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Logo of nihpaAbout Author manuscriptsSubmit a manuscriptNIH Public Access; Author Manuscript; Accepted for publication in peer reviewed journal;
Obesity (Silver Spring). Author manuscript; available in PMC Feb 15, 2011.
Published in final edited form as:
PMCID: PMC3039277
NIHMSID: NIHMS262092

Variants in the CNR1 and the FAAH genes and adiposity traits in the community

Abstract

Pharmacologic blockade of the endocannabinoid receptor 1 leads to weight loss and an improved metabolic risk profile in overweight and obese individuals. We hypothesize that common genetic variants in the CNR1 (encoding endocannabinoid receptor 1) and FAAH genes (encoding fatty acid amide hydrolase, a key enzyme hydrolyzing endocannabinoids) are associated with adiposity traits. We genotyped 18 single nucleotide polymorphisms (SNPs) in the CNR1 and 9 SNPs in the FAAH gene in 2,415 Framingham Offspring Study participants (mean age 61±10 years; 52.6% women; mean BMI 28.2±5.4 kg/m2; 30.3% obese) and studied them for association with cross-sectional and longitudinal measures of adiposity (body mass index [BMI], waist circumference, change over time in BMI and waist circumference, visceral and subcutaneous adipose tissue) using linear mixed-effect models. The selected SNPs captured 85% (r2=0.8) of the common variation (minor allele frequency >5%) at the CNR1 locus and 96% (r2=0.8) of the common variation at the FAAH locus (defined as the genomic segment containing the gene +20 kb upstream and +10 kb downstream). After correction for multiple testing, none of the SNPs in the CNR1 gene or in the FAAH gene displayed statistical evidence for association with BMI, waist circumference and visceral adipose tissue or subcutaneous adipose tissue (all P>0.18). Despite comprehensive SNP mapping across the genes and their regulatory regions in a large unselected sample, we failed to find evidence for an association of common variants in the CNR1 and FAAH genes with measures of adiposity in our community-based sample.

Keywords: CNR1, FAAH, obesity, SAT, V

Introduction

The prevalence of obesity and related metabolic and cardiovascular disorders is increasing rapidly worldwide (1,2). Almost one-third of all adults in the United States have a body mass index (BMI) above 30 kg/m2 and are classified as obese (3). While environmental factors are known to play a significant role in the development of overweight and obesity, we and others have previously reported that several adiposity traits, including BMI (4,5), waist circumference (6), weight change (7), and visceral and subcutaneous adipose tissue (8) have substantial heritable components.

Accumulating evidence from animal models and experimental and clinical studies indicates that the endogenous cannabinoid system plays an important role in the regulation of food consumption and energy balance and thus body weight and adiposity (9,10). Endogenous cannabinoids also modify metabolic and cardiovascular risk factors associated with excess adiposity (11-17). Endogenous cannabinoids (endocannabinoids) are lipid molecules (18) that target two G-protein-coupled receptors, the cannabinoid receptors CB1 and CB2 (19). CB1 is widely expressed in the brain but is also expressed in peripheral organs including liver, adipose tissue, gut, skeletal muscle and pancreas (9). In animal models, CB1 agonists lead to increased food intake (20,21), whereas selective antagonists of CB1 reduce food intake and increase weight loss (21-23). Several placebo-controlled clinical trials in humans have consistently shown that rimonabant, a selective CB1 antagonist, leads to significant weight loss, diminished waist circumference and an improved cardiovascular and metabolic risk profile in non-diabetic overweight and obese individuals and in patients with type 2 diabetes (13-16).

Because of the strong pharmacologic effects of CB1 antagonists on weight loss and waist circumference, we hypothesized that common variants in the CNR1 and the FAAH genes (encoding the fatty acid amide hydrolase [FAAH]) would be associated with cross-sectional and longitudinal measures of adiposity. FAAH is a serine hydrolase, located in the inner cell membrane and represents a key enzyme in the endocannabinoid degradation pathway (18,24). A negative correlation between adipose tissue FAAH expression and circulating endocannabinoid levels has been observed (25). The identification of common genetic variants that influence obesity risk at a population level might enhance the pathophysiologic understanding of obesity, may lead to the development of more effective medications targeting this pathway, and could potentially help identify individuals with a genetic susceptibility to obesity whom might benefit from primary prevention strategies.

Methods and Procedures

Study sample

The Framingham Heart Study (original cohort) was initiated in 1948 when 5,209 residents from Framingham, MA were enrolled in a prospective study on the determinants of cardiovascular disease in the community. In 1971, the Framingham Offspring Study was started and 5,124 spouses and offspring of the original cohort were enrolled (26). Offspring participants have been seen in the Framingham Heart Study clinic on average every 4 years, where a targeted physical examination including detailed anthropometric measures is performed and a medical history is obtained. For the present analyses, 2,415 offspring study participants with clinical measures and available DNA were eligible.

In a sub-sample of 1,422 offspring study participants, a multi-detector computed tomography (CT) of the chest and the abdomen was performed between 2002 and 2005 as previously described (8). The CT study focused on participants from more extended Framingham Heart Study families and on participants living in the broader Framingham/Boston, MA area. Additional inclusion criteria were a minimal age of ≥35 years for men and ≥40 years for women. Pregnancy and weight >160 kg were exclusion criteria for the CT study due to weight limitations of the equipment. Participants not included in the CT substudy were slightly older (62±10 vs. 60±9 years, p<0.000l) but did not differ with respect to sex, BMI or waist circumference from those included in the CT substudy.

All participants provided written informed consent. The study protocols were approved by the Institutional Review Board at the Boston University Medical Center.

Assessment of outcome variables and covariates

Height and weight were measured and BMI was calculated as weight (in kilograms) divided by height (in meters) squared. Waist circumference was measured at the level of the umbilicus. BMI and waist circumference were measured approximately every 4 years. From the serial BMI and waist circumference measurements obtained in each individual, the following variables were derived: BMIage35_50, defined as the mean of all available BMI measurements in a participant between 35 and 50 years of age; BMIage50_65, defined as the mean of all available BMI measurements in a participant between 50 and 65 years of age; waist circumference exam 4-7, defined as the mean of all available waist circumference measurements from exams 4 to 7; and change per year in waist circumference from exam 4 to 7. Current smoking was defined as smoking at least 1 cigarette per day during the year prior to the examination.

Measurement of subcutaneous and visceral adipose tissue

An 8-slice multidetector CT imaging of the abdomen was performed as described in detail previously (8).

Genotyping and SNP selection

The CNR1 gene, encoding the endocannabinoid receptor CB1, is located on chromosome 6q15, consists of 1 exon, and encodes a 472 amino acid protein. The FAAH gene is located on chromosome 1p33, consists of 15 exons and encodes a 579 amino acid protein.

Tagging SNPs were selected with a pair-wise approach at an r2=0.8 to capture all common variation (minor allele frequency >5%) in the chromosomal region +20 kb upstream and +10 kb downstream of each gene using the Tagger function as implemented in Haploview (27), based on the Hapmap release 21a. The observed genotype frequencies of one SNP in the CNR1 gene (rs6928813) deviated from Hardy Weinberg equilibrium (Online Supplemental Table 1). This SNP was not considered in our primary association analyses and in the calculation of genomic coverage of the CNR1 gene.

After genotyping and SNP quality control filters (minor allele frequency >5%; p>0.001 for deviation from Hardy Weinberg equilibrium, minimal call rate: 94%), we had 85% (r2=0.8) coverage of common variation for the CNR1 gene and 96% (r2=0.8) for the FAAH gene (defined as the genomic segment containing the gene +20 kb upstream and +10 kb downstream).

All SNPs were genotyped using allele-specific primer extension of multiplex amplified products with detection by matrix-assisted laser desorption ionization-time of flight mass spectroscopy on an iPLEX Sequenom platform. Genotyping call rates were 99% on average, and the average consensus rate based on 254 duplicate samples was 99%.

Statistical analysis

The following traits were natural logarithmically-transformed prior to the creation of residuals: waist circumference at exams 5 and 7, mean of all available BMI measurements in individuals between 35 and 50 years of age, mean of all available BMI measurements in individuals between 50 and 65 years of age, and BMI at exams 5 and 7.

Sex-specific age-adjusted residuals were generated for each adiposity trait. Associations between SNPs and trait residuals were assessed using linear mixed-effect models as implemented in SOLAR (Sequential Oligogenic Linkage Analysis Routines; version 4.1.3) (28). In a priori primary analyses, we tested each SNP for association with the mean of all available BMI measurements in a participant between 35 and 50 years of age (BMIage35_50), waist circumference at exam 7 and visceral adipose tissue volume. In predefined secondary analyses, we additionally tested each SNP for association with BMI at exam 5 and exam 7, the mean of all available BMI measurements in a participant between 50 and 65 years of age (BMIage50-65), waist circumference at exam 5, mean of all available waist circumference measurements from exams 4 to 7 (waist circumference exam 4-7), change per year in waist circumference from exam 4 to 7, and subcutaneous adipose tissue volume. Given a recent report of a positive association of rs2023239 with obesity and BMI in three different cohorts (29), we tested rs6928813 (perfect proxy of rs2023239) for association with our primary and secondary traits in exploratory analyses.

Calculation of empiric P-values

We used a simulation to determine the distribution of the minimum p-value across all SNPs under the null hypothesis of no association. We simulated 1,000 traits for our sample using SIMQTL in SOLAR. We set the heritability to 35%, although similar distributions were obtained with heritability estimates of 15 and 50%. Each simulated trait was analyzed using linear mixed-effect models, and the minimum p-values over all SNPs were recorded. The empirical p-values correspond to the proportion of simulated minimum p-values smaller or equal to the observed p-values in our data. This approach corrects for the multiple SNPs tested, although it does not account for the multiple traits tested.

Results

Clinical characteristics of the study sample are shown in Table 1. Our sample was a middle-aged (mean 61±10 yrs) cohort with slightly more women than men. Almost one third of the study participants were obese.

Table 1
Characteristics of the study sample (n=2,415).

Association of genetic variants in the CNR1 gene with adiposity traits

Online Supplemental Figure 1 depicts common genetic variation at the CNR1 locus and its LD structure. None of the SNPs in the CNR1 gene displayed significant association with any of the primary phenotypes: mean of all available BMI measurements in a participant between 35 and 50 years of age (BMIage35_50), waist circumference at exam 7, and visceral adipose tissue volume (Table 2). The results for the secondary traits (BMI at exams 5 and 7, waist circumference at exam 5, mean of all available BMI measurements in a participant between 50 and 65 years of age [BMIage50_65], mean of all available waist circumference measurements from exams 4 to 7, change per year in waist circumference from exam 4 to 7, and subcutaneous adipose tissue volume) were likewise null (Online Supplemental Table 2). All empiric P-values were >0.18. Although SNP rs6928813 deviated from HWE (HWE P= 0.00001), we examined it for association with our primary and secondary adiposity traits given a recent publication demonstrating a significant association of SNP rs2023239 (r2=1.0 with rs6928813) with obesity and BMI (29). We observed no evidence for association of rs6928813 with our adiposity traits (empiric P>0.24 for all [Online Supplemental Table 4]).

Table 2
Association of tag SNPs in the CNR1 gene with the mean of all available BMI measurements in a participant between 35 and 50 years of age (BMIage35_50), waist circumference at exam 7, and visceral adipose tissue volume.

Association of genetic variants in the FAAH gene with adiposity traits

All common variation in the FAAH gene and its LD structure is displayed in Online Supplemental Figure 2. None of the SNPs in the FAAH gene were associated with our primary or secondary adiposity traits. (Table 3 and Online Supplemental Table 3).

Table 3
Association of tag SNPs in the FAAH gene with the mean of all available BMI measurements in a participant between 35 and 50 years of age (BMIage35_50), waist circumference at exam 7, and visceral adipose tissue volume.

Power Calculation

Assuming an additive genetic model and a pair-wise correlation between the tested SNPs and a causal variant of r2=0.8, we had a power of 87.8% (significance level of 5%) to identify SNPs explaining 0.5% of the variance of the trait.

Discussion

Principal findings

In this comprehensive analysis of common variation across the CNR1 and FAAH genes including the genes and their 5′ and 3′ regulatory domains, we observed no evidence for association of any variant with several cross-sectional and longitudinal measures of adiposity obtained in an adequately powered community-based sample.

In the context of the current literature

Few previous studies have evaluated the association of SNPs in the CNR1 or FAAH gene with adiposity-related traits, and only one previous study has undertaken comprehensive LD mapping of the CNR1 gene. In a sample of 928 male participants of the Olivetti Prospective Heart Study, the G allele of the 3813 A/G polymorphism (rs12720071) in the CNR1 gene was nominally associated with increased subscapular skinfold thickness (P=0.03) and increased waist circumference (P=0.05). In two additional analyses in a subgroup (n=360) of this cohort, the G allele of this SNP was likewise nominally associated with increased waist circumference (p=0.007 and p=0.04, respectively) (30). These findings were then evaluated for replication in the Wandsworth Heart and Stroke Study (n=216). Consistently, the minor G allele of rs12720071 was associated with higher waist circumference (P=0.006) and higher BMI (P=0.01). In both study samples, a second variant (r2=0.26 between the two SNPs) in the CNR1 gene (4895 A/G variant; rs806368) was tested. This variant displayed no association with any of the quantitative phenotypes analyzed (BMI, waist circumference, subscapular skinfold thickness) in the Olivetti Prospective Heart Study, but the minor allele of rs806368 was nominally associated with higher waist circumference (P=0.047) in the Wandsworth Heart and Stroke Study (30). Whereas these findings are intriguing, the small sample size and lack of correction for multiple testing suggest that these may have been false positive findings. In our larger sample, neither rs12720071 nor rs806368 were associated with any adiposity-related trait.

Another variant in the CNR1 gene (rs1049353) displayed evidence for association with BMI in a relatively small (total n=419) population-based sample from southern Italy (31). Individuals homozygous for the common G allele were overrepresented in overweight and obese individuals (P=0.03 for trend) (31). By contrast, rs1049353 was not associated with obesity (defined as BMI ≥30 kg/m2) in a case-control study (1,064 obese vs. 251 controls) (32), although the minor allele of this genetic variant was related to abdominal obesity (increased waist circumference, P=0.008; higher waist to hip ratio, P=0.009) in a sub-group analysis (n=455) of obese men (32). These findings are similarly limited by the small sample size and presentation of nominal P-values. In our sample, rs1049353 was not associated with adiposity-related traits. Differences in the study design and sample (case-control study including rather young men and pre-menopausal women; mean age 35 and 41 years for controls and cases; the controls being derived from the university and hospital personnel vs. community-based cohort study including pre- and postmenopausal women and men, mean age 61 years) might also account for the observed differences between the paper by Peeters and colleagues and our results (32).

Müller and associates found no evidence for association of 8 CNR1 genetic variants (including rs1049353) with obesity by conducting family-based association tests in German obesity trios and screening the coding region of the CNR1 gene for mutations in 120 Germany obese children (33). Of note, most of these SNPs were located upstream of the promoter of the CNR1 gene (33). Our findings extend this literature by presenting a comprehensive assessment of the genetic variation in the CNR1 gene and evaluation of an adequately powered sample. Despite these strengths, we were unable to confirm any significant association of variants in CNR1 with adiposity traits.

Most recently, rs806381 and rs2023239 were associated with obesity in a French case control study (1,932 obese vs. 1,173 controls; experiment wide P=0.002 for rs806381 and P=0.016 for rs2023239); with BMI in a Swiss cohort of obese adults (n=865; nominal P=0.015 for rs806381 and P=0.02 for rs2023239); and with BMI in a Danish population-based cohort (n=1,780; nominal P=0.0023 for rs806381 and P=0.021 for rs2023239). In our sample, rs6454673 which is highly correlated with rs806381 (r2=0.91), was not associated with adiposity traits. SNP rs6928813 (perfect proxy of rs2023239; r2=1), which failed HWE, was not associated with any adiposity traits in our sample.

Previous studies relating genetic variation in the FAAH gene to obesity have focused on the Pro129Thr polymorphism (rs324420). In one study, carriers of the minor A allele of this SNP were overrepresented in obese when compared with normal weight individuals of European (n=1688; P=0.004) and African (n=614; P=0.049) descent, but not in a smaller sample from Asia (n=365) (34). Furthermore, the AA genotype was positively associated with BMI as a continuous trait in a combined analysis of all three populations (P<0.0001) (34). However, these findings were not replicated in a large population-based study from Denmark including more than 5,000 individuals (35). Our findings are in agreement with the latter study. In our large sample, rs324420 was not associated with adiposity-related measures.

Taken together, our data provide no evidence for association of common genetic variants in the CNR1 or the FAAH genes with adiposity-related traits in a large community-based sample. Further, we found no evidence for an association of common SNPs in the CNR1 or FAAH gene with visceral abdominal or subcutaneous fat.

Strengths and limitations

The large, unselected community-based design, the careful and standardized assessment of clinical covariates and the broad spectrum of adiposity-related traits (including CT measures of subcutaneous and visceral fat in 1,023 individuals), as well as the comprehensive coverage of common genetic variation in and around the CNR1 and the FAAH genes strengthen our study design. We determined subcutaneous and visceral fat using a volume measurement, which likely reflects more accurately the true fat burden in the abdomen as compared to planimetric measurements. In a previous publication we reported differences in the relative amounts of subcutaneous and visceral fat between volumetric and planimetric measurements, likely due to heterogeneity of fat distribution along the longitudinal axis of the abdomen (36). However, some limitations merit consideration. We focused on common genetic variants; thus, we cannot rule out that rare genetic variants (minor allele frequency <5%) in these genes might influence measures of adiposity. Furthermore, we did not cover 100% of the common genetic variation in both genes; it is therefore possible that other SNPs with a minor allele frequency above 5% might affect adiposity-related measures. In addition, we only had adequate power to detect genetic variants that explain 0.5% of the variance in the traits; thus, variants that have even more modest effects could have been missed. The HWE disequilibrium of SNP rs6928813 might have reduced its power to detect an association with our adiposity traits. The literature is divided about whether waist circumference should be measured at the level of the umbilicus (as in our and other studies (30)) or between the iliac crest and lower rib margin (32,35) or at the iliac crest (37). Finally, our results were obtained in individuals of European ancestry. The generalizability of our finding to other ethnicities is unknown.

Conclusion

This comprehensive analysis of common variants within two genes encoding major determinants of endocannabinoid activity found no evidence for association with a comprehensive set of cross-sectional and longitudinal adiposity measures in a large community-based sample.

Supplementary Material

Supplemental Data

Supplementary information description:

Online Supplemental Table 1. Quality criteria of tag SNPs in the CNR1 and FAAH genes.

Online Supplemental Table 2. Association of tag SNPs in the CNR1 gene with secondary phenotypes.

Online Supplemental Table 3. Association of tag SNPs in the FAAH gene with secondary phenotypes.

Online Supplemental Table 4. Association of rs6928813 with primary and secondary adiposity traits.

Legends to online supplemental figures

Online Supplemental Figure 1. All common variation at the CNR1 locus and pairwise D' between the SNPs. Tagging SNPs are marked (Tag SNPs).

Online Supplemental Figure 2. All common variation at the FAAH locus and pairwise D' between the SNPs. Tagging SNPs are marked (Tag SNPs).

Acknowledgments

Supported by the National Heart, Lung, and Blood Institute's Framingham Heart Study (Contract No. N01-HC-25195), an American Diabetes Association Career Development Award (Dr. Meigs), a research grant from sanofi-aventis (Dr. Meigs), and the Boston University Linux Cluster for Genetic Analysis (LinGA) funded by the NIH NCRR Shared Instrumentation grant (1S10RR163736-01A1). Dr. Meigs is also supported by NIDDK K24 DK080140. Dr. Vasan is supported by 2K24HL04334. Dr. Florez is supported by NIH Research Career Award K23 DK65978-04.

Dr. Meigs currently has other research grants from GlaxoSmithKline and sanofi-aventis, and serves on consultancy boards for GlaxoSmithKline, sanofi-aventis, Interleukin Genetics, Kalypsis, and Outcomes Sciences. Dr. Florez has received a consulting honorarium from Publicis Healthcare Communications Group, a global advertising agency engaged by Amylin Pharmaceuticals.

From the Framingham Heart Study of the National Heart Lung and Blood Institute of the National Institutes of Health and Boston University School of Medicine. This work was supported by the National Heart, Lung and Blood Institute's Framingham Heart Study (Contract No. N01-HC-25195)

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