Biology subjects

Aulchenko, Y. S.

Publications and source records attributed to Aulchenko, Y. S..

9 recordsLinked to original sources

bioRxiv2026

The Human Pleiotropic Map of GWAS Associations and Therapeutic Implications

Genetic support for drug targets substantially increases clinical success rates, establishing genome-wide association studies (GWAS) as central to therapeutic hypothesis generation. However, the same genetic evidence that reveals causal gene-disease relationships simultaneously exposes organism-level safety liabilities--a dimension requiring principled, genome-wide quantification. Here we systematically analyse 100,526 GWAS to yield 789,453 credible sets and gene prioritisations for 15,641 genes, with discovery showing no saturation as GWAS expand and increase diversity. We find that 64% of GWAS-implicated genes are pleiotropic, associated with traits across multiple diseases and showing a non-linear relationship between the degree of pleiotropy and clinical success. Highly pleiotropic genes--concentrated in immune, inflammatory, and oncogenic signalling programmes--are enriched in safety-terminated clinical programmes, mouse lethal knockouts, and cancer driver genes, establishing gene-level pleiotropy as a potential measure of genetically-informed organism-level safety liability. Protein-altering variant (PAV) support amplifies therapeutic signal (OR = 6.0), yet PAV targets show higher average pleiotropy, introducing a competing safety liability. Combining PAV support with intermediate pleiotropy (2-5 therapeutic areas) resolves this tension, yielding OR = 10.3 and relative success = 4.8--a profile already satisfied by 52 approved therapies. As GWAS continue to expand in scale and resolution, these findings lay the groundwork for increasingly sophisticated target discovery strategies that yield safer and more effective therapeutic hypotheses.

genomics↗

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

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↗

bioRxiv2021

Identification of tissue-specific and common methylation quantitative trait loci in healthy individuals using MAGAR

BackgroundUnderstanding the influence of genetic variants on DNA methylation is fundamental for the interpretation of epigenomic data in the context of disease. There is a need for systematic approaches not only for determining methylation quantitative trait loci (methQTL) but also for discriminating general from cell-type-specific effects. ResultsHere, we present a two-step computational framework MAGAR, which fully supports identification of methQTLs from matched genotyping and DNA methylation data, and additionally the identification of quantitative cell-type-specific methQTL effects. In a pilot analysis, we apply MAGAR on data in four tissues (ileum, rectum, T-cells, B-cells) from healthy individuals and demonstrate the discrimination of common from cell-type-specific methQTLs. We experimentally validate both types of methQTLs in an independent dataset comprising additional cell types and tissues. Finally, we validate selected methQTLs (PON1, ZNF155, NRG2) by ultra-deep local sequencing. In line with previous reports, we find cell-type-specific methQTLs to be preferentially located in enhancer elements. ConclusionsOur analysis demonstrates that a systematic analysis of methQTLs provides important new insights on the influences of genetic variants to cell-type-specific epigenomic variation.

genetics↗

bioRxiv2021

Same role but different actors: genetic regulation of post-translational modification of two distinct proteins

Post-translational modifications (PTMs) diversify protein functions and dynamically coordinate their signalling networks, influencing most aspects of cell physiology. Nevertheless, their genetic regulation or influence on complex traits is not fully understood. Here, we compare for the first time the genetic regulation of the same PTM of two proteins - glycosylation of transferrin and immunoglobulin G (IgG). By performing genome-wide association analysis of transferrin glycosylation, we identified 10 significantly associated loci, all novel. Comparing these with IgG glycosylation-associated genes, we note protein-specific associations with genes encoding glycosylation enzymes (transferrin - MGAT5, ST3GAL4, B3GAT1; IgG - MGAT3, ST6GAL1) as well as shared associations (FUT6, FUT8). Colocalisation analyses of the latter suggest that different causal variants in the FUT genes regulate fucosylation of the two proteins. We propose that they affect the binding of different transcription factors in different tissues, with fucosylation of IgG being regulated by IKZF1 in B-cells and of transferrin by HNF1A in liver.

genomics↗

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

Insight into the genetic architecture of back pain and its risk factors from a study of 509,000 individuals

Back pain (BP) is a common condition of major social importance and poorly understood pathogenesis. Combining data from the UK Biobank and CHARGE consortium cohorts allowed us to perform a very large GWAS (total N = 509,070) and examine the genetic correlation and pleiotropy between BP and its clinical and psychosocial risk factors. We identified and replicated three BP associated loci, including one novel region implicating SPOCK2/CHST3 genes. We provide evidence for pleiotropic effects of genetic factors underlying BP, height, and intervertebral disc problems. We also identified independent genetic correlations between BP and depression symptoms, neuroticism, sleep disturbance, overweight, and smoking. A significant enrichment for genes involved in central nervous system and skeletal tissue development was observed. The study of pleiotropy and genetic correlations, supported by the pathway analysis, suggests at least two strong molecular axes of BP genesis, one related 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 with the current biopsychosocial model as a paradigm for BP. Overall, the results demonstrate BP to have an extremely complex genetic architecture that overlaps with the genetic predisposition to its biopsychosocial risk factors. The work sheds light on pathways of relevance in the prevention and management of LBP.

genetics↗

bioRxiv2018

Genome-wide Meta-analysis of 158,000 Individuals of European Ancestry Identifies Three Loci Associated with Chronic Back Pain

OBJECTIVESTo conduct a genome-wide association study (GWAS) meta-analysis of chronic back pain (CBP).\n\nMETHODSAdults of European ancestry were included from 16 cohorts in Europe and North America. CBP cases were defined as those reporting back pain present for >3-6 months; non-cases were included as comparisons (\"controls\"). Each cohort conducted genotyping using commercially available arrays followed by imputation. GWAS used logistic regression models with additive genetic effects, adjusting for age, sex, study-specific covariates, and population substructure. The threshold for genome-wide significance in the fixed-effect inverse-variance weighted meta-analysis was p<5x10-8. Suggestive (p<5x10-7) and genome-wide significant (p<5x10-8) variants were carried forward for replication or further investigation in an independent sample.\n\nRESULTSThe discovery sample was comprised of 158,025 individuals, including 29,531 CBP cases. A genome-wide significant association was found for the intronic variant rs12310519 in SOX5 (OR 1.08, p=7.2x10-10). This was subsequently replicated in an independent sample of 283,752 subjects, including 50,915 cases (OR 1.06, p=5.3x10-11), and exceeded genome-wide significance in joint meta-analysis (0R=1.07, p=4.5x10-19). We found suggestive associations at three other loci in the discovery sample, two of which exceeded genome-wide significance in joint meta-analysis: an intergenic variant, rs7833174, located between CCDC26 and GSDMC (OR 1.05, p=4.4x10-13), and an intronic variant, rs4384683, in DCC (OR 0.97, p=2.4x10-10).\n\nDISCUSSIONIn this first reported meta-analysis of GWAS for CBP, we identified and replicated a genetic locus associated with CBP (SOX5). We also identified 2 other loci that reached genome-wide significance in a 2-stage joint meta-analysis (CCDC26/GSDMC and DCC).

genomics↗

bioRxiv2016

Network based conditional genome wide association analysis of human metabolomics

BackgroundGenome-wide association studies (GWAS) have identified hundreds of loci influencing complex human traits, however, their biological mechanism of action remains mostly unknown. Recent accumulation of functional genomics ( omics) including metabolomics data opens up opportunities to provide a new insight into the functional role of specific changes in the genome. Functional genomic data are characterized by high dimensionality, presence of (strong) statistical dependencies between traits, and, potentially, complex genetic control. Therefore, analysis of such data asks for development of specific statistical genetic methods.\n\nResultsWe propose a network-based, conditional approach to evaluate the impact of genetic variants on omics phenotypes (conditional GWAS, cGWAS). For each trait of interest, based on biological network, we select a set of other traits to be used as covariates in GWAS. The network could be reconstructed either from biological pathway databases or directly from the data. We evaluated our approach using data from a population-based KORA study (n=1,784, 1.7 M SNPs) with measured metabolomics data (151 metabolites) and demonstrated that our approach allows for identification of up to five additional loci not detected by conventional GWAS. We show that this gain in power is achieved through increased precision of genetic effect estimates, and in presence of specific contra-intuitive pleiotropic scenarios (when genetic and environmental sources of covariance are acting in opposite manner). We justify existence of such scenarios, and discuss possible applications of our method beyond metabolomics.\n\nConclusionsWe demonstrate that in context of metabolomics network-based, conditional genome-wide association analysis is able to dramatically increase power of identification of loci with specific contra-intuitive pleiotropic architecture. Our method has modest computational costs, can utilize summary level GWAS data, and is applicable to other omics data types. We anticipate that application of our method to new and existing data sets will facilitate progress in understanding genetic bases of control of molecular and complex phenotypes.\n\nShort abstractWe propose a network-based, conditional approach for genome-wide analysis of multivariate omics phenotypes. Our methods can incorporate prior biological knowledge about biological pathways from external sources. We evaluated our approach using metabolomics data and demonstrated that our approach has bigger power and allows for identification of additional loci. We show that gain in power is achieved through increased precision of genetic effect estimates, and in presence of specific contra-intuitive pleiotropic scenarios (when genetic and environmental sources of covariance are acting in opposite manner). We justify existence of such scenarios, and discuss possible applications of our method beyond metabolomics.

genomics↗