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Logo of nihpaAbout Author manuscriptsSubmit a manuscriptNIH Public Access; Author Manuscript; Accepted for publication in peer reviewed journal;
 
Cancer Epidemiol Biomarkers Prev. Author manuscript; available in PMC Nov 1, 2011.
Published in final edited form as:
PMCID: PMC2989404
NIHMSID: NIHMS234242
Eighteen Insulin-like Growth Factor (IGF) pathway genes, circulating levels of IGF-1 and its binding protein (IGFBP-3), and risk of prostate and breast cancer
Fangyi Gu,1,2 Fredrick R. Schumacher,4 Federico Canzian,5 Naomi E. Allen,7 Demetrius Albanes,8 Christine D Berg,9 Sonja I. Berndt,8 Heiner Boeing,10 H. Bas Bueno-de-Mesquita,11 Julie E. Buring,2,12,13,14,15,16 Nathalie Chabbert-Buffet,17 Stephen J. Chanock,8 Françoise Clavel-Chapelon,18,19 Vanessa Dumeaux,20 J. Michael Gaziano,13,14,21 Edward L. Giovannucci,2,3 Christopher A. Haiman,4 Susan E. Hankinson,2,14,22 Richard B. Hayes,8,23 Brian E. Henderson,4 David J. Hunter,1,2 Robert N. Hoover,8 Mattias Johansson,24,25 Timothy J. Key,7 Kay-Tee Khaw,26 Laurence N. Kolonel,27,28 Pagona Lagiou,29 I-Min Lee,2,12,14 Loic LeMarchand,28 Eiliv Lund,20 Jing Ma,14,22 N. Charlotte Onland-Moret,30 Kim Overvad,31 Laudina Rodriguez,32 Carlotta Sacerdote,33 Maria-José Sánchez,34 Meir J. Stampfer,2,3,14,22 Pär Stattin,25 Daniel O. Stram,4 Gilles Thomas,8 Michael J. Thun,35 Anne Tjønneland,36 Dimitrios Trichopoulos,2,37 Rosario Tumino,38 Jarmo Virtamo,39 Stephanie J. Weinstein,8 Walter C. Willett,2,3 Meredith Yeager,8 Shumin M. Zhang,12,14 Rudolf Kaaks,6 Elio Riboli,40 Regina G. Ziegler,8 and Peter Kraft1,2*
1Program in Molecular and Genetic Epidemiology, Harvard School of Public Health, Boston, MA, USA
2Department of Epidemiology, Harvard School of Public Health, Boston, MA, USA
3Department of Nutrition, Harvard School of Public Health, Boston, MA, USA
4Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
5Genomic Epidemiology Group, German Cancer Research Center (DKFZ), Heidelberg, Germany
6Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
7Cancer Epidemiology Unit, University of Oxford, Oxford, OX3 7LF, UK
8Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA
9Division of Cancer Prevention, National Cancer Institute, Bethesda, MD, USA
10Department of Epidemiology, Deutsches Institut für Ernährungsforschung, Potsdam-Rehbrücke, Germany
11National Institute for Public Health and the Environment (RIVM), Bilthoven, The Netherlands
12Division of Preventive Medicine, Boston, MA 02115, USA
13Division of Aging, Department of Medicine, Brigham and Women's Hospital, Boston, MA 02115, USA
14Division of Harvard Medical School, Boston, MA 02115, USA
15Department of Ambulatory Care and Prevention, Harvard Medical School, Boston, MA 02115, USA
16Division for Research and Education in Complementary and Integrative Medical Therapies, Harvard Medical School, Boston, MA 02115, USA
17Gynecology Dept, APHP Hôpital Tenon and Université Pierre et Marie Curie Université Paris 06, 75020 Paris, France
18Inserm, Centre for Research in Epidemiology and Population Health, U1018, Institut Gustave Roussy, F-94805, Villejuif, France
19Paris South University, UMRS 1018, F-94805, Villejuif, France
20Institute of Community Medicine, University of Tromsø, Tromsø, Norway
21Massachusetts Veterans Epidemiology Research and Information Center/VA Cooperative Studies Programs, VA Boston Healthcare System, Boston, MA, USA
22Channing Laboratory, Department of Medicine, Brigham and Women's Hospital, Boston 02115, MA, USA
23Division of Epidemiology, Department of Environmental Medicine, New York University Langone Medical Center, NYU Cancer Institute, New York, NY 10016, USA
24International Agency for Research on Cancer, Lyon, France
25Department of Surgical and Perioperative Sciences, Urology and Andrology, Umeå University, Umeå, Sweden
26Clinical Gerontology Unit, University of Cambridge, Cambridge, UK
27Epidemiology Program, University of Hawaii, Honolulu, Hawaii, USA
28Cancer Research Center, University of Hawaii, Honolulu, Hawaii, USA
29WHO Collaborating Center for Food and Nutrition Policies, Department of Hygiene, Epidemiology and Medical Statistics, University of Athens Medical School, Athens, GR-11527, Greece
30Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, the Netherlands
31Department of Epidemiology, School of Public Health, Aarhus University, Aarhus, Denmark
32Public Health and Participation Directorate Health and Health Care Services Council, Oviedo, Asturias, Spain
33CPO-Piemonte Torino, and Human Genetics Foundation, Torino, Italy
34Andalusian School of Public Health, Granada (Spain) and CIBER de Epidemiología y Salud Pública (CIBERESP), Spain
35Department of Epidemiology, American Cancer Society, Atlanta, Georgia, USA
36The Danish Cancer Society, Institute of Cancer Epidemiology, Copenhagen, Denmark
37Bureau of Epidemiologic Research, Academy of Athens, GR-10679, Greece
38Cancer Registry and Histopathology Unit, "Civile - M.P.Arezzo" Hospital, ASP 7 Ragusa (Italy)
39Department of Chronic Disease Prevention, National Institute for Health and Welfare, Helsinki FIN-00300, Finland
40School of Public Health, Imperial College London, London, UK
*Correspondence to: Peter Kraft, 655 Huntington Avenue, Building II Room 207, Boston, Massachusetts 02115, Phone: 617.432.4271, pkraft/at/hsph.harvard.edu
Background
Circulating levels of insulin-like growth factor I (IGF-1) and its main binding protein, IGF binding protein 3 (IGFBP-3), have been associated with risk of several types of cancer. Heritable factors explain up to 60% of the variation in IGF-1 and IGFBP-3 in studies of adult twins.
Methods
We systematically examined common genetic variation in 18 genes in the IGF signaling pathway for associations with circulating levels of IGF-1 and IGFBP-3. A total of 302 single nucleotide polymorphisms (SNPs) were genotyped in over 5500 Caucasian men and 5500 Caucasian women from the Breast and Prostate Cancer Cohort Consortium (BPC3).
Results
After adjusting for multiple testing, SNPs in the IGF1 and SSTR5 genes were significantly associated with circulating IGF-1 (p<2.1×10−4); SNPs in the IGFBP3 and IGFALS genes were significantly associated with circulating IGFBP-3. Multi-SNP models explained R2=0.62% of the variation in circulating IGF-1 and 3.9% of the variation in circulating IGFBP-3. We saw no significant association between these multi-SNP predictors of circulating IGF-1 or IGFBP-3 and risk of prostate or breast cancers.
Conclusion
Common genetic variation in the IGF1 and SSTR5 genes appears to influence circulating IGF-1 levels, and variation in IGFBP3 and IGFALS appears to influence circulating IGFBP-3. However, these variants explain only a small percentage of the variation in circulating IGF-1 and IGFBP-3 in Caucasian men and women.
Impact
Further studies are needed to explore contributions from other genetic factors such as rare variants in these genes and variation outside of these genes.
Keywords: insulin-like growth factors, genetic association, breast cancer, prostate cancer
The insulin-like growth factor (IGF) signaling pathway plays an important role in regulating cellular proliferation and apoptosis (1). IGF-1 and its major binding protein, IGFBP3, are two of the key molecules in this pathway. Epidemiological studies suggest a positive association between levels of circulating IGF-1 and risk of prostate breast cancers(24). Associations between circulating IGFBP-3 and prostate and breast cancers are inconsistent (2, 45).
The IGF signaling pathway is a complex regulatory system (Fig 1). The main components include IGF-1, IGF-2, the two IGF receptors (IGF-1R and IGF-2R), the six binding proteins [IGFBP-1 through 6], the acid labile subunit (ALS), and the upstream and downstream regulatory factors. The expression of both IGF-1 and IGFBP-3 are upregulated by growth hormone (GH), whose expression is under the regulation of the hypothalamic hormones somatostatin (SST, an inhibitor) and growth hormone-releasing hormone (GHRH, a stimulator). The pituitary-specific transcription factor 1 (POU1F1) is crucial for the synthesis of GH in the pituitary gland. Growth hormone receptor (GHR), growth hormone-releasing hormone receptor (GHRHR), and five somatostatin receptors (SSTR-1 through 5) bind their respective ligands and regulate their function (6).
Figure 1
Figure 1
Overview of IGF signaling pathway upstream of the IGF receptors
In addition to age, gender, smoking, and nutrition, genetic factors may also influence circulating levels of both IGF-1 and IGFBP-3. Twin studies have shown up to 60% of IGF-1 and IGFBP-3 may be explained by genetic factors in adults (78).
Genetic variation in these IGF signaling pathway genes may influence the concentration of IGF-1 and IGFBP-3 in circulation. Epidemiologic studies that have correlated IGF-1 and IGFBP-3 levels with variants in IGF1(912), IGFBP1 (10), IGFBP3 (911), IGFALS (10), and 10 GH-related genes (GHRH, GHRHR, SST(13), SSTR1-SSTR5(1314), POU1F1, and GH)(15) have generally assessed only a few single nucleotide polymorphisms (SNPs) within either one or several genes in this pathway. Most of these studies had insufficient power to detect subtle associations, and SNP panels differed across studies. For many frequently studied SNPs the results are inconsistent (11).
We comprehensively examined the association between common variants in 18 IGF signaling pathway genes and circulating levels of IGF-1 and IGFBP-3 using data collected by the Breast and Prostate Cancer Cohort Consortium (BPC3), a collaboration of nine large prospective studies. The BPC3 genotyped 302 tagging SNPs in these genes and measured circulating levels of IGF-1 and IGFBP-3 in six male and four female nested case-control studies. Our sample size (over 5500 Caucasian women and 5500 Caucasian men) is among the largest to date to examine these associations. The BPC3 previously reported associations between common genetic variation in the IGF1, IGFBP1, and IGFBP3 genes and circulating levels of IGF-1 and IGFBP-3 in men (16)and women (17) separately. We extend this analysis to include 15 other genes in the IGF signaling pathway: GHR, GHRH, GHRHR, IGFALS, IGFBP2, IGFBP4-6, POU1F1, SST, and SSTR1-5. (Fig 1) In separate publications we assess whether the tagging SNPs in these genes are individually associated with risk of prostate or breast cancer (1618); here we test whether markers with strong statistical evidence for association with circulating IGF-1 and IGFBP-3 are collectively associated with risk of prostate or breast cancer.
Study Population
As described previously (19), the BPC3 pooled data from nine well-established cohorts from the United States and Europe to study the association between variation in hormone-related genes and risk of breast and prostate cancers. Each member cohort provided data on prostate/breast cancer cases and their matched controls; seven member cohorts contributed prospectively collected blood samples for hormone assay. In this analysis we used subjects with both successful genotyping data and blood IGF-1 (or IGFBP-3) assay to examine the association of IGF pathway genes with circulating IGF-1 (or IGFBP-3) level. We restricted the analyses to Caucasians, who comprised the majority of participants in all except the Multiethnic Cohort (MEC) (20). Prediagnostic measures of circulating IGF-1 were available from 5583 Caucasian men (2664 prostate cancer patients and 2919 controls) from 6 cohorts and 5533 Caucasian women (2080 breast cancer patients and 3453 controls) from 4 cohorts; data on circulating IGFBP-3 were available from 5565 Caucasian men (2661 cases and 2904 controls) and 5420 Caucasian women (2044 cases and 3376 controls). The 6 male cohorts included the Physicians’ Health Study (PHS) (21), the Health Professionals Follow-Up Study (HPFS) (22), the European Prospective Investigation into Cancer and Nutrition (EPIC) Study (23), the Prostate, Lung, Colorectal, and Ovarian (PLCO) Cancer Screening Trial (24), the Alpha-Tocopherol, Beta-Carotene Cancer Prevention (ATBC) Study (25), and the MEC Study. The 4 female cohorts were the Nurses’ Health Study (NHS) (26), the EPIC, the PLCO, and the MEC.
Cases were initially identified in each cohort by self-report or cancer registries and subsequently confirmed with medical records, including pathological reports. Controls were selected matching on a number of potential confounding factors: age and ethnicity, and in some cohorts, additional criteria, such as region of recruitment in EPIC (1617). Questionnaire data were collected prospectively (i.e. prior to cancer diagnosis). Informed consent was obtained from each individual, and each study was approved by the Institutional Review Board at the respective institutions.
IGF-1 and IGFBP-3 Circulating Levels
Circulating IGF-1 and IGFBP-3 levels were measured by Enzyme-Linked Immunosorbent Assays (Diagnostic System Laboratories, Webster, TX). All of these measurements were made using blood samples collected before diagnosis with cancer, except in the MEC, where the one breast cancer case was prevalent at blood draw. To account for possible reverse causality due to latent cancer, we repeated all association analyses restricting to controls and cases diagnosed more than two years after blood draw (9,164 men and women combined). All the female subjects were non-users of menopausal hormone therapy (and non-users of oral contraceptives in EPIC) at the time of blood draw. Samples from ATBC, HPFS, PHS, NHS and PLCO-women were measured in the Michael Pollak’s laboratory (McGill University), and samples from the remaining studies (EPIC, MEC, and PLCO-men) were measured in the laboratory of the Hormones and Cancer Team at the International Agency for Research on Cancer (IARC). The detailed measurements are described elsewhere (21, 2732).
SNP Discovery and htSNP Selection
SNP discovery and selection of tagging SNPs were based on two approaches, depending on the maturity of the HapMap database at the time tagging SNPs were chosen. For three genes (IGF1, IGFBP1, and IGFBP3), studied before the completion of the HapMap Project, novel SNPs were identified by resequencing coding and evolutionarily conserved regions in a discovery panel of 190 advanced prostate and breast cancer cases from the MEC samples (19 for each cancer and each of five ethnic groups: African American, Latino, Japanese, Native Hawaiian, and Caucasian). A dense set of SNPs in each gene in a multiethnic, cancer-free panel (including 70 MEC Caucasians) was used to determine the linkage disequilibrium (LD) patterns for further use. This set included SNPs with minor allele frequency (MAF) >0.05 from public databases spanning the region from 20 kb upstream of the start of transcription to 10 kb downstream of the end of transcription; it also included novel SNPs detected by resequencing with MAF greater than 5% in any of the five ethnic groups or greater than 1% overall. Using this dense genotyping data, haplotype blocks were determined by a modified version of the Gabriel et al. algorithm (33) in the Haploview program (34). Haplotype-tagging SNPs (htSNPs) were then chosen within these blocks with Rh2>=0.7, calculated by the partition-ligation expectation maximization (PLEM) algorithm in the tagSNPs program (35). A different approach was used to characterize variants in other IGF pathway genes. For these, pairwise tagging SNPs were selected using the HapMap Phase 2 CEU (European ancestry) panel and the algorithm implemented in Tagger (3638). Tag SNPs were chosen so that all SNPs with MAF>0.05 were tagged with r2>0.8. Detailed information on the resequencing results in the discovery panel and on the dense panel of SNPs genotyped in the reference panel can be found at the BPC3 website (39).
Genotyping
Two methods were used to genotype these SNPs: the fluorogenic 5’ endonuclease assay (TaqMan), using reagents and hardware from Applied Biosystems (Foster City, CA, USA) for the IGF1, IGFBP1, and IGFBP3 genes; the Illumina GoldenGate assay (Illumina, San Diego, CA, USA) was used for the others. Genotyping was performed in eight laboratories (University of Southern California, Los Angeles, CA, USA; University of Hawaii, Honolulu, HI, USA; Harvard School of Public Health, Boston, MA, USA; Core Genotyping Facility, National Cancer Institute, Bethesda, MD, USA; Cambridge University, Cambridge, UK; International Agency for Research on Cancer, Lyon, France; Imperial College, London, UK and German Cancer Research Center, Heidelberg, German). Inter-laboratory reproducibility was assessed with 30 SNPs on 94 samples from the Coriell Biorepository (Camden, NJ); the concordance rates were greater than 99% for Taqman assay, and 99.5% for the Illumina OPA assay. The internal quality of each genotyping center was assessed by typing 5–10% blinded samples in duplicate or greater, and the concordance ranged from 97.2 to 99.9%.
Data cleaning and imputation
Samples in which more than 25% of attempted SNPs failed were excluded. Less than 3% of samples were excluded due to low completion rate; the study-specific exclusion rates were less than 5% for all cohorts, except the WHS, which had a 9.5% exclusion rate. Also excluded from each cohort were SNPs with high failure rate (>=25%), departures from Hardy-Weinberg Equilibrium among controls (p <10−5), and those with MAF < 0.01. SNPs that failed in more than three cohorts or differed greatly in European-ancestry allele frequencies across cohorts (fixation index Fst>0.02) were also excluded. The percentage of attempted SNPs that passed the filters described above ranged between 86.0% and 93.5%, calculated by cohort, and most failures were due to low call rates (<75% in a given cohort). Fewer than 1% of the SNPs were excluded from further analysis because of departure from Hardy-Weinberg Equilibrium (p<10−5). Allele frequencies were similar across the cohorts; only two SNPs showed Fst values higher than 2%. See supplementary tables in Canzian et al. (18)for more detail.
Failed SNPs were imputed by study and gender using observed genotypes from the BPC3 subjects and phased HapMap CEU samples (release #22) with the software MACH (4041). Imputed SNPs were excluded from analysis if the estimated correlation between the imputed and true underlying genotypes was less than 30%. The average estimated correlation with the underlying genotypes among SNPs retained for analysis was 0.72; only five SNPs had an estimated correlation less than 0.5. In the combined sample of men and women, we restricted analyses to SNPs that were available for both genders (i.e., SNPs for which genotyping had been attempted and were either genotyped or imputed successfully in both men and women).
Statistical Analysis
Before performing analyses we deleted the batch-specific outliers for circulating IGF-1 and IGFBP-3 levels among both males and females. The outliers were identified based on the generalized extreme studentized deviate many-outlier detection approach, setting alpha to 0.05 (42). This led to the exclusion of 25 IGF-1 samples and 47 IGFBP-3 samples with levels between 1.80 and 6.26 (IGF-1) and 2.35 and 11.16 (IGFBP-3) standard deviations from their respective batch means. The circulating IGF-1 and IGFBP-3 levels were log-transformed to provide approximate normal distributions. We used linear regression to examine the association between each single SNP (coded as 0, 1, or 2 for the number of minor alleles present) and the log-transformed IGF-1 or IGFBP-3 level, adjusting for age at blood draw, batch, and case/control status (prostate cancer for males and breast cancer for females). All the analyses were restricted to Caucasians. We performed the analyses within male cohorts and female cohorts separately and then combined them using fixed-effect meta-analyses (43). Percent change of IGF-1 and IGFBP-3 per copy of minor allele for each SNP was calculated as (eβ −1) × 100%, where the β is the per-allele change in mean log biomarker level. Heterogeneity in SNP-hormone associations between men and women was assessed using Cochrane’s Q statistic (44). Multiple comparison adjustments were performed at the gene level by multiplying nominal p values by the effective number of independent SNPs within each gene (45); because this procedure accounts for correlations among the tested SNPs, it is slightly less conservative than a Bonferroni correction, which assumes the SNPs are independent. We also adjusted for multiple comparisons at the pathway level; the adjusted significance criteria (p<2.1×10−4) was calculated by dividing 0.05 by the sum of the effective number of independent SNPs across all genes (Meff=238).
Genomic control inflation factors λGC were calculated to assess systematic bias due to residual population stratification or departures from analytic assumptions (e.g., homoscedasticity). (The factor λGC is calculated as the median of the observed chi-squared test statistics for marker-trait association divided by its theoretical median under the null, which is 0.45 for a one degree of freedom test.) We also used the genomic control inflation factor to correct for potential systematic biases by calculating corrected chi-square statistics Z2GC, where Z is the Wald statistic from the linear regression of IGF-1 or IGFBP-3 levels on the tested SNP and covariates.
Since the results in men and women are similar (none of the tests for heterogeneity of effects between men and women was significant after adjusting for multiple comparison), we pooled data from men and women to build multi-SNP predictors of IGF-1 and IGFBP-3 levels. We performed backwards stepwise selection, starting with models that contained all SNPs with univariate p-values <0.001; we retained any SNPs that had multivariable adjusted p<0.10. These models retained the design variables gender, age, cancer status, and batch. We calculated percentage of variation of IGF-1 (and IGFBP-3) that was explained by final SNPs kept in the model using the formula (RSScov onlyRSSfull)/RSScov only, where RSScovonly is the residual sum of squares for the model including only covariates, and RSSfull is residual sum of squares for the final full model including both covariates and SNPs.
To test whether the SNPs associated with circulating IGF-1 or IGFBP-3 levels were associated with risk of prostate or breast cancer, we used the SNPs retained in the final model to create separate IGF-1 and IGFBP-3 scores for each subject. We did so by taking a weighted sum of putative risk alleles (those that increased levels of IGF-1 or decreased IGFBP-3) for each subject across the set of SNPs associated with each biomarker. Each allele was weighted by its regression coefficient from the multivariable model of the biomarker on significant SNPs (see previous paragraph). We then examined the associations between the IGF-1 and IGFBP-3 scores and cancer incidence (breast cancer among women and prostate cancer among men), using a logistic regression model. This model included main effects for each of the two scores, as well as main effects for study and age (in five-year age categories).
Population characteristics
Table 1 shows the characteristics of the case-control samples stratified by gender. In men, prostate cancer cases had 2.0 % higher circulating IGF-1 and 3.2 % higher IGFBP-3 levels than controls (p=0.002 and 0.003 respectively), while in women, breast cancer cases and controls showed no significant differences in either IGF-1 or IGFBP-3 levels. Circulating levels of both IGF-1 and IGFBP-3 decreased with age (p<10−4) and differed by cohort (p<10−3) and gender (p<0.01 for IGF-1: p<0.05 for IGFBP-3). Age at blood draw differed by cohort (p<10−4 for both men and women), but was similar between cancer patients and controls.
Table 1
Table 1
Distributions of circulating IGF-1 and IGFBP-3 levels and age at blood draw, by study
Homogeneity between men and women for the association of SNPs and circulating IGF-1/IGFBP-3
We did not detect any significant evidence of heterogeneity between men and women in the association of SNPs in IGF pathway genes with circulating levels of IGF-1 or IGFBP-3. A total of 302 SNPs were available in both the prostate cancer and breast cancer studies. The quantile-quantile plot for the Q test of heterogeneity did not differ from that expected under the null hypothesis (i.e. none of the associations between SNPs and circulating levels differed between men and women). No Q test was significant at the α=0.05 level after adjusting for the number of SNPs tested. Due to the observed lack of heterogeneity between men and women for most SNPs, we combined the data for men and women in further analyses. We report association results for all tested SNPs in men and women separately as well as the combined sample in the Supplementary Data.
Association between SNPs and circulating IGF-1 level
In analyses of men and women combined, two SNPs in IGF1 and four in SSTR5 were significantly associated with circulating IGF-1 levels after adjustment for the number of independent markers tested in the pathway (nominal p-value < 2.1 × 10−4; Table 2). An additional SNP in IGFALS (rs344352) was significantly associated with IGF-1 levels when only adjusting for the number of SNPs tested within each gene.
Table 2
Table 2
Associations between single SNPs and circulating IGF-1
The genomic control inflation factor for IGF-1 levels was 1.59 among men, 1.20 among women, and 1.23 in the meta-analysis combining unadjusted regression parameters for men and women. These inflation factors did not substantially change after stratifying the analyses by study and creating summary association statistics using meta-analysis, or after inverse-normal transforming residuals from a linear regression on the non-genetic covariates (including laboratory batch). The relatively high values for λGC suggest some residual systematic bias (including possible population stratification bias) that we have not adjusted for analytically in the linear regression; they might also reflect an enrichment for SNPs truly associated with IGF1 levels in these eighteen genes. To guard against false positives due to potential biases, we also calculated λGC-corrected p-values (see Methods). After λGC correction, one SNP in IGF1 and one SNP in SSTR5 were significantly associated with circulating IGF-1 levels (corrected p-value < 2.1 × 10−4).
Sensitivity analyses excluding all cases diagnosed less than two years after blood draw did not qualitatively alter these results. The IGF1 and SSTR5 regions still contained the five most significant SNPs. The correlation in effect estimates between the original analyses and the sensitivity analyses for the SNPs in Table 2 was over 0.98, and despite the reduced sample size rs3751830 in SSTR5 remained pathway-wide significant.
Five SNPs remained in a multi-SNP regression model for IGF-1 levels in a backwards stepwise selection analysis beginning with 13 SNPs with p<0.001: rs1520220 (IGF1), rs3751830 (SSTR5), rs213656 (SSTR5), rs344352 (IGFALS), and rs3770473 (IGFBP2/5). These five SNPs explained 0.62% of the variation in circulating IGF-1 within our study population.
Association between SNPs and circulating IGFBP-3 levels
Six SNPs in IGFBP3 and four in IGFALS were significantly associated with circulating IGFBP-3 levels after adjusting for multiple testing at the pathway level (Table 3). The λGC values for IGFBP-3 levels were 1.30 for men, 1.39 for women, and 1.20 for the combined analysis. All six SNPs in IGFBP3 and two of the SNPs in IGFALS remained statistically significant (corrected p-value < 2.1 × 10−4) after λGC-correction.
Table 3
Table 3
Associations between single SNPs and circulating IGFBP-3
Sensitivity analyses excluding all cases diagnosed less than two years after blood draw did not qualitatively alter these results. The IGFBP3 and IGFALS regions still contained the ten most significant SNPs, and SNPs in both of these genes remain significant at the pathway level. The correlation in effect estimates between the original analyses and the sensitivity analyses for the SNPs in Table 3 was over 0.99.
Five SNPs remained in a regression model for IGFBP-3 levels after backwards stepwise selection, starting with the 14 SNPs with p<0.001: rs2854746 (IGFBP3), rs11865665 (IGFALS), rs344352 (IGFALS), rs3770473 (IGFBP2/5), and rs10228265 (IGFBP1). These SNPs explained R2=3.9% of the variation in circulating IGFBP-3 within our study population.
IGF-1 and IGFBP-3 predicting scores and cancer status
Using SNPs that were associated with IGF-1 or IGFBP-3, we created two separate genetic scores (see Methods section) predicting IGF-1 and IGFBP-3 levels respectively and examined their association with cancer status. No significant associations were noted between either score (IGF-1 or IGFBP-3) and either cancer. The odds ratio (OR) associated with a one standard deviation increase in the IGF-1 score was 0.99 (0.93, 1.05) for prostate cancer and 1.01 (0.96, 1.07) for breast cancer. The OR associated with a one standard deviation increase in the IGFBP-3 score was 0.98 (0.93, 1.03) for prostate cancer and 1.00 (0.94, 1.05) for breast cancer.
This large, comprehensive study identified novel associations between common SNPs in SSTR5 and circulating IGF-1 levels and between common SNPs in IGFALS and circulating IGFBP-3 levels. We also replicated previously-reported associations between SNPs in IGF1 and IGF-1 levels and between SNPs in the IGFBP3/BP1 region and IGFBP3 levels (89, 12).
IGFALS gene
In our analyses we found four tagging SNPs in the IGFALS gene that were statistically significantly associated with circulating IGFBP-3 after multiple testing adjustment at the pathway level; one of these SNPs was also statistically significantly associated with IGF-1 levels after adjustment for multiple testing at the gene level. The protein product of this gene (a liver-derived, GH-dependent, 85-kD glycoprotein) is a key component of the 150-kD three-part (ternary) IGF circulating complex (46). More than 80% of IGF-1 circulates as a ternary complex, comprised of one molecule each of IGF-1, IGFBP-3 or 5, and IGFALS (4748). The half-life of IGF-1 carried as this ternary complex is 12 h, much longer than that of binary, bound IGF (30–90 min) or free, unbound IGF-1 (10 min) (49). Our study suggests that common genetic variants may also contribute to the variation of circulating IGF-1 and IGFBP-3. Additional studies will be needed to identity the precise causal variant(s) in this region.
SSTR5 gene
We found four SNPs in SSTR5 that were significantly associated with circulating IGF-1 levels after adjustment for the number of independent tests at the pathway level. A previous (15) study in the EPIC cohort examined 4 SNPs within the SSTR5 gene and reported one synonymous SNP rs642249 in exon 1 associated with IGF-1 level. Another study within the Swedish CAPS project examined five SNPs in the SSTR5 gene and showed strongly significant associations between the missense SNP rs4988483 and reduced circulating levels of both IGF1 and IGFBP3 (13). Data on linkage disequilibrium between rs642249 and the SNPs genotyped in this study are unavailable. Data from the 1000 Genomes Project CEU panel suggest the linkage disequilibrium between rs498843 and SNPs in this study is low (r2<0.05). Thus it is difficult to assess whether the previous results are consistent with those we present here. The SSTR5 gene encodes one of five somatostatin receptors; these structurally related proteins are members of the seven transmembrane-spanning family of G protein-coupled receptors. Somatostatin binds with these receptors to exert its biological functions, including inhibition of GH secretion from the pituitary (50). It is possible that common SNPs located within or near SSTR5 influence its expression or its affinity with SST and thereby inhibit GH production, which eventually affects the production of IGF-1.
IGF1 gene
We found two SNPs in IGF1 (rs1520220 and rs10735380) that were statistically significantly associated with IGF-1 levels in the combined sample of men and women after adjusting for multiple testing at that pathway level. These results are similar to previous analyses of BPC3 data conducted in men and women seperately (1617) These observations are consistent with reports by Tamimi et al. (51) and Al-Zahrani et al. (9). Tamimi et al. found the SNP rs1520220 was associated with mammographic density in women; this association may be mediated through this SNP’s effect on IGF-1 expression. Al-Zahrani et al. also found that the minor allele of rs1520220 was significantly associated with an increase in circulating IGF-1 in women.
More than 10 studies have examined possible associations between circulating IGF-1 levels and a simple sequence length polymorphism (CA)n 1 kb upstream of the IGF1 gene, but the results have been inconsistent (11). We did not genotype IGF1 (CA)n, but according to a previous report, the less common repeat length for this polymorphism is in LD with the minor alleles of rs7965399 and rs35767 (17). We did examine these two SNPs, and one was significant after adjusting for multiple comparisons at the gene level (rs35767: p=0.2 in women, 0.0028 in men, 0.0034 in combined). In a meta-analysis combining nearly 1300 Swedish men and 4 other studies, two tagging SNPs (rs6220 and rs7136446) in the 3’ region of IGF1 gene were reported to be associated with circulating IGF-1 (12). Although we did not genotype rs7136446, we could impute genotypes for this SNP (the estimated correlation between the imputed and true genotypes was greater than 0.67). We found a nominally significant association between the minor allele of rs7136446 and increasing IGF-1 levels (p=0.009 for men and women combined). We could not impute rs6220, because it has not been genotyped in the HapMap CEU reference panel.
Taken together, the previous findings and our study suggest that common genetic variation in the IGF1 gene influences circulating IGF-1 levels. The SNP rs1520220 is consistently associated with circulating IGF-1, although the functional mechanism underlying this association still needs to be explored.
IGFBP3 gene
The positive association of the SNP C-202A (rs2854744) in the promoter region of the IGFBP3 gene with circulating IGFBP-3 levels has been noted consistently in at least eleven studies, including two recent reports from the BPC3 group in men and women (910, 16, 27, 5258). An in vitro study has suggested the A allele of this SNP had higher promoter activity (54). The BPC3 genotyped 16 SNPs in the IGFBP1/3 gene region and their flanking area (8 SNPs in each gene region), among which six SNPs in the IGFBP3 region were significant in our analyses after multiple comparison adjustment at the pathway level, with p values ranging from 4.50 ×10−83 (rs2854746) to 1.20×10−6 (rs2270628) (Table 3); this is consistent with the two BPC3 reports (1617) As noted by multiple studies (16, 27, 59) the most significant SNP was rs2854746, a nonsynonymous polymorphism in exon 1 (Gly32Ala), rather than the extensively reported promoter polymorphism rs2854744 (A-202C). Although the exact functional SNP influencing the IGFBP-3 levels remains to be identified, our analyses together with previous reports suggest the existence of common variants in the IGFBP3 gene that influence circulating IGFBP-3 levels.
IGF-1 and IGFBP-3 predicting scores and cancer status
Although both IGF-1 and IGFBP-3 levels were associated with prostate cancer in our study, no association was observed between either (IGF-1 or IGFBP-3) score predicted by SNP risk alleles and prostate (or breast) cancer. This is consistent with the fact that the SNPs used to create these scores explain only a small percentage of the variation in IGF-1 (<0.7%) or IGFBP-3 levels (<4%). The finding of no association between predicting scores and breast cancer is consistent with the null association between circulating IGF-1 and IGFBP-3 and breast cancer risk in our data. Given that twin studies show that up to 60% IGF-1 and IGFBP-3 in adults are explained by heritable factors (78, 60), the reason behind the small percentages of IGF-1 and IGFBP-3 explained by these pathway genes needs to be explored.
Strengths and limitations
To our knowledge, this is the most comprehensive candidate gene study to examine the genetic factors affecting circulating IGF-1 and IGFBP-3 in terms of 1) the numbers of genes (18), 2) the coverage of each gene (SNPs were chosen to tag both SNPs in the HapMap CEU panel and exonic variants detected by resequencing), and 3) the number of subjects. With germline DNA samples from over 11,000 individuals, we have 99%, 97%, 92%, and 51% power to detect SNPs that can explain 0.3%, 0.25%, 0.15%, and 0.1% (respectively) of IGF-1 (or IGFBP-3) variation at the 0.001 significance level with 2-sided tests. The strict multiple comparison adjustment at the pathway level and the conservative use of genomic control decreases the possibility of false positives. Because ours is a multi-center study, we encountered challenges such as differences in laboratory assays for biomarker measurement and differences in lifestyle and environmental factors that might contribute to IGF-1 (or IGFBP-3) variation. To solve this problem, we adjusted for age and assay batch,. Given the relatively large genomic control inflation factor for the SNP-IGF-1 association among men, we cannot rule out systematic bias such as population stratification; however, after we corrected for genomic control, the top SNPs were still significant.
Conclusion
In summary, our study suggests that common genetic variation in the IGF1 and SSTR5 genes affects circulating IGF-1 levels, and that the same may be true for variation in the IGFALS gene. In addition, common genetic variation in the IGFBP3 and IGFALS genes may influence the concentration of circulating IGFBP-3. Common genetic variation in the other 14 genes in the IGF signaling pathway that we studied had no important impact on circulating IGF-1 or IGFBP-3 levels. Common genetic variation in the pathway accounts for only a small percentage of circulating levels of these biomarkers among Caucasians (0.62% for IGF-1 and 3.9% for IGFBP-3). Other factors such as rare variants in these genes, structural changes in these genes, genetic variation outside of the regions we studied, and gene-environmental interactions, along with lifestyle and environmental exposures, may make their own contributions to IGF-1 and IGFBP-3 variation in population.
Supplementary Material
Acknowledgments
Grant support: This work was supported by the U.S. National Institutes of Health and the National Cancer Institute (cooperative agreements U01-CA98233 to David J. Hunter, U01-CA98710 to Michael J. Thun, U01-CA98216 to Elio Riboli and Rudolf Kaaks, and U01-CA98758 to Brian E. Henderson, and Intramural Research Program of NIH/National Cancer Institute, Division of Cancer Epidemiology and Genetics).
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