Post-GWAS era in genetics of schizophrenia

Genome-wide association studies (GWASs) discovered multiple genetic variants associated with schizophrenia. Te next step (post-GWAS analysis) is aimed at identifying the causal genetic variants and biological mechanisms underlying the associations with disease risk. Te following strategies are considered: the study of transcriptional regulation in neuronal human cells and the use of epigenomic information for searching for regulatory elements involved in the pathogenesis of schizophrenia. Te frst strategy includes identifcation of neuronal enhancers, mapping of potential target genes and functional confrmation of enhancer-promoter interactions. Te second approach is focused on the identifcation of transcriptional factors, which appear to be master regulators of expression.

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Challenges and progress in interpretation of non-coding genetic variants associated with human disease

Experimental Biology and Medicine ◽

10.1177/1535370217713750 ◽

2017 ◽

Vol 242 (13) ◽

pp. 1325-1334 ◽

Cited By ~ 19

Author(s):

Yizhou Zhu ◽

Cagdas Tazearslan ◽

Yousin Suh

Keyword(s):

Molecular Mechanisms ◽

Target Genes ◽

Disease Risk ◽

Association Studies ◽

Genome Wide Association ◽

Genome Wide Association Studies ◽

Genetic Associations ◽

Functional Interpretation ◽

Genome Wide ◽

Coding Variants

Genome-wide association studies have shown that the far majority of disease-associated variants reside in the non-coding regions of the genome, suggesting that gene regulatory changes contribute to disease risk. To identify truly causal non-coding variants and their affected target genes remains challenging but is a critical step to translate the genetic associations to molecular mechanisms and ultimately clinical applications. Here we review genomic/epigenomic resources and in silico tools that can be used to identify causal non-coding variants and experimental strategies to validate their functionalities. Impact statement Most signals from genome-wide association studies (GWASs) map to the non-coding genome, and functional interpretation of these associations remained challenging. We reviewed recent progress in methodologies of studying the non-coding genome and argued that no single approach allows one to effectively identify the causal regulatory variants from GWAS results. By illustrating the advantages and limitations of each method, our review potentially provided a guideline for taking a combinatorial approach to accurately predict, prioritize, and eventually experimentally validate the causal variants.

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In Search of Complex Disease Risk through Genome Wide Association Studies

Mathematics ◽

10.3390/math9233083 ◽

2021 ◽

Vol 9 (23) ◽

pp. 3083

Author(s):

Lorena Alonso ◽

Ignasi Morán ◽

Cecilia Salvoro ◽

David Torrents

Keyword(s):

Genetic Variants ◽

Complex Disease ◽

Disease Risk ◽

Association Studies ◽

Personalised Medicine ◽

Genome Wide Association ◽

Phenotypic Traits ◽

Genome Wide Association Studies ◽

Treatment Protocols ◽

Genome Wide

The identification and characterisation of genomic changes (variants) that can lead to human diseases is one of the central aims of biomedical research. The generation of catalogues of genetic variants that have an impact on specific diseases is the basis of Personalised Medicine, where diagnoses and treatment protocols are selected according to each patient’s profile. In this context, the study of complex diseases, such as Type 2 diabetes or cardiovascular alterations, is fundamental. However, these diseases result from the combination of multiple genetic and environmental factors, which makes the discovery of causal variants particularly challenging at a statistical and computational level. Genome-Wide Association Studies (GWAS), which are based on the statistical analysis of genetic variant frequencies across non-diseased and diseased individuals, have been successful in finding genetic variants that are associated to specific diseases or phenotypic traits. But GWAS methodology is limited when considering important genetic aspects of the disease and has not yet resulted in meaningful translation to clinical practice. This review presents an outlook on the study of the link between genetics and complex phenotypes. We first present an overview of the past and current statistical methods used in the field. Next, we discuss current practices and their main limitations. Finally, we describe the open challenges that remain and that might benefit greatly from further mathematical developments.

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INFERNO - INFERring the molecular mechanisms of NOncoding genetic variants

10.1101/211599 ◽

2017 ◽

Cited By ~ 3

Author(s):

Alexandre Amlie-Wolf ◽

Mitchell Tang ◽

Elisabeth E. Mlynarski ◽

Pavel P. Kuksa ◽

Otto Valladares ◽

...

Keyword(s):

Genetic Variants ◽

Molecular Mechanisms ◽

Target Genes ◽

Association Studies ◽

Regulatory Elements ◽

Genome Wide Association Studies ◽

Cellular Processes ◽

Genome Wide ◽

Causal Variants ◽

Novel Method

AbstractThe majority of variants identified by genome-wide association studies (GWAS) reside in the noncoding genome, where they affect regulatory elements including transcriptional enhancers. We propose INFERNO (INFERring the molecular mechanisms of NOncoding genetic variants), a novel method which integrates hundreds of diverse functional genomics data sources with GWAS summary statistics to identify putatively causal noncoding variants underlying association signals. INFERNO comprehensively infers the relevant tissue contexts, target genes, and downstream biological processes affected by causal variants. We apply INFERNO to schizophrenia GWAS data, recapitulating known schizophrenia-associated genes including CACNA1C and discovering novel signals related to transmembrane cellular processes.

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Epigenetic and Transcriptional Networks Underlying Atrial Fibrillation

Circulation Research ◽

10.1161/circresaha.120.316574 ◽

2020 ◽

Vol 127 (1) ◽

pp. 34-50 ◽

Cited By ~ 6

Author(s):

Antoinette F. van Ouwerkerk ◽

Amelia W. Hall ◽

Zachary A. Kadow ◽

Sonja Lazarevic ◽

Jasmeet S. Reyat ◽

...

Keyword(s):

Atrial Fibrillation ◽

Transcription Factors ◽

Target Genes ◽

Transcriptional Regulatory Network ◽

Association Studies ◽

Regulatory Elements ◽

Genome Wide Association ◽

Transcriptional Networks ◽

Genome Wide Association Studies ◽

Genome Wide

Genome-wide association studies have uncovered over a 100 genetic loci associated with atrial fibrillation (AF), the most common arrhythmia. Many of the top AF-associated loci harbor key cardiac transcription factors, including PITX2, TBX5, PRRX1, and ZFHX3. Moreover, the vast majority of the AF-associated variants lie within noncoding regions of the genome where causal variants affect gene expression by altering the activity of transcription factors and the epigenetic state of chromatin. In this review, we discuss a transcriptional regulatory network model for AF defined by effector genes in Genome-wide association studies loci. We describe the current state of the field regarding the identification and function of AF-relevant gene regulatory networks, including variant regulatory elements, dose-sensitive transcription factor functionality, target genes, and epigenetic states. We illustrate how altered transcriptional networks may impact cardiomyocyte function and ionic currents that impact AF risk. Last, we identify the need for improved tools to identify and functionally test transcriptional components to define the links between genetic variation, epigenetic gene regulation, and atrial function.

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Editing GWAS: experimental approaches to dissect and exploit disease-associated genetic variation

Genome Medicine ◽

10.1186/s13073-021-00857-3 ◽

2021 ◽

Vol 13 (1) ◽

Author(s):

Shuquan Rao ◽

Yao Yao ◽

Daniel E. Bauer

Keyword(s):

Genome Editing ◽

Genetic Variants ◽

Association Studies ◽

Genome Wide Association ◽

Genome Wide Association Studies ◽

Functional Studies ◽

Functional Genetics ◽

Genome Wide ◽

Causal Variants ◽

Experimental Approaches

AbstractGenome-wide association studies (GWAS) have uncovered thousands of genetic variants that influence risk for human diseases and traits. Yet understanding the mechanisms by which these genetic variants, mainly noncoding, have an impact on associated diseases and traits remains a significant hurdle. In this review, we discuss emerging experimental approaches that are being applied for functional studies of causal variants and translational advances from GWAS findings to disease prevention and treatment. We highlight the use of genome editing technologies in GWAS functional studies to modify genomic sequences, with proof-of-principle examples. We discuss the challenges in interrogating causal variants, points for consideration in experimental design and interpretation of GWAS locus mechanisms, and the potential for novel therapeutic opportunities. With the accumulation of knowledge of functional genetics, therapeutic genome editing based on GWAS discoveries will become increasingly feasible.

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CAUSALdb: a database for disease/trait causal variants identified using summary statistics of genome-wide association studies

Nucleic Acids Research ◽

10.1093/nar/gkz1026 ◽

2019 ◽

Cited By ~ 2

Author(s):

Jianhua Wang ◽

Dandan Huang ◽

Yao Zhou ◽

Hongcheng Yao ◽

Huanhuan Liu ◽

...

Keyword(s):

Fine Mapping ◽

Genetic Variants ◽

Association Studies ◽

Complex Trait ◽

Genome Wide Association ◽

Genome Wide Association Studies ◽

Summary Statistics ◽

Genome Wide ◽

Credible Sets ◽

Causal Variants

Abstract Genome-wide association studies (GWASs) have revolutionized the field of complex trait genetics over the past decade, yet for most of the significant genotype-phenotype associations the true causal variants remain unknown. Identifying and interpreting how causal genetic variants confer disease susceptibility is still a big challenge. Herein we introduce a new database, CAUSALdb, to integrate the most comprehensive GWAS summary statistics to date and identify credible sets of potential causal variants using uniformly processed fine-mapping. The database has six major features: it (i) curates 3052 high-quality, fine-mappable GWAS summary statistics across five human super-populations and 2629 unique traits; (ii) estimates causal probabilities of all genetic variants in GWAS significant loci using three state-of-the-art fine-mapping tools; (iii) maps the reported traits to a powerful ontology MeSH, making it simple for users to browse studies on the trait tree; (iv) incorporates highly interactive Manhattan and LocusZoom-like plots to allow visualization of credible sets in a single web page more efficiently; (v) enables online comparison of causal relations on variant-, gene- and trait-levels among studies with different sample sizes or populations and (vi) offers comprehensive variant annotations by integrating massive base-wise and allele-specific functional annotations. CAUSALdb is freely available at http://mulinlab.org/causaldb.

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Improvement in Prediction of Coronary Heart Disease Risk over Conventional Risk Factors Using SNPs Identified in Genome-Wide Association Studies

PLoS ONE ◽

10.1371/journal.pone.0057310 ◽

2013 ◽

Vol 8 (2) ◽

pp. e57310 ◽

Cited By ~ 16

Author(s):

Jennifer L. Bolton ◽

Marlene C. W. Stewart ◽

James F. Wilson ◽

Niall Anderson ◽

Jackie F. Price

Keyword(s):

Risk Factors ◽

Coronary Heart Disease ◽

Heart Disease ◽

Disease Risk ◽

Association Studies ◽

Genome Wide Association ◽

Genome Wide Association Studies ◽

Genome Wide ◽

Heart Disease Risk ◽

Conventional Risk Factors

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GWASdb: a database for human genetic variants identified by genome-wide association studies

Nucleic Acids Research ◽

10.1093/nar/gkr1182 ◽

2011 ◽

Vol 40 (D1) ◽

pp. D1047-D1054 ◽

Cited By ~ 125

Author(s):

Mulin Jun Li ◽

Panwen Wang ◽

Xiaorong Liu ◽

Ee Lyn Lim ◽

Zhangyong Wang ◽

...

Keyword(s):

Genetic Variants ◽

Association Studies ◽

Genome Wide Association ◽

Genome Wide Association Studies ◽

Genome Wide

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bGWAS: an R package to perform Bayesian genome wide association studies

Bioinformatics ◽

10.1093/bioinformatics/btaa549 ◽

2020 ◽

Vol 36 (15) ◽

pp. 4374-4376

Author(s):

Ninon Mounier ◽

Zoltán Kutalik

Keyword(s):

Mendelian Randomization ◽

Causal Effect ◽

Association Studies ◽

R Package ◽

Genome Wide Association ◽

Supplementary Information ◽

Genome Wide Association Studies ◽

Biological Mechanisms ◽

Genome Wide ◽

Related Risk

Abstract Summary Increasing sample size is not the only strategy to improve discovery in Genome Wide Association Studies (GWASs) and we propose here an approach that leverages published studies of related traits to improve inference. Our Bayesian GWAS method derives informative prior effects by leveraging GWASs of related risk factors and their causal effect estimates on the focal trait using multivariable Mendelian randomization. These prior effects are combined with the observed effects to yield Bayes Factors, posterior and direct effects. The approach not only increases power, but also has the potential to dissect direct and indirect biological mechanisms. Availability and implementation bGWAS package is freely available under a GPL-2 License, and can be accessed, alongside with user guides and tutorials, from https://github.com/n-mounier/bGWAS. Supplementary information Supplementary data are available at Bioinformatics online.

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