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Department of Genetics, Southwest Foundation for Biomedical Research (A.G.C., L.J.M., J.B.), San Antonio, Texas 78245; Department of Internal Medicine and Molecular Science, University of Osaka Graduate School of Medicine (T.F., M.T., S.K., S.T., Y.M.), Osaka, Japan; Department of Medicine, TOPS Center for Obesity and Metabolic Research, and the Human and Molecular Genetics Center, Medical College of Wisconsin (G.S., H.J.J., A.E.K.B., D.M., A.K.), Milwaukee, Wisconsin 53226; and Center for Genomic Research, Genset (D.C.), Paris, France
Address all correspondence and requests for reprints to: Ahmed H. Kissebah, M.D., Ph.D., Department of Medicine, Medical College of Wisconsin, 9200 West Wisconsin Avenue, Milwaukee, Wisconsin 53226. E-mail: vdodge{at}mcw.edu
Abstract
Here we present the first genetic analysis of adiponectin levels, a newly identified adipocyte-derived protein. Recent work has suggested that adiponectin may play a role in mediating the effects of body weight as a risk factor for coronary artery disease. For this analysis we assayed serum levels of adiponectin in 1100 adults of predominantly northern European ancestry distributed across 170 families. Quantitative genetic analysis of adiponectin levels detected an additive genetic heritability of 46%. The maximum LOD score detected in a genome wide scan for adiponectin levels was 4.06 (P = 7.7 x 10-6), 35 cM from pter on chromosome 5. The second largest LOD score (LOD = 3.2; P = 6.2 x 10-5) was detected on chromosome 14, 29 cM from pter. The detection of a significant linkage with a quantitative trait locus on chromosome 5 provides strong evidence for a replication of a previously reported quantitative trait locus for obesity-related phenotypes. In addition, several secondary signals offer potential evidence of replications for additional previously reported obesity-related quantitative trait loci on chromosomes 2 and 10. Not only do these results identify quantitative trait loci with significant effects on a newly described, and potentially very important, adipocyte-derived protein, they also reveal the emergence of a consistent pattern of linkage results for obesity-related traits across a number of human populations.
THE 16-kb STRUCTURAL gene Apm1,
which has been mapped to chromosome 3 (3q27), encodes the protein
product adiponectin (also known as ACRP and AdipoQ) (1, 2)
that is structurally similar to cytokines and collections
(3). Adiponectin is an exclusively adipocyte-derived
protein expressed inversely to total fat and is thought to play a role
in mediating the obesity-related risk for coronary artery disease and
type 2 diabetes mellitus (4, 5, 6, 7, 8, 9). The region of chromosome
3 (3q27) that contains the adiponectin structural gene
(Apm1) has also been found to contain a quantitative trait
locus (QTL) with a strong influence on phenotypes of the metabolic
syndrome (10). The promoter region of Apm1
contains consensus sequences for both PPAR
and GR binding and as a
result could be subject to environmental modification with respect to
fat intake and stress. A proteolytic cleavage product of the
adiponectin mouse homolog has been shown to stimulate skeletal muscle
fatty acid oxidation and to limit weight gain in high caloric diet-fed
animals (9). The globular end structure of adiponectin
suggests utilization of a gC1q receptor with broad tissue distribution,
including liver, smooth muscle, endothelium, and immune cells, organs
and tissues likely to be involved in the biology of complex disorders
such as obesity. We recently reported the results from a genome scan
for phenotypes related to the obesity-metabolic syndrome
(i.e. body mass index, waist circumference, and fasting
insulin) where we detected significant linkage signals in the region of
chromosome 3 containing the structural gene for adiponectin
(Apm1) and the portion of chromosome 17 known to contain the
gene for its putative binding protein (gC1q) (10). As a
result, we decided to measure its plasma levels and to conduct a
genome-wide scan to identify quantitative trait loci influencing its
expression.
Subjects and Methods
Subjects
Data for the genetic analyses presented here, included 1100
individuals (297 men and 803 women) of predominately northern European
ancestry distributed among 170 families. As an indication of the
complexity of these pedigrees and their resultant enhanced information
content, Table 1
shows all available
pairwise relationships. These families are among those participating in
the Medical Risks and Complications of Obesity Genes Project, a
broader project designed to investigate the genetics of obesity and the
metabolic syndrome (10), who were recruited through TOPS,
Inc. (Take Off Pounds Sensibly), chapters in the midwestern United
States. Ascertainment for the larger study was based on a family having
at least 2 obese siblings (body mass index, 
30
kg/m2), availability of 1 (preferably both)
parents, as well as at least 1 never-obese sibling and/or parent (body
mass index, 
27 kg/m2). Ages ranged from 13–94
yr, with an average age of 47.4 yr. All protocols were approved by the
institutional review board of the Medical College of Wisconsin. All
subjects received a clear description of the protocol and consented to
participate in the study.
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Circulating levels of adiponectin were measured in plasma samples obtained after an overnight fast. The concentration of plasma adiponectin was measured in collaboration with Funahashi and his collaborators in Japan by ELISA. Details of this procedure, including its sensitivity, specificity, and recovery, have been reported previously (4). Inter- and intraassay coefficients of variation were 3.3% and 7.4%, respectively.
Genotyping
Whole blood was obtained from all consenting family members for DNA extraction. DNA was prepared using commercial kits (Puregene, Gentra Systems, Minneapolis, MN), which use a nonphenol-based method involving ribonuclease A treatment. Genotyping was performed at the Marshfield (Wisconsin) Medical Research Foundation using the Weber screening set 9 (Research Genetics, Inc., Huntsville AL). This procedure used 387 markers, representing short tandem repeat polymorphisms, including 366 autosomal as well as 17 X-linked and 4 Y-linked markers (11) and yielded an average map density of 10 cM. Initial analyses included validation of the reported relationships between individuals, checked by calculating likelihoods of the relationships based on the autosomal genotype data (12). Clear errors in the relations between individuals were identified, and, as a result, data for 8 proband families were discarded. The genotypic data were also examined for Mendelian inconsistencies, and those genotypes proven to be inconsistent were also removed. The autosomal genotype data were 97.6% complete. The average heterozygosity of the markers used was 0.79 ± 0.06, and the sex-averaged genetic spacing was 9.1 ± 3.8 cM. DNA was screened using fluorescently labeled primers from Research Genetics, Inc. (Huntsville, AL). The PCR assay mixture contained 45 ng DNA, 0.075 µM fluorescently labeled primers, 0.12 U AmpliTaq polymerase (Sigma, St. Louis MO), 100 µM of each deoxy-NTP, 25 mM MgCl2, and buffer. PCR conditions included 27 cycles of denaturation (95 C for 30 sec), annealing (55 C for 75 sec), and elongation (72 C for 30 sec), followed by a final 6-min elongation period. Samples were analyzed through automated high throughput scanning fluorescence detectors, each simultaneously detecting 3 separate dyes.
Variance components linkage analysis
A variance component model applied to extended family data was
used to test for evidence of linkage of phenotypes related to the
metabolic syndrome with short tandem repeat loci using a 10-cM
genome-wide map. An extension of the strategy developed by Amos
(13) was used to estimate the genetic variance
attributable to a specific chromosomal location (14). This
approach is based on specifying the expected genetic covariances
between arbitrary relatives as a function of the identity by descent
(IBD) relationships at a given marker locus. The basic method of
variance component linkage analysis also includes a QTL-specific
component, which is used to test for linkage. Using a variance
component model (15), we tested the null hypothesis that
the additive genetic variance due to a QTL
(
q2) equals zero (no linkage)
by comparing the likelihood of this restricted model with that of a
model in which q2 is
estimated. The difference between the two log10
likelihoods produces an LOD score that is the equivalent of the
classical LOD score of linkage analysis. Twice the difference in
loge likelihoods of these models yields a test
statistic that is asymptomatically distributed as a 1/2:1/2 mixture of
a 
2 variable and a point mass at zero
(16). Extensive simulation suggests that the likelihood
ratio test yields expected nominal P values for a wide
variety of reasonable trait distributions (17). This
quantitative trait linkage method has been implemented in the program
package SOLAR (14), which determines whether genetic
variation at a specific chromosomal location can explain the variation
in the phenotype (13, 14, 18).
The use of the variance component approach requires an estimate of the IBD matrix. For the relatively simple TOPS pedigrees, a pairwise maximum likelihood-based procedure was used to estimate IBD probabilities (14). To permit multipoint analysis for QTL mapping, an extension (16) of the technique described by Fulker and colleagues (19) was employed. Estimates of the IBD probabilities were generated at any point on a chromosome using a constrained linear function of observed IBD probabilities of markers at known locations within the region. This multipoint procedure, which yields substantially greater power to localize QTLs than two-point methods, enabled direct localization of the QTL and construction of confidence intervals. For the current dataset, an LOD score evaluation was performed every centimorgan along the chromosome, the distances between markers having been determined using CRI-MAP (20).
Results
Mean levels of adiponectin were 7.24 µg/ml for males and 8.18 µg/ml for females with SDs of 3.52 and 4.10, respectively. The additive genetic heritability of plasma adiponectin concentrations estimated in the additive genetic model as part of this analysis is 0.42 ± 0.06 (P = 1 x10-7), indicating that a substantial portion of the variations in plasma levels is due to the additive effects of genes.
In addition to the previously reported correlations of plasma levels of
adiponectin with total body fat (4) and with measures of
glucose metabolism (8), we found significant
(P = 1 x 10-6) phenotypic
correlations with high density lipoprotein and triglyceride levels
(
P = 0.36 and -0.29, respectively). Using variance
decomposition techniques (21, 22, 23, 24), we also found
significant evidence (P < 0.006) of a genetic
correlation in the plasma levels of adiponectin and both plasma high
density lipoprotein and triglyceride concentrations
(G = 0.32 ± 0.11 and -0.36 ±
0.12, respectively). As the genetic correlation is a direct measure of
pleiotropy, these findings suggest a shared genetic component in their
expression.
We used a robust variance decomposition method to conduct multipoint
linkage analysis. The results of this analysis are summarized by
chromosome in Fig. 1. Our two strongest
signals were on chromosomes 5 and 14, with LOD scores of 4.1
(P = 7.7 x 10 -6) and 3.2
(P = 6.2 x 10 -6),
respectively. In a subsequent oligogenic analysis, conditional on the
chromosome 5 signal, the QTL on chromosome 14 persisted with a LOD
score of 2.1, suggesting that both QTLs influence adiponectin levels.
The maximum LOD score for the chromosome 5 QTL was detected at
approximately 35 cM pter, near marker D5S817 (Fig. 2). The 1-LOD unit support interval (18
cM) surrounding this peak ranges in chromosomal location from 25–43 cM
pter. The maximum LOD score for the chromosome 14 QTL was detected at
approximately 29 cM pter, between markers D14S608 and
D14S599 (Fig. 3). The 1-LOD
unit support interval (26 cM) surrounding this peak ranges in location
from 16–42 cM from pter.
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The present study identifies plasma adiponectin levels as a novel endophenotype that is global to obesity and the metabolic syndrome. As reviewed recently (25) the fundamental features of the metabolic syndrome include glucose intolerance, dyslipidemia, and increased blood pressure. Fundamental to this syndrome is the interaction between total body adiposity, abdominal-visceral fat size, and insulin resistance. The biological precursors of this syndrome involve multiple organs, including skeletal muscle, pancreas, liver, and possibly vascular structures. Several mechanisms, including an overactivity of the hypothalamic arousal system and increased FFA flux into the plasma, have been proposed. We believe that adiponectin might provide a new pathway influencing the genesis of this syndrome.
Until relatively recently the generally accepted view had been that fat was a fairly inert tissue whose sole function was to store excess energy. However, beginning in the mid 1990s this view rapidly began to change based on the identification of a number of secreted adipocyte-specific proteins with autocrine, paracrine, and endocrine functions so that today the concept of adipose tissue as an active secretory organ is widely acknowledged (26, 27, 28). Particularly intriguing is the fact that these proteins appear to have a direct and marked effect on a number of physiological processes with direct connections to an individual’s risk for cardiovascular disease and type II diabetes (5, 6, 7, 8). Indeed, the well established relationship between increasing adiposity and adverse risk for coronary vascular disease as well as type II diabetes may represent a fundamental dysregulation of the endocrine axes to which adipose secreted proteins contribute (26, 27, 28).
Using serum levels of adiponectin, we were able to identify two QTLs with significant evidence of linkage in our genome-wide scan. The 1-LOD unit support interval for the linkage signal on chromosome 5 overlaps the same region Hager and colleagues (29) previously detected a significant linkage for obesity in French families. Although our signal is more refined, at least two rodent QTLs for obesity map to this region of human chromosome 5, Pfat4 (30) and MOB4 (31). In addition, CART (cocaine- and amphetamine-regulated transcript), a strong candidate gene for satiety regulation, is suspected of mapping to this region.
The signal on chromosome 14 is the first reported for this chromosome for an obesity-related phenotype. The 95% confidence interval surrounding this peak contains hepatocyte-specific nuclear factor-3 (HNF-3), a strong positional candidate gene, as it is known to influence the expression of other members of the collectin family of proteins (32).
There are four additional linkage signals that are worth noting (Fig. 1
). These provide potential evidence of replication of other
obesity-related QTLs detected in previous genome scans (Table 2). We detected an LOD score of 2.7 on
chromosome 2 at approximately 50 cM. The confidence interval
surrounding this peak corresponds to the region previously reported by
three independent studies (29, 33, 34) and contains the
candidate gene POMC. A suggestive evidence of linkage
(LOD = 1.9) is detected on chromosome 10 at approximately 67 cM,
which encompasses a QTL previously reported by Hager and colleagues
(29). Potential evidence of linkages (LODs = 1.5 and
1.7, respectively) are also detected on chromosomes 3 and 17 in regions
previously shown to contain obesity-related QTLs in our population
(10). The chromosomes 3 region has been replicated in
samples from French subjects (35) and Pima Indians
(36).
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Acknowledgments
Footnotes
This work was supported by NIH Grants HL-34989, DK-54026, MH-59490, and RR-00058. The genotyping was undertaken through the auspices of the Mammalian Genotyping Service of the NIH, funds being allocated to the Marshfield Medical Research Foundation. TOPS, Inc., provided funds for establishment of the families database, phenotyping, and linkage analysis.
Abbreviations: IBD, Identity by descent; QTL, quantitative trait locus.
Received March 27, 2001.
Accepted May 30, 2001.
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R. Weiss, S. Dufour, A. Groszmann, K. Petersen, J. Dziura, S. E. Taksali, G. Shulman, and S. Caprio Low Adiponectin Levels in Adolescent Obesity: A Marker of Increased Intramyocellular Lipid Accumulation J. Clin. Endocrinol. Metab., May 1, 2003; 88(5): 2014 - 2018. [Abstract] [Full Text] [PDF]
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A. Luke, X. Wu, X. Zhu, D. Kan, Y. Su, and R. Cooper Linkage for BMI at 3q27 Region Confirmed in an African-American Population Diabetes, May 1, 2003; 52(5): 1284 - 1287. [Abstract] [Full Text] [PDF]
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 U. B. Pajvani, X. Du, T. P. Combs, A. H. Berg, M. W. Rajala, T. Schulthess, J. Engel, M. Brownlee, and P. E. Scherer Structure-Function Studies of the Adipocyte-secreted Hormone Acrp30/Adiponectin. IMPLICATIONS FOR METABOLIC REGULATION AND BIOACTIVITY J. Biol. Chem., March 7, 2003; 278(11): 9073 - 9085. [Abstract] [Full Text] [PDF]
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O. Tschritter, A. Fritsche, C. Thamer, M. Haap, F. Shirkavand, S. Rahe, H. Staiger, E. Maerker, H. Haring, and M. Stumvoll Plasma Adiponectin Concentrations Predict Insulin Sensitivity of Both Glucose and Lipid Metabolism Diabetes, February 1, 2003; 52(2): 239 - 243. [Abstract] [Full Text] [PDF]
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 F. Vasseur, N. Helbecque, C. Dina, S. Lobbens, V. Delannoy, S. Gaget, P. Boutin, M. Vaxillaire, F. Lepretre, S. Dupont, et al. Single-nucleotide polymorphism haplotypes in the both proximal promoter and exon 3 of the APM1 gene modulate adipocyte-secreted adiponectin hormone levels and contribute to the genetic risk for type 2 diabetes in French Caucasians Hum. Mol. Genet., October 2, 2002; 11(21): 2607 - 2614. [Abstract] [Full Text] [PDF]
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 M. W. Hulver, D. Zheng, C. J. Tanner, J. A. Houmard, W. E. Kraus, C. A. Slentz, M. K. Sinha, W. J. Pories, K. G. MacDonald, and G. L. Dohm Adiponectin is not altered with exercise training despite enhanced insulin action Am J Physiol Endocrinol Metab, October 1, 2002; 283(4): E861 - E865. [Abstract] [Full Text] [PDF]
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H. Kondo, I. Shimomura, Y. Matsukawa, M. Kumada, M. Takahashi, M. Matsuda, N. Ouchi, S. Kihara, T. Kawamoto, S. Sumitsuji, et al. Association of Adiponectin Mutation With Type 2 Diabetes : A Candidate Gene for the Insulin Resistance Syndrome Diabetes, July 1, 2002; 51(7): 2325 - 2328. [Abstract] [Full Text] [PDF]
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