scholarly journals When GWAS meets the Connectivity Map: drug repositioning for seven psychiatric disorders

2016 ◽  
Author(s):  
Hon-Cheong So ◽  
Carlos K.L. Chau ◽  
Wan-To Chiu ◽  
Kin-Sang Ho ◽  
Cho-Pong Lo ◽  
...  
Keyword(s):  
Psychiatric Disorders ◽  
Developmental Process ◽  
Drug Repositioning ◽  
Expression Profiles ◽  
Association Studies ◽  
Cost Effective ◽  
Genome Wide Association Studies ◽  
Connectivity Map ◽  
Drug Candidates ◽  
Cost Effective Approach

AbstractOur knowledge of disease genetics has advanced rapidly during the past decade, with the advent of high-throughput genotyping technologies such as genome-wide association studies (GWAS). However, few methodologies were developed and systemic studies performed to identify novel drug candidates utilizing GWAS data. In this study we focus on drug repositioning, which is a cost-effective approach to shorten the developmental process of new therapies. We proposed a novel framework of drug repositioning by comparing GWAS-imputed transcriptome with drug expression profiles from the Connectivity Map. The approach was applied to 7 psychiatric disorders. We discovered a number of novel repositioning candidates, many of which are supported by preclinical or clinical evidence. We found that the predicted drugs are significantly enriched for known psychiatric medications, or therapies considered in clinical trials. For example, drugs repurposed for schizophrenia are strongly enriched for antipsychotics (p = 4.69E-06), while those repurposed for bipolar disorder are enriched for antipsychotics (p = 2.26E-07) and antidepressants (p = 1.17E-05). These findings provide support to the usefulness of GWAS signals in guiding drug discoveries and the validity of our approach in drug repositioning. We also present manually curated lists of top repositioning candidates for each disorder, which we believe will serve as a useful resource for researchers.

scholarly journals An Integrative Transcriptome-Wide Analysis of Amyotrophic Lateral Sclerosis for the Identification of Potential Genetic Markers and Drug Candidates

2021 ◽  
Vol 22 (6) ◽  
pp. 3216
Author(s):  
Sungmin Park ◽  
Daeun Kim ◽  
Jaeseung Song ◽  
Jong Wha J. Joo
Keyword(s):  
Amyotrophic Lateral Sclerosis ◽  
Small Molecules ◽  
Expression Profiles ◽  
Association Studies ◽  
Enrichment Analysis ◽  
Genome Wide Association Studies ◽  
Summary Statistic ◽  
Drug Candidates ◽  
Causal Genes ◽  
Lateral Sclerosis

Amyotrophic lateral sclerosis (ALS) is a neurodegenerative neuromuscular disease. Although genome-wide association studies (GWAS) have successfully identified many variants significantly associated with ALS, it is still difficult to characterize the underlying biological mechanisms inducing ALS. In this study, we performed a transcriptome-wide association study (TWAS) to identify disease-specific genes in ALS. Using the largest ALS GWAS summary statistic (n = 80,610), we identified seven novel genes using 19 tissue reference panels. We conducted a conditional analysis to verify the genes’ independence and to confirm that they are driven by genetically regulated expressions. Furthermore, we performed a TWAS-based enrichment analysis to highlight the association of important biological pathways, one in each of the four tissue reference panels. Finally, utilizing a connectivity map, a database of human cell expression profiles cultured with bioactive small molecules, we discovered functional associations between genes and drugs to identify 15 bioactive small molecules as potential drug candidates for ALS. We believe that, by integrating the largest ALS GWAS summary statistic with gene expression to identify new risk loci and causal genes, our study provides strong candidates for molecular basis experiments in ALS.

scholarly journals Identification and Characterization of Alternatively Spliced Transcript Isoforms of IRX4 in Prostate Cancer

Genes ◽  
2021 ◽  
Vol 12 (5) ◽  
pp. 615
Author(s):  
Achala Fernando ◽  
Chamikara Liyanage ◽  
Afshin Moradi ◽  
Panchadsaram Janaththani ◽  
Jyotsna Batra
Keyword(s):  
Prostate Cancer ◽  
Cell Lines ◽  
Expression Profiles ◽  
Association Studies ◽  
Protein Isoforms ◽  
The Cancer Genome Atlas ◽  
Mass Spectrometry Analysis ◽  
Functional Domains ◽  
Homeodomain Protein ◽  
Genome Wide Association Studies

Alternative splicing (AS) is tightly regulated to maintain genomic stability in humans. However, tumor growth, metastasis and therapy resistance benefit from aberrant RNA splicing. Iroquois-class homeodomain protein 4 (IRX4) is a TALE homeobox transcription factor which has been implicated in prostate cancer (PCa) as a tumor suppressor through genome-wide association studies (GWAS) and functional follow-up studies. In the current study, we characterized 12 IRX4 transcripts in PCa cell lines, including seven novel transcripts by RT-PCR and sequencing. They demonstrate unique expression profiles between androgen-responsive and nonresponsive cell lines. These transcripts were significantly overexpressed in PCa cell lines and the cancer genome atlas program (TCGA) PCa clinical specimens, suggesting their probable involvement in PCa progression. Moreover, a PCa risk-associated SNP rs12653946 genotype GG was corelated with lower IRX4 transcript levels. Using mass spectrometry analysis, we identified two IRX4 protein isoforms (54.4 kDa, 57 kDa) comprising all the functional domains and two novel isoforms (40 kDa, 8.7 kDa) lacking functional domains. These IRX4 isoforms might induce distinct functional programming that could contribute to PCa hallmarks, thus providing novel insights into diagnostic, prognostic and therapeutic significance in PCa management.

scholarly journals Prediction of response to drug therapy in psychiatric disorders

Open Biology ◽  
2018 ◽  
Vol 8 (5) ◽  
pp. 180031 ◽  
Author(s):  
Shani Stern ◽  
Sara Linker ◽  
Krishna C. Vadodaria ◽  
Maria C. Marchetto ◽  
Fred H. Gage
Keyword(s):  
Autism Spectrum Disorder ◽  
Bipolar Disorder ◽  
Major Depression ◽  
Personalized Medicine ◽  
Psychiatric Disorders ◽  
Association Studies ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Genome Wide Association Studies ◽  
Patient Reported

Personalized medicine has become increasingly relevant to many medical fields, promising more efficient drug therapies and earlier intervention. The development of personalized medicine is coupled with the identification of biomarkers and classification algorithms that help predict the responses of different patients to different drugs. In the last 10 years, the Food and Drug Administration (FDA) has approved several genetically pre-screened drugs labelled as pharmacogenomics in the fields of oncology, pulmonary medicine, gastroenterology, haematology, neurology, rheumatology and even psychiatry. Clinicians have long cautioned that what may appear to be similar patient-reported symptoms may actually arise from different biological causes. With growing populations being diagnosed with different psychiatric conditions, it is critical for scientists and clinicians to develop precision medication tailored to individual conditions. Genome-wide association studies have highlighted the complicated nature of psychiatric disorders such as schizophrenia, bipolar disorder, major depression and autism spectrum disorder. Following these studies, association studies are needed to look for genomic markers of responsiveness to available drugs of individual patients within the population of a specific disorder. In addition to GWAS, the advent of new technologies such as brain imaging, cell reprogramming, sequencing and gene editing has given us the opportunity to look for more biomarkers that characterize a therapeutic response to a drug and to use all these biomarkers for determining treatment options. In this review, we discuss studies that were performed to find biomarkers of responsiveness to different available drugs for four brain disorders: bipolar disorder, schizophrenia, major depression and autism spectrum disorder. We provide recommendations for using an integrated method that will use available techniques for a better prediction of the most suitable drug.

scholarly journals Characterizing the mechanism behind the progression of NAFLD to hepatocellular carcinoma

2020 ◽  
pp. HEP36
Author(s):  
Pierre Nahon ◽  
Manon Allaire ◽  
Jean-Charles Nault ◽  
Valérie Paradis
Keyword(s):  
Hepatocellular Carcinoma ◽  
Fatty Liver Disease ◽  
Association Studies ◽  
Cost Effective ◽  
Genome Wide Association Studies ◽  
Alcoholic Fatty Liver ◽  
Genome Wide ◽  
High Prevalence ◽  
Prevalence Of Obesity ◽  
Alcoholic Fatty Liver Disease

Hepatocellular carcinoma (HCC) developed in non-alcoholic fatty liver disease (NAFLD) individuals presents substantial clinical and biological characteristics, which remain to be elucidated. Its occurrence in noncirrhotic patients raises issues regarding surveillance strategies, which cannot be considered as cost-effective given the high prevalence of obesity and metabolic syndrome, and furthermore delineates specific oncogenic process that could be targeted in the setting of primary or secondary prevention. In this context, the identification of a genetic heterogeneity modulating HCC risk as well as specific biological pathways have been made possible through genome-wide association studies, development of animal models and in-depth analyses of human samples at the pathological and genomic levels. These advances must be confirmed and pursued to pave the way for personalized management of NAFLD-related HCC.

scholarly journals Genome-wide meta-analysis of depression identifies 102 independent variants and highlights the importance of the prefrontal brain regions

2018 ◽  
Author(s):  
David M. Howard ◽  
Mark J. Adams ◽  
Toni-Kim Clarke ◽  
Jonathan D. Hafferty ◽  
Jude Gibson ◽  
...  
Keyword(s):  
Multiple Testing ◽  
Drug Repositioning ◽  
Association Studies ◽  
Meta Analysis ◽  
Enrichment Analysis ◽  
Brain Regions ◽  
Genome Wide Association Studies ◽  
Multiple Testing Correction ◽  
Synaptic Structure ◽  
Genome Wide

AbstractMajor depression is a debilitating psychiatric illness that is typically associated with low mood, anhedonia and a range of comorbidities. Depression has a heritable component that has remained difficult to elucidate with current sample sizes due to the polygenic nature of the disorder. To maximise sample size, we meta-analysed data on 807,553 individuals (246,363 cases and 561,190 controls) from the three largest genome-wide association studies of depression. We identified 102 independent variants, 269 genes, and 15 gene-sets associated with depression, including both genes and gene-pathways associated with synaptic structure and neurotransmission. Further evidence of the importance of prefrontal brain regions in depression was provided by an enrichment analysis. In an independent replication sample of 1,306,354 individuals (414,055 cases and 892,299 controls), 87 of the 102 associated variants were significant following multiple testing correction. Based on the putative genes associated with depression this work also highlights several potential drug repositioning opportunities. These findings advance our understanding of the complex genetic architecture of depression and provide several future avenues for understanding aetiology and developing new treatment approaches.

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 Rapid Identification of Potential Drug Candidates from Multi-Million Compounds’ Repositories. Combination of 2D Similarity Search with 3D Ligand/Structure Based Methods and In Vitro Screening

Molecules ◽  
2021 ◽  
Vol 26 (18) ◽  
pp. 5593
Author(s):  
Katalin Szilágyi ◽  
Beáta Flachner ◽  
István Hajdú ◽  
Mária Szaszkó ◽  
Krisztina Dobi ◽  
...  
Keyword(s):  
Similarity Search ◽  
Pharmacophore Model ◽  
Cost Effective ◽  
Rapid Identification ◽  
Drug Candidates ◽  
Hit Rate ◽  
Physico Chemical ◽  
Cost Effective Approach ◽  
3D Methods

Rapid in silico selection of target focused libraries from commercial repositories is an attractive and cost-effective approach in early drug discovery. If structures of active compounds are available, rapid 2D similarity search can be performed on multimillion compounds’ databases. This approach can be combined with physico-chemical parameter and diversity filtering, bioisosteric replacements, and fragment-based approaches for performing a first round biological screening. Our objectives were to investigate the combination of 2D similarity search with various 3D ligand and structure-based methods for hit expansion and validation, in order to increase the hit rate and novelty. In the present account, six case studies are described and the efficiency of mixing is evaluated. While sequentially combined 2D/3D similarity approach increases the hit rate significantly, sequential combination of 2D similarity with pharmacophore model or 3D docking enriched the resulting focused library with novel chemotypes. Parallel integrated approaches allowed the comparison of the various 2D and 3D methods and revealed that 2D similarity-based and 3D ligand and structure-based techniques are often complementary, and their combinations represent a powerful synergy. Finally, the lessons we learnt including the advantages and pitfalls of the described approaches are discussed.

A systematic analysis of genetically regulated differences in gene expression and the role of co-expression networks across 16 psychiatric disorders and substance use phenotypes

2021 ◽  
Author(s):  
Zachary F Gerring ◽  
Jackson G Thorp ◽  
Eric R Gamazon ◽  
Eske M Derks
Keyword(s):  
Gene Expression ◽  
Mental Health ◽  
Substance Use ◽  
Prefrontal Cortex ◽  
Psychiatric Disorders ◽  
Developmental Disorders ◽  
Association Studies ◽  
Genetic Correlations ◽  
Autism Spectrum ◽  
Genome Wide Association Studies

ABSTRACTGenome-wide association studies (GWASs) have identified thousands of risk loci for many psychiatric and substance use phenotypes, however the biological consequences of these loci remain largely unknown. We performed a transcriptome-wide association study of 10 psychiatric disorders and 6 substance use phenotypes (collectively termed “mental health phenotypes”) using expression quantitative trait loci data from 532 prefrontal cortex samples. We estimated the correlation due to predicted genetically regulated expression between pairs of mental health phenotypes, and compared the results with the genetic correlations. We identified 1,645 genes with at least one significant trait association, comprising 2,176 significant associations across the 16 mental health phenotypes of which 572 (26%) are novel. Overall, the transcriptomic correlations for phenotype pairs were significantly higher than the respective genetic correlations. For example, attention deficit hyperactivity disorder and autism spectrum disorder, both childhood developmental disorders, showed a much higher transcriptomic correlation (r=0.84) than genetic correlation (r=0.35). Finally, we tested the enrichment of phenotype-associated genes in gene co-expression networks built from prefrontal cortex. Phenotype-associated genes were enriched in multiple gene co-expression modules and the implicated modules contained genes involved in mRNA splicing and glutamatergic receptors, among others. Together, our results highlight the utility of gene expression data in the understanding of functional gene mechanisms underlying psychiatric disorders and substance use phenotypes.

Machine Learning Perspective in Cancer Research

2021 ◽  
pp. 142-163
Author(s):  
Aman Sharma ◽  
Rinkle Rani
Keyword(s):  
Machine Learning ◽  
Cancer Research ◽  
Expression Profiles ◽  
Genetic Diseases ◽  
Cost Effective ◽  
Healthcare Sector ◽  
Diagnosis And Prognosis ◽  
Cost Effective Approach ◽  
Next Generation Sequencing Ngs ◽  
Generation Sequencing

Advancement in genome sequencing technology has empowered researchers to think beyond their imagination. Researchers are trying their hard to fight against various genetic diseases like cancer. Artificial intelligence has empowered research in the healthcare sector. Moreover, the availability of opensource healthcare datasets has motivated the researchers to develop applications which can help in early diagnosis and prognosis of diseases. Further, next-generation sequencing (NGS) has helped to look into detailed intricacies of biological systems. It has provided an efficient and cost-effective approach with higher accuracy. The advent of microRNAs also known as small noncoding genes has begun the paradigm shift in oncological research. We are now able to profile expression profiles of RNAs using RNA-seq data. microRNA profiling has helped in uncovering their relationship in various genetic and biological processes. Here in this chapter, the authors present a review of the machine learning perspective in cancer research.

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