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IGF1R encodes the Insulin-like Growth Factor 1 Receptor, a transmembrane tyrosine kinase receptor located on chromosome 15q26.3, in a region of linkage (LOD=2.53, P=0.00032) to insulin levels at 30 minutes (Insulin30) on an oral glucose tolerance test (OGTT) in the Old Order Amish. Mouse models with beta cell-specific deficiency of IGF1R demonstrate defects in glucose-stimulated insulin secretion.
To test the hypothesis that genetic variation in IGF1R is associated with impaired insulin secretion, we genotyped 54 SNPs in 778 nondiabetic subjects from the Amish Family Diabetes Study who had undergone OGTTs and tested them for association with ln Insulin30 and Insulin Secretion Index (ISI).
No individual SNPs were significantly associated with ln Insulin30 or ISI using a multiple hypothesis testing adjusted P<0.002. Tests of association of 4-SNP haplotypes constructed by a windowing approach revealed an association of the CTTG-variant of a 4-SNP haplotype found in intron 20 (rs1784195-rs2715439-rs8034284-rs12440962) with lower ISI levels (β = 0.18, SE(β) = 0.05, P=0.001).
Sequence variation in IGF1R may influence insulin secretory function, although further studies in other populations will be needed to confirm these findings.
Abnormal insulin secretion is one of the features critical to the development of type 2 diabetes mellitus (T2DM) . While influenced by multiple environmental factors , insulin secretion is highly heritable (h2=0.50–0.58). Recent genome-wide association studies have identified several diabetes susceptibility loci. Accumulating data suggests that these loci influence diabetes risk through their effects on beta cell function [3–5]. In a genome-wide linkage analysis of insulin response to an oral glucose tolerance test (OGTT) in the Amish Family Diabetes Study (AFDS), suggestive linkage was observed on chromosome 15q13-26 to 30 minute OGTT insulin levels (Insulin30) (LOD=2.53, P=0.00032) , highlighting this region as a potential site of a candidate gene for insulin secretion and T2DM.
One strong biological candidate gene in this region is insulin growth factor-1 receptor (IGF1R; MIM: 147370). The IGF1R gene is composed of 21 exons spanning 308 kilobases (kb) on chromosome 15q26.3. IGF1R is a transmembrane tyrosine kinase receptor widely expressed in human tissues with 50% amino acid homology with the insulin receptor (IR). In beta cell-specific IGF1R null mice, beta cells develop normally, but GLUT2 and glucokinase expression are reduced, leading to diminished glucose-stimulated insulin secretion and impaired glucose tolerance . In a combined IGF1R and IR beta cell-specific knockout, beta cells again develop normally, but undergo rapid apoptosis , suggesting that insulin and IGF1 signaling are critical in regulating beta cell mass, and defective signaling may contribute to T2DM development.
Based on these and other studies  and the linkage peak on chromosome 15q, IGF1R was selected as a candidate gene for further study of insulin secretion traits. We genotyped a dense panel of single nucleotide polymorphisms (SNPs) in nondiabetic AFDS participants and performed association analyses with Insulin30 and insulin secretion index (ISI) phenotypes from the OGTT.
The design and methods applied in the AFDS have been described elsewhere . Briefly, the AFDS includes 1,402 subjects from 58 pedigrees, consisting of probands with T2DM and all willing first and second degree relatives of probands and spouses over the age of 18. OGTTs were performed on 883 subjects without a previous diagnosis of diabetes. The absence of diabetes was confirmed in 778 subjects. Table 1 summarizes characteristics of the nondiabetic participants. Insulin30 and ISI (ISI=ln Insulin30 − ln Insulin0) were used as proxy measures for insulin secretory function. The study protocol was approved by the institutional review board at the University of Maryland School of Medicine, and informed consent was obtained from each participant.
SNPs in IGF1R and 10kb flanking sequences were selected for genotyping by first identifying 53 tagging SNPs. Criteria for tagging SNP selection included a minor allele frequency (MAF) ≥0.1 and a minimum r2 ≥0.8 between SNPs, and then forced inclusion of four non-synonymous coding SNPs for a total of 53 SNPs. Tagging SNPs were identified using the tag-SNP selection program Tagger , as implemented in Haploview . The tagging SNPs were supplemented by an additional 46 SNPs from dbSNP in regions with >10kb between tagging SNPs. SNP genotyping was performed using either Ultra-High Throughput Genotyping (SNPStream, Beckman Coulter; Fullerton, CA), Pyrosequencing (PSQ HS96A System, Biotage; Westbrough, MA), or TaqMan SNP Genotyping Assay (ABI PRISM® 7900HT Sequence Detection System, Applied Biosystems Inc.; Foster City, CA).
Prior to analysis, genotype data was analyzed in PEDCHECK  to identify Mendelian inconsistencies. One hundred two blind replicates were included to determine replication rates. Markers with replication rates <95% (5 markers), and call rates <85% (4 markers) were excluded from analyses. Of the remaining SNPs, 36 were monomorphic or had a MAF <0.05 leaving a total of 54 SNPs for further analysis. Hardy-Weinberg equilibrium (HWE) was determined for each SNP using a threshold of P>0.001 to account for multiple comparisons.
We estimated the effects of genotype on ln Insulin30 and ISI levels in 778 subjects confirmed to not have diabetes. These analyses were adjusted for sex, age, age2, BMI and Insulin0, all independently associated with both phenotypes. Because subjects were recruited by family, estimates were obtained for mean ln Insulin30 and ISI levels by genotype conditioning on the pedigree structure in association via a “measured genotype” approach, and statistical significance for the association was determined using a likelihood ratio test under an additive model with one degree of freedom . To account for multiple testing, a principal components-based approach was used correcting for LD between proximal SNPs  adjusting our threshold for statistical significance to P<0.002.
As the average LD block length was 3.5 SNPs, haplotypes were chosen to be four SNPs long to minimize multiple hypothesis testing. Four-SNP haplotypes (Supplemental Table 3) were inferred under an assumption of zero recombinants, as implemented in ZAPLO , with extended pedigrees subdivided into 165 smaller, nuclear pedigrees due to computational complexity in inferring haplotypes for the larger pedigrees. The most probable inferred haplotype pairs were used in analyses. Haplotypic association tests followed a similar approach to genotypic association analysis. To determine the clinical consequences of insulin and ISI-associated SNPs and haplotypes, we then compared SNP and haplotype frequencies between subjects with (n = 139) and without (n = 468) diabetes. The subjects without diabetes were chosen on the basis of their being aged 38 years or older. All analyses were performed using SOLAR .
Joint variance components linkage and association analyses of ln Insulin30 and ISI were then performed in 468 nondiabetic AFDS participants for whom both microsatellite marker and IGF1R SNP genotype data were available in order to determine if the observed association was due to linkage to Insulin30 or ISI on chromosome 15 . These analyses incorporated each SNP or haplotype by adjusting for the genotype as a covariate, along with adjusting for sex, age, age2, BMI, and Insulin0. Insulin30 levels were transformed by their natural logarithm prior to analysis to remove skewness.
Fifty-four SNPs (average distance=5.7 kb), including 50 intronic SNPs, one 3′-untranslated-region SNP and three synonymous coding SNPs were polymorphic and had call rates >85% (Supplemental Table 1). Genotype frequencies for the 54 SNPs did not differ significantly from those predicted under Hardy-Weinberg equilibrium (P>0.001). LD patterns were similar to or higher than those estimated from the Caucasian founders in HapMap (Figure 1).
Of the 54 polymorphic SNPs, none were significantly associated (P<0.002) with variation in Insulin30 (Figure 2A). However, six SNPs with P<0.05 (P=0.011–0.047) were identified (Intron 1: rs874305, rs4966009, rs4966012, rs1319859; Intron 2: rs2670504, rs2715440) (Supplemental Table 2). As with Insulin30, none of the SNPs were significantly associated with ISI at P<0.002 (Figure 2B). Associations for ISI of P<0.05 were observed for three clusters of SNPs in intron 1 (rs874305, rs4966012, rs1319859), intron 2 (rs2670504, rs2684778, rs2715440, rs7165181, rs4966035), and intron 5 (rs3743259, rs4966038) (Supplemental Table 2).
While no 4-SNP haplotypes demonstrated association with Insulin30 (data not shown), the CTTG haplotype of SNPs rs17847195, rs2715439, rs8034284, and rs12440962 (haplotype 48) in intron 20 was associated with lower ISI levels (β = 0.18; 95% CI: 0.08, 0.29; genotypic means: CTTG/CTTG 1.37±0.09 vs. CTTG/CTCG 1.54±0.09 vs. CTCG/CTCG 1.73±0.11) (Figure 3), exceeding our multiple-testing-adjusted significance threshold of α=0.002 with P=0.001. Haplotypes 46 (rs8038056-rs41497346-rs17847195-rs2715439), 47 (rs41497346-rs17847195-rs2715439-rs8034284), 49 (rs2715439-rs8034284-rs12440962-rs2593053), and 50 (rs8034284-rs12440962-rs2593053-rs17847203), which flank haplotype 48, displayed associations with lower ISI consistent with haplotype 48 at P<0.05 (Supplemental Table 4).
We generated posterior estimates of power to detect association of Haplotype 48 with ISI levels in the sample of 468 AFDS members with complete haplotypic and phenotypic data using QUANTO  and found that we had 86% power to detect a statistically significant difference of 0.18 in the ISI levels between genotype at α=0.002 under an additive model for a haplotype with frequency of 0.16.
We tested association of the SNPs in haplotype 48 with T2DM in 139 AFDS T2DM cases and 468 AFDS controls. While none of the SNPs were individually associated with T2DM, the CTTG haplotype was modestly associated with elevated T2DM risk (P=0.02) in a model adjusted for sex, age, age2, BMI, and Insulin0, though not at the gene-wide significance threshold of α=0.002.
To determine if the linkage on chromosome 15q could be attributed to associations with IGF1R SNPs, we performed joint linkage and association analyses of ln Insulin30 and ISI in 468 nondiabetic AFDS subjects with chromosome 15 microsatellite data . Linkage analyses without adjustment for IGF1R genotypes demonstrated peak locus-specific LOD scores (ln Insulin30: LOD=2.23; ISI: LOD=3.13) in the same region of chromosome 15 (53–54cM). Adjustment for associated variants, including haplotype 48, in linkage analyses did not substantially reduce Insulin30 and ISI peak locus-specific LOD scores (ln Insulin30: LOD=1.72–2.17; ISI: LOD=1.61–3.02 ), suggesting that these markers do not account for the previously observed linkage on chromosome 15q .
Previous studies in IGF1R knockout mice showing abnormal insulin secretion and the linkage peak to Insulin30 on chromosome 15q in the Amish under which IGF1R sits make IGF1R a strong positional candidate gene for insulin secretion and T2DM. Based on our detailed examination of IGF1R, we found evidence that IGF1R may influence insulin secretion in humans and thus, may play a role in T2DM pathology. Here, we report an association of a haplotype in intron 20 of IGF1R, the CTTG-variant (frequency = 0.13) of SNPs rs17847195, rs2715439, rs8034284, and rs12440962, with lower ISI (P=0.001) and elevated risk of T2DM (P=0.02).
While some associations of genes with diabetes may be spurious , studies in mice suggest that the association of IGF1R variants with insulin secretion may underlie a true relationship. As described previously, mice with beta cells lacking IGF1R demonstrate defective glucose-driven insulin secretion , suggesting IGF1R is critical for proper beta cell function. Similarly, mice with beta cell-specific knockouts of both IR and IGF1R are born with a normal complement of beta cells, but glucose-stimulated insulin secretion is blunted. As a result, by 3 weeks, the mice developed diabetes .
Previous studies of IGF1R sequence variation and T2DM risk are conflicting. Rasmussen et al.  analyzed IGF1R coding sequence in 82 Danish subjects with T2DM, and found no variants associated with T2DM or related endophenotypes including reduced birth weight and insulin sensitivity index. In contrast, in the Finnish Diabetes Prevention Study , a common synonymous coding variation in IGF1R (GAG1043GAA) (rs2229765) was significantly associated with differences in conversion rates from impaired glucose tolerance (IGT) to T2DM in subjects participating in a weight loss program, suggesting that this polymorphism increases T2DM risk among those with IGT. In our study, rs2229765 was not associated with differences in Insulin30 (P=0.9) or ISI (P=0.9) and was marginally associated with diabetes (P=0.008) using α=0.002 for statistical significance. Rs2229765, however, is present in a haplotype associated with ISI (haplotype 42), and is in strong LD with several of the SNPs in the associated haplotype in intron 20. One possible explanation for the lack of association at rs2229765 in our study may be differences in the LD patterns underlying IGF1R among different populations.
Several features of our study may have improved our power to detect these associations with IGF1R. First, our study was performed in subjects from a genetic isolate, thereby, minimizing confounding effects from population stratification. Second, our background LD is likely higher than in most Northern European samples , such that we may have better gene coverage than other outbred populations. Third, AFDS participants have similar occupational and environmental exposures  as they are predominantly farmers and homemakers from Lancaster, Pennsylvania, thus decreasing confounding effects from these factors. Fourth, our power to detect association with IGF1R may have been increased by analyzing T2DM subphenotypes (Insulin30, ISI) since these subphenotypes are likely to be less genetically complex. Finally, the existence of linkage to insulin levels and T2DM in more than one population near IGF1R [6,22,23] may have reduced the likelihood of a false-positive error due to multiple testing .
Despite the strengths of our study, the study was subject to several limitations. While we found our strongest association with a haplotype, type I error may be present in our analyses as a result of inferring haplotypes with only nuclear pedigrees. Another potential source of error may be the use of the most probable inferred haplotypes, though the average posterior probability for inferred haplotypes was 0.66, indicating that it is unlikely to be a major source of error. To minimize these limitations, an adjusted threshold for statistically significant association was used. Among the genotyped SNPs, we observed high replication rates; however, our call rates were lower than anticipated (average 89%). Similar replication and call rates have been reported by others using SNPStream UHT for genotyping . To ensure that no systematic error was present, we examined genotyping plates and found the proportion of uncalled genotypes to be consistent across plates. Moreover, the fact that all SNPs were in HWE provided no strong evidence for under-calling or over-calling of heterozygosity. Furthermore, no statistically significant differences in trait distributions among those with and without genotype calls were found. Another potential limitation was that 45 of 99 SNPs were monomorphic, had a MAF <0.05 or were unsuccessfully genotyped. Despite this problem, the remaining 54 SNPs demonstrated a high degree of pairwise LD (average r2=0.94) and achieved a high degree of coverage of the gene. Finally, as the Amish are genetically isolated, it is possible that our association of IGF1R with ISI may not be generalizable to other populations; however previous genetic studies of T2DM and related traits in the Amish have found results concordant with other Caucasian populations [26,27], suggesting that our findings in the Amish will likely be relevant to more outbred populations.
While we did not observe statistically significant single-SNP associations, there are several reasons to believe that the observed haplotypic association is real. Haplotypic association may have increased power to detect genetic effects at ungenotyped causal SNPs through indirect association, as haplotypes may be better than individual SNPs at tagging causal SNPs via LD, as exemplified in Martin et al.  for APOE and Alzheimer’s disease and Veal et al.  for PSORS1 and psoriasis, among others [30,31]. Haplotype frequencies may approximate more closely the allele frequency of a causative SNP , which may be the case with haplotype 48. Additionally, haplotypes themselves may contribute to risk for disease, as several studies have identified important haplotypic risk variants for disease such as the APOE ε3/ε4 alleles for Alzheimer’s disease , β2-adrenergic receptor (β2AR) in bronchodilator response , and complement factor H (CFH) in age-related macular degeneration . However, given that the haplotype is only modestly associated with ISI after adjustment for multiple hypothesis testing, this association needs to be replicated in an independent population.
The experience of our study mirrors that of many others insofar as it illustrates the challenges of going from a positive linkage result to identifying a causative SNP. One possibility (among several) is that the effect detected through linkage analysis may be attributable to one or more rare variants, while our strategy for identifying associated SNPs was predicated on a tagging SNP approach geared towards detecting common SNPs. Identifying the functional SNPs whose frequencies are rare remains a very challenging enterprise that may require both additional approaches for SNP discovery (e.g., sequencing) as well as functional assessment of associated SNPs.
In summary, our study suggests that IGF1R variants may influence insulin secretion. However, further studies are needed to confirm these findings.
This work was supported by NIH research grants R01 DK068495 (KDS), R01 DK54261, U01 HL084756-02 (JRO), and training grant T32 HL07024 (ACN); the University of Maryland General Clinical Research Center (M01 RR16500); Hopkins Bayview General Clinical Research Center (M01 RR02719); the Maryland Clinical Nutrition Research Unit (P30 DK072488); and the Baltimore Veterans Administration Geriatric Research and Education Clinical Center. We gratefully acknowledge our Amish liaisons and fieldworkers and the extraordinary cooperation and support of the Amish community, without whom these studies would not be possible.
Conflict of Interest