scholarly journals Integration of genetically regulated gene expression and pharmacological library provides therapeutic drug candidates

2021 ◽  
Author(s):  
Takahiro Konuma ◽  
Kotaro Ogawa ◽  
Yukinori Okada
Keyword(s):  
Gene Expression ◽  
Drug Development ◽  
Association Study ◽  
Large Scale ◽  
Genome Wide Association Study ◽  
Expression Profiles ◽  
Meta Analysis ◽  
Therapeutic Drug ◽  
Summary Statistics ◽  
Regulated Gene Expression

Abstract Approaches toward new therapeutics using disease genomics, such as genome-wide association study (GWAS), are anticipated. Here, we developed Trans-Phar [integration of transcriptome-wide association study (TWAS) and pharmacological database], achieving in silico screening of compounds from a large-scale pharmacological database (L1000 Connectivity Map), which have inverse expression profiles compared with tissue-specific genetically regulated gene expression. Firstly we confirmed the statistical robustness by the application of the null GWAS data and enrichment in the true-positive drug–disease relationships by the application of UK-Biobank GWAS summary statistics in broad disease categories, then we applied the GWAS summary statistics of large-scale European meta-analysis (17 traits; naverage = 201 849) and the hospitalized COVID-19 (n = 900 687), which has urgent need for drug development. We detected potential therapeutic compounds as well as anisomycin in schizophrenia (false discovery rate (FDR)-q = 0.056) and verapamil in hospitalized COVID-19 (FDR-q = 0.068) as top-associated compounds. This approach could be effective in disease genomics-driven drug development.

scholarly journals A transcriptome-wide association study identifies susceptibility genes for Parkinson’s disease

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Shi Yao ◽  
Xi Zhang ◽  
Shu-Cheng Zou ◽  
Yong Zhu ◽  
Bo Li ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Association Study ◽  
Large Scale ◽  
Genome Wide Association Study ◽  
Nucleotide Polymorphisms ◽  
New Genes ◽  
Causal Genes ◽  
Regulated Gene Expression ◽  
Brain Tissues

AbstractGenome-wide association study (GWAS) has seen great strides in revealing initial insights into the genetic architecture of Parkinson’s disease (PD). Since GWAS signals often reside in non-coding regions, relatively few of the associations have implicated specific biological mechanisms. Here, we aimed to integrate the GWAS results with large-scale expression quantitative trait loci (eQTL) in 13 brain tissues to identify candidate causal genes for PD. We conducted a transcriptome-wide association study (TWAS) for PD using the summary statistics of over 480,000 individuals from the most recent PD GWAS. We identified 18 genes significantly associated with PD after Bonferroni corrections. The most significant gene, LRRC37A2, was associated with PD in all 13 brain tissues, such as in the hypothalamus (P = 6.12 × 10−22) and nucleus accumbens basal ganglia (P = 5.62 × 10−21). We also identified eight conditionally independent genes, including four new genes at known PD loci: CD38, LRRC37A2, RNF40, and ZSWIM7. Through conditional analyses, we demonstrated that several of the GWAS significant signals on PD could be driven by genetically regulated gene expression. The most significant TWAS gene LRRC37A2 accounts for 0.855 of the GWAS signal at its loci, and ZSWIM7 accounts for all the GWAS signals at its loci. We further identified several phenotypes previously associated with PD by querying the single nucleotide polymorphisms (SNPs) in the final model of the identified genes in phenome databases. In conclusion, we prioritized genes that are likely to affect PD by using a TWAS approach and identified phenotypes associated with PD.

scholarly journals Transcriptome-wide association study identifies susceptibility genes for rheumatoid arthritis

2021 ◽  
Vol 23 (1) ◽  
Author(s):  
Cuiyan Wu ◽  
Sijian Tan ◽  
Li Liu ◽  
Shiqiang Cheng ◽  
Peilin Li ◽  
...  
Keyword(s):  
Rheumatoid Arthritis ◽  
Association Study ◽  
Mrna Expression ◽  
Signaling Pathway ◽  
Large Scale ◽  
Genome Wide Association Study ◽  
Expression Profiles ◽  
Susceptibility Genes ◽  
Pathway Regulation ◽  
Gene Expression Profile Data

Abstract Objective To identify rheumatoid arthritis (RA)-associated susceptibility genes and pathways through integrating genome-wide association study (GWAS) and gene expression profile data. Methods A transcriptome-wide association study (TWAS) was conducted by the FUSION software for RA considering EBV-transformed lymphocytes (EL), transformed fibroblasts (TF), peripheral blood (NBL), and whole blood (YBL). GWAS summary data was driven from a large-scale GWAS, involving 5539 autoantibody-positive RA patients and 20,169 controls. The TWAS-identified genes were further validated using the mRNA expression profiles and made a functional exploration. Results TWAS identified 692 genes with PTWAS values < 0.05 for RA. CRIPAK (PEL = 0.01293, PTF = 0.00038, PNBL = 0.02839, PYBL = 0.0978), MUT (PEL = 0.00377, PTF = 0.00076, PNBL = 0.00778, PYBL = 0.00096), FOXRED1 (PEL = 0.03834, PTF = 0.01120, PNBL = 0.01280, PYBL = 0.00583), and EBPL (PEL = 0.00806, PTF = 0.03761, PNBL = 0.03540, PYBL = 0.04254) were collectively expressed in all the four tissues/cells. Eighteen genes, including ANXA5, AP4B1, ATIC (PTWAS = 0.0113, downregulated expression), C12orf65, CMAH, PDHB, RUNX3 (PTWAS = 0.0346, downregulated expression), SBF1, SH2B3, STK38, TMEM43, XPNPEP1, KIAA1530, NUFIP2, PPP2R3C, RAB24, STX6, and TLR5 (PTWAS = 0.04665, upregulated expression), were validated with integrative analysis of TWAS and mRNA expression profiles. TWAS-identified genes functionally involved in endoplasmic reticulum organization, regulation of cytokine production, TNF signaling pathway, immune response-regulating signaling pathway, regulation of autophagy, etc. Conclusion We identified multiple candidate genes and pathways, providing novel clues for the genetic mechanism of RA.

scholarly journals Trans-ancestry genome-wide association study identifies novel genetic mechanisms in rheumatoid arthritis

2021 ◽  
Author(s):  
Kazuyoshi Ishigaki ◽  
Saori Sakaue ◽  
Chikashi Terao ◽  
Yang Luo ◽  
Kyuto Sonehara ◽  
...  
Keyword(s):  
Rheumatoid Arthritis ◽  
Association Study ◽  
Fine Mapping ◽  
Large Scale ◽  
Genome Wide Association Study ◽  
Meta Analysis ◽  
Genetic Research ◽  
Genome Wide Association ◽  
Polygenic Risk Score ◽  
Genome Wide

AbstractTrans-ancestry genetic research promises to improve power to detect genetic signals, fine-mapping resolution, and performances of polygenic risk score (PRS). We here present a large-scale genome-wide association study (GWAS) of rheumatoid arthritis (RA) which includes 276,020 samples of five ancestral groups. We conducted a trans-ancestry meta-analysis and identified 124 loci (P < 5 × 10-8), of which 34 were novel. Candidate genes at the novel loci suggested essential roles of the immune system (e.g., TNIP2 and TNFRSF11A) and joint tissues (e.g., WISP1) in RA etiology. Trans-ancestry fine mapping identified putatively causal variants with biological insights (e.g., LEF1). Moreover, PRS based on trans-ancestry GWAS outperformed PRS based on single-ancestry GWAS and had comparable performance between European and East Asian populations. Our study provides multiple insights into the etiology of RA and improves genetic predictability of RA.

scholarly journals A large-scale genome-wide association study meta-analysis of cannabis use disorder

2020 ◽  
Vol 7 (12) ◽  
pp. 1032-1045 ◽  
Author(s):  
Emma C Johnson ◽  
Ditte Demontis ◽  
Thorgeir E Thorgeirsson ◽  
Raymond K Walters ◽  
Renato Polimanti ◽  
...  
Keyword(s):  
Association Study ◽  
Large Scale ◽  
Genome Wide Association Study ◽  
Meta Analysis ◽  
Cannabis Use ◽  
Genome Wide Association ◽  
Cannabis Use Disorder ◽  
Genome Wide

scholarly journals Genetic links between endometriosis and cancers in women

PeerJ ◽  
2019 ◽  
Vol 7 ◽  
pp. e8135 ◽  
Author(s):  
Salma Begum Bhyan ◽  
Li Zhao ◽  
YongKiat Wee ◽  
Yining Liu ◽  
Min Zhao
Keyword(s):  
Gene Expression ◽  
Cancer Progression ◽  
Large Scale ◽  
Cancer Genomics ◽  
Expression Profiles ◽  
Genetic Relationships ◽  
Meta Analysis ◽  
Gene Expression Profiles ◽  
Expression Of Genes ◽  
Genetic Mechanisms

Endometriosis is a chronic disease occurring during the reproductive stage of women. Although there is only limited association between endometriosis and gynecological cancers with regard to clinical features, the molecular basis of the relationship between these diseases is unexplored. We conducted a systematic study by integrating literature-based evidence, gene expression and large-scale cancer genomics data in order to reveal any genetic relationships between endometriosis and cancers in women. We curated 984 endometriosis-related genes from 3270 PubMed articles and then conducted a meta-analysis of the two public gene expression profiles related to endometriosis which identified Differential Expression of Genes (DEGs). Following an overlapping analysis, we identified 39 key endometriosis-related genes common in both literature and DEG analysis. Finally, the functional analysis confirmed that all the 39 genes were associated with the vital processes of tumour formation and cancer progression and that two genes (PGR and ESR1) were common to four cancers of women. From network analysis, we identified a novel linker gene, C3AR1, which had not been implicated previously in endometriosis. The shared genetic mechanisms of endometriosis and cancers in women identified in this study provided possible new avenues of multiple disease management and treatments through early diagnosis.

scholarly journals Gene Expression Correlates of the Cortical Network Underlying Sentence Processing

2020 ◽  
Vol 1 (1) ◽  
pp. 77-103 ◽  
Author(s):  
Xiang-Zhen Kong ◽  
Nathalie Tzourio-Mazoyer ◽  
Marc Joliot ◽  
Evelina Fedorenko ◽  
Jia Liu ◽  
...  
Keyword(s):  
Gene Expression ◽  
Sentence Processing ◽  
Neural Development ◽  
Large Scale ◽  
Expression Profiles ◽  
Meta Analysis ◽  
Gene Expression Profiles ◽  
Functional Network ◽  
Integrative Approach ◽  
Processing Network

A pivotal question in modern neuroscience is which genes regulate brain circuits that underlie cognitive functions. However, the field is still in its infancy. Here we report an integrated investigation of the high-level language network (i.e., sentence-processing network) in the human cerebral cortex, combining regional gene expression profiles, task fMRI, large-scale neuroimaging meta-analysis, and resting-state functional network approaches. We revealed reliable gene expression–functional network correlations using three different network definition strategies, and identified a consensus set of genes related to connectivity within the sentence-processing network. The genes involved showed enrichment for neural development and actin-related functions, as well as association signals with autism, which can involve disrupted language functioning. Our findings help elucidate the molecular basis of the brain’s infrastructure for language. The integrative approach described here will be useful for studying other complex cognitive traits.

scholarly journals Genome-Wide Association Study Highlights APOH as a Novel Locus for Lipoprotein(a) Levels

2020 ◽  
Author(s):  
Mary Hoekstra ◽  
Hao Yu Chen ◽  
Jian Rong ◽  
Line Dufresne ◽  
Jie Yao ◽  
...  
Keyword(s):  
Association Study ◽  
Large Scale ◽  
Genome Wide Association Study ◽  
Meta Analysis ◽  
Genome Wide Association ◽  
European Ancestry ◽  
Linear Regression Models ◽  
Lipoprotein A ◽  
Genome Wide ◽  
A Genome

Objective: Lp(a) (lipoprotein[a]) is an independent risk factor for cardiovascular diseases and plasma levels are primarily determined by variation at the LPA locus. We performed a genome-wide association study in the UK Biobank to determine whether additional loci influence Lp(a) levels. Approach and Results: We included 293 274 White British individuals in the discovery analysis. Approximately 93 095 623 variants were tested for association with natural log-transformed Lp(a) levels using linear regression models adjusted for age, sex, genotype batch, and 20 principal components of genetic ancestry. After quality control, 131 independent variants were associated at genome-wide significance (P ≤5×10 -8 ). In addition to validating previous associations at LPA , APOE , and CETP , we identified a novel variant at the APOH locus, encoding β2GPI (beta2-glycoprotein I). The APOH variant rs8178824 was associated with increased Lp(a) levels (β [95% CI] [ln nmol/L], 0.064 [0.047–0.081]; P =2.8×10 -13 ) and demonstrated a stronger effect after adjustment for variation at the LPA locus (β [95% CI] [ln nmol/L], 0.089 [0.076–0.10]; P =3.8×10 -42 ). This association was replicated in a meta-analysis of 5465 European-ancestry individuals from the Framingham Offspring Study and Multi-Ethnic Study of Atherosclerosis (β [95% CI] [ln mg/dL], 0.16 [0.044–0.28]; P =0.0071). Conclusions: In a large-scale genome-wide association study of Lp(a) levels, we identified APOH as a novel locus for Lp(a) in individuals of European ancestry. Additional studies are needed to determine the precise role of β2GPI in influencing Lp(a) levels as well as its potential as a therapeutic target.

scholarly journals Transcriptome-wide association study identifies susceptibility genes for rheumatoid arthritis

2020 ◽  
Author(s):  
Cuiyan Wu ◽  
Sijia Tan ◽  
Li Liu ◽  
Shiqiang Cheng ◽  
Peilin Li ◽  
...  
Keyword(s):  
Rheumatoid Arthritis ◽  
Association Study ◽  
Mrna Expression ◽  
Large Scale ◽  
Genome Wide Association Study ◽  
Expression Profiles ◽  
Susceptibility Genes ◽  
Genome Wide ◽  
Regulated Expression ◽  
Gene Expression Profile Data

Abstract ObjectiveTo identify rheumatoid arthritis (RA) associated susceptibility genes and pathways through integrating genome-wide association study (GWAS) and gene expression profile data. MethodsA transcriptome-wide association study (TWAS) was conducted by the FUSION software for RA considering EBV-transformed lymphocytes (EL), transformed fibroblasts (TF), peripheral blood (NBL) and whole blood (YBL). GWAS summary data was driven from a large-scale GWAS, involving 5,539 autoantibody-positive RA patients and 20,169 controls. The TWAS-identified genes were further validated using the mRNA expression profiles and made a functional exploration. ResultsTWAS identified 692 genes with PTWAS values < 0.05 for RA. CRIPAK (PEL = 0.01293, PTF = 0.00038, PNBL = 0.02839, PYBL = 0.0978), MUT (PEL = 0.00377, PTF = 0.00076, PNBL = 0.00778, PYBL = 0.00096), FOXRED1 (PEL = 0.03834, PTF = 0.01120, PNBL = 0.01280, PYBL = 0.00583) and EBPL (PEL = 0.00806, PTF = 0.03761, PNBL = 0.03540, PYBL = 0.04254) were collectively expressed in all the four tissues/cells. 18 genes, including ANXA5, AP4B1, ATIC (PTWAS = 0.0113, down-regulated expression), C12orf65, CMAH, PDHB, RUNX3 (PTWAS = 0.0346, down-regulated expression), SBF1, SH2B3, STK38, TMEM43, XPNPEP1, KIAA1530, NUFIP2, PPP2R3C, RAB24, STX6, TLR5 (PTWAS = 0.04665, up-regulated expression), were validated with integrative analysis of TWAS and mRNA expression profiles. TWAS-identified genes functionally involved in endomembrane system organization, endoplasmic reticulum organization, regulation of cytokine production, TNF signaling pathway, etc. ConclusionWe identified multiple candidate genes and pathways, providing novel clues for the genetic mechanism of RA.

Sign in / Sign up

Export Citation Format

Share Document