Scientific research

Scientific research is at the heart of our business. Over the years we have been involved in various projects. These are the areas we currently focus on:

Healthy aging

In our studies of coronary heart disease, chronic pain [1, 2], and healthspan [3], we are beginning to appreciate that many aging-related diseases share genetic determinants, which likely reflects the existence of a limited number of “core” processes that lead to the wide variety of disease outcomes. We want to define and understand these core aging processes. We believe that understanding these will provide improved risk prediction and patient stratification and will facilitate the identification of new biomarkers and therapeutic intervention targets.

Multivariate methods in statistical genomics

Since 2014 we have been working on developing multivariate methods for the genetic analyses of ensembles of traits, mostly “omics” data. We found that a multivariate approach allows for considerably higher power and can facilitate biological interpretation of results obtained in analyses of human metabolome [4]. Whereas previous multivariate studies concentrated solely on methodology, we have developed the first robust replication procedure leading to the discovery of five new loci that affect N-glycosylation of immunoglobulin G [5]. Thus, we empirically demonstrated the value of using multivariate methods to identify 2 novel, reproducible pleiotropic effects. Other recent work is focused on linking GWAS to biology by integrating complex trait GWAS data and functional genomics quantitative trait loci (QTL) data. Here, we developed databases containing several billion associations, as well as tools for analyzing these data. A recent example of this research line is our discovery of several putative causative genes associated with inflammatory bowel disease [6].

Chronic Primary Musculoskeletal Pain

Chronic Primary Musculoskeletal Pain (CPMSP) is the number one cause of years lived with disability worldwide and one of the most common reasons for health care visits in developed countries, yet surprisingly little is known regarding the biology underlying this symptom. Previously, we have investigated one of the most prevalent phenotypes related to CPMSP: back pain [1, 2]. In our studies of more than 500,000 individuals we demonstrated independent genetic correlations between back pain and depression symptoms, neuroticism, sleep disturbance, overweight, and smoking. The studies of pleiotropy and genetic correlations, supported by pathway analysis, suggested at least two strong molecular axes of back pain genesis, one re lated to structural/anatomic factors such as intervertebral disk problems and anthropometrics; and another related to the psychological component of pain perception and pain processing. These findings corroborate the current biopsychosocial model as a paradigm for back pain. Overall, the results demonstrate back pain to have an extremely complex genetic architecture that overlaps with the genetic predisposition to its biopsychosocial risk factors. In the future, we will exploit multivariate methods and big data to study genetic components common and distinct between different types of chronic primary musculoskeletal pain, identify CPMSP biomarkers and potential therapeutic targets.

Regulation of protein glycosylation

Glycosylation —the addition of a carbohydrate moiety— is a common form of co-translational and post-translational modification of proteins. The attachment of a wide range of glycans to the same protein can give rise to hundreds of different glycoforms. Glycosylation of a given glycoprotein can change its physical properties as well as its biological function. Glycosylation is a highly dynamic process that is tissue-specific, cell-type specific, protein-specific, and even protein site-specific. Changes in glycosylation are observed in many diseases. Although glycosylation has been studied extensively at the biochemical level, strikingly little is known about the networks of genes that orchestrate cell- and tissue-specific regulation in vivo. We work on identification of the gene networks that regulate glycosylation from the perspective of quantitative genetics and computational functional genomics [7, 5].


[1] Pradeep Suri, Melody R. Palmer, Yakov A. Tsepilov, Maxim B. Freidin, Cindy G. Boer, et al. “Genome-Wide Meta-Analysis of 158,000 Individuals of European Ancestry Identifies Three Loci Associated With Chronic Back Pain”. In: PLOS Genetics 14.9 (2018), e1007601. DOI:10.1371/journal.pgen.1007601.
[2] Maxim B. Freidin, Yakov A. Tsepilov, Melody Palmer, Lennart C. Karssen, Pradeep Suri, Yurii S. Aulchenko, and Frances M.K. Williams. “Insight Into the Genetic Architecture of Back Pain and Its Risk Factors From a Study of 509,000 Individuals”. In: PAIN 160.6 (2019), pp. 1361–1373. DOI: 10.1097/j.pain.0000000000001514.
[3] Aleksandr Zenin, Yakov Tsepilov, Sodbo Sharapov, Evgeny Getmantsev, L. I. Menshikov, Peter O. Fedichev, and Yurii Aulchenko. “Identification of 12 Genetic Loci Associated With Human Healthspan”. In: Communications Biology 2.1 (2019), p. 41. DOI: 10.1038/s42003-019-0290-0.
[4] Y A Tsepilov, S Z Sharapov, O O Zaytseva, J Krumsek, C Prehn, et al. “A network-based conditional genetic association analysis of the human metabolome”. In: GigaScience 7.12 (Nov. 2018). giy137. ISSN: 2047-217X. DOI: 10.1093/gigascience/giy137.
[5] Sodbo Zh Sharapov, Yakov A Tsepilov, Lucija Klaric, Massimo Mangino, Gaurav Thareja, et al. “Defining the Genetic Control of Human Blood Plasma N-Glycome Using Genome-Wide Association Study”. In: Human Molecular Genetics (2019). DOI: 10.1093/hmg/ddz054.
[6] Yukihide Momozawa, Julia Dmitrieva, Emilie Théâtre, Valérie Deffontaine, Souad Rahmouni, et al. “Ibd Risk Loci Are Enriched in Multi-genic Regulatory Modules Encompassing Putative Causative Genes”. In: Nature Communications 9.1 (2018), p. 2427. DOI: 10.1038/s41467-018-04365-8.
[7] Xia Shen, Lucija Klarić, Sodbo Sharapov, Massimo Mangino, Zheng Ning, et al. “Multivariate Discovery and Replication of Five Novel Loci Associated With Immunoglobulin G N-Glycosylation”. In: Nature Communications 8.1 (2017), p. 447. DOI: 10.1038/s41467-017-00453-3.