We sought to examine the potential genetic basis for population differences in insulin-related phenotypes in a racially/ethnically diverse sample of children. We found that European genetic admixture is associated with insulin-related phenotypes. Next, we determined whether any of the individual 142 AIMs scattered throughout the genome were associated with any of the insulin-related phenotypes. We found a strong association between SI and an AIM at chromosome 2p21 (rs3287), explaining 4.14 per cent of the variance of the trait. Although this effect size may appear large compared with other genetic association studies, our use of refined phenotypes, the inclusion of many covariates and the use of admixed individuals is likely to have increased our ability to detect an effect size of this magnitude. We also found weaker, but statistically significant associations between AIRg and an AIM at chromosome 8q13 (rs1373302) located in the transient receptor potential cation channel, subfamily A, member 1 (TRPA1) gene, and between FI and HOMA-IR and an AIM at chromosome 15q22 (rs12439722) in the gene for the hect (homologous to the E6-AP[UBE3A] carboxyl terminus) domain and RCC1 (CHC1)-like domain RLD) 1 (HERC1). It should be noted that, although we have used a multiple correction for the 142 markers tested, we have not corrected for each of the genetic models tested. If we were to use a Bonferroni correction for all markers and models tested, the p-value threshold would be 8.8 × 10-5. In this case, only the association between rs3287 and SI (p = 5.8 × 10-5) would be considered statistically significant. The four genetic models are likely to be correlated, however, making such a correction overly conservative.
Our finding that insulin-related phenotypes are associated with European admixture is in agreement with previous findings . European admixture is positively associated with favourable insulin-related phenotypes (higher SI and lower AIRg, FI and HOMA-IR). When we examine the association of European admixture within racial/ethnic groups, they are only statistically significant among HAs. It is difficult to interpret these results, however, because of the different sample sizes and different admixture proportion distributions by racial/ethnic group. Among the other covariates examined, we find that total body fat and Tanner stage are the strongest risk factors associated with these insulin-related traits. These results are consistent with those of other studies that have shown that adiposity is a major risk factor for these traits [50, 51]. Insulin-related traits have also previously been found to be associated with pubertal stage [52, 53].
The 2p21 chromosomal region previously has been identified as being associated with type 2 diabetes and related traits via both linkage scans and GWAS. Marker rs3287 is located between two loci, thyroid adenoma associated (THADA) and B-cell chronic lymphocytic leukaemia/lymphoma 11A (BCL11A) at 2p21. These loci have been identified previously in two recent GWAS meta-analyses of type 2 diabetes [21, 54]. This region was also identified in linkage scans for insulin- and diabetes-related traits [55, 56]. It is plausible that, through their effects on cell apoptosis  and/or nutrient transport, these loci may be associated with the progression of type 2 diabetes and/or that different pathways may be involved across populations. Given that the occurrence of the rs3287 risk allele is higher in the West African parental population than in the European and Amerindian parental populations, and that AAs tend to have lower SI, this or another nearby variant that is in admixture LD may explain part of the observed differences in type 2 diabetes susceptibility between AAs and EAs. The markers that we have found to be associated with FI and HOMA-IR are in a region on chromosome 15 that previously has been found to be associated with insulin-related traits in a linkage scan .
Unlike other association studies, we have identified these associations relatively early in the lifespan. It could be that the children with unfavourable insulin-related phenotypes are already on the path towards developing type 2 diabetes. In the long-term, these markers could inform prediction and treatment strategies for early-onset type 2 diabetes and explain population differences. The fact that none of the AIMs showed any significant association with any of the insulin-related phenotypes when performing analyses within racial/ethnic groups may be related to reduced power due to a smaller sample size. Furthermore, our ability to find significant associations by race is strongly influenced by the frequency of the variants and the fact that AIM alleles tend to have a low frequency in one group and a high frequency in another group. By using a multi-ethnic approach, we have the advantage of having more intermediate allele frequencies represented, thus increasing the power to detect associations.
This study had several strengths. First, the use of several endo-phenotypes that are likely to be proximal to the development of type 2 diabetes may have pinpointed more effectively the genetic factors that eventually lead to disease phenotypes. Secondly, the inclusion of individuals from different racial/ethnic backgrounds, and the use of markers that differ in frequency between populations, can lead to a better understanding of the genetic basis for population differences in insulin-related phenotypes and the prevalence of diabetes. Thirdly, the inclusion of environmental and phenotypic measurements enhances the ability to pinpoint the genetic regions that directly influence the disease-causing phenotype.
The study also had some limitations. A relatively small number of genetic markers were used, reducing our ability to provide a high level of resolution with regard to the precise location of potential risk variants. The main weakness of the study was in the small sample size, which raises concerns about the statistical power of the study. Of all the reported associations, the one between marker rs3287 and SI will require the greatest level of statistical power in order to reject the null hypothesis. A power calculation for this association model reveals that at a p-value of 6 × 10-5, our data provides 67 per cent power to estimate the R-squared effect of 0.45 that we obtained for the full model, with a semi-partial R-squared for the marker of 0.04. Although this level of power might not seem sufficient, the concerns of this association being a type 1 error are dissipated by the fact that it represents a form of replication of several previously reported findings at chromosome 2p21. Evidently, this level of detection with a small sample size was aided by the use of precise phenotyping, the consideration of physiological parameters and the inclusion of admixture estimates, as previously discussed.
In conclusion, we have shown that regions on chromosomes 2, 8 and 15 were associated with insulin-related traits in this sample. These results suggest that these regions may harbour causal variants that may also explain population differences in the insulin-related phenotypes and, ultimately, type 2 diabetes prevalence, since the markers tested exhibit large frequency differences between groups. Future studies must combine detailed phenotypic, environmental and genetic measures on similarly diverse but larger sample sizes. The inclusion of different populations is of paramount importance if we are to understand the genetic basis for population differences and fairly implement effective prevention, intervention and treatment strategies.