scholarly journals Advances in Genomics for Drug Development

Genes ◽  
2020 ◽  
Vol 11 (8) ◽  
pp. 942
Author(s):  
Roberto Spreafico ◽  
Leah B. Soriaga ◽  
Johannes Grosse ◽  
Herbert W. Virgin ◽  
Amalio Telenti
Keyword(s):  
Drug Development ◽  
Drug Targets ◽  
Population Genomics ◽  
Target Identification ◽  
Association Studies ◽  
Genome Wide Association Studies ◽  
Genomic Knowledge ◽  
Genome Wide ◽  
Cell Gene Expression ◽  
Preclinical And Clinical Studies

Drug development (target identification, advancing drug leads to candidates for preclinical and clinical studies) can be facilitated by genetic and genomic knowledge. Here, we review the contribution of population genomics to target identification, the value of bulk and single cell gene expression analysis for understanding the biological relevance of a drug target, and genome-wide CRISPR editing for the prioritization of drug targets. In genomics, we discuss the different scope of genome-wide association studies using genotyping arrays, versus exome and whole genome sequencing. In transcriptomics, we discuss the information from drug perturbation and the selection of biomarkers. For CRISPR screens, we discuss target discovery, mechanism of action and the concept of gene to drug mapping. Harnessing genetic support increases the probability of drug developability and approval.

scholarly journals The druggable genome and support for target identification and validation in drug development

2016 ◽  
Author(s):  
Chris Finan ◽  
Anna Gaulton ◽  
Felix Kruger ◽  
Tom Lumbers ◽  
Tina Shah ◽  
...  
Keyword(s):  
Drug Development ◽  
Drug Target ◽  
Complex Disease ◽  
Target Identification ◽  
Association Studies ◽  
Target Selection ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Genome Wide ◽  
Druggable Genome

Target identification (identifying the correct drug targets for each disease) and target validation (demonstrating the effect of target perturbation on disease biomarkers and disease end-points) are essential steps in drug development. We showed previously that biomarker and disease endpoint associations of single nucleotide polymorphisms (SNPs) in a gene encoding a drug target accurately depict the effect of modifying the same target with a pharmacological agent; others have shown that genomic support for a target is associated with a higher rate of drug development success. To delineate drug development (including repurposing) opportunities arising from this paradigm, we connected complex disease- and biomarker-associated loci from genome wide association studies (GWAS) to an updated set of genes encoding druggable human proteins, to compounds with bioactivity against these targets and, where these were licensed drugs, to clinical indications. We used this set of genes to inform the design of a new genotyping array, to enable druggable genome-wide association studies for drug target selection and validation in human disease.

scholarly journals The open targets post-GWAS analysis pipeline

2020 ◽  
Vol 36 (9) ◽  
pp. 2936-2937 ◽  
Author(s):  
Gareth Peat ◽  
William Jones ◽  
Michael Nuhn ◽  
José Carlos Marugán ◽  
William Newell ◽  
...  
Keyword(s):  
Drug Targets ◽  
Gene Expression Regulation ◽  
Association Studies ◽  
Genome Wide Association Studies ◽  
Protein Coding ◽  
Data Resource ◽  
Coding Regions ◽  
Genome Wide ◽  
Causal Genes ◽  
Interactive Data

Abstract Motivation Genome-wide association studies (GWAS) are a powerful method to detect even weak associations between variants and phenotypes; however, many of the identified associated variants are in non-coding regions, and presumably influence gene expression regulation. Identifying potential drug targets, i.e. causal protein-coding genes, therefore, requires crossing the genetics results with functional data. Results We present a novel data integration pipeline that analyses GWAS results in the light of experimental epigenetic and cis-regulatory datasets, such as ChIP-Seq, Promoter-Capture Hi-C or eQTL, and presents them in a single report, which can be used for inferring likely causal genes. This pipeline was then fed into an interactive data resource. Availability and implementation The analysis code is available at www.github.com/Ensembl/postgap and the interactive data browser at postgwas.opentargets.io.

scholarly journals Genomics-driven drug discovery based on disease-susceptibility genes

2021 ◽  
Vol 41 (1) ◽  
Author(s):  
Kyuto Sonehara ◽  
Yukinori Okada
Keyword(s):  
Drug Discovery ◽  
Drug Targets ◽  
Disease Susceptibility ◽  
Mendelian Randomization ◽  
Association Studies ◽  
Drug Repurposing ◽  
Susceptibility Genes ◽  
Genome Wide Association Studies ◽  
Genome Wide ◽  
Disease Associations

AbstractGenome-wide association studies have identified numerous disease-susceptibility genes. As knowledge of gene–disease associations accumulates, it is becoming increasingly important to translate this knowledge into clinical practice. This challenge involves finding effective drug targets and estimating their potential side effects, which often results in failure of promising clinical trials. Here, we review recent advances and future perspectives in genetics-led drug discovery, with a focus on drug repurposing, Mendelian randomization, and the use of multifaceted omics data.

scholarly journals Comment on ‘Large-Scale Cognitive GWAS Meta-Analysis Reveals Tissue-Specific Neural Expression and Potential Nootropic Drug Targets’ by Lam et al.

2018 ◽  
Vol 21 (2) ◽  
pp. 84-88 ◽  
Author(s):  
W. David Hill
Keyword(s):  
Genetic Information ◽  
Drug Targets ◽  
Large Scale ◽  
Association Studies ◽  
Meta Analysis ◽  
Genetic Correlations ◽  
Data Sets ◽  
Genome Wide Association Studies ◽  
Nootropic Drug ◽  
Genome Wide

Intelligence and educational attainment are strongly genetically correlated. This relationship can be exploited by Multi-Trait Analysis of GWAS (MTAG) to add power to Genome-wide Association Studies (GWAS) of intelligence. MTAG allows the user to meta-analyze GWASs of different phenotypes, based on their genetic correlations, to identify association's specific to the trait of choice. An MTAG analysis using GWAS data sets on intelligence and education was conducted by Lam et al. (2017). Lam et al. (2017) reported 70 loci that they described as ‘trait specific’ to intelligence. This article examines whether the analysis conducted by Lam et al. (2017) has resulted in genetic information about a phenotype that is more similar to education than intelligence.

scholarly journals Long Noncoding RNAs and Circular RNAs in Autoimmune Diseases

Biomolecules ◽  
2020 ◽  
Vol 10 (7) ◽  
pp. 1044 ◽  
Author(s):  
Valeria Lodde ◽  
Giampaolo Murgia ◽  
Elena Rita Simula ◽  
Maristella Steri ◽  
Matteo Floris ◽  
...  
Keyword(s):  
Autoimmune Diseases ◽  
Lupus Erythematosus ◽  
Drug Targets ◽  
Association Studies ◽  
Genetic Regulation ◽  
Response To Therapy ◽  
Circular Rnas ◽  
Genome Wide Association Studies ◽  
Genome Wide ◽  
Substantial Progress

Immune responses are essential for the clearance of pathogens and the repair of injured tissues; however, if these responses are not properly controlled, autoimmune diseases can occur. Autoimmune diseases (ADs) are a family of disorders characterized by the body’s immune response being directed against its own tissues, with consequent chronic inflammation and tissue damage. Despite enormous efforts to identify new drug targets and develop new therapies to prevent and ameliorate AD symptoms, no definitive solutions are available today. Additionally, while substantial progress has been made in drug development for some ADs, most treatments only ameliorate symptoms and, in general, ADs are still incurable. Hundreds of genetic loci have been identified and associated with ADs by genome-wide association studies. However, the whole list of molecular factors that contribute to AD pathogenesis is still unknown. Noncoding (nc)RNAs, such as microRNAs, circular (circ)RNAs, and long noncoding (lnc)RNAs, regulate gene expression at different levels in various diseases, including ADs, and serve as potential drug targets as well as biomarkers for disease progression and response to therapy. In this review, we will focus on the potential roles and genetic regulation of ncRNA in four autoimmune diseases—systemic lupus erythematosus, rheumatoid arthritis, multiple sclerosis, and type 1 diabetes mellitus.

Identifying Novel Psoriatic Disease Drug Targets Using a Genetics-Based Priority Index Pipeline

2021 ◽  
pp. 247553032110260
Author(s):  
Audrey Bui ◽  
Jared Liu ◽  
Julie Hong ◽  
Edward Hadeler ◽  
Megan Mosca ◽  
...  
Keyword(s):  
Psoriatic Arthritis ◽  
Drug Targets ◽  
Association Studies ◽  
Therapeutic Targets ◽  
Genome Wide Association Studies ◽  
Priority Index ◽  
Psoriatic Disease ◽  
Genome Wide ◽  
Pathway Crosstalk ◽  
Potential Drug Targets

Background: Despite numerous genome-wide association studies conducted in psoriasis and psoriatic arthritis, only a small fraction of the identified genes has been therapeutically targeted. Objective: We sought to identify and analyze potential therapeutic targets for psoriasis and psoriatic arthritis (PsA) using the priority index (Pi), a genetics-dependent drug target prioritization approach. Methods: Significant genetic variants from GWAS for psoriasis, PsA, and combined psoriatic disease were annotated and run through the Pi pipeline. Potential drug targets were identified based on genomic predictors, annotation predictors, pathway enrichment, and pathway crosstalk. Results: Several gene targets were identified for psoriasis and PsA that demonstrated biological associations to their respective diseases. Some are currently being explored as potential therapeutic targets (i.e. ICAM1, NF-kB, REV3 L, ADRA1B for psoriasis; CCL11 for PsA); others have not yet been investigated (i.e. LNPEP, LCE3 for psoriasis; UBLCP1 for PsA). Additionally, many nodal points of potential intervention were identified as promising therapeutic targets. Of these, some are currently being studied such as TYK2 for psoriasis, and others have yet to be explored (i.e. PPP2CA, YAP1, PI3 K, AKT, FOXO1, RELA, CSF2, IFNGR1, IFNGR2 for psoriasis; GNAQ, PLCB1, GNAI2 for PsA). Conclusion: Through Pi, we identified data-driven candidate therapeutic gene targets and pathways for psoriasis and PsA. Given the sparse PsA specific genetic studies and PsA specific drug targets, this analysis could prove to be particularly valuable in the pipeline for novel psoriatic therapies.

scholarly journals Genome-wide patterns of population structure and linkage disequilibrium in farmed Nile tilapia (Oreochromis niloticus)

2019 ◽  
Author(s):  
Grazyella M. Yoshida ◽  
Agustín Barria ◽  
Katharina Correa ◽  
Giovanna Cáceres ◽  
Ana Jedlicki ◽  
...  
Keyword(s):  
Population Structure ◽  
Linkage Disequilibrium ◽  
Genomic Selection ◽  
Oreochromis Niloticus ◽  
Nile Tilapia ◽  
Population Genomics ◽  
Association Studies ◽  
Demographic History ◽  
Genome Wide Association Studies ◽  
Genome Wide

AbstractNile tilapia (Oreochromis niloticus) is one of the most produced farmed fish in the world and represents an important source of protein for human consumption. Farmed Nile tilapia populations are increasingly based on genetically improved stocks, which have been established from admixed populations. To date, there is scarce information about the population genomics of farmed Nile tilapia, assessed by dense single nucleotide polymorphism (SNP) panels. The patterns of linkage disequilibrium (LD) may affect the success of genome-wide association studies (GWAS) and genomic selection and can also provide key information about demographic history of farmed Nile tilapia populations. The objectives of this study were to provide further knowledge about the population structure and LD patterns, as well as, estimate the effective population size (Ne) for three farmed Nile tilapia populations, one from Brazil (POP A) and two from Costa Rica (POP B and POP C). A total of 55, 56 and 57 individuals from POP A, POP B and POP C, respectively, were genotyped using a 50K SNP panel selected from a whole-genome sequencing (WGS) experiment. Two principal components explained about 20% of the total variation and clearly discriminated between the three populations. Population genetic structure analysis showed evidence of admixture, especially for POP C. The contemporary Ne values calculated based to LD values, ranged from 71 to 141. No differences were observed in the LD decay among populations, with a rapid decrease of r2 when increasing inter-marker distance. Average r2 between adjacent SNP pairs ranged from 0.03 to 0.18, 0.03 to 0.17 and 0.03 to 0.16 for POP A, POP B and POP C, respectively. Based on the number of independent chromosome segments in the Nile tilapia genome, at least 4.2 K SNP are required for the implementation of GWAS and genomic selection in farmed Nile tilapia populations.

scholarly journals Microbial Genome-Wide Association Studies: Lessons from Human GWAS

2016 ◽  
Author(s):  
Robert A. Power ◽  
Julian Parkhill ◽  
Tulio de Oliveira
Keyword(s):  
Disease Transmission ◽  
Drug Targets ◽  
Association Studies ◽  
Genome Wide Association ◽  
Microbial Genome ◽  
Genome Wide Association Studies ◽  
Genome Wide ◽  
Disease Aetiology ◽  
Number Of Microorganisms ◽  
Potential Drug Targets

AbstractThe reduced costs of sequencing have led to the availability of whole genome sequences for a large number of microorganisms, enabling the application of microbial genome wide association studies (GWAS). Given the successes of human GWAS in understanding disease aetiology and identifying potential drug targets, microbial GWAS is likely to further advance our understanding of infectious diseases. By building on the success of GWAS, microbial GWAS have the potential to rapidly provide important insights into pressing global health problems, such as antibiotic resistance and disease transmission. In this review, we outline the methodologies of GWAS, the state of the field of microbial GWAS today, and how lessons from GWAS can direct the future of the field.

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