10 research outputs found
Peter Berger and his sociocultural-phenomenological research
No full textThe phenomenological paradigm in sociocultural research is the relay race of Husserl — Schütz — Luckmann and Berger. Despite the first difference between sociology and phenomenology, the emphasis on design, biography, historical context, subjectivity and experience only complement quantitative research with the necessary quality of humanism. Today, when technocratic line is becoming a leading trend, when people talk about neuro-turnaround in science and social practices, phenomenology must be given credit for its courage in sociocultural subjectivity and the actualization of the philosophy of consciousness. Scientometric absorption of the subject is a dangerous way of deflation of philosophy, its reduction to the functional support of the brain-machine interface.
The sociocultural phenomenologist Peter Berger (1929–2017) died a year after the demise of his and co-author Thomas Luckmann. Last year there was also jubilee of the founder of phenomenology, Edmund Husserl, who turned 160 years old. The scientometric absorption of the subject is a dangerous way of deflation of philosophy, its reduction to the functional support of the brain-machine interface. The study of the heritage of P. Berger in this regard allows us to proceed to the efficient processing of Husserl’s ideas in the field of describing the valuesemantic world of society and culture.
The author proceeds with the study of the model of the socio-cultural and anthropological world, constructed by Peter Ludwig Berger. The subject of the research is the theoretical framework of the phenomenology of society and culture. The main provisions of Berger’s sociocultural phenomenology are: 1) secularization has a heterogeneous porous structure, 2) under capitalism, transcendence is possible as a personal spiritual practice; 3) pluralism of social orders and globalization are the basis for restrained forecasts regarding the society of the future; 4) the clash of bureaucracy and the private is removed by the daily routine of meaning generation. Pursuing issues of the privatization of religion, the theory of modernization, the sociology of knowledge, Berger’s sociocultural phenomenology turns everyday life into a fascinating scientific quest. He easily moves from concrete to abstract and vice versa, but does not throw the reader into the abyss of lifeless ideas. At the same time, the sociologist makes it clear that he is ready to change his mind, he does not close us in a rigid configuration of ideas, yet places the reader in the bootstrap reality.
Berger remained in phenomenological position, describing social structures in terms of construction, typification, collective understanding, legitimization of social memory, horizons of reality, habitualization of meanings, reification of meanings, objectification of the lifeworld of utopias. Main conclusions. The sociocultural phenomenology of P. Berger allows you to value-correlate the sacrifices made by capitalism and communism to build a social order. His phenomenology is the method of contextual correlation of different social worlds — science and religion, secular and transcendental, personal and collective. Bergerian sociocultural subjectivism opposes the reduction of philosophy to the information support of a technogenic society and the maintenance of science
Twelve Years of Genome-Wide Association Studies of Human Protein N-Glycosylation
No full textMost human-secreted and membrane-bound proteins have covalently attached oligosaccharide chains or glycans. Glycosylation influences the physical and chemical properties of proteins, as well as their biological functions. Unsurprisingly, alterations in protein glycosylation have been implicated in a growing number of human diseases, and glycans are increasingly being considered as potential therapeutic targets, an essential part of therapeutics, and biomarkers. Although glycosylation pathways are biochemically well-studied, little is known about the networks of genes that guide the cell- and tissue-specific regulation of these biochemical reactions in humans in vivo. The lack of a detailed understanding of the mechanisms regulating glycome variation and linking the glycome to human health and disease is slowing progress in clinical applications of human glycobiology. Two of the tools that can provide much sought-after knowledge of human in vivo glycobiology are human genetics and genomics, which offer a powerful data-driven agnostic approach for dissecting the biology of complex traits. This review summarizes the current state of human populational glycogenomics. In Section 1, we provide a brief overview of the N-glycan’s structural organization, and in Section 2, we give a description of the major blood plasma glycoproteins. Next, in Section 3, we summarize, systemize, and generalize the results from current N-glycosylation genome-wide association studies (GWASs) that provide novel knowledge of the genetic regulation of the populational variation of glycosylation. Until now, such studies have been limited to an analysis of the human blood plasma N-glycome and the N-glycosylation of immunoglobulin G and transferrin. While these three glycomes make up a rather limited set compared with the enormous multitude of glycomes of different tissues and glycoproteins, the study of these three does allow for powerful analysis and generalization. Finally, in Section 4, we turn to genes in the established loci, paying particular attention to genes with strong support in Section 5. At the end of the review, in Sections 6 and 7, we describe special cases of interest in light of new discoveries, focusing on possible mechanisms of action and biological targets of genetic variation that have been implicated in human protein N-glycosylation
Data for paper: Sharapov et al (2025) "A genome-wide association study in 10,000 individuals links plasma N-glycome to liver disease and anti-inflammatory proteins"
No full textThe dataset contains results of genome-wide association study of human blood plasma glycome. The TPNG_GWAMA_repl_part_aa contains summary statistics for 117 glycome traits from the replication GWAMA (Genome-Wide Association Meta-Analysis) conducted on participants, totaling 3,224 individuals. The 117 files contain association summary statistics for 117 glycome traits, of which 36 were directly measured by UHPLC technology and 81 were derived glycome traits. Funding The work of S.Sh., A.T., D.M., A.S., Y.S.A. was supported by the Research Program at the Moscow State University (MSU) Institute for Artificial Intelligence. The study was conducted using the UK Biobank resource under application #59345. The work of E.E., Y.A.T was supported by the budget project of the Institute of Cytology and Genetics FWNR-2022-0020. European Community’s Seventh Framework Programme funded project PainOmics (602736). TwinsUK is funded by the Wellcome Trust, Medical Research Council, Versus Arthritis, European Union Horizon 2020, Chronic Disease Research Foundation (CDRF), Zoe Ltd and the National Institute for Health Research (NIHR) Clinical Research Network (CRN) and Biomedical Research Centre based at Guy’s and St Thomas’ NHS Foundation Trust in partnership with King’s College London. The TwinsUK Study was approved by London-Westminster Research Ethics Committee (REC reference EC04/015), and Guy’s and St Thomas’ NHS Foundation Trust Research and Development (R&D). The TwinsUK BioBank was approved by the HRA - Liverpool East Research Ethics Committee (REC reference 19/NW/0187), IRAS ID 258513. Glycan analysis performed in Genos was supported by Horizon Europe grants GlycanSwitch (ERC Synergy grant # 101071386), INITIALISE (grant # 101094099) and SynHealth (grant #101159018). All participants provide written, informed consent. We thank Toma Keser, Mirna Šimurina, Marija Vilaj, Jerko Štambuk, Ivan Gudelj, Thomas S. Klarić, Jasminka Krištić, Jelena Šimunović, Julija Jurić, Ana Momčilović, Najda Rudman, and Maja Hanić for their assistance with glycan analysis. Headers rs_id - dbSNP ID. chr - Chromosome number where the SNP is located. bp - Base pair position of the SNP on the chromosome (build GRCh37/hg19). ea - Effect allele. ra - Reference allele. eaf - Effect allele frequency. af_ref - Frequency of the reference allele. beta - Effect size estimate. se - Standard error of the effect size estimate. p - P-value for the association test. n - Sample size used in the analysis. z - Z-score for the association test. info - Imputation information score.Sharapov, S., Timoshchuk, A., Zaytseva, O., Maslov, D., Soplenkova, A., Elgaeva, E., Tiys, E., Mangino, M., Wittenbecher, C., Karssen, L. C., Timofeeva, M., Nostaeva, A., Vučković, F., Trbojevic Akmacic, I., Štambuk, T., Feoktistova, S., Potapova, N., Voroshilova, V., Williams, F., … Aulchenko, Y. (2025). Data for paper: Sharapov et al (2025) "A genome-wide association study in 10,000 individuals links plasma N-glycome to liver disease and anti-inflammatory proteins" [Data set]. Zenodo. https://doi.org/10.5281/zenodo.1516194
Multivariate Genome-Wide Association Study of Concentrations of Seven Elements in Seeds Reveals Four New Loci in Russian Wheat Lines
No full textWheat is a cereal grain that plays an important role in the world’s food industry. The identification of the loci that change the concentration of elements in wheat seeds is an important challenge nowadays especially for genomic selection and breeding of novel varieties. In this study, we performed a multivariate genome-wide association study (GWAS) of the seven traits—concentrations of Zn, Mg, Mn, Ca, Cu, Fe, and K in grain—of the Russian collection of common wheat Triticum aestivum (N = 149 measured in two years in two different fields). We replicated one known locus associated with the concentration of Zn (IAAV1375). We identified four novel loci—BS00022069_51 (associated with concentrations of Ca and K), RFL_Contig6053_3082 (associated with concentrations of Fe and Mn), Kukri_rep_c70864_329 (associated with concentrations of all elements), and IAAV8416 (associated with concentrations of Fe and Mn)—three of them were located near the genes TraesCS6A02G375400, TraesCS7A02G094800, and TraesCS5B02G325400. Our result adds novel information on the loci involved in wheat grain element contents and may be further used in genomic selection
Fast and Simple Protocol for N-Glycome Analysis of Human Blood Plasma Proteome
No full textN-glycome analysis of individual proteins and tissues is crucial for fundamental and applied biomedical research and medical diagnosis and plays an important role in the evaluation of the quality of biopharmaceutical and biotechnological products. The interest in this research area continues to grow annually, thereby increasing the demand for the high-throughput profiling of human blood plasma N-glycome. In response to this need, we have developed an optimized, simple, and rapid protocol for the N-glycome profiling of human plasma proteins. This protocol encompasses the entire analysis cycle, from plasma isolation to N-glycan spectrum quantification. While the proposed method may have lower efficiency compared to already published high-throughput methods, its adaptability makes it suitable for implementation in virtually any molecular biological laboratory
Decomposing the genetic background of chronic back pain
No full textChronic back pain (CBP) is a disabling condition with a lifetime prevalence of 40% and a substantial socioeconomic burden. Because of the high heterogeneity of CBP, subphenotyping may help to improve prediction and support personalized treatment of CBP. To investigate CBP subphenotypes, we decomposed its genetic background into a shared one common to other chronic pain conditions (back, neck, hip, knee, stomach, and head pain) and unshared genetic background specific to CBP. We identified and replicated 18 genes with shared impact across different chronic pain conditions and two genes that were specific for CBP. Among people with CBP, we demonstrated that polygenic risk scores accounting for the shared and unshared genetic backgrounds of CBP may underpin different CBP subphenotypes. These subphenotypes are characterized by varying genetic predisposition to diverse medical conditions and interventions such as diabetes mellitus, myocardial infarction, diagnostic endoscopic procedures, and surgery involving muscles, bones, and joints.</p
Data for paper: Sharapov et al (2025) "A genome-wide association study in 10,000 individuals links plasma N-glycome to liver disease and anti-inflammatory proteins" (Replication GWAMA, N=3224, part 1)
No full textThe dataset contains results of genome-wide association study of human blood plasma glycome. The TPNG_GWAMA_repl_part_aa contains summary statistics for 117 glycome traits from the replication GWAMA (Genome-Wide Association Meta-Analysis) conducted on participants, totaling 3,224 individuals. The 117 files contain association summary statistics for 117 glycome traits, of which 36 were directly measured by UHPLC technology and 81 were derived glycome traits. Funding The work of S.Sh., A.T., D.M., A.S., Y.S.A. was supported by the Research Program at the Moscow State University (MSU) Institute for Artificial Intelligence. The study was conducted using the UK Biobank resource under application #59345. The work of E.E., Y.A.T was supported by the budget project of the Institute of Cytology and Genetics FWNR-2022-0020. European Community’s Seventh Framework Programme funded project PainOmics (602736). TwinsUK is funded by the Wellcome Trust, Medical Research Council, Versus Arthritis, European Union Horizon 2020, Chronic Disease Research Foundation (CDRF), Zoe Ltd and the National Institute for Health Research (NIHR) Clinical Research Network (CRN) and Biomedical Research Centre based at Guy’s and St Thomas’ NHS Foundation Trust in partnership with King’s College London. The TwinsUK Study was approved by London-Westminster Research Ethics Committee (REC reference EC04/015), and Guy’s and St Thomas’ NHS Foundation Trust Research and Development (R&D). The TwinsUK BioBank was approved by the HRA - Liverpool East Research Ethics Committee (REC reference 19/NW/0187), IRAS ID 258513. Glycan analysis performed in Genos was supported by Horizon Europe grants GlycanSwitch (ERC Synergy grant # 101071386), INITIALISE (grant # 101094099) and SynHealth (grant #101159018). All participants provide written, informed consent. We thank Toma Keser, Mirna Šimurina, Marija Vilaj, Jerko Štambuk, Ivan Gudelj, Thomas S. Klarić, Jasminka Krištić, Jelena Šimunović, Julija Jurić, Ana Momčilović, Najda Rudman, and Maja Hanić for their assistance with glycan analysis. Headers rs_id - dbSNP ID. chr - Chromosome number where the SNP is located. bp - Base pair position of the SNP on the chromosome (build GRCh37/hg19). ea - Effect allele. ra - Reference allele. eaf - Effect allele frequency. af_ref - Frequency of the reference allele. beta - Effect size estimate. se - Standard error of the effect size estimate. p - P-value for the association test. n - Sample size used in the analysis. z - Z-score for the association test. info - Imputation information score. Harmonized code Trait Description PGP1 FA2 The percentage of FA2 PGP2 FA2B The percentage of FA2B PGP3 A2BG1 The percentage of A2BG1 PGP4 FA2G1 The percentage of FA2G1 PGP5 FA2G1 The percentage of FA2G1 PGP6 FA2BG1 The percentage of FA2BG1 PGP7 M6 The percentage of M6 PGP8 A2G2 The percentage of A2G2 PGP9 A2BG2 The percentage of A2BG2 PGP10 FA2G2 The percentage of FA2G2 PGP11 FA2BG2 The percentage of FA2BG2 PGP12 M7 + A2G2S1 The percentage of M7+A2G2S1 PGP13 FA2G1S1 The percentage of FA2G1S1 PGP14 A2G2S1 The percentage of A2G2S1+A2G2S1 PGP15 FA2G2S1 The percentage of FA2G2S1 PGP16 FA2BG2S1 The percentage of FA2BG2S1 PGP17 A2G2S2 The percentage of A2G2S2 PGP18 M9 The percentage of M9 PGP19 A2G2S2 The percentage of A2G2S2 PGP20 FA2G2S2 The percentage of FA2G2S2 PGP21 FA2BG2S2 The percentage of FA2BG2S2 PGP22 A3G3S2 The percentage of A3G3S2 PGP23 A3G3S2 The percentage of A3G3S2 PGP24 A3F1G3S2 The percentage of A3F1G3S2 PGP25 A3G3S3 The percentage of A3G3S3 PGP26 A3G3S3 The percentage of A3G3S3 PGP27 A3G3S3 The percentage of A3G3S3 PGP28 FA3G3S3 The percentage of FA3G3S3 PGP29 A3G3S3 The percentage of A3G3S3 PGP30 A3F1G3S3 The percentage of A3F1G3S3 PGP31 FA3G3S3 The percentage of FA3G3S3 PGP32 FA3F1G3S3 The percentage of FA3F1G3S3 PGP33 A4G4S3 The percentage of A4G4S3 PGP34 A4G4S4 The percentage of A4G4S4 PGP35 A4G4S4 The percentage of A4G4S4 PGP36 A4F1G4S4 The percentage of A4F1G4S4 PGP37 FGS/(FG+FGS) The percentage of sialylation of core-fucosylated galactosylated structures without bisecting GlcNAc PGP38 FBGS/(FBG+FBGS) The percentage of sialylation of core-fucosylated galactosylated structures with bisecting GlcNAc PGP39 FGS/(F+FG+FGS) The percentage of sialylation of all core-fucosylated structures without bisecting GlcNAc PGP40 FBGS/(FB+FBG+FBGS) The percentage of sialylation of all core-fucosylated structures with bisecting GlcNAc PGP41 FG1S1/(FG1+FG1S1) The percentage of monosialylation of core-fucosylated monogalactosylated structures without bisecting GlcNAc PGP42 FG2S1/(FG2+FG2S1+FG2S2) The percentage of monosialylation of core-fucosylated digalactosylated structures without bisecting GlcNAc PGP43 FG2S2/(FG2+FG2S1+FG2S2) The percentage of disialylation of core-fucosylated digalactosylated structures without bisecting GlcNAc PGP44 FBG2S1/(FBG2+FBG2S1+FBG2S2) The percentage of monosialylation of core-fucosylated digalactosylated structures with bisecting GlcNAc PGP45 FBG2S2/(FBG2+FBG2S1+FBG2S2) The percentage of disialylation of core-fucosylated digalactosylated structures with bisecting GlcNAc PGP46 FtotalS1/FtotalS2 Ratio of all fucosylated monosialylated and disialylated structures (+/- bisecting GlcNAc) PGP47 FS1/FS2 Ratio of fucosylated monosialylated and disialylated structures (without bisecting GlcNAc) PGP48 FBS1/FBS2 Ratio of fucosylated monosialylated and disialylated structures (with bisecting GlcNAc) PGP49 FtotalS1/FtotalS3 Ratio of all core-fucosylated monosialylated and trisialylated structures (+/- bisecting GlcNAc) PGP50 FS1/FS3 Ratio of core-fucosylated monosialylated and trisialylated structures (without bisecting GlcNAc) PGP51 FtotalS2/FtotalS3 Ratio of all core-fucosylated disialylated and trisialylated structures (+/- bisecting GlcNAc) PGP52 FS2/FS3 Ratio of core-fucosylated disialylated and trisialylated structures (without bisecting GlcNAc) PGP53 FBStotal/FStotal Ratio of all core-fucosylated sialylated structures with and without bisecting GlcNAc PGP54 FBS1/FS1 Ratio of fucosylated monosialylated structures with and without bisecting GlcNAc PGP55 FBS1/(FS1+FBS1) The incidence of bisecting GlcNAc in all fucosylated monosialylated structures PGP56 FBS2/FS2 Ratio of fucosylated disialylated structures with and without bisecting GlcNAc PGP57 FBS2/(FS2+FBS2) The incidence of bisecting GlcNAc in all fucosylated disialylated structures PGP58 FA2n The percentage of FA2 in total neutral plasma glycans (GPn) PGP59 FA2Bn The percentage of FA2B in total neutral plasma glycans (GPn) PGP60 A2BG1n The percentage of A2BG1 in total neutral plasma glycans (GPn) PGP61 FA2G1n The percentage of FA2G1 in total neutral plasma glycans (GPn) PGP62 FA2G1n The percentage of FA2G1 in total neutral plasma glycans (GPn) PGP63 FA2BG1n The percentage of FA2BG1 in total neutral plasma glycans (GPn) PGP64 M6n The percentage of M6 in total neutral plasma glycans (GPn) PGP65 A2G2n The percentage of A2G2 in total neutral plasma glycans (GPn) PGP66 A2BG2n The percentage of A2BG2 in total neutral plasma glycans (GPn) PGP67 FA2G2n The percentage of FA2G2 in total neutral plasma glycans (GPn) PGP68 FA2BG2n The percentage of FA2BG2 in total neutral plasma glycans (GPn) PGP69 M9n The percentage of M9 in total neutral plasma glycans (GPn) PGP70 G0n The percentage of agalactosylated structures in total neutral plasma glycans PGP71 G1n The percentage of monogalactosylated structures in total neutral plasma glycans PGP72 G2n The percentage of digalactosylated structures in total neutral plasma glycans PGP73 Fn total The percentage of all fucosylated structures (+/- bisecting GlcNAc) in total neutral plasma glycans PGP74 FG1n total/G1n The percentage of fucosylation of monogalactosylated structures in total neutral plasma glycans PGP75 FG2n total /G2n The percentage of fucosylation of digalactosylated structures in total neutral plasma glycans PGP76 Fn The percentage of fucosylated structures (without bisecting GlcNAc) in total neutral plasma glycans PGP77 FG0n/G0n The percentage of fucosylation of agalactosylated structures (without bisecting GlcNAc) in total neutral plasma glycans PGP78 FG1n/G1n The percentage of fucosylation of monogalactosylated structures (without bisecting GlcNAc) in total neutral plasma glycans PGP79 FG2n/G2n The percentage of fucosylation of digalactosylated structures (without bisecting GlcNAc) in total neutral plasma glycans PGP80 FBn The percentage of fucosylated structures (with bisecting GlcNAc) in total neutral plasma glycans PGP81 FBG0n/G0n The percentage of fucosylation of agalactosylated structures (with bisecting GlcNAc) in total neutral plasma glycans PGP82 FBG1n/G1n The percentage of fucosylation of monogalactosylated structures (with bisecting GlcNAc) in total neutral plasma glycans PGP83 FBG2n/G2n The percentage of fucosylation of digalactosylated structures (with bisecting GlcNAc) in total neutral plasma glycans PGP84 FBn/Fn Ratio of fucosylated structures with and without bisecting GlcNAc in total neutral plasma glycans PGP85 FBn/Fn total The incidence of bisecting GlcNAc in all fucosylated structures in total neutral plasma glycans PGP86 Fn/(Bn+FBn) Ratio of fucosylated non-bisecting GlcNAc structures and all structures with bisecting GlcNAc in total neutral plasma glycans PGP87 Bn/(Fn+FBn) Ratio of afucosylated structures with bisecting GlcNAc and all fucosylated structures (+/- bisecting GlcNAc) in total neutral plasma glycans PGP88 FBG2n/FG2n Ratio of fucosylated digalactosylated structures with and without bisecting GlcNAc in total neutral plasma glycans PGP89 FBG2n /(FG2n+FBG2n ) The incidence of bisecting GlcNAc in all fucosylated digalactosylated structures in total neutral plasma glycans PGP90 FG2n/(BG2n+FBG2n) Ratio of fucosylated digalactosylated non-bisecting GlcNAc structures and all digalactosylated structures with bisecting GlcNAc in total neutral plasma glycans PGP91 BG2n/(FG2n+FBG2n) Ratio of afucosylated digalactosylated structures with bisecting GlcNAc and all fucosylated digalactosylated structures (+/- bisecting GlcNAc) in total neutral plasma glycans PGP92 FUC-A The percentage of antennary fucosylated structures in total plasma glycans PGP93 FUC-C The percentage of core fucosylated structures in total plasma glycans PGP94 S0total The percentage of neutral glycan structures in total plasma glycans PGP95 S1total the percentage of monosialylated structures in total plasma glycans PGP96 S2total the percentage of bisialylated structures in total plasma glycans PGP97 S3total the percentage of trisialylated structures in total plasma glycans PGP98 S4total the percentage of tetrasialylated structures in total plasma glycans PGP99 G0total The percentage of agalactosylated structures in total plasma glycans PGP100 G1total The percentage of monogalactosylated structures in total plasma glycans PGP101 G2total The percentage of digalactosylated structures in total plasma glycans PGP102 G3total The percentage of trigalactosylated structures in total plasma glycans PGP103 G4total The percentage of tetragalactosylated structures in total plasma glycans PGP104 A2total The percentage of biantennary structures in total plasma glycans PGP105 A3total The percentage of triantennary structures in total plasma glycans PGP106 A4total The percentage of tetraantennary structures in total plasma glycans PGP107 Mtotal The percentage of high-mannose structures in total plasma glycans PGP108 Btotal The percentage of glycan structures with bisecting GlcNAc in total plasma glycans PGP109 G3S2/G3S3 Ratio of disialylated and trisialylated trigalactosylated structures PGP110 G4S3/G4S4 Ratio of trisialylated and tetrasialylated tetragalactosylated structures PGP111 FG3/G3total The percentage of core-fucosylation of trigalactosylated structures PGP112 G3Fa/G3total The percentage of antennary-fucosylation of trigalactosylated structures PGP113 G4Fa/G4total The percentage of antennary-fucosylation of tetragalactosylated structures PGP114 M7n The percentage of M7 in total neutral plasma glycans (GPn) PGP115 Stotal The percentage of sialylated structures in total plasma glycans PGP116 Gtotal The percentage of galactosylated structures in total plasma glycans PGP117 Gntotal The percentage of galactosylated structures in total neutral plasma glycan
A genome-wide association study in 10,000 individuals links plasma N-glycome to liver disease and anti-inflammatory proteins.
No full textpeer reviewedMore than a half of plasma proteins are N-glycosylated. Most of them are synthesized, glycosylated, and secreted to the bloodstream by liver and lymphoid tissues. While associations with N-glycosylation are implicated in the rising number of liver, cardiometabolic, and immune diseases, little is known about the genetic regulation of this process. Here, we performed the largest genome-wide association study of N-glycosylation of the blood plasma proteome in 10,000 individuals. We doubled the number of genetic loci known to be associated with blood N-glycosylation by identifying 16 novel loci and prioritizing 13 novel genes contributing to N-glycosylation. Among these were the GCKR, TRIB1, HP, SERPINA1 and CFH genes. These genes are predominantly expressed in the liver and show a previously unknown genetic link between plasma protein N-glycosylation, metabolic and liver diseases, and inflammatory response. By integrating glycomics, proteomics, transcriptomics, and genomics, we provide a resource that facilitates deeper exploration of disease pathogenesis and supports the discovery of glycan-based biomarkers
The genetic landscape of neuro-related proteins in human plasma
No full textUnderstanding the genetic basis of neuro-related proteins is essential for dissecting the molecular basis of human behavioural traits and the disease aetiology of neuropsychiatric disorders. Here the SCALLOP Consortium conducted a genome-wide association meta-analysis of over 12,000 individuals for 184 neuro-related proteins in human plasma. The analysis identified 125 cis-regulatory protein quantitative trait loci (cis-pQTL) and 164 trans-pQTL. The mapped pQTL capture on average 50% of each protein’s heritability. At the cis-pQTL, multiple proteins shared a genetic basis with human behavioural traits such as alcohol and food intake, smoking and educational attainment, as well as neurological conditions and psychiatric disorders such as pain, neuroticism and schizophrenia. Integrating with established drug information, the causal inference analysis validated 52 out of 66 matched combinations of protein targets and diseases or side effects with available drugs while suggesting hundreds of repurposing and new therapeutic targets
The genetic landscape of neuro-related proteins in human plasma
No full textUnderstanding the genetic basis of neuro-related proteins is essential for dissecting the molecular basis of human behavioural traits and the disease aetiology of neuropsychiatric disorders. Here the SCALLOP Consortium conducted a genome-wide association meta-analysis of over 12,000 individuals for 184 neuro-related proteins in human plasma. The analysis identified 125 cis-regulatory protein quantitative trait loci (cis-pQTL) and 164 trans-pQTL. The mapped pQTL capture on average 50% of each protein’s heritability. At the cis-pQTL, multiple proteins shared a genetic basis with human behavioural traits such as alcohol and food intake, smoking and educational attainment, as well as neurological conditions and psychiatric disorders such as pain, neuroticism and schizophrenia. Integrating with established drug information, the causal inference analysis validated 52 out of 66 matched combinations of protein targets and diseases or side effects with available drugs while suggesting hundreds of repurposing and new therapeutic targets.</p
