scholarly journals EnrichrBot: Twitter bot tracking tweets about human genes

2020 ◽  
Vol 36 (12) ◽  
pp. 3932-3934
Author(s):  
Alon Bartal ◽  
Alexander Lachmann ◽  
Daniel J B Clarke ◽  
Allison H Seiden ◽  
Kathleen M Jagodnik ◽  
...  
Keyword(s):  
Genome Wide Association Study ◽  
Supplementary Information ◽  
Global Trends ◽  
Additional Information ◽  
Gene Sets ◽  
Genome Wide ◽  
Real People ◽  
Human Genes ◽  
Twitter Users ◽  
The Uk

Abstract Motivation Micro-blogging with Twitter to communicate new results, discuss ideas and share techniques is becoming central. While most Twitter users are real people, the Twitter API provides the opportunity to develop Twitter bots and to analyze global trends in tweets. Results EnrichrBot is a bot that tracks and tweets information about human genes implementing six principal functions: (i) tweeting information about under-studied genes including non-coding lncRNAs, (ii) replying to requests for information about genes, (iii) responding to GWASbot, another bot that tweets Manhattan plots from genome-wide association study analysis of the UK Biobank, (iv) tweeting randomly selected gene sets from the Enrichr database for analysis with Enrichr, (v) responding to mentions of human genes in tweets with additional information about these genes and (vi) tweeting a weekly report about the most trending genes on Twitter. Availability and implementation https://twitter.com/botenrichr; source code: https://github.com/MaayanLab/EnrichrBot. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Genome-wide association study of treatment-resistance in depression and meta-analysis of three independent samples

2018 ◽  
Vol 214 (1) ◽  
pp. 36-41 ◽  
Author(s):  
Chiara Fabbri ◽  
Siegfried Kasper ◽  
Alexander Kautzky ◽  
Lucie Bartova ◽  
Markus Dold ◽  
...  
Keyword(s):  
Genome Wide Association Study ◽  
Meta Analysis ◽  
Adenosine Monophosphate ◽  
Research Support ◽  
Antidepressant Treatments ◽  
Advisory Boards ◽  
Gene Sets ◽  
Genome Wide ◽  
A Genome ◽  
Resistant Depression

BackgroundTreatment-resistant depression (TRD) is the most problematic outcome of depression in terms of functional impairment, suicidal thoughts and decline in physical health.AimsTo investigate the genetic predictors of TRD using a genome-wide approach to contribute to the development of precision medicine.MethodA sample recruited by the European Group for the Study of Resistant Depression (GSRD) including 1148 patients with major depressive disorder (MDD) was characterised for the occurrence of TRD (lack of response to at least two adequate antidepressant treatments) and genotyped using the Infinium PsychArray. Three clinically relevant patient groups were considered: TRD, responders and non-responders to the first antidepressant trial, thus outcomes were based on comparisons of these groups. Genetic analyses were performed at the variant, gene and gene-set (i.e. functionally related genes) level. Additive regression models of the outcomes and relevant covariates were used in the GSRD participants and in a fixed-effect meta-analysis performed between GSRD, STAR*D (n = 1316) and GENDEP (n = 761) participants.ResultsNo individual polymorphism or gene was associated with TRD, although some suggestive signals showed enrichment in cytoskeleton regulation, transcription modulation and calcium signalling. Two gene sets (GO:0043949 and GO:0000183) were associated with TRD versus response and TRD versus response and non-response to the first treatment in the GSRD participants and in the meta-analysis, respectively (corrected P = 0.030 and P = 0.027).ConclusionsThe identified gene sets are involved in cyclic adenosine monophosphate mediated signal and chromatin silencing, two processes previously implicated in antidepressant action. They represent possible biomarkers to implement personalised antidepressant treatments and targets for new antidepressants.Declaration of interestD.S. has received grant/research support from GlaxoSmithKline and Lundbeck; has served as a consultant or on advisory boards for AstraZeneca, Bristol-Myers Squibb, Eli Lilly, Janssen and Lundbeck. S.M. has been a consultant or served on advisory boards for: AstraZeneca, Bristol-Myers Squibb, Forest, Johnson & Johnson, Leo, Lundbeck, Medelink, Neurim, Pierre Fabre, Richter. S.K. has received grant/research support from Eli Lilly, Lundbeck, Bristol-Myers Squibb, GlaxoSmithKline, Organon, Sepracor and Servier; has served as a consultant or on advisory boards for AstraZeneca, Bristol-Myers Squibb, GlaxoSmithKline, Eli Lilly, Lundbeck, Pfizer, Organon, Schwabe, Sepracor, Servier, Janssen and Novartis; and has served on speakers' bureaus for AstraZeneca, Eli Lily, Lundbeck, Schwabe, Sepracor, Servier, Pierre Fabre, Janssen and Neuraxpharm. J.Z. has received grant/research support from Lundbeck, Servier, Brainsway and Pfizer, has served as a consultant or on advisory boards for Servier, Pfizer, Abbott, Lilly, Actelion, AstraZeneca and Roche and has served on speakers' bureaus for Lundbeck, Roch, Lilly, Servier, Pfizer and Abbott. J.M. is a member of the Board of the Lundbeck International Neuroscience Foundation and of Advisory Board of Servier. A.S. is or has been consultant/speaker for: Abbott, AbbVie, Angelini, Astra Zeneca, Clinical Data, Boehringer, Bristol Myers Squibb, Eli Lilly, GlaxoSmithKline, Innovapharma, Italfarmaco, Janssen, Lundbeck, Naurex, Pfizer, Polifarma, Sanofi and Servier. C.M.L. receives research support from RGA UK Services Limited.

scholarly journals Genome-Wide Association Meta-Analysis Supports Genes Involved in Valve and Cardiac Development to Associate With Mitral Valve Prolapse

2021 ◽  
Author(s):  
Mengyao Yu ◽  
Sergiy Kyryachenko ◽  
Stephanie Debette ◽  
Philippe Amouyel ◽  
Jean-Jacques Schott ◽  
...  
Keyword(s):  
Mitral Valve ◽  
Association Study ◽  
Genome Wide Association Study ◽  
Cardiac Development ◽  
Meta Analysis ◽  
Genome Wide Association ◽  
Uk Biobank ◽  
Additional Risk ◽  
Genome Wide ◽  
The Uk

Background: Mitral valve prolapse (MVP) is a common cardiac valve disease, which affects 1 in 40 in the general population. Previous genome-wide association study have identified 6 risk loci for MVP. But these loci explained only partially the genetic risk for MVP. We aim to identify additional risk loci for MVP by adding data set from the UK Biobank. Methods: We reanalyzed 1007/479 cases from the MVP-France study, 1469/862 controls from the MVP-Nantes study for reimputation genotypes using HRC and TOPMed panels. We also incorporated 434 MVP cases and 4527 controls from the UK Biobank for discovery analyses. Genetic association was conducted using SNPTEST and meta-analyses using METAL. We used FUMA for post-genome-wide association study annotations and MAGMA for gene-based and gene-set analyses. Results: We found TOPMed imputation to perform better in terms of accuracy in the lower ranges of minor allele frequency below 0.1. Our updated meta-analysis included UK Biobank study for ≈8 million common single-nucleotide polymorphisms (minor allele frequency >0.01) and replicated the association on Chr2 as the top association signal near TNS1 . We identified an additional risk locus on Chr1 ( SYT2 ) and 2 suggestive risk loci on chr8 ( MSRA ) and chr19 ( FBXO46 ), all driven by common variants. Gene-based association using MAGMA revealed 6 risk genes for MVP with pronounced expression levels in cardiovascular tissues, especially the heart and globally part of enriched GO terms related to cardiac development. Conclusions: We report an updated meta-analysis genome-wide association study for MVP using dense imputation coverage and an improved case-control sample. We describe several loci and genes with MVP spanning biological mechanisms highly relevant to MVP, especially during valve and heart development.

Lifestyle factors and genetic variants on two biological age measures: evidence from 94,443 Taiwan Biobank participants

2021 ◽  
Author(s):  
Wan-Yu Lin
Keyword(s):  
Genetic Variants ◽  
Genome Wide Association Study ◽  
Lifestyle Factors ◽  
Biological Age ◽  
Biological Aging ◽  
Nucleotide Polymorphisms ◽  
Single Nucleotide ◽  
Genome Wide ◽  
A Genome ◽  
The Uk

Abstract Background Biological age (BA) can be estimated by phenotypes and is useful for predicting lifespan and healthspan. Levine et al. proposed a PhenoAge and a BioAge to measure BA. Although there have been studies investigating the genetic predisposition to BA acceleration in Europeans, little has been known regarding this topic in Asians. Methods I here estimated PhenoAgeAccel (age-adjusted PhenoAge) and BioAgeAccel (age-adjusted BioAge) of 94,443 Taiwan Biobank (TWB) participants, wherein 25,460 TWB1 subjects formed a discovery cohort and 68,983 TWB2 individuals constructed a replication cohort. Lifestyle factors and genetic variants associated with PhenoAgeAccel and BioAgeAccel were investigated through regression analysis and a genome-wide association study (GWAS). Results A unit (kg/m 2) increase of BMI was associated with a 0.177-year PhenoAgeAccel (95% C.I. = 0.163~0.191, p = 6.0×) and 0.171-year BioAgeAccel (95% C.I. = 0.165~0.177, p = 0). Smokers on average had a 1.134-year PhenoAgeAccel (95% C.I. = 0.966~1.303, p = 1.3×) compared with non-smokers. Drinkers on average had a 0.640-year PhenoAgeAccel (95% C.I. = 0.433~0.847, p = 1.3×) and 0.193-year BioAgeAccel (95% C.I. = 0.107~0.279, p = 1.1×) relative to non-drinkers. A total of 11 and 4 single-nucleotide polymorphisms (SNPs) were associated with PhenoAgeAccel and BioAgeAccel (p<5× in both TWB1 and TWB2), respectively. Conclusions A PhenoAgeAccel-associated SNP (rs1260326 in GCKR) and two BioAgeAccel-associated SNPs (rs7412 in APOE; rs16998073 near FGF5) were consistent with the finding from the UK Biobank. The lifestyle analysis shows that prevention from obesity, cigarette smoking, and alcohol consumption is associated with a slower rate of biological aging.

G2P: a Genome-Wide-Association-Study simulation tool for genotype simulation, phenotype simulation and power evaluation

2019 ◽  
Vol 35 (19) ◽  
pp. 3852-3854 ◽  
Author(s):  
You Tang ◽  
Xiaolei Liu
Keyword(s):  
Association Study ◽  
Genome Wide Association Study ◽  
Genome Wide Association ◽  
Supplementary Information ◽  
Maximum Efficiency ◽  
Genotype Data ◽  
Simulation Tool ◽  
Genome Wide ◽  
Phenotype Data ◽  
Power Evaluation

Abstract Motivation Plenty of Genome-Wide-Association-Study (GWAS) methods have been developed for mapping genetic markers that associated with human diseases and agricultural economic traits. Computer simulation is a nice tool to test the performances of various GWAS methods under certain scenarios. Existing tools are either inefficient in terms of computation and memory efficiency or inconvenient to use to simulate big, realistic genotype data and phenotype data to evaluate available GWAS methods. Results Here, we present a GWAS simulation tool named G2P that can be used to simulate genotype data, phenotype data and perform power evaluation of GWAS methods. G2P is a user-friendly tool with all functions is provided in both graphical user interface and pipeline manners and it is available for Windows, Mac and Linux environments. Furthermore, G2P achieves maximum efficiency in terms of both memory usage and simulation speed; with G2P, the simulation of genotype data that includes 1 000 000 samples and 2 000 000 markers can be accomplished in 5 h. Availability and implementation The G2P software, user manual, and example datasets are freely available at GitHub: https://github.com/XiaoleiLiuBio/G2P. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Varmole: a biologically drop-connect deep neural network model for prioritizing disease risk variants and genes

2020 ◽  
Author(s):  
Nam D Nguyen ◽  
Ting Jin ◽  
Daifeng Wang
Keyword(s):  
Neural Network ◽  
Network Architecture ◽  
Deep Neural Network ◽  
Molecular Mechanisms ◽  
Genome Wide Association Study ◽  
Disease Risk ◽  
Supplementary Information ◽  
Neural Network Architecture ◽  
Risk Variants ◽  
Genome Wide

Abstract Summary Population studies such as genome-wide association study have identified a variety of genomic variants associated with human diseases. To further understand potential mechanisms of disease variants, recent statistical methods associate functional omic data (e.g. gene expression) with genotype and phenotype and link variants to individual genes. However, how to interpret molecular mechanisms from such associations, especially across omics, is still challenging. To address this problem, we developed an interpretable deep learning method, Varmole, to simultaneously reveal genomic functions and mechanisms while predicting phenotype from genotype. In particular, Varmole embeds multi-omic networks into a deep neural network architecture and prioritizes variants, genes and regulatory linkages via biological drop-connect without needing prior feature selections. Availability and implementation Varmole is available as a Python tool on GitHub at https://github.com/daifengwanglab/Varmole. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Expanding the Genetic Architecture of Nicotine Dependence and its Shared Genetics with Multiple Traits: Findings from the Nicotine Dependence GenOmics (iNDiGO) Consortium

2020 ◽  
Author(s):  
Bryan C. Quach ◽  
Michael J. Bray ◽  
Nathan C. Gaddis ◽  
Mengzhen Liu ◽  
Teemu Palviainen ◽  
...  
Keyword(s):  
Nicotine Dependence ◽  
Genetic Architecture ◽  
Genome Wide Association Study ◽  
African Ancestry ◽  
Multiple Traits ◽  
Expression Of Genes ◽  
Genome Wide ◽  
A Genome ◽  
The Uk ◽  
Smoking Index

AbstractCigarette smoking is the leading cause of preventable morbidity and mortality. Knowledge is evolving on genetics underlying initiation, regular smoking, nicotine dependence (ND), and cessation. We performed a genome-wide association study using the Fagerström Test for ND (FTND) in 58,000 smokers of European or African ancestry. Five genome-wide significant loci, including two novel loci MAGI2/GNAI1 (rs2714700) and TENM2 (rs1862416) were identified, and loci reported for other smoking traits were extended to ND. Using the heaviness of smoking index (HSI) in the UK Biobank (N=33,791), rs2714700 was consistently associated, but rs1862416 was not associated, likely reflecting ND features not captured by the HSI. Both variants were cis-eQTLs (rs2714700 for MAGI2-AS3 in hippocampus, rs1862416 for TENM2 in lung), and expression of genes spanning ND-associated variants was enriched in cerebellum. SNP-based heritability of ND was 8.6%, and ND was genetically correlated with 17 other smoking traits (rg=0.40–0.95) and co-morbidities. Our results emphasize the FTND as a composite phenotype that expands genetic knowledge of smoking, including loci specific to ND.

scholarly journals A genome-wide association study of deviated nasal septum using UK Biobank cohort

2020 ◽  
Author(s):  
Li Liu ◽  
CuiYan Wu ◽  
Xiaoxia Dai ◽  
Yan Wen ◽  
Shiqiang Cheng ◽  
...  
Keyword(s):  
Association Study ◽  
Nasal Septum ◽  
Genome Wide Association Study ◽  
Gene Set Enrichment Analysis ◽  
Genome Wide Association ◽  
Genetic Mechanism ◽  
Uk Biobank ◽  
Deviated Nasal Septum ◽  
Gene Sets ◽  
Genome Wide

Abstract Background Deviated nasal septum (DNS) is a common otolaryngology disease. The genetic mechanism underlying DNS remains largely unknown. Methods Totally, 2, 978 DNS patients and 2,978 randomly selected controls from the UK biobank were used in this study. Genotyping was done using the Affymetrix UK BiLEVE Axiom or UK Biobank Axiom array. Genome-wide association study (GWAS) was performed by PLINK 2.0, using age, sex, population structure PC1, PC2 and PC3 as covariates. eQTLs analysis and gene set enrichment analysis (GSEA) were also performed to explore the functional relevance of identified loci with DNS. Results GWAS identified multiple candidate genetic loci for DNS, such as rs75651247 located in DLGAP1 (β=-5.3398, P=9.31×10-8), rs141366706 located in CCND3 (β=-4.7036, P=2.56×10-6), rs76606504 located in FAF1 (β=-4.5013, P=6.76×10-6), and rs142537880 located in SVIL (β= 4.4336, P=9.27×10-6). GSEA detected multiple DNS associated gene sets or pathways, such as KEGG_CALCIUM_SIGNALING_PATHWAY (FDR=3.35×10-3, P=5×10-5) and DAVICIONI_TARGETS_OF_PAX_FOXO1_FUSIONS_UP (FDR=1.60×10-3, P=5×10-5). Conclusions Our study reported multiple candidate genes and gene sets for DNS, providing novel clues for understanding the genetic mechanism of DNS.

scholarly journals Multi-Level Analyses of Genome-Wide Association Study to Reveal Significant Risk Genes and Pathways in Neuromyelitis Optica Spectrum Disorder

2021 ◽  
Vol 12 ◽  
Author(s):  
Ting Li ◽  
He Li ◽  
Yue Li ◽  
Shu-An Dong ◽  
Ming Yi ◽  
...  
Keyword(s):  
Genome Wide Association Study ◽  
Biological Process ◽  
Susceptibility Gene ◽  
Susceptibility Genes ◽  
Cellular Component ◽  
Molecular Function ◽  
Risk Genes ◽  
Kegg Pathways ◽  
Gene Sets ◽  
Genome Wide

BackgroundNeuromyelitis optica spectrum disorder (NMOSD) is an inflammatory disease of the central nervous system and it is understandable that environmental and genetic factors underlie the etiology of NMOSD. However, the susceptibility genes and associated pathways of NMOSD patients who are AQP4-Ab positive and negative have not been elucidated.MethodsSecondary analysis from a NMOSD Genome-wide association study (GWAS) dataset originally published in 2018 (215 NMOSD cases and 1244 controls) was conducted to identify potential susceptibility genes and associated pathways in AQP4-positive and negative NMOSD patients, respectively (132 AQP4-positive and 83 AQP4-negative).ResultsIn AQP4-positive NMOSD cases, five shared risk genes were obtained at chromosome 6 in AQP4-positive NMOSD cases by using more stringent p-Values in both methods (p < 0.05/16,532), comprising CFB, EHMT2, HLA-DQA1, MSH5, and SLC44A4. Fifty potential susceptibility gene sets were determined and 12 significant KEGG pathways were identified. Sixty-seven biological process pathways, 32 cellular-component pathways, and 29 molecular-function pathways with a p-Value of <0.05 were obtained from the GO annotations of the 128 pathways identified. In the AQP4 negative NMOSD group, no significant genes were obtained by using more stringent p-Values in both methods (p < 0.05/16,485). The 22 potential susceptibility gene sets were determined. There were no shared potential susceptibility genes between the AQP4-positive and negative groups, furthermore, four significant KEGG pathways were also identified. Of the GO annotations of the 165 pathways identified, 99 biological process pathways, 37 cellular-component pathways, and 29 molecular-function pathways with a p-Value of <0.05 were obtained.ConclusionThe potential molecular mechanism underlying NMOSD may be related to proteins encoded by these novel genes in complements, antigen presentation, and immune regulation. The new results may represent an improved comprehension of the genetic and molecular mechanisms underlying NMOSD.

scholarly journals Investigation of clinical characteristics and genome associations in the UK Lipoedema cohort

2021 ◽  
Author(s):  
Dionysios Grigoriadis ◽  
Ege Sackey ◽  
Katie Riches ◽  
Malou van Zanten ◽  
Glen Brice ◽  
...  
Keyword(s):  
Clinical Characteristics ◽  
Genome Wide Association Study ◽  
Subcutaneous Fat ◽  
Lower Limbs ◽  
Genome Wide ◽  
A Genome ◽  
History Of ◽  
Different Populations ◽  
The Uk

Lipoedema is a chronic adipose tissue disorder mainly affecting women, causing excess subcutaneous fat deposition on the lower limbs with pain and tenderness. There is often a family history of lipoedema, suggesting a genetic origin, but the contribution of genetics is currently unclear. A tightly phenotyped cohort of 200 lipoedema patients was recruited from two UK specialist clinics. Objective clinical characteristics and measures of quality of life data were obtained. In an attempt to understand the genetic architecture of the disease better, genome-wide single nucleotide polymorphism (SNP) genotype data were obtained, and a genome wide association study (GWAS) performed on 130 of the recruits. The analysis revealed genetic loci suggestively associated with the lipoedema phenotype, with further support provided by an independent cohort taken from the 100,000 Genomes Project. Top SNPs included loci associated with lipoma formation, biosynthesis of hormones and lipid hydroxylation. Exactly how these SNPs relate to a lipoedema disease mechanism is not yet understood but the findings are consistent with existing fat and hormone hypotheses. This first GWAS of a UK lipoedema cohort has identified genetic regions of suggestive association with the disease. Further replication of these findings in different populations is warranted.

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