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Wen J, Sun Q, Huang L, Zhou L, Doyle MF, Ekunwe L, Durda P, Olson NC, Reiner AP, Li Y, Raffield LM. Gene expression and splicing QTL analysis of blood cells in African American participants from the Jackson Heart Study. Genetics 2024; 228:iyae098. [PMID: 39056362 PMCID: PMC11373511 DOI: 10.1093/genetics/iyae098] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2024] [Accepted: 06/05/2024] [Indexed: 07/28/2024] Open
Abstract
Most gene expression and alternative splicing quantitative trait loci (eQTL/sQTL) studies have been biased toward European ancestry individuals. Here, we performed eQTL and sQTL analyses using TOPMed whole-genome sequencing-derived genotype data and RNA-sequencing data from stored peripheral blood mononuclear cells in 1,012 African American participants from the Jackson Heart Study (JHS). At a false discovery rate of 5%, we identified 17,630 unique eQTL credible sets covering 16,538 unique genes; and 24,525 unique sQTL credible sets covering 9,605 unique genes, with lead QTL at P < 5e-8. About 24% of independent eQTLs and independent sQTLs with a minor allele frequency > 1% in JHS were rare (minor allele frequency < 0.1%), and therefore unlikely to be detected, in European ancestry individuals. Finally, we created an open database, which is freely available online, allowing fast query and bulk download of our QTL results.
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Affiliation(s)
- Jia Wen
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27514, USA
| | - Quan Sun
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Le Huang
- Curriculum in Bioinformatics and Computational Biology, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Lingbo Zhou
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Margaret F Doyle
- Department of Pathology and Laboratory Medicine, Larner College of Medicine, University of Vermont, Burlington, VT 05405, USA
| | - Lynette Ekunwe
- Department of Medicine, University of MS Medical Center (UMMC), Jackson, MS 39213, USA
| | - Peter Durda
- Department of Pathology and Laboratory Medicine, Larner College of Medicine, University of Vermont, Burlington, VT 05405, USA
| | - Nels C Olson
- Department of Pathology and Laboratory Medicine, Larner College of Medicine, University of Vermont, Burlington, VT 05405, USA
| | - Alexander P Reiner
- Department of Epidemiology, University of Washington, Seattle, WA 98195, USA
- Division of Public Health Sciences, Fred Hutchinson Cancer Research, Seattle, WA 98109, USA
| | - Yun Li
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27514, USA
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Laura M Raffield
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27514, USA
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2
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Liu W, Zhong W, Giusti-Rodríguez P, Jiang Z, Wang GW, Sun H, Hu M, Li Y. SnapHiC-G: identifying long-range enhancer-promoter interactions from single-cell Hi-C data via a global background model. Brief Bioinform 2024; 25:bbae426. [PMID: 39222061 PMCID: PMC11367764 DOI: 10.1093/bib/bbae426] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2024] [Revised: 07/05/2024] [Accepted: 08/13/2024] [Indexed: 09/04/2024] Open
Abstract
Harnessing the power of single-cell genomics technologies, single-cell Hi-C (scHi-C) and its derived technologies provide powerful tools to measure spatial proximity between regulatory elements and their target genes in individual cells. Using a global background model, we propose SnapHiC-G, a computational method, to identify long-range enhancer-promoter interactions from scHi-C data. We applied SnapHiC-G to scHi-C datasets generated from mouse embryonic stem cells and human brain cortical cells. SnapHiC-G achieved high sensitivity in identifying long-range enhancer-promoter interactions. Moreover, SnapHiC-G can identify putative target genes for noncoding genome-wide association study (GWAS) variants, and the genetic heritability of neuropsychiatric diseases is enriched for single-nucleotide polymorphisms (SNPs) within SnapHiC-G-identified interactions in a cell-type-specific manner. In sum, SnapHiC-G is a powerful tool for characterizing cell-type-specific enhancer-promoter interactions from complex tissues and can facilitate the discovery of chromatin interactions important for gene regulation in biologically relevant cell types.
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Affiliation(s)
- Weifang Liu
- Department of Biostatistics, University of North Carolina at Chapel Hill, 135 Dauer Drive, Chapel Hill, NC 27599, United States
| | - Wujuan Zhong
- Biostatistics and Research Decision Sciences, Merck & Co., Inc., 126 East Lincoln Ave, Rahway, New Jersey 07065, United States
| | - Paola Giusti-Rodríguez
- Department of Psychiatry, University of Florida, 1149 Newel Dr., Gainesville, FL 32611, United States
| | - Zhiyun Jiang
- Department of Genetics, University of North Carolina at Chapel Hill, 120 Mason Farm Road, Chapel Hill, NC 27599, United States
| | - Geoffery W Wang
- Department of Biostatistics, University of North Carolina at Chapel Hill, 135 Dauer Drive, Chapel Hill, NC 27599, United States
| | - Huaigu Sun
- Department of Genetics, University of North Carolina at Chapel Hill, 120 Mason Farm Road, Chapel Hill, NC 27599, United States
| | - Ming Hu
- Department of Quantitative Health Sciences, Lerner Research Institute, Cleveland Clinic Foundation, 9500 Euclid Avenue, Cleveland, OH 44196, United States
| | - Yun Li
- Department of Biostatistics, University of North Carolina at Chapel Hill, 135 Dauer Drive, Chapel Hill, NC 27599, United States
- Department of Genetics, University of North Carolina at Chapel Hill, 120 Mason Farm Road, Chapel Hill, NC 27599, United States
- Department of Computer Science, University of North Carolina at Chapel Hill, 201 S. Columbia St, Chapel Hill, NC 27599, United States
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3
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Murtaza G, Butaney B, Wagner J, Singh R. scGrapHiC: deep learning-based graph deconvolution for Hi-C using single cell gene expression. Bioinformatics 2024; 40:i490-i500. [PMID: 38940151 PMCID: PMC11256916 DOI: 10.1093/bioinformatics/btae223] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/29/2024] Open
Abstract
SUMMARY Single-cell Hi-C (scHi-C) protocol helps identify cell-type-specific chromatin interactions and sheds light on cell differentiation and disease progression. Despite providing crucial insights, scHi-C data is often underutilized due to the high cost and the complexity of the experimental protocol. We present a deep learning framework, scGrapHiC, that predicts pseudo-bulk scHi-C contact maps using pseudo-bulk scRNA-seq data. Specifically, scGrapHiC performs graph deconvolution to extract genome-wide single-cell interactions from a bulk Hi-C contact map using scRNA-seq as a guiding signal. Our evaluations show that scGrapHiC, trained on seven cell-type co-assay datasets, outperforms typical sequence encoder approaches. For example, scGrapHiC achieves a substantial improvement of 23.2% in recovering cell-type-specific Topologically Associating Domains over the baselines. It also generalizes to unseen embryo and brain tissue samples. scGrapHiC is a novel method to generate cell-type-specific scHi-C contact maps using widely available genomic signals that enables the study of cell-type-specific chromatin interactions. AVAILABILITY AND IMPLEMENTATION The GitHub link: https://github.com/rsinghlab/scGrapHiC contains the source code of scGrapHiC and associated scripts to preprocess publicly available datasets to produce the results and visualizations we have discuss in this manuscript.
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Affiliation(s)
- Ghulam Murtaza
- Department of Computer Science, Brown University, 115 Waterman Street, Providence, RI, 02912, United States
| | - Byron Butaney
- Department of Computer Science, Brown University, 115 Waterman Street, Providence, RI, 02912, United States
| | - Justin Wagner
- Material Measurement Laboratory, National Institute of Standards and Technology, Gaithersburg, MD, 20899, United States
| | - Ritambhara Singh
- Department of Computer Science, Brown University, 115 Waterman Street, Providence, RI, 02912, United States
- Center for Computational Molecular Biology, Brown University, 164 Angell Street, Providence, RI, 02912, United States
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4
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Raffo A, Paulsen J. The shape of chromatin: insights from computational recognition of geometric patterns in Hi-C data. Brief Bioinform 2023; 24:bbad302. [PMID: 37646128 PMCID: PMC10516369 DOI: 10.1093/bib/bbad302] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2023] [Revised: 07/05/2023] [Accepted: 08/03/2023] [Indexed: 09/01/2023] Open
Abstract
The three-dimensional organization of chromatin plays a crucial role in gene regulation and cellular processes like deoxyribonucleic acid (DNA) transcription, replication and repair. Hi-C and related techniques provide detailed views of spatial proximities within the nucleus. However, data analysis is challenging partially due to a lack of well-defined, underpinning mathematical frameworks. Recently, recognizing and analyzing geometric patterns in Hi-C data has emerged as a powerful approach. This review provides a summary of algorithms for automatic recognition and analysis of geometric patterns in Hi-C data and their correspondence with chromatin structure. We classify existing algorithms on the basis of the data representation and pattern recognition paradigm they make use of. Finally, we outline some of the challenges ahead and promising future directions.
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Affiliation(s)
- Andrea Raffo
- Department of Biosciences, University of Oslo, 0316 Oslo, Norway
| | - Jonas Paulsen
- Department of Biosciences, University of Oslo, 0316 Oslo, Norway
- Centre for Bioinformatics, Department of Informatics, University of Oslo, 0316 Oslo, Norway
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Wang J, Lu L, Zheng S, Wang D, Jin L, Zhang Q, Li M, Zhang Z. DeCOOC Deconvoluted Hi-C Map Characterizes the Chromatin Architecture of Cells in Physiologically Distinctive Tissues. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2023; 10:e2301058. [PMID: 37515382 PMCID: PMC10520690 DOI: 10.1002/advs.202301058] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/15/2023] [Revised: 07/06/2023] [Indexed: 07/30/2023]
Abstract
Deciphering variations in chromosome conformations based on bulk three-dimensional (3D) genomic data from heterogenous tissues is a key to understanding cell-type specific genome architecture and dynamics. Surprisingly, computational deconvolution methods for high-throughput chromosome conformation capture (Hi-C) data remain very rare in the literature. Here, a deep convolutional neural network (CNN), deconvolve bulk Hi-C data (deCOOC) that remarkably outperformed all the state-of-the-art tools in the deconvolution task is developed. Interestingly, it is noticed that the chromatin accessibility or the Hi-C contact frequency alone is insufficient to explain the power of deCOOC, suggesting the existence of a latent embedded layer of information pertaining to the cell type specific 3D genome architecture. By applying deCOOC to in-house-generated bulk Hi-C data from visceral and subcutaneous adipose tissues, it is found that the characteristic chromatin features of M2 cells in the two anatomical loci are distinctively bound to different physiological functionalities. Taken together, deCOOC is both a reliable Hi-C data deconvolution method and a powerful tool for functional extraction of 3D genome architecture.
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Affiliation(s)
- Junmei Wang
- CAS Key Laboratory of Genome Sciences and InformationBeijing Institute of GenomicsChinese Academy of Sciences and China National Center for BioinformationBeijing100101China
- School of Life ScienceUniversity of Chinese Academy of SciencesBeijing100049China
| | - Lu Lu
- Livestock and Poultry Multiomics Key Laboratory of Ministry of Agriculture and Rural AffairsCollege of Animal Science and TechnologySichuan Agricultural UniversityChengdu611130China
- Animal Breeding and Genetics Key Laboratory of Sichuan ProvinceInstitute of Animal Genetics and BreedingSichuan Agricultural UniversityChengdu611130China
| | - Shiqi Zheng
- CAS Key Laboratory of Genome Sciences and InformationBeijing Institute of GenomicsChinese Academy of Sciences and China National Center for BioinformationBeijing100101China
- School of Life ScienceUniversity of Chinese Academy of SciencesBeijing100049China
| | - Danyang Wang
- CAS Key Laboratory of Genome Sciences and InformationBeijing Institute of GenomicsChinese Academy of Sciences and China National Center for BioinformationBeijing100101China
- School of Life ScienceUniversity of Chinese Academy of SciencesBeijing100049China
- Sars‐Fang Centre & MOE Key Laboratory of Marine Genetics and BreedingCollege of Marine Life SciencesOcean University of ChinaQingdao266100China
| | - Long Jin
- Livestock and Poultry Multiomics Key Laboratory of Ministry of Agriculture and Rural AffairsCollege of Animal Science and TechnologySichuan Agricultural UniversityChengdu611130China
- Animal Breeding and Genetics Key Laboratory of Sichuan ProvinceInstitute of Animal Genetics and BreedingSichuan Agricultural UniversityChengdu611130China
| | - Qing Zhang
- CAS Key Laboratory of Genome Sciences and InformationBeijing Institute of GenomicsChinese Academy of Sciences and China National Center for BioinformationBeijing100101China
| | - Mingzhou Li
- Livestock and Poultry Multiomics Key Laboratory of Ministry of Agriculture and Rural AffairsCollege of Animal Science and TechnologySichuan Agricultural UniversityChengdu611130China
- Animal Breeding and Genetics Key Laboratory of Sichuan ProvinceInstitute of Animal Genetics and BreedingSichuan Agricultural UniversityChengdu611130China
| | - Zhihua Zhang
- CAS Key Laboratory of Genome Sciences and InformationBeijing Institute of GenomicsChinese Academy of Sciences and China National Center for BioinformationBeijing100101China
- School of Life ScienceUniversity of Chinese Academy of SciencesBeijing100049China
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Gilbert BR, Thornburg ZR, Brier TA, Stevens JA, Grünewald F, Stone JE, Marrink SJ, Luthey-Schulten Z. Dynamics of chromosome organization in a minimal bacterial cell. Front Cell Dev Biol 2023; 11:1214962. [PMID: 37621774 PMCID: PMC10445541 DOI: 10.3389/fcell.2023.1214962] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2023] [Accepted: 07/10/2023] [Indexed: 08/26/2023] Open
Abstract
Computational models of cells cannot be considered complete unless they include the most fundamental process of life, the replication and inheritance of genetic material. By creating a computational framework to model systems of replicating bacterial chromosomes as polymers at 10 bp resolution with Brownian dynamics, we investigate changes in chromosome organization during replication and extend the applicability of an existing whole-cell model (WCM) for a genetically minimal bacterium, JCVI-syn3A, to the entire cell-cycle. To achieve cell-scale chromosome structures that are realistic, we model the chromosome as a self-avoiding homopolymer with bending and torsional stiffnesses that capture the essential mechanical properties of dsDNA in Syn3A. In addition, the conformations of the circular DNA must avoid overlapping with ribosomes identitied in cryo-electron tomograms. While Syn3A lacks the complex regulatory systems known to orchestrate chromosome segregation in other bacteria, its minimized genome retains essential loop-extruding structural maintenance of chromosomes (SMC) protein complexes (SMC-scpAB) and topoisomerases. Through implementing the effects of these proteins in our simulations of replicating chromosomes, we find that they alone are sufficient for simultaneous chromosome segregation across all generations within nested theta structures. This supports previous studies suggesting loop-extrusion serves as a near-universal mechanism for chromosome organization within bacterial and eukaryotic cells. Furthermore, we analyze ribosome diffusion under the influence of the chromosome and calculate in silico chromosome contact maps that capture inter-daughter interactions. Finally, we present a methodology to map the polymer model of the chromosome to a Martini coarse-grained representation to prepare molecular dynamics models of entire Syn3A cells, which serves as an ultimate means of validation for cell states predicted by the WCM.
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Affiliation(s)
- Benjamin R. Gilbert
- Department of Chemistry, University of Illinois at Urbana-Champaign, Urbana, IL, United States
| | - Zane R. Thornburg
- Department of Chemistry, University of Illinois at Urbana-Champaign, Urbana, IL, United States
| | - Troy A. Brier
- Department of Chemistry, University of Illinois at Urbana-Champaign, Urbana, IL, United States
| | - Jan A. Stevens
- Molecular Dynamics Group, Groningen Biomolecular Sciences and Biotechnology Institute, University of Groningen, Groningen, Netherlands
| | - Fabian Grünewald
- Molecular Dynamics Group, Groningen Biomolecular Sciences and Biotechnology Institute, University of Groningen, Groningen, Netherlands
| | - John E. Stone
- NVIDIA Corporation, Santa Clara, CA, United States
- NIH Center for Macromolecular Modeling and Bioinformatics, Beckman Institute, University of Illinois at Urbana-Champaign, Urbana, IL, United States
| | - Siewert J. Marrink
- Molecular Dynamics Group, Groningen Biomolecular Sciences and Biotechnology Institute, University of Groningen, Groningen, Netherlands
| | - Zaida Luthey-Schulten
- Department of Chemistry, University of Illinois at Urbana-Champaign, Urbana, IL, United States
- NIH Center for Macromolecular Modeling and Bioinformatics, Beckman Institute, University of Illinois at Urbana-Champaign, Urbana, IL, United States
- NSF Center for the Physics of Living Cells, Department of Physics, University of Illinois at Urbana-Champaign, Urbana, IL, United States
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7
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Wen J, Sun Q, Huang L, Zhou L, Doyle MF, Ekunwe L, Olson NC, Reiner AP, Li Y, Raffield LM. Gene Expression and Splicing QTL Analysis of Blood Cells in African American Participants from the Jackson Heart Study. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.04.26.538455. [PMID: 37163084 PMCID: PMC10168308 DOI: 10.1101/2023.04.26.538455] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/11/2023]
Abstract
Most gene expression and alternative splicing quantitative trait loci (eQTL/sQTL) studies have been biased toward European ancestry individuals. Here, we performed eQTL and sQTL analysis using TOPMed whole genome sequencing-derived genotype data and RNA sequencing data from stored peripheral blood mononuclear cells in 1,012 African American participants from the Jackson Heart Study (JHS). At a false discovery rate (FDR) of 5%, we identified 4,798,604 significant eQTL-gene pairs, covering 16,538 unique genes; and 5,921,368 sQTL-gene-cluster pairs, covering 9,605 unique genes. About 31% of detected eQTL and sQTL variants with a minor allele frequency (MAF) > 1% in JHS were rare (MAF < 0.1%), and therefore unlikely to be detected, in European ancestry individuals. We also generated 17,630 eQTL credible sets and 24,525 sQTL credible sets for genes (gene-clusters) with lead QTL p < 5e-8. Finally, we created an open database, which is freely available online, allowing fast query and bulk download of our QTL results.
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8
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Zhong W, Liu W, Chen J, Sun Q, Hu M, Li Y. Understanding the function of regulatory DNA interactions in the interpretation of non-coding GWAS variants. Front Cell Dev Biol 2022; 10:957292. [PMID: 36060805 PMCID: PMC9437546 DOI: 10.3389/fcell.2022.957292] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2022] [Accepted: 07/21/2022] [Indexed: 01/11/2023] Open
Abstract
Genome-wide association studies (GWAS) have identified a vast number of variants associated with various complex human diseases and traits. However, most of these GWAS variants reside in non-coding regions producing no proteins, making the interpretation of these variants a daunting challenge. Prior evidence indicates that a subset of non-coding variants detected within or near cis-regulatory elements (e.g., promoters, enhancers, silencers, and insulators) might play a key role in disease etiology by regulating gene expression. Advanced sequencing- and imaging-based technologies, together with powerful computational methods, enabling comprehensive characterization of regulatory DNA interactions, have substantially improved our understanding of the three-dimensional (3D) genome architecture. Recent literature witnesses plenty of examples where using chromosome conformation capture (3C)-based technologies successfully links non-coding variants to their target genes and prioritizes relevant tissues or cell types. These examples illustrate the critical capability of 3D genome organization in annotating non-coding GWAS variants. This review discusses how 3D genome organization information contributes to elucidating the potential roles of non-coding GWAS variants in disease etiology.
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Affiliation(s)
- Wujuan Zhong
- Biostatistics and Research Decision Sciences, Merck & Co, Inc, Rahway, NJ, United States
| | - Weifang Liu
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Jiawen Chen
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Quan Sun
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Ming Hu
- Department of Quantitative Health Sciences, Lerner Research Institute, Cleveland Clinic Foundation, Cleveland, OH, United States
| | - Yun Li
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
- Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
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9
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Lyu C, Huang M, Liu N, Chen Z, Lupo PJ, Tycko B, Witte JS, Hobbs CA, Li M. Random field modeling of multi-trait multi-locus association for detecting methylation quantitative trait loci. Bioinformatics 2022; 38:3853-3862. [PMID: 35781319 PMCID: PMC9364381 DOI: 10.1093/bioinformatics/btac443] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2021] [Revised: 06/28/2022] [Accepted: 06/30/2022] [Indexed: 12/24/2022] Open
Abstract
MOTIVATION CpG sites within the same genomic region often share similar methylation patterns and tend to be co-regulated by multiple genetic variants that may interact with one another. RESULTS We propose a multi-trait methylation random field (multi-MRF) method to evaluate the joint association between a set of CpG sites and a set of genetic variants. The proposed method has several advantages. First, it is a multi-trait method that allows flexible correlation structures between neighboring CpG sites (e.g. distance-based correlation). Second, it is also a multi-locus method that integrates the effect of multiple common and rare genetic variants. Third, it models the methylation traits with a beta distribution to characterize their bimodal and interval properties. Through simulations, we demonstrated that the proposed method had improved power over some existing methods under various disease scenarios. We further illustrated the proposed method via an application to a study of congenital heart defects (CHDs) with 83 cardiac tissue samples. Our results suggested that gene BACE2, a methylation quantitative trait locus (QTL) candidate, colocalized with expression QTLs in artery tibial and harbored genetic variants with nominal significant associations in two genome-wide association studies of CHD. AVAILABILITY AND IMPLEMENTATION https://github.com/chenlyu2656/Multi-MRF. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Chen Lyu
- Department of Epidemiology and Biostatistics, Indiana University Bloomington, Bloomington, IN 47405, USA,Department of Population Health, New York University Grossman School of Medicine, New York, NY 10016, USA
| | - Manyan Huang
- Department of Epidemiology and Biostatistics, Indiana University Bloomington, Bloomington, IN 47405, USA
| | - Nianjun Liu
- Department of Epidemiology and Biostatistics, Indiana University Bloomington, Bloomington, IN 47405, USA
| | - Zhongxue Chen
- Department of Epidemiology and Biostatistics, Indiana University Bloomington, Bloomington, IN 47405, USA
| | - Philip J Lupo
- Department of Pediatrics, Baylor College of Medicine, Houston, TX 77030, USA
| | - Benjamin Tycko
- Center for Discovery and Innovation, Nutley, NJ 07110, USA
| | - John S Witte
- Department of Epidemiology and Population Health, Stanford University, Stanford, CA 94305, USA,Department of Biomedical Data Sciences, Stanford University, Stanford, CA 94305, USA
| | - Charlotte A Hobbs
- Rady Children’s Institute for Genomic Medicine, San Diego, CA 92123, USA
| | - Ming Li
- To whom correspondence should be addressed.
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10
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Liu W, Zhong W, Chen J, Huang B, Hu M, Li Y. Understanding Regulatory Mechanisms of Brain Function and Disease through 3D Genome Organization. Genes (Basel) 2022; 13:586. [PMID: 35456393 PMCID: PMC9027261 DOI: 10.3390/genes13040586] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2022] [Revised: 03/17/2022] [Accepted: 03/23/2022] [Indexed: 02/01/2023] Open
Abstract
The human genome has a complex and dynamic three-dimensional (3D) organization, which plays a critical role for gene regulation and genome function. The importance of 3D genome organization in brain development and function has been well characterized in a region- and cell-type-specific fashion. Recent technological advances in chromosome conformation capture (3C)-based techniques, imaging approaches, and ligation-free methods, along with computational methods to analyze the data generated, have revealed 3D genome features at different scales in the brain that contribute to our understanding of genetic mechanisms underlying neuropsychiatric diseases and other brain-related traits. In this review, we discuss how these advances aid in the genetic dissection of brain-related traits.
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Affiliation(s)
- Weifang Liu
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA; (W.L.); (J.C.)
| | - Wujuan Zhong
- Biostatistics and Research Decision Sciences, Merck & Co., Inc., Rahway, NJ 07065, USA;
| | - Jiawen Chen
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA; (W.L.); (J.C.)
| | - Bo Huang
- Department of Pharmaceutical Chemistry, University of California, San Francisco, CA 94143, USA;
- Chan Zuckerberg Biohub, San Francisco, CA 94158, USA
- Department of Biochemistry and Biophysics, University of California, San Francisco, CA 94143, USA
| | - Ming Hu
- Department of Quantitative Health Sciences, Lerner Research Institute, Cleveland Clinic Foundation, Cleveland, OH 44195, USA
| | - Yun Li
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA; (W.L.); (J.C.)
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
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