Ancestry-informative markers on chromosomes 2, 8 and 15 are associated with insulin-related traits in a racially diverse sample of children

Type 2 diabetes prevalence in the paediatric population is increasing, while age at onset is decreasing [1, 2]. Type 2 diabetes also disproportionately affects racial/ethnic minorities in the USA [3]. Twin and familial studies have shown a substantial genetic component to the disease, as well as its related phenotypes [4–10]. Although much is known about environmental contributions to type 2 diabetes, only a very small proportion of the variation due to genetic factors is currently explainable by identified genetic polymorphisms [11–13]. Similarly, little is known about the specific genetic factors that may contribute to population differences in diabetes prevalence. Because the origins of type 2 diabetes are likely to be rooted in childhood, a better understanding of genetic determinants among paediatric populations could lead to a better insight into the aetiology of type 2 diabetes and eventually improved prediction and prevention of the disease.

Endo-phenotypes can be useful in closely dissecting the genetic basis of eventual disease status [14]. For type 2 diabetes, several such measurable phenotypes exist, typically examining measures of glucose and insulin homeostasis. These measures serve as indicators of reduced insulin response and action that may presage type 2 diabetes [15, 16]. Furthermore, previous studies have suggested a genetic basis for racial/ethnic differences in insulin dynamics [17, 18]. Examining the genetic basis for these detailed phenotypes therefore allows for a much better understanding of the link between the genetic and metabolic pathways that underlie the development of type 2 diabetes.

Since the loci recently identified by genome-wide association studies (GWAS)[19–21] occurred predominantly among individuals of European descent, there is considerable uncertainty as to whether these associations translate to other populations. Hispanic Americans (HAs) and African Americans (AAs) suffer from higher rates of type 2 diabetes than European Americans (EAs),[22, 23] and similar differences exist for endo-phenotypes among children, regardless of disease status [24–28]. Admixed populations -- such as HAs, AAs and EAs -- can be examined to determine if there is a genetic basis for these population differences and to identify specific genetic regions associated with both ancestry and insulin-related outcomes [29]. Previous studies have shown that admixture is a strong predictor of diabetes and insulin-related traits but also that this relationship may be mediated through environmental factors such as income or educational level that are correlated with genetic admixture [17, 30–32].

Genetic mapping methods such as admixture mapping capitalise on population differences in a trait and the extended blocks of linkage disequilibrium (LD) created after the admixture process [33]. They are, therefore, most applicable to recently admixed populations such as HAs and AAs. To our knowledge, only one previous study has used this type of method to localise genetic variants associated with type 2 diabetes [34]. Given the large differences in type 2 diabetes and the plausible genetic origins of these differences, there is a great need for additional studies that attempt to pinpoint population-specific genetic risk factors.

In this study, we also capitalise on population differences in a phenotype and the extended LD blocks resulting from admixture in an attempt to identify specific genetic regions that might be involved in the aetiology of population differences in insulin-related traits. Specifically, we examine the association between genetic admixture and four insulin-related outcomes: insulin sensitivity (S_I), acute insulin response to glucose (AIRg), fasting insulin (FI) levels and the homeostasis model assessment of insulin resistance (HOMA-IR). We then examine the association between 142 ancestry-informative markers (AIMs) and each of these traits to identify genetic regions that potentially account for ethnic/racial differences. We performed this study among a multi-ethnic sample of children, adjusting for known influential covariates, including genetic admixture as a control for genetic background, and socio-economic status. We place our findings in the context of other studies that have found associations in similar regions.

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 S_I 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 S_I (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 [17]. European admixture is positively associated with favourable insulin-related phenotypes (higher S_I 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 [57] and/or nutrient transport,[58] 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 S_I, 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 [59].

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 S_I 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.