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

Glycosylation is a common post-translational modification of proteins. Glycosylation is associated with a number of human diseases. 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 measured by Ultra Performance Liquid Chromatography (UPLC) technology. Starting with the 36 original traits measured by UPLC, we computed an additional 77 derived traits leading to a total of 113 glycan traits. We studied associations between these traits and genetic polymorphisms located on human autosomes. We discovered and replicated 12 loci. This allowed us to demonstrate an overlap in genetic control between total plasma protein and IgG glycosylation. The majority of revealed loci contained genes that encode enzymes directly involved in glycosylation (FUT3/FUT6, FUT8, B3GAT1, ST6GAL1, B4GALT1, ST3GAL4, MGAT3 and MGAT5) and a known regulator of plasma protein fucosylation (HNF1A). However, we also found loci that could possibly reflect other more complex aspects of glycosylation process. Functional genomic annotation suggested the role of several genes including DERL3, CHCHD10, TMEM121, IGH and IKZF1. The hypotheses we generated may serve as a starting point for further functional studies in this research area

Glycosylation of immunoglobulin G is regulated by a large network of genes pleiotropic with inflammatory diseases

Effector functions of immunoglobulin G (IgG) are regulated by the composition of a glycan moiety, thus affecting activity of the immune system. Aberrant glycosylation of IgG has been observed in many diseases, but little is understood about the underlying mechanisms. We performed a genome-wide association study of IgG N-glycosylation (N = 8090) and, using a data-driven network approach, suggested how associated loci form a functional network. We confirmed in vitro that knockdown of IKZF1 decreases the expression of fucosyltransferase FUT8, resulting in increased levels of fucosylated glycans, and suggest that RUNX1 and RUNX3, together with SMARCB1, regulate expression of glycosyltransferase MGAT3. We also show that variants affecting the expression of genes involved in the regulation of glycoenzymes colocalize with variants affecting risk for inflammatory diseases. This study provides new evidence that variation in key transcription factors coupled with regulatory variation in glycogenes modifies IgG glycosylation and has influence on inflammatory diseases

Genome-wide association summary statistics for varicose veins of lower extremities

<p>The dataset contains summary statistics for the discovery and the replication stages of the large-scale genome-wide associations study for varicose veins of lower extremities. The discovery stage was based on genetic association data provided by the Neale Lab (<a>http://www.nealelab.is/</a>) for 337,199 UK biobank individuals. Phenotype “varicose veins of lower extremities” was defined based on International Classification of Disease (ICD-10) billing code “I83” present in the electronic patient record. Data were adjusted for two potential confounders – body mass index and deep venous thrombosis. A replication cohort (N=71,256) was generated by means of reverse meta-analysis of two overlapping datasets: genetic association data for 408,455 UK Biobank participants provided by the Gene ATLAS database (<a>http://geneatlas.roslin.ed.ac.uk/</a>), and the above mentioned data provided by the Neale Lab.</p> <p>The data are provided on an "AS-IS" basis, without warranty of any type, expressed or implied, including but not limited to any warranty as to their performance, merchantability, or fitness for any particular purpose. If investigators use these data, any and all consequences are entirely their responsibility. By downloading and using these data, you agree that you will cite the appropriate publication in any communications or publications arising directly or indirectly from these data; for utilisation of data available prior to publication, you agree to respect the requested responsibilities of resource users under 2003 Fort Lauderdale principles; you agree that you will never attempt to identify any participant. </p> <p><strong>When using downloaded data, please cite corresponding paper and this repository:</strong></p> <ol> <li> <p>Shadrina A.S. et al. Varicose veins of lower extremities: insights from the first large-scale genetic study. (Submitted)</p> </li> <li>Alexandra S. Shadrina, Sodbo Zh. Sharapov, Tatiana I. Shashkova, & Yakov A. Tsepilov. (2018). Genome-wide association summary statistics for varicose veins of lower extremities (Version 1) [Data set]. Zenodo. http://doi.org/10.5281/zenodo.1323484</li> </ol> <p><strong>Funding:</strong></p> <p>The work of ASS was supported by the Russian Science Foundation [Project No 17-75-20223]. <br> The work of YAT was supported by the Russian Ministry of Science and Education under the 5-100 Excellence Programme. <br> The work of SZS was supported by the Institute of Cytology and Genetics [Project No 0324-2018-0017].</p> <p><strong>Column headers - discovery</strong></p> <ol> <li>SNP: SNP rsID</li> <li>b: effect size of effect allele</li> <li>se: standard error of effect size</li> <li>chi2: T^2 value of effect allele</li> <li>Pval: P-value of association (without GC correction)</li> <li>N: sample size</li> <li>Chr: chromosome</li> <li>Pos: position (GRCh37 build)</li> <li>A1: effect allele (coded as "1")</li> <li>A2: reference allele (coded as "0")</li> </ol> <p><strong>Column headers - replication</strong></p> <ol> <li>SNP: SNP rsID</li> <li>A1: effect allele (coded as "1")</li> <li>A2: reference allele (coded as "0")</li> <li>N: Total sample size</li> <li>Z: Z-value of effect allele</li> <li>P: P-value of association (without GC correction)</li> </ol

High-Performance Mixed Models Based Genome-Wide Association Analysis with omicABEL software [v1; ref status: indexed, http://f1000r.es/40b]

To raise the power of genome-wide association studies (GWAS) and avoid false-positive results in structured populations, one can rely on mixed model based tests. When large samples are used, and when multiple traits are to be studied in the ’omics’ context, this approach becomes computationally challenging. Here we consider the problem of mixed-model based GWAS for arbitrary number of traits, and demonstrate that for the analysis of single-trait and multiple-trait scenarios different computational algorithms are optimal. We implement these optimal algorithms in a high-performance computing framework that uses state-of-the-art linear algebra kernels, incorporates optimizations, and avoids redundant computations, increasing throughput while reducing memory usage and energy consumption. We show that, compared to existing libraries, our algorithms and software achieve considerable speed-ups. The OmicABEL software described in this manuscript is available under the GNU GPL v. 3 license as part of the GenABEL project for statistical genomics at http: //www.genabel.org/packages/OmicABEL

A Novel Framework for Analysis of the Shared Genetic Background of Correlated Traits

We propose a novel effective framework for the analysis of the shared genetic background for a set of genetically correlated traits using SNP-level GWAS summary statistics. This framework called SHAHER is based on the construction of a linear combination of traits by maximizing the proportion of its genetic variance explained by the shared genetic factors. SHAHER requires only full GWAS summary statistics and matrices of genetic and phenotypic correlations between traits as inputs. Our framework allows both shared and unshared genetic factors to be effectively analyzed. We tested our framework using simulation studies, compared it with previous developments, and assessed its performance using three real datasets: anthropometric traits, psychiatric conditions and lipid concentrations. SHAHER is versatile and applicable to summary statistics from GWASs with arbitrary sample sizes and sample overlaps, allows for the incorporation of different GWAS models (Cox, linear and logistic), and is computationally fast

Genome-wide association study identifies RNF123 locus as associated with chronic widespread musculoskeletal pain

Abstract Background and objectives Chronic widespread musculoskeletal pain (CWP) is a symptom of fibromyalgia and a complex trait with poorly understood pathogenesis. CWP is heritable (48%–54%), but its genetic architecture is unknown and candidate gene studies have produced inconsistent results. We conducted a genome-wide association study to get insight into the genetic background of CWP. Methods Northern Europeans from UK Biobank comprising 6914 cases reporting pain all over the body lasting >3 months and 242 929 controls were studied. Replication of three independent genome-wide significant single nucleotide polymorphisms was attempted in six independent European cohorts (n=43 080; cases=14 177). Genetic correlations with risk factors, tissue specificity and colocalisation were examined. Results Three genome-wide significant loci were identified (rs1491985, rs10490825, rs165599) residing within the genes Ring Finger Protein 123 (RNF123), ATPase secretory pathway Ca 2+ transporting 1 (ATP2C1) and catechol-O-methyltransferase (COMT). The RNF123 locus was replicated (meta-analysis p=0.0002), the ATP2C1 locus showed suggestive association (p=0.0227) and the COMT locus was not replicated. Partial genetic correlation between CWP and depressive symptoms, body mass index, age of first birth and years of schooling were identified. Tissue specificity and colocalisation analysis highlight the relevance of skeletal muscle in CWP. Conclusions We report a novel association of RNF123 locus and a suggestive association of ATP2C1 locus with CWP. Both loci are consistent with a role of calcium regulation in CWP. The association with COMT, one of the most studied genes in chronic pain field, was not confirmed in the replication analysis