499 research outputs found
A comparison of multivariate genome-wide association methods.
No full textpeer reviewedJoint association analysis of multiple traits in a genome-wide association study (GWAS), i.e. a multivariate GWAS, offers several advantages over analyzing each trait in a separate GWAS. In this study we directly compared a number of multivariate GWAS methods using simulated data. We focused on six methods that are implemented in the software packages PLINK, SNPTEST, MultiPhen, BIMBAM, PCHAT and TATES, and also compared them to standard univariate GWAS, analysis of the first principal component of the traits, and meta-analysis of univariate results. We simulated data (N = 1000) for three quantitative traits and one bi-allelic quantitative trait locus (QTL), and varied the number of traits associated with the QTL (explained variance 0.1%), minor allele frequency of the QTL, residual correlation between the traits, and the sign of the correlation induced by the QTL relative to the residual correlation. We compared the power of the methods using empirically fixed significance thresholds (alpha = 0.05). Our results showed that the multivariate methods implemented in PLINK, SNPTEST, MultiPhen and BIMBAM performed best for the majority of the tested scenarios, with a notable increase in power for scenarios with an opposite sign of genetic and residual correlation. All multivariate analyses resulted in a higher power than univariate analyses, even when only one of the traits was associated with the QTL. Hence, use of multivariate GWAS methods can be recommended, even when genetic correlations between traits are weak
Genotype calls from Rare Variant Sanger Sequencing in 1,998 Individuals
No full text<p>Available genotype calls from rare variant sanger sequencing performed on 1,998 individuals.</p>
<p> </p>
<p>From published paper: <em>The Empirical Power of Rare Variant Association Methods: Results from Sanger Sequencing in 1,998 Individuals</em>. Martin Ladouceur, Zari Dastani, Yurii S. Aulchenko, Celia M. T. Greenwood, J. Brent Richards. Published online February 2nd 2012.</p>
<p>DOI: 10.1371/journal.pgen.1002496</p>
<p> </p
GWAS summary statistics for UPLC IgG N-glycosylation traits
No full textThe majority of proteins undergo post-translational glycosylation, in which complex carbohydrates are attached to the surface of proteins. However, when studying glycoproteins, the glycan component is often neglected. Glycosylation can affect protein structure and function, as is the case with Immunoglobulin G, the most abundant antibody in human blood and an important component of the immune system. Effector functions of IgG require the addition of a glycan moiety and are regulated by the composition of the carbohydrate, thus affecting activity of the immune system. Aberrant glycosylation of IgG has been observed in many diseases, but little is understood about the mechanisms behind these changes. Here we show that the synthesis of the glycan fraction is under control of an interconnected set of genes. We performed the largest genome-wide association study of IgG N-glycosylation to date (N=8,090) and found 27 associated loci (15 novel) that explain up to 22% of variance in glycosylation level. We developed a data-driven network approach to propose how these genes form a functional network regulating glycosylation of IgG. From this network we confirmed in-vitro that the transcription factor IKZF1 regulates the expression of glycosyltransferase FUT8, resulting in increased levels of fucosylated glycans. We also found strong in-silico evidence that RUNX1 and RUNX3 transcription factors, together with SMARCB1 chromatin remodelling protein, regulate expression of glycosyltransferase MGAT3. We showed that glycosylation variants supporting this network are pleiotropic with inflammatory and autoimmune diseases, suggesting how variants with an effect on both IgG glycosylation levels and disease risk could influence glycosyltransferases and result in aberrant IgG glycosylation profiles observed in these diseases.Klaric, Lucija; Tsepilov, Yakov A; Aulchenko, Yurii S; Lauc, Gordan; Hayward, Caroline. (2018). GWAS summary statistics for UPLC IgG N-glycosylation traits, [dataset]. MRC Human Genetics Unit. Institute of Genetics and Molecular Medicine. University of Edinburgh. https://doi.org/10.7488/ds/2481
Genome-wide association summary statistics for human healthspan
No full text<p>The dataset contains genome-wide association summary statistics computed for heathspan. The UKB sub-population of 300,447 genetically Caucasian, British individuals were analyzed. For more details see [1].</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. This research has been conducted using the UK Biobank Resource and the use of the data is guided by the principles formulated by the UK Biobank.</p>
<p><strong>When using downloaded data, please cite corresponding paper and this repository:</strong></p>
<ol>
<li>Zenin, A., Tsepilov, Y., Sharapov, S., Getmantsev, E., Menshikov, L. I., Fedichev, P. O., & Aulchenko, Y. (2019). Identification of 12 genetic loci associated with human healthspan. <em>Communications Biology</em>, <em>2</em>(1), 41. http://doi.org/10.1038/s42003-019-0290-0</li>
<li>Aleksandr Zenin, Yakov Tsepilov, Sodbo Sharapov, Evgeny Getmantsev, Leonid Menshikov, Peter Fedichev, & Yurii Aulchenko. (2018). Genome-wide association summary statistics for human healthspan (Version 1) [Data set]. Zenodo. http://doi.org/10.5281/zenodo.1302861</li>
</ol>
<p><strong>Funding</strong></p>
<p>The work was supported by Russian Ministry of Science and Education under 5-100 Excellence Programme. <br>
The work was supported by the Federal Agency of Scientific Organizations via the Institute of Cytology and Genetics (project #0324-2018-0017). <br>
This research has been conducted using the UK Biobank Resource. <br>
The study has been funded by Gero LLC.</p>
<p><strong>Column headers:</strong></p>
<ol>
<li>SNPID - SNP rsID</li>
<li>chr - chromosome</li>
<li>pos - position (GRCh37 build / hg19)</li>
<li>EA - effective allele (coded as "1")</li>
<li>RA - reference allele (coded as "0")</li>
<li>EAF - effective allele frequency</li>
<li>beta - effect size of effective allele</li>
<li>se - standard error of effect size</li>
<li>Z - Z-value of association</li>
<li>-log10(p-value) - minus log10(P-value) of association</li>
</ol>
Biological, clinical and population relevance of 95 loci for blood lipids
No full textPlasma concentrations of total cholesterol, low-density lipoprotein cholesterol, high-density lipoprotein cholesterol and triglycerides are among the most important risk factors for coronary artery disease (CAD) and are targets for therapeutic intervention. We screened the genome for common variants associated with plasma lipids in >100,000 individuals of European ancestry. Here we report 95 significantly associated loci (P<5 x 10(-8)), with 59 showing genome-wide significant association with lipid traits for the first time. The newly reported associations include single nucleotide polymorphisms (SNPs) near known lipid regulators (for example, CYP7A1, NPC1L1 and SCARB1) as well as in scores of loci not previously implicated in lipoprotein metabolism. The 95 loci contribute not only to normal variation in lipid traits but also to extreme lipid phenotypes and have an impact on lipid traits in three non-European populations (East Asians, South Asians and African Americans). Our results identify several novel loci associated with plasma lipids that are also associated with CAD. Finally, we validated three of the novel genes-GALNT2, PPP1R3B and TTC39B-with experiments in mouse models. Taken together, our findings provide the foundation to develop a broader biological understanding of lipoprotein metabolism and to identify new therapeutic opportunities for the prevention of CAD
Identification of heart rate-associated loci and their effects on cardiac conduction and rhythm disorders
No full textElevated resting heart rate is associated with greater risk of cardiovascular disease and mortality. In a 2-stage meta-analysis of genome-wide association studies in up to 181,171 individuals, we identified 14 new loci associated with heart rate and confirmed associations with all 7 previously established loci. Experimental downregulation of gene expression in Drosophila melanogaster and Danio rerio identified 20 genes at 11 loci that are relevant for heart rate regulation and highlight a role for genes involved in signal transmission, embryonic cardiac development and the pathophysiology of dilated cardiomyopathy, congenital heart failure and/or sudden cardiac death. In addition, genetic susceptibility to increased heart rate is associated with altered cardiac conduction and reduced risk of sick sinus syndrome, and both heart rate-increasing and heart rate-decreasing variants associate with risk of atrial fibrillation. Our findings provide fresh insights into the mechanisms regulating heart rate and identify new therapeutic targets
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
A genomic background based method for association analysis in related individuals
No full textBackground. Feasibility of genotyping of hundreds and thousands of single nucleoticle polymorphisms (SNPs) in thousands of study subjects have triggered the need for fast, powerful, and reliable methods for genome-wide association analysis. Here we consider a situation when study participants are genetically related (e.g. due to systematic sampling of families or because a study was performed in a genetically isolated population). Of the available methods that account for relatedness, the Measured Genotype (MG) approach is considered the 'gold standard'. However, MG is not efficient with respect to time taken for the analysis of genome-wide data. In this context we proposed a fast two-step method called Genome-wide Association using Mixed Model and Regression (GRAMMAR) for the analysis of pedigree-based quantitative traits. This method certainly overcomes the drawback of time limitation of the measured genotype (MG) approach, but pays in power. One of the major drawbacks of both MG and GRAMMAR, is that they crucially depend on the availability of complete and correct pedigree data, which is rarely available. Methodology. In this study we first explore type 1 error and relative power of MG, GRAMMAR, and Genomic Control (GCC) approaches for genetic association analysis. Secondly, we propose an extension to GRAMMAR i.e. GRAMMAR-GC. Finally, we propose application of GRAMMAR-GC using the kinship matrix estimated through genomic marker data, instead of (possibly missing and/or incorrect) genealogy. Conclusion. Through simulations we show that MG approach maintains high power across a range of heritabilities and possible pedigree structures, and always outperforms other contemporary methods. We also show that the power of our proposed GRAMMAR-GC approaches to that of the 'gold standard' MG for all models and pedigrees studied. We show that this method is both feasible and powerful and has correct type 1 error in the context of genome-wide association analysis in related individuals
- …
