International Multiconference “Bioinformatics of Genome Regulation and Structure\Systems Biology”

Tag

genome-wide association study

3 RESULTS
Systems Biology and Biomedicine symposiumLoci and genes involved in chronic musculoskeletal pain identified via analysis of genetically independent pain phenotypes

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

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Yakov Tsepilov1, Maxim B. Freidin2, Alexandra S. Shadrina3, Sodbo Z. Sharapov4, Elizaveta E. Elgaeva5, Jan van Zundert6, Lennart РЎ. Karssen7, Pradeep Suri8, Frances M.K. Williams9, Yurii S. Aulchenko10
1Novosibirsk State University, drosophila.simulans@gmail.com
2King’s College London, maxim.freydin@kcl.ac.uk
3Novosibirsk State University, weiner.alexserg@gmail.com
4Novosibirsk State University, sharapovsodbo@gmail.com
5Novosibirsk State University, elizabeth.elgaeva@gmail.com
6Maastricht University Medical Centre, jan.vanzundert@zol.be
7PolyOmica, l.c.karssen@polyomica.com
8VA Puget Sound Health Care System, pradeepsuri1@gmail.com
9King’s College London, frances.williams@kcl.ac.uk
10Novosibirsk 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.

Genomics, transcriptomics, bioinformatics symposiumNovel loci associated with plasma immunoglobulin G N-glycosylation identified by a multivariate analysis

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

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Poster (download)

[pdf-embedder url=”https://bgrssb.icgbio.ru/wp-content/uploads/2020/07/78.pdf”]
Video (download)

Alexandra S. Shadrina1, Alexander S. Zlobin2, Olga O. Zaytseva3, Gordan Lauc4, Lucija Klaric5, Sodbo Z. Sharapov6, Yurii S. Aulchenko7, Yakov A. Tsepilov8
1Laboratory of Glycogenomics, Institute of Cytology and Genetics, Novosibirsk, Russia, weiner.alexserg@gmail.com
2Laboratory of Recombination and Segregation Analysis, Institute of Cytology and Genetics, Novosibirsk, Russia, defrag12@gmail.com
3Genos Glycoscience Research Laboratory, Zagreb, Croatia, lomur00@gmail.com
4Genos Glycoscience Research Laboratory, Zagreb, Croatia, glauc@genos.hr
5MRC Human Genetics Unit, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, United Kingdom, Lucija.Klaric@ed.ac.uk
6Laboratory of Glycogenomics, Institute of Cytology and Genetics, Novosibirsk, Russia, sharapovsodbo@gmail.com
7Laboratory of Glycogenomics, Institute of Cytology and Genetics, Novosibirsk, Russia, y.s.aulchenko@polyomica.com
8Laboratory 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.

Genomics, transcriptomics, bioinformatics symposiumHigh performance pipeline for the calculation of Polygenic Risk Scores

High performance pipeline for the calculation of Polygenic Risk Scores

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Poster (download)

[pdf-embedder url=”https://bgrssb.icgbio.ru/wp-content/uploads/2020/07/109.pdf”]
Video (download)

Arina Nostaeva1, Tatiana Shashkova2, Sodbo Sharapov3, Yakov Tsepilov4, Yurii Aulchenko5, Lennart C. Karssen6
1Laboratory of Recombination and Segregation Analysis, Institute of Cytology and Genetics, Novosibirsk, Russia, avnostaeva@gmail.com
2Laboratory of Recombination and Segregation Analysis, Institute of Cytology and Genetics, Novosibirsk, Russia, shashkova@phystech.edu
3Laboratory of Recombination and Segregation Analysis, Institute of Cytology and Genetics, Novosibirsk, Russia, sharapovsodbo@gmail.com
4Laboratory of Recombination and Segregation Analysis, Institute of Cytology and Genetics, Novosibirsk, Russia, drosophila.simulans@gmail.com
5Laboratory of Recombination and Segregation Analysis, Institute of Cytology and Genetics, Novosibirsk, Russia, yurii.aulchenko@gmail.com
6PolyKnomics, ’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.