.05.

Suggestive linkage with one ILR per genome scan expected by chance.

TABLE 4

Distribution of actual (expected) number of ILRs at different LOD score thresholds and corresponding LOD scores for suggestive and significant linkage*

TraitNumber of actual (expected) ILRs at LOD threshold
Suggestive LODSignificant LOD
0.591.182.083
MES 12 (9.1) 3 (2.4) 2 (0.4) 1 (0.05) 1.57 2.95 
WC 15 (10.5) 4 (3.3) 1 (0.6) 1 (0.1) 1.78 3.30 
Systolic BP 8 (11.7) 1 (4.0) 0 (0.8) 0 (0.1) 1.96 3.49 
Diastolic BP 10 (10.6) 5 (3.3) 0 (0.6) 0 (0.1) 1.80 3.22 
TG 13 (9.4) 8 (2.6) 1 (0.4) 0 (0.05) 1.61 2.98 
HDL cholesterol 18 (10.0) 7 (2.9) 1 (0.5) 0 (0.1) 1.71 3.16 
FPG 19 (13.3) 5 (4.2) 0 (0.5) 0 (0.03) 1.81 2.84 
HOMA%IR 19 (14.3) 3 (6.0) 1 (1.6) 0 (0.4) 2.36 4.08 
TraitNumber of actual (expected) ILRs at LOD threshold
Suggestive LODSignificant LOD
0.591.182.083
MES 12 (9.1) 3 (2.4) 2 (0.4) 1 (0.05) 1.57 2.95 
WC 15 (10.5) 4 (3.3) 1 (0.6) 1 (0.1) 1.78 3.30 
Systolic BP 8 (11.7) 1 (4.0) 0 (0.8) 0 (0.1) 1.96 3.49 
Diastolic BP 10 (10.6) 5 (3.3) 0 (0.6) 0 (0.1) 1.80 3.22 
TG 13 (9.4) 8 (2.6) 1 (0.4) 0 (0.05) 1.61 2.98 
HDL cholesterol 18 (10.0) 7 (2.9) 1 (0.5) 0 (0.1) 1.71 3.16 
FPG 19 (13.3) 5 (4.2) 0 (0.5) 0 (0.03) 1.81 2.84 
HOMA%IR 19 (14.3) 3 (6.0) 1 (1.6) 0 (0.4) 2.36 4.08 
*

Suggestive LOD referred to the LOD score corresponding to one ILR per genome scan expected by chance. Significant LOD referred to the LOD score corresponding to Pgenome-wide = 0.05 at one ILR.

Pgenome-wide < 0.05 by comparing the actual and expected number of ILRs per genome scan estimated by simulation.

This work was supported by grants from the Hong Kong Research Grants Committee; the Chinese University of Hong Kong Strategic Grant Program; the Innovation and Technology Support Fund, Hong Kong (ITS/33/00); and U.S. Public Health Service Grants DK-20595, DK-47486, DK-55889, and DK-58026. G.I.B. is an investigator of the Howard Hughes Medical Institute.

We thank Emily Poon and Patty Tse for technical support and Dr. June Li and Cherry Chiu for help with the recruitment of patients and their family members. We thank William Wen for computational support. We thank all nursing and medical staff at the PWH Diabetes and Endocrine Centre for dedication and professionalism. We thank all patients and their relatives for participating in this study. We are also indebted to the late Professors Robert Turner, Oxford University, and Julian Critchley, The Chinese University of Hong Kong, for inspiration and support.

1.
Reaven GM: Banting Lecture 1988: Role of insulin resistance in human disease.
Diabetes
37
:
1595
–1607,
1988
2.
Alberti KG, Zimmet PZ: Definition, diagnosis and classification of diabetes mellitus and its complications. Part 1. Diagnosis and classification of diabetes mellitus, provisional report of a WHO consultation.
Diabet Med
15
:
539
–553,
1998
3.
Expert Panel on Detection, Evaluation and Treatment of High Blood Cholesterol in Adults: Executive summary of the third report of the National Cholesterol Education Program (NCEP) Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults (Adult Treatment Panel III).
JAMA
285
:
2486
–2497,
2001
4.
Jia WP, Xiang KS, Chen L, Lu JX, Wu YM: Epidemiological study on obesity and its comorbidities in urban Chinese older than 20 years of age in Shanghai, China.
Obes Rev
3
:
157
–165,
2002
5.
Further Study of Risk Factors for Stroke and Coronary Heart Disease Group: The prevalence of metabolic syndrome in a 11 provinces cohort in China.
Zhonghua Yu Fang Yi Xue Za Zhi
36
:
298
–300,
2002
6.
Ford ES, Giles WH, Dietz WH: Prevalence of the metabolic syndrome among US adults: findings from the third National Health and Nutrition Examination Survey.
JAMA
287
:
356
–359,
2002
7.
Meigs JB, Wilson PW, Nathan DM, D’Agostino RB Sr, Williams K, Haffner SM: Prevalence and characteristics of the metabolic syndrome in the San Antonio Heart and Framingham Offspring Studies.
Diabetes
52
:
2160
–2167,
2003
8.
DeFronzo RA, Ferrannini E: Insulin resistance: a multifaceted syndrome responsible for NIDDM, obesity, hypertension, dyslipidemia, and atherosclerotic cardiovascular disease.
Diabetes Care
14
:
173
–194,
1991
9.
Chan JCN, Cheung JCK, Lau EMC, Woo J, Chan AYW, Swaminathan R, Cockram CS: The metabolic syndrome in Hong Kong Chinese: the interrelationships among its components analyzed by structural equation modeling.
Diabetes Care
19
:
953
–959,
1996
10.
Carmelli D, Cardon LR, Fabsitz R: Clustering of hypertension, diabetes, and obesity in adult male twins: same genes or same environments?
Am J Hum Genet
55
:
566
–573,
1994
11.
Hong Y, Pedersen NL, Brismar K, de Faire U: Genetic and environmental architecture of the features of the insulin-resistance syndrome.
Am J Hum Genet
60
:
143
–152,
1997
12.
Liese AD, Mayer-Davis EJ, Tyroler HA, Davis CE, Keil U, Schmidt MI, Brancati FL, Heiss G: Familial components of the multiple metabolic syndrome: the ARIC study.
Diabetologia
40
:
963
–970,
1997
13.
Mitchell BD, Kammerer CM, Mahaney MC, Blangero J, Comuzzie AG, Atwood LD, Haffner SM, Stern MP, MacCluer JW: Genetic analysis of the IRS: pleiotropic effects of genes influencing insulin levels on lipoprotein and obesity measures.
Arterioscler Thromb Vasc Biol
16
:
281
–288,
1996
14.
Rainwater DL, Mitchell BD, Mahaney MC, Haffner SM: Genetic relationship between measures of HDL phenotypes and insulin concentrations.
Arterioscler Thromb Vasc Biol
17
:
3414
–3419,
1997
15.
Perusse L, Rice T, Despres JP, Rao DC, Bouchard C: Cross-trait familial resemblance for body fat and blood lipids: familial correlations in the Quebec Family Study.
Arterioscler Thromb Vasc Biol
17
:
3270
–3277,
1997
16.
Williams JT, Blangero J: Power of variance component linkage analysis to detect quantitative trait loci.
Ann Intern Med
63
:
545
–563,
1999
17.
Kissebah AH, Sonnenberg GE, Myklebust J, Goldstein M, Broman K, James RG, Marks JA, Krakower GR, Jacob HJ, Weber J, Martin L, Blangero J, Comuzzie AG: Quantitative trait loci on chromosomes 3 and 17 influence phenotypes of the metabolic syndrome.
Proc Natl Acad Sci U S A
97
:
14478
–14483,
2000
18.
Arya R, Blangero J, Williams K, Almasy L, Dyer TD, Leach RJ, O’Connell P, Stern MP, Duggirala R: Factors of insulin resistance syndrome-related phenotypes are linked to genetic locations on chromosomes 6 and 7 in nondiabetic Mexican-Americans.
Diabetes
51
:
841
–847,
2002
19.
Loos RJ, Katzmarzyk PT, Rao DC, Rice T, Leon AS, Skinner JS, Wilmore JH, Rankinen T, Bouchard C: Genome-wide linkage scan for the metabolic syndrome in the HERITAGE Family Study.
J Clin Endocrinol Metab
88
:
5935
–5943,
2003
20.
Tang W, Miller MB, Rich SS, North KE, Pankow JS, Borecki IB, Myers RH, Hopkins PN, Leppert M, Arnett DK: Linkage analysis of a composite factor for the multiple metabolic syndrome: the National Heart, Lung, and Blood Institute Family Heart Study.
Diabetes
52
:
2840
–2847,
2003
21.
Francke S, Manraj M, Lacquemant C, Lecoeur C, Lepretre F, Passa P, Hebe A, Corset L, Yan SL, Lahmidi S, Jankee S, Gunness TK, Ramjuttun US, Balgobin V, Dina C, Froguel P: A genome-wide scan for coronary heart disease suggests in Indo-Mauritians a susceptibility locus on chromosome 16p13 and replicates linkage with the metabolic syndrome on 3q27.
Hum Mol Genet
10
:
2751
–2765,
2001
22.
Janus ED:
The Hong Kong Cardiovascular Risk Factor Prevalence Study 1995–1996
. Hong Kong, Department of Clinical Biochemistry, Queen Mary Hospital of Hong Kong,
1997
23.
Ng MCY, So WY, Cox NJ, Lam VKL, Cockram CS, Critchley JAJH, Bell GI, Chan JCN: Genome-wide scan for type 2 diabetes loci in Hong Kong Chinese and confirmation of a susceptibility locus on chromosome 1q21–q25.
Diabetes
53
:
1609
–1613,
2004
24.
International Obesity Task Force:
The Asia-Pacific Perspective: Redefining Obesity and Its Treatment.
Western Pacific Region, World Health Organization,
2000
25.
Boehnke M, Cox NJ: Accurate inference of relationships in sib-pair linkage studies.
Am J Hum Genet
61
:
423
–429,
1997
26.
McPeek MS, Sun L: Statistical tests for detection of misspecified relationships by use of genome-screen data.
Am J Hum Genet
66
:
1076
–1094,
2000
27.
O’Connell JR, Weeks DE: PedCheck: a program for identification of genotype incompatibilities in linkage analysis.
Am J Hum Genet
63
:
259
–266,
1998
28.
Abecasis GR, Cherny SS, Cookson WO, Cardon LR: Merlin—rapid analysis of dense genetic maps using sparse gene flow trees.
Nat Genet
30
:
97
–101,
2002
29.
Sham PC, Purcell S, Cherny SS, Abecasis GR: Powerful regression-based quantitative-trait linkage analysis of general pedigrees.
Am J Hum Genet
71
:
238
–253,
2002
30.
Kong A, Cox NJ: Allele-sharing models: LOD scores and accurate linkage tests.
Am J Hum Genet
61
:
1179
–1188,
1997
31.
Wiltshire S, Cardon LR, McCarthy MI: Evaluating the results of genomewide linkage scans of complex traits by locus counting.
Am J Hum Genet
71
:
1175
–1182,
2002
32.
Lander ES, Kruglyak L: Genetic dissection of complex traits: guidelines for interpreting and reporting linkage results.
Nat Genet
11
:
241
–247,
1995
33.
Almasy L, Blangero J: Multipoint quantitative-trait linkage analysis in general pedigrees.
Am J Hum Genet
62
:
1198
–1211,
1998
34.
McCarthy MI: Growing evidence for diabetes susceptibility genes from genome scan data.
Curr Diab Rep
3
:
159
–167,
2003
35.
Langefeld CD, Wagenknecht LE, Rotter JI, Williams AH, Hokanson JE, Saad MF, Bowden DW, Haffner S, Norris JM, Rich SS, Mitchell BD: Linkage of the metabolic syndrome to 1q23–q31 in Hispanic families: the Insulin Resistance Atherosclerosis Study family study.
Diabetes
53
:
1170
–1174,
2004
36.
Weyer C, Wolford JK, Hanson RL, Foley JE, Tataranni PA, Bogardus C, Pratley RE: Subcutaneous abdominal adipocyte size, a predictor of type 2 diabetes, is linked to chromosome 1q21–q23 and is associated with a common polymorphism in LMNA in Pima Indians.
Mol Genet Metab
72
:
231
–238,
2001
37.
Feitosa MF, Borecki IB, Rich SS, Arnett DK, Sholinsky P, Myers RH, Leppert M, Province MA: Quantitative-trait loci influencing body-mass index reside on chromosomes 7 and 13: the National Heart, Lung, and Blood Institute Family Heart Study.
Am J Hum Genet
70
:
72
–82,
2002
38.
Pajukanta P, Nuotio I, Terwilliger JD, Porkka KV, Ylitalo K, Pihlajamaki J, Suomalainen AJ, Syvanen AC, Lehtimaki T, Viikari JS, Laakso M, Taskinen MR, Ehnholm C, Peltonen L: Linkage of familial combined hyperlipidaemia to chromosome 1q21–q23.
Nat Genet
18
:
369
–373,
1998
39.
Coon H, Myers RH, Borecki IB, Arnett DK, Hunt SC, Province MA, Djousse L, Leppert MF: Replication of linkage of familial combined hyperlipidemia to chromosome 1q with additional heterogeneous effect of apolipoprotein A-I/C-III/A-IV locus: the NHLBI Family Heart Study.
Arterioscler Thromb Vasc Biol
20
:
2275
–2280,
2000
40.
Pei W, Baron H, Muller-Myhsok B, Knoblauch H, Al-Yahyaee SA, Hui R, Wu X, Liu L, Busjahn A, Luft FC, Schuster H: Support for linkage of familial combined hyperlipidemia to chromosome 1q21–q23 in Chinese and German families.
Clin Genet
57
:
29
–34,
2000
41.
Broeckel U, Hengstenberg C, Mayer B, Holmer S, Martin LJ, Comuzzie AG, Blangero J, Nurnberg P, Reis A, Riegger GA, Jacob HJ, Schunkert H: A comprehensive linkage analysis for myocardial infarction and its related risk factors.
Nat Genet
30
:
210
–214,
2002
42.
Elbein SC, Hasstedt SJ: Quantitative trait linkage analysis of lipid-related traits in familial type 2 diabetes: evidence for linkage of triglyceride levels to chromosome 19q.
Diabetes
51
:
528
–535,
2002
43.
Pajukanta P, Lilja HE, Sinsheimer JS, Cantor RM, Lusis AJ, Gentile M, Duan XJ, Soro-Paavonen A, Naukkarinen J, Saarela J, Laakso M, Ehnholm C, Taskinen MR, Peltonen L: Familial combined hyperlipidemia is associated with upstream transcription factor 1 (USF1).
Nat Genet
36
:
371
–376,
2004
44.
Rao DC, Province MA, Leppert MF, Oberman A, Heiss G, Ellison RC, Arnett DK, Eckfeldt JH, Schwander K, Mockrin SC, Hunt SC: A genome-wide affected sibpair linkage analysis of hypertension: the HyperGEN network.
Am J Hypertens
16
:
148
–150,
2003
45.
Palmer LJ, Buxbaum SG, Larkin E, Patel SR, Elston RC, Tishler PV, Redline S: A whole-genome scan for obstructive sleep apnea and obesity.
Am J Hum Genet
72
:
340
–350,
2003
46.
Imperatore G, Knowler WC, Pettitt DJ, Kobes S, Fuller JH, Bennett PH, Hanson RL: A locus influencing total serum cholesterol on chromosome 19p: results from an autosomal genomic scan of serum lipid concentrations in Pima Indians.
Arterioscler Thromb Vasc Biol
20
:
2651
–2656,
2000
47.
Hager J, Dina C, Francke S, Dubois S, Houari M, Vatin V, Vaillant E, Lorentz N, Basdevant A, Clement K, Guy-Grand B, Froguel P: A genome-wide scan for human obesity genes reveals a major susceptibility locus on chromosome 10.
Nat Genet
20
:
304
–308,
1998
48.
Comuzzie AG, Funahashi T, Sonnenberg G, Martin LJ, Jacob HJ, Black AE, Maas D, Takahashi M, Kihara S, Tanaka S, Matsuzawa Y, Blangero J, Cohen D, Kissebah A: The genetic basis of plasma variation in adiponectin, a global endophenotype for obesity and the metabolic syndrome.
J Clin Endocrinol Metab
86
:
4321
–4325,
2001