A platform for storage and analysis of results of genome-wide association studies of sheep

Video (download) Alexander S. Zlobin, zlobin@bionet.nsc.ru, Kurchatov Genomic Center of IC&G Novosibirsk, Russia Anatoliy V. Kirichenko, kianvl@mail.ru, Laboratory of Recombination and Segregation Analysis Institute of Cytology and Genetics SB RAS Novosibirsk, Russia Tatyana I. Shashkova, DEPPT002@gmail.com, Laboratory of Theoretical and Applied Functional Genomics Novosibirsk State University Novosibirsk, Russia Natalya A. Volokova, natavolkova@inbox.ru, L.K. Ernst Federal Science Center for Animal Husbandry, Dubrovitsy, Moscow Region, Russia Pavel M. Borodin, borodin@bionet.nsc.ru, L.K. Ernst Federal Science Center for Animal Husbandry, Dubrovitsy, Moscow Region, Russia Lennart С. Karssen, l.c.karssen@polyomica.com, PolyOmica ‘s-Hertogenbosch, the Netherlands Yakov A. Tsepilov, tsepilov@bionet.nsc.ru, Laboratory of Recombination and Segregation Analysis Institute of Cytology and Genetics SB RAS Novosibirsk, Russia Yurii S. Aulchenko, yurii@bionet.nsc.ru, Kurchatov Genomic Center of IC&G Novosibirsk, Russia We developed a prototype of a platform for aggregation, storage and analysis of the results of genome-wide association scans of different economically important ovine traits.

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Loci and genes involved in chronic musculoskeletal pain identified via analysis of genetically independent pain phenotypes

Yakov Tsepilov1, Maxim B. Freidin2, Alexandra S. Shadrina3, Sodbo Z. Sharapov4, Elizaveta E. Elgaeva5, Jan van Zundert6, Lennart С. Karssen7, Pradeep Suri8, Frances M.K. Williams9, Yurii S. Aulchenko101Novosibirsk State University, drosophila.simulans@gmail.com2King’s College London, maxim.freydin@kcl.ac.uk3Novosibirsk State University, weiner.alexserg@gmail.com4Novosibirsk State University, sharapovsodbo@gmail.com5Novosibirsk State University, elizabeth.elgaeva@gmail.com6Maastricht University Medical Centre, jan.vanzundert@zol.be7PolyOmica, l.c.karssen@polyomica.com8VA Puget Sound Health Care System, pradeepsuri1@gmail.com9King’s College London, frances.williams@kcl.ac.uk10Novosibirsk State University, y.s.aulchenko@polyomica.com We have evaluated four genetically independent pain phenotypes of four common chronic musculoskeletal pains (GIPs). We assume that the first GIP represents a biopsychological component of chronic musculoskeletal pain, related to physiological and psychological aspects and possibly reflecting pain perception and processing.

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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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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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