On region based inference in genome wide association study

Sergey V. Malov1, Alexey Antonik2, Andrey K. Shevchenko31St.-Petersburg State University, malovs@sm14820.spb.edu2St.-Petersburg State University, alexey.antonik@gmail.com3St.-Petersburg State University, andrey.k.shevchenko@gmail.com We consider an advanced framework for genome wide association study (GWAS) based on the signal localization. Instead of looking for single genetic markers associated with the phenotype we attempt to discover regions of genetic markers, which are associated with phenotype. We focus on the localization by moving sums of negative logarithms of p-values, which is efficient for discovery of wide regions of genetic markers associated with phenotype. Particularly, we expect the method should be efficient for searching genes containing a number of genetic markers, if any reconstruction in a gene related to a phenotype. The method is implemented in GWATCH software for visualization and interpretation results of multiple statistical tests for genome associations. We discuss some features of the GWATCH software and apply them for HIV/AIDS cohort study from Botswana.

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High performance pipeline for the calculation of Polygenic Risk Scores

Poster (download) Video (download) Arina Nostaeva1, Tatiana Shashkova2, Sodbo Sharapov3, Yakov Tsepilov4, Yurii Aulchenko5, Lennart C. Karssen61Laboratory of Recombination and Segregation Analysis, Institute of Cytology and Genetics, Novosibirsk, Russia, avnostaeva@gmail.com2Laboratory of Recombination and Segregation Analysis, Institute of Cytology and Genetics, Novosibirsk, Russia, shashkova@phystech.edu3Laboratory of Recombination and Segregation Analysis, Institute of Cytology and Genetics, Novosibirsk, Russia, sharapovsodbo@gmail.com4Laboratory of Recombination and Segregation Analysis, Institute of Cytology and Genetics, Novosibirsk, Russia, drosophila.simulans@gmail.com5Laboratory of Recombination and Segregation Analysis, Institute of Cytology and Genetics, Novosibirsk, Russia, yurii.aulchenko@gmail.com6PolyKnomics, ’s-Hertogenbosch, The Netherlands, l.c.karssen@polyknomics.com A polygenic risk score (PRS) is a value that reflects a person’s predisposition to a disease or any other trait which can (partly) be explained by genetic inheritance. PRSs are often used in reports provided by genetic testing companies like 23andMe, Genotek, etc. Another way of using PRSs is to look at the distribution of PRS values for a group of people and compare them, for example, in a case-control study to find case-dependent traits. PRS models are usually based on summary statistics data from genome-wide association studies (GWAS) and take into account the linkage disequilibrium (LD) structure. We have created a pipeline for high performance PRS calculations across many traits present in the GWAS-MAP platform. The pipeline only requires individual-level data and provides the ability to select a list of traits. This pipeline will be helpful for scientific groups, working with large amounts of individual genotype data, as well as for individuals with their own personal genotype data.

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Genome-wide association study of Parkinson’s disease using MAX3 test

Poster (download) Georgii Ozhegov1, Dmitry Poverin2, Sergey Medvedev3, Suren Zakian4, Yuri Vyatkin5, Sergey Postovalov6 1Kazan Federal University, Kazan, Russia; Novel Software Systems, Ltd., Novosibirsk, Russia, georgii_provisor@mail.ru 2Novosibirsk State Technical University, Novosibirsk, Russia, foxlandg@gmail.com 3Federal Research Center Institute of Cytology and Genetics, Novosibirsk, Russia, medvedev@bionet.nsc.ru 4Federal Research Center Institute of Cytology and Genetics, Novosibirsk, Russia, zakian@bionet.nsc.ru 5Novosibirsk State University, Novosibirsk, Russia; Novel Software Systems, Ltd., Novosibirsk, Russia, yuri@nprog.ru 6Novosibirsk State Technical University, Novosibirsk, Russia; Novosibirsk State University, Novosibirsk, Russia, postovalovsn@gmail.com Whole exomes for a set of patients with Parkinson’s disease (PD) were sequenced to conduct a genome-wide association study (GWAS) using MAX3 test to find novel genomic variants associated with the disease. As a result, several new variants were identified.

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Methylation and expression profiles in Apoe vicinity point to specific neighboring interaction of Apoe and TOMM40 genes: implication for The Alzheimer disease.

Vladimir Babenko11Institute of Cytology and Genetics SB_RAS, bob@bionet.nsc.ru We assessed the dynamics of 8 genes including TOMM40, Apoe and other adjacent ones for overall chromatin marks landscape, including methylation profiles across ENCODE brain cell lines, and histone and ctcf marks. We revealed the region manifests Hi-C topology dynamics in a cell-specific manner. Additionally, based on methylation and histone marks profiles we underscore competitive manner of genes expression implying disrupted locus wide genes expression balance in Alzheimer patients due to Apoe extended locus methylation profile alteration.

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A new method for combining of genetically correlated traits by maximizing of their shared heritability

Video (download) Gulnara R. Svishcheva1, Evgeny S. Tiys2, Sofya G. Feoktistova3, Elizaveta E. Elgaeva4, Sodbo Sharapov5, Yakov A. Tsepilov61Institute of cytology and genetics, gulsvi@mail.ru2Institute of cytology and genetics, tiys@bionet.nsc.ru3Institute of cytology and genetics, sayfutdinovas@gmail.com4Institute of cytology and genetics, elizabeth.elgaeva@gmail.com5Institute of cytology and genetics, sharapovsodbo@gmail.com6Novosibirsk State University, drosophila.simulans@gmail.com Genetic correlations between phenotypic traits are widely observed phenomena. Many groups of traits have a significant level of shared genetic background. In this work, we suggested a new method for extraction of shared genetic component for genetically correlated phenotypic traits. We applied the method to GWAS results for anthropometric traits.

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Genome-wide Association Study Reveals Novel Genetic Variants Associated with HIV-1C Infection in Botswana Population

Andrey Shevchenko1, Sergey V. Malov2, Alexey Antonik31Theodosius Dobzhansky Center for Genome Bioinformatics St.-Petersburg State University St-Petersburg, Russia, andrey.k.shevchenko@gmail.com2Theodosius Dobzhansky Center for Genome Bioinformatics St.-Petersburg State University St-Petersburg, Russia, malovs@sm14820.spb.edu3Theodosius Dobzhansky Center for Genome Bioinformatics St.-Petersburg State University St-Petersburg, Russia, alexey.antonik@gmail.com Genome wide association studies (GWAS) allow to identify common variants associated with the trait in question. In order to efficiently search for the genetic associations we have previously developed Genome-Wide AssociationВ Tracks Chromosome Highway (GWATCH). The broad goal of the Botswana GWAS project is to identify genetic determinants of susceptibility and resistance to infection by HIV-1 subtype C among people severely affected by HIV/AIDS in Botswana. By conducting GWAS analysis on HIV1C case/control dataset consisting of 762 Tswana people (combined from two partly overlapping datasets of 809 microarray and 362 WGS samples), we found several gene regions slightly below significance level.

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