Int J Med Health Sci. Oct 2015,Vol-4;Issue-4 430

International Journal of Medical and Health Sciences

Journal Home Page: http://www.ijmhs.net ISSN:2277-4505

Αssociation between Polymorphisms and Haplotypes in AKR1B1 and Diabetes Type 2

leading to Complications

Sophia V Tachmitzi1*

, Evangelia E Tsironi1, Maria G Kotoula

1, Efthimios Dardiotis

2, Theodoros Eleftheriadis

3,

Dimitrios Z Chatzoulis1, Paraskevi Xanthopoulou

4, Maria Tziastoudi

4, Aristotle G Koutsiaris

1,5, Anatoli

Fotiadou1, Georgios M Hadjigeorgiou

2, Ioannis Stefanidis

3, Elias Zintzaras

6

Departments of 1Ophthalmology,

2 Neurology,

3Nephrology and

4 Biomathematics, University of Thessaly School of

Medicine, Larissa, Greece; 5Bio-medical Informatics and Engineering Lab., School of Health Sciences, T.E.I. of

Thessaly, Larissa, Greece; 6The Institute for Clinical Research and Health Policy Studies, Tufts Medical Center, Tufts

University School of Medicine, Boston, MA, USA.

ABSTRACT

Background: A candidate-gene association study in a south eastern Mediterranean population was conducted to investigate the

association of five AKR1B1 gene variants (rs2259458 G/T, rs2734653 G/A, rs2670230 C/A, rs1790998 C/A, rs17188118 A/C)

with i) diabetes progression and ii) risk of diabetes leading to microvascular complications. Materials and Methods: The cohort

consisted of 169 diabetic cases with complications, 107 diseased controls and 315 healthy controls. The disease progression was

tested using the generalized odds ratio (ORG). The risk of diabetes leading to complications was tested using the ORs of the

additive and co-dominant models. The mode of inheritance was assessed using the degree of dominance index. Results: The

analysis showed that the five AKR1B1 gene variants are not implicated in disease progression. However, the same AKR1B1

variants are associated with the risk of diabetes leading to complications. Significant results were derived for the additive model of

the variant rs2259458 G/T [OR= 1.87 (1.01-3.50)] and the co-dominant model of the variant rs2670230 C/A [OR=1.45 (1.01-

2.04)]. The modes of inheritance for the variants rs2259458 G/T and rs2670230 C/A were “non-dominance” and “dominance of

allele A”, respectively. The frequencies of three haplotypes (T-G-A-C-A, G-G-C-C-A and G-A-C-C-A) were significantly different (P≤0.05) between cases and healthy controls. Conclusion: Genetic variation in AKR1B1 gene may alter susceptibility to diabetes

leading to complications.

KEYWORDS: Aldose reductase gene variants, Diabetic Nephropathy, Diabetic Retinopathy

INTRODUCTION

Diabetic nephropathy (DN) and diabetic retinopathy (DR)

are considered as two major microvascular complications of

diabetes (type 1 and 2) [1]. DN is the primary cause of end-

stage renal failure and is characterized by a progressive

clinical course, ultimately leading to death [2,3]. Diabetic

retinopathy (DR) represents one of the leading causes of

adult acquired blindness [4]. The main risk factor for developing microvascular complications in diabetes is the

poor glycemic control; though, patients with relatively good

glycemic control develop also complications [1]. A familial

clustering of DN and DR indicated that a genetic

predisposition is implicated in the pathogenesis of

microvascular complications [5-7]. Thus, there is evidence

that genetic factors may contribute to the development of

complications in diabetes. However, the genes conferring

susceptibility have not been identified yet [2,8-10].

Aldo-keto reductase family 1, member B1 (aldose

reductase-AR) is an enzyme that in humans is encoded by

the AKR1B1 gene which is located at 7q35 [11]. This gene encodes a member of the aldo/keto reductase superfamily

Original article

Int J Med Health Sci. Oct 2015,Vol-4;Issue-4 431

which catalyzes the reduction of a number of aldehydes,

including glucose. Therefore, AKR1B1 might be implicated

in the development of microvascular complications in

diabetes (such as DN and DR) by catalyzing the reduction of

glucose to sorbitol [11].

The present candidate-gene study examined the hypothesis

of association of five common AKR1B1 gene variants-SNPs

(rs2259458 G/T, rs2734653 G/A, rs2670230 C/A, rs1790998 C/A, rs17188118 A/C) and the progression of diabetes type 2

(i.e. from healthy status to diabetes without complications or

to diabetes leading to complications). Thereafter, the

association of the AKR1B1 variants with the risk of diabetes

leading to complications was tested [2]. The former

hypothesis was tested using the generalized linear odds ratio

(ORG) [12, 13]. The later hypothesis was tested using the co-

dominant and additive models [14-16] and the magnitude of

associations was expressed in terms of odds ratios (ORs) as

a genetic model-free approach. In addition, the mode of

inheritance was estimated based on the degree of dominance index (h-index) [15,16]. Lastly, an analysis of haplotypes

was conducted.

MATERIALS AND METHODS

Subjects

The cohort consisted of 169 diabetic cases (type 2) with microvascular complications (23% with DR, 18% with DN

and 59% with both DR and DN), 107 diseased controls

(diabetics type 2 without microvascular complications) and

315 healthy controls.

The population consisted of males and females (in cases,

diseased and healthy controls the males were: 52%, 51%

and 52%, respectively). The distribution of age was: above

60 years old 89%, 85% and 79% of subjects in cases,

diseased and healthy controls, respectively. In 42% of cases

and 21% of diseased controls, the diabetes duration was

more than 10 years and all subjects were whites of Greek

origin. The study was approved by the University of

Thessaly Ethics Committee. A verbal informed consent was received from all patients. Τhe Ethics committee does not

require a written consent since in the report forms the full

name of the participants is not recorded.

Genotyping

Genomic DNA was extracted from peripheral blood

leukocytes using a salting out method. Based on Hapmap

data for CEU population (Release 27, Phase II+III, Feb09,

on NCBI B36 assembly, dbSNP b126), tag SNPs across the

AKR1B1gene (16.78Kbp spanning from 133777647 to

133794428 in chromosome 7) were selected using the tagger

algorithm (http://www.broadinstitute.org/mpg/tagger/) with

a pair wise approach, an r2 cut-off of ≥ 0.8 and a minor

allele frequency > 0.05.

A total of 5 tag SNPs in four distinct gene regions were

retrieved: in the intronic region between exons 1 and 2

(intron 1-2) (rs17188118, rs1790998), in the intron 3-4

(rs2670230), in the intron 5-6 (rs2734653) and the intron 8-

9 (rs2259458). Tag SNPs genotyping was performed with

TaqMan allele specific discrimination assays on an ABI

PRISM® 7900 Sequence Detection System and analysed

with SDS software (Applied Biosystems, Foster City, USA). At least 10% of the samples were selected randomly for

repeated genotyping, as an internal control. Genotyping was

performed by laboratory personnel blinded to clinical status.

Data Analysis

The association between genotype distribution and clinical

status (i.e. disease progression) was tested using the chi-

square test. The association between disease progression and

genotype distribution was also examined using the

generalized odds ratio ORG [12,13]. The ORG expresses the

probability of a subject being more diseased relative to the

probability of being less diseased, given that diseased

subjects have higher mutational load.

In investigating the susceptibility to diabetes leading to

complications, the co-dominant and additive models of

cases were compared to healthy controls using a logistic

model. These two models were selected since they are orthogonal [14-16]. The magnitude of associations was

expressed in terms of odds ratios (ORs) unadjusted and

adjusted for age and gender with the corresponding 95%

confidence interval (CI).

In healthy controls, deviation of the genotype distribution

from the Hardy-Weinberg equilibrium (HWE) and existence

of linkage disequilibrium (LD) between polymorphisms

were evaluated using exact tests according to Weir [17,18].

The mode of inheritance was estimated using the degree of

dominance index (h-index) [15,16]. A result was considered

statistically significant when p < 0.05.

The unadjusted and adjusted ORs were calculated using

SPSS (SPSS Inc. Version 11.5, Chicago). HWE and LD

were tested using the Genetic Data Analysis (GDA) software [19]. The haplotype frequencies were estimated

and compared by SHEsis [20]. ORG was calculated using

ORGGASMA (http://biomath.med.uth.gr) [12].

RESULTS

Disease progression

The genotype distributions of the five variants in cases,

diseased controls and healthy controls, and the respective

ORGs, are shown in Table 1. The healthy controls were

conformed to HWE for all variants (P ≥ 0.05). None of the

five variants showed significant association between disease

progression and genotype distribution (P ≥ 0.05); a marginal association at P = 0.08 was only shown for rs2670230 C/A.

The ORG produced non-significant results for all variants,

indicating that the risk of disease progression is not related

to the mutational load of the variants (Table 1).

Int J Med Health Sci. Oct 2015,Vol-4;Issue-4 432

Table: 1 Results from testing the association of the AKR1B1 variants with diabetes progression

Variant Genotype Cases

N (%)

Diseased

Controls

N (%)

Healthy

controls

N (%)

P-value ORG (95% CI)

rs2259458 G/T

GG 56(33.9) 43(40.6) 115 (37.6)

0.65 1.11 (0.80-1.53) GT 80(48.5) 50(47.2) 138(45.1)

TT 29(17.6) 13 (12.3) 53(17.3)

rs2734653 G/A

GG 92(57.1) 63(59.4) 177(57.8)

0.93 1.00 (0.70-1.44) GA 60(37.3) 38(35.8) 108(35.3)

AA 9(5.6) 5(4.7) 21(6.9)

rs2670230 C/A

CC 50(30.5) 25(23.8) 108(35.2)

0.08 1.02 (0.75-1.40) CA 87(53.0) 60(57.1) 134(43.6)

AA 27(16.5) 20(19.0) 65(21.2)

rs1790998 C/A

CC 75(45.5) 30(29.1) 123(39.4)

0.13 0.82 (0.59-1.13) CA 64(38.8) 52(50.5) 133(42.6)

AA 26(15.8) 21(20.4) 56(17.9)

rs17188118 A/C

AA 148(90.2) 93(92.1) 267(88.4)

0.65 0.83 (0.45-1.55) AC 14(8.5) 8(7.9) 33(10.9)

CC 2(1.2) 0(0.0) 2(0.7)

Genotype distribution of AKR1B1 gene variants among cases, diseased controls and healthy controls. The P-values and the generalized odds ratio (ORG) for testing the association between genotype distribution of each variant and disease progression (healthy controls, diabet ics without microvascular complications-diseased controls, diabetics with complications-cases) are shown.

Diabetes leading to complications

Single-locus analysis: Table 2 shows the association results for diabetes leading to complications. The model-free

approach (OR) produced a marginally significant

association (P = 0.05) for the additive model of the variant

rs2259458 G/T [ORadjusted = 1.87 (1.01-3.50)] and the co-

dominant model of the variant rs2670230 C/A [OR=1.45

(1.01-2.04)].

For the variant rs2259458 G/T the mode of inheritance is

“non-dominance” (h=0.21, Table 3), indicating that the heterozygote GT has a risk of being diseased that lies in the

middle of the risk-protected GG and risk-exposed TT

homozygous genotypes. For the variant rs2670230 C/A, the

mode of inheritance is “dominance of allele A” (h=3.53),

indicating that the homozygous AA has a greater risk of

being diabetic with complications than the homozygous CC,

and that the heterozygote CA has a risk of diabetes leading

to complications closer to the AA homozygote than to the midpoint between the two homozygotes (Table 3).

Linkage disequlibrium analysis: Table 4 shows the P-values,

and the respective D primes, for testing LD between pairs of

two variants for patients with diabetes leading to

complications and healthy controls. All variants were in LD

(P < 0.05), except for variant rs2670230 C/A for both

populations and the variant rs2734653 G/A for the patients

with diabetes leading to complications.

Analysis of haplotypes: The distribution of the estimated

haplotype frequencies of the five variants, both for cases and

healthy controls, is presented in Table 5. The overall

difference between cases and healthy controls was

marginally significant (Global P = 0.06). T-G-A-C-A, G-G-

C-C-A and G-A-C-C-A haplotypes derived significant results

(P ≤ 0.05).

Int J Med Health Sci. Oct 2015,Vol-4;Issue-4 433

Table :2 Results from testing the association of the AKR1B1 variants with diabetes leading to complications

SNP Genetic model OR (95% CIs) p-value ORadjusted (95% CIs) P-value

rs2259458 G/T Additive 1.12 (0.65-1.96) 0.68 1.87 (1.01-3.50) 0.05

Co-dominant 1.15 (0.78-1.67) 0.48 1.14 (0.75-1.72) 0.55

rs2734653 G/A Additive 0.83 (0.36-1.87) 0.65 0.94 (0.38-2.32) 0.89

Co-dominant 1.09 (0.73-1.61) 0.67 0.98 (0.64-1.52) 0.95

rs2670230 C/A Additive 0.90 (0.51-1.57) 0.70 0.77 (0.42-1.43) 0.41

Co-dominant 1.45 (1.01-2.04) 0.05 1.39 (0.91-2.13) 0.12

rs1790998 C/A Additive 0.87 (0.66-1.15) 0.33 0.80 (0.59-1.08) 0.14

Co-dominant 0.85 (0.58-1.25) 0.42 0.74 (0.49-1.12) 0.16

rs17188118

A/C Additive

1.34 (0.50-3.60) 0.56 1.04 (0.37-2.91) 0.94

Co-dominant 0.76 (0.40-1.47) 0.41 0.63 (0.31-1.30) 0.21

Odds Ratio (OR) and the corresponding 95% Confidence Intervals (CIs) for testing the association of the AKR1B1 gene variants with diabetes leading to complications for the additive and co-dominant models. The ORs adjusted for age and sex are also shown.

Table:3 The h-index and the respective mode of inheritance for the significant variants.

SNP h-index Mode of inheritance

rs2259458 G/T 0.21* None-dominance (additiveness)

rs2670230 C/A 3.53† Dominance of allele A

*based on adjusted values, †based on unadjusted values

Table:4 P-value/D for testing linkage disequilibrium between pairs of variants in cases and healthy controls (in brackets).

rs2734653 rs2670230 rs1790998 rs17188118

rs2259458 <0.01/1

(<0.01/1)

<0.01/0.75

(<0.01/0.80)

<0.01/0.72

(<0.01/0.72)

0.02/1.00

(0.01/1.00)

rs2734653 <0.01/0.94

(<0.01/1.00)

<0.01/0.70

(<0.01/0.60)

0.07/0.66

(0.02/0.79)

rs2670230 0.45/0.09

(0.05/0.04)

0.04/0.76

(<0.01/0.82)

rs1790998 <0.01/1.00

(<0.01/1.00)

Int J Med Health Sci. Oct 2015,Vol-4;Issue-4 434

Table: 5 Estimated haplotype frequencies for the five AKR1B1 gene variants (SNP1- SNP2 - SNP3 - SNP4 - SNP5)

Haplotype

SNP1-SNP2-SNP3-

SNP4-SNP5

Estimated frequencies

Cases Healthy

controls

P-value P-value

global

G-A-A-A-A 0.02 0.05 0.07

0.06

G-A-A-A-C 0.01 0.00 0.45

G-A-A-C-A 0.21 0.19 0.63

G-A-C-C-A 0.01 0.00 0.05

G-G-A-A-A 0.12 0.10 0.34

G-G-A-A-C 0.00 0.00 0.86

G-G-A-C-A 0.02 0.05 0.07

G-G-C-A-A 0.12 0.14 0.41

G-G-C-A-C 0.05 0.06 0.58

G-G-C-C-A 0.03 0.01 0.05

T-G-A-A-A 0.02 0.03 0.74

T-G-A-C-A 0.02 0.01 0.04

T-G-C-A-A 0.01 0.02 0.64

T-G-C-A-C 0.00 0.00 0.21

T-G-C-C-A 0.36 0.35 0.81

Estimated haplotype frequencies for the five AKR1B1 gene variants (SNP1: rs2259458 G/T, SNP2: rs2734653 G/A, SNP3: rs2670230 C/A, SNP4: rs1790998 C/A,

SNP5: rs17188118 A/C). The P-values for comparing each haplotype between cases (diabetics with complications) and healthy controls, and the global P-value for comparing the overall difference in haplotypes are shown.

DISCUSSION

The present study investigated whether certain AKR1B1

gene variants are associated with the diabetes disease

progression and with the development of diabetes leading to

complications.

In examining the association of AKR1B1 gene variants with

diabetes progression, the results showed that none of the

variants is implicated in disease progression.

In examining the association of AKR1B1 gene variants with

diabetes leading to complications, two variants (rs2259458

G/T and rs2670230 C/A) were found to be associated with diabetes leading to complications. The degree of dominance

index (h-index) indicated that the mode of inheritance is

“none-dominance” for the variant rs2259458 G/T and

“dominance of allele A” for the variant rs2670230 C/A

[15,16]. The genotype distributions of the examined variants

conform to HWE in the healthy control group indicating

lack of population stratification and genotyping mistakes

[21].

Haplotype analysis revealed that three haplotypes are

implicated in the development of complications in diabetes

(Table 5). The haplotype G-G-C-C-A may confer protection

for diabetes leading to complications, whereas allele T of

rs2259458 G/T and allele A of rs2670230 C/A may

contribute to the risk of diabetes leading to complications

when haplotypes are considered. The same applies for allele

A of rs2734653 G/A and allele A of rs1790998 C/A.

DN and DR are complex diseases with multifactorial

etiology and involve epistatic and gene-environment interactions. Therefore, in addition to hypothesis-driven

studies (i.e. the gene-candidate association studies),

hypothesis-free studies such as GWAS [14,22,23],

microarrays gene expression analyses [24,25] and whole

genome linkage scans [26,27] may assist in providing more

conclusive evidence regarding the significance of AKR1B1

as a marker in diabetes leading to complication. This can be

achieved by examining the genomic convergence of these

different types of studies [23].

Int J Med Health Sci. Oct 2015,Vol-4;Issue-4 435

Although GWAS represent a superior strategy for

unraveling genetic complexity [22], the findings of gene-

candidate association studies may be supportive in

replicating existed evidence and in revealing genuine

genetic effects that could merit prioritization in future

studies. GWAS themselves lack replication and therefore, replication of their findings from different investigators and

different methodologies (such as gene-candidate association

studies) are essential to interpret the mass of associations

likely to result from GWAS [14,26,28]. In addition, this

work fulfills the minimum requirements for the association

study to be informative [23].

In conclusion, the present study showed that genetic

variation in AKR1B1 gene may alter susceptibility to

diabetes leading to complications; though, it is not

implicated in disease progression. The results suggest that

AKR1B1 variants and haplotypes are involved in the

pathogenesis of diabetes leading to complications. However,

additional studies (including gene-gene and gene-interaction studies [14,29] and a genetic convergence analysis of

different data sources are needed in order to produce more

conclusive claims of the association between AKR1B1 and

disease progression or diabetes leading to complications.

Acknowledgements

We thank Almpanidou Pavlina for technical assistance. This

work was supported in part by the grant (code:2989) of the

University of Thessaly Research Committee. EET was the

principal investigator of the study and responsible for the

study conduct. ET, DZC, GMH, IS conceived and designed

the study. SVT, EET, MGK, TE, DZC, AF, IS screened the

patients and collected the blood samples and assembled the

GAS patient database. SVT, ED, AGK, GMH performed the

genotyping. VX and MT assisted in the statistical analysis of

the data. GMH, IS and AGK assisted in drafting the manuscript. EZ performed the statistical analysis of the data

and he drafted the manuscript.

Declaration of interest

The authors report no conflicts of interest. The authors alone

are responsible for the content and writing if the paper.

REFERENCES

1. Rich SS. Genetics of diabetes and its complications. J Am

Soc Nephrol 2006; 17:353-360.

2. Zintzaras E, Papathanasiou AA, Stefanidis I. Endothelial

nitric oxide synthase gene polymorphisms and diabetic

nephropathy: a HuGE review and meta-analysis. Genet

Med 2009; 11:695-706.

3. Gross JL, de Azevedo MJ, Silveiro SP, Canani LH,

Caramori ML, Zelmanovitz T. Diabetic nephropathy:

diagnosis, prevention, and treatment. Diabetes Care

2005; 28:164-176.

4. Congdon N, O' Colmain B, Klaver CC, Klein R, Muñoz

B, Friedman DS, Kempen J, Taylor HR, Mitchell P.

Eye Diseases Prevalence Research Group. Causes and

prevalence of visual impairment among adults in the

United States. Arch Ophthalmol 2004; 122:477-485.

5. Borch-Johnsen K, Nørgaard K, Hommel E, Mathiesen

ER, Jensen JS, Deckert T, Parving HH. Is diabetic

nephropathy an inherited complication? Kidney Int

1992; 41:719-722.

6. Quinn M, Angelico MC, Warram JH, Krolewski AS.

Familial factors determine the development of diabetic

nephropathy in patients with IDDM. Diabetologia

1996; 39:940-945.

7. Strojek K, Grzeszczak W, Morawin E, Adamski M,

Lacka B, Rudzki H, Schmidt S, Keller C, Ritz E.

Nephropathy of type II diabetes: evidence for hereditary

factors? Kidney Int 1997; 51:1602-1607.

8. Zintzaras E, Uhlig K, Koukoulis GN, Papathanasiou AA,

Stefanidis I. Methylenetetrahydrofolate reductase gene

polymorphism as a risk factor for diabetic nephropathy:

a meta-analysis. J Hum Genet 2007; 52:881-890.

9. Zintzaras E, Chatzoulis DZ, Karabatsas CH, Stefanidis I.

The relationship between C677T

methylenetetrahydrofolate reductase gene

polymorphism and retinopathy in type 2 diabetes: a

meta-analysis. J Hum Genet 2005; 50:267-275.

10. Zintzaras, E., Stefanidis, I. Association between the

GLUT1 gene polymorphism and the risk of diabetic

nephropathy: a meta-analysis. J Hum Genet 2005;

50:84-91.

11. Graham A, Heath P, Morten JE, Markham AF. The

human aldose reductase gene maps to chromosome region 7q35. Hum Genet 1991; 86, 509-514.

12. Zintzaras E. The generalized odds ratio as a measure of

genetic risk effect in the analysis and meta-analysis of

association studies. Stat Appl Genet Mol Biol 2010a;

9:Article21.

13. Zintaras E. The power of generalized odds ratio in

assessing association in genetic studies with known

mode of inheritance. Journal of Applied Statistics 2012;

39:2569-2581.

14. Zintzaras E, Lau J. Synthesis of genetic association

studies for pertinent gene-disease associations requires

appropriate methodological and statistical approaches. J Clin Epidemiol 2008a; 61:634-645.

15. Zintzaras E, Santos M. Estimating the mode of

inheritance in genetic association studies of qualitative

traits based on the degree of dominance index. BMC

Med Res Methodol 2011; 11:171.

16. Zintzaras E, Santos M. Performance of MAX test and

degree of dominance index in predicting the mode of

inheritance. Stat Appl Genet Mol Biol 2012; 1:Article 4.

Int J Med Health Sci. Oct 2015,Vol-4;Issue-4 436

17. Weir BS. Genetic Data Analysis: Method for discrete

population genetic data. Sunderland, MA:

Sinauerassociates, 1990.

18. Hernández JL, Weir BS. A disequilibrium coefficient

approach to Hardy-Weinberg testing. Biometrics 1989;

45:53-70.

19. Lewis PO, Zaykin D: Genetic Data Analysis. Computer

program for the analysis of allelic data. Version 1.0

(d16c). Available at

http://lewis.eeb.uconn.edu/lewishome/software.html,

2001.

20. Shi YY, He L. SHEsis, a powerful software platform for

analyses of linkage disequilibrium, haplotype

construction, and genetic association at polymorphism

loci. Cell Res 2005; 15:97-98.

21. Zintzaras E. Impact of Hardy-Weinberg equilibrium

deviation on allele-based risk effect of genetic

association studies and meta-analysis. Eur J Epidemiol 2010b; 25:553-560.

22. Kitsios GD, Zintzaras E. Genome-wide association

studies: hypothesis-"free" or "engaged"? Transl Res

2009a; 154:161-164.

23. Kitsios GD, Zintzaras E. Genomic convergence of

genome-wide investigations for complex traits. Ann

Hum Genet 2009b; 73:514-519.

24. Zintzaras E, Ioannidis JP. Meta-analysis for ranked

discovery datasets: theoretical framework and empirical

demonstration for microarrays. Comput Biol Chem

2008; 32:38-46.

25. Zintzaras E, Ioannidis JP. METRADISC-XL: a program

for meta-analysis of multidimensional ranked discovery oriented datasets including microarrays. Comput

Methods Programs Biomed 2012; 108:1243-1246.

26. Thomas DC. Are we ready for genome-wide association

studies? Cancer Epidemiol Biomarkers Prev 2006;

15:595-598.

27. Storey JD, Tibshirani R. Statistical significance for

genomewide studies. Proc Natl Acad Sci USA 2003; 16,

9440-9445.

28. Zintzaras E, Ioannidis JP. HEGESMA: genome search

meta-analysis and heterogeneity testing. Bioinformatics

2005b; 21:3672-3673.

29. Clayton D, McKeigue PM. Epidemiological methods for studying genes and environmental factors in complex

diseases. Lancet 2001; 358:1356-1360.

____________________________________________

*Corresponding author: Sophia V Tachmitzi

E-Mail: sophitach@yahoo.gr