2009 Vienna - European Society of Human Genetics

Statistical genetics, includes Mapping, linkage and association methods
P08.48
Genetic polymorphism in the matrix metalloproteinases genes
and risk of occupational chronic bronchitis
O. Tselousova1 , G. Korytyna1 , L. Akhmadishina1 , T. Victorova1,2 ;
1 2 1Institute of Biochemistry and Genetics, Ufa, Russian Federation, Bashkir
State Medical University, Ufa, Russian Federation.
Active occupational exposure to respiratory irritants is the major risk
factors for occupational chronic bronchitis (OCB), but OCB develops
in some workers. It’s suggested a significant genetic role. Matrix metalloproteinases
(MMPs) are proteolytic enzymes associated with inflammation
and airway remodelling in respiratory diseases. The aim of this
study was to investigate the role of MMPs polymorphisms in the development
of occupational chronic bronchitis in workers frome Russia.
The MMP1 (interstitial collagenase), MMP9 (gelatinase B) and MMP12
(macrophage elastase) were genotyped by PCR-RFLP analysis in 110
workers with OCB and 157 healthy workers.
The overall, the genotype distribution and the allele frequencies of
polymorphisms G(-1607)GG of MMP1 gene, A(-82)G of MMP12 gene
and C(-1562)T of MMP9 gene didn’t significantly differ in groups (all
P values are above 0.05). The most common MMP1 genotype was
GG/GG in the patients with OCB (60.26%). The MMP9 frequency distribution
CC genotype was similar in the workers with OCB and in the
healthy subjects (80.91% and 73.95%, respectively). The frequency of
MMP12 AA genotype between the workers with OCB (75.73%) and the
healthy workers (72.61%) was not significant.
Our results show no significant difference between the workers with
occupational chronic bronchitis and the healthy workers for MMPs.
It’s needed more studies to define genetic risk factors for occupational
chronic bronchitis.
P08.49
Genome-wide parametric linkage analysis of adult height
T. I. Axenovich 1,2 , I. V. Zorkoltseva 1 , N. M. Belonogova 1,2 , A. V. Kirichenko 1 , B.
A. Oostra 3 , C. M. van Duijn 3 , Y. S. Aulchenko 1,3 ;
1 Institute of Cytology and Genetics, Novosibirsk, Russian Federation, 2 Novosibirsk
State University, Novosibirsk, Russian Federation, 3 Erasmus MC Rotterdam,
Rotterdam, The Netherlands.
Despite extensive research of genetic determinants of human adult
height, the current knowledge allows us to predict only a small portion
of the trait’s genetic variation. In this study we analyzed 2940 genotyped
and phenotyped individuals in a large pedigree including more
than 23000 members in 18 generations. The pedigree was derived
from an isolated Dutch population, where genetic heterogeneity is expected
to be low and linkage disequilibrium to be extensive.
Complex segregation analysis confirmed high heritability of adult
height, and suggested involvement of a major gene in the control of
height in this population. The estimates obtained from complex segregation
analysis were used to perform parametric linkage analysis.
Parametric linkage analysis indicated at least four suggestive and
three genome-wide significant loci. Significant peaks were located at
the chromosome regions 1p32.2 (LOD score = 3.35), 2p16.3-21 (LOD
score = 3.29) and 16q24.1-24.2 (LOD score = 3.94). First peak was
close to already known candidate gene COL9A2. The second peak
was in the middle of neurexin 1 gene (NRXN1), which is expressed
in hypothalamic area and may influence growth hormone production.
The locus 16q24.1-24.2 showing the strongest linkage signal was not
mapped earlier as contributing to population diversity of human stature.
Despite of relatively small number of genotyped and phenotyped individuals
a genome-wide parametric linkage identified four loci with suggestive
linkage and three with significant linkage. This demonstrates
high power of the used approach, which combines advantages of genetically
isolated population, large pedigree and parametric methods
of linkage analysis.
P08.50
A 3’UtR transition within DEFB1 is associated with chronic and
aggressive periodontitis
G. M. Richter 1 , A. S. Schäfer 1 , M. Nothnagel 2 , M. L. Laine 3 , A. Rühling 4 , C.
Schäfer 5 , N. Cordes 4 , B. Noack 6 , M. Folwaczny 7 , J. Glas 7 , C. Dörfer 4 , H. Dommisch
5 , B. Groessner-Schreiber 4 , S. Jepsen 5 , B. G. Loos 8 , S. Schreiber 1 ;
1 Institute for Clinical Molecular Biology, University Medical Center Schleswig-
Holstein, Kiel, Germany, 2 Institute of Medical Informatics and Statistics, Uni-
versity Medical Center Schleswig-Holstein, Kiel, Germany, 3 Academic Center
for Dentistry, VU, Amsterdam, The Netherlands, 4 Department of Operative
Dentistry and Periodontology, University Medical Center Schleswig-Holstein,
Kiel, Germany, 5 Department of Periodontology, Operative and Preventive Dentistry,
Bonn, Germany, 6 University Medical Center Carl Gustav Carus, Dresden,
Germany, 7 Department of Preventive Dentistry and Periodontology, Munich,
Germany, 8 Departement of Periodontology, Academic Centre for Dentistry Amsterdam
(ACTA), VU, Amsterdam, The Netherlands.
Periodontal diseases are complex inflammatory diseases and affect
up to 20% of the worldwide population. An unbalanced reaction of the
immune system to microbial pathogens is considered the key factor in
the development of periodontitis. Defensins have a strong antimicrobial
function and are important contributors of the immune system in
maintaining health. We present the first systematic association study
of DEFB1. Using a tagging SNP approach including described promoter
SNPs of DEFB1, we investigated the associations of the selected
variants in a large population (N = 1,337 cases and 2,887 ethnically
matched controls). The 3’UTR SNP rs1047031 showed the most significant
association signal for homozygous carriers of the rare A allele
(P = 0.002) with an increased genetic risk of 1.3 (95% confidence interval
1.11-1.57). The association was consistent with the specific periodontitis
forms chronic periodontitis (odd’s ratio = 2.2 [95% confidence
interval 1.16-4.35], P = 0.02), and aggressive periodontitis (odd’s ratio
= 1.3 [95% confidence interval 1.04-1.68], P = 0.02). Sequencing of
regulatory and exonic regions of DEFB1 identified no other associated
variant, pointing to rs1047031 as likely being the causative variant.
Prediction for microRNA targets identified a potential microRNA binding
site at the position of rs1047031.
P08.51
Pharmacogenetic investigation in complex traits using Genome
Wide Association study
E. Salvi 1 , C. Barlassina 1 , L. Citterio 2 , C. Lanzani 2 , S. Lupoli 3 , F. Torri 1 , A. Orro 4 ,
C. Cosentino 1 , F. Taddeo 1 , V. Tieran 1 , D. Cusi 5 , G. Bianchi 2 , F. Macciardi 1 ;
1 University of Milan, Milan, Italy, 2 University Vita Salute San Raffaele, Milan,
Italy, 3 INSPE, HSR Scientific Institute, Milan, Italy, 4 Institute of Biomedical Technologies,
CNR, Segrate, Milan, Italy, 5 San Carlo Borromeo Hospital, Milan, Italy.
Pharmacogenetic association studies help to identify DNA variants
which impact on the individual response to drugs. The knowledge of
sample variability in drug response can allow to personalize drug dosing
and treatment regimes. The fundamental question concerns whether
it is possible to differentiate the patients with potentially responses
to the treatment (R) from those with the greatest risk of no response
(NR). Our approach to these questions is to identify SNPs that segregate
with drug efficacy either in candidate genes (CG) that relate to the
mechanism of action of the drug or in other DNA regions detected with
a Genome Wide Association Study (GWAS). SNPs identified from CG
or GWAS could allow NR to be excluded from subsequent clinical trial
studies, therefore allowing enriched, smaller, faster, less expensive
clinical studies on patients with a better chance of responding favorably.
Here, we present an example of this approach: we adopted a
genetic association design, where the phenotype of interest was measured
as a quantitative trait and the variables affecting the distribution
of the phenotype are the SNPs, the therapy and the SNP*therapy interaction.
Once we identified those genes, we developed an algorithm
to detect the genotypic profiles that best discriminate R from the NR.
In a final step, we merged the best predictive SNPs found from our
GWAS strategy with those from a CG approach. Both categories of
SNPs convey a specific predictive power that is magnified by their joint
integration into a unified model.
P08.52
The hereditary risk factors of socially significant multifactorial
diseases
O. A. Gra 1,2 , Z. M. Kozhekbaeva 2,3 , D. V. Gra 4 , M. D. Fedorova 4 , O. I. Skotnikova
5 , N. P. Kisseljova 4 , F. L. Kisseljov 4 , I. V. Goldenkova-Pavlova 2 , T. V.
Nasedkina 1 ;
1 Engelhardt Institute of Molecular Biology Russian Academy of Sciences, Moscow,
Russian Federation, 2 Vavilov Institute of General Genetics, Russian Academy
of Sciences, Moscow, Russian Federation, 3 Institute for Human Genomics,
University of Miami Miller School of Medicine, Miami, FL, United States,
4 Institute of Carcinogenesis, N. N. Blokhin Cancer Research Centre, Russian
Academy of Medical Science, Moscow, Russian Federation, 5 The Central Sci-