PMCCPMCCPMCC

Search tips
Search criteria 

Advanced

 
Logo of nihpaAbout Author manuscriptsSubmit a manuscriptHHS Public Access; Author Manuscript; Accepted for publication in peer reviewed journal;
 
Cancer Epidemiol Biomarkers Prev. Author manuscript; available in PMC 2010 July 2.
Published in final edited form as:
PMCID: PMC2896274
NIHMSID: NIHMS117296

IGF-I and IGFBP-3 polymorphisms in relation to circulating levels among African American and Caucasian women

Abstract

Circulating insulin-like growth factor-one (IGF-I) and IGF binding protein-3 (IGFBP-3) levels have been associated with common diseases. Although family-based studies suggest that genetic variation contributes to circulating IGF-I and IGFBP-3 levels, analyses of associations with multiple IGF-I and IGFBP-3 single nucleotide polymorphisms (SNPs) have been limited, especially among African Americans. We evaluated 30 IGF-I and 15 IGFBP-3 SNPs and estimated diplotypes in association with plasma IGF-I and IGFBP-3 among 984 premenopausal African American and Caucasian women. In both races, IGFBP-3 rs2854746 (Ala32Gly) was positively associated with plasma IGFBP-3 (CC versus GG mean difference among Caucasians = 631 ng/ml, 95% confidence interval: 398, 864; African Americans = 897 ng/ml, 95% confidence interval: 656, 1138), and IGFBP-3 diplotypes with the rs2854746 GG genotype had lower mean IGFBP-3 levels than referent diplotypes with the CG genotype, while IGFBP-3 diplotypes with the CC genotype had higher mean IGFBP-3 levels. IGFBP-3 rs2854744 (−202 A/C) was in strong linkage disequilibrium with rs2854746 in Caucasians only, but was associated with plasma IGFBP-3 in both races. Eight additional IGFBP-3 SNPs were associated with 5% or greater differences in mean IGFBP-3 levels, with generally consistent associations between races. Twelve IGF-I SNPs were associated with 10% or greater differences in mean IGF-I levels, but associations were generally discordant between races. Diplotype associations with plasma IGF-I did not parallel IGF-I SNP associations. Our study supports that common IGFBP-3 SNPs, especially rs2854746, influence plasma IGFBP-3 levels among African Americans and Caucasians, but provides less evidence that IGF-I SNPs affect plasma IGF-I levels.

Keywords: insulin-like growth factor I, insulin-like growth factor binding protein 3, genetic polymorphisms, women, African Americans

INTRODUCTION

Insulin-like growth factor-one (IGF-I), a peptide with structural similarities to insulin, has been implicated in many biologic processes, including cell cycle regulation, differentiation, proliferation, hormone secretion, and apoptosis. IGF-binding proteins (IGFBPs) help regulate the activity of IGFs by influencing their bioavailablity and degradation, and they may also have independent effects through interactions with cell surface molecules (1, 2). IGFBP-3 binds approximately 90% of circulating IGF-I (3) and has also been reported to inhibit growth and promote apoptosis (4, 5) independent of its effects on IGF-I.

Circulating IGF-I levels, and to a lesser extent IGFBP-3 levels, have been studied in association with cardiovascular disease, diabetes, and cancer (6-8). Estimates from twin- or family-based studies suggest that genetic factors may account for up to 50% of the inter-individual variation in plasma IGF-I levels (9, 10) and up to 60% of the variation in plasma IGFBP-3 levels (9, 11). In adults, age is the nongenetic factor most consistently associated with IGF-I blood levels, with lower levels associated with advancing age (3, 12-21). Women have lower circulating IGF-I (13, 16, 17, 19, 21, 22) but higher IGFBP-3 levels (13, 16, 17, 21, 22) than men, and two studies have suggested that African American women have higher circulating IGF-I levels than Caucasian women (17, 23).

Our research goal was to investigate relations between IGF-I and IGFBP-3 polymorphisms and their circulating protein levels among African American and Caucasian women. Prior analysis of dense single nucleotide polymorphisms (SNPs) and IGF-I and IGFBP-3 levels among African Americans has been limited to the Multiethnic Cohort Study, which included a random sample of about 150 African Americans in their IGF SNP analyses (24). We selected 45 SNPs in IGF-I and IGFBP-3 and examined whether these SNPs and their estimated diplotypes (paired haplotypes) were associated with plasma IGF-I and IGFBP-3 levels in premenopausal African American and Caucasian women who participated in the National Institute for Environmental Health Sciences (NIEHS) Uterine Fibroid Study (UFS).

MATERIALS AND METHODS

Study Population

The current study population consisted of 984 premenopausal women (582 African Americans and 402 Caucasians) that participated in the NIEHS UFS and had available DNA samples.. The UFS was designed to estimate the prevalence of uterine leiomyomata (fibroids) among African American and Caucasian women and to evaluate potential etiologic factors for fibroids. Details of the parent study were previously described (25, 26). Briefly, a random sample of 2,384 George Washington University female health plan members, aged 35 to 49, was identified for potential enrollment into the parent study (25, 26). The study was approved by Institutional Review Boards at NIEHS and George Washington University, and the consent form specified use of specimens for genetic polymorphism analyses.

UFS eligibility criteria were met by 1,786 of the 2,102 women that consented to eligibility screening. Most ineligible women were excluded because they no longer attended the health plan clinic where the parent study was based (71%) or they had been misidentified as a 35-49 year-old female (16%). Enrollment occurred from 1996 through 1999. Approximately 20% of eligible women refused participation, resulting in a total of 1,430 participants in the parent study (26). Demographic characteristics, reproductive history, smoking status, and alcohol use were assessed from telephone interviews and self-administered questionnaires. Body weight was measured at the clinic visit.

We restricted the current study to women who self-identified as African American or Caucasian (n = 1,323) to facilitate race-specific analyses, and excluded postmenopausal women because they did not attend the UFS clinic visit for ultrasound screening and blood collection (n = 178). Race and menopausal status criteria for the current study were met by 1,145 women, and DNA was extracted for 984 of the 1,003 women with blood samples.

Sample Collection and Assays

Fasting blood samples were collected by venipuncture, and plasma was stored at −80°C. Plasma IGF-I was measured at NIEHS using a double-antibody radioimmunoassay by extraction method (Nichols Institute Diagnostics, San Juan Capistrano, CA), with a reported detection limit of 0.06 ng/mL. Plasma IGFBP-3 was measured at NIEHS by a double-antibody immunoradiometric assay (Diagnostic Systems Laboratories, Inc., Webster, TX), with a reported detection limit of 0.05 ng/mL The mean inter-assay coefficient of variation on replicate quality control samples was 8.8% for IGF-I and 4.2% for IGFBP-3.

Genomic DNA was extracted from whole blood using a phenol:chloroform procedure or a modified salt precipitation protocol (GenQuik Protocol, Orochem Technologies Inc.).

Genetic Polymorphisms

Race-specific tag SNPs in IGF-I and IGFBP-3 were selected using Genome Variation Server (GVS) software1. We used the Seattle SNPs database as the African American and Caucasian reference population for IGF-I, and we used the HapMap database for IGFBP-3 because it had not been evaluated by Seattle SNPs. We expanded coverage to include five kilobases (kb) outside the 5′ and 3′ ends of each gene, specified a pairwise correlation coefficient (r2) of 0.8 to identify tag SNPs that capture variation across each gene, and selected tag SNPs with a minor allele frequency (MAF) of at least 5% among the African American or Caucasian reference populations. The GVS software identified 29 tag SNPs for IGF-I and 12 tag SNPs for IGFBP-3 that met these criteria. In addition, we selected five SNPs a priori including one nonsynonymous IGF-I SNP (rs17884626), one synonymous IGF-I SNP (rs3729846), two nonsynonymous IGFBP-3 SNPs (rs2854746, rs9282734), and an IGFBP-3 promoter SNP (rs2854744, −202 A/C) previously evaluated in association with circulating IGFBP-3 levels and health outcomes (24, 27-35).

Genotyping was performed using the TaqMan genotyping approach (36-38) at the Mammalian Genotyping Core, Lineberger Comprehensive Cancer Center (Chapel Hill, NC). Allele-specific oligonucleotide probes for 39 selected SNPs were purchased from Applied Biosystems (ABI; Foster City, CA) “TaqMan® Validated and Coding SNP or Pre-Designed SNP Genotyping Assays”. ABI attempted to develop custom assays for the 6 remaining SNPs through their “Custom TaqMan® SNP Genotyping Assays” service. Two IGF-I tag SNPs were dropped from analyses, including one for which a custom assay could not be developed, and one with a pre-designed assay that did not meet ABI technical specifications. In addition, we genotyped an alternate IGF-I tag SNP to replace one that was inconsistent with Hardy-Weinberg equilibrium (HWE) in our African American population. We genotyped 30 IGF-I and 15 IGFBP-3 SNPs (Supplemental Figures 1-2).

PCR amplification was performed on an ABI GeneAmp® PCR System 9700 thermal cycler with dual 384-well-blocks, and endpoint plates were read using the ABI 7900HT system. Fluorescent VIC and 6-FAM reporter dyes differentiated wild type and variant alleles. The Sequence Detection System (SDS) 2.3 software automatically called alleles; experienced operators reviewed the software output. The samples’ DNA concentrations were validated using a NanoDrop® ND-1000 Spectrophotometer prior to dilution to five ng/ul using DNA grade sterile water. Samples were placed into 96-well microtiter plates with two blank and two known DNA standard (Control DNA CEPH Individual 1347-02, ABI) samples, and were subsequently aliquoted into 384-well PCR plates. Quality control measures included blinded genotyping of 28 duplicate samples representing 22 women, which produced concordant results for all samples. The overall call rate was 98.8%, and only five women had less than 50% of complete allele calls for the 45 SNPs assayed. We confirmed that SNP genotype frequencies were consistent with HWE within each racial group using the exact test statistic with one degree of freedom (alpha = 0.01) (39).

Diplotype Estimation

To simultaneously evaluate associations between linked polymorphic loci and their plasma protein levels, we estimated race-specific IGF-I and IGFBP-3 diplotypes. SNPs were excluded from race-specific diplotype analyses if their MAF in our study population was below 5% for tag SNPs or below 3% for a priori SNPs within the racial group being evaluated. Women missing genotype data for more than 50% of the SNPs considered for diplotype analyses within a gene were excluded from diplotype estimation for that gene (one Caucasian and three African Americans for IGFBP-3, three Caucasians and two African Americans for IGF-I). We examined race-specific linkage disequilibrium (LD) patterns using Haploview software (40) to identify SNPs in each gene that could be combined for estimating diplotypes. First, we identified LD blocks consisting of individual SNPs (with MAF at least 5%) in strong LD (95% of pairwise SNP comparisons with one-sided 95% confidence intervals (CI) for D’ within 0.70 to 0.98) (41). Next, we used the Tagger approach (42) in Haploview to identify pairs of redundant SNPs in strong LD (pairwise r2 values of at least 0.8), and excluded one member of each redundant pair from diplotype estimation unless both SNPs were selected a priori. Race-specific pairwise r2 values are available for IGFBP-3 and IGF-I SNPs in Supplemental Tables 1-4.

Race-specific diplotypes representing defined groups of SNPs in each gene were estimated using PHASE version 2.1 (43, 44), which allocates the most likely diplotype to each subject, with the prior assumption that frequently observed haplotypes with less ambiguity due to homozygosity are more probable. PHASE also provides a posterior probability estimate that expresses the uncertainty associated with each diplotype assignment. To reduce the number of race-specific diplotype groups for analyses, we combined single (unlinked) SNPs with an adjacent LD block and adjacent LD blocks with each other, if the larger groups resulted in diplotypes estimated with at least 90% certainty (posterior probability) for at least 90% of observations. Otherwise, diplotype groups were composed of individual SNPs or LD blocks. We assigned women to their most probable diplotype for each group; however, if their most probable diplotype had a posterior probability below 90%, we classified their diplotype group as missing.

Statistical Analyses

Primary statistical analyses were stratified by race and conducted using SAS v9.1 (SAS Institute Inc., Cary, NC). We used linear regression to estimate associations between IGF-I and IGFBP-3 SNP variants and their respective circulating protein levels. Estimated associations are unadjusted since there are no known factors other than race that would predict both plasma levels and SNP variants.

For individual SNP analyses, we generally used codominant inheritance models that estimated associations separately for heterozygous and homozygous variants relative to the referent genotype, which was defined as the most common race-specific homozygous genotype in our study population. However, when there were 10 or fewer women with the homozygous variant genotype, we used a dominant model that combined heterozygous and homozygous variants for comparison with the referent genotype. We evaluated the concordance of estimated associations between races (i.e., the difference in mean differences between races) (Supplemental Tables 5-6) by combining the data for both races and applying linear regression models that included multiplicative genotype by race interaction terms with separate parameters for race and genotypes.

To estimate diplotype associations with plasma IGF-I or IGFBP-3 levels, we used separate race-specific models for each diplotype group, with the most common diplotype as the reference category. Diplotypes assigned to 5 or fewer women were combined into one “rare diplotype” category. We used an empirical-Bayes method of information-weighted averaging to enhance the validity and precision of regression estimates (45). Specifically, we assumed a prior mean of 0, since we had no prior information to group diplotypes according to the anticipated direction of associations with plasma levels. We specified a prior variance corresponding to +/− one standard deviation of the mean plasma levels (2*standard deviation/3.92)2 of IGF-I (prior variances: African Americans, 1,419; Caucasians, 901) and IGFBP-3 (prior variances: African Americans, 186,819; Caucasians, 174,161). This method shrinks regression estimates toward the prior mean such that imprecise estimates based on smaller numbers of observations are shifted further toward the prior mean than more precise estimates. We applied the shrinkage estimator for each diplotype and report posterior medians (50th percentile of the posterior probability distribution) and 95% posterior limits (2.5th and 97.5th percentiles of the posterior probability distribution). Regression estimates and 95% confidence intervals estimated by linear regression are available in Supplemental Tables 7-10.

RESULTS

Participant Characteristics

Race-specific mean plasma IGF1 and IGFBP-3 levels and other characteristics of the study population are displayed in Table 1. African Americans were less likely than Caucasians to have a college or graduate degree (33% vs. 87%), to report regular alcohol consumption (42% vs. 78%), or to be nulliparous at UFS enrollment (21% vs. 59%). African Americans were more likely than Caucasians to be overweight or obese (75% vs. 41%) and to report current smoking (30% vs. 8%). Few women were currently taking oral contraceptives.

Table 1
Characteristics of premenopausal Caucasian and African American women with genotype information from NIEHS Uterine Fibroid Study

IGFBP-3

For plasma IGFBP-3, we focused on estimated differences in mean levels of at least 200 ng/ml for index genotypes or diplotypes relative to the referent (i.e., roughly +/− 5% of the estimated mean level for the referent genotype or diplotype, which ranged from 3798 to 4693 ng/ml). We disregarded imprecise associations with rare SNPs having 10 or fewer observations with heterozygous and homozygous variants.

SNP Analyses

The variants for 10 IGFBP-3 SNPs (rs903889, rs924140, rs2854744, rs2854746, rs2471551, rs3110697, rs2453840, rs2453839, rs2270628, rs12671457) were associated with differences of 200 ng/ml or greater in estimated mean IGFBP-3 levels among at least one racial group when compared with referent genotypes (Table 2). In both races, variants for rs924140, rs2854744, and rs2854746 were associated with differences in plasma IGFBP-3 levels of approximately 500-900 ng/ml for homozygous variants and approximately 300-500 ng/ml for heterozygotes relative to the estimated mean levels for referent genotypes. Pairwise r2 values for all three SNPs were at least 0.8 among Caucasians, indicating strong LD in our study population. Among African Americans, rs924140 and rs2854744 were also in strong LD (r2 = 0.82); however, neither SNP was in LD with rs2854746 (r2 = 0.30-0.34). In both races, rs3110697 was also in moderate LD with rs924140 and rs2854744 (r2 = 0.55-0.65), and its variants were inversely associated with plasma IGFBP-3 (about 430 ng/ml lower in Caucasians and 550 ng/ml lower in African Americans with the AA genotype, with smaller differences estimated for the AG genotype relative to those with the GG genotype). Plasma IGFBP-3 was also inversely associated with rs2471551 variants among African Americans (460ng/ml lower for the CC genotype and 260ng/ml lower for the CG genotype relative to the GG genotype).

Table 2
Estimated mean differences in plasma IGFBP-3 levels associated with IGFBP-3 SNP variants relative to estimated mean IGFBP-3 levels for referent genotypes (italicized), based on linear regression models for premenopausal Caucasian and African American ...

Inverse associations between homozygous variants for five SNPs (rs903889, rs2471551, rs2453840, rs2453839, rs2270628) and plasma IGFBP-3 were noted among Caucasians, but estimates were relatively imprecise since they were based on fewer than 25 observations. Two of these SNPS, rs2453840 and rs2453839, were in strong LD (r2 = 0.87) among Caucasians. Among African Americans, combined homozygous and heterozygous variants for rs2453840 and rs12671457 were positively and inversely associated with plasma IGFBP-3 respectively, with differences of about 200 ng/ml relative to referent genotypes.

SNP associations with plasma IGFBP-3 showed little evidence of discordance by race based on estimated African American versus Caucasian differences in these associations (Supplemental Table 5). Three possible exceptions were rs2854746 (CC versus GG genotype difference between races, 266; 95% CI: −70, 602), rs2453840 (AA and AC versus CC genotype difference between races, 298; 95% CI: 45, 551) and rs2270628 (TT versus CC genotype difference between races, 400; 95% CI: −88, 888). However, estimated differences in mean differences between races, especially for homozygous variants, were fairly imprecise.

Diplotype Analyses

Among Caucasians, three LD blocks accounted for 10 of 12 IGFBP-3 SNPs included in diplotype analyses, with two SNPs outside LD blocks. After excluding two redundant IGFBP-3 SNPs (r2 ≥ 0.8), we created two diplotype groups, as described previously (Table 2), and completed diplotype estimation in each group for 93% to 98% of Caucasians. Overall, we estimated 48 unique diplotypes, including 20 classified as rare based on assignment to five or fewer women.

Among African Americans, four LD blocks accounted for 11 of 14 IGFBP-3 SNPs included in diplotype analyses, with three SNPs outside LD blocks. After excluding one redundant IGFBP-3 SNP (r2 ≥ 0.8), we created three diplotype groups (Table 2), and completed diplotype estimation in each group for 94% to 99% of African Americans. Overall, we estimated 71 unique diplotypes, which included 33 rare diplotypes.

Six Caucasian IGFBP-3 group 1 diplotypes (1a-1f) were associated with decreases of 200 ng/ml or greater in estimated mean IGFBP-3 levels relative to the referent diplotype (Figure 1). All six diplotypes (1a-1f) included the GG genotype for rs2854746 (3rd diplotype position), and five diplotypes (1a, 1b, 1d-1f) included the CC genotype for rs2854744 (2nd diplotype position). The only two Caucasian IGFBP-3 group 1 diplotypes (1m, 1n) with the CC genotype for rs2854746 were positively associated with plasma IGFBP-3.

Figure 1
Estimated differences from mean plasma IGFBP-3 levels associated with IGFBP-3 diplotypes for premenopausal Caucasian and African American women, based on race-specific linear regression models of each IGFBP-3 diplotype group on plasma IGFBP-3 levels. ...

Ten African American IGFBP-3 group 1 diplotypes (1a-1j) were inversely associated with plasma IGFBP-3 relative to the referent diplotype, including eight (1a-1h) with the GG genotype for rs2854746 (3rd diplotype position), three (1a, 1c, 1e) with the CC genotype for rs2854744 (2nd position), and the only one (1c) with the CC genotype for rs2471551 (4th diplotype position) (Figure 1). In addition, the only African American IGFBP-3 group 1 diplotype (1r) with the CC genotype for rs2854746 was positively associated with plasma IGFBP-3. One African American IGFBP-3 group 2 diplotype (2a) and two group 3 diplotypes (3a, 3b) were associated with lower mean IGFBP-3 levels relative to the referent diplotype while one group 3 diplotype (3l) was associated with a higher mean level.

IGF-I

For plasma IGF-I, we focused on estimated differences in mean levels of at least 16-18 ng/ml for index genotypes or diplotypes relative to the referent (i.e., about +/− 10% of the estimated mean level for the referent genotype or diplotype, which ranged from 158 to 176 ng/ml). We did not consider imprecise associations with rare SNPs having 10 or fewer observations with heterozygous and homozygous variants.

SNP Analyses

Among Caucasians, homozygous variants for two common IGF-I SNPs (rs1520220, rs6214) and variants for five rare (MAF < 5%) IGF-I SNPs (rs5742612, rs5742614, rs5742657, rs5742692, rs3730204) were associated with 10% or greater differences in estimated mean IGF-I levels relative to referent genotypes (Table 3). However, with the exception of the positive association with the rs6214 TT genotype, estimates were relatively imprecise due to 30 or fewer observations with variants. None of the IGF-I SNPs noted above were in LD (r2 < 0.4), except for strong LD between rs5742657 and rs5742692 (r2 = 0.94).

Table 3
Estimated mean differences in plasma IGF-I levels associated with IGF-I SNP variants relative to estimated mean IGF-I levels for referent genotypes (italicized), based on linear regression models for premenopausal Caucasian and African American women

Among African Americans, combined homozygous and heterozygous variants for three rare (MAF < 5%) IGF-I SNPs (rs2033178, rs17727841, rs11111262) were associated with differences of approximately 15% to 25% relative to estimated mean IGF-I levels for the referent genotypes although the association with the rs11111262 variants was based on fewer than 30 observations (Table 3). In addition, variants for two more common IGF-I SNPs (rs6219, rs2946834) were associated with 10% increases in estimated mean IGF-I levels relative to referent genotypes. Of the five IGF-I SNPs noted above, only rs17727841 and rs11111262 were in moderate LD (r2 = 0.56).

Based on models that included interaction terms with race, estimated mean differences in IGF-I levels for 13 IGF-I SNP variants varied by at least 16 ng/ml in African Americans versus Caucasians (Supplemental Table 6). These included four SNPs within or near exon 4, which was where plasma IGF-I associations were predominantly noted: rs11111262 (AG/AA versus GG genotype difference between races, 44; 95% CI: 13, 75), rs1520220 (GG versus CC genotype difference between races, 43; 95% CI: 1, 85), rs6219 (CT/TT versus CC genotype difference between races, 21; 95% CI: −1, 44), and rs2946834 (AA versus GG genotype difference between races, 24; 95% CI: −4, 52). However, estimated differences in mean differences between races were relatively imprecise.

Diplotype Analyses

Among Caucasians, three LD blocks accounted for 15 of 17 IGF-I SNPs included in diplotype analyses, with two SNPs outside LD blocks. After excluding six redundant IGF-I SNPs (r2 ≥ 0.8), we created four diplotype groups (Table 3) and completed diplotype estimation in each group for 97% to 99% of Caucasians. Overall, we estimated 40 unique diplotypes, including 13 classified as rare based on assignment to five or fewer women.

Among African Americans, four LD blocks accounted for 16 of 20 IGF-I SNPs included in diplotype analyses, with four SNPs outside LD blocks. After excluding six redundant IGF-I SNPs (r2 ≥ 0.8), we created four diplotype groups (Table 3) and completed diplotype estimation in each group for 95% to 99% of African Americans. Overall, we estimated 73 unique diplotypes, which included 25 rare diplotypes.

Five Caucasian IGF-I group 3 diplotypes (3f-3j) were associated with 10% or greater increases in estimated mean IGF-I levels relative to the referent diplotype, although two estimates (3g, 3j) were relatively imprecise due to few observations with either diplotype (Figure 2). One African American IGF-I group 1 diplotype (1g) and one group 4 diplotype (4e) were associated with increases of 15% to 20% in mean IGF-I levels relative to the referent diplotypes. In addition, one African American IGF-I group 1 diplotype (1a), four group 2 diplotypes (2a, 2m-2o) and two group 3 diplotypes (3a, 3q) were associated with 10% or greater differences in mean IGF-I levels compared to the referent diplotypes, although estimates were fairly imprecise due to small numbers of observations. IGF-I diplotype associations with plasma IGF-I did not parallel SNP associations, unlike associations between IGFBP-3 diplotypes and plasma IGFBP-3.

Figure 2
Estimated differences from mean plasma IGF-I levels associated with IGF-I diplotypes for premenopausal Caucasian and African American women, based on race-specific linear regression models of each IGF-I diplotype group on plasma IGF-I levels. Posterior ...

DISCUSSION

Evidence of a causal association was strongest for the nonsynonymous IGFBP-3 SNP, rs2854746, with plasma IGFBP-3 levels. In both races, the rs2854746 CC genotype was associated with higher mean IGFBP-3 levels than were estimated for the GG genotype, while mean levels for the CG genotype were intermediate. In addition, IGFBP-3 diplotypes with the rs2854746 GG genotype had consistently lower mean IGFBP-3 levels than those estimated for referent diplotypes with the CG genotype in both races, while IGFBP-3 diplotypes with the CC genotype had higher mean IGFBP-3 levels.

Biologic evidence supports a causal relation of rs2854746 with plasma IGFBP-3, since this SNP results in an amino acid change from alanine to glycine, and protein sequence analysis suggests that the amino acid coded by rs2854746 is within the region of IGFBP-3 responsible for IGF-I binding (46). The Multiethnic Cohort Study, a large study of Caucasian women from the Breast and Prostate Cancer Cohort Consortium, and a small Caucasian study reported associations between rs2854746 and plasma IGFBP-3 that were consistent with our findings (24, 35, 47). Therefore, individual rs2854746 associations and correspondence with IGFBP-3 diplotype findings among both races, in addition to biologic evidence, support a causal association between rs2854746 and plasma IGFBP-3.

Other genetic studies of circulating IGFBP-3 have not evaluated rs2854746, but several have examined the IGFBP-3 promoter SNP, rs2854744 (−202 A/C), predominantly among Caucasians. Several studies reported higher mean IGFBP-3 levels among individuals with the AA genotype compared to those with the CC genotype, and intermediate levels among those with the AC genotype (24, 27-35). We also noted increases in mean IGFBP-3 levels for rs2854744 AA versus CC genotypes in both races although plasma IGFBP-3 associations with diplotypes that included rs2854744 variants were not as consistent as those with diplotypes that included rs2854746 variants, especially among African Americans. Consistent with the Multiethnic Cohort Study (24), we noted strong LD between rs2854744 and rs2854746 (r2 = 0.82) in Caucasians that may partly explain associations between rs2854744 variants and plasma IGFBP-3, although these two SNPs were not in LD among African Americans (r2 = 0.34). Deal, et al. reported that promoter activity was increased in vitro in association with the rs2854744 A allele (28), which suggests that rs2854744 may influence circulating IGFBP-3 levels independent of its association with rs2854746. Similar to our study, four studies reported decreased IGFBP-3 levels in association with rs3110697 variants relative to the referent genotype (24, 33-35), and the Multiethnic Cohort Study also reported that rs3110697 was not in strong LD with rs2854744 or rs2854746 among both races (24).

We also reported consistent inverse associations between IGFBP-3 rs2471551 variants and plasma IGFBP-3 among both races. This SNP has potential functional relevance as it is located near a splice site (< 20 base pairs from the 5′ side of exon 2). Canzian, et al. and Diorio, et al. reported that the rs2471551 CC genotype was inversely associated with circulating IGFBP-3 relative to the GG genotype within Caucasian women (27, 34).

Many epidemiologic studies of IGF-I have focused on the dinucleotide CA repeat polymorphism (position −969) located in the promoter approximately one kb upstream of the transcription site; however, associations between CA repeat polymorphisms and circulating IGF-I levels have been inconsistent (23, 30, 31, 48-55). Methodological differences in the categorization of repeat genotypes and the potential for substantial misclassification during genotyping make it difficult to compare results across studies (56). Although we did not evaluate this repeat polymorphism, we evaluated three IGF-I SNPs within five kb of the 5′ and 3′ ends of the gene, and found that two were associated with 10% or greater differences in mean IGF-I levels (rs5742612 among Caucasians and rs2946834 among African Americans). However, Diorio, et al. reported no association between rs5742612 variants and plasma IGF-I within their study of Caucasian women (34). There have been no reports of LD between the IGF-I repeat polymorphism and any of the SNPs in our study, with the exception of rs5742612 in a Chinese population (57). Due to rs5742612 MAF differences for Chinese versus African Americans or Caucasians, it is unlikely that rs5742612 would be in LD with the repeat polymorphism in our study population.

We estimated higher mean IGF-I levels in association with the rs6214 TT versus CC genotypes among Caucasians. In contrast, Al-Zahrani, et al. and Canzian, et al. reported no association between rs6214 and circulating IGF-I within predominantly Caucasian study populations (27, 29). Consistent with the Multiethnic Cohort Study (24), we found no association between rs35767 and plasma IGF-I in either racial group. However, Canzian, et al. and Patel, et al. noted associations between rs35767 and circulating IGF-I in large studies of Caucasian women (27, 35). We also noted an inverse association between plasma IGF-I and the rs1520220 GG versus CC genotypes among Caucasians and a positive association with rs2946834 AA versus GG genotypes among African Americans, although estimated differences in race-specific associations may not be meaningful given their imprecision. Al-Zahrani, et al. and Patel, et al. reported higher mean IGF-I levels in association with rs1520220 and rs2946834 variants relative to referent genotypes in Caucasian women (29, 35), although Al-Zahrani, et al. reported that only the association with rs1520220 variants remained after adjustment for rs2946834 (29). However, rs1520220 and rs2946834 variants were not associated with plasma IGF-I in the Multiethnic Cohort Study (24), and Diorio, et al. reported no association with rs1520220 variants (34). Comparison of race-specific IGF-I SNP and diplotype associations with plasma IGF-I suggests that an untyped functional polymorphism may lie near or within the untranslated region of exon 4, but we could not identify this polymorphism from evaluation of the literature.

A strength of this study is that participants were randomly selected from health plan membership roles, with response rates of about 80% for both races. However, selection bias could exist if eligible women excluded from our analysis differed from the women that were included with respect to their plasma IGF-I or IGFBP-3 levels or genotypes. In particular, 161 (14%) eligible women lacked DNA for genotyping primarily because of no available blood samples, which included a slightly greater proportion of missing African Americans (16%) than Caucasians (12%).

The use of diplotype analysis strengthened our study as it provided support for detecting which SNPs may be causally associated with circulating protein levels, and it assisted with identifying regions where untyped SNPs that influence circulating protein levels may reside. Diplotype associations were unlikely to be biased by the exclusion of women who had diplotypes estimated with low certainty (posterior probability < 90%), since only 1% to 7% of women were excluded from analyses of each diplotype group based on this criterion. Rare diplotypes were assigned with lower certainty, as the PHASE software assumes that frequently observed haplotypes with less ambiguity are more probable. We combined rare diplotypes assigned to five or fewer women into a single category, but we did not interpret associations with these categories due to their heterogeneity.

Despite restricting our study to Caucasians and African Americans and stratifying analyses by race, population stratification within each racial group is a potential limitation of our study. Population stratification is more likely to bias results within African Americans due to their inherently greater admixture than Caucasians. However, the degree of bias depends on the number of ethnicities and the range of their genotype frequencies within the racial group, in addition to the true magnitude of genotype association with the outcome (58-60). Concordance between races for IGFBP-3 SNP findings suggests that population stratification was less likely to bias these results although population stratification within races may still be present. However, population stratification within races may have more strongly influenced the IGF-I SNP findings as there were notable differences between races.

Our use of information-weighted averaging intentionally biased estimates of associations with race-specific diplotypes towards the null since we assumed a null value for the prior mean. However, this approach increased the precision of estimates, particularly for diplotypes assigned to small numbers of women. Despite the increase in bias with estimating posterior medians, a reduction in the overall mean square error based on a greater decrease in variance of estimates has been shown with simulation studies and an occupational cohort study (61). Although we did not interpret our results based on hypothesis tests, this approach also reduces the likelihood of type I error with multiple comparisons (62-64).

The parent study obtained only one measurement of plasma IGF-I and IGFBP-3 from study participants; however, the Nurses’ Health Study reported high correlations (> 0.8) for plasma IGF-I and IGFBP-3 measurements in premenopausal women that were repeated over time (65). Age and sex are strong predictors of circulating IGF-I and IGFBP-3; however, these factors were unlikely to influence our results since our study population was restricted to premenopausal women within a 15-year age range (35 to 49 years), and adjusting for age did not affect results (data not shown).

A major strength of our study was the large number of African American participants, since previous research has mostly focused on relations between IGF-I and IGFBP-3 SNPs and their circulating protein levels in Caucasians. Because African Americans have more genetic heterogeneity than Caucasians, the frequency of etiologically relevant SNPs may differ, and may at least partly explain racial disparities in the burden of cancer and cardiovascular disease. Therefore, assessing IGF-I and IGFBP-3 SNPs that predict circulating IGF-I and IGFBP-3 levels will improve our understanding of the biological role of IGF-I and IGFBP-3 in the etiology of common diseases.

Supplementary Material

Acknowledgments

The epidemiologic study was managed by Glenn Heartwell, and the clinical coordinator was Dr. Joel Schechtman. Dr. Mary Watson prepared DNA samples for genotyping. Dr. Jason Luo performed genotyping within the Mammalian Genotyping Core of The University of North Carolina at Chapel Hill. Dr. Sue Edelstein prepared the figures. Drs. Abee Boyles and Stephanie London reviewed an earlier version of the manuscript.

Funding: This research was supported by the Intramural Research Program of the National Institutes of Health (NIH), National Institute of Environmental Health Sciences and the NIH Office of Research on Minority Health. Genotyping was supported by the UNC-GSK Center of Excellence in Pharmacoepidemiology and Public Health. Dr. Poole was supported in part by a grant from the National Institute of Environmental Health Sciences (P30ES10126).

Footnotes

1Genome Variation Server (GVS) Version 1.04. Seattle (WA): Seattle SNPs Program for Genomic Applications (PGA). [updated 2006 June 16; cited 2006 July 9]. Available from: http://gvs.gs.washington.edu/GVS/

REFERENCES

1. Jones JI, Clemmons DR. Insulin-like growth factors and their binding proteins: biological actions. Endocr Rev. 1995;16:3–34. [PubMed]
2. Hwa V, Oh Y, Rosenfeld RG. The insulin-like growth factor-binding protein (IGFBP) superfamily. Endocr Rev. 1999;20:761–87. [PubMed]
3. Juul A, Main K, Blum WF, Lindholm J, Ranke MB, Skakkebaek NE. The ratio between serum levels of insulin-like growth factor (IGF)-I and the IGF binding proteins (IGFBP-1, 2 and 3) decreases with age in healthy adults and is increased in acromegalic patients. Clin Endocrinol (Oxf) 1994;41:85–93. [PubMed]
4. Gill ZP, Perks CM, Newcomb PV, Holly JM. Insulin-like growth factor-binding protein (IGFBP-3) predisposes breast cancer cells to programmed cell death in a non-IGF-dependent manner. J Biol Chem. 1997;272:25602–7. [PubMed]
5. Schedlich LJ, Graham LD. Role of insulin-like growth factor binding protein-3 in breast cancer cell growth. Microsc Res Tech. 2002;59:12–22. [PubMed]
6. Giovannucci E. Insulin-like growth factor-I and binding protein-3 and risk of cancer. Horm Res. 1999;51:34–41. [PubMed]
7. Renehan AG, Zwahlen M, Minder C, O’Dwyer ST, Shalet SM, Egger M. Insulin-like growth factor (IGF)-I, IGF binding protein-3, and cancer risk: systematic review and meta-regression analysis. Lancet. 2004;363:1346–53. [PubMed]
8. Sandhu MS. Insulin-like growth factor-I and risk of type 2 diabetes and coronary heart disease: molecular epidemiology. Endocr Dev. 2005;9:44–54. [PubMed]
9. Harrela M, Koistinen H, Kaprio J, et al. Genetic and environmental components of interindividual variation in circulating levels of IGF-I, IGF-II, IGFBP-1, and IGFBP-3. J Clin Invest. 1996;98:2612–5. [PMC free article] [PubMed]
10. Pantsulaia I, Trofimov S, Kobyliansky E, Livshits G. Genetic regulation of the variation of circulating insulin-like growth factors and leptin in human pedigrees. Metabolism. 2005;54:975–81. [PubMed]
11. Pantsulaia I, Trofimov S, Kobyliansky E, Livshits G. Pedigree-based quantitative genetic analysis of interindividual variation in circulating levels of IGFBP-3. J Bone Miner Metab. 2002;20:156–63. [PubMed]
12. LeRoith D. Insulin-like growth factors. N Engl J Med. 1997;336:633–40. [PubMed]
13. Kaklamani VG, Linos A, Kaklamani E, Markaki I, Mantzoros C. Age, sex, and smoking are predictors of circulating insulin-like growth factor 1 and insulin-like growth factor-binding protein 3. J Clin Oncol. 1999;17:813–7. [PubMed]
14. Holmes MD, Pollak MN, Hankinson SE. Lifestyle correlates of plasma insulin-like growth factor I and insulin-like growth factor binding protein 3 concentrations. Cancer Epidemiol Biomarkers Prev. 2002;11:862–7. [PubMed]
15. Lukanova A, Toniolo P, Akhmedkhanov A, et al. A cross-sectional study of IGF-I determinants in women. Eur J Cancer Prev. 2001;10:443–52. [PubMed]
16. Probst-Hensch NM, Wang H, Goh VH, Seow A, Lee HP, Yu MC. Determinants of circulating insulin-like growth factor I and insulin-like growth factor binding protein 3 concentrations in a cohort of Singapore men and women. Cancer Epidemiol Biomarkers Prev. 2003;12:739–46. [PubMed]
17. DeLellis K, Rinaldi S, Kaaks RJ, Kolonel LN, Henderson B, Le Marchand L. Dietary and lifestyle correlates of plasma insulin-like growth factor-I (IGF-I) and IGF binding protein-3 (IGFBP-3): the multiethnic cohort. Cancer Epidemiol Biomarkers Prev. 2004;13:1444–51. [PubMed]
18. Harris TB, Kiel D, Roubenoff R, et al. Association of insulin-like growth factor-I with body composition, weight history, and past health behaviors in the very old: the Framingham Heart Study. J Am Geriatr Soc. 1997;45:133–9. [PubMed]
19. Goodman-Gruen D, Barrett-Connor E. Epidemiology of insulin-like growth factor-I in elderly men and women. The Rancho Bernardo Study. Am J Epidemiol. 1997;145:970–6. [PubMed]
20. Johansson H, Baglietto L, Guerrieri-Gonzaga A, et al. Factors associated with circulating levels of insulin-like growth factor-I and insulin-like growth factor binding protein-3 in 740 women at risk for breast cancer. Breast Cancer Res Treat. 2004;88:63–73. [PubMed]
21. Morimoto LM, Newcomb PA, White E, Bigler J, Potter JD. Variation in plasma insulin-like growth factor-1 and insulin-like growth factor binding protein-3: personal and lifestyle factors (United States) Cancer Causes Control. 2005;16:917–27. [PubMed]
22. Chang S, Wu X, Yu H, Spitz MR. Plasma concentrations of insulin-like growth factors among healthy adult men and postmenopausal women: associations with body composition, lifestyle, and reproductive factors. Cancer Epidemiol Biomarkers Prev. 2002;11:758–66. [PubMed]
23. Jernstrom H, Chu W, Vesprini D, et al. Genetic factors related to racial variation in plasma levels of insulin-like growth factor-1: implications for premenopausal breast cancer risk. Mol Genet Metab. 2001;72:144–54. [PubMed]
24. Cheng I, Henderson KD, Haiman CA, et al. Genetic Determinants of Circulating IGF-I, IGFBP-1 and IGFBP-3 Levels in a Multiethnic Population. J Clin Endocrinol Metab. 2007;92:3660–6. [PubMed]
25. Baird D Day, Dunson DB, Hill MC, Cousins D, Schectman JM. High cumulative incidence of uterine leiomyoma in black and white women: ultrasound evidence. Am J Obstet Gynecol. 2003;188:100–7. [PubMed]
26. Baird DD, Dunson DB, Hill MC, Cousins D, Schectman JM. Association of physical activity with development of uterine leiomyoma. Am J Epidemiol. 2007;165:157–63. [PubMed]
27. Canzian F, McKay JD, Cleveland RJ, et al. Polymorphisms of genes coding for insulin-like growth factor 1 and its major binding proteins, circulating levels of IGF-I and IGFBP-3 and breast cancer risk: results from the EPIC study. Br J Cancer. 2006;94:299–307. [PMC free article] [PubMed]
28. Deal C, Ma J, Wilkin F, et al. Novel promoter polymorphism in insulin-like growth factor-binding protein-3: correlation with serum levels and interaction with known regulators. J Clin Endocrinol Metab. 2001;86:1274–80. [PubMed]
29. Al-Zahrani A, Sandhu MS, Luben RN, et al. IGF1 and IGFBP3 tagging polymorphisms are associated with circulating levels of IGF1, IGFBP3 and risk of breast cancer. Hum Mol Genet. 2006;15:1–10. [PubMed]
30. Jernstrom H, Deal C, Wilkin F, et al. Genetic and nongenetic factors associated with variation of plasma levels of insulin-like growth factor-I and insulin-like growth factor-binding protein-3 in healthy premenopausal women. Cancer Epidemiol Biomarkers Prev. 2001;10:377–84. [PubMed]
31. Lai JH, Vesprini D, Zhang W, Yaffe MJ, Pollak M, Narod SA. A polymorphic locus in the promoter region of the IGFBP3 gene is related to mammographic breast density. Cancer Epidemiol Biomarkers Prev. 2004;13:573–82. [PubMed]
32. Schernhammer ES, Hankinson SE, Hunter DJ, Blouin MJ, Pollak MN. Polymorphic variation at the −202 locus in IGFBP3: Influence on serum levels of insulin-like growth factors, interaction with plasma retinol and vitamin D and breast cancer risk. Int J Cancer. 2003;107:60–4. [PubMed]
33. Ren Z, Cai Q, Shu XO, et al. Genetic polymorphisms in the IGFBP3 gene: association with breast cancer risk and blood IGFBP-3 protein levels among Chinese women. Cancer Epidemiol Biomarkers Prev. 2004;13:1290–5. [PubMed]
34. Diorio C, Brisson J, Berube S, Pollak M. Genetic polymorphisms involved in insulin-like growth factor (IGF) pathway in relation to mammographic breast density and IGF levels. Cancer Epidemiol Biomarkers Prev. 2008;17:880–8. [PubMed]
35. Patel AV, Cheng I, Canzian F, et al. IGF-1, IGFBP-1, and IGFBP-3 polymorphisms predict circulating IGF levels but not breast cancer risk: findings from the Breast and Prostate Cancer Cohort Consortium (BPC3) PLoS ONE. 2008;3:e2578. [PMC free article] [PubMed]
36. Behrens M, Lange R. A highly reproducible and economically competitive SNP analysis of several well characterized human mutations. Clin Lab. 2004;50:305–16. [PubMed]
37. Livak KJ. Allelic discrimination using fluorogenic probes and the 5′ nuclease assay. Genet Anal. 1999;14:143–9. [PubMed]
38. McGuigan FE, Ralston SH. Single nucleotide polymorphism detection: allelic discrimination using TaqMan. Psychiatr Genet. 2002;12:133–6. [PubMed]
39. Wigginton JE, Cutler DJ, Abecasis GR. A note on exact tests of Hardy-Weinberg equilibrium. Am J Hum Genet. 2005;76:887–93. [PubMed]
40. Barrett JC, Fry B, Maller J, Daly MJ. Haploview: analysis and visualization of LD and haplotype maps. Bioinformatics. 2005;21:263–5. [PubMed]
41. Gabriel SB, Schaffner SF, Nguyen H, et al. The structure of haplotype blocks in the human genome. Science. 2002;296:2225–9. [PubMed]
42. de Bakker PI, Yelensky R, Pe’er I, Gabriel SB, Daly MJ, Altshuler D. Efficiency and power in genetic association studies. Nat Genet. 2005;37:1217–23. [PubMed]
43. Stephens M, Donnelly P. A comparison of bayesian methods for haplotype reconstruction from population genotype data. Am J Hum Genet. 2003;73:1162–9. [PubMed]
44. Stephens M, Smith NJ, Donnelly P. A new statistical method for haplotype reconstruction from population data. Am J Hum Genet. 2001;68:978–89. [PubMed]
45. Greenland S. Bayesian perspectives for epidemiological research: I. Foundations and basic methods. Int J Epidemiol. 2006;35:765–75. [PubMed]
46. Bairoch A, Apweiler R, Wu CH, et al. The Universal Protein Resource (UniProt) Nucleic Acids Res. 2005;33:D154–9. [PMC free article] [PubMed]
47. Morimoto LM, Newcomb PA, White E, Bigler J, Potter JD. Variation in plasma insulin-like growth factor-1 and insulin-like growth factor binding protein-3: genetic factors. Cancer Epidemiol Biomarkers Prev. 2005;14:1394–401. [PubMed]
48. Rosen CJ, Kurland ES, Vereault D, et al. Association between serum insulin growth factor-I (IGF-I) and a simple sequence repeat in IGF-I gene: implications for genetic studies of bone mineral density. J Clin Endocrinol Metab. 1998;83:2286–90. [PubMed]
49. Jernstrom H, Sandberg T, Bageman E, Borg A, Olsson H. Insulin-like growth factor-1 (IGF1) genotype predicts breast volume after pregnancy and hormonal contraception and is associated with circulating IGF-1 levels: implications for risk of early-onset breast cancer in young women from hereditary breast cancer families. Br J Cancer. 2005;92:857–66. [PMC free article] [PubMed]
50. Rietveld I, Janssen JA, Hofman A, Pols HA, van Duijn CM, Lamberts SW. A polymorphism in the IGF-I gene influences the age-related decline in circulating total IGF-I levels. Eur J Endocrinol. 2003;148:171–5. [PubMed]
51. Missmer SA, Haiman CA, Hunter DJ, et al. A sequence repeat in the insulin-like growth factor-1 gene and risk of breast cancer. Int J Cancer. 2002;100:332–6. [PubMed]
52. Vaessen N, Heutink P, Janssen JA, et al. A polymorphism in the gene for IGF-I: functional properties and risk for type 2 diabetes and myocardial infarction. Diabetes. 2001;50:637–42. [PubMed]
53. DeLellis K, Ingles S, Kolonel L, et al. IGF1 genotype, mean plasma level and breast cancer risk in the Hawaii/Los Angeles multiethnic cohort. Br J Cancer. 2003;88:277–82. [PMC free article] [PubMed]
54. Wen W, Gao YT, Shu XO, et al. Insulin-like growth factor-I gene polymorphism and breast cancer risk in Chinese women. Int J Cancer. 2005;113:307–11. [PubMed]
55. Giovannucci E, Haiman CA, Platz EA, Hankinson SE, Pollak MN, Hunter DJ. Dinucleotide repeat in the insulin-like growth factor-I gene is not related to risk of colorectal adenoma. Cancer Epidemiol Biomarkers Prev. 2002;11:1509–10. [PubMed]
56. Tran N, Bharaj BS, Diamandis EP, Smith M, Li BD, Yu H. Short tandem repeat polymorphism and cancer risk: influence of laboratory analysis on epidemiologic findings. Cancer Epidemiol Biomarkers Prev. 2004;13:2133–40. [PubMed]
57. Wong HL, Delellis K, Probst-Hensch N, et al. A new single nucleotide polymorphism in the insulin-like growth factor I regulatory region associates with colorectal cancer risk in singapore chinese. Cancer Epidemiol Biomarkers Prev. 2005;14:144–51. [PubMed]
58. Wang Y, Localio R, Rebbeck TR. Evaluating bias due to population stratification in case-control association studies of admixed populations. Genet Epidemiol. 2004;27:14–20. [PubMed]
59. Ardlie KG, Lunetta KL, Seielstad M. Testing for population subdivision and association in four case-control studies. Am J Hum Genet. 2002;71:304–11. [PubMed]
60. Wacholder S, Rothman N, Caporaso N. Population stratification in epidemiologic studies of common genetic variants and cancer: quantification of bias. J Natl Cancer Inst. 2000;92:1151–8. [PubMed]
61. Greenland S, Poole C. Empirical-Bayes and semi-Bayes approaches to occupational and environmental hazard surveillance. Arch Environ Health. 1994;49:9–16. [PubMed]
62. Poole C. Multiple comparisons? No problem! Epidemiology. 1991;2:241–3. [PubMed]
63. Greenland S, Robins JM. Empirical-Bayes adjustments for multiple comparisons are sometimes useful. Epidemiology. 1991;2:244–51. [PubMed]
64. Greenland S. Multiple comparisons and association selection in general epidemiology. Int J Epidemiol. 2008;37:430–4. [PubMed]
65. Missmer SA, Spiegelman D, Bertone-Johnson ER, Barbieri RL, Pollak MN, Hankinson SE. Reproducibility of plasma steroid hormones, prolactin, and insulin-like growth factor levels among premenopausal women over a 2- to 3-year period. Cancer Epidemiol Biomarkers Prev. 2006;15:972–8. [PubMed]