Relation of type 2 diabetes to individual admixture and candidate gene polymorphisms in the Hispanic American population of San Luis Valley, Colorado.

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ELECTRONIC LETTER
Relation of type 2 diabetes to individual admixture and
candidate gene polymorphisms in the Hispanic American
population of San Luis Valley, Colorado
E J Parra, C J Hoggart, C Bonilla, S Dios, J M Norris, J A Marshall, R F Hamman, R E Ferrell,
P M McKeigue, M D Shriver
...............................................................................................................................
J Med Genet 2004;41:e116 (http://www.jmedgenet.com/cgi/content/full/41/11/e116). doi: 10.1136/jmg.2004.018887
T
and Native Americans, than in populations of European
ancestry.1One approach to distinguishing between environ-
mental and genetic explanations for this difference is to
study the relationship of type 2 diabetes risk to individual
admixture proportions (the proportions of an individual’s
genome that are of European and Native American ancestry).
With only a few markers informative for ancestry, it is
possible to estimate the average admixture proportions of any
Hispanic American population. In such analyses, it has been
possible to demonstrate that the prevalence of type 2 diabetes
in Hispanic Americans in the south western United States
varies with the average Native American admixture propor-
tion of these populations.2–4In the Native American popula-
tion of Gila River, Arizona, USA, European admixture is
associated with lower prevalence of type 2 diabetes.5
However, it has not been possible to demonstrate an
association of type 2 diabetes with individual admixture
proportions within an Hispanic American population. To
estimate the admixture proportions of an individual accu-
rately requires a larger panel of markers: at least 40 markers
with average frequency differentials of 0.6 are required to
estimate the admixture of an individual with a standard error
of no more than 0.1.6It is now possible to identify relatively
large numbers of such ancestry informative markers from
data accumulating in the public domain. For this study we
typed a panel of 21 markers chosen to have large differences
in frequency between European, Native American, and West
African ancestry.
The possible relationship of type 2 diabetes risk to
individual admixture proportions within Hispanic American
populations complicates the interpretation of associations of
type 2 diabetes with candidate gene polymorphisms within
these populations. If admixture proportions vary between
individuals (hidden population stratification) and the risk of
type 2 diabetes varies with individual admixture proportions,
this will confound allelic associations with type 2 diabetes at
any loci where allele frequencies differ between Europeans
and Native Americans. We have shown that in recently
admixed populations associations are often observed between
unlinked genetic markers.7–9
association studies in admixed populations, it is necessary
to control for possible confounding by population stratifica-
tion. The classic approach to this has been to type parents as
controls, but for a late onset disease such as type 2 diabetes
parents of cases are not usually available for study. By typing
ancestry informative markers, we can estimate individual
admixture and control for it as a confounder. The most
satisfactory approach to this is to fit a statistical model
of population admixture, individual admixture, and the
he prevalence of type 2 diabetes is higher in populations
of Native American ancestry, and in Hispanic American
populations formed by admixture between Europeans
Thus, when carrying out
relationship of disease risk to individual admixture. Tests
for allelic association with the disease can then be adjusted
for the confounder. Although the statistical model is based
on a straightforward application of the laws of mendelian
genetics, to fit such a model in practice requires bayesian
computationally intensive methods. We have developed a
general purpose program (ADMIXMAP) for modelling
admixture based on this approach, and have demonstrated
the ability to distinguish associations of a trait with alleles at
loci that are linked to a trait locus from associations with
unlinked loci that are generated by population stratifica-
tion.9 10Where two or more loci in the same gene have been
typed, the program can also model the unobserved haplo-
types, given phase-unknown genotype data.
In this paper, we evaluated the association of type 2
diabetes, fasting insulin, and body mass index (BMI) with
polymorphisms in five candidate genes in a sample from the
Hispanic American population of San Luis Valley, Colorado,
USA. The candidate genes are calpain 10 (CAPN10), guanine
nucleotide binding protein, beta polypeptide 3 (GNB3),
Key points
N The prevalence of type 2 diabetes is higher in Hispanic
American populations than in populations of European
ancestry. The objectives of this study were to distinguish
between genetic and environmental explanations for
this ethnic difference in disease risk, and to test
candidate gene polymorphisms for association with
type 2 diabetes in the Hispanic American population of
San Luis Valley, Colorado, USA.
N We genotyped 11 single nucleotide polymorphisms in
five candidate genes (CAPN10, GNB3, PPARG, and
ABCC8/KCNJ11), together with a panel of 21
ancestry informative markers, in a sample of 261
controls and 185 diabetic individuals. The ADMIXMAP
program was used to model the effects of admixture.
N Type 2 diabetes risk varies with proportion of Native
American ancestry in San Luis Valley Hispanics, but
this relationship is confounded by socioeconomic
factors. We were able to confirm modest effects of
ABCC8/KCNJ11 variants on type 2 diabetes risk, but
observed no association of type 2 diabetes with four
CAPN10 markers.
Abbreviations: AIMs, ancestry informative markers; LD, linkage
disequilibrium; MCMC simulation, Markov chain Monte Carlo
simulation; SLVDS, San Luis Valley Diabetes Study
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peroxisome
(PPARG), ATP binding cassette, subfamily C, member 8
(ABCC8), and potassium inwardly rectifying channel, sub-
family J, member 11 (KCNJ11). We analysed four markers
within the CAPN10 gene (UCSNP 19, 43, 44, and 63
polymorphisms), one marker within the GNB3 gene (C825T
polymorphism), two markers within the PPARG gene
(Pro12Ala and exon 6 CRT polymorphisms), and four
markers located within the ABCC8 (SUR1) and KCNJ11
genes, which are closely linked on chromosome 11 (ABCC8
exon 16 splice acceptor site, ABCC8 exon 31 GRA poly-
morphism, ABCC8 exon 33 GRT polymorphism, and
KCNJ11-E32K polymorphism). In previous studies, associa-
tions of type 2 diabetes or related traits such as obesity and
insulin resistance have been detected with these polymorph-
isms, or other sites in the same genes. However, with the
exception of CAPN10, there have been few studies of these
associations in Hispanic American populations.
proliferativeactivatedreceptor,gamma
METHODS
Populations
The study sample was selected from participants of the San
Luis Valley Diabetes Study (SLVDS), a geographically based
study of the natural history, incidence, and risk factors for
type 2 diabetes conducted in the counties of Alamosa and
Conejos in southern Colorado. These counties are 43.6%
Hispanic American. Informed consent from all participants
and approval by the Institutional Review Board of the
University of Colorado were obtained prior to data collection.
Additional approval of this work was obtained through the
Penn State University Institutional Review Board (ORC#
00M0453). The procedure for selecting the SLVDS study
subjects has been described in detail by Hamman et al.11In
summary, persons with type 2 diabetes in the study area were
identified through all health care facilities and through
advertisement in local newspapers, presentations to local
organisations, and local radio programs. Eligible subjects
with a medical diagnosis of type 2 diabetes were 20–75 years
of age, residents of the study area, mentally competent, and
spoke either Spanish or English. The baseline data collection
clinic (1984–1988) was attended by 82% of eligible subjects
(n=440). Controls were selected using a two stage sampling
method. First, 57% of all occupied structures in the two
county area were sampled and enumerated. Enumerated
persons 20–75 years of age were the sampling frame for the
second stage of control selection where subjects were
randomly selected within age, sex, ethnic group, and county
strata to match the age and sex distribution of persons with
type 2 diabetes. Some 67% of eligible controls (n=1351)
attended the baseline clinic. The total sample described above
comprised unrelated individuals of Hispanic ancestry and
English or Anglo ancestry. We have focused our analyses only
on the persons who identified as Hispanic (Mexican,
Mexican American or Chicano, Spanish/Hispanic). The
SLVDS Hispanic sample included 185 individuals with a
history of diabetes confirmed by oral glucose tolerance test
and 261 controls with confirmed normal glucose tolerance. A
total of 128 participants reported their ethnicity (based on the
1980 US census question) as Mexican, Mexican American, or
Chicano, and 318 as other Spanish/Hispanic. Data on other
relevant phenotypes, such as fasting insulin and BMI, were
also collected in the SLVDS. Data on household income and
years of education were used to assess socioeconomic status.
Age at baseline visit ranged between 21 and 75 years old.
Given the relatively young age of some of the subjects, it was
expected that some of the control subjects would develop
type 2 diabetes as they grew older. As such our analysis is
conservative with respect to testing for associations with
either ancestry or locus specific tests. Table 1 summarises the
characteristics of this Hispanic sample from San Luis Valley.
Allele frequencies in unadmixed Europeans were estimated
from samples from Spain, Germany, Ireland, and Britain.
Allele frequencies in unadmixed Native Americans were
estimated from Mayans and from populations in the south
western United States (Pima, Cheyenne, and Pueblo). Allele
frequencies in West Africans were estimated from samples
from Nigeria, Sierra Leone, and Central African Republic.
More information on these unadmixed samples is available
indbSNP(http://www.ncbi.nlm.nih.gov/SNP/index.html),
under the submitter handle PSU-ANTH.
Genetic analysis
We typed 11 polymorphisms within five candidate genes. The
description of the markers in each gene, the primer
sequences, and the PCR conditions are given in table 2. The
sequence surrounding the polymorphisms of the Calpain-10
gene was kindly provided to us by Dr Nancy Cox. The
polymorphism of UCSNP-19 is based on a difference of 32 bp
between the two alternative alleles, and this marker was
genotyped using conventional agarose electrophoresis. The
characterisation of the remaining polymorphisms was based
on the presence/absence of a restriction site. In some cases,
due to the absence of a natural restriction site, primer
sequences were modified to create a restriction site poly-
morphism (table 2). After initial denaturation for 5 min at
94˚C, DNA samples were amplified at the denaturation/
annealing/extension temperatures specified for each marker,
followed by a final extension for 5 min at 72˚C. After PCR,
these markers were digested using the appropriate restriction
enzyme, following the recommendations of the supplier. For
details of the restriction enzymes used, see table 2. After
digestion, genotypes were characterised by means of conven-
tional agarose electrophoresis (SNP 43, SNP 44, and SNP 63),
or alternatively, by melting curve analysis. Details of the
melting curve analysis method employed have been described
in a previous manuscript.12For a subset of the samples,
genotypes were characterised using both the McSNP method
and conventional agarose electrophoresis, with consistent
results.
A panel of 21 ancestry informative markers (AIMs) was
also genotyped in the San Luis Valley sample. These markers
Table 1
Description of the SLVDS Hispanic sample
SLVDS casesSLVD controls
n
Males
Females
Age at baseline visit
Mean
SD
Minimum/maximum
BMI
Mean
SD
Minimum/maximum
Fasting insulin (mU/l)
Mean
SD
Minimum/maximum
Income*
Mean
SD
Minimum/maximum
Education (years)
Mean
SD
Minimum/maximum
185
70
115
261
123
138
57.14
10.85
26/74
52.54
12.59
21/75
29.32
4.95
17.23/48.38
26.17
4.44
15.84/40.03
22.82
12.51
7/69
13.46
7.15
3/44
3.59
2.36
1/9
4.68
2.38
1/10
9.47
3.45
0/17
10.81
3.75
0/17
*Total household income in ten categories.
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show large differences in frequency between the parental
populations (mainly European and Native American), and
were used to control for the presence of genetic structure due
to admixture. The panel of AIMs includes an Alu insertion
polymorphism (PV92), four short insertion–deletion poly-
morphisms (MID-575, MID-52, MID-161, and MID-93), and
16 single nucleotide polymorphisms (SNPs). Relevant infor-
mation about these markers is provided in table 3. We have
Table 2
Candidate gene polymorphisms typed
Markers Variation59–39 Forward/reverse primersPCR (35 cycles)MgCl2(mM) Enzyme
CAPN10
UCSNP-19
(Nancy Cox:
pers. comm.)
UCSNP-43
(Nancy Cox:
pers. comm.)
UCSNP-44
(Nancy Cox:
pers. comm.)
UCSNP-63
(Nancy Cox:
pers. comm.)
GNB3
GNB3-C825T
NCBI rs5443
2 or 3 repeats of
a 32 bp unit
F-GTTTGGTTCTCTTCAGCGTGGAG
R-ATGAACCCTGGCAGGGTCTAAG
94˚C 30 s
60˚C 40 s
72˚C 40 s
94˚C 45 s
55˚C 45 s
72˚C 60 s
94˚C 45 s
61˚C 45 s
72˚C 45 s
94˚C 60 s
66.5˚C 60 s
72˚C 60 s
1.5
GRA F-CACGCTTGCTGTGAAGTAATGC
R-CTCTGATTCCCATGGTCTGTAG
1.5NsiI
CRT F-CAGGGCGCTCACGCTTGCCG
R-TCCTCACCAAGTCAAGGCTTA
2.0 BstUI
CRTF-AGGGCCTGACGGGGGTGGCG
R-AGCACTCCCAGCTCCTGATC
3.0 HhaI
CRT F-CATCATCTGCGGCATCAC
R-AATAGTAGGCGGCCACTGAG
94˚C 30 s
58˚C 30 s
72˚C 30 s
1.5 BseDI
PPARG
PPARG-Pro12Ala
NCBI rs1801282
GRCF-CAAACCCCTATTCCATGCTG
R-AGTGAAGGAATCGCTTTCCG
94˚C 30 s
58˚C 30 s
72˚C 30 s
94˚C 30 s
60˚C 30 s
72˚C 30 s
2.5HpaII
PPARG-E6
NCBI rs3856806
CRTF-CTCAGACAGATTGTCACGGAAC
R-TTCTTGATCACCTGCAGTAGC
1.5 NlaIII
ABCC8 (SUR1)
ABCC8-E16 splice
acceptor site
NCBI rs1799854
CRTF-GTAATGGTTGTTCAGACTCC 94˚C 30 s2.5PstI
R-CTAGAAGGAGCGAGGACT57˚C 30 s
72˚C 30 s
94˚C 30 s
56˚C 30 s
72˚C 30 s
94˚C 30 s
58˚C 30 s
72˚C 30 s
ABCC8-E31
NCBI rs4148643
GRA F-GGAGTACATCGGTGCATGTG
R-CCATTAGGGGGTAGGTAAGG
1.5 BslI
ABCC8-E33
NCBI rs757110
GRTF-GCTACGACAGCTCCCTGAAG
R-CCCGTGCTCTGACCTTCT
2.0 MwoI
KCNJ11
KCNJ11-E23K
NCBI rs5219
GRA F-ATCATCCCCGAGGAATACG
R-GCCTTTCTTGGACACAAGGC
94˚C 30 s
58˚C 30 s
72˚C 30 s
2.5 BanII
For some markers, primers have been modified to introduce a restriction site polymorphism, and the altered base is shown in bold letters.
Table 3
allele 1
Ancestry informative markers with estimated ancestry specific frequencies of
Marker*
NCBI rs
Chromosomal
locationEuropean
Native
AmericanAfrican
MID-575
FY-null
F13B
TSC1102055
WI-11153
MID-52
SGC30610
WI-17163
WI-4019
WI-11909
D11S429
TYR
DRD2 ‘‘Taqd’’
DRD2 ‘‘Bcl I’’
WI-14319
CYP19
PV92
WI-7423
CKM
MID-161
MID-93
rs140864
rs2814778
rs6003
rs2065160
rs17203
rs16344
rs3309
rs3340
rs2161
rs2695
rs594689
rs1042602
rs1800498
rs1079598
rs2862
rs4646
rs3138523
rs2816
rs4884
rs16440
rs16383
1p34.3
1q23.2
1q31.3
1q32.1
3p12.3
4q24
5q11.2
5q33.2
7q22.1
9q21.31
11q13.1
11q14.3
11q23.2
11q23.2
15q14
15q21.2
16q23.3
17p13.1
19q13.32
20q11.22
22q13.2
0.584
0.993
0.104
0.921
0.172
0.082
0.300
0.197
0.295
0.845
0.440
0.485
0.630
0.135
0.142
0.287
0.171
0.517
0.257
0.508
0.082
0.007
1.000
0.018
0.137
0.819
0.763
0.699
0.690
0.618
0.181
0.119
0.034
0.045
0.665
0.716
0.741
0.792
0.058
0.904
0.109
0.763
0.124
0.001
0.704
0.487
0.785
0.263
0.401
0.054
0.430
0.805
0.087
0.005
0.135
0.063
0.386
0.332
0.225
0.000
0.164
0.637
0.263
*DRD2 ‘‘Taqd’’ and DRD2 ‘‘BclI’’ are closely linked on chromosome 11, located 4.7 kb apart.
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reported in a recent manuscript the allele frequencies of these
21 AIMs in the sample from San Luis Valley and also in
samples of the relevant parental populations.13
Statistical analysis
To test for association of the markers located within the type
2 diabetes candidate genes with the traits under study, we
used theprogramADMIXMAP10
www.lshtm.ac.uk/eu/genetics/index.html#admix). This is a
general purpose program for modelling population admixture
with genotype and phenotype data, based on a combination
of bayesian and classical methods. As we have described the
statistical methods used in this program in detail previously10
and demonstrated their application to studies of skin
pigmentation in this Hispanic American population sample,
only an outline is given here. For this analysis, the Hispanic
American population was modelled as formed by admixture
between three subpopulations: European, Native American,
and West African. The program fits a hierarchical model for
the distribution of admixture proportions in the population,
the admixture proportions of each parental gamete, and the
ancestry of the gene copies at each locus. The variation
between three states of ancestry on chromosomes of mixed
descent is modelled as the outcome of three independent
Poisson arrival processes. This requires only one extra
parameter—the sum of the intensities of the arrival
processes—to be specified in the model. Allele and haplotype
frequencies are estimated by combining information from
unadmixed and admixed population samples (using the
posterior distribution of allele frequencies obtained from data
on unadmixed individuals as a prior distribution for the
corresponding ancestry specific allele frequencies in the
admixed population). Where two or more SNPs in the same
gene have been typed, these loci are grouped into a single
‘‘compound locus’’ and the program models the unobserved
haplotypes, given the observed (phase unknown) genotypes
at each compound locus. A generalised linear model is
specified for the relation of the dependent variable (type 2
diabetes, insulin, or BMI) to individual admixture and other
covariates such as age, sex, and socioeconomic variables. For
type 2 diabetes, this is a logistic regression model. For insulin
and BMI, this is a linear regression model. For fasting insulin,
only those individuals who were classified as controls at
baseline visit were included in the regression model.
The model is specified as a bayesian full probability model,
in which all unobserved variables—such as haplotypes,
ancestry states at each locus, gamete admixture proportions,
and population level parameters—are ‘‘missing data’’. Non-
informative prior distributions are specified for the distribu-
tion of admixture proportions in the population, and for the
parameters of the regression model. The posterior distribu-
tion of the missing data, given the observed data, is then
generated by Markov chain Monte Carlo (MCMC) simula-
tion. Inference about the parameters of the regression model
is based on the posterior distribution. In large samples, the
posterior means and 95% central posterior intervals (‘‘95%
credible intervals’’) are asymptotically equivalent to max-
imum likelihood estimates and 95% confidence intervals
(95% CI).
Score tests for allelic association with the trait, conditional
on individual admixture and any other covariates, are
constructed as described previously.10The parameter tested
is the coefficient b for the effect of the allele or haplotype
under study (coded as 0, 1, or 2 copies) in a regression model
that includes admixture and other covariates such as age and
sex. For each SNP, a positive score value indicates association
of the trait with the allele being tested. To test the null
hypothesis that b=0, the score (gradient of the log-
likelihood) and the observed information (curvature of the
(available at http://
log-likelihood) at b=0 are calculated by averaging over the
posterior distribution of the missing data (the haplotypes and
individual admixture values). The score test correctly allows
for uncertainty about haplotype assignments and estimation
of individual admixture proportions, because it is based on
the likelihood of the observed data as a function of the
parameter (b) that is being tested.
The ratio of observed to complete information in the score
test can be interpreted as a measure of the efficiency of the
analysis, compared to a study design in which haplotypes
have been observed directly and individual admixture
proportions measured without error. Where an allele or
haplotype is found to show significant association with the
trait, it is possible to estimate the size of the effect of that
allele by fitting a model in which the allele or haplotype
(coded for each individual as 0, 1, or 2 copies) is included as
an explanatory variable in the regression model. Inference is
then based upon the posterior distribution of the regression
coefficient. This, however, requires a separate run of the
sampler for each hypothesis under test, whereas the score
test procedure allows all loci and all haplotypes to be tested
for association in a single run of the MCMC sampler. An
approximation to the maximum likelihood estimate of the
effect size (as the natural logarithm of the odds ratio, for a
logistic regression model) can be obtained from the score test
by dividing the score by the observed information. In large
samples, this is asymptotically equivalent to computing the
maximum likelihood estimate directly.
The fit of the observed genotype frequencies to Hardy-
Weinberg proportions was estimated by a Fisher exact test.
Linkage disequilibrium (LD) between markers was estimated
using the 3LOCUS.PAS program, kindly provided by Dr Jeff
Long. LD is expressed as the D9 coefficient, in which the
observed gametic disequilibrium (D) is standardised by the
theoretical maximum disequilibrium (Dmax).14
RESULTS
Fit of the genotype frequencies to the Hardy-
Weinberg proportions
We tested independently in cases and controls if genotype
frequencies deviate from the theoretical Hardy-Weinberg
proportions (HW). We detected significant departures of HW
in four markers in the sample of controls (FY-null, p=0.01;
GNB3, p=0.009; MID-161, p=0.02; and MID-93, p=0.015).
No significant deviations were observed in the sample of
diabetics. Overall, the number of significant tests (four out of
64, or 6%) is very close to the expected proportion (5%).
Associations with individual admixture
The mean admixture proportions of the population were
estimated as 65% European, 34% Native American, and less
than 1% African. The sum of intensities parameter was
estimated as 7.1 (95% CI 4.9 to 12.4) per 100 cM, implying
that the average time back to unadmixed ancestors in this
population is at least seven generations. The average Native
American ancestry is higher in the cases than in the controls
(34.3 v 33.2%, respectively). The estimated distribution of
individual admixture proportions for the total sample is
shown in fig 1.
Table 4 shows the results of logistic regression analyses
with type 2 diabetes as a dependent variable. In a model with
age, sex, and individual admixture only, the odds ratio for
type 2 diabetes associated with unit change in Native
American admixture proportion (from 0 to 1) was estimated
as 8.1 (95% CI 1.3 to 59). Adjustment for BMI had little effect
on this odds ratio (table 4). When income and education
were included as explanatory variables in the model, there
was a strong inverse relation of type 2 diabetes risk to
income: the odds ratio associated with an increase of one unit
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in income category was 0.88 (95% CI 0.83 to 0.94). With
income and education in the model, the odds ratio associated
with Native American admixture was 5.5 (95% CI 0.8 to 41).
With fasting insulin or BMI as dependent variable in the
regression model, there was no evidence of a relationship of
these variables to individual admixture (data not shown).
Linkage disequilibrium between markers within
compound loci
We estimated the extent of linkage disequilibrium (LD)
between markers within each compound locus. LD was very
high between the four markers located in the CAP10 locus
(D9 between 85 and 100%) and also between the two markers
located within the PPARG locus (D9=88%). LD was also
close to the maximum possible value between three of the
four markers located within the ABCC8/KCNJ11 genes
(ABCC8 exon 31 GRA, ABCC8 exon 33 GRT, and KCNJ11-
E32K; D9 between 94 and 100%). However, in spite of being
located only some kilobases apart on chromosome 11
(approximately 30 kb), LD was very low between the
ABCC8 exon 16 splice acceptor site and the other three
markers within the ABCC8/KCNJ11 genes (D9 between 9 and
19%). For this reason, in this genomic region we constructed
haplotypes on the basis of the three markers showing tight
linkage disequilibrium, and the exon 6 polymorphism was
analysed independently.
Associations with candidate gene polymorphisms
Table 5 shows the allele frequencies of the candidate gene
polymorphisms analysed in the sample of cases and controls
and table 6 depicts the results of tests for associations of type
2 diabetes with the SNPs in each compound locus, tested one
at a time. Table 7 shows the results of score tests for
associations of type 2 diabetes with the haplotypes estimated
at each of the four compound loci. At each compound locus,
Figure 1
population of San Luis Valley. (A) European genetic contribution;
(B) native American genetic contribution; (C) West African genetic
contribution. Individual ancestry was estimated using the program
ADMIXMAP, as described in the Statistical analysis section. Eur,
European; Nam, North American; Wafr, West African.
Distribution of individual admixture in the Hispanic American
Table 4
95% CI) in logistic regression models with diabetes as
outcome variable
Estimated odds ratios (posterior means, with
Posterior mean95% CI 97.5% Quantile
Model 1
Age
Sex*
NAM?
Model 2
Age
Sex*
Income
Education
NAM?
Model 3
Age
Sex*
BMI
NAM?
1.033
1.42
8.1
1.024
1.10
1.3
1.044
1.77
58.6
1.022
1.31
0.88
0.96
5.47
1.011
1.04
0.83
0.92
0.81
1.033
1.65
0.93
1.00
40.9
1.040
1.19
1.17
7.85
1.029
0.93
1.13
1.19
1.051
1.55
1.20
58.0
*Males=1, females=2; ?Native American admixture proportion.
Table 5
polymorphisms in the sample of cases and controls from
the San Luis Valley Hispanic sample
Allele frequency data for candidate gene
Locus
SLVDS casesSLVDS controls
npnp
CAPN10
CAPN10–43
Allele G
Allele A
CAPN10–19
3 Repeats
2 Repeats
CAPN10–63
Allele T
Allele C
CAPN10–44
Allele T
Allele C
PPARG
PPARG-E6
Allele C
Allele T
PPARG-Pro12Ala
Allele Ala12
Allele Pro12
ABCC8/KCNJ11
ABCC8-E16
Allele T
Allele C
ABCC8-E31
Allele A
Allele G
ABCC8-E33
Allele T
Allele G
KCNJ11-E23K
Allele A
Allele G
GNB3
GNB3 C825T
Allele T
Allele C
175 0.766
0.234
2550.733
0.267
1760.540
0.460
253 0.573
0.427
177 0.175
0.825
250 0.156
0.844
180 0.892
0.108
260 0.862
0.138
1810.870
0.130
247 0.913
0.087
1760.134
0.866
261 0.115
0.885
182 0.533
0.467
2610.492
0.508
1760.392
0.608
2420.314
0.686
182 0.690
0.310
261 0.617
0.383
177 0.302
0.698
2500.382
0.618
179 0.385
0.615
2500.361
0.639
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rare haplotypes have been grouped into a single category to
construct the test for association, although the statistical
model evaluates all possible haplotypes. At each locus the
haplotypes are tested for association one a time, and in
addition a chi-squared test statistic was calculated to test the
null hypothesis that all haplotypes have no effect. Test
statistics are not calculated for haplotypes where the
observed information is less than 1 because for these rare
haplotypes the asymptotic properties of the score test do not
hold.
In a model adjusting for age and sex only, associations
were significant at the 5% level for one of the two SNPs in the
PPARG gene (PPARG-E6), and for three of the four SNPs in
the ABCC8/KCNJ11 gene (ABCC8-E31, ABCC8-E33, and
KCNJ11-E23K) (table 6). When each haplotype was tested
successively, there were no significant associations with any
of the four PPARG haplotypes. In the ABCC8/KCNJ11 gene,
the A-T-G haplotype was positively associated with type 2
diabetes, and the G-G-A haplotype was negatively associated
with type 2 diabetes. The summary test for association with
all haplotypes at ABCC8/KCNJ11 was also significant
(p=0.027) (data not shown). None of the SNPs or
haplotypes in CAPN10 showed any evidence of association
with type 2 diabetes. In a model adjusting for individual
admixture proportions in addition to age and sex, the
associations with SNPs in PPARG and ABCC8/KCNJ11 were
changed only slightly (tables 6 and 7). Additional adjustment
for BMI had little effect on these associations (data not
shown). For two of the ancestry informative markers—
MID52 and D11S429—associations with type 2 diabetes were
significant at the 5% level without adjustment for admixture,
but not after adjustment for individual admixture.
There were no significant associations of fasting insulin
levels with SNPs or haplotypes in CAPN10, PPARG, or
ABCC8/KCNJ11 (data not shown). The T allele at locus GNB3
was associated with higher insulin levels in an analysis
adjusting for age and sex only (p=0.02). This association
persisted after adjustment for admixture (p=0.02), but was
weakened by adjustment for BMI (score 22.44, observed
information 2.78, p=0.09). None of the candidate gene
polymorphisms showed any evidence of association with
BMI.
DISCUSSION
The prevalence of type 2 diabetes is higher in many
populations of Native American ancestry than in people of
European ancestry living in similar environments. In San
Luis Valley, prevalence of type 2 diabetes in the admixed
Table 6
Tests for association of type 2 diabetes with SNPs within compound loci (alleles tested are indicated in parenthesis)
Locus
Adjusted for age, sex onlyAdjusted for age, sex, admixture
ScoreObs Info % Info ORpScore Obs Info % InfoORp
CAPN10–43 (A)
CAPN10–19 (2 repeats)
CAPN10–63 (C)
CAPN10–44 (C)
PPARG-E6 (T)
PPARG-Pro12Ala (Pro12)
ABCC8-E16 (C)
ABCC8-E31 (G)
ABCC8-E33 (G)
KCN1J11-E23K (G)
GNB3 C825T (C)
25.19
4.95
25.33
27.90
9.16
24.94
27.60
214.35
215.40
16.12
26.60
37.43
47.09
26.34
22.43
18.31
22.08
52.10
42.98
50.18
48.37
41.46
99.9
99.9
99.8
99.5
100
99.9
99.8
99.9
100
100
100
0.87
1.11
0.82
0.70
1.65
0.80
0.86
0.72
0.74
1.40
0.85
0.40
0.47
0.30
0.09
0.03
0.29
0.29
0.03
0.03
0.02
0.31
24.28
4.09
23.36
27.59
7.26
23.05
24.95
211.75
213.97
14.50
26.31
35.20
44.42
23.81
20.88
15.69
18.85
47.66
38.76
46.85
44.94
39.69
96.7
96.9
94.6
96.1
90.3
90.1
95.4
94.5
96.7
96.4
98.0
0.89
1.10
0.87
0.70
1.59
0.85
0.90
0.74
0.74
1.38
0.85
0.47
0.54
0.49
0.10
0.07
0.48
0.47
0.06
0.04
0.03
0.32
OR, odds ratio.
Table 7
Tests for association of type 2 diabetes with SNP alleles (or haplotypes at compound loci)
LocusHaplotype
Adjusted for age, sex only Adjusted for age, sex, admixture
ScoreObs Info % InfoOR pScoreObs Info % Info ORp
Candidate genes
CAPN10
CAPN10
CAPN10
CAPN10
CAPN10
CAPN10
CAPN10
UCSNP 43-19-63-44
G-3-C-T
G-2-T-T
G-2-C-T
G-2-C-C
A-3-C-T
Others
0.42
6.14
6.04
26.80
25.03
20.78
39.87
26.56
25.5
21.26
36.9
0.48
94
96
92
97
96
1.01
1.26
1.27
0.73
0.87
0.95
0.23
0.23
0.14
0.41
0.52
4.23
6.84
26.32
24.55
20.71
38.13
24.45
24.05
20.05
35.20
0.46
92
92
90
94
93
1.01
1.19
1.33
0.73
0.88
0.93
0.39
0.16
0.16
0.44
PPARG
PPARG
PPARG
PPARG
PPARG
E6-Pro12Ala
C-Ala
C-Pro
T-Ala
T-Pro
1.25
28.78
5.20
2.32
5.493
24.44
17.38
2.576
93
99
97
90
1.26
0.70
1.35
2.46
0.59
0.08
0.21
0.15
0.73
27.74
4.34
2.67
5.57
23.26
14.90
2.50
85
93
90
73
1.14
0.72
1.34
2.91
0.76
0.11
0.26
0.09
ABCC8/KCNJ11
ABCC8/KCNJ11
ABCC8/KCNJ11
ABCC8/KCNJ11
ABCC8/KCNJ11
E31-E33-E23K
A-T-G
G-T-G
G-G-A
Others
13.96
1.525
216.6
1.116
44.06
46.18
49.82
0.4966
96
96
100
1.37
1.03
0.72
0.03
0.82
0.02
11.28
2.549
214.87
1.04
39.25
41.77
45.83
0.5004
90
92
95
1.33
1.06
0.72
0.07
0.69
0.02
ABCC8-ex16
GNB3
27.60
26.60
52.10
41.46
99.8
100
0.86
0.85
0.29
0.31
24.95
26.31
47.66
39.69
95.4
98
0.90
0.85
0.47
0.32
For haplotypes where the observed information is less than 1, p values are omitted.
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Hispanic American population compared with non-Hispanic
American whites has been estimated to be 2.1-fold higher in
men and 4.8-fold higher in women.11If the excess risk of type
2 diabetes in Hispanic Americans compared with Europeans
has a genetic basis, we would expect to observe within
Hispanic American populations an association of type 2
diabetes with the proportion of the genome that is of Native
American ancestry. The estimated size of effect in this study
(OR=8.1 without adjustment for socioeconomic status,
OR=5.1 after adjustment for socioeconomic status) is
compatible with a genetic explanation for this ethnic
difference, but as the interval estimate for the adjusted odds
ratio overlaps 1, we cannot exclude an environmental
explanation based on factors associated with low socio-
economic status that confound the association with indivi-
dual admixture. The 95% CIs for the effect of European/
Native American admixture proportions on risk of type 2
diabetes are wide in this dataset: first, because individual
admixture proportions vary only over a modest range in this
population; and second, because with only 21 markers
informative for ancestry we cannot accurately estimate the
proportion of the genome that is of Native American
ancestry.
Even though we cannot measure individual admixture
accurately, we can still control for confounding by individual
admixture when testing for associations with alleles or
haplotypes after adjusting for individual admixture propor-
tions, because the score test (by integrating over the posterior
distribution of all missing data, including individual admix-
ture) allows for uncertainty in estimates of individual
admixture.10For most of the candidate gene polymorphisms
that were included in this study, the confounding effect of
individual admixture is weak, and thus the effect of the
candidate gene polymorphism, adjusted for confounding, can
be estimated accurately even though the confounder has not
been measured accurately. The proportion of information
extracted (ratio of observed to complete information) in the
score test can be interpreted as a measure of the efficiency of
the analysis, compared with the analysis of a dataset in
which individual admixture is estimated accurately (with a
large panel of ancestry informative markers) and individual
haplotypes are assigned without error. Even though haplo-
types are not observed directly in this analysis but inferred
from unphased genotype data, the proportion of information
extracted in score tests for association with each haplotype is
greater than 70% except where the haplotype is rare. As
theory shows, when studying haplotype effects on disease
risk it is generally more efficient to study a large sample of
unrelated individuals and to model the effects of the
unobserved haplotypes than to type other family members
in order to infer phase.15
In this study we typed polymorphisms in five candidate
genes (CAPN10, GNB3, PPARG, ABCC8/KCNJ11) and eval-
uated their association with type 2 diabetes and fasting
insulin. Because we have strong prior evidence of associa-
tions with these candidate gene polymorphisms, it is
reasonable to interpret even modest significance levels
(p,0.05) as evidence of association. The calpain-10 gene is
of particular interest, because the association of this gene
with type 2 diabetes was first detected in a Hispanic
American population.16Horikawa et al reported that the
highest diabetes risk in this population was associated with
the haplotype pair 112/121 (here referred to as G2T/G3C) at
the three polymorphic sites CAPN10-43, CAPN10-19, and
CAPN10-63. Subsequent studies of these SNPs and others in
the CAPN10 gene (for example, CAPN10-44) have not
consistently replicated this result. While some studies found
a modest effect on type 2 diabetes or associated pheno-
types,17–20other reports indicated no association.21–24Several
factors can explain the heterogeneous findings observed in
studies involving CAPN10 and type 2 diabetes: variation in
statistical power between studies, ethnic differences in allele
or haplotype frequencies, and the presence of gene–gene and
gene–environment interactions, among others. Two recent
meta-analyses of CAPN10 family based and population based
studies have reported modest pooled odds ratios for the allele
UCSNP-44 C (OR,1.225) and the UCSNP-43 G/G homozygote
(OR,1.226). This means that very large sample sizes are
required to detect the effect of these variants on type 2
diabetes risk. In the present study, we have not observed any
significant association of CAPN10 alleles or haplotypes with
type2 diabetes,fastinginsulin,
Additionally, no significant effect was detected when testing
the G2T/G3C haplotype pair (data not shown). Although in
our sample of Hispanic Americans the relative frequency of
one of the haplotypes hypothesised to increase type 2
diabetes risk (112 or G2T) is higher than in populations of
European ancestry (approximately 17 v 3–8%, respectively),
we have not observed a significant effect of CAPN10
polymorphisms on type 2 diabetes or fasting insulin levels.
PPARG is a member of the nuclear hormone receptor
subfamily of transcription factors, and has an important role
in insulin action and fat metabolism.27A recent meta-
analysis28
including more
European ancestry indicated that the common Pro12 allele
slightly increases type 2 diabetes risk. However, in a Native
American population, the Oji-Cree from Western Ontario,29
the Ala12 allele was associated with type 2 diabetes in
women. In the San Luis Valley sample association was
detected with C161T, a common silent polymorphism,30 31but
not with the Pro12Ala locus. The analysis at the haplotype
level did not show any significant effect.
The ABCC8 (SUR1) and KCNJ11 genes encode the two
subunits that constitute the ATP sensitive potassium channel
of the pancreatic beta cells. This channel is the target of the
sulfonylurea class of drugs. Both genes are located 4.5 kb
apart on chromosome 11, and have been widely studied in
relation to risk of type 2 diabetes. We analysed three common
polymorphisms in the ABCC8 gene and one site in the
KCNJ11 gene in the San Luis Valley sample. The variants in
the ABCC8 gene are a C/T mutation in the splice acceptor site
of exon 16, which has been associated with effects on insulin
secretion and type 2 diabetes,32–35a silent G/A mutation in
exon 31 that has been associated with hyperinsulinaemia and
type 2 diabetes,36 37and a non-synonymous G/T substitution
in exon 33.38The G/A mutation in codon 23 (E23K) of the
KCNJ11 gene has also been associated with type 2 diabetes in
numerous studies, including two recent meta-analysis in
European populations.39–42Three of these SNPs were asso-
ciated with type 2 diabetes in the San Luis Valley sample
(ABCC8 exon 31 allele A, ABCC8 exon 33 allele T, and
KCNJ11 E23K allele G; see table 6). In fact, these three
markers are in strong linkage disequilibrium (LD), with D9
values between 94 and 100%, while the LD between these
sites and the marker located upstream in exon 16 is very low
(D9 between 9 and 19%). Interestingly, the ABCC8 exon 31-A
allele has been previously reported to be associated with
hyperinsulinaemia in non-diabetic Mexican Americans.36
Ancestry specific haplotype frequencies were estimated using
ADMIXMAP, which combines the information from un-
admixed ‘‘parental’’ samples and the SLV sample (table 8).
The three common haplotypes in the compound ABCC8/
KCNJ11 locus accounted for an estimated 98% of haplotypes
in the San Luis Valley population. Haplotypes bearing A-T-G
at the three loci listed above (Ex31-Ex33-E23K) were
positively associated with type 2 diabetes, and haplotypes
bearing G-G-A at the three loci listed above were inversely
associated with type 2 diabetes. The haplotype increasing
or BMI(table 6).
than3000 individuals of
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type 2 diabetes risk (A-T-G) is more frequent in Native
American populations than in European populations (50 v
28%). One problem with testing for haplotype effects is that
without a strong prior hypothesis about which haplotypes are
associated with disease risk, detection of effects relies on
summary tests over all haplotypes, which have multiple
degrees of freedom and thus low statistical power. It is
important to note that the KCNJ11 E23K allele associated
with type 2 diabetes in the San Luis Valley population (allele
G) is not the same allele that has been reported to be
associated with type 2 diabetes in populations of European
ancestry (allele A).39–42In fact, the odds ratio associated with
the presence of the E23K G allele in San Luis Valley
(OR,1.40, CI: 1.05 to 1.85), does not overlap with the values
reported in Europeans, where a recent meta-analysis of case
control data indicated a pooled odds ratio associated with the
presence of the E23K A allele of 1.23 (CI: 1.12 to 1.3640).
Because the E23K A allele occurs predominantly on the same
haplotype (G-G-A) in both Europeans and Native Americans,
opposite directions of association with diabetes mellitus in
these two populations cannot be explained by occurrence on
different haplotypes. These contradictory results could be due
in part to differences with respect to the ethnic background
in the samples, but additional studies in Native American
and Hispanic populations are needed to confirm this point.
The C825T polymorphism of the gene encoding the G
protein beta-3 subunit (GNB3) has been repeatedly asso-
ciated with obesity, hypertension, and type 2 diabetes.43–46
This gene plays a key role in intracellular signalling and the
825T mutation creates a splice variant that is functional and
is associated with enhanced G protein activation.46We
observed no association with type 2 diabetes, but in non-
diabetic individuals the T allele was associated with higher
fasting insulin concentrations.
We have thus detected in this Hispanic American popula-
tion an association of type 2 diabetes with markers located on
the ABCC8 and KCNJ11 genes, closely linked on chromosome
11. We have also described evidence of association of the
G825T polymorphism with fasting insulin in the non-diabetic
sample. We did not observe an association of four CAPN10
markers with type 2 diabetes. In the score tests used in this
study, the observed information for the log odds ratio
associated with a common allele or haplotype is typically
about 30, equivalent to a standard error of 0.18. We can thus
estimate that our study had adequate (90%) statistical power
to detect at 5% significance a log odds ratio for type 2 diabetes
of about 0.6 (OR,1.8) associated with one extra copy of any
common allele or haplotype. Therefore, our study would have
detected effects of the magnitude described in the Mexican
American sample in which the original association of
CAPN10 and type 2 diabetes was reported
However, as pointed by Song et al,26neither this or, for that
matter, most of the previous CAPN10 studies can individually
achieve enough statistical power to detect the modest effects
(OR.2).
that have been described in recent meta-analyses of markers
such as the UCSNP-44 C allele (OR,1.1925), the UCSNP-43
G/G genotype (OR,1.1926), or the PPARG Pro12 allele
(OR,1.2528).
We have also demonstrated the ability to control for
confounding by population stratification when studying
genetic associations within a recently admixed population,
using a panel of markers informative for ancestry and
bayesian computationally intensive methods for statistical
analysis. This makes it possible to study genetic associations
in stratified populations using ordinary case control and cross
sectional designs, rather than family based designs which
require parents or sibs of affected individuals to be collected.10
We note also that the San Luis Valley population, with about
34% average Native American admixture, is an ideal setting
in which to apply a novel approach that exploits admixture to
localise genes underlying ethnic differences in risk of type 2
diabetes. However this will require a much larger panel of
markers informative for Native American versus European
ancestry.
ACKNOWLEDGEMENTS
We thank the San Luis Valley Study participants for their help.
ELECTRONIC-DATABASE INFORMATION
The URLs mentioned in this paper are as follows:
dbSNP, see NCBI website at http://www.ncbi.nlm.
nih.gov/SNP/index.html; ADMIXMAP, available at
http://www.lshtm.ac.uk/eu/genetics/
index.html#admix.Is
Authors’ affiliations
E J Parra, Department of Anthropology, University of Toronto at
Mississauga, Mississauga, Canada ON L5L 1C6
C J Hoggart, P M McKeigue, Noncommunicable Disease Epidemiology
Unit, London School of Hygiene and Tropical Medicine, London, WC1E
7HT, UK
C Bonilla, S Dios, M D Shriver, Department of Anthropology,
Pennsylvania State University, University Park, PA 16802, USA
J M Norris, J A Marshall, R F Hamman, Department of Preventive
Medicine and Biometrics, University of Colorado Health Sciences Center,
Denver, CO 80262, USA
R E Ferrell, Department of Human Genetics, Graduate School of Public
Health, University of Pittsburgh, Pittsburgh, PA, USA
.....................
This work was supported in part by grants from NIH/NIDDK (DK53958)
and NIH/NHGRI (HG02154) to MDS. The development of the
ADMIXMAP program was supported by NIH grant MH60343 to PMM.
Conflict of interest: none declared.
Correspondence to: Esteban J Parra, Department of Anthropology,
University of Toronto at Mississauga, 3359 Mississauga Road North,
Room 2002 South Building, Mississauga, Canada ON L5L 1C6;
eparra@utm.utoronto.ca
Revised version received 13 April 2004
Accepted for publication 25 May 2004
REFERENCES
1 American Diabetes Association, http://www.diabetes.org.
2 Gardner Jr LI, Stern MP, Haffner SM, Gaskill SP, Hazuda HP, Relethford JH,
Eifler CW. Prevalence of diabetes in Mexican Americans: relationship to
percent of gene pool derived from Native American sources. Diabetes
1984;33:86–92.
3 Chakraborty R, Ferrell R-E, Stern M-P, Haffner S-M, Hazuda H-P,
Rosenthal M. Relationship of prevalence of non-insulin-dependent diabetes
mellitus to Amerindian admixture in the Mexican Americans of San Antonio,
Texas. Genet Epidemiol 1986;3:435–54.
4 Weiss K-M. Genetic variation and human disease: principles and evolutionary
approaches. Cambridge: Cambridge University Press, 1993.
5 Williams R-C, Long J-C, Hanson R, Sievers M-L, Knowler W-C. Individual
estimates of European genetic admixture associated with lower risks of type 2
Table 8
specific haplotype frequencies estimated using
ADMIXMAP
ABCC8/KCNJ11 E31-E33-E23K ancestry
HaplotypeNative Americans Europeans
A-T-A
A-T-G
A-G-A
A-G-G
G-T-A
G-T-G
G-G-A
G-G-G
0.0075
0.4951
0.0043
0.0018
0.0029
0.1536
0.3202
0.0146
0.0002
0.2785
0.0050
0.0002
0.0002
0.3549
0.3606
0.0004
8 of 9 Electronic letter
www.jmedgenet.com

Page 9

diabetes and quantitative predictors of hyperglycemia and obesity in Pima
Indians. Am J Hum Genet 2000;66:527–38.
6 McKeigue P-M, Carpenter J, Parra E-J, Shriver M-D. Estimation of admixture
and detection of linkage in admixed populations by a Bayesian approach:
applications to African-American populations. Ann Hum Genet
2000;64:171–86.
7 Parra EJ, Kittles RA, Argyropoulos G, Pfaff CL, Hiester K, Bonilla C,
Sylvester N, Parrish-Gause D, Garvey WT, Jin L, McKeigue PM, Kamboh MI,
Ferrell RE, Pollitzer WS, Shriver MD. Ancestral proportions and admixture
dynamics in geographically defined African Americans living in South
Carolina. Am J Phys Anthropol 2001;114:18–29.
8 Pfaff CL, Parra EJ, Bonilla C, Hiester K, McKeigue PM, Kamboh MI,
Hutchinson RG, Ferrell RE, Boerwinkle E, Shriver MD. Population structure in
admixed populations: effect of admixture dynamics on the pattern of linkage
disequilibrium. Am J Hum Genet 2001;68:198–207.
9 Shriver MD, Parra EJ, Dios S, Bonilla C, Norton H, Jovel C, Pfaff C, Jones C,
Massac A, Cameron N, Baron A, Jackson T, Argyropoulos G, Jin L,
Hoggart CJ, McKeigue PM, Kittles RA. Skin pigmentation, biogeographical
ancestry and admixture mapping. Hum Genet 2003;112:387–99.
10 Hoggart C-J, Parra E-J, Shriver M-D, Kittles R-A, Clayton D-G, McKeigue P-M.
Control of confounding of genetic associations in stratified populations.
Am J Hum Genet 2003;72:1492–504.
11 Hamman RF, Marshall JA, Baxter J, Kahn LB, Mayer EJ, Orleans M,
Murphy JR, Lezotte DC. Methods and prevalence of non-insulin-dependent
diabetes mellitus in a biethnic Colorado population: the San Luis Valley
diabetes study. Am J Epidemiol 1989;129:295–311.
12 Akey J-M, Sosnoski D, Parra E, Dios S, Hiester K, Su B, Bonilla C, Jin L,
Shriver MD. Melting curve analysis of SNPs (McSNP): a gel-free and
inexpensive approach for SNP genotyping. BioTechniques 2001;30:358–67.
13 Bonilla C, Parra EJ, Pfaff CL, Dios S, Marshall JA, Hamman RF, Ferrell RE,
Hoggart CL, McKeigue PM, Shriver MD. Admixture in the Hispanics of the San
Luis Valley, Colorado, and its implications for complex trait gene mapping.
Ann Hum Genet 2004;68:139–53.
14 Thompson EA, Deeb S, Walker D, Motulsky AG. The detection of linkage
disequilibrium between closely linked markers: RFLP at the AI-CIII
apolipoprotein genes. Am J Hum Genet 1988;42:113–24.
15 McKeigue PM. Efficiency of estimation of haplotype frequencies: use of marker
phenotypes of unrelated individuals versus counting of phase-known gametes.
Am J Hum Genet 2000;67:1626–7.
16 Horikawa Y, Oda N, Cox NJ, Li X, Orho-Melander M, Hara M, Hinokio Y,
Lindner TH, Mashima H, Schwarz PE, del Bosque-Plata L, Horikawa Y, Oda Y,
Yoshiuchi I, Colilla S, Polonsky KS, Wei S, Concannon P, Iwasaki N, Schulze J,
Baier LJ, Bogardus C, Groop L, Boerwinkle E, Hanis CL, Bell GI. Genetic
variation in the gene encoding calpain-10 is associated with type 2 diabetes
mellitus. Nat Genet 2000;26:163–75.
17 Baier LJ, Permana PA, Yang X, Pratley RE, Hanson RL, Shen GQ, Mott D,
Knowler WC, Cox NJ, Horikawa Y, Oda N, Bell GI, Bogardus C. A calpain-
10 gene polymorphism is associated with reduced muscle mRNA levels and
insulin resistance. J Clin Invest 2000;106:819–21.
18 Elbein SC, Chu W, Ren Q, Hemphill C, Schay J, Cox NJ, Hanis CL, Hasstedt SJ.
Role of calpain-10 gene variants in familial type 2 diabetes in Caucasians.
J Clin Endocrinol Metab 2002;87:650–4.
19 Orho-Melander M, Klannemark M, Svensson M-K, Ridderstrale M,
Lindgren C-M, Groop L. Variants in the calpain-10 gene predispose to insulin
resistance and elevated free fatty acid levels. Diabetes 2002;51:2658–64.
20 Cassell PG, Jackson AE, North BV, Evans JC, Syndercombe-Court D,
Phillips C, Ramachandran A, Snehalatha C, Gelding SV, Vijayaravaghan S,
Curtis D, Hitman GA. Haplotype combinations of calpain gene polymorphisms
associate with increased risk of impaired glucose tolerance and type 2
diabetes in South Indians. Diabetes 2002;51:1622–8.
21 Hegele RA, Harris SB, Zinman B, Hanley AJ, Cao H. Absence of association of
type 2 diabetes with CAPN10 and PC-1 polymorphisms in Oji-Cree. Diabetes
Care 2001;24:1498–9.
22 Tsai HJ, Sun G, Weeks DE, Kaushal R, Wolujewicz M, McGarvey ST, Tufa J,
Viali S, Deka R. Type 2 diabetes and three calpain-10 gene polymorphisms in
Samoans: no evidence of association. Am J Hum Genet 2001;69:1236–44.
23 Fingerlin TE, Erdos MR, Watanabe RM, Wiles KR, Stringham HM, Mohlke KL,
Silander K, Valle TT, Buchanan TA, Tuomilehto J, Bergman RN, Boehnke M,
Collins FS. Variation in three single nucleotide polymorphisms in the calpain-
10 gene not associated with type 2 diabetes in a large Finish cohort. Diabetes
2002;51:1644–8.
24 Horikawa Y, Oda N, Yu L, Imamura S, Fujiwara K, Makino M, Seino Y,
Itoh M, Takeda J. Genetic variations in calpain-10 gene are not a major factor
in the occurrence of type 2 diabetes in Japanese. J Clin Endocrinol Metab
2003;88:244–7.
25 Weedon MN, Schwarz PE, Horikawa Y, Iwasaki N, Illig T, Holle R,
Rathmann W, Selisko T, Schulze J, Owen KR, Evans J, Del Bosque-Plata L,
Hitman G, Walker M, Levy JC, Sampson M, Bell GI, McCarthy MI,
Hattersley AT, Frayling TM. Meta-analysis and a large association study
confirm a role for calpain-10 variation in type 2 diabetes susceptibility.
Am J Hum Genet 2003;73:1208–12.
26 Song Y, Niu T, Manson JE, Kwiatlkowski DJ, Liu S. Are variants in the
CAPN10 gene related to risk of type 2 diabetes? A quantitative assessment of
population and family-based association studies. Am J Hum Genet
2004;74:208–22.
27 Schwartz M-W, Kahn S-E. Insulin resistance and obesity. Nature
1999;402:860–1.
28 Altshuler D, Hirschhorn JN, Klannermark M, Lindgren CM, Vohl MC,
Nemesh J, Lane CR, Schaffner SF, Bolk S, Brewer C, Tuomi T, Gaudet D,
Hudson TJ, Daly M, Groop L, Lander ES. The common PPARc Pro12Ala
polymorphism is associated with decreased risk of type 2 diabetes. Nat Genet
2000;26:76–9.
29 Hegele R-A, Cao H, Harris S-B, Zinman B, Hanley A-J, Anderson C-M.
Peroxisome proliferator-activated receptor-gamma2 P12A and type 2
diabetes in Canadian Oji-Cree. J Clin Endocrinol Metab 2000;85:2014–9.
30 Meirhaeghe A, Fajas L, Helbecque N, Cottel D, Lebel P, Dallongeville J,
Deeb S, Auwerx J, Amouyel P. A genetic polymorphism of the peroxisome
proliferator-activated receptor c gene influences plasma leptin levels in obese
humans. Hum Mol Genet 1998;7:435–40.
31 Koch M, Rett K, Maerker E, Volk A, Haist K, Deninger M, Rettig A, Renn W,
Haring HU. The silent PPARgamma exon 6 CAC(His)RCAT(His)
polymorphism does not affect the plasma leptin levels in a collective of first
degree relatives of type 2 diabetes patients from South West Germany. Exp
Clin Endocrinol Diabetes 2000;108:341–6.
32 Inoue H, Ferrer J, Welling CM, Elbein SC, Hoffman M, Mayorga R, Warren-
Perry M, Zhang Y, Millns H, Turner R, Province M, Bryan J, Permutt MA,
Aguilar-Bryan L. Sequence variants in the sulfonylurea receptor (SUR) gene
are associated with NIDDM in Caucasians. Diabetes 1996;45:825–31.
33 Hart LM, de Knijff P, Dekker JM, Stolk RP, Nijpels G, van der Does FE,
Ruige JB, Grobbee DE, Heine RJ, Maassen JA. Variants in the sulphonylurea
receptor gene: association of the exon 16-3t variant with type II diabetes
mellitus in Dutch Caucasians. Diabetologia 1999;42:617–20.
34 Hart LM, Dekker JM, van Haeften TW, Ruige JB, Stehouwer CD, Erkelens DW,
Heine RJ, Maassen JA. Reduced second phase insulin secretions in carriers of
a sulphonylurea receptor gene variant associating with type 2 diabetes
mellitus. Diabetologia 2000;43:515–9.
35 Meirhaeghe A, Helbecque N, Cottel D, Arveiler D, Ruidavets JB, Haas B,
Ferrieres J, Tauber JP, Bingham A, Amouyel P. Impact of sulfonylurea receptor
1 genetic variability on non-insulin-dependent diabetes mellitus prevalence
and treatment: a population study. Am J Med Genet 2001;101:4–8.
36 Goksel D-L, Fischbach K, Duggirala R, Mitchell BD, Aguilar-Bryan L,
Blangero J, Stern MP, O’Connell P. Variant in sulfonylurea receptor-1 gene is
associated with high insulin concentrations in non-diabetic Mexican
Americans: SUR-1 gene variant and hyperinsulinemia. Hum Genet
1998;103:280–5.
37 Reis A-F, Ye W-Z, Dubois-Laforgue D, Bellanne-Chantelot C, Timsit J, Velho G.
Association of a variant in exon 31 of the sulfonylurea receptor 1 (SUR1) gene
with type 2 diabetes mellitus in French Caucasians. Hum Genet
2000;107:138–44.
38 Rissanen J, Markkanen A, Karkkainen P, Pihlajamaki J, Kekalainen P,
Mykkanen L, Kuusisto J, Karhapaa P, Niskanen L, Laakso M. Sulfonylurea
receptor 1 gene variants are associated with gestational diabetes and type 2
diabetes but not with altered secretion of insulin. Diabetes Care
2000;23:70–3.
39 Nielsen EM, Hansen L, Carstensen B, Echwald SM, Drivsholm T, Glumer C,
Thorsteinsson B, Borch-Johnsen K, Hansen T, Pedersen O. The E23K variant of
Kir6.2 associates with impaired post-OGTT serum insulin response and
increased risk of type 2 diabetes. Diabetes 2003;52:573–7.
40 Gloyn AL, Weedon MN, Owen KR, Turner MJ, Knight BA, Hitman G,
Walker M, Levy JC, Sampson M, Halford S, McCarthy MI, Hattersley AT,
Frayling TM. Large-scale association studies of variants in genes encoding the
pancreatic beta-cell KATP channel subunits Kir6.2 (KCNJ11) and SUR1
(ABCC8) confirm that the KCNJ11 E23K variant is associated with type 2
diabetes. Diabetes 2003;52:568–72.
41 Love-Gregory L, Wasson J, Lin J, Skolnick G, Suarez B, Permutt M-A. E23K
single nucleotide polymorphism in the islet ATP-sensitive potassium channel
gene (Kir6.2) contributes as much to the risk of type II diabetes in Caucasians
as the PPARgamma Pro12Ala variant. Diabetologia 2003;46:136–7.
42 Barroso I, Luan J, Middleberg RP, Harding AH, Franks PW, Jakes RW,
Clayton D, Schafer AJ, O’Rahilly S, Wareham NJ. Candidate gene
association study in type 2 diabetes indicates a role for genes involved in
b-cell function as well as insulin action. PLoS Biol 2003;1:41–55.
43 Siffert W, Rosskopf D, Siffert G, Busch S, Moritz A, Erbel R, Sharma AM,
Ritz E, Wichmann HE, Jakobs KH, Horsthemke B. Association of a human
G-protein b3 subunit variant with hypertension. Nat Genet 1998;18:45–8.
44 Siffert W, Forster P, Jockel KH, Mvere DA, Brinkmann B, Naber C, Crookes R,
Du P Heyns A, Epplen JT, Fridey J, Freedman BI, Muller N, Stolke D,
Sharma AM, Al Moutaery K, Grosse-Wilde H, Buerbaum B, Ehrlich T,
Ahmad HR, Horsthemke B, Du Toit ED, Tiilikainen A, Ge J, Wang Y, Yang D,
Husing J, Rosskopf D. Worldwide ethnic distribution of the G protein beta3
subunit 825T allele and its association with obesity in Caucasian, Chinese,
and Black African individuals. J Am Soc Nephrol 1999;10:1921–30.
45 Rosskopf D, Frey U, Eckhardt S, Schmidt S, Ritz E, Hofmann S, Jaksch M,
Muller N, Husing J, Siffert W, Jocke KH. Interaction of the G protein beta 3
subunit T825 allele and the IRS-1 Arg972 variant in type 2 diabetes. Eur J Med
Res 2000;5:484–90.
46 Fernandez-Real JM, Penarroja G, Richart C, Castro A, Vendrell J, Broch M,
Lopez-Bermejo A, Ricart W. G protein beta3 gene variant, vascular function,
and insulin sensitivity in type 2 diabetes. Hypertension 2003;41:124–9.
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