33162C 02e7e52db238b91963000000

User Manual: 33162C

Open the PDF directly: View PDF .
Page Count: 6

Association between polymorphisms in the nuclear respiratory factor 1 gene and type 2 diabetes mellitus in the Korean population

Diabetologia (2005) 48: 2033–2038

DOI 10.1007/s00125-005-1855-7

ARTICLE

Y. M. Cho .H. D. Shin .B. L. Park .J. H. Kim .

K. S. Park .S. Y. Kim .H. K. Lee

Association between polymorphisms in the nuclear respiratory

factor 1 gene and type 2 diabetes mellitus in the Korean

population

Received: 16 November 2004 / Accepted: 2 April 2005 / Published online: 5 August 2005

#Springer-Verlag 2005

Abstract Aims/hypothesis: Dysfunction in mitochondrial

oxidative phosphorylation plays a central role in insulin

resistance and type 2 diabetes. Nuclear respiratory factor 1

(NRF1) is a transcription factor that acts on nuclear genes

encoding respiratory subunits and components of the mi-

tochondrial transcription and replication machinery. Thus,

we investigated its genetic association with type 2 dia-

betes. Methods: The NRF1 gene was sequenced to iden-

tify polymorphisms in 24 Korean DNA samples and then

common variants were genotyped in 766 patients with

type 2 diabetes and 303 non-diabetic subjects. Results:

Twelve single nucleotide polymorphisms and one inser-

tion/deletion polymorphism were identified. Six common

variants among them were genotyped in a larger study.

Although three individual polymorphisms appeared to be

associated with type 2 diabetes (g.−46350insdel A,g.+ 141G>T

and g.+54529A>G), the effects were only marginal. How-

ever, a haplotype (H2) was associated with a decreased risk

of type 2 diabetes and another haplotype (H4) was associated

with an increased risk of type 2 diabetes (pvalues for the

Haplo.Score test were 0.009 and 0.004, respectively).

Conclusions/interpretation: We demonstrated that two com-

mon haplotypes of NRF1 gene are associated with type 2

diabetes in the Korean population.

Keywords Mitochondria .Nuclear respiratory factor 1 .

Oxidative phosphorylation .Polymorphism .Type 2

diabetes mellitus

Abbreviations FOXC1: fork head-related activator-3 .

LD: linkage disequilibrium .NRF1: nuclear respiratory

factor 1 .OR: odds ratio .PGC1: peroxisome proliferator-

activated receptor γ-coactivator 1 .SNP: single nucleotide

polymorphism .TFAM: mitochondrial transcription factor

A.UTR: untranslated region

Introduction

Insulin resistance in the skeletal muscle of insulin-resistant

offspring of type 2 diabetic patients is associated with

decreased mitochondrial phosphorylation, which might be

an inherited defect in mitochondrial oxidative phosphory-

lation [1]. Furthermore, age-associated insulin resistance

also could be attributable to an age-associated reduction in

mitochondrial oxidative phosphorylation capacity [2].

Therefore, dysfunction in mitochondrial oxidative phos-

phorylation might play an important role in the pathogen-

esis of insulin resistance and type 2 diabetes.

It has been suggested that the oxidative phosphorylation

and electron transport chain system in mitochondria rely on

the functional interplay of gene products expressed from

both nuclear and mitochondrial genomes [3]. Since mito-

chondrial DNA encodes only 13 subunits of ∼100 struc-

tural subunits of respiratory proteins and ATP synthase in

the human mitochondria, the nucleo-mitochondrial inter-

action is essential to maintain normal cellular function [4].

Using microarray data, Patti et al. [5] and Mootha et al. [6]

independently observed that the genes of oxidative metab-

olism regulated by nuclear-encoded proteins are coordi-

nately reduced in the skeletal muscle of human subjects

with insulin resistance or diabetes, which is in line with the

fact that mitochondrial oxidative metabolism is crucial in

maintaining normal insulin sensitivity and normal glucose

metabolism [1,2]. Interestingly, those genes downregu-

lated in insulin resistance or diabetes are under the control

Y. M. Cho .J. H. Kim .K. S. Park .S. Y. Kim .H. K. Lee (*)

Department of Internal Medicine,

Seoul National University College of Medicine,

28 Yongon-dong, Chongno-gu,

Seoul, 110-744, South Korea

e-mail: hkleemd@snu.ac.kr

Tel.: +82-2-20722266

Fax: +82-2-7657966

Y. M. Cho .J. H. Kim .K. S. Park .H. K. Lee

Genome Research Centre for Diabetes and Endocrine Disease,

Clinical Research Institute, Seoul National University Hospital,

Seoul, South Korea

H. D. Shin .B. L. Park

Department of Genetic Epidemiology, SNP Genetics,

Seoul, South Korea

of nuclear respiratory factor 1 (NRF1) and peroxisome pro-

liferator-activated receptor γ-coactivator 1 (PGC1) [5,6].

Among them, NRF1 (Gene map locus 7q32; OMIM#

600879) is a transcription factor that acts on nuclear genes

encoding respiratory subunits and components of mito-

chondrial transcription and replication machinery [7,8].

NRF1 binds to and activates the promoters of many genes

of the mitochondrial electron transport system such as

cytochrome c, NADH dehydrogenase subunit 8, some cy-

tochrome oxidase subunits, some ATP synthase subunits,

mitochondrial transcription factor A (TFAM), and so on

(reviewed in [9]). There is a growing body of evidence that

links NRF1 physiologically to glucose metabolism. Aer-

obic exercise increases NRF1 expression in skeletal muscle

[10] and muscle-specific overexpression of human NRF1

in transgenic mice increases glucose transport capacity in

skeletal muscle by increasing glucose transporter 4 ex-

pression [11]. NRF1 mRNA expression in human skeletal

muscle is decreased in diabetes and inversely correlated

with fasting glucose [5]. In this regard, NRF1 is an at-

tractive candidate gene for type 2 diabetes. However, to the

best of our knowledge, there is no study on the association

between NRF1 polymorphism and type 2 diabetes. Thus, in

this study, we examined the genetic association between

NRF1 and type 2 diabetes.

Subjects and methods

Subjects

For initial sequencing, 24 Koreans were randomly selected

from unrelated local residents without any history of fa-

milial diseases. With 24 samples, one can expect to identify

more than 90% of polymorphisms with a frequency greater

than 0.05 [12]. Subsequently, we studied 766 unrelated

patients with type 2 diabetes from the Diabetes Clinic of

Seoul National University Hospital (age: 59±10 years, 357

men, 409 women) and 303 non-diabetic control subjects

(age: 65±4 years, 139 men, 164 women). The age of onset

of diabetes was 50±10 years. All subjects enrolled in this

study were of Korean ethnicity. Type 2 diabetes was diag-

nosed according to World Health Organization criteria

[13]. Subjects with positive GAD antibodies were ex-

cluded. Selection of the non-diabetic control subjects was

according to the following criteria: 60 years or older, no

past history of diabetes, no diabetes in first-degree rel-

atives, a fasting plasma glucose concentration of less than

6.1 mmol/l, and an HbA

c value of less than 5.8%. This

study was carried out in accordance with the Declaration of

Helsinki as revised in 2000 (http://www.wma.net/e/policy/

17cnote.pdf).

The Institutional Review Board of the Clinical Research

Institute in Seoul National University Hospital approved

the study protocol and written informed consent for genetic

analysis was obtained from each subject.

All study subjects were examined in the morning after an

overnight fast. Height, weight, circumferences of waist and

hip and blood pressure were measured. Blood samples were

drawn for biochemical measurements (fasting plasma glu-

cose, fasting plasma insulin, HbA

c, total cholesterol, tri-

glyceride and HDL-cholesterol) and DNA extraction.

Sequencing analysis of the human NRF1 gene

We sequenced all exons, including exon–intron boundaries

and promoter region (∼1.5 kb), to discover polymorphisms

in 24 Korean DNA samples using the ABI PRISM 3700

DNA Analyzer (Applied Biosystems, Foster City, CA,

USA). Seventeen primer sets for the amplification and

sequencing analysis were designed based on GenBank

sequences (NM_005011). Information regarding the prim-

ers is available at http://www.snp-genetics.com/reference/

NRF1_add_info.doc. Sequence variants were verified by

chromatograms.

Genotyping with fluorescence polarisation detection

For genotyping of polymorphic sites, amplifying primers

and probes were designed for TaqMan. Primer Express

(Applied Biosystems) was used to design both the PCR

primers and the MGB TaqMan probes. One allelic probe

was labelled with the FAM dye and the other with the

fluorescent VIC dye. PCRs were run in a TaqMan Uni-

versal Master mix without UNG (Applied Biosystems)

with PCR primer concentrations of 900 nmol/l and MGB

TaqMan probe concentrations of 200 nmol/l. Reactions

were performed in a 384-well format in a total reaction

volume of 5 μl using 20 ng genomic DNA. The plates were

then placed in a thermal cycler (PE 9700; Applied Bio-

systems) and heated at 50°C for 2 min and 95°C for

10 min followed by 40 cycles of 95°C for 15 s and 60°C

for 1 min. The TaqMan assay plates were transferred to a

Prism 7900HT instrument (Applied Biosystems) where

the fluorescence intensity in each well of the plate was

read.

Fluorescence data files from each plate were analysed by

automated software (SDS 2.1). Genotyping quality control

was performed in 10% of samples by duplicate checking

(rate of concordance in duplicates >99%). The genotype

success rate was >97.5%. Information regarding the prim-

ers is available on our website http://www.snp-genetics.

com/reference/NRF1_add_info.doc.

Statistical analyses

We used chi-square tests to determine whether individual

polymorphisms were in Hardy–Weinberg equilibrium. Lo-

gistic regression analyses were used for calculating odds

ratio (ORs), 95% CIs and corresponding pvalues, con-

trolling for age, sex and BMI as covariates. Genotypes were

given codes of 0, 1 and 2 and 0, 1 and 1 or 0, 0 and 1 in the

additive, dominant or recessive models, respectively. In the

additive model, the OR was expressed per difference in

number of rare allele. Multiple regressions, whilst adjusting

2034

for age and sex, were used for association analyses of

diabetes-related phenotypes. Haplotype associations were

also estimated using Haplo.Score (http://www.biostat.wustl.

edu/genetics/geneticssoft), which imputes score statistics to

test associations between haplotypes and a wide variety of

traits, including binary, ordinal, quantitative and Poisson

[14]. This software provides several haplotype-specific tests

for association, as well as adjustment for non-genetic covar-

iates and computation of simulation pvalues, using the

assumption that all subjects are unrelated and that hap-

lotypes are ambiguous due to unknown linkage phases of the

genetic markers. To predict putative transcription factor

binding sites, we used MatInspector (http://www.genoma

tix.de/products/MatInspector/index.html)[15]. A pvalue of

less than 0.05 was considered statistically significant.

Results

Identification of polymorphisms in the NRF1 gene

In this study, the NRF1 gene including all exons, ±50 bp

exon–intron boundaries and the −1,500 bp 5′-flanking

region were sequenced in 24 Korean DNA samples and 12

single nucleotide polymorphisms (SNPs) and one insertion/

deletion polymorphism were identified (Fig. 1a). Five

variants were in the 5′region: g.−46656G>A,g.−46568G>A,

g.−46350insdel A,g.−46185G>C,andg.−45903C>T.Two

SNPs were found in the exons, where both predicted silent

substitutions: g.+141G>Tin exon 2 encoding 47Ser and

g.+33162C>Tin exon 5 encoding 191Asp. Three SNPs were

in introns: g.+14382T>C,g.+54529A>G,andg.+59745A>G.

And three SNPs were in the 3′-untranslated region (UTR)

(g.+97884G>A,g.+97893G>Tand g.+ 98560A>G). By

pair-wise linkage analysis, we have found that five sets of

SNPs (g.−46568G>A:g.−46185G>C,g.−46568G>A:

g.−45903C>T,g.−46185G>C:g.−45903C>T,g.+141G>T:

g.+54529A>G,g.+14382T>C:g.+97893G>T) were in ab-

solute linkage disequilibrium (LD) (LD D′=1 and r

=1) (see

Fig. 1a). Among identified polymorphisms, six common

variants (g.−46568G>A,g.−46350insdel A,g.+ 141G>T,

g.+33162C>T,g.+54529A>Gand g.+59745A>G) were

selected for larger scale genotyping. The LD patterns did

not show a break among SNPs genotyped (all D′s are >0.9,

see Fig. 1b). Genotype distributions of all loci in NRF1 were

in Hardy–Weinberg equilibrium (p>0.05).

Ex1 Ex2 Ex3 Ex4 Ex 5 Ex6,7,8 Ex9 Ex10 Ex11 Ex12

g.-46656 G>A (0.021)

g.-46568 G>A (0.13)*

g.-46350 insdelA (0.48)*

g.-46185 G>C (0.125)

g.-45903 C>T (0.125)

g.+141 G>T (S47S) (0.395)*

g.+14382 T>C (0.02)

g.+33162 C>T (D191D) (0.104)*

g.+54529 A>G (0.395)*

g.+59745 A>G (0.083)*

g.+97884 G>A (0.041)

g.+97893 G>T (0.02)

g.+98560 A>G (0.026)

r2=1

5 kb

-0.0570.0080.0530.105

0.007g.+59745A>G

1.000-0.1500.9760.4980.126

g.+54529A>G

1.0001.000-0.1480.068

0.856g.+33162C>T

0.9600.9941.000-0.5130.125

g.+141G>T

0.9300.9450.9010.954-0.074

g.-46350insdelA

1.0000.9780.9840.9781.000-

g.-46568G>A

g.-46350insdelA

g.+141G>T

g.+33162C>T

g.+54529A>G

g.+59745A>G

|D’|

Map of NRF1 on chromosome 7q32

LDs among NRF1 polymorphisms

Fig. 1 Gene map (a) and LD pattern (b)oftheNRF1 gene.

aPolymorphisms identified in NRF1, with coding exons marked

by closed blocks and 5′- and 3′-UTRs by open blocks.*=SNPs that

were genotyped in the larger population. The frequencies of poly-

morphisms were based on sequencing data (n=24). The first

nucleotide of the translation start site is denoted as nucleotide +1.

bLD coefficients (|D′| and r

) among polymorphisms in NRF1

2035

Association with type 2 diabetes

We assessed the association of each genotype by logistic

regression analyses adjusting for age, sex and BMI. Three

individual polymorphisms were found to be associated with

the risk of type 2 diabetes (Table 1). The g.−46350insdel A

showed an increased risk of type 2 diabetes when applying a

recessive genetic model (OR=1.631, 95% CI 1.139−2.337,

p=0.008). Also, the nucleotide position −46,350 was pre-

dicted as a 3′-flanking site of the fork head-related activator-3

(FOXC1) consensus sequence. The g.+141G>Tshowed

an increased risk of type 2 diabetes with an additive

model (OR=1.341 [1.075–1.671], p=0.009) and with a

dominant model (OR=1.396 [1.050–1.855], p=0.022).

The g.+54529A>Gpolymorphism, which is in complete

LD with g.+141G>T, showed an increased risk of type 2

diabetes with an additive model (OR=1.302 [1.046–1.619],

p= 0.018) and with a dominant model (OR=1.398 [1.053–

1.857], p=0.021). When Bonferroni’s correction for the

multiple comparisons was strictly applied, no polymor-

phism remained significant in terms of the association with

type 2 diabetes.

Among the six different polymorphisms genotyped, we

identified five common haplotypes (frequency >0.05) that

account for 97.6% of the observed haplotypes (Table 2).

The haplotype H2 was associated with a decreased risk of

type 2 diabetes (pvalue for Haplo.Score=0.009) and the

haplotype H4 was associated with an increased risk of type

2 diabetes (pvalue for Haplo.Score=0.004). Comparing

the elements of the protective haplotype H2 and the sus-

Table 1 Associations between polymorphisms in NRF1 and the risk of type 2 diabetes

SNPs Genotype Number Additive Dominant Recessive Statistical

power

Diabetes Control OR (95% CI) pOR (95% CI) pOR (95% CI) p

g.−46568G>AGG 667

(87.5%)

268

(88.7%)

GA 94

(12.3%)

(10.3%)

1.04

(0.697–1.595)

0.803 1.154

(0.741–1.795)

0.527 0.144

(0.014–1.493)

0.104 0.034

AA 1 (0.1%) 3 (1.0%)

g.−46350

insdel A

ins/ins 200

(26.8%)

(27.4%)

ins/del 364

(48.8%)

164

(54.8%)

1.174

(0.959–1.438)

0.12 0.989

(0.718–1.362)

0.944 1.631

(1.139–2.337)

0.008 0.215

del/del 182

(24.4%)

(17.7%)

g.+141G>TGG 306

(40.1%)

143

(47.5%)

GT 371

(48.6%)

134

(44.5%)

1.341

(1.075–1.671)

0.009 1.396

(1.050–1.855)

0.022 1.598

(0.974–2.621)

0.063 0.550

TT 86

(11.3%)

(8.0%)

g.+33162C>TCC 652

(85.8%)

261

(87.3%)

CT 107

(14.1%)

(11.7%)

1.076

(0.723–1.602)

0.717 1.171

(0.768–1.786)

0.462 0.143

(0.014–1.475)

0.102 0.046

TT 1 (0.1%) 3 (1.0%)

g.+54529A>GAA 310

(40.7%)

145

(48.0%)

AG 368

(48.3%)

130

(43.0%)

1.302

(1.046–1.619)

0.018 1.398

(1.053–1.857)

0.021 1.384

(0.859–1.119)

0.182 0.464

GG 84

(11.0%)

(8.9%)

g.+59745A>GAA 613

(80.7%)

245

(80.7%)

AG 141

(18.6%)

(18.9%)

1.080

(0.765–1.524)

0.661 1.030

(0.720–1.473)

0.871 ––0.053

GG 6 (0.8%) 0 (0.0%)

Genotype distributions are shown as number (%). Odds ratios (ORs), 95% CI, and pvalues were from logistic regression analyses with

additive, dominant, and recessive models controlling for age, sex and BMI as covariates. In additive models, ORs are expressed per

difference in number of rare alleles. pvalues were not corrected for multiple comparisons (18 tests)

Statistical powers calculated with the given OR of additive model, frequency and subject numbers at a significance level of 0.05

2036

ceptible haplotype H4, two polymorphisms (g.+141G>T

and g.+54529A>G) were regarded as the determinants

modifying the risk of type 2 diabetes, although their indi-

vidual effect was modest, as shown in Table 1.

Association with diabetes-related phenotypes

For the association analyses of the diabetes-related phe-

notypes, only non-diabetic subjects were used because, in

diabetic subjects, treatment of diabetes might have affect-

ed those parameters. However, analyses of fasting plasma

glucose, homeostasis model assessment of insulin resis-

tance, waist circumference, BMI, total cholesterol, trigly-

ceride and HDL-cholesterol did not reveal any strong

associations with genotypes or haplotypes when adjusting

for multiple comparisons (data not shown). Also, there

were no strong associations between the diabetes-related

phenotypes and the protective haplotype H2 or the sus-

ceptible haplotype H4 (Table 3).

Discussion

In this study, we demonstrated that two common haplo-

types of the NRF1 gene are associated with type 2 diabetes

in the Korean population. To the best of our knowledge,

this is the first study to demonstrate that NRF1 polymor-

phisms are associated with type 2 diabetes. However, we

did not find any association with diabetes-related pheno-

types. Before concluding that the polymorphisms in NRF1

really are associated with the risk of type 2 diabetes, this

result should be replicated.

At this time, we can only speculate on the mechanism

linking genetic variations of NRF1 to the susceptibility for

type 2 diabetes. Although the nucleotide position −46,350

Table 3 Comparison of diabetes-related phenotypes according to haplotypes in the non-diabetic subjects

Haplotype

combination

Haplotype H2 p value

Haplotype H4 p value

−/−−/H2 H2/H2 −/−−/H4 H4/H4

Number 264 38 1 –180 109 14 –

Fasting plasma

glucose (mmol/l)

5.1±0.5 5.2±0.5 5.4 0.285 5.1±0.5 5.1±0.4 4.8±0.4 0.360

HOMA-IR

1.58 (0.18–5.71) 1.87 (0.69–4.83) 2.25 0.925 1.59 (0.36–4.83) 1.86 (0.18–5.71) 1.72 (0.47–3.01) 0.712

Cholesterol

(mmol/l)

5.02±0.90 5.40±0.85 5.61 0.071 5.05±0.94 5.01±0.80 4.91±0.78 0.931

Triglyceride

(mmol/l)

1.36 (0.38–5.94) 1.51 (0.55–2.87) 1.79 0.805 1.44 (0.38–4.63) 1.51 (0.52–5.94) 1.46 (0.75–2.41) 0.330

HDL-cholesterol

(mmol/l)

1.17±0.35 1.19±0.28 1.16 0.380 1.16±0.33 1.19±0.36 1.24±0.34 0.298

BMI (kg/m

) 23.5±3.2 25.0±2.4 23.7 0.013 23.6±3.2 23.7±3.2 23.5±2.8 0.517

Waist circumference

(cm)

82.6±8.4 85.9±5.5 86.0 0.348 82.5±7.9 83.7±8.8 81.9±6.1 0.407

Data are shown as means±SD in the case of normal distribution, otherwise as median (range). This table compares diabetes-related

phenotypes according to the additive models with haplotypes H2 and H4, which showed a significant association with type 2 diabetes

Homeostasis model assessment of insulin resistance (HOMA-IR) and triglyceride were log-transformed before analyses

pvalues of haplotype associations of additive models were calculated by the Haplo.Score algorithm developed by Schaid et al. [14], while

controlling for age, sex and BMI as covariates. They were not corrected for multiple comparisons (seven parameters)

Table 2 Association between haplotypes and the risk of type 2 diabetes

Haplotype Locus Frequency pvalue

−46,567 −46,350 141 33,162 54,529 59,745 Diabetes Control

H1 G insA G C A A 0.499 0.533 0.138

H2 G delA G

A 0.042 0.066 0.009

H3 G delA G C A G 0.097 0.087 0.626

H4 G delA T

A 0.277 0.226 0.004

H5 A delA T T G A 0.062 0.061 0.880

Each haplotype with a frequency >0.05 is shown

pvalues of haplotype associations were calculated by the Haplo.Score algorithm developed by Schaid et al. [14], while controlling for age,

sex and BMI as covariates. They were not corrected for multiple comparisons (five tests)

Alleles regarded as the determinants modifying susceptibility to type 2 diabetes after examining the allelic differences between the

protective (H2) and susceptible (H4) haplotypes

2037

was predicted as 3′-flanking site of the FOXC1 consensus

sequence, haplotype analysis revealed that the −46,350A

variant was neutral to the risk of type 2 diabetes in that it

appeared both in protective (H2) and in susceptible (H4)

haplotypes. Similarly, comparing the elements of H2 and H4,

two polymorphisms (g.+141G>Tand g.+54529A>G)were

regarded as the determinants modifying the susceptibility to

type 2 diabetes. However, their individual association with

type 2 diabetes was only modest. Moreover, g.+141G>Tis a

synonymous substitution and g.+54529A>Gis located in

intron. It is possible that these polymorphisms did not reach

a robust association with type 2 diabetes in this study be-

cause the sample size was too small to identify such a modest

genetic effect. Since all genotyped loci were in a single LD

block, it is unlikely that we failed to detect a disease as-

sociation due to any polymorphism within this region that was

not covered in genotyping. However, it is possible that the

polymorphism(s) upstream from the promoter region se-

quenced by us or the gene(s) in the same or nearby locus of

NRF1 could have an influence on susceptibility to type 2

diabetes.

Type 2 diabetes is a multifactorial disease and influenced

by gene–gene and gene–environment interactions [16–18].

Of note, there are interactions among PGC1,NRF1 and

TFAM in the biogenesis and maintenance of normal

function of mitochondria [9,19,20]. Recently, evaluation

of high-order gene–gene interactions has become possible,

thanks to newly developed methods such as multifactor

dimensionality reduction [21–23]. The examination of

gene–gene interactions among PGC1,NRF1 and TFAM

would help reveal the genetic link between mitochondrial

oxidative phosphorylation and type 2 diabetes.

In conclusion, haplotypes of the NRF1 gene, which reg-

ulates mitochondrial oxidative phosphorylation, are asso-

ciated with type 2 diabetes in the Korean population and this

association should be tested in other different populations for

replication.

Acknowledgements Y.M. Cho and H.D. Shin contributed equally

to this work. This work was supported by a grant from the Korea

Health 21 R & D Project, Ministry of Health & Welfare, Republic of

Korea (02-PJ1-PG1-CH04-0001 and 00-PJ3-PG6-GN07-001) and

by an intramural grant of the National Institute of Health, Korea.

References

1. Petersen KF, Dufour S, Befroy D, Garcia R, Shulman GI (2004)

Impaired mitochondrial activity in the insulin-resistant offspring of

patients with type 2 diabetes. N Engl J Med 350:664–671

2. Petersen KF, Befroy D, Dufour S et al (2003) Mitochondrial

dysfunction in the elderly: possible role in insulin resistance.

Science 300:1140–1142

3. Gopalakrishnan L, Scarpulla RC (1995) Structure, expression,

and chromosomal assignment of the human gene encoding

nuclear respiratory factor 1. J Biol Chem 270:18019–18025

4. Poyton RO, McEwen JE (1996) Crosstalk between nuclear and

mitochondrial genomes. Annu Rev Biochem 65:563–607

5. Patti ME, Butte AJ, Crunkhorn S et al (2003) Coordinated

reduction of genes of oxidative metabolism in humans with

insulin resistance and diabetes: potential role of PGC1 and

NRF1. Proc Natl Acad Sci U S A 100:8466–8471

6. Mootha VK, Lindgren CM, Eriksson KF et al (2003) PGC-1

alpha-responsive genes involved in oxidative phosphorylation

are coordinately downregulated in human diabetes. Nat Genet

34:267–273

7. Evans MJ, Scarpulla RC (1989) Interaction of nuclear factors

with multiple sites in the somatic cytochrome c promoter.

Characterization of upstream NRF-1, ATF, and intron Sp1

recognition sequences. J Biol Chem 264:14361–14368

8. Evans MJ, Scarpulla RC (1990) NRF-1: a trans-activator of

nuclear-encoded respiratory genes in animal cells. Genes Dev

4:1023–1034

9. Scarpulla RC (2002) Nuclear activators and coactivators in

mammalian mitochondrial biogenesis. Biochim Biophys Acta

1576:1–14

10. Baar K, Wende AR, Jones TE et al (2002) Adaptations of

skeletal muscle to exercise: rapid increase in the transcriptional

coactivator PGC-1. FASEB J 16:1879–1886

11. Baar K, Song Z, Semenkovich CF et al (2003) Skeletal muscle

overexpression of nuclear respiratory factor 1 increases glucose

transport capacity. FASEB J 17:1666–1673

12. Eberle MA, Kruglyak L (2000) An analysis of strategies for

discovery of single-nucleotide polymorphisms. Genet Epide-

miol 19(Suppl 1):S29–S35

13. Alberti KG, Zimmet PZ (1998) Definition, diagnosis and

classification of diabetes mellitus and its complications part 1:

diagnosis and classification of diabetes mellitus provisional

report of a WHO consultation. Diabet Med 15:539–553

14. Schaid DJ, Rowland CM, Tines DE, Jacobson RM, Poland GA

(2002) Score tests for association between traits and haplotypes

when linkage phase is ambiguous. Am J Hum Genet 70:425–

434

15. Quandt K, Frech K, Karas H, Wingender E, Werner T (1995)

MatInd and MatInspector: new fast and versatile tools for

detection of consensus matches in nucleotide sequence data.

Nucleic Acids Res 23:4878–4884

16. Froguel P, Velho G (2001) Genetic determinants of type 2

diabetes. Recent Prog Horm Res 56:91–105

17. Elbein SC (2002) Perspective: the search for genes for type 2

diabetes in the post-genome era. Endocrinology 143:2012–

2018

18. Gloyn AL, McCarthy MI (2001) The genetics of type 2

diabetes. Best Pract Res Clin Endocrinol Metab 15:293–308

19. Lowell BB, Spiegelman BM (2000) Towards a molecular

understanding of adaptive thermogenesis. Nature 404:652–660

20. Wu Z, Puigserver P, Andersson U et al (1999) Mechanisms

controlling mitochondrial biogenesis and respiration through

the thermogenic coactivator PGC-1. Cell 98:115–124

21. Ritchie MD, Hahn LW, Roodi N et al (2001) Multifactor-

dimensionality reduction reveals high-order interactions among

estrogen-metabolism genes in sporadic breast cancer. Am J

Hum Genet 69:138–147

22. Ritchie MD, Hahn LW, Moore JH (2003) Power of multifactor

dimensionality reduction for detecting gene–gene interactions

in the presence of genotyping error, missing data, phenocopy,

and genetic heterogeneity. Genet Epidemiol 24:150–157

23. Cho YM, Ritchie MD, Moore JH et al (2004) Multifactor-

dimensionality reduction shows a two-locus interaction asso-

ciated with type 2 diabetes mellitus. Diabetologia 47:549–554

2038

33162C 02e7e52db238b91963000000

Navigation menu

Versions of this User Manual:

Views

Navigation