The Experts below are selected from a list of 42213 Experts worldwide ranked by ideXlab platform
Manolis Kellis - One of the best experts on this subject based on the ideXlab platform.
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joint bayesian inference of risk variants and tissue specific epigenomic enrichments across multiple complex human diseases
Nucleic Acids Research, 2016Co-Authors: Yue Li, Manolis KellisAbstract:Genome wide association studies (GWAS) provide a powerful approach for uncovering disease-associated variants in human, but fine-mapping the causal variants remains a challenge. This is partly remedied by prioritization of disease-associated variants that overlap GWAS-enriched epigenomic annotations. Here, we introduce a new Bayesian model RiVIERA (Risk Variant Inference using Epigenomic Reference Annotations) for inference of driver variants from summary statistics across multiple traits using hundreds of epigenomic annotations. In simulation, RiVIERA promising power in detecting causal variants and causal annotations, the multi-trait joint inference further improved the detection power. We applied RiVIERA to model the existing GWAS summary statistics of 9 autoimmune diseases and Schizophrenia by jointly harnessing the potential causal enrichments among 848 tissue-specific Epigenomics annotations from ENCODE/Roadmap consortium covering 127 cell/tissue types and 8 major epigenomic marks. RiVIERA identified meaningful tissue-specific enrichments for enhancer regions defined by H3K4me1 and H3K27ac for Blood T-Cell specifically in the nine autoimmune diseases and Brain-specific enhancer activities exclusively in Schizophrenia. Moreover, the variants from the 95% credible sets exhibited high conservation and enrichments for GTEx whole-blood eQTLs located within transcription-factor-binding-sites and DNA-hypersensitive-sites. Furthermore, joint modeling the nine immune traits by simultaneously inferring and exploiting the underlying epigenomic correlation between traits further improved the functional enrichments compared to single-trait models.
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riviera beta joint bayesian inference of risk variants and tissue specific epigenomic enrichments across multiple complex human diseases
bioRxiv, 2016Co-Authors: Yue Li, Manolis KellisAbstract:Genome wide association studies (GWAS) provide a powerful approach for uncovering disease-associated variants in human, but fine-mapping the causal variants remains a challenge. This is partly remedied by prioritization of disease-associated variants that overlap GWAS-enriched epigenomic annotations. Here, we introduce a new Bayesian model RiVIERA-beta (Risk Variant Inference using Epigenomic Reference Annotations) for inference of driver variants by modelling summary statistics p-values in Beta density function across multiple traits using hundreds of epigenomic annotations. In simulation, RiVIERA-beta promising power in detecting causal variants and causal annotations, the multi-trait joint inference further improved the detection power.} We applied RiVIERA-beta to model the existing GWAS summary statistics of 9 autoimmune diseases and Schizophrenia by jointly harnessing the potential causal enrichments among 848 tissue-specific Epigenomics annotations from ENCODE/Roadmap consortium covering 127 cell/tissue types and 8 major epigenomic marks. RiVIERA-beta identified meaningful tissue-specific enrichments for enhancer regions defined by H3K4me1 and H3K27ac for Blood T-Cell specifically in the 9 autoimmune diseases and Brain-specific enhancer activities exclusively in Schizophrenia. Moreover, the variants from the 95% credible sets exhibited high conservation and enrichments for GTEx whole-blood eQTLs located within transcription-factor-binding-sites and DNA-hypersensitive-sites. Furthermore, joint modeling the nine immune traits by simultaneously inferring and exploiting the underlying epigenomic correlation between traits further improved the functional enrichments compared to single-trait models.
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systematic chromatin state comparison of epigenomes associated with diverse properties including sex and tissue type
Nature Communications, 2015Co-Authors: Angela Yen, Manolis KellisAbstract:Epigenomic data sets provide critical information about the dynamic role of chromatin states in gene regulation, but a key question of how chromatin state segmentations vary under different conditions across the genome has remained unaddressed. Here we present ChromDiff, a group-wise chromatin state comparison method that generates an information-theoretic representation of epigenomes and corrects for external covariate factors to better isolate relevant chromatin state changes. By applying ChromDiff to the 127 epigenomes from the Roadmap Epigenomics and ENCODE projects, we provide novel group-wise comparative analyses across sex, tissue type, state and developmental age. Remarkably, we find that distinct sets of epigenomic features are maximally discriminative for different group-wise comparisons, in each case revealing distinct enriched pathways, many of which do not show gene expression differences. Our methodology should be broadly applicable for epigenomic comparisons and provides a powerful new tool for studying chromatin state differences at the genome scale.
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The NIH Roadmap Epigenomics Mapping Consortium
Nature biotechnology, 2010Co-Authors: Bradley E. Bernstein, Manolis Kellis, John A. Stamatoyannopoulos, Joseph F. Costello, Bing Ren, Aleksandar Milosavljevic, Alexander Meissner, Marco A. Marra, Arthur L. Beaudet, Joseph R. EckerAbstract:The NIH Roadmap Epigenomics Mapping Consortium aims to produce a public resource of epigenomic maps for stem cells and primary ex vivo tissues selected to represent the normal counterparts of tissues and organ systems frequently involved in human disease.
Andrea H. Brand - One of the best experts on this subject based on the ideXlab platform.
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Public Health Genomics— public health goes personalized?
European journal of public health, 2011Co-Authors: Andrea H. BrandAbstract:High five?—5 years ago, we wrote the first Editorial1 and Viewpoint2 on Public Health Genomics for the European Journal of Public Health . We concluded that ‘public health in the future will be quite different from public health in the past’. Today, 5 years later, have we been right? Where are we now? The highly technology and bioinformatics-driven dynamics of genomics as a ‘moving target’ from the Human Genome Project (HGP) to the Personal Genome Project (PGP) is currently ‘shaking’ public health research, policy making and practice in a very fundamental way. Let us be prepared for rethinking our public health interventions, since we face a time when boundaries of disciplines are crossed and the understanding of diseases is changed as it happened before with the discovery of microscopy and the jump from the macroscopic point of view in anatomy to the microscopic view in cell structure. Firstly, Epigenomics plays a crucial role for public health by bridging social and biomedical sciences and being the blueprint for the understanding and measurement of genome–environment interactions and the interplay of all health determinants in disease aetiology. It suggests measurable mechanisms (DNA methylation patterns) whereby environmental factors such as stress, nutrients or a virus influence gene expression (‘from society to cell’). For example, nightshift seems to be not ‘just’ an environmental risk factor, but also appear to have an epigenomic effect in …
Yue Li - One of the best experts on this subject based on the ideXlab platform.
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joint bayesian inference of risk variants and tissue specific epigenomic enrichments across multiple complex human diseases
Nucleic Acids Research, 2016Co-Authors: Yue Li, Manolis KellisAbstract:Genome wide association studies (GWAS) provide a powerful approach for uncovering disease-associated variants in human, but fine-mapping the causal variants remains a challenge. This is partly remedied by prioritization of disease-associated variants that overlap GWAS-enriched epigenomic annotations. Here, we introduce a new Bayesian model RiVIERA (Risk Variant Inference using Epigenomic Reference Annotations) for inference of driver variants from summary statistics across multiple traits using hundreds of epigenomic annotations. In simulation, RiVIERA promising power in detecting causal variants and causal annotations, the multi-trait joint inference further improved the detection power. We applied RiVIERA to model the existing GWAS summary statistics of 9 autoimmune diseases and Schizophrenia by jointly harnessing the potential causal enrichments among 848 tissue-specific Epigenomics annotations from ENCODE/Roadmap consortium covering 127 cell/tissue types and 8 major epigenomic marks. RiVIERA identified meaningful tissue-specific enrichments for enhancer regions defined by H3K4me1 and H3K27ac for Blood T-Cell specifically in the nine autoimmune diseases and Brain-specific enhancer activities exclusively in Schizophrenia. Moreover, the variants from the 95% credible sets exhibited high conservation and enrichments for GTEx whole-blood eQTLs located within transcription-factor-binding-sites and DNA-hypersensitive-sites. Furthermore, joint modeling the nine immune traits by simultaneously inferring and exploiting the underlying epigenomic correlation between traits further improved the functional enrichments compared to single-trait models.
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riviera beta joint bayesian inference of risk variants and tissue specific epigenomic enrichments across multiple complex human diseases
bioRxiv, 2016Co-Authors: Yue Li, Manolis KellisAbstract:Genome wide association studies (GWAS) provide a powerful approach for uncovering disease-associated variants in human, but fine-mapping the causal variants remains a challenge. This is partly remedied by prioritization of disease-associated variants that overlap GWAS-enriched epigenomic annotations. Here, we introduce a new Bayesian model RiVIERA-beta (Risk Variant Inference using Epigenomic Reference Annotations) for inference of driver variants by modelling summary statistics p-values in Beta density function across multiple traits using hundreds of epigenomic annotations. In simulation, RiVIERA-beta promising power in detecting causal variants and causal annotations, the multi-trait joint inference further improved the detection power.} We applied RiVIERA-beta to model the existing GWAS summary statistics of 9 autoimmune diseases and Schizophrenia by jointly harnessing the potential causal enrichments among 848 tissue-specific Epigenomics annotations from ENCODE/Roadmap consortium covering 127 cell/tissue types and 8 major epigenomic marks. RiVIERA-beta identified meaningful tissue-specific enrichments for enhancer regions defined by H3K4me1 and H3K27ac for Blood T-Cell specifically in the 9 autoimmune diseases and Brain-specific enhancer activities exclusively in Schizophrenia. Moreover, the variants from the 95% credible sets exhibited high conservation and enrichments for GTEx whole-blood eQTLs located within transcription-factor-binding-sites and DNA-hypersensitive-sites. Furthermore, joint modeling the nine immune traits by simultaneously inferring and exploiting the underlying epigenomic correlation between traits further improved the functional enrichments compared to single-trait models.
Joseph R. Ecker - One of the best experts on this subject based on the ideXlab platform.
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methylpipe and compepitools a suite of r packages for the integrative analysis of Epigenomics data
BMC Bioinformatics, 2015Co-Authors: Kamal Kishore, Joseph R. Ecker, Stefano De Pretis, Ryan Lister, Marco J Morelli, Valerio Bianchi, Bruno Amati, Mattia PelizzolaAbstract:Background Numerous methods are available to profile several epigenetic marks, providing data with different genome coverage and resolution. Large epigenomic datasets are then generated, and often combined with other high-throughput data, including RNA-seq, ChIP-seq for transcription factors (TFs) binding and DNase-seq experiments. Despite the numerous computational tools covering specific steps in the analysis of large-scale Epigenomics data, comprehensive software solutions for their integrative analysis are still missing. Multiple tools must be identified and combined to jointly analyze histone marks, TFs binding and other -omics data together with DNA methylation data, complicating the analysis of these data and their integration with publicly available datasets.
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epigenome wide inheritance of cytosine methylation variants in a recombinant inbred population
Genome Research, 2013Co-Authors: Robert J Schmitz, Oswaldo Valdeslopez, Saad M Khan, Trupti Joshi, Mark A Urich, Joseph R Nery, Brian W Diers, Gary Stacey, Joseph R. EckerAbstract:Cytosine DNA methylation is one avenue for passing information through cell divisions. Here, we present epigenomic analyses of soybean recombinant inbred lines (RILs) and their parents. Identification of differentially methylated regions (DMRs) revealed that DMRs mostly cosegregated with the genotype from which they were derived, but examples of the uncoupling of genotype and epigenotype were identified. Linkage mapping of methylation states assessed from whole-genome bisulfite sequencing of 83 RILs uncovered widespread evidence for local methylQTL. This Epigenomics approach provides a comprehensive study of the patterns and heritability of methylation variants in a complex genetic population over multiple generations, paving the way for understanding how methylation variants contribute to phenotypic variation.
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The NIH Roadmap Epigenomics Mapping Consortium
Nature biotechnology, 2010Co-Authors: Bradley E. Bernstein, Manolis Kellis, John A. Stamatoyannopoulos, Joseph F. Costello, Bing Ren, Aleksandar Milosavljevic, Alexander Meissner, Marco A. Marra, Arthur L. Beaudet, Joseph R. EckerAbstract:The NIH Roadmap Epigenomics Mapping Consortium aims to produce a public resource of epigenomic maps for stem cells and primary ex vivo tissues selected to represent the normal counterparts of tissues and organ systems frequently involved in human disease.
Yu Zhao - One of the best experts on this subject based on the ideXlab platform.
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Understanding Epigenomics based on the rice model.
TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik, 2020Co-Authors: Dao-xiu Zhou, Yu ZhaoAbstract:The purpose of this paper provides a comprehensive overview of the recent researches on rice Epigenomics, including DNA methylation, histone modifications, noncoding RNAs, and three-dimensional genomics. The challenges and perspectives for future research in rice are discussed. Rice as a model plant for epigenomic studies has much progressed current understanding of epigenetics in plants. Recent results on rice epigenome profiling and three-dimensional chromatin structure studies reveal specific features and implication in gene regulation during rice plant development and adaptation to environmental changes. Results on rice chromatin regulator functions shed light on mechanisms of establishment, recognition, and resetting of epigenomic information in plants. Cloning of several rice epialleles associated with important agronomic traits highlights importance of epigenomic variation in rice plant growth, fitness, and yield. In this review, we summarize and analyze recent advances in rice Epigenomics and discuss challenges and directions for future research in the field.
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Understanding Epigenomics based on the rice model
TAG Theoretical and Applied Genetics, 2020Co-Authors: Dao-xiu Zhou, Yu ZhaoAbstract:Key message The purpose of this paper provides a comprehensive overview of the recent researches on rice Epigenomics, including DNA methylation, histone modifications, noncoding RNAs, and three-dimensional genomics. The challenges and perspectives for future research in rice are discussed. Rice as a model plant for epigenomic studies has much progressed current understanding of epigenetics in plants. Recent results on rice epigenome profiling and three-dimensional chromatin structure studies reveal specific features and implication in gene regulation during rice plant development and adaptation to environmental changes. Results on rice chromatin regulator functions shed light on mechanisms of establishment, recognition, and resetting of epigenomic information in plants. Cloning of several rice epialleles associated with important agronomic traits highlights importance of epigenomic variation in rice plant growth, fitness, and yield. In this review, we summarize and analyze recent advances in rice Epigenomics and discuss challenges and directions for future research in the field.