Introduction There is a worldwide epidemic of obesity and type 2 diabetes across all age groups, especially in industrialized countries [1]. In the United States alone, over two-thirds of the population has a body mass index (BMI) of 25 kg/m2 or greater and is thus overweight [2,3]. Being overweight is a well-established risk factor for many chronic diseases, such as type 2 diabetes, hypertension, and cardiovascular events [4], and increases in BMI are associated with higher all-cause mortality [5,6]. The economic cost attributable to obesity in the United States has been estimated to be as high as $100 billion/yr [7], and includes not only direct health care costs but also the cost of lost productivity in affected individuals [8]. Individual susceptibility to obesity is thought to be determined by interactions between an individual's genetic make-up and behavior and the environment. Thus, the increased prevalence of obesity likely reflects the exposure of genetically susceptible individuals to unhealthy secular trends in environmental and behavioral factors, such as diet and exercise [9]. In industrialized countries, between 60%–70% of the variation in obesity-related phenotypes appears to be heritable [10,11]. The traditional approach for mapping disease genes relies on linkage mapping followed by progressive fine-mapping of candidate linkage peaks [12]. While the approach has been extremely successful at identifying genes that predispose carriers to rare Mendelian disorders [13], it has met only limited success when applied to complex traits such as obesity. We have taken advantage of recent advances in genotyping technology that enable detailed assessment of entire genomes [14,15]. These advances have already allowed the identification of genes that influence quantitative variation in heart disease–related phenotypes [16] and of susceptibility genes for age-related macular degeneration [17], inflammatory bowel disease [18], and type 2 diabetes [19]. We recruited and phenotyped 6,148 individuals, male and female, ages 14–102 y, from a cluster of four towns in the Lanusei Valley in the Sardinian province of Ogliastra [20]. By studying an isolated population, we expected to increase the genetic and environmental homogeneity of our sample, increasing power [21,22]. Our cohort included >30,000 relative pairs and represents >60% of the population eligible for participation in the study; a detailed account of the family structures we examined is available elsewhere [20]. We took advantage of the relatedness among individuals in our sample to substantially reduce study costs [23]. Specifically, because our sample includes many large families, we reasoned that genotyping a relatively small number of markers in all individuals would allow us to identify shared haplotype stretches within each family. We could then genotype a subset of the individuals in each family at higher density to characterize the haplotypes in each stretch and impute missing genotypes in other individuals in the family [23,24]. For the analyses presented here, we genotyped 3,329 individuals using the Affymetrix 10,000 SNP Mapping Array and we genotyped an additional 1,412 individuals using the Affymetrix 500,000 SNP Mapping Array Set. The genotyped individuals were selected to represent the largest families in our sample, without respect to phenotype. The high-density arrays were generally used to genotype both parents and one child (in larger sibships) or just the parents (in smaller sibships); the lower density arrays were used to genotype everyone else. Except when parents and offspring were genotyped in the same family, we tried to ensure that individuals genotyped with the high-density array were only distantly related to one another. For the 2,893 individuals that were genotyped with the 10,000 SNP arrays only, we used a modified version of the Lander-Green algorithm [25,26] to probabilistically infer missing genotypes [24]. Our approach for estimating missing genotypes is implemented in MERLIN (http://www.sph.umich.edu/csg/abecasis/MERLIN/) and described in detail elsewhere [24]. Our initial analysis focused on evaluating the additive effects of 362,129 SNPs (Table S1) that passed quality control checks [27,28]. The remaining SNPs failed quality checks (∼2.9% of SNPs failed checks for data completeness, Hardy–Weinberg equilibrium, and Mendelian incompatibilities) or had a minor allele frequency of 400kb (Figure 2). KIAA1005, a gene of unknown function, also maps nearby. The FTO gene has not been previously implicated in obesity, but it maps to a region where linkage to BMI has been reported in two previous genome-wide linkage scans (LOD = 3.2 in the Framingham Heart Study [32] and LOD = 2.2 in the families with white ancestry from the Family Blood Pressure Program [33]). Furthermore, a syndrome that results from deletion of this region of Chromosome 16q includes obesity as one of its features [34]. Figure 2 Association Results and LD Patterns in Region Surrounding the FTO Gene (A) Summary of the association between SNPs in the region and BMI. The SNP showing strongest association (rs9930506) is highlighted. Other SNPs are colored according to their degree of disequilibrium with rs9930506 ranging from high (red), to intermediate (green), to low (blue). Transcripts are indicated at the bottom of the graph, with an arrow indicating transcript direction. (B) Summary of the patterns of disequilibrium in the region in Sardinia and in two of the HapMap populations (CEU and YRI) [55]. The grey bar marks the region of association and facilitates comparisons between the panels. Although multiple SNPs within FTO show evidence for association, these do not point to multiple independently associated SNPs—rather, it is likely they are all in disequilibrium with the same causal variant(s). In a sequential analysis in which we selected the best SNP for each trait and then conditioned on it to successively select the next best SNP, only one FTO SNP was selected (results presented in Table S2). This result is consistent with the fact that the SNPs fall in a region of strong linkage disequilibrium, both in Sardinia and in the HapMap (Figure 2B). Our FDR analysis of BMI selected one additional SNP outside this cluster, rs6602024 (Figure 3). This SNP maps to Chromosome 10 and shows association with BMI (p = 4.9 × 10 − 6), weight (1.6 × 10 − 5), and hip circumference (p = 0.00047). The SNP maps to the platelet-type phosphofructokinase (PFKP) gene, which acts as a major rate-limiting enzyme in glycolysis, converting D-fructose-6-phosphate to fructose-1,6-bisphosphate [35]. Alterations in the structure or regulation of PFKP could alter the balance between glycolysis and glycogen production, ultimately leading to obesity. Figure 3 Association Results and LD Patterns in Region Surrounding the PFKP Gene (A) Summary of the association between SNPs in the region and BMI. The SNP showing strongest association (rs6602024) is highlighted. Other SNPs are colored according to their degree of disequilibrium with rs6602024, ranging from high (orange) to low (blue). Transcripts are indicated at the bottom of the graph, with an arrow indicating transcript direction. (B) Summary of the patterns of disequilibrium in the region in Sardinia and in two of the HapMap populations (CEU and YRI) [55]. The grey bar marks the region of association and facilitates comparisons between the panels. Table 2 shows the phenotypic effects associated with each of the two SNPs in our sample. Because rs9930506 is more common, it shows more significant association despite being associated with smaller phenotypic effects (the two homozygotes differ, on average, by ∼1.5 BMI units). A rarer polymorphism, such as rs6602024, impacts only a smaller proportion of the population and shows less significant association, despite a larger difference between homozygote means (which differ, on average, by ∼2.9 BMI units). In each case, a more accurate estimate of the effect is provided by the regression model with age, sex, and (where appropriate) height as covariates. In a study, such as ours, that estimates effect sizes for many SNPs, statistical fluctuation means that some estimates will be slightly high and others will be low. SNPs that reach statistical significance are likely to include those for which effect size estimates are inflated (this is the winner's curse phenomenon) [36], and thus we proceeded to replicate our top association signals in additional large samples. Table 2 Effects Associated with the rs9930506 and rs6602024 SNPs To further investigate the association between rs9930506 and rs6602024 and obesity-related traits, we genotyped these SNPs in the GenNet study [37]. The study includes a series of families recruited through probands with elevated blood pressure. The families included in this analysis comprise 3,467 individuals in total (1,101 African Americans [AA] in 369 families, 839 Hispanic Americans [HA] in 223 families, and 1,496 European Americans [EA] in 457 families). Overall, individuals in GenNet are heavier than those in our original Sardinian sample. Nevertheless, our findings strongly confirm evidence for association between rs9930506 and the three BMI-related traits (weight, hip circumference, and BMI). Specifically, rs9930506 showed association with all three traits among EA and HA in the GenNet study (meta-analysis of the EA and HA samples results in a p-value between 0.0005 and 0.001, depending on trait; see Table 3). The association is significant and in the same direction as in our original sample. The allele frequencies are also similar in all three samples, with a frequency of 0.46 in our Sardinian sample for allele “G” of rs9930506 and of 0.44 and 0.33 in the GenNet EA and HA samples, respectively. In the GenNet sample, homozygotes for the two rs9930506 alleles differ in weight by ∼1.0 BMI units on average. Table 3 Replication of Association between rs9930506/FTO/G Allele and Obesity-Related Traits We also examined the relationship between rs9930506 and the three traits in AA, but did not observe evidence for association within that group. In AA, allele “G” of marker rs9930506 has a somewhat lower frequency of 0.21. In addition, AA show quite distinct patterns of linkage disequilibrium (LD) and thus it is not surprising that the association does not replicate. For example, in the HapMap sample of Utah residents with ancestry from northern and western Europe (CEU), the eight SNPs that show association with obesity-related traits in our sample are strongly associated with each other and tag a total of 38 different variants (r 2 > 0.80). In contrast, in the HapMap Yoruba in Ibadan, Nigeria (YRI) the strength of LD in the region is greatly reduced such that rs9930506 is not in strong LD (r 2 0.8, HapMap CEU) with the eight SNPs that are associated with obesity-related traits in our Sardinian sample. We selected an additional seven SNPs in the region to tag these 38 variants in samples with reduced LD. Together with rs9930506, these seven variants capture the other 30 SNPs with r 2 > 0.58 (average r 2 = 0.87, HapMap YRI). The results are summarized in Table 4 and show that, whereas all the variants show association in EA and HA, none of the variants shows association in AA. One possible explanation is that obesity in AA has a different genetic architecture. Alternatively, it is possible that because some of the variants are quite common in EA and HA but rare in AA, much larger sample sizes will be required to adequately gauge their effects (for example, rs1421085 and rs3751812 have minor allele frequencies >0.25 in these first two populations, but 0.73), it is important to note that apart from the SNPs that overlap FTO, other strongly associated SNPs differed among the traits (see Tables 1 and S2). In contrast to FTO, PFKP is a critical enzyme within the well-studied pathway of glucose metabolism but, to our knowledge, has not been previously implicated in obesity in humans. PFKP is one of the three phosphofructokinase subunit proteins that show partially overlapping patterns of expression and form hetero-tetramers in diverse cells and tissues. The subunits are encoded by different genes. One form is highly expressed in muscle (PFKM); a second, in liver (PFKL); and the third, PFKP, is the only form in platelets and is also highly expressed in subregions of the brain [42]. None of the forms has been previously implicated in obesity in humans, although PFKM is mutated in some cases of impaired glycogen synthesis (glycogen storage disease VII; see Online Mendelian Inheritance in Man, http://www.ncbi.nlm.nih.gov/entrez/dispomim.cgi?id=232800) [35]. It is of considerable interest that compared to the other isozymes, PFKP has lower affinity for fructose-6-phosphate and decreased inhibition by ATP [43]. Consequently, PFKP is the most stringently regulated, responding to small changes at typical metabolic levels of effectors [44]. Genetic variants in the enzyme could thus adjust the rate of glycolysis, shifting the balance of metabolism between gluconeogenesis and glucose assimilation—a possible step in the etiology of obesity. Additionally, it is intriguing that in mice a locus associated with obesity has been mapped to a 127-kb interval that includes Pfkp [39]. The mouse locus shows strong evidence of interaction with diet, with different effects in mice fed high-fat and low-fat diets. One possibility is that greater homogeneity of diet in Sardinia facilitated mapping, but made replication in other populations more difficult. How significant are the associations observed? The replication of the FTO association in two different populations indicates that it is likely important not only in Sardinia, but in many different populations. In contrast, the failure to replicate the PFKP association in other populations suggests that (a) the association we identified may refer to rarer, population-specific variants; (b) the effects of the locus may depend on genetic or environmental background; or (c) the association identified in our original sample is due to the statistical fluctuations inherent in testing hundreds of thousands of SNPs. As for the public health impact of the observed associations, a 1-unit increment in BMI has been associated with an 8% increase in the risk of coronary heart disease [45] and excess weight in middle life is associated with increased overall risk of death [46]. Thus, the alleles reported here, which shift BMI by 1–1.5 units, have effects that are not only statistically significant but could also have important health consequences. Furthermore, apart from the direct contribution of these gene variants, they provide an entrée to the analysis of genes and pathways that contribute additionally, and open new routes to possible eventual intervention. Note: After completing this manuscript, we became aware of additional evidence that supports our report of association between FTO and obesity-related traits. First, genotyping of 1,780 individuals from the SUVIMAX study [47,48] replicated association of allele rs9930506 with increased BMI (p = 0.006). Combined evidence from SUVIMAX, GenNet EA, and GenNet HA resulted in a replication p-value of 1.5 × 10 − 5. In addition, two other large independent studies also show association of SNPs in FTO with increased BMI [49,50]. Genotyping of the SUVIMAX sample did not provide evidence for association between rs6602024 and BMI. Materials and Methods Study sample. We recruited and phenotyped 6,148 individuals, male and female, ages 14–102 y, from a cluster of four towns in the Lanusei Valley [20]. During physical examination of each individual, a blood sample was collected (for DNA extraction) and anthropometric traits were recorded. Here, we report analyses of hip circumference, weight, and the derived quantity BMI (which is calculated from a combination of height and weight). Genotyping was carried out using the Affymetrix 10K and 500K chips (http://affymetrix.com/) using standard protocols. Summary assessments of genotype data quality are provided in the Results section and in Table S1. To follow up on SNPs rs9930506 and rs6602024, we genotyped and examined the association between these two SNPs and BMI, hip circumference, and body weight in the GenNet study. The study comprises 3,467 individuals in total, recruited between 1995 and 2004 (1,101 AA, 839 HA, and 1,496 EA). Individuals were recruited at two field centers: EA were recruited from Tecumseh, Michigan, and AA and HA were recruited from Maywood, Illinois. Participants were recruited from families starting from a proband with high blood pressure. DNA was available for 3,205 individuals (968 AA, 824 HA, and 1471 EA). SNP genotyping was performed using the 5′-nuclease–based assay (TaqMan; ABI, http://www.appliedbiosystems.com/) analyzed on an ABI Prism 7900 Real Time PCR System. Within each ethnic group, genotype completeness rates exceed 98% and there was no evidence for deviation from Hardy–Weinberg equilibrium (p > 0.05). Statistical analysis. To ensure adequate control of type I error rates, we applied an inverse normal transformation to each trait prior to analysis [20]. The inverse normal transformation reduces the impact of outliers and deviations from normality on statistical analysis. The transformation involves ranking all available phenotypes, transforming these ranks into quantiles and, finally, converting the resulting quantiles into normal deviates. We included sex, age, and age2 as covariates in all analysis. Height was significantly associated with weight and hip circumference and was included as an additional covariate in analysis of those traits. We fitted a simple regression model to each trait and used a variance component approach to account for correlation between different observed phenotypes within each family. For individuals who had genotype data available, we coded genotypes as 0, 1, and 2 (depending on the number of copies of the allele being tested). For individuals with missing genotype data, we used the Lander–Green algorithm to estimate an expected genotype score (between 0 and 2) for each individual [24]. Briefly, to estimate each genotype score we first calculate the likelihood of the observed genotype data. Then, we instantiate each missing genotype to a specific value and update the pedigree likelihood. The ratio of the two likelihoods gives a posterior probability that the instantiated genotype is true, conditional on all available data. Due to computational constraints, we divided large pedigrees into subunits with “bit-complexity” of 19 or less (typically, 20–25 individuals) before estimating missing genotypes. Our analytical approach considers all observed or estimated genotypes (rather than focusing on alleles transmitted from heterozygous parents) and thus is not immune to effects of population stratification. In homogenous populations, this type of analysis is expected to be more powerful [51,52]. To adjust for the effects of population structure and cryptic relatedness among sampled individuals, we used the genomic control method to adjust our test statistics for each trait separately [29]. FDRs were calculated with R's p.adjust() procedure using the method of Benjamini and Hochberg [30]. Since the initial analysis often identified clusters of nearby SNPs that all showed similar levels of association, we also carried out a sequential stepwise analysis. In this analysis, we selected the best SNP for each trait, and then conditioned on it to successively select the next best SNP. This sequential analysis can help identify regions with multiple independent association signals. The stepwise analysis was repeated for five rounds. Candidate gene analysis. We selected 74 candidate genes previously tested for association with obesity in humans [53]. For each gene, we first evaluated the ability of the Affymetrix SNPs to tag common SNPs (MAF > 0.05) within +/− 5 kb of the gene (r 2 > 0.50 or r 2 > 0.80) using the HapMap CEU database [54]. We then evaluated evidence for association using all Affymetrix SNPs within each gene as well as neighboring Affymetrix SNPs that could be used to improve coverage (r 2 > 0.5). For each gene, we report coverage statistics as well as the SNP that showed strongest evidence for association. We selected 74 genes that were previously targeted in associations studies aiming to identify genetic determinants of obesity in humans [53]: ACE, ACTN, ADIPOQ, ADIPOR1, ADIPOR2, ADRB1, ADRB2, AGER, AHSG, APOA2, APOA4, APOA5, AR, BDNF, CASQ1, COL1A1, COMT, CRP, CYP11B2, DIO1, ENPP1, ESR1, ESR2, FABP2, FOXC2, GAD2, GFPT1, GHRHR, GNAS, GNB3, GPR40, H6PD, HSD11B1, HTR2C, ICAM1, IGF1, IGF2, IL6, IL6R, KCNJ11, KL, LEP, LEPR, LIPC, LPL, LTA, MC4R, MCHR1, MKKS, MTHFR, MTTP, NMB, NOS3, NPY, NPY2R, NR0B2, NTRK2, PARD6A, PLIN, PPARG, PPARGC1A, PRDM2, PTPN1, PYY, RETN, SCD, SELE, SERPINE1, TAS2R38, TNF, UCP1, UCP2, UCP3, and VDR. We did not consider genes associated with drug-induced body weight gain or mitochondrial genes [53]. The following genes have previously been investigated for their role in obesity and related traits but are not well tagged by SNPs in the Affymetrix array: ADRB3, DRD4, INS, and APOE. Supporting Information Table S1 Genotype Data for Sardinian Cohort (47 KB DOC) Click here for additional data file. Table S2 Results of Stepwise Analysis to Identify Independent Risk Alleles To generate this table, we first sought the most significantly associated allele in the genome. We then added this allele to our baseline model and repeated our genome scan to identify the next associated SNPs. (55 KB DOC) Click here for additional data file. Table S3 Tag SNP That Shows Strongest Association with BMI for Each Previously Identified Candidate Gene The first column indicates the name of a previously identified candidate. The second column indicates the number of SNPs in our Affymetrix arrays that are either in the gene or constitute the best available tag (r2 > 0.5) for a genic SNP. The next column indicates the number of HapMap SNPs within +/− 5 kb of the gene and the proportion of these that are covered at r 2 > 0.50 or r 2 > 0.80. The next columns indicate the SNP that showed strongest association in our analysis, the p-value, the tested allele and its frequency, and the estimated additive effect. The last column corresponds to the FDR incurred when all tested SNPs are considered and this test is declared significant. (162 KB DOC) Click here for additional data file. Table S4 Tag SNP That Shows Strongest Association with Hip Circumference for Each Previously Identified Candidate Gene The first column indicates the name of a previously identified candidate. The second column indicates the number of SNPs in our Affymetrix arrays that are either in the gene or constitute the best available tag (r 2 > 0.5) for a genic SNP. The next column indicates the number of HapMap SNPs within +/− 5 kb of the gene and the proportion of these that are covered at r 2 > 0.50 or r 2 > 0.80. The next columns indicate the SNP that showed strongest association in our analysis, the p-value, the tested allele and its frequency, and the estimated additive effect. The last column corresponds to the FDR incurred when all tested SNPs are considered and this test is declared significant. (163 KB DOC) Click here for additional data file. Table S5 Tag SNP That Shows Strongest Association with Weight for Each Previously Identified Candidate Gene The first column indicates the name of a previously identified candidate. The second column indicates the number of SNPs in our Affymetrix arrays that are either in the gene or constitute the best available tag (r 2 > 0.5) for a genic SNP. The next column indicates the number of HapMap SNPs within +/− 5 kb of the gene and the proportion of these that are covered at r 2 > 0.50 or r 2 > 0.80. The next columns indicate the SNP that showed strongest association in our analysis, the p-value, the tested allele and its frequency, and the estimated additive effect. The last column corresponds to the FDR incurred when all tested SNPs are considered and this test is declared significant. (163 KB DOC) Click here for additional data file.
