Hydroxymethylation changes during early embryonic development in zebrafish

Artem Nedoluzhko1, Paula Berrutti2, Igo GuimarГЈes3, Ioannis Konstantinidis4, Igor Babiak5, Jorge M.O. Fernandes61Nord University, artem.nedoluzhko@nord.no2Nord University, paula.d.berrutti@nord.no3Universidade Federal de GoiГЎs, igoguimaraes@ufg.br4Nord University, ioannis.konstantinidis@nord.no5Nord University, igor.s.babiak@nord.no6Nord University, jorge.m.fernandes@nord.no A large number of studies in organisms across the eukaryotic phyla have shown that DNA 5-methylcytosine (5mC) modification is one of many mechanisms that suppress gene expression. Cytosine hydroxymethylation has been described during the past years; this DNA modification is increasingly recognized as an important component of epigenetic regulation in eukaryotes. In the present study, we investigated if hydroxymethylation may be involved in fish embryonic development and demonstrated for the first time at a genome-wide level and single nucleotide resolution the hydroxymethylome changes during zebrafish (Danio rerio) embryogenesis from one-cell stage to hatching. DNA hydroxymethylation was profiled by reduced representation hydroxymethylation profiling (RRHP), as shortly described in Fig. 1. Taken together with recently published data on 5-methylcytosine (5mC) modification events in D. rerio, our data unveil a new role for DNA hydroxymethylation in epigenetic regulation of fish embryonic development.

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Statistical relations between N-glycome of circulating immunoglobuline G and total plasma N-Glycome

Sofya Feoktistova1, Sodbo Sharapov2, Yakov A. Tsepilov3, Tim Spector4, Gordan Lauc5, Frano Vuckovic6, Yurii S. Aulchenko71Laboratory of Glycogenomics Institute of Cytology and Genetics Novosibirsk, Russia, sayfutdinovas@gmail.com2Laboratory of Glycogenomics Institute of Cytology and Genetics Novosibirsk, Russia, sharapovsodbo@gmail.com3Laboratory of Theoretical and Applied Functional Genomics Novosibirsk State University Novosibirsk, Russia, drosophila.simulans@gmail.com4Department of Twin Research and Genetic Epidemiology, School of Life Course Sciences King’s College London London, United Kingdom, tim.spector@kcl.ac.uk5Genos Glycoscience Research Laboratory Zagreb, Croatia, glauc@genos.hr6Genos Glycoscience Research Laboratory Zagreb, Croatia, fvuckovic@genos.hr7Laboratory of Glycogenomics Institute of Cytology and Genetics Novosibirsk, Russia, y.s.aulchenko@polyomica.com Glycosylation is the most common co-translational and post-translational modification of proteins. Glycans influence the physical properties of proteins as well as their biological functions. Alteration in glycosylation is observed in many human diseases.Defining genetic factors, altering glycosylation, can provide a basis for novel approaches to diagnostic and pharmaceutical applications. Predictionof IgG N-glycome from total plasma N-Glycome (TPNG) will allow to increase the power of genetic analysisof tissue specific glycosylation processes.

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Computer methods for visualization chromosome-specific DNA sequences in FISH images

Poster (download) Video (download) Bogomolov A.G.1, Karamysheva T.V.2, Rubtsov N.B.31Institute of Cytology and Genetics SB RAS, Novosibirsk, Russia; Novosibirsk State University Novosibirsk, Russia, mantis_anton@bionet.nsc.ru2Institute of Cytology and Genetics SB RAS, Novosibirsk, Russia, kary@bionet.nsc.ru3Institute of Cytology and Genetics SB RAS, Novosibirsk, Russia; Novosibirsk State University Novosibirsk, Russia, rubt@bionet.nsc.ru A great number of interspersed repetitive sequences in chromosomes make it difficult to identify chromosomal material via fluorescence in situ hybridization (FISH). The traditional approach to solve this problem is chromosome in situ suppression hybridization (CISS- hybridization). Unfortunately, it is impossible to be performed or fails with chromosomes of many eukaryote species. The aim of this study was to consider the image enhance procedure [1] and the in silico method of chromosome specific signal visualization (method VISSIS) [2] as alternatives to CISS-hybridization. The effectiveness of approaches for identification of specific signals was estimated by signal-to-background ratio (SNR). The computer methods were applied to images of human chromosomes, obtained with FISH of the whole chromosome painting DNA probes. Results showed that effectiveness of image processing methods depends on ratio of short and line interspersed elements (SINEs/LINEs) in DNA probes. The closer chromosomes in ratio of SINEs/LINEs, the higher specific signal intensities and signal-to-background ratios could be achieved. This suggests that computer methods can be efficient only with application of DNA probes derived from chromosomes characterized with similar ratio of SINE and LINE contents.

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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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Competition and collaboration in the miRNA science field

Artemiy Firsov1, Igor Titov21Institute of Informatics Systems, a.firsov@g.nsu.ru2Institute of Cytology and Genetics, titov@bionet.nsc.ru In this work we present the analysis of the characteristics of institutions interactions in the miRNA science field using the data from PubMed digital library. We identified the leaders of the field – China, USA -, characterized the interactions and described the country level features of co-authorship. We observed that the USA were leading in the publication activity until China took the lead 4 years ago. However, the USA are the main co-authorship driver in this field. We have also identified the pioneers and show, that they are the leaders of co-authorship activity. We compare the publications activity patterns on the organization level, identifying leaders. We compare the organization interaction graph with the authors interaction graph, and provide additional insights of miRNA science field evolution.

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lncRNAs – their potential in regulation of hypertension and behavior of ISIAH rats

Poster (download) Ivan Sidorenko1, Vladimir Babenko2, Arcady Markel3, Olga Redina41Institute of Cytology and Genetics SB RAS, vanyasidorenko22@gmail.com2Institute of Cytology and Genetics SB RAS, bob@bionet.nsc.ru3Institute of Cytology and Genetics SB RAS, markel@bionet.nsc.ru4Institute of Cytology and Genetics SB RAS, oredina@bionet.nsc.ru Long non-coding RNAs (lncRNAs) play an important role in the control of many biological processes in the body, including the development of cardiovascular diseases and hypertension. It is believed that lncRNAs play a central role in the epigenetic control of gene expression, however, the understanding of lncRNA biological functions and interactions is still far from being complete. In this work, we identified the lncRNAs differentially expressed in the hypothalami of hypertensive ISIAH and normotensive WAG rats, and revealed lncRNA-associated differentially expressed genes (DEGs) related to hypertension and behavioral characteristics of ISIAH rats (grooming, vertical activity, hyperactivity, abnormal emotion/affect behavior (including abnormal response to novelty). The work was carried out using transcriptome sequencing (RNA-Seq method). Three lncRNAs (Bc1, RGD1562890, and Snhg4) were found, the expression of which differed in the hypothalami of hypertensive ISIAH and normotensive WAG rats. The largest number of co-regulated genes, both associated with hypertension and behavior, was found for Snhg4. These findings may be useful for further understanding the role of lncRNAs in regulating the protein coding genes and modulating processes associated with both hypertension and behavior.

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The limits of the additive model for adult height

Poster (download) Ivan A. Kuznetsov1, Sergei A. Slavskii2, Tatiana I. Shashkova3, Georgii A. Bazykin4, Tatiana I. Axenovich5, Fyodor A. Kondrashov6, Yurii S. Aulchenko71Center of Life Sciences, Skolkovo Institute of Science and Technology, Moscow, Russia; Laboratory of Theoretical and Applied Functional Genomics, Novosibirsk State University, Novosibirsk, Russia, Ivan.Kuznetsov@skoltech.ru2Laboratory of Theoretical and Applied Functional Genomics, Novosibirsk State University, Novosibirsk, Russia, slavsky@phystech.edu3Laboratory of Theoretical and Applied Functional Genomics, Novosibirsk State University, Novosibirsk, Russia, deppt002@gmail.com4Center of Life Sciences, Skolkovo Institute of Science and Technology, Moscow, Russia, g.bazykin@skoltech.ru5Laboratory of recombination and segregation analysis, Institute of Cytology and Genetics SB RAS, Novosibirsk, Russia, tatiana.aksenovich@gmail.com6Institute of Science and Technology, Vienna, Austria, fkondrashov@gmail.com7Laboratory of recombination and segregation analysis, Institute of Cytology and Genetics SB RAS, Novosibirsk, Russia, yurii@bionet.nsc.ru The classical approach to analysis of polygenic quantitative traits assumes use of normal approximation and the additivity of effects. For more than a century, adult height served as an exemplary trait justifying this type of analysis and serving as a test case. We demonstrate that the classical approach to analysis of height has met its limits in contemporary large populations. In particular, we demonstrate the existence of weak, but highly significant non-additive interactions of genetic and environmental factors. In the conventional model, the better fit to the data that was achieved by accounting for these interactions came at the expense of the mean-model’s increased complexity. The complexity of the model could be reduced if log-normal approximation was used.

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Peak caller comparison through quality control of ChIP-Seq datasets

Ruslan N. Sharipov1, Yury V. Kondrakhin2, Semyon K. Kolmykov3, Ivan S. Yevshin4, Anna S. Ryabova5, Fedor A. Kolpakov61BIOSOFT.RU, LLC; Novosibirsk State University Novosibirsk, Russia, shrus79@biosoft.ru2Institute of Computational Technologies SB RAS; BIOSOFT.RU, LLC, Novosibirsk, Russia, yvkondrat@mail.ru3FRC Institute of Cytology and Genetics SB RAS; Institute of Computational Technologies SB RAS, Novosibirsk, Russia, kolmykovsk@gmail.com4Institute of Computational Technologies SB RAS; BIOSOFT.RU, LLC Novosibirsk, Russia, ivan@biosoft.ru5Institute of Computational Technologies SB RAS; BIOSOFT.RU, LLC Novosibirsk, Russia, anna@biosoft.ru6Institute of Computational Technologies SB RAS; BIOSOFT.RU, LLC Novosibirsk, Russia, fedor@biosoft.ru Chromatin immunoprecipitation followed by high throughput sequencing, i.e. ChIP-Seq, is a widely used experimental technology for the identification of functional protein-DNA interactions. Nowadays, such databases as GTRD, ChIP-Atlas and ReMap systematically collect and annotate a large number of ChIP-Seq datasets generated by distinct peak callers, including MACS2. The quality control of such datasets is currently indispensable, since the peak callers may produce different results for the same ChIP-seq experiment. We have performed a comparative analysis of intensively used peak callers with the help of two metrics that control false positive/negative rates. We have found that MACS2 outperformed its competitors.

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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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Novel loci associated with plasma immunoglobulin G N-glycosylation identified by a multivariate analysis

Poster (download) Video (download) Alexandra S. Shadrina1, Alexander S. Zlobin2, Olga O. Zaytseva3, Gordan Lauc4, Lucija Klaric5, Sodbo Z. Sharapov6, Yurii S. Aulchenko7, Yakov A. Tsepilov81Laboratory of Glycogenomics, Institute of Cytology and Genetics, Novosibirsk, Russia, weiner.alexserg@gmail.com2Laboratory of Recombination and Segregation Analysis, Institute of Cytology and Genetics, Novosibirsk, Russia, defrag12@gmail.com3Genos Glycoscience Research Laboratory, Zagreb, Croatia, lomur00@gmail.com4Genos Glycoscience Research Laboratory, Zagreb, Croatia, glauc@genos.hr5MRC Human Genetics Unit, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, United Kingdom, Lucija.Klaric@ed.ac.uk6Laboratory of Glycogenomics, Institute of Cytology and Genetics, Novosibirsk, Russia, sharapovsodbo@gmail.com7Laboratory of Glycogenomics, Institute of Cytology and Genetics, Novosibirsk, Russia, y.s.aulchenko@polyomica.com8Laboratory of Theoretical and Applied Functional Genomics, Novosibirsk State University, Novosibirsk, Russia, drosophila.simulans@gmail.com Immunoglobulin G (IgG) is the most prevalent human plasma N-glycosylated protein, which makes a significant impact into the total plasma protein glycosylation profile. Glycosylation of IgG is known to affect its biological properties; for example, sialylation of glycans attached to IgG is known to have an anti-inflammatory effect, while the absence of a core fucose in the IgG glycan structure can increase antigen-dependent cell cytotoxicity. The biochemical processes underlying protein glycosylation are well-studied; at the same time, little is known about biological network regulating these reactions. In the present study, we performed a multivariate analysis based on the summary statistics obtained in the previously published IgG N-glycome GWAS in order to discover new loci influencing IgG N-glycosylation patterns. We revealed thirty-four loci associated with the levels of plasma IgG N-glycosylation. Of these loci, eight loci have not been reported in previous works. Our results significantly expand the number of identified IgG N-glycome-associated loci and contribute to understanding the mechanisms of the genetic control of glycosylation.

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