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Seoighe C, Connaire S, Chopra M. Probing the limits of cis-acting gene regulation using a model of allelic imbalance quantitative trait loci. PLoS Genet 2025; 21:e1011446. [PMID: 40305626 PMCID: PMC12068699 DOI: 10.1371/journal.pgen.1011446] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2024] [Revised: 05/12/2025] [Accepted: 03/27/2025] [Indexed: 05/02/2025] Open
Abstract
Imbalance in gene expression between alleles is a hallmark of cis-acting expression quantitative trait loci (eQTLs) and several methods have been developed to exploit allelic imbalance to support the identification of eQTLs. Allelic imbalance is also of scientific and, potentially, clinical interest as it can erode the degree to which the effects of deleterious variants are buffered in a diploid organism and has been reported to be associated with the penetrance of pathological genomic variants. Here, we develop and apply a statistical model that is designed to evaluate whether the genotype of a locus is associated with the degree of allelic imbalance of a gene and refer to such loci as allelic imbalance quantitative trait loci (aiQTLs). An advantage of our approach is that it does not depend on linkage disequilibrium between the aiQTL and the associated gene and is, therefore, suited to the identification of eQTLs that act in cis over very large distances. We applied our model to data from the GTEx consortium and examined the relationship between the distance of an eQTL from the TSS of the associated gene and the evidence that the eQTL acts in cis. Previous studies have used a distance of 1Mb from the target gene as an indication that an eQTL acts in cis; however, our results suggest that the majority of eQTLs at distances more than 500 kb from the TSS of the target gene are likely to act in trans (and thus to affect both gene copies). The model used here is also well suited to comparing the overall extent of allelic imbalance between samples. We show that in some tissues allelic imbalance is correlated with age; however, this correlation may be due to changes in the abundance of immune cell populations with age, as we found strong correlations between sample-level allelic imbalance and the inferred abundance of multiple immune cell types across whole blood samples.
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Affiliation(s)
- Cathal Seoighe
- School of Mathematical and Statistical Sciences, University of Galway, Galway, Ireland
- Research Ireland Centre for Research Training in Genomics Data Science, University of Galway, Galway, Ireland
| | - Seán Connaire
- School of Mathematical and Statistical Sciences, University of Galway, Galway, Ireland
| | - Mehak Chopra
- School of Mathematical and Statistical Sciences, University of Galway, Galway, Ireland
- Research Ireland Centre for Research Training in Genomics Data Science, University of Galway, Galway, Ireland
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2
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Simmons SK, Adiconis X, Haywood N, Parker J, Lin Z, Liao Z, Tuncali I, Al’Khafaji AM, Shin A, Jagadeesh K, Gosik K, Gatzen M, Smith JT, El Kodsi DN, Kuras Y, Baecher-Allan C, Serrano GE, Beach TG, Garimella K, Rozenblatt-Rosen O, Regev A, Dong X, Scherzer CR, Levin JZ. Experimental and Computational Methods for Allelic Imbalance Analysis from Single-Nucleus RNA-seq Data. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2025:2024.08.13.607784. [PMID: 39185246 PMCID: PMC11343128 DOI: 10.1101/2024.08.13.607784] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/27/2024]
Abstract
Single-cell RNA-seq (scRNA-seq) is emerging as a powerful tool for understanding gene function across diverse cells. Recently, this has included the use of allele-specific expression (ASE) analysis to better understand how variation in the human genome affects RNA expression at the single-cell level. We reasoned that because intronic reads are more prevalent in single-nucleus RNA-Seq (snRNA-Seq), and introns are under lower purifying selection and thus enriched for genetic variants, that snRNA-seq should facilitate single-cell analysis of ASE. Here we demonstrate how experimental and computational choices can improve the results of allelic imbalance analysis. We explore how experimental choices, such as RNA source, read length, sequencing depth, genotyping, etc., impact the power of ASE-based methods. We developed a new suite of computational tools to process and analyze scRNA-seq and snRNA-seq for ASE. As hypothesized, we extracted more ASE information from reads in intronic regions than those in exonic regions and show how read length can be set to increase power. Additionally, hybrid selection improved our power to detect allelic imbalance in genes of interest. We also explored methods to recover allele-specific isoform expression levels from both long- and short-read snRNA-seq. To further investigate ASE in the context of human disease, we applied our methods to a Parkinson's disease cohort of 94 individuals and show that ASE analysis had more power than eQTL analysis to identify significant SNP/gene pairs in our direct comparison of the two methods. Overall, we provide an end-to-end experimental and computational approach for future studies.
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Affiliation(s)
- Sean K. Simmons
- Aligning Science Across Parkinson’s (ASAP) Collaborative Research Network, Chevy Chase, MD 20815, USA
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Xian Adiconis
- Aligning Science Across Parkinson’s (ASAP) Collaborative Research Network, Chevy Chase, MD 20815, USA
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Nathan Haywood
- Aligning Science Across Parkinson’s (ASAP) Collaborative Research Network, Chevy Chase, MD 20815, USA
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Jacob Parker
- Aligning Science Across Parkinson’s (ASAP) Collaborative Research Network, Chevy Chase, MD 20815, USA
- Stephen & Denise Adams Center for Parkinson’s Disease Research of Yale School of Medicine, New Haven, CT 06510, USA
| | - Zechuan Lin
- Aligning Science Across Parkinson’s (ASAP) Collaborative Research Network, Chevy Chase, MD 20815, USA
- Stephen & Denise Adams Center for Parkinson’s Disease Research of Yale School of Medicine, New Haven, CT 06510, USA
| | - Zhixiang Liao
- Aligning Science Across Parkinson’s (ASAP) Collaborative Research Network, Chevy Chase, MD 20815, USA
- Precision Neurology Program of Brigham & Women’s Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Idil Tuncali
- Aligning Science Across Parkinson’s (ASAP) Collaborative Research Network, Chevy Chase, MD 20815, USA
- Precision Neurology Program of Brigham & Women’s Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Aziz M. Al’Khafaji
- Broad Clinical Labs, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Asa Shin
- Broad Clinical Labs, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Karthik Jagadeesh
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Kirk Gosik
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Michael Gatzen
- Data Sciences Platform, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Jonathan T. Smith
- Data Sciences Platform, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Daniel N. El Kodsi
- Aligning Science Across Parkinson’s (ASAP) Collaborative Research Network, Chevy Chase, MD 20815, USA
- Precision Neurology Program of Brigham & Women’s Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Yuliya Kuras
- Aligning Science Across Parkinson’s (ASAP) Collaborative Research Network, Chevy Chase, MD 20815, USA
- Precision Neurology Program of Brigham & Women’s Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Clare Baecher-Allan
- Dept. of Neurology, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA 02115, USA
| | - Geidy E. Serrano
- Aligning Science Across Parkinson’s (ASAP) Collaborative Research Network, Chevy Chase, MD 20815, USA
- Banner Sun Health Research Institute, Sun City, AZ 85351, USA
| | - Thomas G. Beach
- Aligning Science Across Parkinson’s (ASAP) Collaborative Research Network, Chevy Chase, MD 20815, USA
- Banner Sun Health Research Institute, Sun City, AZ 85351, USA
| | - Kiran Garimella
- Data Sciences Platform, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Orit Rozenblatt-Rosen
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- Present address: Genentech, South San Francisco, CA 94080, USA
| | - Aviv Regev
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- Present address: Genentech, South San Francisco, CA 94080, USA
| | - Xianjun Dong
- Aligning Science Across Parkinson’s (ASAP) Collaborative Research Network, Chevy Chase, MD 20815, USA
- Stephen & Denise Adams Center for Parkinson’s Disease Research of Yale School of Medicine, New Haven, CT 06510, USA
| | - Clemens R. Scherzer
- Aligning Science Across Parkinson’s (ASAP) Collaborative Research Network, Chevy Chase, MD 20815, USA
- Stephen & Denise Adams Center for Parkinson’s Disease Research of Yale School of Medicine, New Haven, CT 06510, USA
| | - Joshua Z. Levin
- Aligning Science Across Parkinson’s (ASAP) Collaborative Research Network, Chevy Chase, MD 20815, USA
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
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3
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Zhang D, Cheng J, Li X, Huang K, Yuan L, Zhao Y, Xu D, Zhang Y, Zhao L, Yang X, Ma Z, Xu Q, Li C, Wang X, Zheng C, Tang D, Nian F, Yue X, Li W, Tian H, Weng X, Hu P, Feng Y, Kalds P, Jiang Z, Zhao Y, Zhang X, Li F, Wang W. Comprehensive multi-tissue epigenome atlas in sheep: A resource for complex traits, domestication, and breeding. IMETA 2024; 3:e254. [PMID: 39742295 PMCID: PMC11683475 DOI: 10.1002/imt2.254] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/26/2024] [Revised: 11/19/2024] [Accepted: 11/26/2024] [Indexed: 01/03/2025]
Abstract
Comprehensive functional genome annotation is crucial to elucidate the molecular mechanisms of agronomic traits in livestock, yet systematic functional annotation of the sheep genome is lacking. Here, we generated 92 transcriptomic and epigenomic data sets from nine major tissues, along with whole-genome data from 2357 individuals across 29 breeds worldwide, and 4006 phenotypic data related to tail fat weight. We constructed the first multi-tissue epigenome atlas in terms of functional elements, chromatin states, and their functions and explored the utility of the functional elements in interpreting phenotypic variation during sheep domestication and improvement. Particularly, we identified a total of 753,723 nonredundant functional elements, with over 60% being novel. We found tissue-specific promoters and enhancers related to sensory abilities and immune response that were highly enriched in genomic regions influenced by domestication, while longissimus dorsi tissue-specific active enhancers and tail fat tissue-specific active promoters were highly enriched in genomic regions influenced by breeding and improvement. Notably, a variant, Chr13:51760995A>C, located in an enhancer region, was identified as a causal variant for tail fat deposition based on multi-layered data sets. Overall, this research provides foundational resources and a successful case for future investigations of complex traits in sheep through the integration of multi-omics data sets.
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Affiliation(s)
- Deyin Zhang
- State Key Laboratory of Herbage Improvement and Grassland Agro‐ecosystems; Key Laboratory of Grassland Livestock Industry Innovation, Ministry of Agriculture and Rural Afairs; Engineering Research Center of Grassland Industry, Ministry of Education; College of Pastoral Agriculture Science and TechnologyLanzhou UniversityLanzhouChina
| | - Jiangbo Cheng
- State Key Laboratory of Herbage Improvement and Grassland Agro‐ecosystems; Key Laboratory of Grassland Livestock Industry Innovation, Ministry of Agriculture and Rural Afairs; Engineering Research Center of Grassland Industry, Ministry of Education; College of Pastoral Agriculture Science and TechnologyLanzhou UniversityLanzhouChina
| | - Xiaolong Li
- State Key Laboratory of Herbage Improvement and Grassland Agro‐ecosystems; Key Laboratory of Grassland Livestock Industry Innovation, Ministry of Agriculture and Rural Afairs; Engineering Research Center of Grassland Industry, Ministry of Education; College of Pastoral Agriculture Science and TechnologyLanzhou UniversityLanzhouChina
| | - Kai Huang
- State Key Laboratory of Herbage Improvement and Grassland Agro‐ecosystems; Key Laboratory of Grassland Livestock Industry Innovation, Ministry of Agriculture and Rural Afairs; Engineering Research Center of Grassland Industry, Ministry of Education; College of Pastoral Agriculture Science and TechnologyLanzhou UniversityLanzhouChina
| | - Lvfeng Yuan
- Lanzhou Veterinary Research InstituteChinese Academy of Agricultural Sciences (CAAS)LanzhouChina
| | - Yuan Zhao
- State Key Laboratory of Herbage Improvement and Grassland Agro‐ecosystems; Key Laboratory of Grassland Livestock Industry Innovation, Ministry of Agriculture and Rural Afairs; Engineering Research Center of Grassland Industry, Ministry of Education; College of Pastoral Agriculture Science and TechnologyLanzhou UniversityLanzhouChina
| | - Dan Xu
- College of Animal Science and TechnologyGansu Agricultural UniversityLanzhouChina
| | - Yukun Zhang
- State Key Laboratory of Herbage Improvement and Grassland Agro‐ecosystems; Key Laboratory of Grassland Livestock Industry Innovation, Ministry of Agriculture and Rural Afairs; Engineering Research Center of Grassland Industry, Ministry of Education; College of Pastoral Agriculture Science and TechnologyLanzhou UniversityLanzhouChina
| | - Liming Zhao
- State Key Laboratory of Herbage Improvement and Grassland Agro‐ecosystems; Key Laboratory of Grassland Livestock Industry Innovation, Ministry of Agriculture and Rural Afairs; Engineering Research Center of Grassland Industry, Ministry of Education; College of Pastoral Agriculture Science and TechnologyLanzhou UniversityLanzhouChina
| | - Xiaobin Yang
- College of Animal Science and TechnologyGansu Agricultural UniversityLanzhouChina
| | - Zongwu Ma
- College of Animal Science and TechnologyGansu Agricultural UniversityLanzhouChina
| | - Quanzhong Xu
- State Key Laboratory of Herbage Improvement and Grassland Agro‐ecosystems; Key Laboratory of Grassland Livestock Industry Innovation, Ministry of Agriculture and Rural Afairs; Engineering Research Center of Grassland Industry, Ministry of Education; College of Pastoral Agriculture Science and TechnologyLanzhou UniversityLanzhouChina
| | - Chong Li
- College of Animal Science and TechnologyGansu Agricultural UniversityLanzhouChina
| | - Xiaojuan Wang
- College of Animal Science and TechnologyGansu Agricultural UniversityLanzhouChina
| | - Chen Zheng
- College of Animal Science and TechnologyGansu Agricultural UniversityLanzhouChina
| | - Defu Tang
- College of Animal Science and TechnologyGansu Agricultural UniversityLanzhouChina
| | - Fang Nian
- College of ScienceGansu Agricultural UniversityLanzhouChina
| | - Xiangpeng Yue
- State Key Laboratory of Herbage Improvement and Grassland Agro‐ecosystems; Key Laboratory of Grassland Livestock Industry Innovation, Ministry of Agriculture and Rural Afairs; Engineering Research Center of Grassland Industry, Ministry of Education; College of Pastoral Agriculture Science and TechnologyLanzhou UniversityLanzhouChina
| | - Wanhong Li
- State Key Laboratory of Herbage Improvement and Grassland Agro‐ecosystems; Key Laboratory of Grassland Livestock Industry Innovation, Ministry of Agriculture and Rural Afairs; Engineering Research Center of Grassland Industry, Ministry of Education; College of Pastoral Agriculture Science and TechnologyLanzhou UniversityLanzhouChina
| | - Huibin Tian
- State Key Laboratory of Herbage Improvement and Grassland Agro‐ecosystems; Key Laboratory of Grassland Livestock Industry Innovation, Ministry of Agriculture and Rural Afairs; Engineering Research Center of Grassland Industry, Ministry of Education; College of Pastoral Agriculture Science and TechnologyLanzhou UniversityLanzhouChina
| | - Xiuxiu Weng
- State Key Laboratory of Herbage Improvement and Grassland Agro‐ecosystems; Key Laboratory of Grassland Livestock Industry Innovation, Ministry of Agriculture and Rural Afairs; Engineering Research Center of Grassland Industry, Ministry of Education; College of Pastoral Agriculture Science and TechnologyLanzhou UniversityLanzhouChina
| | - Peng Hu
- Key Laboratory of Exploration and Utilization of Aquatic Genetic Resources, Ministry of EducationShanghai Ocean UniversityShanghaiChina
| | - Yuanqing Feng
- Department of GeneticsUniversity of PennsylvaniaPhiladelphiaPAUSA
| | | | - Zhihua Jiang
- Department of Animal Sciences and Center for Reproductive BiologyWashington State University (WSU)PullmanWAUSA
| | - Yunxia Zhao
- Key Laboratory of Agricultural Animal Genetics, Breeding, and Reproduction of the Ministry of EducationHuazhong Agricultural UniversityWuhanChina
| | - Xiaoxue Zhang
- College of Animal Science and TechnologyGansu Agricultural UniversityLanzhouChina
| | - Fadi Li
- State Key Laboratory of Herbage Improvement and Grassland Agro‐ecosystems; Key Laboratory of Grassland Livestock Industry Innovation, Ministry of Agriculture and Rural Afairs; Engineering Research Center of Grassland Industry, Ministry of Education; College of Pastoral Agriculture Science and TechnologyLanzhou UniversityLanzhouChina
| | - Weimin Wang
- State Key Laboratory of Herbage Improvement and Grassland Agro‐ecosystems; Key Laboratory of Grassland Livestock Industry Innovation, Ministry of Agriculture and Rural Afairs; Engineering Research Center of Grassland Industry, Ministry of Education; College of Pastoral Agriculture Science and TechnologyLanzhou UniversityLanzhouChina
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4
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Qi G, Battle A. Computational methods for allele-specific expression in single cells. Trends Genet 2024; 40:939-949. [PMID: 39127549 PMCID: PMC11537817 DOI: 10.1016/j.tig.2024.07.003] [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] [Received: 03/31/2024] [Revised: 07/16/2024] [Accepted: 07/17/2024] [Indexed: 08/12/2024]
Abstract
Allele-specific expression (ASE) is a powerful signal that can be used to investigate multiple molecular mechanisms, such as cis-regulatory effects and imprinting. Single-cell RNA-sequencing (scRNA-seq) enables ASE characterization at the resolution of individual cells. In this review, we highlight the computational methods for processing and analyzing single-cell ASE data. We first describe a bioinformatics pipeline to obtain ASE counts from raw reads synthesized from previous literature. We then discuss statistical methods for detecting allelic imbalance and its variability across conditions using scRNA-seq data. In addition, we describe other methods that use single-cell ASE to address specific biological questions. Finally, we discuss future directions and emphasize the need for an integrated, optimized bioinformatics pipeline, and further development of statistical methods for different technologies.
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Affiliation(s)
- Guanghao Qi
- Department of Biostatistics, University of Washington, Seattle, WA 98195, USA.
| | - Alexis Battle
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA; Department of Computer Science, Johns Hopkins University, Baltimore, MD 21218, USA; Department of Genetic Medicine, Johns Hopkins University, Baltimore, MD 21205, USA.
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5
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Ghosal S, Schatz MC, Venkataraman A. BEATRICE: Bayesian fine-mapping from summary data using deep variational inference. Bioinformatics 2024; 40:btae590. [PMID: 39360993 PMCID: PMC11496888 DOI: 10.1093/bioinformatics/btae590] [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] [Received: 05/27/2024] [Revised: 08/30/2024] [Accepted: 10/01/2024] [Indexed: 10/09/2024] Open
Abstract
MOTIVATION We introduce a novel framework BEATRICE to identify putative causal variants from GWAS statistics. Identifying causal variants is challenging due to their sparsity and high correlation in the nearby regions. To account for these challenges, we rely on a hierarchical Bayesian model that imposes a binary concrete prior on the set of causal variants. We derive a variational algorithm for this fine-mapping problem by minimizing the KL divergence between an approximate density and the posterior probability distribution of the causal configurations. Correspondingly, we use a deep neural network as an inference machine to estimate the parameters of our proposal distribution. Our stochastic optimization procedure allows us to sample from the space of causal configurations, which we use to compute the posterior inclusion probabilities and determine credible sets for each causal variant. We conduct a detailed simulation study to quantify the performance of our framework against two state-of-the-art baseline methods across different numbers of causal variants and noise paradigms, as defined by the relative genetic contributions of causal and noncausal variants. RESULTS We demonstrate that BEATRICE achieves uniformly better coverage with comparable power and set sizes, and that the performance gain increases with the number of causal variants. We also show the efficacy BEATRICE in finding causal variants from the GWAS study of Alzheimer's disease. In comparison to the baselines, only BEATRICE can successfully find the APOE ϵ2 allele, a commonly associated variant of Alzheimer's. AVAILABILITY AND IMPLEMENTATION BEATRICE is available for download at https://github.com/sayangsep/Beatrice-Finemapping.
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Affiliation(s)
- Sayan Ghosal
- Chan Zuckerberg Initiative Foundation, Redwood City, CA 94065, United States
| | - Michael C Schatz
- Department of Computer Science, Johns Hopkins University, Baltimore, MD 21218, United States
| | - Archana Venkataraman
- Department of Electrical and Computer Engineering, Boston University, Boston, MA 02215, United States
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6
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Sakaue S, Weinand K, Isaac S, Dey KK, Jagadeesh K, Kanai M, Watts GFM, Zhu Z, Brenner MB, McDavid A, Donlin LT, Wei K, Price AL, Raychaudhuri S. Tissue-specific enhancer-gene maps from multimodal single-cell data identify causal disease alleles. Nat Genet 2024; 56:615-626. [PMID: 38594305 PMCID: PMC11456345 DOI: 10.1038/s41588-024-01682-1] [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] [Received: 03/07/2023] [Accepted: 02/07/2024] [Indexed: 04/11/2024]
Abstract
Translating genome-wide association study (GWAS) loci into causal variants and genes requires accurate cell-type-specific enhancer-gene maps from disease-relevant tissues. Building enhancer-gene maps is essential but challenging with current experimental methods in primary human tissues. Here we developed a nonparametric statistical method, SCENT (single-cell enhancer target gene mapping), that models association between enhancer chromatin accessibility and gene expression in single-cell or nucleus multimodal RNA sequencing and ATAC sequencing data. We applied SCENT to 9 multimodal datasets including >120,000 single cells or nuclei and created 23 cell-type-specific enhancer-gene maps. These maps were highly enriched for causal variants in expression quantitative loci and GWAS for 1,143 diseases and traits. We identified likely causal genes for both common and rare diseases and linked somatic mutation hotspots to target genes. We demonstrate that application of SCENT to multimodal data from disease-relevant human tissue enables the scalable construction of accurate cell-type-specific enhancer-gene maps, essential for defining noncoding variant function.
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Affiliation(s)
- Saori Sakaue
- Center for Data Sciences, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Divisions of Genetics and Rheumatology, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Kathryn Weinand
- Center for Data Sciences, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Divisions of Genetics and Rheumatology, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Shakson Isaac
- Center for Data Sciences, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Divisions of Genetics and Rheumatology, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Kushal K Dey
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, MA, USA
| | - Karthik Jagadeesh
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, MA, USA
| | - Masahiro Kanai
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Center for Computational and Integrative Biology, Massachusetts General Hospital, Boston, MA, USA
| | - Gerald F M Watts
- Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Zhu Zhu
- Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Michael B Brenner
- Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Andrew McDavid
- Department of Biostatistics and Computational Biology, University of Rochester Medical Center, Rochester, NY, USA
| | - Laura T Donlin
- Hospital for Special Surgery, New York, NY, USA
- Weill Cornell Medicine, New York, NY, USA
| | - Kevin Wei
- Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Alkes L Price
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, MA, USA
- Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA, USA
| | - Soumya Raychaudhuri
- Center for Data Sciences, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
- Divisions of Genetics and Rheumatology, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.
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7
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Akhtar F, Ruiz JH, Liu YG, Resendez RG, Feliers D, Morales LD, Diaz-Badillo A, Lehman DM, Arya R, Lopez-Alvarenga JC, Blangero J, Duggirala R, Mummidi S. Functional characterization of the disease-associated CCL2 rs1024611G-rs13900T haplotype: The role of the RNA-binding protein HuR. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.10.31.564937. [PMID: 37961304 PMCID: PMC10635030 DOI: 10.1101/2023.10.31.564937] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
CC-chemokine ligand 2 (CCL2) is involved in the pathogenesis of several diseases associated with monocyte/macrophage recruitment, such as HIV-associated neurocognitive disorder (HAND), tuberculosis, and atherosclerosis. The rs1024611 (alleles:A>G; G is the risk allele) polymorphism in the CCL2 cis-regulatory region is associated with increased CCL2 expression in vitro and ex vivo, leukocyte mobilization in vivo, and deleterious disease outcomes. However, the molecular basis for the rs1024611-associated differential CCL2 expression remains poorly characterized. It is conceivable that genetic variant(s) in linkage disequilibrium (LD) with rs1024611 could mediate such effects. Previously, we used rs13900 (alleles:_C>T) in the CCL2 3' untranslated region (3' UTR) that is in perfect LD with rs1024611 to demonstrate allelic expression imbalance (AEI) of CCL2 in heterozygous individuals. Here we tested the hypothesis that the rs13900 could modulate CCL2 expression by altering mRNA turnover and/or translatability. The rs13900 T allele conferred greater stability to the CCL2 transcript when compared to the rs13900 C allele. The rs13900 T allele also had increased binding to Human Antigen R (HuR), an RNA-binding protein, in vitro and ex vivo. The rs13900 alleles imparted differential activity to reporter vectors and influenced the translatability of the reporter transcript. We further demonstrated a role for HuR in mediating allele-specific effects on CCL2 expression in overexpression and silencing studies. The presence of the rs1024611G-rs13900T conferred a distinct transcriptomic signature related to inflammation and immunity. Our studies suggest that the differential interactions of HuR with rs13900 could modulate CCL2 expression and explain the interindividual differences in CCL2-mediated disease susceptibility.
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Affiliation(s)
- Feroz Akhtar
- Department of Health and Behavioral Sciences, Texas A&M University- San Antonio, Texas, USA
| | - Joselin Hernandez Ruiz
- Utah Center for Genetic Discovery, Department of Human Genetics, University of Utah, Salt Lake City, Utah, USA
| | - Ya-Guang Liu
- Department of Pathology, School of Medicine, University of Texas Health San Antonio, San Antonio, Texas, USA
| | - Roy G. Resendez
- Department of Health and Behavioral Sciences, Texas A&M University- San Antonio, Texas, USA
| | - Denis Feliers
- Department of Medicine, School of Medicine, University of Texas Health San Antonio, San Antonio, Texas, USA
| | - Liza D. Morales
- South Texas Diabetes and Obesity Institute, Department of Genetics, School of Medicine, University of Texas Rio Grane Valley, Brownsville, USA
| | - Alvaro Diaz-Badillo
- Department of Health and Behavioral Sciences, Texas A&M University- San Antonio, Texas, USA
| | - Donna M. Lehman
- Department of Health and Behavioral Sciences, Texas A&M University- San Antonio, Texas, USA
| | - Rector Arya
- Department of Health and Behavioral Sciences, Texas A&M University- San Antonio, Texas, USA
| | - Juan Carlos Lopez-Alvarenga
- Department of Population Health and Biostatistics, School of Medicine, University of Texas Rio Grande Valley, Harlingen, Texas, USA
| | - John Blangero
- South Texas Diabetes and Obesity Institute, Department of Genetics, School of Medicine, University of Texas Rio Grane Valley, Brownsville, USA
| | - Ravindranath Duggirala
- Department of Health and Behavioral Sciences, Texas A&M University- San Antonio, Texas, USA
| | - Srinivas Mummidi
- Department of Health and Behavioral Sciences, Texas A&M University- San Antonio, Texas, USA
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8
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Antontseva EV, Degtyareva AO, Korbolina EE, Damarov IS, Merkulova TI. Human-genome single nucleotide polymorphisms affecting transcription factor binding and their role in pathogenesis. Vavilovskii Zhurnal Genet Selektsii 2023; 27:662-675. [PMID: 37965371 PMCID: PMC10641029 DOI: 10.18699/vjgb-23-77] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2023] [Revised: 03/24/2023] [Accepted: 03/30/2023] [Indexed: 11/16/2023] Open
Abstract
Single nucleotide polymorphisms (SNPs) are the most common type of variation in the human genome. The vast majority of SNPs identified in the human genome do not have any effect on the phenotype; however, some can lead to changes in the function of a gene or the level of its expression. Most SNPs associated with certain traits or pathologies are mapped to regulatory regions of the genome and affect gene expression by changing transcription factor binding sites. In recent decades, substantial effort has been invested in searching for such regulatory SNPs (rSNPs) and understanding the mechanisms by which they lead to phenotypic differences, primarily to individual differences in susceptibility to diseases and in sensitivity to drugs. The development of the NGS (next-generation sequencing) technology has contributed not only to the identification of a huge number of SNPs and to the search for their association (genome-wide association studies, GWASs) with certain diseases or phenotypic manifestations, but also to the development of more productive approaches to their functional annotation. It should be noted that the presence of an association does not allow one to identify a functional, truly disease-associated DNA sequence variant among multiple marker SNPs that are detected due to linkage disequilibrium. Moreover, determination of associations of genetic variants with a disease does not provide information about the functionality of these variants, which is necessary to elucidate the molecular mechanisms of the development of pathology and to design effective methods for its treatment and prevention. In this regard, the functional analysis of SNPs annotated in the GWAS catalog, both at the genome-wide level and at the level of individual SNPs, became especially relevant in recent years. A genome-wide search for potential rSNPs is possible without any prior knowledge of their association with a trait. Thus, mapping expression quantitative trait loci (eQTLs) makes it possible to identify an SNP for which - among transcriptomes of homozygotes and heterozygotes for its various alleles - there are differences in the expression level of certain genes, which can be located at various distances from the SNP. To predict rSNPs, approaches based on searches for allele-specific events in RNA-seq, ChIP-seq, DNase-seq, ATAC-seq, MPRA, and other data are also used. Nonetheless, for a more complete functional annotation of such rSNPs, it is necessary to establish their association with a trait, in particular, with a predisposition to a certain pathology or sensitivity to drugs. Thus, approaches to finding SNPs important for the development of a trait can be categorized into two groups: (1) starting from data on an association of SNPs with a certain trait, (2) starting from the determination of allele-specific changes at the molecular level (in a transcriptome or regulome). Only comprehensive use of strategically different approaches can considerably enrich our knowledge about the role of genetic determinants in the molecular mechanisms of trait formation, including predisposition to multifactorial diseases.
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Affiliation(s)
- E V Antontseva
- Institute of Cytology and Genetics of the Siberian Branch of the Russian Academy of Sciences, Novosibirsk, Russia
| | - A O Degtyareva
- Institute of Cytology and Genetics of the Siberian Branch of the Russian Academy of Sciences, Novosibirsk, Russia
| | - E E Korbolina
- Institute of Cytology and Genetics of the Siberian Branch of the Russian Academy of Sciences, Novosibirsk, Russia
| | - I S Damarov
- Institute of Cytology and Genetics of the Siberian Branch of the Russian Academy of Sciences, Novosibirsk, Russia
| | - T I Merkulova
- Institute of Cytology and Genetics of the Siberian Branch of the Russian Academy of Sciences, Novosibirsk, Russia
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9
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Wu EY, Singh NP, Choi K, Zakeri M, Vincent M, Churchill GA, Ackert-Bicknell CL, Patro R, Love MI. SEESAW: detecting isoform-level allelic imbalance accounting for inferential uncertainty. Genome Biol 2023; 24:165. [PMID: 37438847 PMCID: PMC10337143 DOI: 10.1186/s13059-023-03003-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2022] [Accepted: 06/29/2023] [Indexed: 07/14/2023] Open
Abstract
Detecting allelic imbalance at the isoform level requires accounting for inferential uncertainty, caused by multi-mapping of RNA-seq reads. Our proposed method, SEESAW, uses Salmon and Swish to offer analysis at various levels of resolution, including gene, isoform, and aggregating isoforms to groups by transcription start site. The aggregation strategies strengthen the signal for transcripts with high uncertainty. The SEESAW suite of methods is shown to have higher power than other allelic imbalance methods when there is isoform-level allelic imbalance. We also introduce a new test for detecting imbalance that varies across a covariate, such as time.
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Affiliation(s)
- Euphy Y Wu
- Department of Biostatistics, University of North Carolina-Chapel Hill, Chapel Hill, NC, USA
| | - Noor P Singh
- Department of Computer Science, University of Maryland, College Park, MD, USA
| | | | - Mohsen Zakeri
- Department of Computer Science, University of Maryland, College Park, MD, USA
| | | | | | - Cheryl L Ackert-Bicknell
- Department of Orthopedics, School of Medicine, University of Colorado, Anschutz Campus, Aurora, CO, USA
| | - Rob Patro
- Department of Computer Science, University of Maryland, College Park, MD, USA
| | - Michael I Love
- Department of Biostatistics, University of North Carolina-Chapel Hill, Chapel Hill, NC, USA.
- Department of Genetics, University of North Carolina-Chapel Hill, Chapel Hill, NC, USA.
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10
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Deshpande D, Chhugani K, Chang Y, Karlsberg A, Loeffler C, Zhang J, Muszyńska A, Munteanu V, Yang H, Rotman J, Tao L, Balliu B, Tseng E, Eskin E, Zhao F, Mohammadi P, P. Łabaj P, Mangul S. RNA-seq data science: From raw data to effective interpretation. Front Genet 2023; 14:997383. [PMID: 36999049 PMCID: PMC10043755 DOI: 10.3389/fgene.2023.997383] [Citation(s) in RCA: 34] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2022] [Accepted: 02/24/2023] [Indexed: 03/14/2023] Open
Abstract
RNA sequencing (RNA-seq) has become an exemplary technology in modern biology and clinical science. Its immense popularity is due in large part to the continuous efforts of the bioinformatics community to develop accurate and scalable computational tools to analyze the enormous amounts of transcriptomic data that it produces. RNA-seq analysis enables genes and their corresponding transcripts to be probed for a variety of purposes, such as detecting novel exons or whole transcripts, assessing expression of genes and alternative transcripts, and studying alternative splicing structure. It can be a challenge, however, to obtain meaningful biological signals from raw RNA-seq data because of the enormous scale of the data as well as the inherent limitations of different sequencing technologies, such as amplification bias or biases of library preparation. The need to overcome these technical challenges has pushed the rapid development of novel computational tools, which have evolved and diversified in accordance with technological advancements, leading to the current myriad of RNA-seq tools. These tools, combined with the diverse computational skill sets of biomedical researchers, help to unlock the full potential of RNA-seq. The purpose of this review is to explain basic concepts in the computational analysis of RNA-seq data and define discipline-specific jargon.
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Affiliation(s)
- Dhrithi Deshpande
- Department of Pharmacology and Pharmaceutical Sciences, USC Alfred E. Mann School of Pharmacy and Pharmaceutical Sciences, Los Angeles, CA, United States
| | - Karishma Chhugani
- Department of Pharmacology and Pharmaceutical Sciences, USC Alfred E. Mann School of Pharmacy and Pharmaceutical Sciences, Los Angeles, CA, United States
| | - Yutong Chang
- Department of Pharmacology and Pharmaceutical Sciences, USC Alfred E. Mann School of Pharmacy and Pharmaceutical Sciences, Los Angeles, CA, United States
| | - Aaron Karlsberg
- Department of Clinical Pharmacy, USC Alfred E. Mann School of Pharmacy and Pharmaceutical Sciences, Los Angeles, CA, United States
| | - Caitlin Loeffler
- Department of Computer Science, University of California, Los Angeles, CA, United States
| | - Jinyang Zhang
- Beijing Institutes of Life Science, Chinese Academy of Sciences, Beijing, China
| | - Agata Muszyńska
- Małopolska Centre of Biotechnology, Jagiellonian University, Krakow, Poland
- Institute of Automatic Control, Electronics and Computer Science, Silesian University of Technology, Gliwice, Poland
| | - Viorel Munteanu
- Department of Computers, Informatics and Microelectronics, Technical University of Moldova, Chisinau, Moldova
| | - Harry Yang
- Department of Microbiology, Immunology and Molecular Genetics, University of California Los Angeles, Los Angeles, CA, United States
| | - Jeremy Rotman
- Department of Clinical Pharmacy, USC Alfred E. Mann School of Pharmacy and Pharmaceutical Sciences, Los Angeles, CA, United States
| | - Laura Tao
- Department of Computational Medicine, David Geffen School of Medicine at UCLA, CHS, Los Angeles, CA, United States
| | - Brunilda Balliu
- Department of Computational Medicine, David Geffen School of Medicine at UCLA, CHS, Los Angeles, CA, United States
| | | | - Eleazar Eskin
- Department of Computer Science, University of California, Los Angeles, CA, United States
- Department of Computational Medicine, David Geffen School of Medicine at UCLA, CHS, Los Angeles, CA, United States
- Department of Human Genetics, David Geffen School of Medicine at UCLA, Los Angeles, CA, United States
| | - Fangqing Zhao
- Beijing Institutes of Life Science, Chinese Academy of Sciences, Beijing, China
- Key Laboratory of Systems Biology, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou, China
| | - Pejman Mohammadi
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA, United States
| | - Paweł P. Łabaj
- Małopolska Centre of Biotechnology, Jagiellonian University, Krakow, Poland
- Department of Biotechnology, Boku University Vienna, Vienna, Austria
| | - Serghei Mangul
- Department of Clinical Pharmacy, USC Alfred E. Mann School of Pharmacy and Pharmaceutical Sciences, Los Angeles, CA, United States
- Department of Quantitative and Computational Biology, USC Dornsife College of Letters, Arts and Sciences, Los Angeles, CA, United States
- *Correspondence: Serghei Mangul,
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11
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Wattacheril JJ, Raj S, Knowles DA, Greally JM. Using epigenomics to understand cellular responses to environmental influences in diseases. PLoS Genet 2023; 19:e1010567. [PMID: 36656803 PMCID: PMC9851565 DOI: 10.1371/journal.pgen.1010567] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023] Open
Abstract
It is a generally accepted model that environmental influences can exert their effects, at least in part, by changing the molecular regulators of transcription that are described as epigenetic. As there is biochemical evidence that some epigenetic regulators of transcription can maintain their states long term and through cell division, an epigenetic model encompasses the idea of maintenance of the effect of an exposure long after it is no longer present. The evidence supporting this model is mostly from the observation of alterations of molecular regulators of transcription following exposures. With the understanding that the interpretation of these associations is more complex than originally recognised, this model may be oversimplistic; therefore, adopting novel perspectives and experimental approaches when examining how environmental exposures are linked to phenotypes may prove worthwhile. In this review, we have chosen to use the example of nonalcoholic fatty liver disease (NAFLD), a common, complex human disease with strong environmental and genetic influences. We describe how epigenomic approaches combined with emerging functional genetic and single-cell genomic techniques are poised to generate new insights into the pathogenesis of environmentally influenced human disease phenotypes exemplified by NAFLD.
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Affiliation(s)
- Julia J. Wattacheril
- Department of Medicine, Center for Liver Disease and Transplantation, Columbia University Irving Medical Center, New York Presbyterian Hospital, New York, New York, United States of America
| | - Srilakshmi Raj
- Division of Genomics, Department of Genetics, Albert Einstein College of Medicine, Bronx, New York, United States of America
| | - David A. Knowles
- New York Genome Center, New York, New York, United States of America
- Department of Computer Science, Columbia University, New York, New York, United States of America
- Department of Systems Biology, Columbia University, New York, New York, United States of America
| | - John M. Greally
- Division of Genomics, Department of Genetics, Albert Einstein College of Medicine, Bronx, New York, United States of America
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12
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Bruscadin JJ, Cardoso TF, da Silva Diniz WJ, de Souza MM, Afonso J, Vieira D, Malheiros J, Andrade BGN, Petrini J, Ferraz JBS, Zerlotini A, Mourão GB, Coutinho LL, de Almeida Regitano LC. Differential Allele-Specific Expression Revealed Functional Variants and Candidate Genes Related to Meat Quality Traits in B. indicus Muscle. Genes (Basel) 2022; 13:genes13122336. [PMID: 36553605 PMCID: PMC9777870 DOI: 10.3390/genes13122336] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2022] [Revised: 12/03/2022] [Accepted: 12/07/2022] [Indexed: 12/14/2022] Open
Abstract
Traditional transcriptomics approaches have been used to identify candidate genes affecting economically important livestock traits. Regulatory variants affecting these traits, however, remain under covered. Genomic regions showing allele-specific expression (ASE) are under the effect of cis-regulatory variants, being useful for improving the accuracy of genomic selection models. Taking advantage of the better of these two methods, we investigated single nucleotide polymorphisms (SNPs) in regions showing differential ASE (DASE SNPs) between contrasting groups for beef quality traits. For these analyses, we used RNA sequencing data, imputed genotypes and genomic estimated breeding values of muscle-related traits from 190 Nelore (Bos indicus) steers. We selected 40 contrasting unrelated samples for the analysis (N = 20 animals per contrasting group) and used a beta-binomial model to identify ASE SNPs in only one group (i.e., DASE SNPs). We found 1479 DASE SNPs (FDR ≤ 0.05) associated with 55 beef-quality traits. Most DASE genes were involved with tenderness and muscle homeostasis, presenting a co-expression module enriched for the protein ubiquitination process. The results overlapped with epigenetics and phenotype-associated data, suggesting that DASE SNPs are potentially linked to cis-regulatory variants affecting simultaneously the transcription and phenotype through chromatin state modulation.
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Affiliation(s)
- Jennifer Jessica Bruscadin
- Center of Biological Sciences and Health, Federal University of São Carlos, São Carlos 13560-000, SP, Brazil
- Embrapa Pecuária Sudeste, São Carlos 13560-000, SP, Brazil
| | | | | | | | - Juliana Afonso
- Embrapa Pecuária Sudeste, São Carlos 13560-000, SP, Brazil
| | - Dielson Vieira
- Embrapa Pecuária Sudeste, São Carlos 13560-000, SP, Brazil
- Department of Internal Medicine, Yale University School of Medicine, New Haven, CT 06520, USA
| | - Jessica Malheiros
- Federal University of Latin American Integration-UNILA, Foz do Iguaçu 85851-000, PR, Brazil
| | | | - Juliana Petrini
- Center for Functional Genomics, Department of Animal Science, 13400-000, University of São Paulo (ESALQ—USP), Piracicaba 13400-000, SP, Brazil
| | - José Bento Sterman Ferraz
- Department of Veterinary Medicine, University of São Paulo (FMVZ—USP), Pirassununga 13630-000, SP, Brazil
| | | | - Gerson Barreto Mourão
- Center for Functional Genomics, Department of Animal Science, 13400-000, University of São Paulo (ESALQ—USP), Piracicaba 13400-000, SP, Brazil
| | - Luiz Lehmann Coutinho
- Center for Functional Genomics, Department of Animal Science, 13400-000, University of São Paulo (ESALQ—USP), Piracicaba 13400-000, SP, Brazil
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13
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Wang QS, Huang H. Methods for statistical fine-mapping and their applications to auto-immune diseases. Semin Immunopathol 2022; 44:101-113. [PMID: 35041074 PMCID: PMC8837575 DOI: 10.1007/s00281-021-00902-8] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2021] [Accepted: 10/22/2021] [Indexed: 01/07/2023]
Abstract
Although genome-wide association studies (GWAS) have identified thousands of loci in the human genome that are associated with different traits, understanding the biological mechanisms underlying the association signals identified in GWAS remains challenging. Statistical fine-mapping is a method aiming to refine GWAS signals by evaluating which variant(s) are truly causal to the phenotype. Here, we review the types of statistical fine-mapping methods that have been widely used to date, with a focus on recently developed functionally informed fine-mapping (FIFM) methods that utilize functional annotations. We then systematically review the applications of statistical fine-mapping in autoimmune disease studies to highlight the value of statistical fine-mapping in biological contexts.
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Affiliation(s)
- Qingbo S Wang
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Osaka, Japan.
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA.
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
| | - Hailiang Huang
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA.
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- Department of Medicine, Harvard Medical School, Boston, MA, USA.
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14
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Marion-Poll L, Forêt B, Zielinski D, Massip F, Attia M, Carter AC, Syx L, Chang HY, Gendrel AV, Heard E. Locus specific epigenetic modalities of random allelic expression imbalance. Nat Commun 2021; 12:5330. [PMID: 34504093 PMCID: PMC8429725 DOI: 10.1038/s41467-021-25630-3] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2020] [Accepted: 08/19/2021] [Indexed: 01/02/2023] Open
Abstract
Most autosomal genes are thought to be expressed from both alleles, with some notable exceptions, including imprinted genes and genes showing random monoallelic expression (RME). The extent and nature of RME has been the subject of debate. Here we investigate the expression of several candidate RME genes in F1 hybrid mouse cells before and after differentiation, to define how they become persistently, monoallelically expressed. Clonal monoallelic expression is not present in embryonic stem cells, but we observe high frequencies of monoallelism in neuronal progenitor cells by assessing expression status in more than 200 clones. We uncover unforeseen modes of allelic expression that appear to be gene-specific and epigenetically regulated. This non-canonical allelic regulation has important implications for development and disease, including autosomal dominant disorders and opens up therapeutic perspectives.
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Affiliation(s)
- Lucile Marion-Poll
- Institut Curie, PSL Research University, CNRS UMR3215, INSERM U934, Paris, France.
- Directors' research, European Molecular Biology Laboratory (EMBL), Heidelberg, Germany.
| | - Benjamin Forêt
- Institut Curie, PSL Research University, CNRS UMR3215, INSERM U934, Paris, France
| | - Dina Zielinski
- Institut Curie, PSL Research University, CNRS UMR3215, INSERM U934, Paris, France
- Institut Curie, PSL Research University, INSERM U900, Mines ParisTech, Paris, France
| | - Florian Massip
- Berlin Institute for Medical Systems Biology, Max Delbrück Center for Molecular Medicine, Berlin, Germany
| | - Mikael Attia
- Institut Curie, PSL Research University, CNRS UMR3215, INSERM U934, Paris, France
| | - Ava C Carter
- Center for Personal Dynamic Regulomes, Stanford University, Stanford, CA, USA
| | - Laurène Syx
- Institut Curie, PSL Research University, CNRS UMR3215, INSERM U934, Paris, France
- Institut Curie, PSL Research University, INSERM U900, Mines ParisTech, Paris, France
| | - Howard Y Chang
- Center for Personal Dynamic Regulomes, Stanford University, Stanford, CA, USA
| | - Anne-Valerie Gendrel
- Institut Curie, PSL Research University, CNRS UMR3215, INSERM U934, Paris, France.
| | - Edith Heard
- Institut Curie, PSL Research University, CNRS UMR3215, INSERM U934, Paris, France.
- Directors' research, European Molecular Biology Laboratory (EMBL), Heidelberg, Germany.
- Collège de France, Paris, France.
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15
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Wu W, Lovett JL, Shedden K, Strassmann BI, Vincenz C. Targeted RNA-seq improves efficiency, resolution, and accuracy of allele specific expression for human term placentas. G3 (BETHESDA, MD.) 2021; 11:jkab176. [PMID: 34009305 PMCID: PMC8496276 DOI: 10.1093/g3journal/jkab176] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/25/2021] [Accepted: 05/12/2021] [Indexed: 12/30/2022]
Abstract
Genomic imprinting is an epigenetic mechanism that results in allele-specific expression (ASE) based on the parent of origin. It is known to play a role in the prenatal and postnatal allocation of maternal resources in mammals. ASE detected by whole transcriptome RNA-seq (wht-RNAseq) has been widely used to analyze imprinted genes using reciprocal crosses in mice to generate large numbers of informative SNPs. Studies in humans are more challenging due to the paucity of SNPs and the poor preservation of RNA in term placentas and other tissues. Targeted RNA-seq (tar-RNAseq) can potentially mitigate these challenges by focusing sequencing resources on the regions of interest in the transcriptome. Here, we compared tar-RNAseq and wht-RNAseq in a study of ASE in known imprinted genes in placental tissue collected from a healthy human cohort in Mali, West Africa. As expected, tar-RNAseq substantially improved the coverage of SNPs. Compared to wht-RNAseq, tar-RNAseq produced on average four times more SNPs in twice as many genes per sample and read depth at the SNPs increased fourfold. In previous research on humans, discordant ASE values for SNPs of the same gene have limited the ability to accurately quantify ASE. We show that tar-RNAseq reduces this limitation as it unexpectedly increased the concordance of ASE between SNPs of the same gene, even in cases of degraded RNA. Studies aimed at discovering associations between individual variation in ASE and phenotypes in mammals and flowering plants will benefit from the improved power and accuracy of tar-RNAseq.
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Affiliation(s)
- Weisheng Wu
- BRCF Bioinformatics Core, University of Michigan, Ann Arbor, MI 48109, USA
| | - Jennie L Lovett
- Department of Anthropology, University of Michigan, Ann Arbor, MI 48109, USA
| | - Kerby Shedden
- Department of Statistics, University of Michigan, Ann Arbor, MI 48109, USA
| | - Beverly I Strassmann
- Department of Anthropology, University of Michigan, Ann Arbor, MI 48109, USA
- Research Center for Group Dynamics, Institute for Social Research, University of Michigan, Ann Arbor, MI 48106, USA
| | - Claudius Vincenz
- Research Center for Group Dynamics, Institute for Social Research, University of Michigan, Ann Arbor, MI 48106, USA
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16
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Abstract
Diploidy has profound implications for population genetics and susceptibility to genetic diseases. Although two copies are present for most genes in the human genome, they are not necessarily both active or active at the same level in a given individual. Genomic imprinting, resulting in exclusive or biased expression in favor of the allele of paternal or maternal origin, is now believed to affect hundreds of human genes. A far greater number of genes display unequal expression of gene copies due to cis-acting genetic variants that perturb gene expression. The availability of data generated by RNA sequencing applied to large numbers of individuals and tissue types has generated unprecedented opportunities to assess the contribution of genetic variation to allelic imbalance in gene expression. Here we review the insights gained through the analysis of these data about the extent of the genetic contribution to allelic expression imbalance, the tools and statistical models for gene expression imbalance, and what the results obtained reveal about the contribution of genetic variants that alter gene expression to complex human diseases and phenotypes.
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Affiliation(s)
- Siobhan Cleary
- School of Mathematics, Statistics and Applied Mathematics, National University of Ireland, Galway H91 H3CY, Ireland;
| | - Cathal Seoighe
- School of Mathematics, Statistics and Applied Mathematics, National University of Ireland, Galway H91 H3CY, Ireland;
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17
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Degtyareva AO, Antontseva EV, Merkulova TI. Regulatory SNPs: Altered Transcription Factor Binding Sites Implicated in Complex Traits and Diseases. Int J Mol Sci 2021; 22:6454. [PMID: 34208629 PMCID: PMC8235176 DOI: 10.3390/ijms22126454] [Citation(s) in RCA: 37] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2021] [Revised: 06/15/2021] [Accepted: 06/15/2021] [Indexed: 12/19/2022] Open
Abstract
The vast majority of the genetic variants (mainly SNPs) associated with various human traits and diseases map to a noncoding part of the genome and are enriched in its regulatory compartment, suggesting that many causal variants may affect gene expression. The leading mechanism of action of these SNPs consists in the alterations in the transcription factor binding via creation or disruption of transcription factor binding sites (TFBSs) or some change in the affinity of these regulatory proteins to their cognate sites. In this review, we first focus on the history of the discovery of regulatory SNPs (rSNPs) and systematized description of the existing methodical approaches to their study. Then, we brief the recent comprehensive examples of rSNPs studied from the discovery of the changes in the TFBS sequence as a result of a nucleotide substitution to identification of its effect on the target gene expression and, eventually, to phenotype. We also describe state-of-the-art genome-wide approaches to identification of regulatory variants, including both making molecular sense of genome-wide association studies (GWAS) and the alternative approaches the primary goal of which is to determine the functionality of genetic variants. Among these approaches, special attention is paid to expression quantitative trait loci (eQTLs) analysis and the search for allele-specific events in RNA-seq (ASE events) as well as in ChIP-seq, DNase-seq, and ATAC-seq (ASB events) data.
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Affiliation(s)
- Arina O. Degtyareva
- Department of Molecular Genetic, Institute of Cytology and Genetics, 630090 Novosibirsk, Russia; (A.O.D.); (E.V.A.)
| | - Elena V. Antontseva
- Department of Molecular Genetic, Institute of Cytology and Genetics, 630090 Novosibirsk, Russia; (A.O.D.); (E.V.A.)
| | - Tatiana I. Merkulova
- Department of Molecular Genetic, Institute of Cytology and Genetics, 630090 Novosibirsk, Russia; (A.O.D.); (E.V.A.)
- Department of Natural Sciences, Novosibirsk State University, 630090 Novosibirsk, Russia
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18
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Liang Y, Aguet F, Barbeira AN, Ardlie K, Im HK. A scalable unified framework of total and allele-specific counts for cis-QTL, fine-mapping, and prediction. Nat Commun 2021; 12:1424. [PMID: 33658504 PMCID: PMC7930098 DOI: 10.1038/s41467-021-21592-8] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2020] [Accepted: 01/29/2021] [Indexed: 01/31/2023] Open
Abstract
Genetic studies of the transcriptome help bridge the gap between genetic variation and phenotypes. To maximize the potential of such studies, efficient methods to identify expression quantitative trait loci (eQTLs) and perform fine-mapping and genetic prediction of gene expression traits are needed. Current methods that leverage both total read counts and allele-specific expression to identify eQTLs are generally computationally intractable for large transcriptomic studies. Here, we describe a unified framework that addresses these needs and is scalable to thousands of samples. Using simulations and data from GTEx, we demonstrate its calibration and performance. For example, mixQTL shows a power gain equivalent to a 29% increase in sample size for genes with sufficient allele-specific read coverage. To showcase the potential of mixQTL, we apply it to 49 GTEx tissues and find 20% additional eQTLs (FDR < 0.05, per tissue) that are significantly more enriched among trait associated variants and candidate cis-regulatory elements comparing to the standard approach.
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Affiliation(s)
- Yanyu Liang
- Section of Genetic Medicine, The University of Chicago, Chicago, IL, USA.
| | - François Aguet
- The Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Alvaro N Barbeira
- Section of Genetic Medicine, The University of Chicago, Chicago, IL, USA
| | - Kristin Ardlie
- The Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Hae Kyung Im
- Section of Genetic Medicine, The University of Chicago, Chicago, IL, USA.
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19
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Robles-Espinoza CD, Mohammadi P, Bonilla X, Gutierrez-Arcelus M. Allele-specific expression: applications in cancer and technical considerations. Curr Opin Genet Dev 2021; 66:10-19. [PMID: 33383480 PMCID: PMC7985293 DOI: 10.1016/j.gde.2020.10.007] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2020] [Revised: 10/26/2020] [Accepted: 10/31/2020] [Indexed: 11/18/2022]
Abstract
Allele-specific gene expression can influence disease traits. Non-coding germline genetic variants that alter regulatory elements can cause allele-specific gene expression and contribute to cancer susceptibility. In tumors, both somatic copy number alterations and somatic single nucleotide variants have been shown to lead to allele-specific expression of genes, many of which are considered drivers of tumor growth. Here, we review recent studies revealing the pervasive presence of this phenomenon in cancer susceptibility and progression. Furthermore, we underscore the importance of careful experimental design and computational analysis for accurate allelic expression quantification and avoidance of false positives. Finally, we discuss additional methodological challenges encountered in cancer studies and in the burgeoning field of single-cell transcriptomics.
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Affiliation(s)
- Carla Daniela Robles-Espinoza
- Laboratorio Internacional de Investigación sobre el Genoma Humano, Universidad Nacional Autónoma de México, Campus Juriquilla, Boulevard Juriquilla 3001, Santiago de Querétaro 76230, Mexico; Wellcome Sanger Institute, Hinxton, Cambridgeshire CB10 1SA, UK
| | - Pejman Mohammadi
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA, USA; Scripps Translational Science Institute, The Scripps Research Institute, La Jolla, CA, USA
| | - Ximena Bonilla
- Department of Computer Science, ETH Zurich, Universitätsstr. 6, 8092 Zürich, Switzerland; Swiss Institute of Bioinformatics, Quartier Sorge - Bâtiment Amphipôle, Lausanne 1015, Switzerland; University Hospital Zurich, Rämistrasse 100, 8091 Zürich, Switzerland
| | - Maria Gutierrez-Arcelus
- Center for Data Sciences, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA; Division of Genetics, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA; Division of Rheumatology, Inflammation and Immunity, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA; Program in Medical and Population Genetics, Broad Institute, Cambridge, MA 02142, USA; Division of Immunology, Department of Pediatrics, Boston Children's Hospital, Harvard Medical School, Boston, Massachusetts, USA.
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20
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Wang AT, Shetty A, O'Connor E, Bell C, Pomerantz MM, Freedman ML, Gusev A. Allele-Specific QTL Fine Mapping with PLASMA. Am J Hum Genet 2020; 106:170-187. [PMID: 32004450 PMCID: PMC7011109 DOI: 10.1016/j.ajhg.2019.12.011] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2019] [Accepted: 12/29/2019] [Indexed: 12/22/2022] Open
Abstract
Although quantitative trait locus (QTL) associations have been identified for many molecular traits such as gene expression, it remains challenging to distinguish the causal nucleotide from nearby variants. In addition to traditional QTLs by association, allele-specific (AS) QTLs are a powerful measure of cis-regulation that are concordant with traditional QTLs but typically less susceptible to technical/environmental noise. However, existing methods for estimating causal variant probabilities (i.e., fine mapping) cannot produce valid estimates from asQTL signals due to complexities in linkage disequilibrium (LD). We introduce PLASMA (Population Allele-Specific Mapping), a fine-mapping method that integrates QTL and asQTL information to improve accuracy. In simulations, PLASMA accurately prioritizes causal variants over a wide range of genetic architectures. Applied to RNA-seq data from 524 kidney tumor samples, PLASMA achieves a greater power at 50 samples than conventional QTL-based fine mapping at 500 samples, with more than 17% of loci fine mapped to within five causal variants, compared to 2% by QTL-based fine mapping, and a 6.9-fold overall reduction in median credible set size compared to QTL-based fine mapping when applied to H3K27AC ChIP-seq from just 28 prostate tumor/normal samples. Variants in the PLASMA credible sets for RNA-seq and ChIP-seq were enriched for open chromatin and chromatin looping, respectively, at a comparable or greater degree than credible variants from existing methods while containing far fewer markers. Our results demonstrate how integrating AS activity can substantially improve the detection of causal variants from existing molecular data.
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Affiliation(s)
- Austin T Wang
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA 02142, USA; Department of Biology, Massachusetts Institute of Technology, Cambridge, MA 02142, USA; Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA 02215, USA.
| | - Anamay Shetty
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA 02215, USA; Cambridge University, Cambridge CB2 1TN, UK
| | - Edward O'Connor
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA 02215, USA
| | - Connor Bell
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA 02215, USA
| | - Mark M Pomerantz
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA 02215, USA
| | - Matthew L Freedman
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA 02215, USA; The Eli and Edythe L. Broad Institute, Cambridge, MA 02142, USA; Center for Functional Cancer Epigenetics, Dana-Farber Cancer Institute, Boston, MA 02215, USA
| | - Alexander Gusev
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA 02215, USA; The Eli and Edythe L. Broad Institute, Cambridge, MA 02142, USA; Brigham & Women's Hospital, Division of Genetics, Boston, MA 02215, USA.
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21
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Frochaux MV, Bou Sleiman M, Gardeux V, Dainese R, Hollis B, Litovchenko M, Braman VS, Andreani T, Osman D, Deplancke B. cis-regulatory variation modulates susceptibility to enteric infection in the Drosophila genetic reference panel. Genome Biol 2020; 21:6. [PMID: 31948474 PMCID: PMC6966807 DOI: 10.1186/s13059-019-1912-z] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2019] [Accepted: 12/05/2019] [Indexed: 02/07/2023] Open
Abstract
BACKGROUND Resistance to enteric pathogens is a complex trait at the crossroads of multiple biological processes. We have previously shown in the Drosophila Genetic Reference Panel (DGRP) that resistance to infection is highly heritable, but our understanding of how the effects of genetic variants affect different molecular mechanisms to determine gut immunocompetence is still limited. RESULTS To address this, we perform a systems genetics analysis of the gut transcriptomes from 38 DGRP lines that were orally infected with Pseudomonas entomophila. We identify a large number of condition-specific, expression quantitative trait loci (local-eQTLs) with infection-specific ones located in regions enriched for FOX transcription factor motifs. By assessing the allelic imbalance in the transcriptomes of 19 F1 hybrid lines from a large round robin design, we independently attribute a robust cis-regulatory effect to only 10% of these detected local-eQTLs. However, additional analyses indicate that many local-eQTLs may act in trans instead. Comparison of the transcriptomes of DGRP lines that were either susceptible or resistant to Pseudomonas entomophila infection reveals nutcracker as the only differentially expressed gene. Interestingly, we find that nutcracker is linked to infection-specific eQTLs that correlate with its expression level and to enteric infection susceptibility. Further regulatory analysis reveals one particular eQTL that significantly decreases the binding affinity for the repressor Broad, driving differential allele-specific nutcracker expression. CONCLUSIONS Our collective findings point to a large number of infection-specific cis- and trans-acting eQTLs in the DGRP, including one common non-coding variant that lowers enteric infection susceptibility.
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Affiliation(s)
- Michael V. Frochaux
- Laboratory of Systems Biology and Genetics, Institute of Bioengineering, Ecole Polytechnique Fédérale de Lausanne (EPFL) and Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Maroun Bou Sleiman
- Laboratory of Systems Biology and Genetics, Institute of Bioengineering, Ecole Polytechnique Fédérale de Lausanne (EPFL) and Swiss Institute of Bioinformatics, Lausanne, Switzerland
- Current Address: Laboratory of Integrative Systems Physiology, Institute of Bioengineering, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Vincent Gardeux
- Laboratory of Systems Biology and Genetics, Institute of Bioengineering, Ecole Polytechnique Fédérale de Lausanne (EPFL) and Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Riccardo Dainese
- Laboratory of Systems Biology and Genetics, Institute of Bioengineering, Ecole Polytechnique Fédérale de Lausanne (EPFL) and Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Brian Hollis
- Laboratory of Systems Biology and Genetics, Institute of Bioengineering, Ecole Polytechnique Fédérale de Lausanne (EPFL) and Swiss Institute of Bioinformatics, Lausanne, Switzerland
- Current Address: Department of Biological Sciences, University of South Carolina, Columbia, South Carolina USA
| | - Maria Litovchenko
- Laboratory of Systems Biology and Genetics, Institute of Bioengineering, Ecole Polytechnique Fédérale de Lausanne (EPFL) and Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Virginie S. Braman
- Laboratory of Systems Biology and Genetics, Institute of Bioengineering, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Tommaso Andreani
- Computational Biology and Data Mining Group, Institute of Molecular Biology, Johannes Gutenberg-Universität Mainz, Mainz, Germany
| | - Dani Osman
- Faculty of Sciences III and Azm Center for Research in Biotechnology and its Applications, LBA3B, EDST, Lebanese University, Tripoli, 1300 Lebanon
| | - Bart Deplancke
- Laboratory of Systems Biology and Genetics, Institute of Bioengineering, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
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