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A Genome-Wide Approach Accounting for Body Mass Index Identifies Genetic Variants Influencing Fasting Glycemic Traits and Insulin Resistance

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A Genome-Wide Approach Accounting for Body Mass Index Identifies Genetic Variants Influencing Fasting Glycemic Traits and Insulin Resistance ARTICLES A genome-wide approach accounting for body mass index identifies genetic variants influencing fasting glycemic traits and insulin resistance Recent genome-wide association studies have described many loci implicated in type 2 diabetes (T2D) pathophysiology and b-cell dysfunction but have contributed little to the understanding of the genetic basis of insulin resistance. We hypothesized that genes implicated in insulin resistance pathways might be uncovered by accounting for differences in body mass index (BMI) and potential interactions between BMI and genetic variants. We applied a joint meta-analysis approach to test associations with fasting insulin and glucose on a genome-wide scale. We present six previously unknown loci associated with fasting insulin at P < 5 × 10−8 in combined discovery and follow-up analyses of 52 studies comprising up to 96,496 non-diabetic individuals. Risk variants were associated with higher triglyceride and lower high-density lipoprotein (HDL) cholesterol levels, suggesting a role for these loci in insulin resistance pathways. The discovery of these loci will aid further characterization of the role of insulin resistance in T2D pathophysiology. In contrast to recent progress in the discovery of genetic variants under- for the potential for adiposity levels to perturb the physiological lying T2D pathophysiology and β-cell function, the understanding milieu in which genetic variants in insulin signaling pathways oper- of the genetic basis of insulin resistance remains limited1. Partly ate. Adiposity may also hinder the identification of genetic variants because early case-control studies of T2D were designed to maximize influencing insulin resistance by introducing variance in the outcome the likelihood of detecting variants that directly increase T2D risk that is not attributable to genetic variation5, suggesting that adjust- rather than those that affect risk through the mediation of adiposity, ment for adiposity per se may be necessary. most of the associated loci discovered in these studies mapped to A joint test that investigates the association between an outcome genes related to β-cell dysfunction2. More recently, we have shown and a genetic variant, while allowing for possible effect modifica- that the genetic architectures of quantitative indices of β-cell function tion by an environmental variable, has been proposed13. Moreover, and of insulin resistance differ markedly: given the same individuals, a statistical method was developed that extends this joint test to a © 2012 Nature America, Inc. All rights reserved. America, Inc. © 2012 Nature sample sizes and biochemical measurements, we described a larger meta-analysis context14. This enabled us to simultaneously test both number of signals for β-cell function than for insulin resistance3,4. the main genetic effect, adjusted for BMI, and potential interaction Although this observation is consistent with the higher reported between each genetic variant and BMI. This joint meta-analysis npg heritability of insulin secretion compared to resistance, overall (JMA) approach can provide increased power for detecting genetic heritability estimates of insulin resistance in individuals of European loci when underlying interaction effects are suspected but unknown13, ancestry of 25–44% suggest that many loci remain to be discovered and, notably, as shown in simulation studies, this approach does not and that new strategies are required for their identification5. reduce power to detect the main genetic effects in the absence of Obesity is an important determinant of insulin resistance6. It was interaction14. Within the Meta-Analyses of Glucose- and Insulin- postulated that adiposity might modulate the genetic determinants of related traits Consortium (MAGIC), we implemented this approach insulin resistance and contribute to the heterogeneity of T2D etiology. and performed a genome-wide JMA to search for SNPs significantly It has been shown that the heritability of insulin resistance increases associated with glycemic traits, while simultaneously adjusting for with higher BMI7, and some candidate gene studies have observed BMI and allowing for interaction with BMI. Using this method, we that genetic effect size varies with adiposity level8–10, findings that are successfully identified loci that are associated with fasting insulin compatible with the presence of an underlying interaction between levels at genome-wide significance levels. BMI and genetic variants for insulin resistance. Furthermore, the adipokine hormones and proinflammatory cytokines that are pro- RESULTS duced by adipose tissue can influence insulin signaling via diverse Study overview mechanisms11,12, and these processes may interact with genetic vari- As a first phase, we conducted a discovery genome-wide JMA of the ants influencing insulin resistance pathways. Therefore, to identify main effects of SNPs and of SNP by BMI (SNP × BMI) interaction variants associated with insulin resistance, it may also be important for four diabetes-related quantitative traits: fasting insulin levels, to account for gene variant by BMI interaction, which would allow fasting glucose levels and surrogate measures of β-cell function A full list of authors and affiliations appears at the end of the paper. Received 7 July 2011; accepted 13 April 2012; published online 13 May 2012; doi:10.1038/ng.2274 NATURE GENETICS VOLUME 44 | NUMBER 6 | JUNE 2012 659 ARTICLES a Fasting insulin b Fasting glucose 30 30 G6PC2* GCK** MTNR1B*** GCKR GCKR SLC30A8 DGKB FADS1 CRY2 20 20 ADCY5 MADD ) ) VPS13C P P SLC2A2 ( ( IGF1 PROX1 TCF7L2 10 10 GLIS3 –log 10 –log 10 5 5 0 0 30 30 LYPLAL1 PPP1R3B COBLL1-GRB14 IRS1 UHRF1BP1 DPYSL5 ARAP1 20 20 FOXA2 ) ) PDGFC PCSK1 P OR4S1 P PPP1R3B ( ( PDX1 10 10 –log –log 10 10 5 5 0 0 1 2 3 4 5 6 7 9 11 13 16 19 1 2 3 4 5 6 7 9 11 13 16 19 Chromosome Chromosome Figure 1 Genome-wide association plots of the discovery JMA. Association results are shown for fasting insulin (a) and fasting glucose (b) levels. We observed 17 loci with known associations (red) and took 50 loci forward to follow-up analysis (light and dark blue). Of these loci, 12 reached genome- wide significance in the combined discovery and follow-up JMA (dark blue). The P values of these 12 loci from the models fit in the combined discovery and follow-up analyses are shown below the plots: red, JMA; orange, main genetic effects, adjusting for BMI; green, interaction, with continuous BMI; blue, interaction, with dichotomous BMI. *G6PC2 JMA P = 1.7 × 10−113; **GCK JMA P = 8.3 × 10−56; ***MTNR1B JMA P = 4.38 × 10−105. (HOMA-B) and insulin resistance (HOMA-IR)15. Measures of fasting for fasting glucose and fasting insulin analyses, respectively insulin, HOMA-B and HOMA-IR were log transformed. (Supplementary Table 1). We combined study-specific results from The discovery-stage JMA for fasting insulin levels of approximately both the discovery (phase 1) and the follow-up (phase 2) studies 2.4 million SNPs in 51,750 non-diabetic individuals from 29 studies for the 50 index SNPs (Supplementary Table 3) for total sample (Supplementary Table 1) showed previously reported associations sizes of 96,496 for fasting glucose levels and 85,573 for fasting insulin of variants in IGF1 and GCKR with fasting insulin at genome-wide levels. In the combined JMA, we identified six loci associated with © 2012 Nature America, Inc. All rights reserved. America, Inc. © 2012 Nature significance (Fig. 1a and Supplementary Table 2) and identified 31 fasting insulin levels and seven loci associated with fasting glucose previously unreported loci tentatively associated (P < 1 × 10−5) with levels at genome-wide levels of significance (P < 5 × 10−8), includ- fasting insulin (Fig. 1a, Supplementary Fig. 1 and Supplementary ing PPP1R3B, which was associated with both fasting insulin and 3 npg Table 3a) . The discovery JMA for fasting glucose in 58,074 individu- fasting glucose levels (Fig. 1, Table 1 and Supplementary Table 3). als showed association of 16 loci previously described in MAGIC Descriptions of noteworthy genes around each association signal are meta-analyses of fasting glucose levels (Fig. 1b, Supplementary given (Boxes 1 and 2). Fig. 1 and Supplementary Table 2) and identified 20 previously unre- As the JMA simultaneously tested both main genetic effects and ported loci tentatively associated with fasting glucose (P < 1 × 10−5) interaction effects, in order to further characterize the association (Fig. 1b and Supplementary Table 3b). The JMA approach identi- signals observed using the JMA, we performed additional analyses fied an excess of previously undetected signals relative to the number examining main effects and interaction effects separately. These expected by chance (Supplementary Fig. 1). The SNPs that showed additional analyses included meta-analysis of regression models of association at P < 1 × 10−5 in the discovery JMA for HOMA-IR and the main effects, with and without adjustment for BMI, univariate HOMA-B largely overlapped with those identified for fasting insulin meta-analysis of interaction effects using continuous BMI and meta- and fasting glucose levels, without identifying additional loci. We analyses of the main effects of SNPs in strata defined by a BMI cutoff therefore focused all subsequent analyses on fasting insulin and of 28 kg/m2, which was chosen on the basis of the median BMI of fasting glucose levels. Results for associations with HOMA-IR and the largest cohorts included in our discovery stage JMA (BMI strata HOMA-B are provided (Supplementary Table 4). were categorized as leaner, where BMI <28 kg/m2, and heavier, where At each locus that reached P < 1 × 10−5 in the discovery JMA, we BMI ≥28 kg/m2). chose an index SNP to represent the association signal in the cor- responding genomic region. We selected a total of 50 SNPs (30 SNPs Associations with fasting insulin levels associated with fasting insulin levels, 19 SNPs associated with fasting The loci examined in follow-up analysis that associated with fast- glucose levels and 1 SNP associated with both) for the second, follow- ing insulin levels (natural log transformed) at genome-wide levels up phase of analysis.
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