A Common Genetic Variant Is Associated with Adult and Childhood Obesity

Obesity is associated with an increased risk of type 2 diabetes mellitus, heart disease, metabolic syndrome, hypertension, stroke, and some forms of cancer (1). It is commonly assessed by calculating an individual's body mass index (BMI) [weight/(height)² in kg/m²] as a surrogate measurement. Individuals with a BMI ≥ 25 kg/m² are classified as overweight, and those with a BMI ≥ 30 kg/m² are considered obese. Having a BMI over 25 kg/m² increases the risk of death (2). Presently, 65% of Americans are overweight and 30% are obese (3). Genetic factors contribute significantly to the etiology of obesity (4, 5), with estimates of the heritability of BMI ranging from 30 to 70% (6–9).

To identify common genetic variants associated with elevated BMI, we have studied individuals from the National Heart, Lung, and Blood Institute (NHLBI)–Framingham Heart Study (FHS) (10). The participants were enrolled from the community without being selected for a particular trait or disease and were followed over 24 years (table S1). In this population, heritability estimates for BMI range between 37 and 54% (11, 12).

Using families from this sample, we performed a genome-wide association analysis, using a testing strategy for quantitative traits in family-based designs (13, 14). To bypass the multiple comparisons, we used a two-stage testing strategy implemented in the software package PBAT (15, 16). In the screening step, parental genotypes are used to select single-nucleotide polymorphisms (SNPs) and genetic models that best predict offspring phenotypes. Formally, the screening step estimates the power to detect an association in the second step (Fig. 1 and fig. S1). The second step uses a family-based association test (FBAT) (17) to test the selected SNPs for association with BMI levels using measured offspring genotypes. The FBAT is a generalization of the transmission disequilibrium test (18) and assesses whether the over- or undertransmission of an allele is correlated with offspring phenotypes. Because the allele that is transmitted from the parent to the offspring is selected stochastically, the test step is statistically independent of the screening step, which conditions only on parental genotypes (19, 20). Thus, the power estimates from the screening step do not bias the significance level of any subsequently computed FBAT statistic in the test step (13, 14, 19, 20). Hence, the FBAT results need to be adjusted only for the number of comparisons performed during the test step. A SNP that reaches significance after adjustment for the number of tests performed in the test step is considered significant at a genome-wide level (13). Following the guidelines of Van Steen et al. (13), we tested the 10 SNPs with the highest power estimates in the screening step for association, using the FBAT (21, 22).

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We genotyped 116,204 SNPs in 694 participants from the FHS offspring cohort (20). After exclusions, 86,604 SNPs were tested for association with BMI. We used FBAT-PC, which incorporates BMI data across multiple exams and increases our power to detect a genetic effect (14). Of the top 10 SNPs tested under a recessive model [which we determined during the screening procedure had greatest power (Table 1)], only SNP rs7566605 reached overall significance (unadjusted FBAT-PC P value, 0.0026). Other genetic models that did not capture the underlying biology of the SNP were less robust. In such cases, rs7566605 was not ranked in the top 10 for power. The frequency of the rs7566605 C allele is 0.37, and the SNP is in Hardy-Weinberg equilibrium (Table 2). We found no association with BMI for Affymetrix SNPs close to rs7566605 (Fig. 2A and Table 2), This result was expected because none of these SNPs are good proxies for rs7566605 at a r² threshold of 0.8 (Fig. 2B) (23).

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Another analysis based on a larger sample of 923 FHS individuals, including those already tested, indicated that the SNP is a significant predictor of BMI. For all exams, rs7566605 CC homozygotes are about 1 BMI unit heavier than individuals with GC or GG genotypes (P < 0.0001), regardless of sex or age (Fig. 3). CC homozygotes were also more likely to be obese than non-obese [for example, at exam 5, odds ratio (OR) = 1.33, 95% confidence interval (CI) (1.20 to 1.48)].

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Many reported associations with common gene polymorphisms have not been replicated, presumably because of factors such as population stratification, inadequate statistical power, and genotyping errors (24). Therefore, we sought to confirm the association between BMI and rs7566605 in five additional unrelated samples of varying ethnicity and age.

We examined the association of rs7566605 with obesity in 3996 participants of the KORA S4 cohort, a population of Western European ancestry in a town near Munich, Germany (table S2) (25). Genetic determinants of BMI have been studied in this cohort using candidate gene–based approaches (26). The mean BMI of rs7566605 CC homozygotes under a recessive model was 0.60 kg/m² higher than for GC and GG genotypes combined (P = 0.008). The CC genotype was also significantly more frequent in obese individuals [BMI ≥ 30 kg/m², OR = 1.32, 95% CI (1.05 to 1.66), P = 0.0167] than in non-obese individuals, again with a recessive model. These results confirmed the association that we found in the original genome scan of the FHS sample.

We also tested for an association in a case-control study consisting of self-described white or Caucasian subjects from Poland and the United States (1775 cases and 926 controls, table S3). Cases were drawn from the 90th to 97th percentile of the BMI distribution and controls were drawn from the 5th to 12th percentile, with distributions determined for each combination of gender, country of origin, and age by decade. The results for rs7566605 showed no evidence of heterogeneity between the Polish and U.S. subsamples, so we performed a combined analysis using a Mantel-Haenzsel procedure, as described (27). The CC genotype showed a significant association of the SNP with obesity under a recessive model [OR = 1.37, 95% CI (1.05 to 1.78), P = 0.02], with an odds ratio similar to that estimated from the KORA data.

The SNP was also genotyped in the Nurses Health Study cohort, using the 2726 control DNAs from two nested case-control studies examining diabetes (28) and breast cancer cases (29) (table S4). The participants are, by self-report, >95% Caucasian. No significant association or trend was observed between rs7566605 and BMI levels. There are fewer individuals with a high BMI in this sample as compared to the KORA and FHS samples (20). The lack of replication thus may reflect a different BMI distribution in the Nurses Health Study or differences in environment and lifestyle.

The rs7566605 SNP was also typed in 368 Western European parent-child trios in which either a child or adolescent offspring was obese (mean BMI percentile 98.4 ± 1.93) (30) (table S5). Analysis using the transmission disequilibrium test (18) revealed an overtransmission of the C allele to the obese offspring (P = 0.0017), indicating that rs7566605 is associated with BMI from an early age.

The finding was also replicated in two samples from a self-reported African American population from Maywood, Illinois (table S6). The samples consisted of 866 individuals from a family-based sample consisting of nuclear families and sibships and 402 unrelated individuals (186 chosen from the top and bottom quartiles of the BMI distribution, plus the parents from the family-based sample, whose phenotypic information is not used in the family-based analysis). The samples were dichotomized into obese (BMI ≥ 30 kg/m²) and lean (BMI < 30 kg/m²) individuals. The CC genotype was associated with obesity under recessive models in both the family-based (P = 0.009) and unrelated samples (OR = 2.36, P = 0.04 by Fisher's exact test), adding further confirmation to our finding.

The rs7566605 CC genotype was associated with obesity in three different family-based samples and three studies of unrelated individuals. A meta-analysis of all the case-control samples showed that the CC genotype was significantly associated with obesity under a recessive model, with an OR of 1.22 [95% CI (1.05 to 1.42), P = 0.008; Table 3].

The result from both Western Europeans and African Americans suggests that the risk allele predates human migration out of Africa. By comparing chromosomes of individuals with different ancestry from the HapMap project (31) in a 750–kilobase (kb) region near INSIG2 (insulin-induced gene 2), it is apparent that rs7566605 is present on a single haplotype in the European samples (haplotype 2) and on two haplotypes in the Yoruba samples (haplotypes 2 and 8) (Fig. 4; the rs7566605 C allele is shown in red). All of these haplotypes extend to include the intergenic region near INSIG2 and the nearby FLJ10996 gene. We also typed the rs11684454 SNP on European haplotype 2, located within an intron of FLJ10996. It too was associated with BMI in KORA samples (P = 0.0093) and in Western European parent-child trio samples (P = 0.0015), confirming that the associated region in Europeans extends at least 70 kb. Nonsynonymous SNPs (rs2229616 and rs17512204) in FLJ10996 were not associated with BMI in Western European parent-child trio samples. We are not aware of any nonsynonymous SNPs in INSIG2. The region harboring the causal variant (Fig. 4) may be narrowed by detailed analysis of African haplotypes 2 and 8, because they are only similar close to rs7566605.

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rs7566605 is 10 kb upstream of the transcription start site of INSIG2. INSIG2 is an attractive candidate gene for the quantitative trait locus affecting BMI because its protein product inhibits the synthesis of fatty acid and cholesterol (32). For example, in the ZDF (fa/fa) rat model, overexpression of insig2 in the liver reduces plasma triglyceride levels (33). A model in which altered insig2 activity leads to obesity by elevating plasma triglyceride levels with subsequent storage in adipose tissue is certainly plausible. Indeed, the INSIG2 region has been implicated as a factor in obesity by linkage studies in mice (34) and humans (35).

By studying a population not selected for a particular phenotype (the FHS Cohort), we hoped to identify common genetic variants affecting BMI levels. The high frequency of the rs7566605 C allele suggests that it is ancient and is consistent with the hypothesis that such alleles have become deleterious only in modern times (36). Although these variants are likely to carry low relative risk, their impact on health is substantial because of their prevalence in the population.

We expect that other common variants affecting BMI were not detected in our screen because of either lack of power in the screening step or insufficient coverage of the genome at the density provided by the Affymetrix 100K SNP chip. Taken together, the findings in four of the five samples from different populations using two different study designs confirm a consistent association between the rs7566605 polymorphism and obesity.