Original Article

Variation Within the Gene Encoding the Upstream
Stimulatory Factor 1 Does Not Influence Susceptibility to
Type 2 Diabetes in Samples From Populations With
Replicated Evidence of Linkage to Chromosome 1q
Eleftheria Zeggini,

1,2
Coleen M. Damcott,

3
Robert L. Hanson,

4
Mohammad A. Karim,

5,6

N. William Rayner,
1,2

Christopher J. Groves,
1

Leslie J. Baier,
4

Terri C. Hale,
5,6

Andrew T. Hattersley,
7

Graham A. Hitman,
8

Sarah E. Hunt,
9

William C. Knowler,
4

Braxton D. Mitchell,
3

Maggie C.Y. Ng,
10,11

Jeffrey R. O’Connell,
3

Toni I. Pollin,
3

Martine Vaxillaire,
12

Mark Walker,
13

Xiaoqin Wang,
5,6

Pamela Whittaker,
9

Xiang Kunsun,
14

Weiping Jia,
14

Juliana C. N. Chan,
11

Philippe Froguel,
12,15

Panos Deloukas,
9

Alan R. Shuldiner,
3

Steven C. Elbein,
5,6

and Mark I. McCarthy,
1,2

for the

International Type 2 Diabetes 1q Consortium*

The gene encoding the transcription factor upstream stim-
ulatory factor (USF)1 influences susceptibility to familial
combined hyperlipidemia (FCHL) and triglyceride levels.
Phenotypic overlap between FCHL and type 2 diabetes
makes USF1 a compelling positional candidate for the
widely replicated type 2 diabetes linkage signal on chromo-
some 1q. We typed 22 variants in the F11R/USF1 region (1
per 3 kb), including those previously implicated in FCHL-
susceptibility (or proxies thereof) in 3,726 samples pref-

erentially enriched for 1q linkage. We also examined
glucose- and lipid-related continuous traits in an overlap-
ping set of 1,215 subjects of European descent. There was
no convincing evidence for association with type 2 diabetes
in any of seven case-control comparisons, individually or
combined. Family-based association analyses in 832 Pima
subjects were similarly negative. At rs3737787 (the variant
most strongly associated with FCHL), the combined odds
ratio, per copy of the rarer A-allele, was 1.10 (95% CI
0.97–1.24, P � 0.13). In 124 Utah subjects, rs3737787 was
significantly associated (P � 0.002) with triglyceride lev-
els, but direction of this association was opposite to pre-
vious reports, and there was no corroboration in three
other samples. These data exclude USF1 as a major con-
tributor to type 2 diabetes susceptibility and the basis for
the chromosome 1q linkage. They reveal only limited evi-
dence for replication of USF1 effects on continuous meta-
bolic traits. Diabetes 55:2541–2548, 2006

P
ositional cloning within regions previously high-
lighted by genome-wide linkage analysis repre-
sents one of the dominant strategies for
identification of sequence variants influencing

individual risk of type 2 diabetes. A region of chromosome
1q (from 147 to 175 Mb on the NCBI35 assembly) has
emerged as among the strongest signals (1) with linkage to
type 2 diabetes demonstrated in diverse populations of
European (2–7), East-Asian (8,9), Native-American (10),
and African-American origin (S.C.E., unpublished observa-
tions). Moreover, signals for several diabetes-related traits
have also been mapped to this region (11–14). The Inter-
national Type 2 Diabetes 1q Consortium represents a
collaborative effort between many of the groups with 1q
linkage signals to identify the variant(s) responsible. In
addition to dense-map indirect linkage disequilibrium (LD)
mapping of the region, the most compelling positional
candidates are being targeted for detailed analysis.

The gene encoding upstream stimulatory factor (USF)1
is one such candidate. USF1 maps in the middle of the
region of interest (�158 Mb), close to the peak of linkage
in several of the type 2 diabetes-linked populations

From the 1Oxford Centre for Diabetes, Endocrinology and Metabolism,
University of Oxford, Oxford, U.K.; the 2Wellcome Trust Centre for Human
Genetics, University of Oxford, Oxford, U.K.; the 3Division of Endocrinology,
Diabetes and Nutrition, University of Maryland School of Medicine, Baltimore,
Maryland; the 4Phoenix Epidemiology and Clinical Research Section, National
Institute of Diabetes and Digestive and Kidney Diseases, Phoenix, Arizona; the
5Endocrinology Section, Medical Service, Central Arkansas Veterans Health-
care System, Little Rock, Arkansas; the 6Division of Endocrinology and
Metabolism, Department of Internal Medicine, College of Medicine, University
of Arkansas for Medical Sciences, Little Rock, Arkansas; the 7Institute of
Clinical and Biomedical Science, Peninsula Medical School, Exeter, U.K.; the
8Centre for Diabetes and Metabolic Medicine, Bart’s and the London Queen
Mary’s School of Medicine and Dentistry, London, U.K.; the 9Wellcome Trust
Sanger Institute, Hinxton, U.K.; the 10Department of Medicine, University of
Chicago, Chicago, Illinois; the 11Department of Medicine and Therapeutics,
Chinese University of Hong Kong, Shatin, Hong Kong; the 12Institut de
Biologie de Lille, Lille, France; the 13Department of Medicine, University of
Newcastle, Newcastle, U.K.; the 14Department of Endocrinology and Metabo-
lism, Shanghai Diabetes Institute, Shanghai Jiaotong University No.6
People’s Hospital, Shanghai, China; and the 15Faculty of Life Sciences,
Imperial College, London, U.K.

Address correspondence and reprint requests to Mark McCarthy, Robert
Turner Professor of Diabetes, Oxford Centre for Diabetes, Endocrinology and
Metabolism, University of Oxford, Churchill Hospital, Old Road, Headington,
Oxford OX3 7LJ, U.K. E-mail: mark.mccarthy@drl.ox.ac.uk.

Received for publication 19 January 2006 and accepted in revised form 19
June 2006.

*A list of additional members of the consortium can be found in the
ACKNOWLEDGMENTS.

Additional information for this article can be found in an online appendix at
http://diabetes.diabetesjournals.org.

DI, disposition index; FCHL, familial combined hyperlipidemia; FSIGT,
frequently sampled intravenous glucose tolerance test; LD, linkage disequilib-
rium; MAF, minor allele frequency; SNP, single nucleotide polymorphism;
USF, upstream stimulatory factor.

DOI: 10.2337/db06-0088
© 2006 by the American Diabetes Association.
The costs of publication of this article were defrayed in part by the payment of page

charges. This article must therefore be hereby marked “advertisement” in accordance

with 18 U.S.C. Section 1734 solely to indicate this fact.

DIABETES, VOL. 55, SEPTEMBER 2006 2541



(2,3,8,10). The gene encodes a ubiquitously expressed
member of the basic helix-loop-helix leucine zipper tran-
scription family, which, either as a homodimer or through
heterodimerization with the related USF2 protein, binds
the E-box motif found in the promoters of many genes
involved in glucose and lipid metabolism (15,16). Although
the relative functional importance of USF1 and USF2 (and
of sterol regulatory element– binding protein 1, which
interacts with the same motif) remains unclear (17,18);
considerable evidence implicates the USFs in the regula-
tion of key metabolic genes including liver pyruvate kinase
(19), fatty acid synthase (16,20), glucokinase (21), and
pyruvate dehydrogenase complex component X (22).

The biological plausibility of USF1 as a positional
candidate for type 2 diabetes is enhanced by evidence
implicating the gene in susceptibility to familial combined
hyperlipidemia (FCHL) (23,24). In Finnish pedigrees seg-
regating FCHL, in which prior evidence for 1q linkage had
been reported (25), several single nucleotide polymor-
phisms (SNPs) within USF1 (and to a lesser extent, within
the neighboring F11R [or JAM] gene) were associated
with FCHL and triglyceride levels. Though no single caus-
ative variant was identified, the strongest associations
involved the common allele at two highly correlated SNPs,
rs3737787 (within the 3� untranslated region) and
rs2073658 (within intron 7). These findings were recently
replicated in families from Utah (24). Given the strong
phenotypic overlap between FCHL and type 2 diabetes
(which is characterized by an FCHL-like dyslipidemia) and
growing suggestions that the �-cell dysfunction and insulin
resistance of type 2 diabetes may, in fact, be secondary to

disturbances of lipid metabolism, USF1 emerges as a
strong candidate vis-à-vis the 1q linkage signal.

There have been only limited studies addressing the role
of USF1 variation and glucose metabolism to date. In 800
young men from the European Atherosclerosis Research
Study, Putt et al. (26) tested three intronic USF1 SNPs
(one of which, rs2073655, was in complete LD with the
SNPs displaying the strongest FCHL association signal in
Finns) reporting weak haplotypic associations with glu-
cose levels during an oral glucose tolerance test. A recent
study in Hong Kong Chinese reported associations of
USF1 SNPs (including rs3737787) with type 2 diabetes and
metabolic syndrome in one case-control analysis (using
family cases showing 1q linkage), but these were not
replicated in a second Chinese sample (27). Most recently,
a study of eight USF1 SNPs (including rs3737787 and
rs2073658) reported no association with type 2 diabetes in
1,475 French case-control samples (28).

The present study has made use of dense-map, large-
scale genotyping data from the consortium’s mapping
efforts on chromosome 1q to evaluate the influence of
USF1 variation on type 2 diabetes risk and related meta-
bolic traits, making explicit use of populations and sam-
ples selected because of their prior evidence for linkage to
the region.

RESEARCH DESIGN AND METHODS

We used three complementary approaches to the analysis of USF1 variation:
1) a large multiethnic case-control analysis in seven 1q-linked populations, 2)
family-based association analyses in an enlarged Pima dataset, and 3) analysis

TABLE 1
Characteristics of the samples studied

n Male (%) Ethnicity
Age at diagnosis or age

at study (years)*
BMI

(kg/m2)

U.K.
Sibpair probands 449 54 European descent 54.7 � 8.1 28.8 � 5.1
Control subjects 450 56 European descent — —

French
Diabetic case subjects 259 47 European descent 43.2 � 9.5 26.2 � 3.8
Control subjects 288 37 European descent 59.9 � 13.6* 24.9 � 4.2

Shanghai
Sibpair probands 80 50 East Asian 35.3 � 3.9 23.1 � 3.4
Chinese control subjects 80 69 East Asian 74.2 � 5.5* 21.1 � 2.3

Hong Kong
Sibpair probands 64 42 East Asian 38.0 � 8.7 27.8 � 4.0
Control subjects 64 42 East Asian 42.2 � 8.9* 21.5 � 2.0

Utah
Case subjects 182 70 European descent 62.7 � 9.8 31.5 � 5.8
Control subjects 167 39 European descent 51.3 � 14.9* 28.1 � 6.3
Intermediate trait 124 43 European descent 40.0 � 11.1* 27.9 � 6.0

Amish
Familial case subjects 150 32 European descent 59.6 � 11.2 29.4 � 5.7
Control subjects 361 52 European descent 51.7 � 10.5* 27.4 � 4.6
Other samples 203 38 European descent 50.5 � 16.7* 28.3 � 5.3

Pima
Early-onset case subjects 200 35 Native American 17.8 � 4.5 35.1 � 8.1
Elderly control subjects 199 50 Native American 61.3 � 9.3* 32.1 � 6.3
Other family members 733 37 Native American 36.3 � 10.4 33.6 � 8.1

49.6 � 6.9*
Arkansas

Intermediate trait 181 38 European descent 35.7 � 9.1* 29.7 � 6.0

Data as means � SD. Age at diagnosis was taken for case subjects only. *Age at study was taken for control subjects or other nondiabetic
subjects.

USF1 AND TYPE 2 DIABETES

2542 DIABETES, VOL. 55, SEPTEMBER 2006



of diabetes-related quantitative traits in Amish, Utah, and Arkansas subjects of
European origin.
Multiethnic case-control study. We examined 2,993 case-control samples
from seven populations with evidence for 1q linkage (Table 1 and online
appendix [available at http://diabetes.diabetesjournals.org]). For five of the
populations, cases included probands from some (U.K.) or all (French,
Shanghai, Hong Kong, and Utah) pedigrees from the relevant genome scans
(2–5,8,9), allowing us to exploit the increase in power when cases are selected
for membership of a multiplex sibship (29). In French and Utah samples,
additional cases were recruited from other sources (online appendix). In these
five samples, cases were unrelated, and ethnically matched locally recruited
control subjects were used (online appendix).

All of the Amish and a quarter of the Pima case-control samples were
recruited from the pedigrees typed in the relevant linkage studies (7,10). The
Pima case-control sample compared 200 case (age of diagnosis �25 years)
with 199 control subjects (nondiabetic after age 45 years). No case or control
subject was a first-degree relative of another individual in the sample. The
Amish case-control set consisted of 150 type 2 diabetic case and 361 control
subjects (normal glucose tolerance beyond the age of 38 years). Given
sustained endogamy within Amish communities, ascertainment of unrelated
case and control subjects was not feasible. Thus, analyses in the Amish take
account of the correlations among related individuals (see below).

Differences in clinical parameters of case and control subjects (Table 1) are
consistent with recognized ethnic and geographic variation in anthropometry
and the clinical presentation of type 2 diabetes (for example, the early onset
of diabetes in Pima Indians and the relatively low adiposity of Chinese
subjects). Note that the Hong Kong and French samples reported here
represent a subset of those separately analyzed for USF1 variation at selected
SNPs by Ng et al. (27) (64 case and 64 control subjects in common) and
Gibson et al. (28) (172 case and 282 control subjects in common).
Family-based association study. A further 733 Pima samples from the
original linkage pedigrees were typed and, with 99 individuals from the
case-control sample, analyzed using family-based association methods. These
832 Pima samples include 570 type 2 diabetic subjects (diagnosed before age
45 years), 104 nondiabetic siblings (aged �45 years), and 158 parents (to
reconstruct family relationships).
Quantitative trait study: Amish. In addition to case-control samples, the 1q
consortium genotyped a further 203 Amish individuals from the same pedi-
grees, including 159 with impaired glucose tolerance and/or impaired fasting
glucose and 44 “linking” individuals to allow pedigree reconstruction. Fasting
levels of (serum) total and HDL cholesterol and triglycerides (Quest Diagnos-
tics, Baltimore, MD) were available for 631 of 714 individuals, including 103
type 2 diabetic case subjects, 342 subjects with normal glucose tolerance, 150
subjects with impaired glucose homeostasis, and 36 of the linking subjects (24
with normal and 12 with unknown glucose tolerance).
Utah and Arkansas. To examine the impact of USF1 SNPs on insulin
sensitivity (Si), insulin secretion (AIRg), and �-cell function (disposition index
[DI]), we analyzed two samples. The first consisted of 124 nondiabetic
members of Utah families with evidence of chromosome 1q linkage (2,5). The
second sample included 181 unrelated Arkansas Caucasians with normal
glucose tolerance tests, ascertained primarily for age 18 –50 years. Character-
istics of the latter population, not previously reported, are summarized in
Table 1.

All Utah subjects and 105 of those from Arkansas underwent a tolbut-
amide-modified frequently sampled intravenous glucose tolerance test
(FSIGT) (30,31). Subjects on lipid-lowering medications were excluded from
analysis. Tolbutamide became unavailable during the Arkansas study, and the
remaining 76 were studied with an insulin-modified FSIGT. After baseline
sampling, the FSIGT was initiated with a 11.4 g/m2 50% dextrose bolus over 1
min; a further 25 samples were collected over 3 h. At 20 min, subjects were
given tolbutamide (125 mg/m2) or insulin (0.04 units/kg). If the glucose
between 120 and 180 min differed by �0.25 mmol/l or showed a clear upward
or downward trend, sampling was continued to 240 min.

In addition, we analyzed trait data from those members of the Utah
case-control population (see above) with BMI, cholesterol, and triglyceride
measures (116 case and 163 control subjects). Subjects from this group on
lipid-lowering medications were not excluded from analyses.

For Utah samples, insulin was measured with a radioimmunoassay with
appreciable proinsulin crossreactivity. For Arkansas samples, insulin was
measured by the General Clinical Research Center Core Laboratory using an
insulin-specific immunochemilluminometric assay (MLT Assay, Wales, U.K.).
Plasma glucose was measured by a glucose oxidase assay, and blood lipids
were measured using standard clinical assays by LabCorp (Burlington, NC).
Ethical information. All samples were ascertained with written informed
consent under protocols approved by respective local ethics committees or
institutional review boards. Use of samples for 1q consortium purposes was,
where necessary, supplemented by further specific approval. The Oxfordshire

Local Research Ethics Committee assessed all ethical documentation and
provided specific approval for each sample genotyped in the U.K.
SNPs and genotyping. 1q consortium samples were typed for 22 SNPs
covering USF1 and F11R (Fig. 1). These included 15 SNPs typed as part of the
consortium’s effort to cover the 1q region of interest at 5 kb density and 7
SNPs specifically chosen given the FCHL association data (23). All SNPs were
typed using the Illumina Golden Gate assay (32) at the Wellcome Trust Sanger
Institute, as part of two 1536-plex assays.

The Utah and Arkansas intermediate trait samples were independently
genotyped at the University of Arkansas using Pyrosequencing (PSQ-96;
Biotage, Uppsala, Sweden). Using genotypes gathered in 96 Utah subjects for
10 USF1 SNPs selected from prior data (23), six tag SNPs (rs1556259,
rs2516838, rs2516839, rs2516840, rs2516841, and rs3737787) were defined
using pairwise LD methods (LDSelect; r2 � 0.9) (33). Further details of primer
sequences and assay conditions are available from the authors.
Data quality control. Consortium genotyping data from the Illumina plat-
form were subjected to extensive quality control, which required SNPs to pass
criteria for call rates, Illumina quality score, and duplicate error rates. We also
tested for departures from Hardy-Weinberg equilibrium and expected allele
frequencies, for excessive within-sample plate-to-plate variation, and, in Pima
and Amish samples, for Mendelian inconsistency. As a result, SNPs rs3813609
and 7556492 were excluded from analysis in all populations; rs790055 from
Pima, French, Shanghai, and U.K. populations; rs2988726 from French,
Shanghai, and U.K. populations; and rs2774279 from the Hong Kong popula-
tion. For SNPs passing this quality control pipeline, we estimate, on the basis
of �450,000 cryptic duplicate genotypes, an overall genotyping error rate of
�0.2%.
Statistical methods

Multiethnic case-control study. For the case-control samples, between-
group differences in genotype frequency were evaluated on a population-
specific basis using standard contingency table methods with the additive
model as default. Exact P values were calculated, where necessary, using
Stata SE version 8 and STATXACT (Cytel, Cambridge, MA). In the Amish
sample, population structure results in substantial relatedness within (and
between) case and control subjects; failure to allow for this will underestimate
the variance and upwardly bias the test statistic. We therefore repeated
case-control comparisons in the Amish by modeling a residual familial effect
as a random effect using SOLAR. Specifically, we used the measured genotype
approach to assess the effect of genotype on diabetes status while simulta-
neously estimating the residual correlations in phenotype among related
individuals (34).

Single-point data from the seven case-control samples were combined
using the Mantel-Haenszel fixed-effects method (Stata SE version 8 and
STATXACT). For these analyses, we first established the homogeneity of odds
ratios (ORs) across combined strata before generating combined ORs under
dominant, additive, and recessive models. The Mantel-Haenszel method
assumes independence of case and control data. To account for departures
from this assumption in the Amish, this analysis was repeated using an inverse
variance-based method (35). The logarithm of the OR (logOR) (under the
additive model) and its variance were calculated for each population by
logistic regression and the study-wide common logOR estimated as the
weighted sum of the study-specific values, with weights taken as the inverse
of the variance estimates. In the Amish, the corrected value of the logOR
variance was taken as the P value for the measured genotype approach using
the Wald statistic. Pairwise measures of LD were calculated, and haplotype
analyses were performed using haplotype trend regression (36) implemented
in HelixTree (GoldenHelix, Bozeman, MT).
Family-based association analysis. Analysis of the extended Pima data
set (n � 832) was conducted in two ways. First, case-control comparisons
were made across the full resource (using a cutoff for age at diagnosis of 45
years) using binomial generalized estimating equations to control for family
membership. Second, we conducted an explicit family-based association
analysis using the method of Abecasis (37) to partition the association into
between- and within-family components.
Quantitative trait analyses. In the Amish, full pedigree structure was
explicitly specified and analyses conducted in a regression framework with
trait measures as the dependent variable. In Utah and Arkansas datasets, Si
was estimated from glucose and insulin measures during the FSIGT using the
MinMod (Utah) or MinMod Millennium (Arkansas) programs (38,39). AIRg
was calculated as the mean insulin response above basal from 2 to 10 min
following the glucose bolus and DI as Si � AIRg. Genotypic effects on Si, AIRg,
DI, and lipids were tested using mixed-effect general linear models in SPSS
version 12. Primary tests were conducted under an additive model unless
homozygosity for the rare allele was observed in �10 cases, in which case a
recessive model for the common allele was tested. Skewed variables (Si, AIRg,
DI, BMI, triglycerides, and free fatty acid levels) were ln-transformed to
normality before analysis. All models included age (and BMI, as appropriate)

E. ZEGGINI AND ASSOCIATES

DIABETES, VOL. 55, SEPTEMBER 2006 2543



as covariates and sex and genotype as fixed factors. Additionally, diagnosis
and pedigree membership were included in analysis of family members from
the Utah study and protocol type (tolbutamide or insulin) as a fixed factor for
Arkansas subjects. The significance of associations detected was examined by
comparison of marginal means using the least significant difference test. We
considered P � 0.05 to be significant throughout without correction for
multiple testing.

RESULTS

Multiethnic case-control study. Following quality con-
trol, between 18 and 20 (depending on the population) of
the original 22 SNPs (covering a 60-kb range) were avail-
able for analysis. Within the immediate USF1 region (�11
kb), complete data were available from nine SNPs. These
include three of four SNPs showing association in the
Finnish FCHL study (23) (rs3737787, rs2516839, and
rs2516838 but not rs2073658 for which assay design
failed). Using deep resequencing and genotype data from
Seattle SNPs (http://pga.mbt.washington.edu/), these
typed SNPs discriminate over 93% of all USF1 haplotypes
observed in 46 chromosomes of European origin (SNPs
with minor allele frequency [MAF] �5%) and tag �95% of

all SNPs with MAF �5% (the exception being rs10908821
and its associated haplotype, both with a frequency of 7%)
with a r2 � 0.8. The untyped FCHL-associated SNP
(rs2073658) is in almost complete LD with rs3737787 in
samples of European descent (23,26). LD plots for the
seven samples are shown in online appendix Table 1.

Single-point results for each of the seven case-control
populations are summarized in Fig. 1 and detailed in
online appendix Table 2. The Amish case-control data
shown were derived using the measured genotype ap-
proach to allow for family structure (34). There was no
suggestion of association with rs3737787 in any popula-
tion. In the U.K. case-control analysis, nominally signifi-
cant (P � 0.05) associations were seen for several
variants, including a pool of highly correlated SNPs within
F11R and rs2073653 and rs1556259 within USF1 but none
was replicated in the other samples (see Fig. 1). In the
Hong Kong sample, there was nominal evidence of an
association with type 2 diabetes at rs2516838 (P � 0.03,
additive model); again, this was not substantiated in other
samples. Finally, haplotype analyses of the nine core USF1

FIG. 1. Single-point case-control association analyses for the USF1 gene region. The central part of the figure shows the regional gene structure
(for USF1, F11R, and ARHGAP30), the SNPs typed, and those associated with FCHL or triglycerides (Pajukanta et al. [23]), as well as the SNPs
attempted in this study (n � 22) and those successfully typed in at least one population (n � 20). SNPs rs7556492 and 3813609 are shown in
parentheses, as they failed quality control in all populations. The three asterisked SNPs failed only in some populations. The right panel displays
the case-control association P values for the four samples from European descent. For the Amish, these are corrected for family structure by the
measured genotype method. The left panel displays the case-control association P values for the East-Asian and Pima samples. The dotted vertical
lines define P � 0.05 (�log

10
P � 1.3).

USF1 AND TYPE 2 DIABETES

2544 DIABETES, VOL. 55, SEPTEMBER 2006



SNPs (from rs4339888 to rs1556259) showed no associa-
tion with type 2 diabetes in any of the populations studied
(online appendix Table 3).

Analyses combining data from these seven case-control
comparisons are summarized in Table 2 (additive model
only). At rs3737787, no evidence for association was
observed under the additive (Table 2), dominant (for the
rarer allele A: OR 1.08 [95% CI 0.93–1.25], P � 0.32), or
recessive (1.15 [0.87–1.51], P � 0.33) models. Nominally
significant associations under the additive model were
seen for a cluster of highly correlated variants in F11R and
for SNPs 3766383 and 2516839 within USF1 (generating
ORs between 1.10 and 1.15). With the exception of
rs3766383 (model recessive for G, P � 0.02), combined
analyses under the dominant and recessive models were
not significant (data not shown).
Family-based association study. The same set of 19
SNPs were also typed in the full Pima dataset (n � 832).
Case-control analysis (allowing for family structure) re-
vealed no significant associations (online appendix Table
4). The most significant P value was seen at rs7516231
(MAF 0.33 in control subjects, 0.42 in cases, P � 0.10). At
rs3737787, the equivalent allele frequencies were 0.35
(unaffected) and 0.33 (affected) (P � 0.96). The explicit
test of within-family association was again nonsignificant
for all SNPs (including rs3737787, P � 0.22).
Quantitative trait study. In the full set of 714 Amish
subjects (631 with trait measures), there was no evidence
of association with lipid or adiposity traits. For example,
at rs3737787, there was no hint of association with lipid
(triglycerides P � 0.51; HDL cholesterol P � 0.80) or
anthropometric (BMI P � 0.36; waist-to-hip ratio P � 0.10;
leptin P � 0.19) measures.

The Utah and Arkansas FSIGT subjects were genotyped
separately for six tag SNPs. No associations with Si, AIRg,
DI, or free fatty acid levels were seen among the 124

nondiabetic Utah family members. However, in this sam-
ple, several SNPs (rs2516838, rs2516839, and rs2516841)
showed associations with cholesterol levels (P � 0.001,
P � 0.01, and P � 0.03, respectively) when corrected for
age, sex, BMI, and family membership (Table 3). Further-
more, SNPs rs2516839 and 3737787 were associated with
triglyceride levels (P � 0.03 and P � 0.007, respectively).
Crucially, however, it is the rare allele (A) at rs3737787
that is associated with raised triglycerides (in contrast
with other reports [23,24]). In the second FSIGT sample
(Arkansas), we found no significant associations with mea-
sures of Si, �-cell function, glycemia, or lipids for any SNP.

Finally, we tested each of these six SNPs in the Utah
case-control subjects with BMI, cholesterol, and triglycer-
ide measures (116 case and 163 control subjects) after
adjusting for BMI (in the lipid analyses), sex, and diabetes
status. In contrast to the younger nondiabetic Utah family
members, we found no association of any USF1 SNP with
cholesterol or triglyceride concentrations.

DISCUSSION

The main finding is that analysis of �3,700 subjects (using
case-control and family-based association methods) found
no reproducible evidence that variation within the USF1
gene (or its neighbor F11R) is associated with type 2
diabetes. Though several nominally significant associa-
tions were found in the case-control study, these were not
observed in the family-based association analysis. In any
event, the variants displaying these associations have low
prior odds for functional involvement (since none was
strongly associated with FCHL) (23), and the estimated
effect sizes are modest, particularly in the context of the 1q
consortium’s ongoing LD mapping efforts across the re-
gion, which have already identified many variants display-
ing far stronger replicated associations with type 2

TABLE 2
Single SNP analyses for association with type 2 diabetes for all seven samples combined

Additive Additive (inverse variance)
rs no. NCBI35 position Alleles (1,2) OR (95% CI)* P* OR (95% CI)† P†

836 157781297 C,T 0.91 (0.83–1.01) 0.07 0.88 (0.78–0.99) 0.03
790055 157784219 A,G 0.96 (0.65–1.43) 0.83 0.90 (0.66–1.23) 0.50
2481080 157789410 C,T 0.93 (0.85–1.02) 0.10 0.89 (0.79–1.00) 0.06
2988726 157791827 A,G 1.01 (0.98–1.03) 0.61 0.91 (0.75–1.10) 0.33
2251597 157800920 C,G 1.10 (0.99–1.22) 0.06 1.15 (1.01–1.29) 0.03
2481084 157802687 C,T 1.10 (0.99–1.22) 0.07 1.14 (1.01–1.29) 0.04
7546890 157808465 A,G 0.91 (0.83–1.00) 0.06 0.90 (0.80–1.01) 0.06
11799478 157809827 C,T 1.11 (1.00–1.23) 0.06 1.15 (1.02–1.31) 0.02
4339888 157820399 C,G 1.09 (0.99–1.21) 0.09 1.14 (1.01–1.28) 0.04
3766383 157821645 G,T 1.18 (1.01–1.38) 0.03 1.14 (0.99–1.31) 0.06
3737787 157822596 A,G 1.06 (0.96–1.16) 0.23 1.10 (0.97–1.24) 0.13
2516840 157824390 C,T 1.05 (0.85–1.30) 0.65 1.04 (0.92–1.18) 0.50
2073656 157824428 C,G 0.97 (0.90–1.04) 0.35 0.93 (0.82–1.05) 0.23
2073653 157825833 A,G 1.17 (1.00–1.37) 0.05 1.13 (0.98–1.30) 0.08
2516839 157826194 A,G 1.11 (1.01–1.23) 0.04 1.14 (1.02–1.28) 0.02
2516838 157827443 C,G 0.92 (0.79–1.07) 0.27 0.94 (0.83–1.06) 0.29
1556259 157827722 C,T 0.88 (0.75–1.02) 0.09 0.90 (0.78–1.03) 0.12
2774279 157830629 A,G 1.07 (0.88–1.29) 0.49 1.04 (0.92–1.18) 0.49
3813610 157835712 C,G 1.10 (1.00–1.21) 0.05 1.11 (1.00–1.24) 0.05
7516231 157839152 A,T 1.24 (1.03–1.49) 0.02 1.17 (1.01–1.35) 0.04

Allele designations (1,2) are in line with Illumina convention (i.e., alphabetical for base name; see third column). *P values based on
assumptions of independence. Where appropriate (any cell with fewer than six observations), exact methods were used. OR and 95% CIs are
calculated per copy of allele 1 (with reference to genotype 22) by modification of the method by Liu and Agresti (40). †P values estimated
using inverse-variance method to take into account of lack of independence in the Amish sample. OR is calculated per copy of allele 1.

E. ZEGGINI AND ASSOCIATES

DIABETES, VOL. 55, SEPTEMBER 2006 2545



diabetes (41). Crucially, the direction of these weak asso-
ciations (favoring haplotypes carrying the more rare allele
at rs3737787) is opposite to that reported in previous
studies (23–27). While we cannot formally exclude the
possibility that these nominal associations reflect true
susceptibility effects (perhaps involving LD with other
untyped variants), it is clear that previously reported type
2 diabetes susceptibility effects involving rs3737787 and
other USF1 variants (26,27) are not substantiated in this
larger, more informative dataset in which cases were
selected to be enriched for evidence of linkage to the
USF1 region.

In any study of this type, it is vital to be explicit about
the extent to which the variants analyzed allow exclusion
of effects at other untyped variants. In terms of capturing
common variation in the USF1 region, the 20 SNPs typed
provide excellent coverage, as shown by their high density
(close to one per kilobase in the USF1 coding region),
their documented capacity to tag hidden variation (at least
in Europeans and East Asians), and by comparison to
variants typed in previous studies (23,24,26). Recent evi-
dence (42) suggests that rs2073658 has the best credentials
as a functional etiological variant within USF1. Though we
were unable to generate a working assay for this SNP,
it is clear (from genotypes in Finns [23], French [26],
and Caucasians [http://pga.mbt.washington.edu/]) that
rs2073658 and rs3737787 are in almost complete LD (r2

exceeding 0.99) in Europeans. LD patterns in the region
suggest the same is likely to be true in the other popula-
tions studied. Thus, failure to type this SNP directly will
have had minimal adverse effect on power. As with any
indirect association study using common tag SNPs, we
cannot exclude a role for rare variation within the gene
(MAFs �5%) (43).

In terms of effect size, our study focused on populations
with documented 1q linkage and preferentially sampled
cases from multiplex families contributing to that linkage
signal. Both maneuvers substantially boost power to de-
tect variation contributing to the linkage signal (28,44).

Taking these factors into account, we estimate that the
current sample has (for 	 � 10
3, MAF of 10%, multipli-
cative model), 55% power for a variant with an allelic OR
of 1.2 and 96% for an OR of 1.3. Given the relative power
of linkage and association approaches when the etiologi-
cal variants are typed (45), we can demonstrate, using
reasonable assumptions, over 95% power to detect a
variant contributing to at least 10% of the 1q linkage signal.

While the conclusions regarding USF1 variation and
type 2 diabetes seem clear, interpretation of the findings
with respect to the other traits (especially lipid parame-
ters) is less straightforward. In one sample (124 nondia-
betic relatives of type 2 diabetic subjects from Utah), we
did detect statistically strong associations with certain
USF1 SNPs (including rs3737787). However, the associa-
tion between rs3737787 and triglycerides lies in the oppo-
site direction to that described in Finns (23) and a different
set of Utah pedigrees (24), and none of these associations
could be replicated in the other samples (combined n �
1,091) or in a recent study of USF1 variation in French
subjects (28). The possibility should also be considered
that USF1 variants having modest effects on lipid param-
eters in other populations may be relatively less penetrant
in the Amish, owing to the generally higher degree of
physical activity in this population. While the analysis of
lipid traits was not a major aim of our study, our data
certainly suggest that the trait associations evident in
several previous studies (23–27) are not reproducible in all
other samples.

Despite the strong biological candidacy of USF1 and its
location within the well-replicated peak of linkage on
chromosome 1q, we conclude, with confidence engen-
dered by the large sample size, comprehensive capture of
common variation within and around the gene (and the
detailed analytical plan) that common variation within
USF1 has no discernible impact on type 2 diabetes sus-
ceptibility and that USF1 makes no detectable contribu-
tion to the replicated linkage signal on chromosome 1q.

TABLE 3
Metabolic traits by USF1 genotype in Utah family members

SNP (rs)
Allele
1, 2

Model (with
respect to
allele 1) 1,1 (n)

Trait values
for 1/1

1,2 (or
combined
group) (n)

Trait values for
1,2 (or

combined
group) 2,2 (n)

Trait values
for 2,2 P value

Cholesterol
(mmol/l)
2516838 C,G General 22 5.08 � 0.19 60 4.29 � 0.14 40 4.79 � 0.15 0.001

Dominant 22 5.09 � 0.19 100 4.53 � 0.11 — 0.010
Recessive — 82 4.45 � 0.13 40 4.84 � 0.16 0.047

2516839 A,G General 52 4.96 � 0.14 62 4.52 � 0.14 11 4.23 � 0.26 0.010
Dominant 52 4.96 � 0.14 73 4.55 � 0.13 — 0.014

2516841 C, T General 65 4.84 � 0.13 45 4.45 � 0.16 5 4.01 � 0.37 0.026
Dominant 65 4.83 � 0.13 50 4.45 � 0.15 — 0.033

Triglycerides
(mmol/l)
2516839 A,G General 52 1.41 (1.18–1.69) 62 1.19 (0.99–1.43) 11 0.89 (0.63–1.24) 0.032
3737787 A,G General 11 1.87 (1.31–2.66) 35 1.42 (1.17–1.73) 79 1.08 (0.90–1.28) 0.007

Recessive — 46 1.52 (1.27–1.82) 79 1.07 (0.90–1.26) 0.002

Data are marginal means � SE or means (95% CI). Marginal means, adjusted for age, ln(BMI), sex, and pedigree membership are shown by
genotype under general (three genotypes compared), dominant, and recessive models (with respect to allele 1 as designated in the second
column). All values are in mmol/l. To convert to mass units, multiply cholesterol by 38.67 and triglycerides by 88.57. P values are shown for
comparison of marginal means using the least significant difference test with 2 df under general and 1 df under dominant or recessive models.
Triglycerides, which were ln-transformed for analysis, were converted back to a linear scale. Only statistically significant findings at P � 0.05
are shown.

USF1 AND TYPE 2 DIABETES

2546 DIABETES, VOL. 55, SEPTEMBER 2006



ACKNOWLEDGMENTS

The principal funding was provided as a supplement to
National Institute of Diabetes and Digestive and Kidney
Diseases award U01-DK58026. Other major support has
been provided by the National Institutes of Health (T32-
AG00219, R01-DK54261, K24-DK02673, K07-CA67960, R01-
DK39311, and intramural funds), the University of
Maryland and Arkansas General Clinical Research Cen-
ters, the National Center for Research Resources
(M01RR14288), the Department of Veteran Affairs, and the
American Diabetes Association (U.S.); “200 Familles pour
vaincre le Diabète et l’Obésité” and the Association Fran-
çaise des Diabétiques (France); Diabetes UK (U.K.); The
Hong Kong Research Grants Committee (CUHK 4292/99M;
1/04C), the Chinese University of Hong Kong Strategic
Grant Program (SRP9902) and the Hong Kong Innovation
and Technology Support Fund (ITS/33/00) (Hong Kong);
and the National Nature Science Foundation of China
(39630150), the Shanghai Medical Pioneer Development
Project (96-3-004; 996024), and the Shanghai Science Tech-
nology Development Foundation (01ZB14047) (China).
Additional funding and support is detailed in the online
appendix.

We thank all the subjects participating in this study and
the involvement of the following additional members of
the 1q Consortium: Nancy Cox (Chicago); Clifton Bogar-
dus, Michal Prochazka, and Peter Kovacs (Phoenix); Lon
Cardon, Steven Wiltshire, Simon Fiddy, and Richard Mott
(Oxford); Christian Dina (Lille); Mao Fu and Mona Sabra
(Baltimore); Winston Chu (Little Rock); Alan Herbert,
Josee Dupuis, James Meigs, and Adrienne Cupples (Bos-
ton); and Rudy Leibel and Wendy Chung (New York).

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