scholarly journals Gene-based association analysis identified 190 genes with polymorphisms affecting neuroticism

2020 ◽  
Author(s):  
Nadezhda M. Belonogova ◽  
Irina V. Zorkoltseva ◽  
Yakov A. Tsepilov ◽  
Tatiana I. Axenovich
Keyword(s):  
Gene Expression ◽  
Detailed Analysis ◽  
Association Analysis ◽  
Genetic Variants ◽  
Bioinformatics Analysis ◽  
Summary Statistics ◽  
Uk Biobank ◽  
Protein Coding ◽  
Pathogenic Variants ◽  
Genome Wide

AbstractRecent genome-wide studies have reported about 600 genes potentially influencing neuroticism. Little is known about the mechanisms of their action. Here, we aimed to conduct a more detailed analysis of genes whose polymorphisms can regulate the level of neuroticism. Using UK Biobank-based GWAS summary statistics, we performed a gene-based association analysis using four sets of genetic variants within a gene differing in their protein coding properties. To guard against the influence of strong GWAS signals outside the gene, we used the specially designed procedure. As a result, we identified 190 genes associated with neuroticism due to their polymorphisms. Thirty eight of these genes were novel. Within all genes identified, we distinguished two slightly overlapping groups comprising genes that demonstrated association when using protein-coding and non-coding SNPs. Many genes from the first group included potentially pathogenic variants. For some genes from the second group, we found evidence of pleiotropy with gene expression. We demonstrated that the association of almost two hundred known genes could be inflated by the GWAS signals outside the gene. Using bioinformatics analysis, we prioritized the neuroticism genes and showed that the genes influencing the trait by their polymorphisms are the most appropriate candidate genes.

scholarly journals Gene-based association analysis identifies 190 genes affecting neuroticism

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Nadezhda M. Belonogova ◽  
Irina V. Zorkoltseva ◽  
Yakov A. Tsepilov ◽  
Tatiana I. Axenovich
Keyword(s):  
Association Analysis ◽  
Bioinformatics Analysis ◽  
Specific Protein ◽  
Gene Variants ◽  
Summary Statistics ◽  
Uk Biobank ◽  
Protein Coding ◽  
Pathogenic Variants ◽  
Genome Wide ◽  
Coding Variants

AbstractNeuroticism is a personality trait, which is an important risk factor for psychiatric disorders. Recent genome-wide studies reported about 600 genes potentially influencing neuroticism. Little is known about the mechanisms of their action. Here, we aimed to conduct a more detailed analysis of genes that can regulate the level of neuroticism. Using UK Biobank-based GWAS summary statistics, we performed a gene-based association analysis using four sets of within-gene variants, each set possessing specific protein-coding properties. To guard against the influence of strong GWAS signals outside the gene, we used a specially designed procedure called “polygene pruning”. As a result, we identified 190 genes associated with neuroticism due to the effect of within-gene variants rather than strong GWAS signals outside the gene. Thirty eight of these genes are new. Within all genes identified, we distinguished two slightly overlapping groups obtained from using protein-coding and non-coding variants. Many genes in the former group included potentially pathogenic variants. For some genes in the latter group, we found evidence of pleiotropy with gene expression. Using a bioinformatics analysis, we prioritized the neuroticism genes and showed that the genes that contribute to neuroticism through their within-gene variants are the most appropriate candidate genes.

scholarly journals Evaluation and application of summary statistic imputation to discover new height-associated loci

2017 ◽  
Author(s):  
Sina Rüeger ◽  
Aaron McDaid ◽  
Zoltán Kutalik
Keyword(s):  
Genetic Variants ◽  
Association Studies ◽  
Low Frequency ◽  
Cost Effective ◽  
Genotype Imputation ◽  
Genome Wide Association Studies ◽  
Summary Statistics ◽  
Uk Biobank ◽  
Genome Wide ◽  
The Uk

AbstractAs most of the heritability of complex traits is attributed to common and low frequency genetic variants, imputing them by combining genotyping chips and large sequenced reference panels is the most cost-effective approach to discover the genetic basis of these traits. Association summary statistics from genome-wide meta-analyses are available for hundreds of traits. Updating these to ever-increasing reference panels is very cumbersome as it requires reimputation of the genetic data, rerunning the association scan, and meta-analysing the results. A much more efficient method is to directly impute the summary statistics, termed as summary statistics imputation. Its performance relative to genotype imputation and practical utility has not yet been fully investigated. To this end, we compared the two approaches on real (genotyped and imputed) data from 120K samples from the UK Biobank and show that, while genotype imputation boasts a 2- to 5-fold lower root-mean-square error, summary statistics imputation better distinguishes true associations from null ones: We observed the largest differences in power for variants with low minor allele frequency and low imputation quality. For fixed false positive rates of 0.001, 0.01, 0.05, using summary statistics imputation yielded an increase in statistical power by 15, 10 and 3%, respectively. To test its capacity to discover novel associations, we applied summary statistics imputation to the GIANT height meta-analysis summary statistics covering HapMap variants, and identified 34 novel loci, 19 of which replicated using data in the UK Biobank. Additionally, we successfully replicated 55 out of the 111 variants published in an exome chip study. Our study demonstrates that summary statistics imputation is a very efficient and cost-effective way to identify and fine-map trait-associated loci. Moreover, the ability to impute summary statistics is important for follow-up analyses, such as Mendelian randomisation or LD-score regression.Author summaryGenome-wide association studies (GWASs) quantify the effect of genetic variants and traits, such as height. Such estimates are called association summary statistics and are typically publicly shared through publication. Typically, GWASs are carried out by genotyping ~ 500′000 SNVs for each individual which are then combined with sequenced reference panels to infer untyped SNVs in each’ individuals genome. This process of genotype imputation is resource intensive and can therefore be a limitation when combining many GWASs. An alternative approach is to bypass the use of individual data and directly impute summary statistics. In our work we compare the performance of summary statistics imputation to genotype imputation. Although we observe a 2- to 5-fold lower RMSE for genotype imputation compared to summary statistics imputation, summary statistics imputation better distinguishes true associations from null results. Furthermore, we demonstrate the potential of summary statistics imputation by presenting 34 novel height-associated loci, 19 of which were confirmed in UK Biobank. Our study demonstrates that given current reference panels, summary statistics imputation is a very efficient and cost-effective way to identify common or low-frequency trait-associated loci.

scholarly journals CAUSALdb: a database for disease/trait causal variants identified using summary statistics of genome-wide association studies

2019 ◽  
Author(s):  
Jianhua Wang ◽  
Dandan Huang ◽  
Yao Zhou ◽  
Hongcheng Yao ◽  
Huanhuan Liu ◽  
...  
Keyword(s):  
Fine Mapping ◽  
Genetic Variants ◽  
Association Studies ◽  
Complex Trait ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Summary Statistics ◽  
Genome Wide ◽  
Credible Sets ◽  
Causal Variants

Abstract Genome-wide association studies (GWASs) have revolutionized the field of complex trait genetics over the past decade, yet for most of the significant genotype-phenotype associations the true causal variants remain unknown. Identifying and interpreting how causal genetic variants confer disease susceptibility is still a big challenge. Herein we introduce a new database, CAUSALdb, to integrate the most comprehensive GWAS summary statistics to date and identify credible sets of potential causal variants using uniformly processed fine-mapping. The database has six major features: it (i) curates 3052 high-quality, fine-mappable GWAS summary statistics across five human super-populations and 2629 unique traits; (ii) estimates causal probabilities of all genetic variants in GWAS significant loci using three state-of-the-art fine-mapping tools; (iii) maps the reported traits to a powerful ontology MeSH, making it simple for users to browse studies on the trait tree; (iv) incorporates highly interactive Manhattan and LocusZoom-like plots to allow visualization of credible sets in a single web page more efficiently; (v) enables online comparison of causal relations on variant-, gene- and trait-levels among studies with different sample sizes or populations and (vi) offers comprehensive variant annotations by integrating massive base-wise and allele-specific functional annotations. CAUSALdb is freely available at http://mulinlab.org/causaldb.

scholarly journals Tri-methylation of Histone H3 Lysine 4 Facilitates Gene Expression in Ageing Cells

2017 ◽  
Author(s):  
Cristina Cruz ◽  
Monica Della Rosa ◽  
Christel Krueger ◽  
Qian Gao ◽  
Lucy Field ◽  
...  
Keyword(s):  
Gene Expression ◽  
Budding Yeast ◽  
Histone H3 ◽  
Specific Factor ◽  
Protein Coding ◽  
Replicative Lifespan ◽  
Protein Coding Genes ◽  
Genome Wide ◽  
Normal Expression ◽  
Transcriptional Induction

AbstractTranscription of protein coding genes is accompanied by recruitment of COMPASS to promoter-proximal chromatin, which deposits di- and tri-methylation on histone H3 lysine 4 (H3K4) to form H3K4me2 and H3K4me3. Here we determine the importance of COMPASS in maintaining gene expression across lifespan in budding yeast. We find that COMPASS mutations dramatically reduce replicative lifespan and cause widespread gene expression defects. Known repressive functions of H3K4me2 are progressively lost with age, while hundreds of genes become dependent on H3K4me3 for full expression. Induction of these H3K4me3 dependent genes is also impacted in young cells lacking COMPASS components including the H3K4me3-specific factor Spp1. Remarkably, the genome-wide occurrence of H3K4me3 is progressively reduced with age despite widespread transcriptional induction, minimising the normal positive correlation between promoter H3K4me3 and gene expression. Our results provide clear evidence that H3K4me3 is required to attain normal expression levels of many genes across organismal lifespan.

scholarly journals A transcriptome-wide Mendelian randomization study to uncover tissue-dependent regulatory mechanisms across the human phenome

2019 ◽  
Author(s):  
Tom G Richardson ◽  
Gibran Hemani ◽  
Tom R Gaunt ◽  
Caroline L Relton ◽  
George Davey Smith
Keyword(s):  
Gene Expression ◽  
Genetic Variants ◽  
Complex Traits ◽  
Mendelian Randomization ◽  
Drug Repositioning ◽  
Association Studies ◽  
Thyroid Tissue ◽  
Genome Wide Association Studies ◽  
Tissue Specific ◽  
Genome Wide

AbstractBackgroundDeveloping insight into tissue-specific transcriptional mechanisms can help improve our understanding of how genetic variants exert their effects on complex traits and disease. By applying the principles of Mendelian randomization, we have undertaken a systematic analysis to evaluate transcriptome-wide associations between gene expression across 48 different tissue types and 395 complex traits.ResultsOverall, we identified 100,025 gene-trait associations based on conventional genome-wide corrections (P < 5 × 10−08) that also provided evidence of genetic colocalization. These results indicated that genetic variants which influence gene expression levels in multiple tissues are more likely to influence multiple complex traits. We identified many examples of tissue-specific effects, such as genetically-predicted TPO, NR3C2 and SPATA13 expression only associating with thyroid disease in thyroid tissue. Additionally, FBN2 expression was associated with both cardiovascular and lung function traits, but only when analysed in heart and lung tissue respectively.We also demonstrate that conducting phenome-wide evaluations of our results can help flag adverse on-target side effects for therapeutic intervention, as well as propose drug repositioning opportunities. Moreover, we find that exploring the tissue-dependency of associations identified by genome-wide association studies (GWAS) can help elucidate the causal genes and tissues responsible for effects, as well as uncover putative novel associations.ConclusionsThe atlas of tissue-dependent associations we have constructed should prove extremely valuable to future studies investigating the genetic determinants of complex disease. The follow-up analyses we have performed in this study are merely a guide for future research. Conducting similar evaluations can be undertaken systematically at http://mrcieu.mrsoftware.org/Tissue_MR_atlas/.

scholarly journals A compendium of uniformly processed human gene expression and splicing quantitative trait loci

2021 ◽  
Vol 53 (9) ◽  
pp. 1290-1299
Author(s):  
Nurlan Kerimov ◽  
James D. Hayhurst ◽  
Kateryna Peikova ◽  
Jonathan R. Manning ◽  
Peter Walter ◽  
...  
Keyword(s):  
Gene Expression ◽  
Quantitative Trait ◽  
Target Genes ◽  
Genome Wide Association Study ◽  
Cell Types ◽  
Summary Statistics ◽  
Genome Wide ◽  
Cell Type Specific ◽  
Trait Locus ◽  
Complex Human Traits

AbstractMany gene expression quantitative trait locus (eQTL) studies have published their summary statistics, which can be used to gain insight into complex human traits by downstream analyses, such as fine mapping and co-localization. However, technical differences between these datasets are a barrier to their widespread use. Consequently, target genes for most genome-wide association study (GWAS) signals have still not been identified. In the present study, we present the eQTL Catalogue (https://www.ebi.ac.uk/eqtl), a resource of quality-controlled, uniformly re-computed gene expression and splicing QTLs from 21 studies. We find that, for matching cell types and tissues, the eQTL effect sizes are highly reproducible between studies. Although most QTLs were shared between most bulk tissues, we identified a greater diversity of cell-type-specific QTLs from purified cell types, a subset of which also manifested as new disease co-localizations. Our summary statistics are freely available to enable the systematic interpretation of human GWAS associations across many cell types and tissues.

scholarly journals Genome-wide association analysis of common genetic variants of resistant hypertension

2018 ◽  
Vol 19 (3) ◽  
pp. 295-304 ◽  
Author(s):  
Nihal El Rouby ◽  
◽  
Caitrin W. McDonough ◽  
Yan Gong ◽  
Leslie A. McClure ◽  
...  
Keyword(s):  
Association Analysis ◽  
Resistant Hypertension ◽  
Genetic Variants ◽  
Genome Wide Association ◽  
Genome Wide Association Analysis ◽  
Genome Wide ◽  
Common Genetic Variants

scholarly journals Gene Expression in Trypanosomatid Parasites

2010 ◽  
Vol 2010 ◽  
pp. 1-15 ◽  
Author(s):  
Santiago Martínez-Calvillo ◽  
Juan C. Vizuet-de-Rueda ◽  
Luis E. Florencio-Martínez ◽  
Rebeca G. Manning-Cela ◽  
Elisa E. Figueroa-Angulo
Keyword(s):  
Gene Expression ◽  
Dna Sequences ◽  
Transcription Initiation ◽  
Histone Variants ◽  
Protein Coding ◽  
Functional Studies ◽  
Polycistronic Transcription ◽  
General Transcription Factors ◽  
Genome Wide ◽  
Trypanosomatid Parasites

The parasitesLeishmaniaspp.,Trypanosoma brucei,andTrypanosoma cruziare the trypanosomatid protozoa that cause the deadly human diseases leishmaniasis, African sleeping sickness, and Chagas disease, respectively. These organisms possess unique mechanisms for gene expression such as constitutive polycistronic transcription of protein-coding genes and trans-splicing. Little is known about either the DNA sequences or the proteins that are involved in the initiation and termination of transcription in trypanosomatids.In silicoanalyses of the genome databases of these parasites led to the identification of a small number of proteins involved in gene expression. However, functional studies have revealed that trypanosomatids have more general transcription factors than originally estimated. Many posttranslational histone modifications, histone variants, and chromatin modifying enzymes have been identified in trypanosomatids, and recent genome-wide studies showed that epigenetic regulation might play a very important role in gene expression in this group of parasites. Here, we review and comment on the most recent findings related to transcription initiation and termination in trypanosomatid protozoa.

Sign in / Sign up

Export Citation Format

Share Document