New approaches to the selection of genetic markers associated with multifactorial phenotypic traits


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Abstract

Modern approaches to searching for associations between the studied phenotype and structural variations of the human genome are analyzed. Most complex phenotypic traits, including diseases, do not follow the laws of Mendelian inheritance, but have a multi-factor nature, that is, a significant contribution to their development is made by the genetic component in combination with the influence of environmental factors. In General, there are several approaches to the design of a limited set of polymorphic markers for point genotyping. Selection of individual molecular genetic markers is carried out based on either their statistically significant Association with the studied multivariate feature, or their functional significance for the implementation of this feature. The «candidate gene» approach allows you to focus on one or more polymorphic variants in the region of a gene (allelic variant), the product of which is likely involved in the development of a disease or trait. The cheaper procedure for full-genome screening using ultra-high-density microchips has made available another approach for searching for genetic predispositions - full - genome Association search. We believe that the unification of both approaches into a single algorithm for the choice of molecular genetic markers to conduct point genotyping will allow for both markers selected based on a priori assumptions about the functional significance of candidate genes, and Association with the studied trait on the basis of genome-wide associations search. This approach will optimize the diagnostic efficiency of the created test suite.

About the authors

G. G. Kutelev

Military Medical Academy. S. M. Kirov

Author for correspondence.
Email: vmeda-nio@mil.ru
Russian Federation, Saint Petersburg

A. B. Krivoruchko

Military Medical Academy. S. M. Kirov

Email: vmeda-nio@mil.ru
Russian Federation, Saint Petersburg

A. E. Trandina

Military Medical Academy. S. M. Kirov

Email: vmeda-nio@mil.ru
Russian Federation, Saint Petersburg

A. M. Ivanov

Military Medical Academy. S. M. Kirov

Email: vmeda-nio@mil.ru
Russian Federation, Saint Petersburg

D. V. Cherkashin

Military Medical Academy. S. M. Kirov

Email: vmeda-nio@mil.ru
Russian Federation, Saint Petersburg

A. A. Marchenko

Military Medical Academy. S. M. Kirov

Email: vmeda-nio@mil.ru
Russian Federation, Saint Petersburg

S. L. Grishaev

Military Medical Academy. S. M. Kirov

Email: vmeda-nio@mil.ru
Russian Federation, Saint Petersburg

References

Supplementary files

Supplementary Files
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1. JATS XML
2. Fig. 1. Manhattan EY plot, where each point represents an SNP. The X-axis is the location of SNPs in the genome, the Y-axis is the level of association of each SNP (the stronger the association with a trait, the lower its p-value, which means the higher the negative logarithm of this value and the corresponding "column"). The graph shows SNPs from GWAS 2013 and 74 recently discovered loci (marked with red crosses) associated with learning [23]

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3. Fig. 2. Algorithm for studying the molecular mechanism of the formation of a multifactorial trait

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Copyright (c) 2020 Kutelev G.G., Krivoruchko A.B., Trandina A.E., Ivanov A.M., Cherkashin D.V., Marchenko A.A., Grishaev S.L.

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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