Biology subjects

Sharapov, S.

Publications and source records attributed to Sharapov, S..

7 recordsLinked to original sources

bioRxiv2026

Reconstructing N-Glycan Profiles of Individual Glycoproteins from the Total Blood Plasma N-Glycome: Implications for Immunoglobulin G N-Glycosylation GWAS

The genetic regulation of the plasma N-glycome variation in human populations is not fully characterized, partly due to the limited sample size in glyco-genetics studies. Here, we aimed to demonstrate that protein-specific N-glycan profiles, like those of immunoglobulin G (IgG), can be accurately reconstructed from the total plasma N-glycome (TPNG), enabling us to find new regulators of this complex process re-analysing existing datasets. By testing multiple linear and non-linear machine learning approaches we built a model to reconstruct IgG N-glycans from TPNG data, training on the TwinsUK cohort and validating on CEDAR. We reconstruct GWAS summary statistics for IgG N-glycans by applying the trained linear model to plasma glycan GWAS summary statistics, i.e., as GWAS of linear combinations of plasma glycan traits. The majority of the identified loci had been implicated in IgG N-glycosylation GWAS. Additionally, we found four new loci and suggested the role of FCRLA, KDELR2, HHEX, and TCF3 in the regulation of IgG N-glycosylation. In conclusion, we showed that our method enables the creation of protein-specific N-glycome datasets, allowing for powerful meta-analyses without the need to profile new samples.

genetics↗

bioRxiv2025

Dissecting the Cellular Genetics of Cardiovascular Disease Through Endothelial and Immune Compartments Profiling

BackgroundNon-communicable diseases such as coronary artery disease, atrial fibrillation, type 2 diabetes, hypertension, and others share endothelial dysfunction as one of their underlying features. The endothelium, as the interface between blood and vasculature, shapes disease onset and progression through its response to environmental cues. However, while the genetic component of these diseases has been captured by genome wide association studies (GWAS), which also highlighted a shared immune component, it remains unclear which of these disease loci exerts their effects through endothelial cells. This study identifies, and quantifies, the genetic determinants of endothelial cells molecular traits and their overlap to the common genetic variation component of these diseases. MethodsWe generated genotype, RNA-sequencing, H3K27ac ChIP-sequencing, ATAC-sequencing, and endothelial cells barrier stimuli response measurements for 100 samples of human umbilical vein endothelial cells. These were used to identify quantitative trait loci (QTL) for gene expression, transcriptional isoform usage, splice junction usage, chromatin activity and barrier response. We applied statistical colocalisation to identify the overlap between data layers, and to explain molecular QTLs contribution to GWAS disease loci. ResultsWe used molecular QTLs to identify the regulatory features of 8,214 genes, representing 36% of all expressed genes in endothelial cells. We also identified the molecular mechanisms underlying 815 loci across 16 disease GWAS. These represent between 29% and 40% of all loci for each disease, compared to the previous average of 23%. This is due to the choice of a cell type often underrepresented in tissue level data, and the inclusion of isoform, splicing and chromatin activity datasets. Furthermore, we compared the endothelial cells molecular QTLs with similar datasets in monocytes, neutrophils and CD4 T lymphocytes to shed light on the interplay between the endothelial and the immune compartments in these diseases. We identified loci acting through both the endothelial and the immune compartment, mostly with the same directionality of effect, and endothelial specific ones. ConclusionsThis work expands the knowledge of the mechanisms and genes underlying the effect of common genetic variation on non-communicable diseases having endothelial dysfunction as a shared feature. It also illustrates the interplay between endothelial cells and immune cell types in these diseases, highlighting shared and unique pathways.

genomics↗

bioRxiv2025

SAFE-LD: A novel method for the estimation of linkage disequilibrium from summary statistics

Genome-wide association studies (GWAS) have greatly advanced our understanding of the genetic architecture of complex traits. Downstream analyses of GWAS summary statistics require accurate in-sample LD, the variant correlations in the same individuals used for the GWAS, as even small discrepancies can propagate into substantial error. In practice, privacy and consent restrictions prevent sharing of individual-level genotypes, forcing researchers either to rely on external reference panels, which reduce accuracy and power, or to store and distribute massive precomputed LD matrices that are inflexible and difficult to analyze. Here we introduce SAFE-LD (Shrinkage and Anonymisation Framework for LD Estimation), a novel method that produces pseudo-genotypes designed to reproduce the exact in-sample LD of a cohort, while discarding all individual-level genetic content. SAFELD surrogates can be stored in VCF/PGEN formats and used seamlessly with standard pipelines, providing LD estimates indistinguishable from the originals but free from privacy concerns. Using extensive simulations on UK Biobank data, we show that SAFE-LD is robust across genomic regions and population sizes. Notably, SAFE-LD achieves finemapping accuracy on par with internal LD, and significantly outperforms external LD even under best-case conditions with cohort-matched reference panels. We further extend this framework to existing GWAS summary statistics through SAFE-LDss, which exploits existing published summary statistics where numerous traits have been analyzed on the same samples. SAFE-LD offers a scalable, privacy-preserving, and highly accurate alternative to traditional LD estimation, enabling easy sharing and seamless utilization with standard tools. By storing compact pseudo-genotypes instead of massive precomputed LD matrices, it also provides a highly efficient solution in terms of disk space and data management, while safeguarding participant privacy and supporting precise fine-mapping.

genetics↗

bioRxiv2025

Replication of 10 novel loci involved in human plasma protein N-glycosylation using MALDI-MS and UHPLC-FD data

N-glycans are essential components of glycoproteins, influencing their properties and functions. While biochemical pathways of glycosylation are well-characterized, their genetic regulation remains poorly understood. This study utilizes matrix-assisted laser desorption/ionization-mass spectrometry (MALDI-MS) and ultra-high performance liquid chromatography-fluorescence detection (UHPLC-FD) to strengthen replication and further characterize previously identified genome-wide association signals for the total human plasma N-glycome (TPNG). Univariate and multivariate genetic association meta-analyses involved 3,385 samples across 143 N-glycome traits from the Hoorn Diabetes Care System and DiaGene cohorts as well as 3,224 samples across 117 N-glycome traits from TwinsUK, CEDAR, QMDiab and SABRE cohorts. We successfully replicated ten previously identified but not replicated glycosylation quantitative trait loci (glyQTLs) and prioritized five high-confidence putative causal genes, including the glycosyltransferase MGAT4B and inflammation-related genes - C3 and FCGR2B. The linkage-specific sialic acid derivatization in MALDI-MS enabled delineation of genetic effects on 2,3- and 2,6-sialylation. Mass spectrometry analysis also provided evidence for glucuronic acid-containing glycans in human blood plasma. These findings advance our understanding of the genetic regulation of protein N-glycosylation and highlight the complementarity of different analytical approaches in glycomics research.

genetics↗

bioRxiv2019

Analysis of genetically independent phenotypes identifies shared genetic factors associated with chronic musculoskeletal pain at different anatomic sites

Chronic musculoskeletal pain has a negative impact on all aspects of human life. Genetic studies of pain are complicated by the high complexity and heterogeneity of pain phenotypes. In this research, we aimed to reduce phenotype heterogeneity and reveal genes and pathways shared by chronic musculoskeletal pain at four locations: back, neck/shoulder, hip, and knee. Our study was based on the results of genome-wide association studies performed using UK Biobank data with a total sample size of 456,000 individuals. We applied principal component analysis based on the matrix of genetic covariances between the studied pain traits and constructed four genetically independent phenotypes (GIPs). The leading GIP (GIP1) explains the largest proportion of the genetic variance of and covariance between the analyzed phenotypes (78.4%), and the later GIPs (GIP2-4) explain progressively less. We identified and replicated five loci associated with GIP1 and one locus associated with GIP2. The genes decisively prioritized for the GIP1-associated loci were SLC39A8, ECM1, and FOXP2. For the remaining two GIP1-associated loci, we proposed several candidates (AMIGO3, BSN, RBM6, FAM212A, RNF123, UBA7 and MIR7114, NSMF, NOXA1, GRIN1), but were unable to prioritize any of them convincingly. The most likely causal gene in the locus associated with GIP2 was GDF5. For GIP1, gene set/tissue/cell type enrichment analyses identified multiple terms related to the nervous system. Genetic correlations analysis revealed a genetic overlap between GIP1 and osteoarthritis as well as a set of anthropometric (such as overweight and waist circumference), sociodemographic (such as age of first birth and college completion) and psychiatric/personality (such as depressive symptoms and neuroticism) traits. We suggest that GIP1 represents a biopsychological component of chronic musculoskeletal pain, related to physiological and psychological aspects and likely reflecting pain perception and processing.

genomics↗

bioRxiv2018

Defining the genetic control of human blood plasma N-glycome using genome-wide association study

Glycosylation is a common post-translational modification of proteins. It is known, that glycans are directly involved in the pathophysiology of every major disease. Defining genetic factors altering glycosylation may provide a basis for novel approaches to diagnostic and pharmaceutical applications. Here, we report a genome-wide association study of the human blood plasma N-glycome composition in up to 3811 people. We discovered and replicated twelve loci. This allowed us to demonstrate a clear overlap in genetic control between total plasma and IgG glycosylation. Majority of loci contained genes that encode enzymes directly involved in glycosylation (FUT3/FUT6, FUT8, B3GAT1, ST6GAL1, B4GALT1, ST3GAL4, MGAT3, and MGAT5). We, however, also found loci that are likely to reflect other, more complex, aspects of plasma glycosylation process. Functional genomic annotation suggested the role of DERL3, which potentially highlights the role of glycoprotein degradation pathway, and such transcription factor as IKZF1.

genetics↗

bioRxiv2018

Identification of 12 genetic loci associated with human healthspan

The mounting challenge of preserving the quality of life in an aging population directs the focus of longevity science to the regulatory pathways controlling healthspan. To understand the nature of the relationship between the healthspan and lifespan and uncover the genetic architecture of the two phenotypes, we studied the incidence of major age-related diseases in the UK Biobank (UKB) cohort. We observed that the incidence rates of major chronic diseases increase exponentially. The risk of disease acquisition doubled approximately every eight years, i.e., at a rate compatible with the doubling time of the Gompertz mortality law. Assuming that aging is the single underlying factor behind the morbidity rates dynamics, we built a proportional hazards model to predict the risks of the diseases and therefore the age corresponding to the end of healthspan of an individual depending on their age, gender, and the genetic background. We suggested a computationally efficient procedure for the determination of the effect size and statistical significance of individual gene variants associations with healthspan in a form suitable for a Genome-Wide Association Studies (GWAS). Using the UKB sub-population of 300,447 genetically Caucasian, British individuals as a discovery cohort, we identified 12 loci associated with healthspan and reaching the whole-genome level of significance. We observed strong (|{rho}g| > 0.3) genetic correlations between healthspan and the incidence of specific age-related disease present in our healthspan definition (with the notable exception of dementia). Other examples included all-cause mortality (as derived from parental survival, with{rho} g = -0.76), life-history traits (metrics of obesity, age at first birth), levels of different metabolites (lipids, amino acids, glycemic traits), and psychological traits (smoking behaviour, cognitive performance, depressive symptoms, insomnia). We conclude by noting that the healthspan phenotype, suggested and characterized here, offers a promising new way to investigate human longevity by exploiting the data from genetic and clinical data on living individuals.

epidemiology↗