SNP Genotypes and Technology of the Effective Genes Analysis;
Recommendations for Cardiology
*Aleksandr Nagay, Kurbanov RD, Khamidullaeva GA, Srojidiniva NZ and Tursunova NB
Department of Arterial Hypertension and Molecular Genetics Research, Republican specialized center of cardiology, China
Received: June 09, 2017; Published: June 14, 2017
Corresponding author: Aleksandr Nagay, Department of Arterial Hypertension and Molecular Genetics Research, Republican Specialized Center of
Cardiology, 4, Osiyo Str Mirzo-Ulugbek, 100052, Tashkent, Uzbekistan, China
Each patient has a combination of the genes bound to
cardiovascular illnesses. Today there are 3000 illnesses to which
individual genetic tests (Gene Tests Med. Gen. Information
Resource 2013) are available. However to interpret such results,
for example for polygenic diseases as an essential hypertension
without computer systems is extremely difficult. Usually it is bound
to a larger flow of information and shortage of clinical geneticists.
Besides cognitive abilities of the cardiologist are limited by lack of
knowledge in the field of bioinformatics and biotechnology.
Differences between genomic and clinical tests
Unprecedented depreciation of the chip-sequencing, made
possible to conduct large researches of cardiovascular diseases.
These technologies were always followed by 2 main problems:
I. choice of management of a huge array of genomic data;
II. choice of computing methods of the analysis [1-3].
Despite it, use of genomic results for 10 years became
ubiquitous. Technically genetic tests don’t differ from any other
medical test. Genomic tests as all other clinical tests, are caused
by the probabilistic size where a certain path physiological state
is present (that is the diagnosis), or will be present (that is, the
forecast) [4]. Anyway, genomic or usual tests are used for clinical
decision-making in the context of an asymptomatic or pathological
condition of the patient. As a result clinical genomic data have the
same purpose as other medical tests: to provide information at
diagnosis and treatment of the patient. However, genetic tests have
also some difference from clinical:
a. high probability of the unforeseen obtained data;
b. confidentiality of the obtained information (only for
family members);
c. The obtained data don’t guarantee that the patient
understands character of the provided information (for example:
this category includes information on future risks of development of
pathologies). Today there are some doubts that highly technological
genetic tests of SNP won’t be more expensive than medical
researches [5]. Today the most important difference between
genomic tests and clinical - the fact that medical institutions and
medical industrial infrastructure believe that genetic and usual
medical tests will be very different because of complexity of
interpreting results.
Superfine phenotype, molecular information and their
clinical context
To understand pathophysiology, clinical, imaging, functional,
molecular, genomic, markers need to be connected, and further
linked to outcomes. Such connection presents a formidable
challenge to data storage, management and analysis, IT capacity,
and accessibility. Innovative statistics will be needed addressing a
multiplicity of potential biomarkers, identifying reliable and valid
measurements.
Genomic test in CVD
Integration of biological markers for specific disease processes
has the potential to identify patients at risk for first cardiovascular
events (arterial hypertension, cerebrovascular or coronary heart
diseases, cardiac arrhythmia, heart failure, diabetes type II) to allow
to target therapies to patients who are most likely to benefit, and to
allow better prediction of unwanted side-effects of therapy [6-7].
Technology of the effective genes analysis
The technology of the effective genes analysis is the combined
method of the sequential analysis, ROC-curve and relative risk of
RR (Altman’s theorem) with use of basic mathematical algorithms (Bayes’ theorem) in diagnostics of in vitro. Thus, using DNA
identification, the developed algorithm can recommend an
individual diet and a safe dose of drug (Warfarin, Klopidogrel,
Simvastatin, Perindopril). The algorithm is capable to transform
pathogenic genetic data to the diagnostic tool which in a
combination with clinical observation and biometric data can
create the clinical decision.
Recommendations
Despite numerous disadvantages of the genomic tests, the
analysis of a big flow of genetic and clinical information it became
real. We recommend considering several moments on the first
steps. in - the first to pay attention to an analytical stage. To provide
sensory management of all knots of system. It is necessary to
consider in advance all working platforms which will be served
by cardio-genetic lab. Use Bayesian approach to frame a matrix of
recognition of the damaging alleles and haplotype. It is necessary to
adhere to standard forms of biological data. To strengthen enough
genetic information, clinical geneticist has to be able to structure
a superfine phenotype. For stratification of risk of rising of the
ABP (the high, increased, average risk) use multi-sample option.
It will lead to specificity increasing while sensitivity will remain
invariable. Use heuristic and statistical algorithms; they will be able
to identify the damaging genotype, without conflicting to clinical
information of the patient. We also recommend to limit access to
genetic information. It is bound to the fact that information on
existence of the inherited pathologies can affect a work arrangement
and receiving insurance. In some countries laws of genetic non-discrimination
(US genetic information nondiscrimination act
GINA 2008) are adopted.
Despite a technological capability to process and make
more exact clinical assessment of big data array, the problem
of inaccessibility of resources and mechanisms of exchange of
the annotated genetic information remains unresolved. Ability
to authentically predict phenotypical results on the basis of a
genotype, still remains a DNA lab task of the next generation.
ISSN: 2574 -1241
