2008 Barcelona - European Society of Human Genetics

Genomics, technology, bioinformatics
From all the subjects 4745 samples have been tested for PCR-functionality
and monitored in case of sample mix-ups or contamination .
During QC only 0,8% of the samples had to be excluded .
P08.08
Using the bioinformatic tools to choose the sNPs with highly
possible phenotypic effect
D. Ozhegova1 , M. Freidin1 , V. Puzyrev2 ;
1 2 Siberian State Medicin University, Tomsk, Russian Federation, Research
Institute of Medical Genetics, Tomsk, Russian Federation.
Common polymorphisms, such as SNPs, in human gene promoters
are the significant factors influencing differential gene expression underlying
natural phenotypic variation . A number of bioinformatic tools
were developed recently, which are useful in prediction of “on its own”
functionally important SNP in promoters utilizing the knowledge about
transcription factors binding the DNA . We used such the resources for
a pilot search of functional SNPs in seven immune response modifying
genes: STAT1, IL10, IL12B, IFNG, IFNGR1, MCP-1, TLR-2 . Firstly,
all 61 the SNPs were chosen in the promoters of these genes using
dbSNP (http://www .ncbi .nlm .nih .gov/SNP), and Ensembl (http://www .
ensembl.org). Then, the selection of a minimal sufficient number of
SNPs was done using SNPselector (http://www .snpselector .Duhs .
duke .edu) and PupaSNPFinder (http://www .pupasnp .org) . Finally, Genomatix
MathInsprector (http://www .genomatix .de/matinspector .html)
was utilised to predict a possible transcription factors (TFs) binding
efficiency change due to the SNPs chosen. Eighteen SNPs in promoter
region of seven genes were analyzed by MathInspector and
it was found that the nucleotide substitution in seven SNPs caused
new binding sites for TFs . The potential functionality of these SNPs
is under current experimental validation in our group . Thus, bioinformatic
approaches to the analysis of gene promoters allows reducing
the search space for candidate SNPs and focusing on the SNPs with
specific characteristics. Such in silico analysis facilitate understanding
of specific features of gene promoters under the study and provide
information on the genes functional variability .
P08.09
Breast cancer diagnostics: cscE screening using the
BioNumerics® software.
K. Janssens, B. Pot, L. Vauterin, P. Vauterin;
Applied Maths NV, Sint-Martens-Latem, Belgium.
INTRODUCTION . CSCE technology can be used for indirect mutation
scanning (e .g . BRCA1/2 mutation detection) . The method is sensitive
and more rapid than full gene sequencing and is therefore time and
cost saving . By the use of multi-capillary sequencers, high throughput
routine screening becomes feasible, but requires the availability of reliable
automatic mutation detection software .
DATA ANALYSIS is performed directly on the ABI .FSA files. Files are
imported in batches through the use of a BioNumerics® plugin, a script
based dynamic extension of the software that uses a file naming strategy
to automatically import up to 200 traces with up to 20 samples
each . BioNumerics® provides an adapted database environment to
store all imported data and takes care of all data management activities
. The plugin offers a proper analysis tool for the mutation detection:
Peak matching is done using a proprietary algorithm that uses five
user-adjustable curve parameters allowing to compare normalized
peak shapes . The result is a fast, sensitive and reliable peak matching
that can be used to discriminate typical ‘wild type samples’ from ‘heterozygous
mutants’ . For each target PCR product, one or more target
variants can be defined, allowing the creation of polymorphic variants.
The result is displayed in a clear overview report with color indication
of reference peaks, positive matches, mismatches, failed peaks and
problem cases for which verification is required. For the latter click and
zoom functions are available to quickly evaluate all matching parameters
on the screen .
P08.10
High Quality mutation Detection
L. Xu, S. Jankowski, E. Fraser, E. Vennemeyer;
Applied Biosystems, Foster City, CA, United States.
Accurate mutation calling and quality data have been identified as key
components of direct sequencing by many clinical researchers . We
used a new bioinformatics software, Variant Reporter TM to detect muta-
tions in large volume of data sets . This analysis tool provides improved
algorithm for SNP detection that are trained to discover accurate sequence
variations and report review status for traceability . It helps to
create expressive Quality Control Data reports for large data sets and
create annotated projects that contain trace files and data annotation.
Data sharing abilities between users, such as between a bioinformatics
team and end users, will be demonstrated. The guided workflow
gives a new or advanced user confidence in a short period of time. In
this poster we will highlight how core team can use the new Quality
Control metrics and how end users can share the accurate results .
P08.11
Prevalence of mutations in troponin t (tNNt2) and troponin i
(TNNI3) in Czech hypertrophic cardiomyopathy (HKMP) patients.
L. Benesova 1 , K. Curila 2 , M. Penicka 2 , D. Zemanek 3 , P. Tomasov 3 , M. Minarik 1 ;
1 Laboratory for molecular genetics and oncology, Genomac International, Ltd.,
Prague, Czech Republic, 2 Cardiocenter, Charles University, Prague, Czech Republic,
3 Cardiovascular center, Faculty Hospital Motol, Prague, Czech Republic.
Hypertrophic cardiomyopathy (HCMP) is a serious cardiovascular
disease with autosomal dominant inheritance, caused by mutation of
genes coding for structural or regulation proteins of sarcomers of the
heart muscle . Troponin T (TNNT2) and Troponin I (TNNI3) are important
part of sarcomere of heart muscle and mutations in their genes are
responsible for development of HCM .
We have performed complete sequencing of TNNT2 and TNNI3 genes
in 100 HCMP patients, previously diagnosed by Electrocardiography .
We have recorded a total of 4 positives . Of the different mutation types
detected, there was 1 novel mutation, which, to date, was not recorded
in any of the major HCMP databases . A wide variability of the mutation
malignancy was recorded with respect to the disease manifestation for
different mutation types .
This project was supported by the Czech Ministry of Health Grant
Agency project no .NR9164 .
P08.12
Disentangling molecular relationships with a causal inference
test
J. Millstein, B. Zhang, J. Zhu, E. E. Schadt;
Rosetta Inpharmatics LLC, wholy owned subsidary of Merck & Co., Inc., Seattle,
WA, United States.
There has been intense effort over the past couple of decades to identify
loci underlying quantitative traits as a key step in the process of elucidating
the etiology of complex diseases . However, a stumbling block
has been the difficult question of how to leverage this information to
identify molecular mechanisms that explain quantitative trait loci (QTL) .
We have developed a formal statistical test to quantify the strength of
a causal inference pertaining to a measured factor, e .g ., a molecular
species, which potentially mediates the causal association between
a locus and a quantitative trait . We applied the test to infer causal
relationships between transcript abundances and obesity traits in mice
and also to reconstruct transcriptional regulatory networks in yeast . We
treat the causal inference as a ‘chain’ of mathematical conditions that
must be satisfied to conclude that the potential mediator is causal for
the trait, where the inference is only as good as the weakest link in the
chain . This perspective naturally leads to the Intersection-Union Test
framework in which a series of statistical tests are combined to form
an omnibus test . Using computer simulated mouse crosses, we show
that type I error is low under a variety of non-causal null models . We
show that power under a simple causal model is comparable to other
model selection techniques as well as Bayesian network reconstruction
methods . We further show empirically that this method compares
favorably to TRIGGER for reconstructing transcriptional regulatory
networks in yeast, recovering 6 out of 8 known regulators .
P08.13
Identification of a potential regulatory element forming a hairpin
structure within the 3’UTR of CDK5R1
M. Venturin 1 , S. Moncini 1 , P. Zuccotti 1 , A. Nicolin 2 , P. Riva 1 ;
1 Department of Biology and Genetics for Medical Sciences, University of Milan,
Milan, Italy, 2 Department of Pharmacology, Chemotherapy and Medical Toxicology,
University of Milan, Milan, Italy.
CDK5R1 encodes for p35, a regulatory subunit of CDK5 kinase, which
is fundamental for normal neural development and function . CDK5R1