Am. J. Hum. Genet. 70:1247–1256, 2002
A Combined Analysis of Genomewide Linkage Scans for Body Mass Index,
from the National Heart, Lung, and Blood Institute Family Blood Pressure
Program
Xiaodong Wu,1 Richard S. Cooper,1 Ingrid Borecki,2 Craig Hanis,3 Molly Bray,3 Cora E. Lewis,4
Xiaofeng Zhu,1 Donghui Kan,1 Amy Luke,1 and David Curb5
1
Department of Preventive Medicine and Epidemiology, Loyola University Medical Center, Maywood, IL; 2Division of Biostatistics,
Washington University School of Medicine, St. Louis; 3Institute for Molecular Medicine and Human Genetics Center, University of Texas
Houston Health Science Center, Houston; 4Division of Preventive Medicine, Department of Medicine, University of Alabama, Birmingham,
AL; and 5Pacific Health Research Institute, Honolulu
A combined analysis of genome scans for obesity was undertaken using the interim results from the National Heart,
Lung, and Blood Institute Family Blood Pressure Program. In this research project, four multicenter networks of
investigators conducted eight individual studies. Data were available on 6,849 individuals from four ethnic groups
(white, black, Mexican American, and Asian). The sample represents the largest single collection of genomewide
scan data that has been analyzed for obesity and provides a test of the reproducibility of linkage analysis for a
complex phenotype. Body mass index (BMI) was used as the measure of adiposity. Genomewide linkage analyses
were first performed separately in each of the eight ethnic groups in the four networks, through use of the variancecomponent method. Only one region in the analyses of the individual studies showed significant linkage with BMI:
3q22.1 (LOD 3.45, for the GENOA network black sample). Six additional regions were found with an associated
LOD 12, including 3p24.1, 7p15.2, 7q22.3, 14q24.3, 16q12.2, and 17p11.2. Among these findings, the linkage
at 7p15.2, 7q22.3, and 17p11.2 has been reported elsewhere. A modified Fisher’s omnibus procedure was then
used to combine the P values from each of the eight genome scans. A complimentary approach to the meta-analysis
was undertaken, combining the average allele-sharing identity by descent (p̂ ) for whites, blacks, and Mexican
Americans. Using this approach, we found strong linkage evidence for a quantitative-trait locus at 3q27 (marker
D3S2427; LOD 3.40, P p .03). The same location has been shown to be linked with obesity-related traits and
diabetes in at least two other studies. These results (1) confirm the previously reported obesity-susceptibility locus
on chromosomes 3, 7, and 17 and (2) demonstrate that combining samples from different studies can increase the
power to detect common genes with a small-to-moderate effect, so long as the same gene has an effect in all samples
considered.
Introduction
The prevalence of obesity has been increasing rapidly in
the last several decades, especially in the industrialized
countries. Obese individuals are at higher risk of morbidity and mortality from many chronic diseases, such as
hypertension, non–insulin-dependent diabetes, lipid abnormalities, and coronary heart disease (Spadano et al.
1999). It is known that obesity-related traits, including
BMI, are influenced by both genetic and environmental
factors (Bouchard et al. 1998). In the majority of studies,
heritability has been estimated to be 40%–90% (Borecki
Received November 28, 2001; accepted for publication February
19, 2002; electronically published March 28, 2002.
Address for correspondence and reprints: Dr. Xiaodong Wu, Department of Preventive Medicine and Epidemiology, Loyola University
Medical Center, 2160 South First Avenue, Maywood, IL 60153. Email: xwu@apache.medctr.luhs.org
䉷 2002 by The American Society of Human Genetics. All rights reserved.
0002-9297/2002/7005-0015$15.00
et al. 1998b; Bouchard et al. 1998; Barsh et al. 2000;
Luke et al. 2001). Mutations responsible for several rare
Mendelian types of obesity have been identified (Chagnon
et al. 2000), although the genes that confer susceptibility
to the common form of obesity are largely unknown.
Segregation analyses have provided evidence of major
genes influencing obesity-related traits (Price et al. 1990;
Borecki et al. 1998a); however, other studies have inferred
that multiple genes contribute, each having small effects
individually (Bouchard et al. 1988; Hasstedt et al. 1997;
Lecomte et al. 1997; Borecki 1998b). To search for the
multiple unknown loci that are hypothesized, linkage
analysis has been used with STR markers spread across
the genome. Genomewide linkage analysis has been performed in several different populations, including people
of European descent (Hager et al. 1998; Lee et al. 1999;
Kissebah et al. 2000; Öhman et al. 2000; Van der Kallen
et al. 2000; Watanabe et al. 2000; Perola et al. 2001),
Mexican Americans (Comuzzie et al. 1997; Mitchell et
1247
1248
al. 1999), Pima Indians (Hanson et al. 1998; Norman et
al. 1998; Walder et al. 2000), and African Americans (Zhu
et al. 2002).
Identifying the susceptibility genes for complex traits
such as obesity, diabetes, and hypertension represents a
challenging task for human geneticists. Multiple genes
interacting with environmental factors usually affect
these traits. Because of the moderate effect of each gene
on the trait, it is difficult to acquire enough power to
detect it with a moderate sample size. One potential
solution to this challenge is to combine data from multiple studies (Li and Rao 1996; Gu et al. 1998; Lonjou
et al. 1999; Palmer et al. 2001). In the present study,
we first performed genome scans for BMI in the eight
samples from the National Heart, Lung, and Blood Institute (NHLBI) Family Blood Pressure Program (FBPP).
We subsequently conducted linkage analysis in the combined data set by means of a meta-analysis combining
P values and also by pooling average allele-sharing identity by descent (IBD), p̂.
Subjects and Methods
Family Ascertainment
Subjects were selected from the four component networks (GenNet, GENOA, HyperGEN, and SAPPHIRe)
of the ongoing NHLBI FBPP. In brief, GenNet sampled
black and white nuclear families through identification
of young-to-middle aged probands with elevated blood
pressure (BP). Both GENOA and HyperGEN sampled
black and white sibships containing sib pairs with essential hypertension. GENOA also sampled Mexican
American sibships containing sib pairs with hypertension, as well as some nonhypertensive sibs in all three
ethnic groups. SAPPHIRe recruited three groups of Japanese and Chinese sib pairs: concordant for hypertension, concordant for hypotension, and extremely discordant. Anthropometric measurements were obtained
at the time of the clinic visit. The pooled data set analyzed here currently includes genotype data and 1120
measured and derived phenotypic variables in 6,849 participants, representing 160% of the total FBPP sample.
In the present study, we used BMI as the measure of
adiposity, because it is highly correlated with other measures of fat mass (Borecki et al. 1991) and is available
for the largest number of individuals. Family relations
were verified using the marker data; pedigree errors were
corrected, and unrelated individuals were eliminated
from the analysis.
Genotyping
DNA was extracted from whole blood by standard
methods at each of the four networks and was sent to
the Mammalian Genotyping Service in Marshfield, WI
Am. J. Hum. Genet. 70:1247–1256, 2002
for genotyping (Center for Medical Genetics Web site).
Screening Set 8 (372 highly polymorphic microsatellite
markers) was used for all four networks. This screening
set has an average heterozygosity of ∼80% and an average intermarker distance of 10 cM and covers ∼95%
of the human genome.
Analytical Methods
The genome scans were performed separately for each
of eight sampled populations—that is, for the GenNet
white and black samples; the GENOA white, black, and
Mexican American samples; the HyperGEN white and
black samples, and the SAPPHIRe Asian sample—using
the multipoint variance-component (VC) method implemented in the software GENEHUNTER 2 (Kruglyak et
al. 1996; Kruglyak Lab Software Programs Web site).
The VC method specifies the expected genetic covariances between relatives as a function of the estimated
proportion of alleles shared IBD at a marker locus. The
IBD probabilities were estimated using a multipoint approach that considers all available genotypes. The likelihood-ratio test was applied to test the null hypothesis
of no additive genetic variance due to a quantitativetrait locus (QTL) at a particular location. Outliers for
BMI were removed from each population before any
analysis. Outliers were chosen as BMI observations that
are not only far from the mean (e.g., 14 SD) but also
separated from the nearest interior observation by ⭓1
SD. BMI was transformed to approximate normality by
taking the inverse normal transformation, since it is
known that VC methods can be sensitive to departures
from normality in the trait distribution (Allison et al.
1999). We also performed the genome scan using BMI
without data transformation, and the results were quite
similar (data not shown). Sex, age, and age squared were
incorporated as covariates in the analysis. To evaluate
significance of linkage, we performed simulations for
groups with LOD 13.3, the proposed genomewide significant level (Pratt et al. 2000). We retained phenotypes
and pedigree structure in the data sets and randomly
generated genotypes under the hypothesis of no linkage.
The simulations were repeated 100 times to obtain empiric P values for genomewide significance.
To combine the linkage evidence across the eight data
sets, we used Fisher’s omnibus procedure (Fisher 1932).
This method is based on the observation that if n independent tests are made of the same hypothesis, resulting in P values P1, P2, …, Pn, then we can calculate
n
a combined P value for all n tests by Sip1
(⫺2 ln P)i , which
2
is asymptotically distributed as a x distribution with 2n
df. A modification of this method is needed to avoid bias
in the pooling of nonparametric LOD scores from
GENEHUNTER that are exactly zero (Province 2001).
We used .0012 (LOD 2.0) as the suggested significance
1249
Wu et al.: Combined Linkage Analysis of BMI
level for the Fisher test statistic. Since linkage results may
differ by ethnic group, we also produced a meta-analysis
scan separately for whites and for blacks.
We further combined eight individual studies by pooling average allele-sharing IBD (p̂) for each pair of individuals, estimated separately from each individual
study, and performed genome scans using the original
Haseman-Elston (HE) regression method (Haseman and
Elston 1972) with the pooled p̂. Because the frequency
of individual alleles can vary substantially between different ethnic groups, and the estimation of IBD sharing
is sensitive to allele frequencies in the absence of parental
genotype data, we first calculated p̂ for each pair of
individuals from each individual study separately and
then pooled p̂ across the studies.
The probability of sharing i (i p 0, 1, or 2) alleles
IBD (ˆf0, ˆf1, or ˆf2, respectively) for each of the sib pairs
was estimated using GENEHUNTER2 (the “dump ibd”
subroutine) across the genome in each of the eight studies separately. The proportion of alleles shared IBD for
each pair of relatives was calculated as p̂ p 12 ˆf1 ⫹ fˆ2.
BMI was regressed against sex, age, and age squared
within the study to obtain a residual suitable for analysis.
This residual was considered as the phenotype in our
linkage analysis, and we analyzed only sib pairs with
nonmissing phenotype and genotype data. We then
pooled samples by race over the different networks
(whites and blacks). Because only one Mexican American population was available and because we had already combined the Chinese and Japanese samples for
the individual genome scan, there was no pooling for
Mexican Americans and Asians. Assuming that the variance of residuals was solely due to the genetic effect
and a random environmental effect, we performed an
HE regression using the model yi p a ⫹ bpˆ i ⫹ i, where
yi is the squared difference of the residuals of BMI adjusted for sex, age, and age squared for the ith sib pair,
and p̂i is an estimate of the marker locus IBD proportion.
We further pooled whites, blacks, and Mexican Americans together. We did not include Asians in this final
pooling exercise, because the mean and variance of the
adjusted BMI were quite different from the other three
groups and could add substantial heterogeneity to the
analysis. To evaluate the significance of the combined
IBD analysis, we permuted the age, age squared, and
sex-adjusted phenotypes in the HE regression and recalculated the LOD scores through use of these permuted
phenotypes. We did the permutation 100 times, to obtain
the empiric genomewide P value.
Results
Family Characteristics
The clinical characteristics of the 6,849 family members
are presented in table 1 for each study. The mean BMI
values for whites, blacks, and Mexican Americans were
similar but higher than that for Asians. The proportion
of participants with a BMI 130 (a common definition of
obesity) was much higher among whites, blacks, and
Mexican Americans than among Asians. The lower average BMI in the subjects in the SAPPHIRe network is
the result of their primary recruitment in Taiwan, where
obesity is less common than in the United Sates. The heritability of BMI was calculated by use of the VC method
and was 0.43–0.63 in the eight studies (table 1).
Single Genomewide Screen
The results of the genome screen in the eight studies
considered separately are presented in figure 1. All of
the regions with a LOD 12.0 from multipoint VC analysis are listed in table 2. If a nearby region has a LOD
11.0 in other populations, we also listed these regions
in table 2 as “replicates.” The analysis revealed one region with multipoint LOD scores 13.3, which is the approximate level of genomewide significance for this
Table 1
Characteristics of Family Members for BMI Genome Scan from the FBPP Study
Study and Population (N)
GenNet:
White (606)
Black (621)
GENOA:
White (753)
Black (617)
Mexican American (788)
HyperGEN:
White (1,138)
Black (1,256)
SAPPHIRe:
Asian (1,070)
Age
(years)
% Male
BMI
% BMI 130
Heritability
45.0 Ⳳ 14.1
40.5 Ⳳ 11.7
47.2
40.5
29.2 Ⳳ 6.1
30.2 Ⳳ 8.3
38.5
42.3
.59
.57
56.3 Ⳳ 9.8
56.5 Ⳳ 9.4
55.8 Ⳳ 11.6
47.3
34.5
38.85
30.0 Ⳳ 6.2
30.4 Ⳳ 6.3
30.8 Ⳳ 6.1
41.4
45.5
50.4
.43
.45
.63
60.8 Ⳳ 9.4
50.7 Ⳳ 11.0
47.31
35.33
30.4 Ⳳ 6.0
32.4 Ⳳ 7.6
43.4
56.9
.56
.62
54.0 Ⳳ 12.2
43.6
25.6 Ⳳ 3.8
9.9
.57
Figure 1
Multipoint VC LOD scores for linkage to BMI in each of the eight groups. 1 p GenNet white; 2 p GenNet black; 3 p GENOA
white; 4 p GENOA black; 5 p GENOA Mexican American; 6 p HyperGEN white; 7 p HyperGEN black; and 8 p SAPPHIRe Asian. The
X-axis is the chromosome location. Each chromosome is scaled according to its length from the genetic map and is separated from the others
by vertical dotted lines.
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Wu et al.: Combined Linkage Analysis of BMI
Table 2
Results of Linkage Analysis from Eight Genome Scans
Chromosome
and Marker
Location
(cM)
Region
(cM)
LOD Score
31.17
145.45
0–37
138–155
2.03
3.45
HyperGEN (white)
GENOA (black)
24.89
124.39
146.62
0–32
108–137
124–151
2.66
2.36
1.29a
SAPPHIRe (Asian)
HyperGEN (black)
GENOA (Mexican American)
100.71
88–112
2.15
HyperGen (black)
78.71
66–88
2.55
GENOA (white)
31.33
47.66
23–48
36–57
2.47
1.87a
GENOA (white)
GENOA (Mexican American)
Study (Population)
3:
D3S1259
D3S1764
7:
D7S3051
D7S2847
D7S1824
14:
D14S617
16:
GATA67G11
17:
D17S947
D17S2196
a
Replicate at the same region with LOD 12.
method of analysis at a p 0.05 (Pratt et al. 2000). This
region was centered at 3q22.1 (LOD 3.45 at marker
D3S1764 in the GENOA black sample). In 100 simulated genomewide scans, we found a LOD 13.45 only
once, which gave an empiric P value of .01. Six more
regions were found with a LOD 12 (table 2) in at least
one study. The regions are 3p24.1 (LOD 2.03, at marker
D3S1259), 7p15.2 (LOD 2.66, at marker D7S3051),
7q22.3 (LOD 2.36, at marker D7S2847), 14q24.3 (LOD
2.15, at marker D14S617), 16q12.2 (LOD 2.55, at
Figure 2
marker GATA67G11), and 17p11.2 (LOD 2.47, at
marker D17S947).
Combined Analyses
The genome scan results pooling the studies with
Fisher’s method are presented in figure 2. We first combined the P values for the three white populations and
the three black populations separately. Two regions
showed suggestive linkage, with LOD 12, in whites:
Results from combining P values by means of the modified Fisher’s method. 1 p combined white; 2 p combined black; and
3 p all groups combined. Scaling and separation of each chromosome are as described in figure 1.
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Am. J. Hum. Genet. 70:1247–1256, 2002
Figure 3
Results from combining IBD-sharing method. 1 p combined white; 2 p combined black; 3 p combined white, black, and
Mexican American. Scaling and separation of each chromosome are as described in figure 1.
16q12.2 (LOD 2.15, at marker GATA67G11) and 17
(LOD 2.18, at marker D17S947). Next we combined P
values across all eight studies; however, no region
showed evidence of linkage with LOD 12. The highest
LOD score is around marker D3S1764 (151.3 cM, with
LOD 1.58). This is the only region showing strong linkage with BMI in individual studies.
The genome scan result for the combined IBD analysis
is presented in figure 3. Table 3 shows the number of
sib pairs selected in the analysis. Although no region
showed evidence of linkage with LOD 12 in either the
black or white samples, both show some evidence of
linkage at the same location on chromosome 3 (186.97
cM, at marker D3S2427, with LOD 1.90 in whites vs.
LOD 1.88 in blacks). When we combined white, black,
and Mexican American populations, we obtained a LOD
of 3.40 at the same region (186.97 cM, at marker
D3S2427). In 100 permutated genome scans, we found
a maximum multipoint LOD 13.40 only three times,
which gave a genomewide empiric P value of .03.
Discussion
In the present study, strong linkage evidence was found
for BMI on chromosome 3 (3q27, with LOD 3.40 at
marker D3S2427; P p .03), through use of a combined
IBD analysis. At least two other analyses support linkage
to this region. In a genome scan involving 507 nuclear
white families, Kissebah et al. (2000) reported that a QTL
on chromosome 3 (3q27) was strongly linked to six traits
representing the fundamental phenotypes of metabolic
syndrome, including BMI and waist and hip circumferences. The 1-LOD interval for BMI (183–200 cM) from
the Kissebah et al. (2000) study is very similar to our
result (180–193 cM). Vionnet et al. (2000) also report
significant linkage with diabetes or glucose intolerance at
age !45 years at the same region in French whites. It is
known that obesity is correlated with diabetes, and it is
possible that the same variants have an effect on both
obesity and diabetes. Several potential candidate genes
have been identified around this region. Among these
genes, the adiponectin gene (MIM 605441) (at 3q27)
seems the most interesting. Adiponectin is the most abundant gene transcript specific to adipose tissue (Maeda et
al. 1996). It encodes a secreted protein that circulates in
serum of normal individuals. Although the precise function of the adiponectin in energy expenditure and fat parTable 3
Number of Sib Pairs Selected in the
Combined IBD Analysis
Population and Study
White:
GenNet
GENOA
HyperGEN
Black:
GenNet
GENOA
HyperGEN
Mexican-American:
GENOA
No. of Sib Pairs
384
924
1,055
297
893
800
468
Wu et al.: Combined Linkage Analysis of BMI
titioning remains unclear, it is known that its circulating
level is inversely correlated with BMI (Arita et al. 1999),
and its mRNA level is suppressed in the adipose tissue of
obese animals and humans (Hu et al. 1996). Kissebah
further suggested that the gene for one of glucose transporters (GLUT2 [MIM 138160], at 3q26-q27) might be
relevant, at least for the metabolic syndrome (Kissebah et
al. 2000). Another candidate gene in this region is apolipoprotein D (Apo-D [MIM 164160]) (at 3q26). Apo-D
is a component of high-density lipoprotein and is closely
associated with the enzyme lecithin:cholesterol acyltransferase, an enzyme involved in lipoprotein metabolism. In
an association study, Vijayaraghavan et al. (1994) found
that a marker in this gene was more common in persons
with obesity (P p .006).
The only significant linkage in a single genome scan
was found at 3q22.1, with a LOD of 3.45 (P p .01) in
GENOA blacks. However, this region is ∼30 cM away
from the 3q27 region mentioned above, and it is hard
to tell whether the two QTLs are the same. Further
replication will be required to establish linkage with this
region.
Positive evidence for linkage and association with
obesity has been reported elsewhere for the leptin gene
(MIM 164160) on chromosome 7 (Clement et al. 1996;
Duggirala et al. 1996; Reed et al. 1996; Lapsys et al.
1997; Roth et al. 1997). Although we did not show
strong evidence for linkage in our pooled analysis, we
did find some evidence of linkage in this region in the
HyperGEN black (LOD 2.36, at marker D7S2847) and
GENOA Mexican American (LOD 1.29, at marker
D7S1824) samples.
Kissebah et al. (2000) recently published strong linkage
evidence for leptin level, on chromosome 17 (17p11)
(LOD 4.97, at marker D17S947; 31–45 cM). Two of
our samples show some evidence of linkage for BMI in
the same region—namely, the GENOA whites (LOD
2.47, at marker D17S947; 23–48 cM) and GENOA
Mexican Americans (LOD 1.87, at marker D17S2196;
36–57 cM). The candidate genes in this region include
solute carrier family 4 of the insulin-specific facilitated
glucose transporter (GLUT4 [MIM 138190]) and the receptor protein known to bind to globular “heads” of the
complement C1q (gC1qR).
Another interesting region emerged at 7p15.2, where
“suggestive” evidence for linkage was seen in Asians
(LOD 2.66, at marker D7S3051) (table 2). An obvious
candidate in this region is neuropeptide Y (NPY [MIM
162640]). NPY is one of the two critical neurochemicals
that have been shown to influence feeding behavior.
Intracerebroventricular infusion of NPY into the brains
of rats results in insatiable eating and weight gain (Morley 1987; Beck et al. 1992). Bray et al. (1999) showed
evidence of linkage of NPY with body weight (P p
1253
.020) and a composite measure of body mass and size
(P p .048) in a Mexican American population.
The genome scans for human complex traits published
to date are mostly based on relatively small sample sizes,
and it is almost certain that some true linkage will be
missed in a single genome scan study in which each gene
has a moderate effect. This has recently been shown in
a simulation study by Hirschhorn et al. (2001). Thus,
replication of some of these findings across multiple studies is critical to identify the true linkage. In obesity genome scan studies, we are beginning to see some of these
replications, as was recently reviewed by Comuzzie et al.
(2001); this development is particularly encouraging.
Four regions (on chromosomes 3, 7, and 17) in the present study have been associated with significant linkage
to obesity in publications before, which extends these
positive developments.
An obvious step to increase power in linkage analysis
of complex traits is to pool data across studies (Lander
and Kruglyak 1995; Allison and Heo 1998; Palmer et
al. 2001). In a recent review article, Altmuller et al.
(2001) compared 31 whole-genome scans for different
human complex diseases, with regard to design, methods, and results. They found that the most obvious differences influencing success in finding linkage across
studies was sample size. Two methods were used in the
present study to combine all eight studies from four
networks: combining P value and combining p̂. Fisher’s
method for combining P value is very general and easy
to implement; however, highly significant P values from
a single study can determine the significance of the
Fisher test statistic (Province 2001). Guerra et al. (1999)
showed that pooling raw data usually has greater power
and results in fewer false-positive results than does combining P values. By combining the original IBD-sharing
estimates from all eight studies, we detected a QTL for
BMI on chromosome 3 with significant support, although the evidence was weak in any of the individual
studies.
We did not perform ascertainment correction when
we applied the VC method, even though the families in
the FBPP study were ascertained through hypertension
and it is known that there is correlation between hypertension and obesity. This was guided by several considerations. First, on the basis of the simulation study
by de Andrade and Amos (2000), the power to detect
a major locus and the mean likelihood-ratio test were
similar whether the data were corrected for ascertainment or not, although there is some excess of falsepositive findings when there is a large genetic background. Their finding is consistent with that of Allison
et al. (1999). Second, it is not clear which kind of ascertainment would be the most efficient in our case.
When an inefficient ascertainment correction is used, it
can decrease power (Comuzzie and Williams 1999).
1254
In summary, by combining the data from eight studies, we found strong evidence for a QTL influencing
BMI, in FBPP data, on chromosome 3 (3q27). The same
region has been shown to be linked to several traits
related to adiposity and diabetes, with highly significant
statistical support in at least two other studies. We also
replicated the findings on chromosomes 3, 7, and 17 in
some of the individual genome scans. Our study shows
that by combining different data sets—and, hence, increasing the sample size—we can improve the power to
detect genes affecting human complex traits with moderate heritability, such as BMI.
Acknowledgments
We thank Dr. D. C. Rao, of Washington University, for reviewing the manuscript. This work was supported by National
Heart, Lung, and Blood Institute grants UO1 HL54485,
HL54466, HL65702, HL54481, HL45508, HL47910,
HL51021, HL54464, HL54473, HL54496, HL54472,
HL54515, HL54495, HL54471, HL54509, 2HHZ598, and
HL65702. Genotyping was conducted by the NHLBI Mammalian Genotyping Service, Marshfield, WI.
Electronic-Database Information
Accession numbers and URLs for data in this article are as
follows:
Center for Medical Genetics, Marshfield Medical Research
Foundation, http://research.marshfieldclinic.org/genetics/ (for
genetic maps)
Kruglyak Lab Software Programs, http://www.fhcrc.org/labs/
kruglyak/Downloads/ (for GENEHUNTER)
Online Mendelian Inheritance in Man (OMIM), http://www
.ncbi.nlm.nih.gov/Omim/ (for adiponectin [MIM 605441],
GLUT2 [MIM 138160], Apo-D [MIM 107740], leptin
[MIM 164160], GLUT4 [MIM 138190], and NPY [MIM
162640])
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