We tested 362,129 SNPs for association with three obesity-related quantitative traits (BMI, hip circumference, and weight). Height was included as a covariate in analysis of hip circumference and weight. In addition, we included age and sex as covariates in every analysis. The genomic control parameter [29
] for our initial analysis of each trait ranged from 1.07 to 1.09, indicating that our estimated test statistics might be slightly inflated. This is likely due to unaccounted-for distant relationships among the sampled individuals. All results presented in our tables have been adjusted using the method of genomic control [29
]. After adjustment, we observed no significant excess of results exceeding liberal significance thresholds. For example, the proportion of test statistics that were significant at α = 0.001 was 0.00098.
Results of our initial association analysis are summarized in and in . We used the false-discovery rate (FDR) to select a small set of very promising trait SNP associations for rapid replication. Using an FDR [30
] of 20% highlighted a small set of SNPs for each trait. This set include the top eight SNP association results for hip circumference and weight (FDR = 0.013 and FDR = 0.16, respectively) and the top nine SNP association results for BMI (FDR = 0.20).
Negative Log of p-Value for Single Marker Association Analysis with Three Obesity-Related Traits
Markers Showing Strongest Evidence for Association
Eight of the SNPs listed in overlap among the three traits. In particular, SNP rs9930506 and a cluster of nearby SNPs on Chromosome 16 show strong association with BMI (p = 8.6 × 10−7), hip circumference (p = 3.4 × 10−8) and weight (p = 9.1 × 10−7). Two of the associated SNPs in the cluster, rs9939609 and rs9926289, fall within an intronic region where sequence is strongly conserved across species. For comparative purposes, using a conservative Bonferroni correction aimed at an overall type I error rate of 0.05 (one false positive per 20 genome-scans), would result in a significance threshold of 1.4 × 10−7.
This cluster of SNPs on Chromosome 16 overlaps the FTO
] gene, an extremely large gene whose exons span >400kb (). 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
Association Results and LD Patterns in Region Surrounding the FTO Gene
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 (B).
Our FDR analysis of BMI selected one additional SNP outside this cluster, rs6602024 (). 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.
Association Results and LD Patterns in Region Surrounding the PFKP Gene
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.
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 ). 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.
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 (r2 > 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 (r2 < 0.3) with any of the other Chromosome 16 SNPs that show association in Sardinia.
In an attempt to fine-map association in the region, we decided to genotype the region of strong association in greater detail. In general, the study of samples from AA participants can afford an opportunity to fine-map association signals and even facilitate identification of the causal variants [38
]. As noted above, a total of 38 different variants are in LD (r2
> 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 r2
> 0.58 (average r2
= 0.87, HapMap YRI). The results are summarized in 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 <0.11 in AA).
Fine-Mapping Results for FTO Region in GenNet Sample
In contrast to rs9930506, we did not replicate association between SNP rs6602024 in the PFKP
gene and the three obesity-related traits. The “A” allele was rare in all populations, with a frequency of 0.12 in our Sardinian sample, 0.11 in the HA and EA GenNet subsamples and 0.25 in the AA GenNet subsample. The results are summarized in and show that, although homozygotes for the rare “A” allele at rs6602024 were on average heavier by ~1.0–3.0 BMI units than homozygotes for the “G” allele at the SNP, these homozygotes were rare and, overall, there was no significant association. Corroborating evidence that PFKP
and rs6602024 are associated with BMI is the observation that a region of ~120 kb including the Pfkp
gene has been implicated in a mouse model of obesity [39
] (see Discussion
). A definite assessment of the impact of PFKP on obesity-related quantitative traits in human populations will likely require examination of much larger sample sizes.
Replication of Association between rs6602024/PFKP/A Allele and Obesity-Related Traits
Our genotyping results also hint at the possible importance in Sardinia of other genes previously investigated as candidates influencing obesity and related traits (Tables S3
). When we evaluated evidence for association across previously identified candidate genes, we observed a small excess of nominally significant p
-values. (We tested 837 candidate SNPs in 74 candidate genes against three traits and found that 145 tests were significant at p
< 0.05, corresponding to 5.8% of the 2,511 tests. We observed no such excess when the whole genome was considered.) Among the interesting candidates that show association in our sample are the two adiponectin receptor genes [40
(best single SNP p
-value = 0.013, 0.027, and 0.016 for BMI, hip circumference, and weight) and ADIPOR2
-values = 0.018, 0.019, 0.013) and the lipoprotein lipase gene, LPL
] (best p
-values = 0.014, 0.006, 0.018). Nevertheless, all the association signals observed in any of these previous candidate genes are far less significant than those in FTO