JEPEGMIX2: improved gene-level joint analysis of eQTLs in cosmopolitan cohorts

AbstractMotivationTo increase detection power, researchers use gene level analysis methods to aggregate weak marker signals. Due to gene expression controlling biological processes, researchers proposed aggregating signals for expression Quantitative Trait Loci (eQTL). Most gene-level eQTL methods make statistical inferences based on i) summary statistics from genome-wide association studies (GWAS) and ii) linkage disequilibrium (LD) patterns from a relevant reference panel. While most such tools assume homogeneous cohorts, our Gene-level Joint Analysis of functional SNPs in Cosmopolitan Cohorts (JEPEGMIX) method accommodates cosmopolitan cohorts by using heterogeneous panels. However, JEPGMIX relies on brain eQTLs from older gene expression studies and does not adjust for background enrichment in GWAS signals.ResultsWe propose JEPEGMIX2, an extension of JEPEGMIX. When compared to JPEGMIX, it uses i) cis-eQTL SNPs from the latest expression studies and ii) brains specific (sub)tissues and tissues other than brain. JEPEGMIX2 also i) avoids accumulating averagely enriched polygenic information by adjusting for background enrichment and ii), to avoid an increase in false positive rates for studies with numerous highly enriched (above the background) genes, it outputs gene q-values based on Holm adjustment of [email protected] informationSupplementary material is available at Bioinformatics online.

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Microglia in Brain Development, Homeostasis, and Neurodegeneration

Annual Review of Genetics ◽

10.1146/annurev-genet-112618-043515 ◽

2019 ◽

Vol 53 (1) ◽

pp. 263-288 ◽

Cited By ~ 10

Author(s):

Christopher J. Bohlen ◽

Brad A. Friedman ◽

Borislav Dejanovic ◽

Morgan Sheng

Keyword(s):

Alzheimer Disease ◽

Brain Development ◽

Neurodegenerative Disease ◽

Human Genetics ◽

Association Studies ◽

Therapeutic Interventions ◽

Genome Wide Association Studies ◽

Expression Studies ◽

Genome Wide ◽

Gene Expression Studies

Advances in human genetics have implicated a growing number of genes in neurodegenerative diseases, providing insight into pathological processes. For Alzheimer disease in particular, genome-wide association studies and gene expression studies have emphasized the pathogenic contributions from microglial cells and motivated studies of microglial function/dysfunction. Here, we summarize recent genetic evidence for microglial involvement in neurodegenerative disease with a focus on Alzheimer disease, for which the evidence is most compelling. To provide context for these genetic discoveries, we discuss how microglia influence brain development and homeostasis, how microglial characteristics change in disease, and which microglial activities likely influence the course of neurodegeneration. In all, we aim to synthesize varied aspects of microglial biology and highlight microglia as possible targets for therapeutic interventions in neurodegenerative disease.

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emeraLD: Rapid Linkage Disequilibrium Estimation with Massive Data Sets

10.1101/301366 ◽

2018 ◽

Cited By ~ 1

Author(s):

Corbin Quick ◽

Christian Fuchsberger ◽

Daniel Taliun ◽

Gonçalo Abecasis ◽

Michael Boehnke ◽

...

Keyword(s):

Linkage Disequilibrium ◽

Association Studies ◽

Random Access ◽

Supplementary Information ◽

Data Sets ◽

Genome Wide Association Studies ◽

Summary Statistics ◽

Genome Wide ◽

Wide Range ◽

Supplementary Material

AbstractSummaryEstimating linkage disequilibrium (LD) is essential for a wide range of summary statistics-based association methods for genome-wide association studies (GWAS). Large genetic data sets, e.g. the TOPMed WGS project and UK Biobank, enable more accurate and comprehensive LD estimates, but increase the computational burden of LD estimation. Here, we describe emeraLD (Efficient Methods for Estimation and Random Access of LD), a computational tool that leverages sparsity and haplotype structure to estimate LD orders of magnitude faster than existing tools.Availability and ImplementationemeraLD is implemented in C++, and is open source under GPLv3. Source code, documentation, an R interface, and utilities for analysis of summary statistics are freely available at http://github.com/statgen/[email protected] informationSupplementary data are available at Bioinformatics online.

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TWENTY-FOUR GENES ARE UPREGULATED IN PATIENTS WITH HYPOSPADIAS

Balkan Journal of Medical Genetics ◽

10.2478/bjmg-2013-0030 ◽

2013 ◽

Vol 16 (2) ◽

pp. 39-43 ◽

Cited By ~ 6

Author(s):

R. Karabulut ◽

Z. Turkyilmaz ◽

K. Sonmez ◽

G. Kumas ◽

Sg. Ergun ◽

...

Keyword(s):

Gene Expression ◽

Expression Profiles ◽

Association Studies ◽

Gene Expression Profiles ◽

Ventral Surface ◽

Genome Wide Association Studies ◽

Expression Array ◽

Large Patient ◽

Expression Studies ◽

Genome Wide

ABSTRACT Hypospadias is a congenital hypoplasia of the penis, with displacement of the urethral opening along the ventral surface, and has been reported to be one of the most common congenital anomalies, occurring in approximately 1:250 to 1:300 live births. As hypospadias is reported to be an easily diagnosed malformation at the crossroads of genetics and environment, it is important to study the genetic component in order to elucidate its etiology. In this study, the gene expression profiles both in human hypospadias tissues and normal penile tissues were studied by Human Gene Expression Array. Twentyfour genes were found to be upregulated. Among these, ATF3 and CYR61 have been reported previously. Other genes that have not been previously reported were also found to be upregulated: BTG2, CD69, CD9, DUSP1, EGR1, EIF4A1, FOS, FOSB, HBEGF, HNRNPUL1, IER2, JUN, JUNB, KLF2, NR4A1, NR4A2, PTGS2, RGS1, RTN4, SLC25A25, SOCS3 and ZFP36 (p <0.05). Further studies including genome-wide association studies (GWAS) with expression studies in a large patient group will help us for identifiying the candidate gene(s) in the etiology of hypospadias

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GREP: genome for REPositioning drugs

Bioinformatics ◽

10.1093/bioinformatics/btz166 ◽

2019 ◽

Vol 35 (19) ◽

pp. 3821-3823 ◽

Cited By ~ 6

Author(s):

Saori Sakaue ◽

Yukinori Okada

Keyword(s):

Large Scale ◽

Human Genetics ◽

Association Studies ◽

Enrichment Analysis ◽

Supplementary Information ◽

Clinical Indication ◽

Genome Wide Association Studies ◽

Expression Studies ◽

Gene Expression Studies ◽

Approved Drugs

AbstractSummaryMaking use of accumulated genetic knowledge for clinical practice is our next goal in human genetics. Here we introduce GREP (Genome for REPositioning drugs), a standalone python software to quantify an enrichment of the user-defined set of genes in the target of clinical indication categories and to capture potentially repositionable drugs targeting the gene set. We show that genes identified by the large-scale genome-wide association studies were robustly enriched in the approved drugs to treat the trait of interest. This enrichment analysis was also highly applicable to other sets of biological genes such as those identified by gene expression studies and genes somatically mutated in cancers. This software should accelerate investigators to reposition drugs to other indications with the guidance of known genomics.Availability and implementationGREP is available at https://github.com/saorisakaue/GREP as a python source code.Supplementary informationSupplementary data are available at Bioinformatics online.

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A unifying framework for joint trait analysis under a non-infinitesimal model

10.1101/293803 ◽

2018 ◽

Author(s):

Ruth Johnson ◽

Huwenbo Shi ◽

Bogdan Pasaniuc ◽

Sriram Sankararaman

Keyword(s):

Complex Traits ◽

Association Studies ◽

Supplementary Information ◽

Genome Wide Association Studies ◽

Posterior Density ◽

Trait Analysis ◽

Genome Wide ◽

Genetic Overlap ◽

Causal Variants ◽

Supplementary Material

AbstractMotivationA large proportion of risk regions identified by genome-wide association studies (GWAS) are shared across multiple diseases and traits. Understanding whether this clustering is due to sharing of causal variants or chance colocalization can provide insights into shared etiology of complex traits and diseases.ResultsIn this work, we propose a flexible, unifying framework to quantify the overlap between a pair of traits called UNITY (Unifying Non-Infinitesimal Trait analYsis). We formulate a Bayesian generative model that relates the overlap between pairs of traits to GWAS summary statistic data under a non-infinitesimal genetic architecture underlying each trait. We propose a Metropolis-Hastings sampler to compute the posterior density of the genetic overlap parameters in this model. We validate our method through comprehensive simulations and analyze summary statistics from height and BMI GWAS to show that it produces estimates consistent with the known genetic makeup of both traits.AvailabilityThe UNITY software is made freely available to the research community at: https://github.com/bogdanlab/[email protected] informationSupplementary data are available at Bioinformatics online.

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Faculty Opinions recommendation of Meta-Analysis of Genome-Wide Association Studies and Network Analysis-Based Integration with Gene Expression Data Identify New Suggestive Loci and Unravel a Wnt-Centric Network Associated with Dupuytren's Disease.

Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature ◽

10.3410/f.726582766.793522025 ◽

2016 ◽

Author(s):

Rik Lories

Keyword(s):

Gene Expression ◽

Network Analysis ◽

Gene Expression Data ◽

Association Studies ◽

Meta Analysis ◽

Genome Wide Association ◽

Genome Wide Association Studies ◽

Expression Data ◽

Dupuytren's Disease ◽

Genome Wide

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Transcriptome-wide Mendelian randomization study prioritising novel tissue-dependent genes for glioma susceptibility

Scientific Reports ◽

10.1038/s41598-021-82169-5 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Jamie W. Robinson ◽

Richard M. Martin ◽

Spiridon Tsavachidis ◽

Amy E. Howell ◽

Caroline L. Relton ◽

...

Keyword(s):

Gene Expression ◽

Association Studies ◽

Tissue Expression ◽

Tissue Type ◽

Mendelian Randomisation ◽

Genome Wide Association Studies ◽

Causal Pathways ◽

Genome Wide ◽

Glioma Risk ◽

Brain Tissues

AbstractGenome-wide association studies (GWAS) have discovered 27 loci associated with glioma risk. Whether these loci are causally implicated in glioma risk, and how risk differs across tissues, has yet to be systematically explored. We integrated multi-tissue expression quantitative trait loci (eQTLs) and glioma GWAS data using a combined Mendelian randomisation (MR) and colocalisation approach. We investigated how genetically predicted gene expression affects risk across tissue type (brain, estimated effective n = 1194 and whole blood, n = 31,684) and glioma subtype (all glioma (7400 cases, 8257 controls) glioblastoma (GBM, 3112 cases) and non-GBM gliomas (2411 cases)). We also leveraged tissue-specific eQTLs collected from 13 brain tissues (n = 114 to 209). The MR and colocalisation results suggested that genetically predicted increased gene expression of 12 genes were associated with glioma, GBM and/or non-GBM risk, three of which are novel glioma susceptibility genes (RETREG2/FAM134A, FAM178B and MVB12B/FAM125B). The effect of gene expression appears to be relatively consistent across glioma subtype diagnoses. Examining how risk differed across 13 brain tissues highlighted five candidate tissues (cerebellum, cortex, and the putamen, nucleus accumbens and caudate basal ganglia) and four previously implicated genes (JAK1, STMN3, PICK1 and EGFR). These analyses identified robust causal evidence for 12 genes and glioma risk, three of which are novel. The correlation of MR estimates in brain and blood are consistently low which suggested that tissue specificity needs to be carefully considered for glioma. Our results have implicated genes yet to be associated with glioma susceptibility and provided insight into putatively causal pathways for glioma risk.

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GWASpro: a high-performance genome-wide association analysis server

Bioinformatics ◽

10.1093/bioinformatics/bty989 ◽

2018 ◽

Vol 35 (14) ◽

pp. 2512-2514 ◽

Cited By ~ 4

Author(s):

Bongsong Kim ◽

Xinbin Dai ◽

Wenchao Zhang ◽

Zhaohong Zhuang ◽

Darlene L Sanchez ◽

...

Keyword(s):

High Performance ◽

Large Scale ◽

Linear Mixed Model ◽

Association Studies ◽

Learning Curves ◽

Experimental Designs ◽

Genome Wide Association ◽

Supplementary Information ◽

Genome Wide Association Studies ◽

Genome Wide

Abstract Summary We present GWASpro, a high-performance web server for the analyses of large-scale genome-wide association studies (GWAS). GWASpro was developed to provide data analyses for large-scale molecular genetic data, coupled with complex replicated experimental designs such as found in plant science investigations and to overcome the steep learning curves of existing GWAS software tools. GWASpro supports building complex design matrices, by which complex experimental designs that may include replications, treatments, locations and times, can be accounted for in the linear mixed model. GWASpro is optimized to handle GWAS data that may consist of up to 10 million markers and 10 000 samples from replicable lines or hybrids. GWASpro provides an interface that significantly reduces the learning curve for new GWAS investigators. Availability and implementation GWASpro is freely available at https://bioinfo.noble.org/GWASPRO. Supplementary information Supplementary data are available at Bioinformatics online.

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

10.1101/563379 ◽

2019 ◽

Cited By ~ 2

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/.

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