Recent studies have demonstrated the importance of sex effects on the underlying genetic architecture of insulin-related traits. To explore sex-specific genetic effects on fasting insulin, we tested for genotype-by-sex interaction and conducted linkage analysis of fasting insulin in Hypertension Genetic Epidemiology Network families. Hypertensive siblings and their first-degree relatives were recruited from five field centers. We performed a genome scan for quantitative trait loci influencing fasting insulin among 1,505 European Americans and 1,616 African Americans without diabetes. Sex-stratified linear regression models, adjusted for race, center, and age, were explored. The Mammalian Genotyping Service typed 391 microsatellite markers, spaced roughly 9 cM. Variance component linkage analysis was performed in SOLAR using ethnic-specific marker allele frequencies and multipoint IBDs calculated in MERLIN. We detected a quantitative trait locus influencing fasting insulin in female subjects (logarithm of odds [LOD] = 3.4) on chromosome 2 at 95 cM (between GATA69E12 and GATA71G04) but not in male subjects (LOD = 0.0, P for interaction = 0.007). This sex-specific signal at 2p13.2 was detected in both European-American (LOD = 2.1) and African-American (LOD = 1.2) female subjects. Our findings overlap with several other linkage reports of insulin-related traits and demonstrate the importance of considering complex context-dependent interactions in the search for insulin-related genes.

Insulin resistance and hyperinsulinemia are associated with glucose intolerance, lipid abnormalities, and hypertension (1). Hyperinsulinemia and its associated metabolic abnormalities are predictors of incident type 2 diabetes (2,3) and cardiovascular disease (4,5). Insulin affects multiple anabolic pathways with effects on glucose homeostasis, protein synthesis, and fat storage; it is likely that some of these effects vary in male and female subjects. Fasting insulin concentration correlates with measures of insulin resistance using whole-body glucose uptake in clamp studies in subjects with normoglycemia or impaired glucose tolerance (6), and, therefore, it has been used in population studies as a proxy for β-cell function and insulin resistance.

The genetic basis for fasting insulin levels and glucose homeostasis have been demonstrated in several studies (713). The importance of sex effects on the underlying genetic architecture of insulin-related traits such as human fatness (1416) has also been recently documented. Previous analysis of the Hypertension Genetic Epidemiology Network (HyperGEN) data revealed a substantial genetic component of variation in fasting insulin concentration but failed to show genome-wide significant linkage to this trait (8). In this study, we examined the sex-specific genetic regulation of fasting insulin levels in HyperGEN families.

HyperGEN methods have previously been reported (17). In brief, hypertensive sibships and their offspring and/or parents were recruited from five field centers. The present report includes 1,505 European-American and 1,616 African-American participants without type 2 diabetes. Additionally, a random sample of age-matched individuals from the same base populations (182 Caucasians and 198 African-American subjects) was included to estimate population parameters. The sample of examined individuals included 2,433 relative pairs: 360 parent-offspring pairs, 1,213 sibling pairs, 534 avuncular pairs (aunt/uncle/niece/nephew), and 152 first-cousin pairs (online appendix Table 1 [available at http://diabetes.diabetesjournals.org]).

Phenotype and covariates.

Venipuncture was performed on fasting participants following standard protocol (17). Anthropometric measurements were obtained as previously reported (17). Serum concentration of insulin was measured using an automated immunoassay instrument. All other variables were collected through interviews performed by trained interviewers. Informed consent was obtained from all participants, and this project was approved by the institutional review boards of all participating institutions.

Genotypes.

Genotyping was provided by the National Heart, Blood, and Lung Institute Mammalian Genotyping Service (Marshfield, WI). Details on gel preparation and PCR conditions are available from the Mammalian Genotyping Service website (http://research.marshfieldclinic.org/genetics/home/index.asp). The Cooperative Human Linkage Center screening set 8 was used, which included 391 microsatellite markers equally spaced (∼9 cM distance) throughout the genome. The average marker heterozygosity was 77.7%. Analyses and assignment of the marker alleles were done using computerized algorithms. Relationship status among the purportedly full sibs was tested using ASPEX, a likelihood-based method (18), and MERLIN (19). We used the University of California Santa Cruz (http://genome.uscs.edu/) and Online Mendelian Inheritance in Man (http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=OMIM) to determine the cytogenic location of markers and to search for candidate genes.

Analytical methods.

Variance component linkage analysis (SOLAR version 2.1.2) (20) was used to detect and localize quantitative trait loci (QTL) influencing fasting insulin levels among the combined sample of European-American and African-American participants and stratified by race and sex. We computed exact conditional probabilities using the Lander-Green algorithm as implemented in MERLIN (19). Allele frequencies from founders were calculated separately in African-American and European-American groups.

Genotype-by-sex interaction analysis.

Genotype-by-sex interaction on fasting insulin levels was explored using a three-step strategy. We initially tested for the evidence of additive genotype-by-sex interaction, by accounting for the genetic covariance among female and male relative pairs. In these analyses, the likelihood of a model including genotype-by-sex interaction is compared with the likelihood of restricted models in which such interactions are excluded. We tested for differential additive genetic effects among female and male subjects [genetic correlation (rhoG) ≠ 1]; for differences in the magnitude of the genetic effects among female and male subjects [genetic variance (σg) ≠ among female and male subjects]; and for differences in residual environmental interaction with sex status [environment variances (σe) ≠ among female and male subjects]. Second, we performed linkage analysis for male and female subjects (sex-stratified subsets) and compared the model results with the results of the combined sample to restrict the number of regions considered in the QTL-specific genotype-by-sex interaction analysis. Finally, we examined the evidence for a QTL-specific genotype-by-sex interaction at regions identified in the linkage analysis. The likelihood of the model including QTL-specific genotype-by-sex interaction was compared with the likelihood of the restricted model in which such interaction was excluded using a likelihood-ratio test (21).

Application to HyperGEN data.

We excluded 10 individuals who were part of monozygotic twin pairs. In addition, six European-American individuals were excluded from Alabama because a sample size of six was too small to consider as a separate race-center grouping. The insulin variable was logarithmically transformed, and outliers (any observation >4 SDs from the mean and at least 1 SD from the nearest data point) were also excluded (number excluded varies by analysis, less than five). We screened covariates for statistical significance using backward- and forward-stepwise linear regression implemented in SAS 8.02 (Cary, NC). All covariates whose effects were significant at the P ≤ 0.10 were retained in subsequent analysis. Residuals were generated for models and used in all genetic analyses. We considered two different models of covariate adjustment. In model 1, fasting insulin phenotype was stratified by sex and race and adjusted for the effects of age, study center, and age-by-sex interactions. In model 2, we adjusted for additional covariates such as ever smoker, ever drinker, self-reported physical activity (number of hours of exercise per week), and hypertension status in male subjects and for ever smoker, ever drinker, self-reported physical activity, hypertension status, and estrogen use in female subjects. We did not adjust for BMI and other obesity-related traits because these traits are highly genetically correlated with the insulin trait (rhoG = 0.64, P < 0.001).

Because a nonnormal trait distribution increases type I error in the variance component method, we calculated the distribution of logarithm of odds (LOD) scores under the assumption of multivariate normality, using 10,000 replicates and simulation methods (“-lodadj ” command in SOLAR). We determined the robust LOD score by multiplying the observed LOD score by a correction coefficient, calculated by regressing the expected LOD scores on the observed simulated LOD scores (22). Only robust LOD scores are reported in this study. In addition, we determined the 1-LOD unit drop support interval for all linkage results with a LOD score ≥1.8 (23).

Overall, ∼75% of subjects had hypertension (Table 1). Female and male subjects had similar age and physical activity levels within race, but female subjects had lower alcohol consumption and tobacco exposure than males. African-American female subjects had the highest fasting insulin levels among all groups.

The heritability of fasting insulin concentration was ∼0.48 ± 0.09 for female subjects and 0.40 ± 0.13 for males, after adjusting for covariates. The additive genetic correlation (rhoG) between female and male subjects in the combined sample was 0.65 ± 0.21 (P = 0.07), and the additive genetic variance of males was 4.05 ± 0.66 and of female subjects 4.10 ± 0.40 (P = 0.96). In analyses stratified by race, the rhoG was 0.49 (P = 0.02) for European-American subjects and 0.64 (P = 0.19) for African-American subjects, with no differences in the additive genetic variance of male and female subjects in both racial groups.

As hypothesis testing for genotype-by-sex interaction is notably underpowered and we found significant evidence for aggregate interaction in European-American subjects, we proceeded with sex-specific linkage analysis and QTL-specific interaction hypothesis. QTL-specific interaction hypothesis testing was performed only in those regions where suggestive evidence for linkage was observed in the stratified linkage analyses in either male or female subjects. The results of these analyses are reported in Table 2 (for LOD scores ≥1.8), and an extended table with all sex-specific and combined sample LOD scores can be found in online appendix Table 2. A QTL influencing variance of fasting insulin concentration on chromosome 2 at 95 cM (between markers GATA69E12 and GATA71G04) was detected in female subjects (adjusted LOD = 3.0, model 1) but not in males (LOD = 0.0) (interaction P value = 0.0071) (Table 2 and Fig. 1). The 1-LOD support interval spanned from 78 to 101 cM. This sex-specific signal at 2p13.2 was detected in both European-American (LOD = 2.0) and African-American (LOD = 1.2) female subjects. The LOD was greatly reduced in the combined model of female and male subjects (LOD = 0.7) and, therefore, would not be detected without the gene-by-sex interaction analysis.

We also detected suggestive evidence of linkage (23) on chromosomes 1q, 4q, 7q, 9q, 13q, 17q, and 19p and a second linkage peak on chromosome 2p (Table 2). Some of the linkages were detected only in male subjects (i.e., 1p32.2). The QTL-specific interaction P values for these analyses are displayed in online appendix Table 2. Supporting evidence of linkage to the regions identified in our study is summarized in Table 3. Supplemental figures display linkage results for all chromosomes (supplemental online Fig. 1).

We detected a QTL-specific genotype-by-sex interaction influencing variation of fasting insulin concentration in female subjects (LOD = 3.4) on chromosome 2p. This sex-specific linkage signal was detected in both African-American and European-American families and is substantially higher than the linkage signal from the total sample (LOD = 0.7). Interestingly, this region of the genome would not be detected without considering gene-by-sex interactions, stressing the importance of considering complex context-dependent interactions in the search for insulin-related genes.

Sex-specific genetic effects have been previously described for fat deposition and distribution (15,16) and blood pressure (24), traits associated with insulin resistance. It is possible that the regulation of genes influencing insulin is modulated by environment factors such as the hormonal environment in men and women. In addition, these sex-specific effects may be due to gene-gene interaction including interactions with genes in sex chromosomes.

The region of 2p overlaps with positive findings for several insulin-related traits and has been detected in multiple ethnic populations (8,11,13,25,26) (Table 3). For example, suggestive linkage on chromosome 2 at 88 cM (LOD = 2.3) for the homeostatis model assessment of insulin resistance was described for Japanese and Chinese descents enrolled in the Stanford Asian Pacific Program in Hypertension and Insulin Resistance study (26). Among participants from four different ethnic groups (Hispanics, African Americans, European Americans, and Asians) in the Family Blood Pressure Program project, a meta-analysis of fasting glucose and insulin concentration identified suggestive linkage to chromosome 2p at 99 cM (11). Studies including only subjects of European ancestry (the Framingham Offspring Study and the Finland-U.S. Investigation of Non-Insulin-Dependent Diabetes Mellitus Genetics Study) have also identified suggestive evidence for linkage on 2p13 (13,25). Except for our study, none of these previous studies have accounted for gene-by-sex interactions in their analysis, and, therefore, one wonders whether their ability to detect a genome-wide linkage was hampered by not considering complex gene-environment interactions.

Among 172 genes present in the 1-LOD support region, 4 are involved in pathways of glucose or complex carbohydrate metabolism (glutamine-fructose-6-phosphate transaminase 1 [GFPT1], glucosidase I [GCS1], hexokinase 2 [HK2], and N-acetylglucosamine kinase [NAGK]). The GFPT1 gene encodes a rate-limiting enzyme of the hexosamine pathway and this pathway has been implicated in the pathogenesis of insulin resistance and diabetes vascular complications (27). However, studies have failed to show association of GFPT1 variants with diabetes and diabetic nephropathy (28,29).

Other candidate genes located in the 1-LOD unit support interval of the 2p12 signal are four genes from the Reg family, which are involved in regulating β-cell islets mass (30). Regenerating islet-derived 1 β gene A (REG1A) encodes a protein secreted by the exocrine pancreas and has been associated with islet cell regeneration. Another gene located at 2q13 is the Alstrom syndrome 1 (ALMS1) gene that is associated with an autosomal recessive syndrome characterized by childhood obesity, type 2 diabetes, and neurosensory degeneration (31). However, a study of coding ALMS1 variants failed to detect any significant association with type 2 diabetes (32).

We identified several other regions with suggestive evidence of linkage to fasting insulin concentration (Table 2). Most of these regions have been previously described (Table 3). Of interest is the 9q32 region, nearby the region where a putative gene for diabetes (carboxyl ester lipase [CEL] 9q34.3) has recently been identified (33). CEL product is involved in the hydrolysis and absorption of dietary lipids. The enzyme is a component of the exocrine pancreas, and the findings link β-cell dysfunction to exocrine pancreas injury (33). In our study, the highest LOD score at this region was detected in African-American female subjects, who also had the highest mean fasting insulin concentration.

We also identified suggestive linkage of fasting insulin concentration to 1q32.2, a region previously identified only for fasting glucose and homeostatis model assessment of insulin resistance traits in studies of European Americans and American Indians (7,9,25,34). Other regions of suggestive evidence of linkage were 2p21, 4q13.3, 7q31.33, 9q32, 13q12.12, and 17q25.3.

In summary, we detected a QTL-specific gene-by-sex interaction influencing variation of fasting insulin concentration at 2p13.2. The genome-wide linkage was specific to female subjects and was present across racial backgrounds. These results demonstrate the importance of considering complex context-dependent interactions in our search for insulin related genes.

FIG. 1.

Chromosome 2 multipoint LOD scores for fasting insulin by sex.

FIG. 1.

Chromosome 2 multipoint LOD scores for fasting insulin by sex.

TABLE 1

Descriptive characteristics of HyperGEN participants by race and sex

Male subjects
Female subjects
European AmericanAfrican AmericanEuropean AmericanAfrican American
n 716 547 789 1,069 
No. of relative pairs 565 221 559 759 
Age (years) 57 ± 13 48 ± 13 57 ± 13 48 ± 13 
Fasting insulin (mU/l) 8.2 ± 5.7 8.7 ± 7.7 7.2 ± 4.7 10.9 ± 9.9 
Hypertensive (%) 75 75 74 81 
Smoking     
    Current 62 (9) 208 (38) 72 (9) 238 (22) 
    Ever 400 (56) 372 (68) 289 (37) 449 (42) 
Alcohol consumption     
    Current 273 (38) 232 (42) 186 (24) 166 (16) 
    Ever 372 (52) 389 (71) 237 (30) 342 (32) 
Physical activity (mean hours/week) 2.8 ± 3.0 2.7 ± 2.7 2.4 ± 2.8 2.6 ± 2.7 
Ever estrogen users — — 390 (49) 360 (34) 
Male subjects
Female subjects
European AmericanAfrican AmericanEuropean AmericanAfrican American
n 716 547 789 1,069 
No. of relative pairs 565 221 559 759 
Age (years) 57 ± 13 48 ± 13 57 ± 13 48 ± 13 
Fasting insulin (mU/l) 8.2 ± 5.7 8.7 ± 7.7 7.2 ± 4.7 10.9 ± 9.9 
Hypertensive (%) 75 75 74 81 
Smoking     
    Current 62 (9) 208 (38) 72 (9) 238 (22) 
    Ever 400 (56) 372 (68) 289 (37) 449 (42) 
Alcohol consumption     
    Current 273 (38) 232 (42) 186 (24) 166 (16) 
    Ever 372 (52) 389 (71) 237 (30) 342 (32) 
Physical activity (mean hours/week) 2.8 ± 3.0 2.7 ± 2.7 2.4 ± 2.8 2.6 ± 2.7 
Ever estrogen users — — 390 (49) 360 (34) 

Data are means ± SD or n (%) unless otherwise indicated.

TABLE 2

Estimated LOD scores suggestive of linkage (LOD ≥1.8) in the combined sample and stratified by sex for multipoint quantitative trait linkage analyses of fasting insulin concentration

SampleChromosomecMChromosomal regionLOD score
Combined male subjects     
    Model 1 226 1q32.2 2.10 
Combined female subjects     
    Model 1 94 2p13.2 2.95 
    Model 2  95  3.39 
European-American female subjects     
    Model 1  89  2.00 
    Model 2  95  2.14 
African-American female subjects     
    Model 1 70 2p21 2.09 
Combined female subjects    2.51 
    Model 1  70  2.09 
    Model 2  70   
Combined all male and female subjects     
    Model 2 81 4q13.3 2.45 
African-American female subjects     
    Model 2 128 7q31.33 1.88 
African-American female subjects     
    Model 2 120 9q32 1.82 
African-American male subjects     
    Model 1 13 13q12.12 2.02 
    Model 2  12  1.43 
Combined all male and female subjects     
    Model 1 17 116 17q25.3 2.33 
    Model 2  118  2.06 
Combined all male and female subjects     
    Model 2 19 19p13.3 1.98 
SampleChromosomecMChromosomal regionLOD score
Combined male subjects     
    Model 1 226 1q32.2 2.10 
Combined female subjects     
    Model 1 94 2p13.2 2.95 
    Model 2  95  3.39 
European-American female subjects     
    Model 1  89  2.00 
    Model 2  95  2.14 
African-American female subjects     
    Model 1 70 2p21 2.09 
Combined female subjects    2.51 
    Model 1  70  2.09 
    Model 2  70   
Combined all male and female subjects     
    Model 2 81 4q13.3 2.45 
African-American female subjects     
    Model 2 128 7q31.33 1.88 
African-American female subjects     
    Model 2 120 9q32 1.82 
African-American male subjects     
    Model 1 13 13q12.12 2.02 
    Model 2  12  1.43 
Combined all male and female subjects     
    Model 1 17 116 17q25.3 2.33 
    Model 2  118  2.06 
Combined all male and female subjects     
    Model 2 19 19p13.3 1.98 

Model 1 is adjusted for age and center within sex and race. Model 2 is adjusted for age, center, ever smoker, ever drinker, self-reported physical activity, and hypertension status in male subjects and adjusted for ever smoker, ever drinker, self-reported physical activity, hypertension status, and estrogen use in female subjects, both within sex and race.

TABLE 3

Supporting evidence of linkage to fasting insulin

Our evidence
Supporting evidence
Maximum LOD scoreChromosome regionPhenotypeLOD or P valueCytogenic location or nearest marker location (cM)References
2.1 at 226 cM 1q32.2 Fasting glucose (Pima Indians) 1.5* 217.9 Pratley et al., 1998 
  Fasting glucose 1.8 218 Meigs et al., 2002 
  HOMA-IR 1.2 224 Panhuysen et al., 2003 
  Diabetes (AA) 1.9 226 Avery et al., 2004 
      Longitudinal changes in fasting glucose 5.2 × 10−8 176–218 Jun et al., 2003 
2.1–3.4 at 95 cM 2p13.2 Fasting glucose 1.3 108 Meigs et al., 2002 
  20-year mean fasting glucose 1.0 101 Meigs et al., 2002 
  HOMA-IR 2.3 88 Chiu et al., 2005 
  Fasting Insulin (EA) 1.4 99 Freedman et al., 2005 
  Fasting glucose (AA) 1.9 96 Freedman et al., 2005 
  Fasting insulin 2.4 99.41 An et al., 2005 
  HOMA-IR 2.3–2.6 99.41–103.16 An et al., 2005 
2.5 at 81 cM 4q13.3 Fasting glucose 1.5* 77.6 Pratley et al., 1998 
  Fasting insulin 1.5 77.6 Pratley et al., 1998 
  Fasting glucose 1.9* 94.3 Pratley et al., 1998 
  HOMA (EA) 2.5 81 Freedman et al., 2005 
  Fasting insulin (EA) 2.3 81 Freedman et al., 2005 
  Bivariate glucose/insulin (EA) 1.2 81 Freedman et al., 2005 
  Fasting insulin 2.3–2.5 78.43–88.35 An et al., 2005 
1.9 at 160 cM 7q36.1 HOMA (AA) 1.7 182 Freedman et al., 2005 
  Fasting glucose 1.8 163 Li et al., 2004 
  Fasting glucose 2.3 163 An et al., 2005 
  Fasting insulin 2.3 163 An et al., 2005 
  HOMA-IR 3.2–2.6 163.03–173.71 An et al., 2005 
1.8 at 120 cM 9q32 2-h glucose (Pima Indians) 1.3 107.4 Pratley et al., 1998 
  Type 2 diabetes 1.4 140 Iwasaki et al., 2003 
  Acute insulin response to glucose 2.0 114.5 Watanabe et al., 2000 
2.0 at 9 cM 13q12.12 Bivariate: glucose and insulin (AA) 1.5 12 Freedman et al., 2005 
2.3 at 116 cM 17q25.3 Type 2 diabetes/IGH† 1.5 109 Hsueh et al., 2003 
2.0 at 0 cM 19p13.3 Fasting insulin (Pima Indians) 1.3 18 Pratley et al., 1998 
  Large young-onset diabetes 1.3 36.9 Vionnet et al., 2000 
  Bivariate glucose/insulin (AA) 2.5 11 Freedman et al., 2005 
  HOMA-IR (AA) 2.3 11 Freedman et al., 2005 
  Fasting insulin (AA) 2.3 11 Freedman et al., 2005 
  Fasting insulin 0.0036 59.6 Lakka et al., 2003 
  Diabetes (EA) 2.0 Avery et al., 2004 
Our evidence
Supporting evidence
Maximum LOD scoreChromosome regionPhenotypeLOD or P valueCytogenic location or nearest marker location (cM)References
2.1 at 226 cM 1q32.2 Fasting glucose (Pima Indians) 1.5* 217.9 Pratley et al., 1998 
  Fasting glucose 1.8 218 Meigs et al., 2002 
  HOMA-IR 1.2 224 Panhuysen et al., 2003 
  Diabetes (AA) 1.9 226 Avery et al., 2004 
      Longitudinal changes in fasting glucose 5.2 × 10−8 176–218 Jun et al., 2003 
2.1–3.4 at 95 cM 2p13.2 Fasting glucose 1.3 108 Meigs et al., 2002 
  20-year mean fasting glucose 1.0 101 Meigs et al., 2002 
  HOMA-IR 2.3 88 Chiu et al., 2005 
  Fasting Insulin (EA) 1.4 99 Freedman et al., 2005 
  Fasting glucose (AA) 1.9 96 Freedman et al., 2005 
  Fasting insulin 2.4 99.41 An et al., 2005 
  HOMA-IR 2.3–2.6 99.41–103.16 An et al., 2005 
2.5 at 81 cM 4q13.3 Fasting glucose 1.5* 77.6 Pratley et al., 1998 
  Fasting insulin 1.5 77.6 Pratley et al., 1998 
  Fasting glucose 1.9* 94.3 Pratley et al., 1998 
  HOMA (EA) 2.5 81 Freedman et al., 2005 
  Fasting insulin (EA) 2.3 81 Freedman et al., 2005 
  Bivariate glucose/insulin (EA) 1.2 81 Freedman et al., 2005 
  Fasting insulin 2.3–2.5 78.43–88.35 An et al., 2005 
1.9 at 160 cM 7q36.1 HOMA (AA) 1.7 182 Freedman et al., 2005 
  Fasting glucose 1.8 163 Li et al., 2004 
  Fasting glucose 2.3 163 An et al., 2005 
  Fasting insulin 2.3 163 An et al., 2005 
  HOMA-IR 3.2–2.6 163.03–173.71 An et al., 2005 
1.8 at 120 cM 9q32 2-h glucose (Pima Indians) 1.3 107.4 Pratley et al., 1998 
  Type 2 diabetes 1.4 140 Iwasaki et al., 2003 
  Acute insulin response to glucose 2.0 114.5 Watanabe et al., 2000 
2.0 at 9 cM 13q12.12 Bivariate: glucose and insulin (AA) 1.5 12 Freedman et al., 2005 
2.3 at 116 cM 17q25.3 Type 2 diabetes/IGH† 1.5 109 Hsueh et al., 2003 
2.0 at 0 cM 19p13.3 Fasting insulin (Pima Indians) 1.3 18 Pratley et al., 1998 
  Large young-onset diabetes 1.3 36.9 Vionnet et al., 2000 
  Bivariate glucose/insulin (AA) 2.5 11 Freedman et al., 2005 
  HOMA-IR (AA) 2.3 11 Freedman et al., 2005 
  Fasting insulin (AA) 2.3 11 Freedman et al., 2005 
  Fasting insulin 0.0036 59.6 Lakka et al., 2003 
  Diabetes (EA) 2.0 Avery et al., 2004 
*

Single-point LOD. †Among nondiabetic subjects. AA, African American; EA, European American; HOMA, homeostasis model assessment; HOMA-IR, HOMA of insulin resistance; IGH, impaired glucose homeostasis.

Additional information for this article can be found in an online appendix at available at http://diabetes.diabetesjournals.org.

The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked “advertisement” in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.

This hypertension network is funded by cooperative agreements (U10) with National Heart, Blood, and Lung Institute (HL54471, HL54472, HL54473, HL54495, HL54496, HL54497, HL54509, and HL54515).

We acknowledge the participants of the HyperGEN Study. We also acknowledge the HyperGEN participating institutions and principal staff: Network Center/University of Utah Field Center: Steven C. Hunt, Roger R. Williams (deceased), Hilary Coon, Paul N. Hopkins, Janet Hood, Lily Wu, and Jan Skuppin; University of Alabama at Birmingham Field Center: Albert Oberman, Cora E. Lewis, Michael T. Weaver, Phillip Johnson, Susan Walker, and Christie Oden; Boston University/Framingham Field Center: R. Curtis Ellison, Richard H. Myers, Yuqing Zhang, Luc Djoussé, Jemma B. Wilk, and Greta Lee Splansky; University of Minnesota Field Center: Donna Arnett, Aaron R. Folsom, Mike Miller, Jim Pankow, Gregory Feitl, and Barb Lux; University of North Carolina Field Center: Gerardo Heiss, Barry I. Freedman, Kari North, Kathryn Rose, and Amy Haire; Data Coordinating Center, Washington University: D.C. Rao, Michael A. Province, Ingrid B. Borecki, Avril Adelman, Derek Morgan, Karen Schwander, David Lehner, Aldi Kraja, and Stephen Mandel; Central Biochemistry Lab, University of Minnesota: John H. Eckfeldt, Ronald C. McGlennen, Michael Y. Tsai, Catherine Leiendecker-Foster, Greg Rynders, and Jean Bucksa; Molecular Genetics Laboratory, University of Utah: Mark Leppert, Steven C. Hunt, Jean-Marc Lalouel, and Robert Weiss; and National Heart, Lung, and Blood Institute: Susan E. Old, Millicent Higgins (retired), Cashell Jaquish, Dina Paltoo, Martha Lundberg, andMariana Gerschenson. We also thank the University of Minnesota Supercomputing Institute for Digital Simulation and Advanced Computation for use of the IBM SP supercomputer.

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Supplementary data