scholarly journals INFERNO - INFERring the molecular mechanisms of NOncoding genetic variants

2017 ◽  
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.

scholarly journals Post-GWAS era in genetics of schizophrenia

2019 ◽  
pp. 6-7
Author(s):  
V. E. Golimbet ◽  
A. K. Golov ◽  
N. V. Kondratyev
Keyword(s):  
Genetic Variants ◽  
Target Genes ◽  
Disease Risk ◽  
Association Studies ◽  
Regulatory Elements ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Biological Mechanisms ◽  
Master Regulators ◽  
Genome Wide

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.

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

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.

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

2019 ◽  
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.

scholarly journals Addressing the Missing Heritability Problem With the Help of Regulatory Features

2019 ◽  
Vol 15 ◽  
pp. 117693431986086
Author(s):  
Shan-Shan Dong ◽  
Yan Guo ◽  
Tie-Lin Yang
Keyword(s):  
Target Genes ◽  
Association Studies ◽  
Complex Diseases ◽  
Regulatory Elements ◽  
Genome Wide Association Studies ◽  
Nucleotide Polymorphisms ◽  
Susceptibility Loci ◽  
Missing Heritability ◽  
Genome Wide ◽  
Missing Heritability Problem

Genome-wide association studies (GWASs) have successfully identified thousands of susceptibility loci for human complex diseases. However, missing heritability is still a challenging problem. Considering most GWAS loci are located in regulatory elements, we recently developed a pipeline named functional disease-associated single-nucleotide polymorphisms (SNPs) prediction (FDSP), to predict novel susceptibility loci for complex diseases based on the interpretation of regulatory features and published GWAS results with machine learning. When applied to type 2 diabetes and hypertension, the predicted susceptibility loci by FDSP were proved to be capable of explaining additional heritability. In addition, potential target genes of the predicted positive SNPs were significantly enriched in disease-related pathways. Our results suggested that taking regulatory features into consideration might be a useful way to address the missing heritability problem. We hope FDSP could offer help for the identification of novel susceptibility loci for complex diseases.

scholarly journals Identification of atrial fibrillation associated genes and functional non-coding variants

2019 ◽  
Vol 10 (1) ◽  
Author(s):  
Antoinette F. van Ouwerkerk ◽  
Fernanda M. Bosada ◽  
Karel van Duijvenboden ◽  
Matthew C. Hill ◽  
Lindsey E. Montefiori ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
Genetic Variants ◽  
Target Gene ◽  
Target Genes ◽  
Association Studies ◽  
Regulatory Region ◽  
Regulatory Elements ◽  
Genome Wide Association Studies ◽  
Cx43 Expression

Abstract Disease-associated genetic variants that lie in non-coding regions found by genome-wide association studies are thought to alter the functionality of transcription regulatory elements and target gene expression. To uncover causal genetic variants, variant regulatory elements and their target genes, here we cross-reference human transcriptomic, epigenomic and chromatin conformation datasets. Of 104 genetic variant regions associated with atrial fibrillation candidate target genes are prioritized. We optimize EMERGE enhancer prediction and use accessible chromatin profiles of human atrial cardiomyocytes to more accurately predict cardiac regulatory elements and identify hundreds of sub-threshold variants that co-localize with regulatory elements. Removal of mouse homologues of atrial fibrillation-associated regions in vivo uncovers a distal regulatory region involved in Gja1 (Cx43) expression. Our analyses provide a shortlist of genes likely affected by atrial fibrillation-associated variants and provide variant regulatory elements in each region that link genetic variation and target gene regulation, helping to focus future investigations.

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

2017 ◽  
Vol 242 (13) ◽  
pp. 1325-1334 ◽  
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.

scholarly journals Gene-centric functional dissection of human genetic variation uncovers regulators of hematopoiesis

2018 ◽  
Author(s):  
Satish K Nandakumar ◽  
Sean K McFarland ◽  
Laura Marlene Mateyka ◽  
Caleb A Lareau ◽  
Jacob C Ulirsch ◽  
...  
Keyword(s):  
Candidate Genes ◽  
Target Genes ◽  
Association Studies ◽  
Human Diseases ◽  
Genome Wide Association Studies ◽  
Loss Of Function ◽  
Genome Wide ◽  
Causal Variants ◽  
Genomic Regions

Genome-wide association studies (GWAS) have identified thousands of variants associated with human diseases and traits. However, the majority of GWAS-implicated variants are in non-coding genomic regions and require in depth follow-up to identify target genes and decipher biological mechanisms. Here, rather than focusing on causal variants, we have undertaken a pooled loss-of-function screen in primary hematopoietic cells to interrogate 389 candidate genes contained in 75 loci associated with red blood cell traits. Using this approach, we identify 77 genes at 38 GWAS loci, with most loci harboring 1-2 candidate genes. Importantly, the hit set was strongly enriched for genes validated through orthogonal genetic approaches. Genes identified by this approach are enriched in relevant biological pathways, allowing regulators of human erythropoiesis and blood disease modifiers to be defined. More generally, this functional screen provides a paradigm for gene-centric follow up of GWAS for a variety of human diseases and traits.

scholarly journals An integrated platform to systematically identify causal variants and genes for polygenic human traits

2019 ◽  
Author(s):  
Damien J. Downes ◽  
Ron Schwessinger ◽  
Stephanie J. Hill ◽  
Lea Nussbaum ◽  
Caroline Scott ◽  
...  
Keyword(s):  
Genetic Variants ◽  
Association Studies ◽  
Significant Proportion ◽  
Genome Wide Association Studies ◽  
Effector Genes ◽  
Genome Wide ◽  
Common Genetic Variants ◽  
Integrated Platform ◽  
Causal Variants

ABSTRACTGenome-wide association studies (GWAS) have identified over 150,000 links between common genetic variants and human traits or complex diseases. Over 80% of these associations map to polymorphisms in non-coding DNA. Therefore, the challenge is to identify disease-causing variants, the genes they affect, and the cells in which these effects occur. We have developed a platform using ATAC-seq, DNaseI footprints, NG Capture-C and machine learning to address this challenge. Applying this approach to red blood cell traits identifies a significant proportion of known causative variants and their effector genes, which we show can be validated by direct in vivo modelling.

scholarly journals A novel quantile regression approach for eQTL discovery

2016 ◽  
Author(s):  
Xiaoyu Song ◽  
Gen Li ◽  
Iuliana Ionita-Laza ◽  
Ying Wei
Keyword(s):  
Gene Expression ◽  
Genetic Variation ◽  
Linear Regression ◽  
Genetic Variants ◽  
Molecular Mechanisms ◽  
Association Studies ◽  
Expression Level ◽  
Special Focus ◽  
Genome Wide Association Studies ◽  
Genome Wide

AbstractOver the past decade, there has been a remarkable improvement in our understanding of the role of genetic variation in complex human diseases, especially via genome-wide association studies. However, the underlying molecular mechanisms are still poorly characterized, impending the development of therapeutic interventions. Identifying genetic variants that influence the expression level of a gene, i.e. expression quantitative trait loci (eQTLs), can help us understand how genetic variants influence traits at the molecular level. While most eQTL studies focus on identifying mean effects on gene expression using linear regression, evidence suggests that genetic variation can impact the entire distribution of the expression level. Indeed, several studies have already investigated higher order associations with a special focus on detecting heteroskedasticity. In this paper, we develop a Quantile Rank-score Based Test (QRBT) to identify eQTLs that are associated with the conditional quantile functions of gene expression. We have applied the proposed QRBT to the Genotype-Tissue Expression project, an international tissue bank for studying the relationship between genetic variation and gene expression in human tissues, and found that the proposed QRBT complements the existing methods, and identifies new eQTLs with heterogeneous effects genome-wideacross different quantile levels. Notably, we show that the eQTLs identified by QRBT but missed by linear regression are more likely to be tissue specific, and also associated with greater enrichment in genome-wide significant SNPs from the GWAS catalog. An R package implementing QRBT is available on our website.

scholarly journals Cascading epigenomic analysis for identifying disease genes from the regulatory landscape of GWAS variants

PLoS Genetics ◽  
2021 ◽  
Vol 17 (11) ◽  
pp. e1009918
Author(s):  
Bernard Ng ◽  
William Casazza ◽  
Nam Hee Kim ◽  
Chendi Wang ◽  
Farnush Farhadi ◽  
...  
Keyword(s):  
Genetic Variants ◽  
Target Genes ◽  
Association Studies ◽  
Disease Genes ◽  
Genome Wide Association Studies ◽  
Cascading Effects ◽  
Risk Alleles ◽  
Gene Sets ◽  
Genome Wide ◽  
Regulatory Landscape

The majority of genetic variants detected in genome wide association studies (GWAS) exert their effects on phenotypes through gene regulation. Motivated by this observation, we propose a multi-omic integration method that models the cascading effects of genetic variants from epigenome to transcriptome and eventually to the phenome in identifying target genes influenced by risk alleles. This cascading epigenomic analysis for GWAS, which we refer to as CEWAS, comprises two types of models: one for linking cis genetic effects to epigenomic variation and another for linking cis epigenomic variation to gene expression. Applying these models in cascade to GWAS summary statistics generates gene level statistics that reflect genetically-driven epigenomic effects. We show on sixteen brain-related GWAS that CEWAS provides higher gene detection rate than related methods, and finds disease relevant genes and gene sets that point toward less explored biological processes. CEWAS thus presents a novel means for exploring the regulatory landscape of GWAS variants in uncovering disease mechanisms.

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