Fine mapping with epigenetic information and 3D structure

Risk Variants ◽

Increase Risk

AbstractSince 2005, thousands of genome-wide association studies (GWAS) have been published, identifying hundreds of thousands of genetic variants that increase risk of complex traits such as autoimmune diseases. This wealth of data has the potential to improve patient care, through personalized medicine and the identification of novel drug targets. However, the potential of GWAS for clinical translation has not been fully achieved yet, due to the fact that the functional interpretation of risk variants and the identification of causal variants and genes are challenging. The past decade has seen the development of great advances that are facilitating the overcoming of these limitations, by utilizing a plethora of genomics and epigenomics tools to map and characterize regulatory elements and chromatin interactions, which can be used to fine map GWAS loci, and advance our understanding of the biological mechanisms that cause disease.

GPA-MDS: A Visualization Approach to Investigate Genetic Architecture among Phenotypes Using GWAS Results

International Journal of Genomics ◽

10.1155/2016/6589843 ◽

2016 ◽

Vol 2016 ◽

pp. 1-6 ◽

Cited By ~ 2

Author(s):

Wei Wei ◽

Paula S. Ramos ◽

Kelly J. Hunt ◽

Bethany J. Wolf ◽

Gary Hardiman ◽

...

Keyword(s):

Complex Traits ◽

Genetic Architecture ◽

Association Studies ◽

Genetic Relationships ◽

Joint Analysis ◽

Risk Variants ◽

Novel Approach ◽

Medical Benefits ◽

Rigorous Framework

Genome-wide association studies (GWAS) have identified tens of thousands of genetic variants associated with hundreds of phenotypes and diseases, which have provided clinical and medical benefits to patients with novel biomarkers and therapeutic targets. Recently, there has been accumulating evidence suggesting that different complex traits share a common risk basis, namely, pleiotropy. Previously, a statistical method, namely, GPA (Genetic analysis incorporating Pleiotropy and Annotation), was developed to improve identification of risk variants and to investigate pleiotropic structure through a joint analysis of multiple GWAS datasets. While GPA provides a statistically rigorous framework to evaluate pleiotropy between phenotypes, it is still not trivial to investigate genetic relationships among a large number of phenotypes using the GPA framework. In order to address this challenge, in this paper, we propose a novel approach, GPA-MDS, to visualize genetic relationships among phenotypes using the GPA algorithm and multidimensional scaling (MDS). This tool will help researchers to investigate common etiology among diseases, which can potentially lead to development of common treatments across diseases. We evaluate the proposed GPA-MDS framework using a simulation study and apply it to jointly analyze GWAS datasets examining 18 unique phenotypes, which helps reveal the shared genetic architecture of these phenotypes.

A global overview of pleiotropy and genetic architecture in complex traits

10.1101/500090 ◽

2018 ◽

Cited By ~ 19

Author(s):

Kyoko Watanabe ◽

Sven Stringer ◽

Oleksandr Frei ◽

Maša Umićević Mirkov ◽

Tinca J.C. Polderman ◽

...

Keyword(s):

Genetic Variants ◽

Complex Traits ◽

Genetic Architecture ◽

Human Genetics ◽

Association Studies ◽

Regulatory Elements ◽

Multiple Trait ◽

Current Availability ◽

Flanking Regions

ABSTRACTAfter a decade of genome-wide association studies (GWASs), fundamental questions in human genetics are still unanswered, such as the extent of pleiotropy across the genome, the nature of trait-associated genetic variants and the disparate genetic architecture across human traits. The current availability of hundreds of GWAS results provide the unique opportunity to gain insight into these questions. In this study, we harmonized and systematically analysed 4,155 publicly available GWASs. For a subset of well-powered GWAS on 558 unique traits, we provide an extensive overview of pleiotropy and genetic architecture. We show that trait associated loci cover more than half of the genome, and 90% of those loci are associated with multiple trait domains. We further show that potential causal genetic variants are enriched in coding and flanking regions, as well as in regulatory elements, and how trait-polygenicity is related to an estimate of the required sample size to detect 90% of causal genetic variants. Our results provide novel insights into how genetic variation contributes to trait variation. All GWAS results can be queried and visualized at the GWAS ATLAS resource (http://atlas.ctglab.nl).

Combining SNP-to-gene linking strategies to pinpoint disease genes and assess disease omnigenicity

10.1101/2021.08.02.21261488 ◽

2021 ◽

Author(s):

Steven Gazal ◽

Omer Weissbrod ◽

Farhad Hormozdiari ◽

Kushal Dey ◽

Joseph Nasser ◽

...

Keyword(s):

Fine Mapping ◽

Complex Traits ◽

Target Genes ◽

Disease Risk ◽

Association Studies ◽

Common Disease ◽

Disease Genes ◽

Genome Wide

Although genome-wide association studies (GWAS) have identified thousands of disease-associated common SNPs, these SNPs generally do not implicate the underlying target genes, as most disease SNPs are regulatory. Many SNP-to-gene (S2G) linking strategies have been developed to link regulatory SNPs to the genes that they regulate in cis, but it is unclear how these strategies should be applied in the context of interpreting common disease risk variants. We developed a framework for evaluating and combining different S2G strategies to optimize their informativeness for common disease risk, leveraging polygenic analyses of disease heritability to define and estimate their precision and recall. We applied our framework to GWAS summary statistics for 63 diseases and complex traits (average N=314K), evaluating 50 S2G strategies. Our optimal combined S2G strategy (cS2G) included 7 constituent S2G strategies (Exon, Promoter, 2 fine-mapped cis-eQTL strategies, EpiMap enhancer-gene linking, Activity-By-Contact (ABC), and Cicero), and achieved a precision of 0.75 and a recall of 0.33, more than doubling the precision and/or recall of any individual strategy; this implies that 33% of SNP-heritability can be linked to causal genes with 75% confidence. We applied cS2G to fine-mapping results for 49 UK Biobank diseases/traits to predict 7,111 causal SNP-gene-disease triplets (with S2G-derived functional interpretation) with high confidence. Finally, we applied cS2G to genome-wide fine-mapping results for these traits (not restricted to GWAS loci) to rank genes by the heritability linked to each gene, providing an empirical assessment of disease omnigenicity; averaging across traits, we determined that the top 200 (1%) of ranked genes explained roughly half of the heritability linked to all genes. Our results highlight the benefits of our cS2G strategy in providing functional interpretation of GWAS findings; we anticipate that precision and recall will increase further under our framework as improved functional assays lead to improved S2G strategies.

Open Targets Genetics: An open approach to systematically prioritize causal variants and genes at all published human GWAS trait-associated loci

10.1101/2020.09.16.299271 ◽

2020 ◽

Cited By ~ 1

Author(s):

Edward Mountjoy ◽

Ellen M. Schmidt ◽

Miguel Carmona ◽

Gareth Peat ◽

Alfredo Miranda ◽

...

Keyword(s):

Complex Traits ◽

Drug Targets ◽

Association Studies ◽

Single Gene ◽

Cell Types ◽

Causal Variant ◽

Open Approach ◽

Protein Coding ◽

Causal Genes

AbstractGenome-wide association studies (GWAS) have identified many variants robustly associated with complex traits but identifying the gene(s) mediating such associations is a major challenge. Here we present an open resource that provides systematic fine-mapping and protein-coding gene prioritization across 133,441 published human GWAS loci. We integrate diverse data sources, including genetics (from GWAS Catalog and UK Biobank) as well as transcriptomic, proteomic and epigenomic data across many tissues and cell types. We also provide systematic disease-disease and disease-molecular trait colocalization results across 92 cell types and tissues and identify 729 loci fine-mapped to a single coding causal variant and colocalized with a single gene. We trained a machine learning model using the fine mapped genetics and functional genomics data using 445 gold standard curated GWAS loci to distinguish causal genes from background genes at the same loci, outperforming a naive distance based model. Genes prioritized by our model are enriched for known approved drug targets (OR = 8.1, 95% CI: [5.7, 11.5]). These results will be regularly updated and are publicly available through a web portal, Open Targets Genetics (OTG, http://genetics.opentargets.org), enabling users to easily prioritize genes at disease-associated loci and assess their potential as drug targets.

Pan-Cancer Study Detects Novel Genetic Risk Variants and Shared Genetic Basis in Two Large Cohorts

10.1101/635367 ◽

2019 ◽

Cited By ~ 3

Author(s):

Sara R. Rashkin ◽

Rebecca E. Graff ◽

Linda Kachuri ◽

Khanh K. Thai ◽

Stacey E. Alexeeff ◽

...

Keyword(s):

Genetic Basis ◽

Association Studies ◽

Large Population ◽

Regulatory Elements ◽

European Ancestry ◽

Risk Variants ◽

Genome Wide ◽

Pan Cancer ◽

Shared Genetic

AbstractDeciphering the shared genetic basis of distinct cancers has the potential to elucidate carcinogenic mechanisms and inform broadly applicable risk assessment efforts. However, no studies have investigated pan-cancer pleiotropy within single, well-defined populations unselected for phenotype. We undertook novel genome-wide association studies (GWAS) and comprehensive evaluations of heritability and pleiotropy across 18 cancer types in two large, population-based cohorts: the UK Biobank (413,870 European ancestry individuals; 48,961 cancer cases) and the Kaiser Permanente Genetic Epidemiology Research on Adult Health and Aging cohorts (66,526 European ancestry individuals; 16,001 cancer cases). The GWAS detected 21 novel genome-wide significant risk variants. In addition, numerous cancer sites exhibited clear heritability. Investigations of pleiotropy identified 12 cancer pairs exhibiting either positive or negative genetic correlations and 43 pleiotropic loci. We identified 158 pleiotropic variants, many of which were enriched for regulatory elements and influenced cross-tissue gene expression. Our findings demonstrate widespread pleiotropy and offer further insight into the complex genetic architecture of cross-cancer susceptibility.

Using regulatory genomics data to interpret the function of disease variants and prioritise genes from expression studies

F1000Research ◽

10.12688/f1000research.13577.1 ◽

2018 ◽

Vol 7 ◽

pp. 121 ◽

Cited By ~ 1

Author(s):

Enrico Ferrero

Keyword(s):

Gene Expression ◽

Large Scale ◽

Association Studies ◽

Cell Types ◽

Regulatory Elements ◽

New Drugs ◽

Expression Studies ◽

Regulatory Genomics

The identification of therapeutic targets is a critical step in the research and developement of new drugs, with several drug discovery programmes failing because of a weak linkage between target and disease. Genome-wide association studies and large-scale gene expression experiments are providing insights into the biology of several common and complex diseases, but the complexity of transcriptional regulation mechanisms often limit our understanding of how genetic variation can influence changes in gene expression. Several initiatives in the field of regulatory genomics are aiming to close this gap by systematically identifying and cataloguing regulatory elements such as promoters and enhacers across different tissues and cell types. In this Bioconductor workflow, we will explore how different types of regulatory genomic data can be used for the functional interpretation of disease-associated variants and for the prioritisation of gene lists from gene expression experiments.

Non-coding RNAs underlie genetic predisposition to breast cancer

Genome Biology ◽

10.1186/s13059-019-1876-z ◽

2020 ◽

Vol 21 (1) ◽

Cited By ~ 5

Author(s):

Mahdi Moradi Marjaneh ◽

Jonathan Beesley ◽

Tracy A. O’Mara ◽

Pamela Mukhopadhyay ◽

Lambros T. Koufariotis ◽

...

Keyword(s):

Breast Cancer ◽

Breast Cancer Risk ◽

Cancer Risk ◽

Complex Traits ◽

Regulatory Elements ◽

Chromatin Interaction ◽

Alternative Mechanism ◽

Risk Variants ◽

Non Coding Rnas

Abstract Background Genetic variants identified through genome-wide association studies (GWAS) are predominantly non-coding and typically attributed to altered regulatory elements such as enhancers and promoters. However, the contribution of non-coding RNAs to complex traits is not clear. Results Using targeted RNA sequencing, we systematically annotated multi-exonic non-coding RNA (mencRNA) genes transcribed from 1.5-Mb intervals surrounding 139 breast cancer GWAS signals and assessed their contribution to breast cancer risk. We identify more than 4000 mencRNA genes and show their expression distinguishes normal breast tissue from tumors and different breast cancer subtypes. Importantly, breast cancer risk variants, identified through genetic fine-mapping, are significantly enriched in mencRNA exons, but not the promoters or introns. eQTL analyses identify mencRNAs whose expression is associated with risk variants. Furthermore, chromatin interaction data identify hundreds of mencRNA promoters that loop to regions that contain breast cancer risk variants. Conclusions We have compiled the largest catalog of breast cancer-associated mencRNAs to date and provide evidence that modulation of mencRNAs by GWAS variants may provide an alternative mechanism underlying complex traits.

Beyond association: successes and challenges in linking non-coding genetic variation to functional consequences that modulate Alzheimer’s disease risk

Molecular Neurodegeneration ◽

10.1186/s13024-021-00449-0 ◽

2021 ◽

Vol 16 (1) ◽

Author(s):

Gloriia Novikova ◽

Shea J. Andrews ◽

Alan E. Renton ◽

Edoardo Marcora

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Disease Risk ◽

Association Studies ◽

Regulatory Elements ◽

Causal Variant ◽

Cell Type ◽

Risk Variants ◽

Cellular Phenotypes

AbstractAlzheimer’s disease (AD) is the most common type of dementia, affecting millions of people worldwide; however, no disease-modifying treatments are currently available. Genome-wide association studies (GWASs) have identified more than 40 loci associated with AD risk. However, most of the disease-associated variants reside in non-coding regions of the genome, making it difficult to elucidate how they affect disease susceptibility. Nonetheless, identification of the regulatory elements, genes, pathways and cell type/tissue(s) impacted by these variants to modulate AD risk is critical to our understanding of disease pathogenesis and ability to develop effective therapeutics. In this review, we provide an overview of the methods and approaches used in the field to identify the functional effects of AD risk variants in the causal path to disease risk modification as well as describe the most recent findings. We first discuss efforts in cell type/tissue prioritization followed by recent progress in candidate causal variant and gene nomination. We discuss statistical methods for fine-mapping as well as approaches that integrate multiple levels of evidence, such as epigenomic and transcriptomic data, to identify causal variants and risk mechanisms of AD-associated loci. Additionally, we discuss experimental approaches and data resources that will be needed to validate and further elucidate the effects of these variants and genes on biological pathways, cellular phenotypes and disease risk. Finally, we discuss future steps that need to be taken to ensure that AD GWAS functional mapping efforts lead to novel findings and bring us closer to finding effective treatments for this devastating disease.

Pig genome functional annotation enhances the biological interpretation of complex traits and human disease

Nature Communications ◽

10.1038/s41467-021-26153-7 ◽

2021 ◽

Vol 12 (1) ◽

Author(s):

Zhangyuan Pan ◽

Yuelin Yao ◽

Hongwei Yin ◽

Zexi Cai ◽

Ying Wang ◽

...

Keyword(s):

Adaptive Evolution ◽

Complex Traits ◽

Functional Annotation ◽

Molecular Mechanisms ◽

Association Studies ◽

Regulatory Elements ◽

Neutral Evolution ◽

Tissue Specific ◽

Slow Evolution

AbstractThe functional annotation of livestock genomes is crucial for understanding the molecular mechanisms that underpin complex traits of economic importance, adaptive evolution and comparative genomics. Here, we provide the most comprehensive catalogue to date of regulatory elements in the pig (Sus scrofa) by integrating 223 epigenomic and transcriptomic data sets, representing 14 biologically important tissues. We systematically describe the dynamic epigenetic landscape across tissues by functionally annotating 15 different chromatin states and defining their tissue-specific regulatory activities. We demonstrate that genomic variants associated with complex traits and adaptive evolution in pig are significantly enriched in active promoters and enhancers. Furthermore, we reveal distinct tissue-specific regulatory selection between Asian and European pig domestication processes. Compared with human and mouse epigenomes, we show that porcine regulatory elements are more conserved in DNA sequence, under both rapid and slow evolution, than those under neutral evolution across pig, mouse, and human. Finally, we provide biological insights on tissue-specific regulatory conservation, and by integrating 47 human genome-wide association studies, we demonstrate that, depending on the traits, mouse or pig might be more appropriate biomedical models for different complex traits and diseases.

Using regulatory genomics data to interpret the function of disease variants and prioritise genes from expression studies

F1000Research ◽

10.12688/f1000research.13577.2 ◽

2018 ◽

Vol 7 ◽

pp. 121

Author(s):

Enrico Ferrero

Keyword(s):

Gene Expression ◽

Large Scale ◽

Association Studies ◽

Cell Types ◽

Regulatory Elements ◽

New Drugs ◽

Expression Studies ◽

Regulatory Genomics

The identification of therapeutic targets is a critical step in the research and developement of new drugs, with several drug discovery programmes failing because of a weak linkage between target and disease. Genome-wide association studies and large-scale gene expression experiments are providing insights into the biology of several common diseases, but the complexity of transcriptional regulation mechanisms often limits our understanding of how genetic variation can influence changes in gene expression. Several initiatives in the field of regulatory genomics are aiming to close this gap by systematically identifying and cataloguing regulatory elements such as promoters and enhacers across different tissues and cell types. In this Bioconductor workflow, we will explore how different types of regulatory genomic data can be used for the functional interpretation of disease-associated variants and for the prioritisation of gene lists from gene expression experiments.