Y.
1
Zhang ,
E.M.
1
Smith ,
T.
1
Mersha ,
J.
1
Gunnell ,
A.
3
DelaForest ,
C.J.
3
Hillard ,
A.H.
1,4
Kissebah ,
M.
1,2
Olivier ,
and R.A.
1,3,4
Wilke
1Human
and Molecular Genetics Center,2Department of Physiology,3Department of Pharmacology,4Department of Medicine, Medical College of Wisconsin, 8701 Watertown Plank Road, Milwaukee,
WI 53226
Abstract
For centuries, marijuana (Cannabis sativa) has been an intriguing
subject for its psychoactive and medicinal effects in humans. Recent
studies have revealed a complicated network of endogenous cannabinoid
(eCB) signaling pathways (Fig.1) in which, both exogenous and
endogenous ligands act on a selective group of receptors, CB1 and CB2
receptors. Two endogenous ligands, e.g. N-arachidonylethanolamine (AEA)
and 2-arachidonylglycerol (2-AG), have been identified for the CB1
receptor. Because these lipid transmitters are not found to be kept in
storage, but rather synthesized “on demand”, fine-tuning of their levels
relies on regulated turnover by two key enzymes, fatty acid amide
hydrolase (FAAH) and monoacylglycerol lipase (MGLL).
Alterations in the eCB system are related to a variety of complex
diseases including the metabolic syndrome. Genetic and pharmacologic
evidence support the hypothesis that increased eCB/CB1 signaling overrides the normal satiety signals to stimulate inappropriate food
consumption. Very recently, our group conducted association tests in a
family-based cohort using tag single nucleotide polymorphisms (tagSNPs)
in the CB1 receptor gene (CNR1) (see poster by Mersha et al.). The tests
revealed several relationships between lipids and CNR1. CNR1 tagSNPs
influence lipids directly, independent of obesity.
In the current study, the contribution of FAAH gene to obesity-related
lipid traits is being characterized using a similar approach. Five FAAH
tagSNPs representing linkage disequilibrium within the entire FAAH gene
region have been selected using the human HapMap. We present here the
preliminary data from one of these tagSNPs, a coding SNP (cSNP;
rs324420). As shown previously (Sipe 2005), we observed that this FAAH
variant is associated with body mass index (BMI). We also extend these
observations, now demonstrating that this cSNP is also associated with
obesity-related lipid traits defining the metabolic syndrome, i.e., high
triglycerides and low HDL cholesterol.
Study Population (MRC-OB Cohort)
1560 individuals from 503 families were recruited through the TOPS (Take Off
Pounds Sensibly, Inc.) membership in 10 Midwest states, with the following criteria:
 At least two obese siblings (BMI>30).
 Availability of one (preferably both) parents.
 One or more never obese (BMI<27) sibling.
Exclusion criteria included:
 Pregnancy.
 Type 1 diabetes.
 History of cancer, renal or hepatic disease.
 Severe coronary artery disease.
 Substance abuse.
 History of weight loss >10% in the last 12 months.
 Individuals receiving lipid lowering medication.
rs324420
Figure 2. Structure and physical location of the FAAH gene on Chromosome 6, along with the position of 19
FAAH SNPs identified in the CEPH population of the Human HapMap. Pairwise comparison (red: r2 > 0.8) reveals
the LD structure of these SNPs, and two blocks are recognized by the algorithm of Gabriel et al [2002]. Six haplotypes
were observed within the CEPH population at frequency > 1% (five of these occurring at frequency >5%), and five
haplotype tagging SNPs (tagSNPs, pointed by arrowheads) can be used to represent the variation within these
haplotypes. The typed coding SNP rs324420 is pointed out.
Results and Conclusions
1.The genetic contribution of a key component of the eCB
system, the FAAH gene, to the obesity-related phenotypes
are investigated.
Data analysis
Table 1. Subject characteristics.
Variable
Age (yrs)
2
BMI (kg/m )
Waist (cm)
Hip (cm)
Waist/Hip
Glucose (mg/dl)
Insulin (pmol/l)
HOMA-IR
T Chol (mg/dl)
LDL (mg/dl)
HDL (mg/dl)
TG (mg/dl)
N
1531
1530
1594
1593
1593
1475
1497
1475
1511
1316
1512
1511
Mean
46.35
31.94
102.2
116.5
0.8757
89
16.60
3.778
197.5
121.8
39.09
127.4
SD
15.47
8.08
26.9
27.4
0.1004
29
13.20
4.114
44.1
38.8
11.40
105.8
Min
13.00
17.10
44.0
43.0
0.5300
36
2.00
0.384
66.0
13.0
11.00
27.0
Max
90.00
75.31
855.0
985.0
1.4500
379
172.37
58.705
458.0
360.0
116.00
2564.0
FAAH
3a. Our results demonstrate an association between this
cSNP and BMI (replicated).
3b. Our results also demonstrate associations between this
cSNP and dyslipidemia (novel).
4.These results suggest that people with variations in FAAH
gene may have different extent of susceptibility to onset
of obesity and dyslipideimia.
1.The rest of the tagSNPs of FAAH are being investigated.
Inactivated
Table 2. The single SNP association analysis of rs324420 for obesity-related traits.
trait
SNP
rs324420
Figure 1. CB1 receptor intracellular signaling cascades in the brain. The activation of CB1 receptors in
the presynaptic cell is subjected to the available amount of endocannabinoid molecules, which in turn is
regulated by inactivation through FAAH.
Adapted from http://www.endocannabinoid.net/EcsnMedia.axd?id=39.
2.TagSNPs were selected based on HapMap and the only
coding tagSNP has been typed in a family cohort of
northern European descent.
Total
Chol
ns
LDL
Chol
ns
HDL Chol
<0.01
Triglyceride
0.022
BMI
0.042
Waist
Circumference
ns
Glucose
Waist/Hip
0.0899
Insulin
0.0995
ns
HOMA_IR
ns
References
1. Gabriel, S. B., S. F. Schaffner, et al. (2002). "The structure of haplotype blocks in the human genome."
Science 296(5576): 2225-9.
2. Sipe JC, Waalen J, Gerber A and Beutler E. (2005). Overweight and obesity associated with a missense
polymorphism in fatty acid amide hydrolase (FAAH). International Journal of Obesity 29: 755_759.
3. Wilke RA, Reif DG, Moore JH: Combinatorial pharmacogenetics. Nature Reviews Drug
Discovery 4: 911-918, 2005.
4. Carillo MW, Wilke RA, Ritchie MD: Computational approaches for pharmacogenomics. Pac
Symp Biocomput. 544-546, 2006.
5. McCarty CA, Mukesh BN, Giampietro PF, Wilke RA. Healthy People 2010 disease prevalence in
the Marshfield Clinic Personalized Medicine Research Project: Opportunities for public health
genomic research. Personalized Medicine 4: 183-190, 2007.
6. Wilke RA, Lin D, Roden DM, Watkins PB, Flockhart D, Zineh I, Giacomini KM, Krauss RM:
Identifying genetic risk factors for serious adverse drug reactions – current progress and
challenges. Nature Reviews Drug Discovery 6: 904-916, 2007.
7. Wilke RA, Mareedu RK, Moore JH: The pathway less traveled – moving from candidate genes
to candidate pathways in the analysis of genome-wide data from large scale pharmacogenetic
association studies. Current Pharmacogenomics and Personalized Medicine (in press)