1
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Ibeh N, Kusuma P, Crenna Darusallam C, Malik SG, Sudoyo H, McCarthy DJ, Gallego Romero I. Profiling genetically driven alternative splicing across the Indonesian archipelago. Am J Hum Genet 2024:S0002-9297(24)00339-2. [PMID: 39383868 DOI: 10.1016/j.ajhg.2024.09.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2024] [Revised: 09/11/2024] [Accepted: 09/12/2024] [Indexed: 10/11/2024] Open
Abstract
One of the regulatory mechanisms influencing the functional capacity of genes is alternative splicing (AS). Previous studies exploring the splicing landscape of human tissues have shown that AS has contributed to human biology, especially in disease progression and the immune response. Nonetheless, this phenomenon remains poorly characterized across human populations, and it is unclear how genetic and environmental variation contribute to AS. Here, we examine a set of 115 Indonesian samples from three traditional island populations spanning the genetic ancestry cline that characterizes Island Southeast Asia. We conduct a global AS analysis between islands to ascertain the degree of functionally significant AS events and their consequences. Using an event-based statistical model, we detected over 1,500 significant differential AS events across all comparisons. Additionally, we identify over 6,000 genetic variants associated with changes in splicing (splicing quantitative trait loci [sQTLs]), some of which are driven by Papuan-like genetic ancestry, and only show partial overlap with other publicly available sQTL datasets derived from other populations. Computational predictions of RNA binding activity reveal that a fraction of these sQTLs directly modulate the binding propensity of proteins involved in the splicing regulation of immune genes. Overall, these results contribute toward elucidating the role of genetic variation in shaping gene regulation in one of the most diverse regions in the world.
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Affiliation(s)
- Neke Ibeh
- School of BioSciences, University of Melbourne, Parkville, VIC 3010, Australia; Melbourne Integrative Genomics, University of Melbourne, Parkville, VIC 3010, Australia; Bioinformatics and Cellular Genomics, St Vincents Institute of Medical Research, Fitzroy, VIC 3065, Australia; Human Genomics and Evolution, St Vincent's Institute of Medical Research, Fitzroy, VIC 3065, Australia
| | - Pradiptajati Kusuma
- Genome Diversity and Disease Laboratory, Mochtar Riady Institute of Nanotechnology, Tangerang 15811, Indonesia
| | - Chelzie Crenna Darusallam
- Genome Diversity and Disease Laboratory, Mochtar Riady Institute of Nanotechnology, Tangerang 15811, Indonesia
| | - Safarina G Malik
- Genome Diversity and Disease Laboratory, Mochtar Riady Institute of Nanotechnology, Tangerang 15811, Indonesia
| | - Herawati Sudoyo
- Genome Diversity and Disease Laboratory, Mochtar Riady Institute of Nanotechnology, Tangerang 15811, Indonesia
| | - Davis J McCarthy
- Melbourne Integrative Genomics, University of Melbourne, Parkville, VIC 3010, Australia; Bioinformatics and Cellular Genomics, St Vincents Institute of Medical Research, Fitzroy, VIC 3065, Australia; School of Mathematics and Statistics, Faculty of Science, University of Melbourne, Parkville, VIC 3010, Australia
| | - Irene Gallego Romero
- School of BioSciences, University of Melbourne, Parkville, VIC 3010, Australia; Melbourne Integrative Genomics, University of Melbourne, Parkville, VIC 3010, Australia; Human Genomics and Evolution, St Vincent's Institute of Medical Research, Fitzroy, VIC 3065, Australia; Centre for Genomics, Evolution and Medicine, Institute of Genomics, University of Tartu, 51010 Tartu, Estonia.
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2
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Wenz BM, He Y, Chen NC, Pickrell JK, Li JH, Dudek MF, Li T, Keener R, Voight BF, Brown CD, Battle A. Genotype inference from aggregated chromatin accessibility data reveals genetic regulatory mechanisms. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.09.04.610850. [PMID: 39282458 PMCID: PMC11398312 DOI: 10.1101/2024.09.04.610850] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 09/21/2024]
Abstract
Background Understanding the genetic causes for variability in chromatin accessibility can shed light on the molecular mechanisms through which genetic variants may affect complex traits. Thousands of ATAC-seq samples have been collected that hold information about chromatin accessibility across diverse cell types and contexts, but most of these are not paired with genetic information and come from diverse distinct projects and laboratories. Results We report here joint genotyping, chromatin accessibility peak calling, and discovery of quantitative trait loci which influence chromatin accessibility (caQTLs), demonstrating the capability of performing caQTL analysis on a large scale in a diverse sample set without pre-existing genotype information. Using 10,293 profiling samples representing 1,454 unique donor individuals across 653 studies from public databases, we catalog 23,381 caQTLs in total. After joint discovery analysis, we cluster samples based on accessible chromatin profiles to identify context-specific caQTLs. We find that caQTLs are strongly enriched for annotations of gene regulatory elements across diverse cell types and tissues and are often strongly linked with genetic variation associated with changes in expression (eQTLs), indicating that caQTLs can mediate genetic effects on gene expression. We demonstrate sharing of causal variants for chromatin accessibility and diverse complex human traits, enabling a more complete picture of the genetic mechanisms underlying complex human phenotypes. Conclusions Our work provides a proof of principle for caQTL calling from previously ungenotyped samples, and represents one of the largest, most diverse caQTL resources currently available, informing mechanisms of genetic regulation of gene expression and contribution to disease.
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Affiliation(s)
- Brandon M Wenz
- Genetics and Epigenetics Program, Cell and Molecular Biology Graduate Group, Biomedical Graduate Studies, University of Pennsylvania - Perelman School of Medicine, Philadelphia PA 19104
| | - Yuan He
- Department of Biomedical Engineering, Johns Hopkins University; Baltimore, MD, 21218
| | - Nae-Chyun Chen
- Department of Computer Science, Johns Hopkins University; Baltimore, MD, 21218
| | | | | | - Max F Dudek
- Graduate Group in Genomics and Computational Biology, University of Pennsylvania, Philadelphia, PA 19104
| | - Taibo Li
- Department of Biomedical Engineering, Johns Hopkins University; Baltimore, MD, 21218
| | - Rebecca Keener
- Department of Biomedical Engineering, Johns Hopkins University; Baltimore, MD, 21218
| | - Benjamin F Voight
- Department of Genetics, University of Pennsylvania - Perelman School of Medicine, Philadelphia, PA, 19104
- Department of Systems Pharmacology and Translational Therapeutics, University of Pennsylvania - Perelman School of Medicine, Philadelphia PA, 19104
- Institute for Translational Medicine and Therapeutics, University of Pennsylvania - Perelman School of Medicine, Philadelphia, PA, 19104
| | - Christopher D Brown
- Department of Genetics, University of Pennsylvania - Perelman School of Medicine, Philadelphia, PA, 19104
| | - Alexis Battle
- Department of Biomedical Engineering, Johns Hopkins University; Baltimore, MD, 21218
- Department of Computer Science, Johns Hopkins University; Baltimore, MD, 21218
- Department of Genetic Medicine, Johns Hopkins University; Baltimore, MD, 21218
- Malone Center for Engineering in Healthcare, Johns Hopkins University, Baltimore, MD, 21218
- Data Science and AI Institute, Johns Hopkins University, Baltimore, MD, 21218
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3
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Opris CE, Suciu H, Jung I, Flamand S, Harpa MM, Opris CI, Popa C, Kovacs Z, Gurzu S. Significance of Fibrillin-1, Filamin A, MMP2 and SOX9 in Mitral Valve Pathology. Int J Mol Sci 2024; 25:9410. [PMID: 39273357 PMCID: PMC11394784 DOI: 10.3390/ijms25179410] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2024] [Revised: 08/23/2024] [Accepted: 08/27/2024] [Indexed: 09/15/2024] Open
Abstract
Genetic factors play a significant role in the pathogenesis of mitral valve diseases, including mitral valve prolapse (MVP) and mitral valve regurgitation. Genes like Fibrillin-1 (FBN1), Filamin A (FLNA), matrix metalloproteinase 2 (MMP2), and SRY-box transcription factor 9 (SOX9) are known to influence mitral valve pathology but knowledge of the exact mechanism is far from clear. Data regarding serum parameters, transesophageal echocardiography, and genetic and histopathologic parameters were investigated in 54 patients who underwent cardiovascular surgery for mitral valve regurgitation. The possible association between Fibrillin-1, Filamin A, MMP2, and SOX9 gene expressions was checked in relationship with the parameters of systemic inflammatory response. The mRNA expression levels (RQ-relative quantification) were categorized into three distinct groups: low (RQ < 1), medium/normal (RQ = 1-2), and high (RQ > 2). Severe fibrosis of the mitral valve was reflected by high expression of FBN1 and low expression of MMP2 (p < 0.05). The myxoid degeneration level was associated with the mRNA expression level for FBN1 and a low lymphocyte-monocyte ratio was associated with an increased mRNA expression of FBN1 (p < 0.05). A high number of monocytes was associated with high values of FBN1 whereas the increase in the number of lymphocytes was associated with high levels of MMP2. In addition, we observed that the risk of severe hyalinization was enhanced by a low mRNA expression of FLNA and/or SOX9. In conclusion, a lower FLNA mRNA expression can reflect the aging process that is highlighted in mitral valve pathology as a higher risk for hyalinization, especially in males, that might be prevented by upregulation of the SOX9 gene. FBN1 and MMP2 influence the inflammation-related fibrotic degeneration of the mitral valve. Understanding the genetic base of mitral valve pathology can provide insights into disease mechanisms, risk stratification, and potential therapeutic targets.
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Affiliation(s)
- Carmen Elena Opris
- Department of Adult and Children Cardiovascular Recovery, Emergency Institute for Cardio-Vascular Diseases and Transplantation, 540139 Targu Mures, Romania
- Department of Pathology, George Emil Palade University of Medicine, Pharmacy, Science and Technology, 540139 Targu Mures, Romania
| | - Horatiu Suciu
- Department of Surgery, George Emil Palade University of Medicine, Pharmacy, Science and Technology, 540139 Targu Mures, Romania
- Romanian Academy of Medical Sciences, 030173 Bucharest, Romania
| | - Ioan Jung
- Department of Pathology, George Emil Palade University of Medicine, Pharmacy, Science and Technology, 540139 Targu Mures, Romania
- Romanian Academy of Medical Sciences, 030173 Bucharest, Romania
| | - Sanziana Flamand
- Department of Surgery, George Emil Palade University of Medicine, Pharmacy, Science and Technology, 540139 Targu Mures, Romania
| | - Marius Mihai Harpa
- Department of Surgery, George Emil Palade University of Medicine, Pharmacy, Science and Technology, 540139 Targu Mures, Romania
| | - Cosmin Ioan Opris
- Department of Surgery, George Emil Palade University of Medicine, Pharmacy, Science and Technology, 540139 Targu Mures, Romania
| | - Cristian Popa
- Faculty of European Studies, Babes-Bolyai University, 400006 Cluj-Napoca, Romania
| | - Zsolt Kovacs
- Department of Biochemistry and Environmental Chemistry, George Emil Palade University of Medicine, Pharmacy, Sciences and Technology, 540139 Targu Mures, Romania
- Research Center for Oncopathology and Translational Medicine (CCOMT), George Emil Palade University of Medicine, Pharmacy, Science and Technology, 540139 Targu Mures, Romania
| | - Simona Gurzu
- Department of Pathology, George Emil Palade University of Medicine, Pharmacy, Science and Technology, 540139 Targu Mures, Romania
- Romanian Academy of Medical Sciences, 030173 Bucharest, Romania
- Research Center for Oncopathology and Translational Medicine (CCOMT), George Emil Palade University of Medicine, Pharmacy, Science and Technology, 540139 Targu Mures, Romania
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4
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Zhu Q, Nambiar R, Schultz E, Gao X, Liang S, Flamand Y, Stevenson K, Cole PD, Gennarini L, Harris MH, Kahn JM, Ladas EJ, Athale UH, Hoa Tran T, Michon B, Welch JJG, Sallan SE, Silverman LB, Kelly KM, Yao S. Genome-wide study identifies novel genes associated with bone toxicities in children with acute lymphoblastic leukaemia. Br J Haematol 2024. [PMID: 39143423 DOI: 10.1111/bjh.19696] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2024] [Accepted: 07/27/2024] [Indexed: 08/16/2024]
Abstract
Bone toxicities are common among paediatric patients treated for acute lymphoblastic leukaemia (ALL) with potentially major negative impact on patients' quality of life. To identify the underlying genetic contributors, we conducted a genome-wide association study (GWAS) and a transcriptome-wide association study (TWAS) in 260 patients of European-descent from the DFCI 05-001 ALL trial, with validation in 101 patients of European-descent from the DFCI 11-001 ALL trial. We identified a significant association between rs844882 on chromosome 20 and bone toxicities in the DFCI 05-001 trial (p = 1.7 × 10-8). In DFCI 11-001 trial, we observed a consistent trend of this variant with fracture. The variant was an eQTL for two nearby genes, CD93 and THBD. In TWAS, genetically predicted ACAD9 expression was associated with an increased risk of bone toxicities, which was confirmed by meta-analysis of the two cohorts (meta-p = 2.4 × 10-6). In addition, a polygenic risk score of heel quantitative ultrasound speed of sound was associated with fracture risk in both cohorts (meta-p = 2.3 × 10-3). Our findings highlight the genetic influence on treatment-related bone toxicities in this patient population. The genes we identified in our study provide new biological insights into the development of bone adverse events related to ALL treatment.
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Affiliation(s)
- Qianqian Zhu
- Department of Biostatistics and Bioinformatics, Roswell Park Comprehensive Cancer Center, Buffalo, New York, USA
| | - Ram Nambiar
- Department of Biostatistics and Bioinformatics, Roswell Park Comprehensive Cancer Center, Buffalo, New York, USA
| | - Emily Schultz
- Department of Biostatistics and Bioinformatics, Roswell Park Comprehensive Cancer Center, Buffalo, New York, USA
| | - Xinyu Gao
- Department of Biostatistics and Bioinformatics, Roswell Park Comprehensive Cancer Center, Buffalo, New York, USA
| | - Shuyi Liang
- Department of Biostatistics and Bioinformatics, Roswell Park Comprehensive Cancer Center, Buffalo, New York, USA
| | - Yael Flamand
- Department of Data Science, Dana-Farber Cancer Institute, Boston, Massachusetts, USA
| | - Kristen Stevenson
- Department of Data Science, Dana-Farber Cancer Institute, Boston, Massachusetts, USA
| | - Peter D Cole
- Division of Pediatric Hematology/Oncology, Rutgers Cancer Institute of New Jersey, New Brunswick, New Jersey, USA
| | - Lisa Gennarini
- Division of Pediatric Hematology, Oncology and Cellular Therapy, Children's Hospital at Montefiore, Albert Einstein College of Medicine, Bronx, New York, USA
| | - Marian H Harris
- Department of Pathology, Boston Children's Hospital, Boston, Massachusetts, USA
| | - Justine M Kahn
- Division of Pediatric Hematology/Oncology/Stem Cell Transplantation, Columbia University Irving Medical Center, New York, New York, USA
| | - Elena J Ladas
- Division of Pediatric Hematology/Oncology/Stem Cell Transplantation, Columbia University Irving Medical Center, New York, New York, USA
| | - Uma H Athale
- Division of Pediatric Hematology/Oncology, McMaster University, Hamilton, Ontario, Canada
| | - Thai Hoa Tran
- Division of Pediatric Hematology and Oncology, Charles-Bruneau Cancer Center, CHU Sainte-Justine, University of Montreal, Montreal, Quebec, Canada
| | - Bruno Michon
- Division of Hematology-Oncology, Centre Hospitalier Universitaire de Québec, Quebec City, Quebec, Canada
| | - Jennifer J G Welch
- Division of Pediatric Hematology-Oncology, Hasbro Children's Hospital, Warren Alpert Medical School of Brown University, Providence, Rhode Island, USA
| | - Stephen E Sallan
- Department of Pediatric Oncology, Dana-Farber Cancer Institute and Division of Pediatric Hematology-Oncology, Boston Children's Hospital, Boston, Massachusetts, USA
| | - Lewis B Silverman
- Department of Pediatric Oncology, Dana-Farber Cancer Institute and Division of Pediatric Hematology-Oncology, Boston Children's Hospital, Boston, Massachusetts, USA
| | - Kara M Kelly
- Department of Pediatric Oncology, Roswell Park Comprehensive Cancer Center, Buffalo, New York, USA
- Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, New York, USA
| | - Song Yao
- Department of Cancer Prevention and Control, Roswell Park Comprehensive Cancer Center, Buffalo, New York, USA
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5
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Smail C, Montgomery SB. RNA Sequencing in Disease Diagnosis. Annu Rev Genomics Hum Genet 2024; 25:353-367. [PMID: 38360541 DOI: 10.1146/annurev-genom-021623-121812] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/17/2024]
Abstract
RNA sequencing (RNA-seq) enables the accurate measurement of multiple transcriptomic phenotypes for modeling the impacts of disease variants. Advances in technologies, experimental protocols, and analysis strategies are rapidly expanding the application of RNA-seq to identify disease biomarkers, tissue- and cell-type-specific impacts, and the spatial localization of disease-associated mechanisms. Ongoing international efforts to construct biobank-scale transcriptomic repositories with matched genomic data across diverse population groups are further increasing the utility of RNA-seq approaches by providing large-scale normative reference resources. The availability of these resources, combined with improved computational analysis pipelines, has enabled the detection of aberrant transcriptomic phenotypes underlying rare diseases. Further expansion of these resources, across both somatic and developmental tissues, is expected to soon provide unprecedented insights to resolve disease origin, mechanism of action, and causal gene contributions, suggesting the continued high utility of RNA-seq in disease diagnosis.
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Affiliation(s)
- Craig Smail
- Genomic Medicine Center, Children's Mercy Research Institute, Children's Mercy Kansas City, Kansas City, Missouri, USA;
| | - Stephen B Montgomery
- Department of Biomedical Data Science, Department of Genetics, and Department of Pathology, Stanford University School of Medicine, Stanford, California, USA;
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6
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Cote AC, Young HE, Huckins LM. Critical reasoning on the co-expression module QTL in the dorsolateral prefrontal cortex. HGG ADVANCES 2024; 5:100311. [PMID: 38773772 PMCID: PMC11214266 DOI: 10.1016/j.xhgg.2024.100311] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2023] [Revised: 05/16/2024] [Accepted: 05/16/2024] [Indexed: 05/24/2024] Open
Abstract
Expression quantitative trait locus (eQTL) analysis is a popular method of gaining insight into the function of regulatory variation. While cis-eQTL resources have been instrumental in linking genome-wide association study variants to gene function, complex trait heritability may be additionally mediated by other forms of gene regulation. Toward this end, novel eQTL methods leverage gene co-expression (module-QTL) to investigate joint regulation of gene modules by single genetic variants. Here we broadly define a "module-QTL" as the association of a genetic variant with a summary measure of gene co-expression. This approach aims to reduce the multiple testing burden of a trans-eQTL search through the consolidation of gene-based testing and provide biological context to eQTLs shared between genes. In this article we provide an in-depth examination of the co-expression module eQTL (module-QTL) through literature review, theoretical investigation, and real-data application of the module-QTL to three large prefrontal cortex genotype-RNA sequencing datasets. We find module-QTLs in our study that are disease associated and reproducible are not additionally informative beyond cis- or trans-eQTLs for module genes. Through comparison to prior studies, we highlight promises and limitations of the module-QTL across study designs and provide recommendations for further investigation of the module-QTL framework.
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Affiliation(s)
- Alanna C Cote
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA.
| | - Hannah E Young
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Laura M Huckins
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT 06511, USA.
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7
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Caudal É, Loegler V, Dutreux F, Vakirlis N, Teyssonnière É, Caradec C, Friedrich A, Hou J, Schacherer J. Pan-transcriptome reveals a large accessory genome contribution to gene expression variation in yeast. Nat Genet 2024; 56:1278-1287. [PMID: 38778243 PMCID: PMC11176082 DOI: 10.1038/s41588-024-01769-9] [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: 02/14/2023] [Accepted: 04/24/2024] [Indexed: 05/25/2024]
Abstract
Gene expression is an essential step in the translation of genotypes into phenotypes. However, little is known about the transcriptome architecture and the underlying genetic effects at the species level. Here we generated and analyzed the pan-transcriptome of ~1,000 yeast natural isolates across 4,977 core and 1,468 accessory genes. We found that the accessory genome is an underappreciated driver of transcriptome divergence. Global gene expression patterns combined with population structure showed that variation in heritable expression mainly lies within subpopulation-specific signatures, for which accessory genes are overrepresented. Genome-wide association analyses consistently highlighted that accessory genes are associated with proportionally more variants with larger effect sizes, illustrating the critical role of the accessory genome on the transcriptional landscape within and between populations.
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Affiliation(s)
- Élodie Caudal
- Université de Strasbourg, CNRS GMGM UMR 7156, Strasbourg, France
| | - Victor Loegler
- Université de Strasbourg, CNRS GMGM UMR 7156, Strasbourg, France
| | - Fabien Dutreux
- Université de Strasbourg, CNRS GMGM UMR 7156, Strasbourg, France
| | | | | | - Claudia Caradec
- Université de Strasbourg, CNRS GMGM UMR 7156, Strasbourg, France
| | - Anne Friedrich
- Université de Strasbourg, CNRS GMGM UMR 7156, Strasbourg, France
| | - Jing Hou
- Université de Strasbourg, CNRS GMGM UMR 7156, Strasbourg, France.
| | - Joseph Schacherer
- Université de Strasbourg, CNRS GMGM UMR 7156, Strasbourg, France.
- Institut Universitaire de France (IUF), Paris, France.
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8
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Wang L, Khunsriraksakul C, Markus H, Chen D, Zhang F, Chen F, Zhan X, Carrel L, Liu DJ, Jiang B. Integrating single cell expression quantitative trait loci summary statistics to understand complex trait risk genes. Nat Commun 2024; 15:4260. [PMID: 38769300 DOI: 10.1038/s41467-024-48143-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2023] [Accepted: 04/22/2024] [Indexed: 05/22/2024] Open
Abstract
Transcriptome-wide association study (TWAS) is a popular approach to dissect the functional consequence of disease associated non-coding variants. Most existing TWAS use bulk tissues and may not have the resolution to reveal cell-type specific target genes. Single-cell expression quantitative trait loci (sc-eQTL) datasets are emerging. The largest bulk- and sc-eQTL datasets are most conveniently available as summary statistics, but have not been broadly utilized in TWAS. Here, we present a new method EXPRESSO (EXpression PREdiction with Summary Statistics Only), to analyze sc-eQTL summary statistics, which also integrates 3D genomic data and epigenomic annotation to prioritize causal variants. EXPRESSO substantially improves existing methods. We apply EXPRESSO to analyze multi-ancestry GWAS datasets for 14 autoimmune diseases. EXPRESSO uniquely identifies 958 novel gene x trait associations, which is 26% more than the second-best method. Among them, 492 are unique to cell type level analysis and missed by TWAS using whole blood. We also develop a cell type aware drug repurposing pipeline, which leverages EXPRESSO results to identify drug compounds that can reverse disease gene expressions in relevant cell types. Our results point to multiple drugs with therapeutic potentials, including metformin for type 1 diabetes, and vitamin K for ulcerative colitis.
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Affiliation(s)
- Lida Wang
- Department of Public Health Sciences; Pennsylvania State University College of Medicine, Hershey, Pennsylvania, USA
| | - Chachrit Khunsriraksakul
- Bioinformatics and Genomics PhD Program; Pennsylvania State University College of Medicine, Hershey, Pennsylvania, USA
- Institute for Personalized Medicine; Pennsylvania State University College of Medicine, Hershey, Pennsylvania, USA
| | - Havell Markus
- Bioinformatics and Genomics PhD Program; Pennsylvania State University College of Medicine, Hershey, Pennsylvania, USA
- Institute for Personalized Medicine; Pennsylvania State University College of Medicine, Hershey, Pennsylvania, USA
| | - Dieyi Chen
- Department of Public Health Sciences; Pennsylvania State University College of Medicine, Hershey, Pennsylvania, USA
| | - Fan Zhang
- Bioinformatics and Genomics PhD Program; Pennsylvania State University College of Medicine, Hershey, Pennsylvania, USA
| | - Fang Chen
- Department of Public Health Sciences; Pennsylvania State University College of Medicine, Hershey, Pennsylvania, USA
| | - Xiaowei Zhan
- Department of Statistical Science, Southern Methodist University, Dallas, TX, US
- Quantitative Biomedical Research Center, Department of Population and Data Sciences, University of Texas Southwestern Medical Center, Dallas, TX, US
- Center for Genetics of Host Defense, University of Texas Southwestern Medical Center, Dallas, TX, US
| | - Laura Carrel
- Department of Biochemistry and Molecular Biology; Pennsylvania State University College of Medicine, Hershey, Pennsylvania, USA.
| | - Dajiang J Liu
- Department of Public Health Sciences; Pennsylvania State University College of Medicine, Hershey, Pennsylvania, USA.
- Bioinformatics and Genomics PhD Program; Pennsylvania State University College of Medicine, Hershey, Pennsylvania, USA.
- Department of Statistical Science, Southern Methodist University, Dallas, TX, US.
| | - Bibo Jiang
- Department of Public Health Sciences; Pennsylvania State University College of Medicine, Hershey, Pennsylvania, USA.
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9
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Randolph HE, Aracena KA, Lin YL, Mu Z, Barreiro LB. Shaping immunity: The influence of natural selection on population immune diversity. Immunol Rev 2024; 323:227-240. [PMID: 38577999 DOI: 10.1111/imr.13329] [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] [Indexed: 04/06/2024]
Abstract
Humans exhibit considerable variability in their immune responses to the same immune challenges. Such variation is widespread and affects individual and population-level susceptibility to infectious diseases and immune disorders. Although the factors influencing immune response diversity are partially understood, what mechanisms lead to the wide range of immune traits in healthy individuals remain largely unexplained. Here, we discuss the role that natural selection has played in driving phenotypic differences in immune responses across populations and present-day susceptibility to immune-related disorders. Further, we touch on future directions in the field of immunogenomics, highlighting the value of expanding this work to human populations globally, the utility of modeling the immune response as a dynamic process, and the importance of considering the potential polygenic nature of natural selection. Identifying loci acted upon by evolution may further pinpoint variants critically involved in disease etiology, and designing studies to capture these effects will enrich our understanding of the genetic contributions to immunity and immune dysregulation.
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Affiliation(s)
- Haley E Randolph
- Committee on Genetics, Genomics, and Systems Biology, University of Chicago, Chicago, Illinois, USA
- Department of Pediatrics, Columbia University Irving Medical Center, New York, New York, USA
| | | | - Yen-Lung Lin
- Section of Genetic Medicine, Department of Medicine, University of Chicago, Chicago, Illinois, USA
| | - Zepeng Mu
- Committee on Genetics, Genomics, and Systems Biology, University of Chicago, Chicago, Illinois, USA
| | - Luis B Barreiro
- Committee on Genetics, Genomics, and Systems Biology, University of Chicago, Chicago, Illinois, USA
- Department of Human Genetics, University of Chicago, Chicago, Illinois, USA
- Section of Genetic Medicine, Department of Medicine, University of Chicago, Chicago, Illinois, USA
- Committee on Immunology, University of Chicago, Chicago, Illinois, USA
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10
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Wigdor EM, Samocha KE, Eberhardt RY, Chundru VK, Firth HV, Wright CF, Hurles ME, Martin HC. Investigating the role of common cis-regulatory variants in modifying penetrance of putatively damaging, inherited variants in severe neurodevelopmental disorders. Sci Rep 2024; 14:8708. [PMID: 38622173 PMCID: PMC11018828 DOI: 10.1038/s41598-024-58894-y] [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: 04/19/2023] [Accepted: 04/04/2024] [Indexed: 04/17/2024] Open
Abstract
Recent work has revealed an important role for rare, incompletely penetrant inherited coding variants in neurodevelopmental disorders (NDDs). Additionally, we have previously shown that common variants contribute to risk for rare NDDs. Here, we investigate whether common variants exert their effects by modifying gene expression, using multi-cis-expression quantitative trait loci (cis-eQTL) prediction models. We first performed a transcriptome-wide association study for NDDs using 6987 probands from the Deciphering Developmental Disorders (DDD) study and 9720 controls, and found one gene, RAB2A, that passed multiple testing correction (p = 6.7 × 10-7). We then investigated whether cis-eQTLs modify the penetrance of putatively damaging, rare coding variants inherited by NDD probands from their unaffected parents in a set of 1700 trios. We found no evidence that unaffected parents transmitting putatively damaging coding variants had higher genetically-predicted expression of the variant-harboring gene than their child. In probands carrying putatively damaging variants in constrained genes, the genetically-predicted expression of these genes in blood was lower than in controls (p = 2.7 × 10-3). However, results for proband-control comparisons were inconsistent across different sets of genes, variant filters and tissues. We find limited evidence that common cis-eQTLs modify penetrance of rare coding variants in a large cohort of NDD probands.
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Affiliation(s)
- Emilie M Wigdor
- Human Genetics Programme, Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, UK.
| | - Kaitlin E Samocha
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, USA
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, USA
| | - Ruth Y Eberhardt
- Human Genetics Programme, Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, UK
| | - V Kartik Chundru
- Human Genetics Programme, Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, UK
- Department of Clinical and Biomedical Sciences, University of Exeter Medical School, Royal Devon and Exeter Hospital, Exeter, UK
| | - Helen V Firth
- Department of Medical Genetics, Addenbrooke's Hospital, Cambridge University Hospitals, Cambridge, UK
| | - Caroline F Wright
- Institute of Biomedical and Clinical Science, University of Exeter Medical School, Royal Devon and Exeter Hospital, Exeter, UK
| | - Matthew E Hurles
- Human Genetics Programme, Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, UK
| | - Hilary C Martin
- Human Genetics Programme, Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, UK.
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11
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Guo X, Chatterjee N, Dutta D. Subset-based method for cross-tissue transcriptome-wide association studies improves power and interpretability. HGG ADVANCES 2024; 5:100283. [PMID: 38491773 PMCID: PMC10999697 DOI: 10.1016/j.xhgg.2024.100283] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2023] [Revised: 03/09/2024] [Accepted: 03/09/2024] [Indexed: 03/18/2024] Open
Abstract
Integrating results from genome-wide association studies (GWASs) and studies of molecular phenotypes such as gene expressions can improve our understanding of the biological functions of trait-associated variants and can help prioritize candidate genes for downstream analysis. Using reference expression quantitative trait locus (eQTL) studies, several methods have been proposed to identify gene-trait associations, primarily based on gene expression imputation. To increase the statistical power by leveraging substantial eQTL sharing across tissues, meta-analysis methods aggregating such gene-based test results across multiple tissues or contexts have been developed as well. However, most existing meta-analysis methods have limited power to identify associations when the gene has weaker associations in only a few tissues and cannot identify the subset of tissues in which the gene is "activated." For this, we developed a cross-tissue subset-based transcriptome-wide association study (CSTWAS) meta-analysis method that improves power under such scenarios and can extract the set of potentially associated tissues. To improve applicability, CSTWAS uses only GWAS summary statistics and pre-computed correlation matrices to identify a subset of tissues that have the maximal evidence of gene-trait association. Through numerical simulations, we found that CSTWAS can maintain a well-calibrated type-I error rate, improves power especially when there is a small number of associated tissues for a gene-trait association, and identifies an accurate associated tissue set. By analyzing GWAS summary statistics of three complex traits and diseases, we demonstrate that CSTWAS could identify biological meaningful signals while providing an interpretation of disease etiology by extracting a set of potentially associated tissues.
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Affiliation(s)
- Xinyu Guo
- Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, CA 90007, USA
| | - Nilanjan Chatterjee
- Department of Biostatistics, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD 21218, USA; Department of Oncology, School of Medicine, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Diptavo Dutta
- Integrative Tumor Epidemiology Branch, Division of Cancer Epidemiology & Genetics, National Cancer Institute, Rockville, MD 20850, USA.
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12
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Wang L, Babushkin N, Liu Z, Liu X. Trans-eQTL mapping in gene sets identifies network effects of genetic variants. CELL GENOMICS 2024; 4:100538. [PMID: 38565144 PMCID: PMC11019359 DOI: 10.1016/j.xgen.2024.100538] [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: 05/10/2023] [Revised: 12/08/2023] [Accepted: 03/13/2024] [Indexed: 04/04/2024]
Abstract
Nearly all trait-associated variants identified in genome-wide association studies (GWASs) are noncoding. The cis regulatory effects of these variants have been extensively characterized, but how they affect gene regulation in trans has been the subject of fewer studies because of the difficulty in detecting trans-expression quantitative loci (eQTLs). We developed trans-PCO for detecting trans effects of genetic variants on gene networks. Our simulations demonstrate that trans-PCO substantially outperforms existing trans-eQTL mapping methods. We applied trans-PCO to two gene expression datasets from whole blood, DGN (N = 913) and eQTLGen (N = 31,684), and identified 14,985 high-quality trans-eSNP-module pairs associated with 197 co-expression gene modules and biological processes. We performed colocalization analyses between GWAS loci of 46 complex traits and the trans-eQTLs. We demonstrated that the identified trans effects can help us understand how trait-associated variants affect gene regulatory networks and biological pathways.
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Affiliation(s)
- Lili Wang
- The Committee on Genetics, Genomics and Systems Biology, University of Chicago, Chicago, IL 60637, USA; Department of Medicine, Section of Genetic Medicine, University of Chicago, Chicago, IL 60637, USA
| | - Nikita Babushkin
- Department of Medicine, Section of Genetic Medicine, University of Chicago, Chicago, IL 60637, USA
| | - Zhonghua Liu
- Department of Biostatistics, Columbia University, New York, NY 10032, USA
| | - Xuanyao Liu
- The Committee on Genetics, Genomics and Systems Biology, University of Chicago, Chicago, IL 60637, USA; Department of Medicine, Section of Genetic Medicine, University of Chicago, Chicago, IL 60637, USA; Department of Human Genetics, University of Chicago, Chicago, IL 60637, USA.
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13
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Tsouris A, Brach G, Friedrich A, Hou J, Schacherer J. Diallel panel reveals a significant impact of low-frequency genetic variants on gene expression variation in yeast. Mol Syst Biol 2024; 20:362-373. [PMID: 38355920 PMCID: PMC10987670 DOI: 10.1038/s44320-024-00021-0] [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: 09/15/2023] [Revised: 01/29/2024] [Accepted: 01/30/2024] [Indexed: 02/16/2024] Open
Abstract
Unraveling the genetic sources of gene expression variation is essential to better understand the origins of phenotypic diversity in natural populations. Genome-wide association studies identified thousands of variants involved in gene expression variation, however, variants detected only explain part of the heritability. In fact, variants such as low-frequency and structural variants (SVs) are poorly captured in association studies. To assess the impact of these variants on gene expression variation, we explored a half-diallel panel composed of 323 hybrids originated from pairwise crosses of 26 natural Saccharomyces cerevisiae isolates. Using short- and long-read sequencing strategies, we established an exhaustive catalog of single nucleotide polymorphisms (SNPs) and SVs for this panel. Combining this dataset with the transcriptomes of all hybrids, we comprehensively mapped SNPs and SVs associated with gene expression variation. While SVs impact gene expression variation, SNPs exhibit a higher effect size with an overrepresentation of low-frequency variants compared to common ones. These results reinforce the importance of dissecting the heritability of complex traits with a comprehensive catalog of genetic variants at the population level.
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Affiliation(s)
- Andreas Tsouris
- Université de Strasbourg, CNRS, GMGM UMR 7156, Strasbourg, France
| | - Gauthier Brach
- Université de Strasbourg, CNRS, GMGM UMR 7156, Strasbourg, France
| | - Anne Friedrich
- Université de Strasbourg, CNRS, GMGM UMR 7156, Strasbourg, France
| | - Jing Hou
- Université de Strasbourg, CNRS, GMGM UMR 7156, Strasbourg, France.
| | - Joseph Schacherer
- Université de Strasbourg, CNRS, GMGM UMR 7156, Strasbourg, France.
- Institut Universitaire de France (IUF), Paris, France.
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14
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Lin W, Wall JD, Li G, Newman D, Yang Y, Abney M, VandeBerg JL, Olivier M, Gilad Y, Cox LA. Genetic regulatory effects in response to a high-cholesterol, high-fat diet in baboons. CELL GENOMICS 2024; 4:100509. [PMID: 38430910 PMCID: PMC10943580 DOI: 10.1016/j.xgen.2024.100509] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/01/2023] [Revised: 11/20/2023] [Accepted: 02/05/2024] [Indexed: 03/05/2024]
Abstract
Steady-state expression quantitative trait loci (eQTLs) explain only a fraction of disease-associated loci identified through genome-wide association studies (GWASs), while eQTLs involved in gene-by-environment (GxE) interactions have rarely been characterized in humans due to experimental challenges. Using a baboon model, we found hundreds of eQTLs that emerge in adipose, liver, and muscle after prolonged exposure to high dietary fat and cholesterol. Diet-responsive eQTLs exhibit genomic localization and genic features that are distinct from steady-state eQTLs. Furthermore, the human orthologs associated with diet-responsive eQTLs are enriched for GWAS genes associated with human metabolic traits, suggesting that context-responsive eQTLs with more complex regulatory effects are likely to explain GWAS hits that do not seem to overlap with standard eQTLs. Our results highlight the complexity of genetic regulatory effects and the potential of eQTLs with disease-relevant GxE interactions in enhancing the understanding of GWAS signals for human complex disease using non-human primate models.
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Affiliation(s)
- Wenhe Lin
- Department of Human Genetics, The University of Chicago, Chicago, IL 60637, USA.
| | - Jeffrey D Wall
- Institute for Human Genetics, University of California, San Francisco, San Francisco, CA 94143, USA
| | - Ge Li
- Center for Precision Medicine, Wake Forest University School of Medicine, Winston-Salem, NC 27157, USA
| | - Deborah Newman
- Southwest National Primate Research Center, Texas Biomedical Research Institute, San Antonio, TX 78229, USA
| | - Yunqi Yang
- Committee on Genetics, Genomics and System Biology, The University of Chicago, Chicago, IL 60637, USA
| | - Mark Abney
- Department of Human Genetics, The University of Chicago, Chicago, IL 60637, USA
| | - John L VandeBerg
- Department of Human Genetics, South Texas Diabetes and Obesity Institute, University of Texas Rio Grande Valley, Brownsville, TX 78520, USA
| | - Michael Olivier
- Center for Precision Medicine, Wake Forest University School of Medicine, Winston-Salem, NC 27157, USA
| | - Yoav Gilad
- Department of Human Genetics, The University of Chicago, Chicago, IL 60637, USA; Department of Medicine, Section of Genetic Medicine, The University of Chicago, Chicago, IL 60637, USA.
| | - Laura A Cox
- Center for Precision Medicine, Wake Forest University School of Medicine, Winston-Salem, NC 27157, USA; Southwest National Primate Research Center, Texas Biomedical Research Institute, San Antonio, TX 78229, USA.
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15
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Einson J, Minaeva M, Rafi F, Lappalainen T. The impact of genetically controlled splicing on exon inclusion and protein structure. PLoS One 2024; 19:e0291960. [PMID: 38478511 PMCID: PMC10936842 DOI: 10.1371/journal.pone.0291960] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2023] [Accepted: 09/08/2023] [Indexed: 03/17/2024] Open
Abstract
Common variants affecting mRNA splicing are typically identified though splicing quantitative trait locus (sQTL) mapping and have been shown to be enriched for GWAS signals by a similar degree to eQTLs. However, the specific splicing changes induced by these variants have been difficult to characterize, making it more complicated to analyze the effect size and direction of sQTLs, and to determine downstream splicing effects on protein structure. In this study, we catalogue sQTLs using exon percent spliced in (PSI) scores as a quantitative phenotype. PSI is an interpretable metric for identifying exon skipping events and has some advantages over other methods for quantifying splicing from short read RNA sequencing. In our set of sQTL variants, we find evidence of selective effects based on splicing effect size and effect direction, as well as exon symmetry. Additionally, we utilize AlphaFold2 to predict changes in protein structure associated with sQTLs overlapping GWAS traits, highlighting a potential new use-case for this technology for interpreting genetic effects on traits and disorders.
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Affiliation(s)
- Jonah Einson
- Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY, United States of America
- New York Genome Center, New York, NY, United States of America
| | - Mariia Minaeva
- Science for Life Laboratory, Department of Gene Technology, KTH Royal Institute of Technology, Stockholm, Sweden
| | - Faiza Rafi
- New York Genome Center, New York, NY, United States of America
- Department of Biotechnology, The City College of New York, New York, NY, United States of America
| | - Tuuli Lappalainen
- New York Genome Center, New York, NY, United States of America
- Science for Life Laboratory, Department of Gene Technology, KTH Royal Institute of Technology, Stockholm, Sweden
- Department of Systems Biology, Columbia University Irving Medical Center, New York, NY, United States of America
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16
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Wang Y, Wu J, Zhao J, Xu T, Zhang M, Liu J, Wang Y, Wang Q, Song X. Global characterization of RNA editing in genetic regulation of multiple ovarian cancer subtypes. MOLECULAR THERAPY. NUCLEIC ACIDS 2024; 35:102127. [PMID: 38352860 PMCID: PMC10863325 DOI: 10.1016/j.omtn.2024.102127] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/09/2023] [Accepted: 01/18/2024] [Indexed: 02/16/2024]
Abstract
RNA editing plays an extensive role in the initiation and progression of cancer. However, the overall profile and molecular functions of RNA editing in different ovarian cancer subtypes have not been fully characterized and elucidated. Here, we conducted a study on RNA editing in four cohorts of ovarian cancer subtypes through large-scale parallel reporting and bioinformatics analysis. Our findings revealed that RNA editing patterns exhibit subtype-specific characteristics within cancer subtypes. The expression pattern of ADAR and the number of differential editing sites varied under different conditions. CCOC and EOC exhibited significant editing deficiency, whereas HGSC and MOC displayed significant editing excess. The sites within the turquoise module of the coedited network also revealed their correlation with ovarian cancer. In addition, we identified an average of over 40,000 cis-edQTLs in the four subtypes. Finally, we explored the association between RNA editing and drug response, uncovering several potentially effective editing-drug pairs (EDP) and suggesting the conceivable utility of RNA editing sites as therapeutic targets for cancer treatment. Overall, our comprehensive study has identified and characterized RNA editing events in various subtypes of ovarian cancer, providing a new perspective for ovarian cancer research and facilitating the development of medical interventions and treatments.
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Affiliation(s)
- Yulan Wang
- Department of Biomedical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China
| | - Jing Wu
- School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing 211166, China
| | - Jian Zhao
- Department of Biomedical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China
| | - Tianyi Xu
- National Genomics Data Center & CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China
| | - Meng Zhang
- Department of Biomedical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China
| | - Jingjing Liu
- Department of Biomedical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China
| | - Yixuan Wang
- Department of Biomedical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China
| | - Quan Wang
- Department of Biomedical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China
| | - Xiaofeng Song
- Department of Biomedical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China
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17
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Strober BJ, Tayeb K, Popp J, Qi G, Gordon MG, Perez R, Ye CJ, Battle A. SURGE: uncovering context-specific genetic-regulation of gene expression from single-cell RNA sequencing using latent-factor models. Genome Biol 2024; 25:28. [PMID: 38254214 PMCID: PMC10801966 DOI: 10.1186/s13059-023-03152-z] [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: 12/22/2022] [Accepted: 12/20/2023] [Indexed: 01/24/2024] Open
Abstract
Genetic regulation of gene expression is a complex process, with genetic effects known to vary across cellular contexts such as cell types and environmental conditions. We developed SURGE, a method for unsupervised discovery of context-specific expression quantitative trait loci (eQTLs) from single-cell transcriptomic data. This allows discovery of the contexts or cell types modulating genetic regulation without prior knowledge. Applied to peripheral blood single-cell eQTL data, SURGE contexts capture continuous representations of distinct cell types and groupings of biologically related cell types. We demonstrate the disease-relevance of SURGE context-specific eQTLs using colocalization analysis and stratified LD-score regression.
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Affiliation(s)
- Benjamin J Strober
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Karl Tayeb
- Department of Human Genetics, University of Chicago, Chicago, IL, USA
| | - Joshua Popp
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Guanghao Qi
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - M Grace Gordon
- Biological and Medical Informatics Graduate Program, University of California, San Francisco, CA, USA
- Division of Rheumatology, Department of Medicine, University of California, San Francisco, CA, USA
- Institute for Human Genetics, University of California, San Francisco, CA, USA
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, CA, USA
| | - Richard Perez
- Institute for Human Genetics, University of California, San Francisco, CA, USA
| | - Chun Jimmie Ye
- Division of Rheumatology, Department of Medicine, University of California, San Francisco, CA, USA
- Institute for Human Genetics, University of California, San Francisco, CA, USA
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, CA, USA
- Institute for Computational Health Sciences, University of California, San Francisco, San Francisco, CA, USA
- Chan-Zuckerberg Biohub, San Francisco, CA, USA
| | - Alexis Battle
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA.
- Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA.
- Department of Genetic Medicine, Johns Hopkins University, Baltimore, MD, USA.
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18
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Ehsan N, Kotis BM, Castel SE, Song EJ, Mancuso N, Mohammadi P. Haplotype-aware modeling of cis-regulatory effects highlights the gaps remaining in eQTL data. Nat Commun 2024; 15:522. [PMID: 38225224 PMCID: PMC10789818 DOI: 10.1038/s41467-024-44710-8] [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: 01/31/2022] [Accepted: 12/30/2023] [Indexed: 01/17/2024] Open
Abstract
Expression Quantitative Trait Loci (eQTLs) are critical to understanding the mechanisms underlying disease-associated genomic loci. Nearly all protein-coding genes in the human genome have been associated with one or more eQTLs. Here we introduce a multi-variant generalization of allelic Fold Change (aFC), aFC-n, to enable quantification of the cis-regulatory effects in multi-eQTL genes under the assumption that all eQTLs are known and conditionally independent. Applying aFC-n to 458,465 eQTLs in the Genotype-Tissue Expression (GTEx) project data, we demonstrate significant improvements in accuracy over the original model in estimating the eQTL effect sizes and in predicting genetically regulated gene expression over the current tools. We characterize some of the empirical properties of the eQTL data and use this framework to assess the current state of eQTL data in terms of characterizing cis-regulatory landscape in individual genomes. Notably, we show that 77.4% of the genes with an allelic imbalance in a sample show 0.5 log2 fold or more of residual imbalance after accounting for the eQTL data underlining the remaining gap in characterizing regulatory landscape in individual genomes. We further contrast this gap across tissue types, and ancestry backgrounds to identify its correlates and guide future studies.
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Affiliation(s)
- Nava Ehsan
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA, USA
| | - Bence M Kotis
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA, USA
| | - Stephane E Castel
- Department of Systems Biology, Columbia University, New York, NY, USA
- New York Genome Center, New York, NY, USA
| | - Eric J Song
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA, USA
| | - Nicholas Mancuso
- Center for Genetic Epidemiology, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern, California, CA, USA
| | - Pejman Mohammadi
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA, USA.
- Center for Immunity and Immunotherapies, Seattle Children's Research Institute, Seattle, WA, USA.
- Department of Pediatrics, University of Washington School of Medicine, Seattle, WA, USA.
- Department of Genome Sciences, University of Washington, Seattle, WA, USA.
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19
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Tsouris A, Brach G, Schacherer J, Hou J. Non-additive genetic components contribute significantly to population-wide gene expression variation. CELL GENOMICS 2024; 4:100459. [PMID: 38190102 PMCID: PMC10794783 DOI: 10.1016/j.xgen.2023.100459] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Revised: 09/19/2023] [Accepted: 11/09/2023] [Indexed: 01/09/2024]
Abstract
Gene expression variation, an essential step between genotype and phenotype, is collectively controlled by local (cis) and distant (trans) regulatory changes. Nevertheless, how these regulatory elements differentially influence gene expression variation remains unclear. Here, we bridge this gap by analyzing the transcriptomes of a large diallel panel consisting of 323 unique hybrids originating from genetically divergent Saccharomyces cerevisiae isolates. Our analysis across 5,087 transcript abundance traits showed that non-additive components account for 36% of the gene expression variance on average. By comparing allele-specific read counts in parent-hybrid trios, we found that trans-regulatory changes underlie the majority of gene expression variation in the population. Remarkably, most cis-regulatory variations are also exaggerated or attenuated by additional trans effects. Overall, we showed that the transcriptome is globally buffered at the genetic level mainly due to trans-regulatory variation in the population.
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Affiliation(s)
- Andreas Tsouris
- Université de Strasbourg, CNRS, GMGM UMR, 7156 Strasbourg, France
| | - Gauthier Brach
- Université de Strasbourg, CNRS, GMGM UMR, 7156 Strasbourg, France
| | - Joseph Schacherer
- Université de Strasbourg, CNRS, GMGM UMR, 7156 Strasbourg, France; Institut Universitaire de France (IUF), Paris, France.
| | - Jing Hou
- Université de Strasbourg, CNRS, GMGM UMR, 7156 Strasbourg, France.
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20
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Zhang X, Gomez L, Below JE, Naj AC, Martin ER, Kunkle BW, Bush WS. An X Chromosome Transcriptome Wide Association Study Implicates ARMCX6 in Alzheimer's Disease. J Alzheimers Dis 2024; 98:1053-1067. [PMID: 38489177 DOI: 10.3233/jad-231075] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/17/2024]
Abstract
Background The X chromosome is often omitted in disease association studies despite containing thousands of genes that may provide insight into well-known sex differences in the risk of Alzheimer's disease (AD). Objective To model the expression of X chromosome genes and evaluate their impact on AD risk in a sex-stratified manner. Methods Using elastic net, we evaluated multiple modeling strategies in a set of 175 whole blood samples and 126 brain cortex samples, with whole genome sequencing and RNA-seq data. SNPs (MAF > 0.05) within the cis-regulatory window were used to train tissue-specific models of each gene. We apply the best models in both tissues to sex-stratified summary statistics from a meta-analysis of Alzheimer's Disease Genetics Consortium (ADGC) studies to identify AD-related genes on the X chromosome. Results Across different model parameters, sample sex, and tissue types, we modeled the expression of 217 genes (95 genes in blood and 135 genes in brain cortex). The average model R2 was 0.12 (range from 0.03 to 0.34). We also compared sex-stratified and sex-combined models on the X chromosome. We further investigated genes that escaped X chromosome inactivation (XCI) to determine if their genetic regulation patterns were distinct. We found ten genes associated with AD at p < 0.05, with only ARMCX6 in female brain cortex (p = 0.008) nearing the significance threshold after adjusting for multiple testing (α = 0.002). Conclusions We optimized the expression prediction of X chromosome genes, applied these models to sex-stratified AD GWAS summary statistics, and identified one putative AD risk gene, ARMCX6.
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Affiliation(s)
- Xueyi Zhang
- Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, OH, USA
| | - Lissette Gomez
- John P. Hussman Institute for Human Genomics, Miller School of Medicine, University of Miami, Miami, FL, USA
| | - Jennifer E Below
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Adam C Naj
- Department of Biostatistics, Epidemiology, and Informatics, Penn Neurodegeneration Genomics Center, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
- Department of Pathology and Laboratory Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Eden R Martin
- John P. Hussman Institute for Human Genomics, Miller School of Medicine, University of Miami, Miami, FL, USA
| | - Brian W Kunkle
- John P. Hussman Institute for Human Genomics, Miller School of Medicine, University of Miami, Miami, FL, USA
| | - William S Bush
- Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, OH, USA
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21
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Sasse A, Ng B, Spiro AE, Tasaki S, Bennett DA, Gaiteri C, De Jager PL, Chikina M, Mostafavi S. Benchmarking of deep neural networks for predicting personal gene expression from DNA sequence highlights shortcomings. Nat Genet 2023; 55:2060-2064. [PMID: 38036778 DOI: 10.1038/s41588-023-01524-6] [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: 03/13/2023] [Accepted: 09/08/2023] [Indexed: 12/02/2023]
Abstract
Deep learning methods have recently become the state of the art in a variety of regulatory genomic tasks1-6, including the prediction of gene expression from genomic DNA. As such, these methods promise to serve as important tools in interpreting the full spectrum of genetic variation observed in personal genomes. Previous evaluation strategies have assessed their predictions of gene expression across genomic regions; however, systematic benchmarking is lacking to assess their predictions across individuals, which would directly evaluate their utility as personal DNA interpreters. We used paired whole genome sequencing and gene expression from 839 individuals in the ROSMAP study7 to evaluate the ability of current methods to predict gene expression variation across individuals at varied loci. Our approach identifies a limitation of current methods to correctly predict the direction of variant effects. We show that this limitation stems from insufficiently learned sequence motif grammar and suggest new model training strategies to improve performance.
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Affiliation(s)
- Alexander Sasse
- Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, WA, USA
| | - Bernard Ng
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, USA
| | - Anna E Spiro
- Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, WA, USA
| | - Shinya Tasaki
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, USA
| | - David A Bennett
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, USA
| | - Christopher Gaiteri
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, USA
- Department of Psychiatry, SUNY Upstate Medical University, Syracuse, NY, USA
| | - Philip L De Jager
- Center for Translational & Computational Neuroimmunology, Department of Neurology, and the Taub Institute for the Study of Alzheimer's Disease and the Aging Brain, Columbia University Irving Medical Center, New York, NY, USA
| | - Maria Chikina
- Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, PA, USA.
| | - Sara Mostafavi
- Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, WA, USA.
- Canadian Institute for Advanced Research, Toronto, Ontario, Canada.
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22
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Moore A, Marks JA, Quach BC, Guo Y, Bierut LJ, Gaddis NC, Hancock DB, Page GP, Johnson EO. Evaluating 17 methods incorporating biological function with GWAS summary statistics to accelerate discovery demonstrates a tradeoff between high sensitivity and high positive predictive value. Commun Biol 2023; 6:1199. [PMID: 38001305 PMCID: PMC10673847 DOI: 10.1038/s42003-023-05413-w] [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: 04/05/2022] [Accepted: 10/03/2023] [Indexed: 11/26/2023] Open
Abstract
Where sufficiently large genome-wide association study (GWAS) samples are not currently available or feasible, methods that leverage increasing knowledge of the biological function of variants may illuminate discoveries without increasing sample size. We comprehensively evaluated 17 functional weighting methods for identifying novel associations. We assessed the performance of these methods using published results from multiple GWAS waves across each of five complex traits. Although no method achieved both high sensitivity and positive predictive value (PPV) for any trait, a subset of methods utilizing pleiotropy and expression quantitative trait loci nominated variants with high PPV (>75%) for multiple traits. Application of functionally weighting methods to enhance GWAS power for locus discovery is unlikely to circumvent the need for larger sample sizes in truly underpowered GWAS, but these results suggest that applying functional weighting to GWAS can accurately nominate additional novel loci from available samples for follow-up studies.
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Affiliation(s)
- Amy Moore
- Genomics and Translational Research Center, RTI International, Research Triangle Park, NC, 27709, USA.
| | - Jesse A Marks
- Genomics and Translational Research Center, RTI International, Research Triangle Park, NC, 27709, USA
| | - Bryan C Quach
- Genomics and Translational Research Center, RTI International, Research Triangle Park, NC, 27709, USA
| | - Yuelong Guo
- GeneCentric Therapeutics, Inc., Cary, NC, USA
| | - Laura J Bierut
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, USA
| | - Nathan C Gaddis
- Genomics and Translational Research Center, RTI International, Research Triangle Park, NC, 27709, USA
| | - Dana B Hancock
- Genomics and Translational Research Center, RTI International, Research Triangle Park, NC, 27709, USA
| | - Grier P Page
- Genomics and Translational Research Center, RTI International, Research Triangle Park, NC, 27709, USA
- Fellow Program, RTI International, Research Triangle Park, NC, 27709, USA
| | - Eric O Johnson
- Genomics and Translational Research Center, RTI International, Research Triangle Park, NC, 27709, USA.
- Fellow Program, RTI International, Research Triangle Park, NC, 27709, USA.
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23
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Lin W, Wall JD, Li G, Newman D, Yang Y, Abney M, VandeBerg JL, Olivier M, Gilad Y, Cox LA. Genetic regulatory effects in response to a high cholesterol, high fat diet in baboons. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.08.01.551489. [PMID: 37577666 PMCID: PMC10418186 DOI: 10.1101/2023.08.01.551489] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/15/2023]
Abstract
Steady-state expression quantitative trait loci (eQTLs) explain only a fraction of disease-associated loci identified through genome-wide association studies (GWAS), while eQTLs involved in gene-by-environment (GxE) interactions have rarely been characterized in humans due to experimental challenges. Using a baboon model, we found hundreds of eQTLs that emerge in adipose, liver, and muscle after prolonged exposure to high dietary fat and cholesterol. Diet-responsive eQTLs exhibit genomic localization and genic features that are distinct from steady-state eQTLs. Furthermore, the human orthologs associated with diet-responsive eQTLs are enriched for GWAS genes associated with human metabolic traits, suggesting that context-responsive eQTLs with more complex regulatory effects are likely to explain GWAS hits that do not seem to overlap with standard eQTLs. Our results highlight the complexity of genetic regulatory effects and the potential of eQTLs with disease-relevant GxE interactions in enhancing the understanding of GWAS signals for human complex disease using nonhuman primate models.
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Affiliation(s)
- Wenhe Lin
- Department of Human Genetics, The University of Chicago, Chicago, USA
| | - Jeffrey D. Wall
- Institute for Human Genetics, University of California San Francisco, San Francisco, CA, USA
- Present address: Galatea Bio, Hialeah, FL, USA
| | - Ge Li
- Center for Precision Medicine, Wake Forest University School of Medicine, Winston-Salem, NC, USA
| | - Deborah Newman
- Southwest National Primate Research Center, Texas Biomedical Research Institute, San Antonio, TX, USA
| | - Yunqi Yang
- Committee on Genetics, Genomics and System Biology, The University of Chicago, Chicago, USA
| | - Mark Abney
- Department of Human Genetics, The University of Chicago, Chicago, USA
| | - John L. VandeBerg
- Department of Human Genetics, South Texas Diabetes and Obesity Institute, University of Texas Rio Grand Valley, Brownsville, TX, USA
| | - Michael Olivier
- Center for Precision Medicine, Wake Forest University School of Medicine, Winston-Salem, NC, USA
| | - Yoav Gilad
- Department of Human Genetics, The University of Chicago, Chicago, USA
- Department of Medicine, Section of Genetic Medicine, The University of Chicago, Chicago, IL, USA
- Lead contact
| | - Laura A. Cox
- Center for Precision Medicine, Wake Forest University School of Medicine, Winston-Salem, NC, USA
- Southwest National Primate Research Center, Texas Biomedical Research Institute, San Antonio, TX, USA
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24
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DeGorter MK, Goddard PC, Karakoc E, Kundu S, Yan SM, Nachun D, Abell N, Aguirre M, Carstensen T, Chen Z, Durrant M, Dwaracherla VR, Feng K, Gloudemans MJ, Hunter N, Moorthy MPS, Pomilla C, Rodrigues KB, Smith CJ, Smith KS, Ungar RA, Balliu B, Fellay J, Flicek P, McLaren PJ, Henn B, McCoy RC, Sugden L, Kundaje A, Sandhu MS, Gurdasani D, Montgomery SB. Transcriptomics and chromatin accessibility in multiple African population samples. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.11.04.564839. [PMID: 37986808 PMCID: PMC10659267 DOI: 10.1101/2023.11.04.564839] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/22/2023]
Abstract
Mapping the functional human genome and impact of genetic variants is often limited to European-descendent population samples. To aid in overcoming this limitation, we measured gene expression using RNA sequencing in lymphoblastoid cell lines (LCLs) from 599 individuals from six African populations to identify novel transcripts including those not represented in the hg38 reference genome. We used whole genomes from the 1000 Genomes Project and 164 Maasai individuals to identify 8,881 expression and 6,949 splicing quantitative trait loci (eQTLs/sQTLs), and 2,611 structural variants associated with gene expression (SV-eQTLs). We further profiled chromatin accessibility using ATAC-Seq in a subset of 100 representative individuals, to identity chromatin accessibility quantitative trait loci (caQTLs) and allele-specific chromatin accessibility, and provide predictions for the functional effect of 78.9 million variants on chromatin accessibility. Using this map of eQTLs and caQTLs we fine-mapped GWAS signals for a range of complex diseases. Combined, this work expands global functional genomic data to identify novel transcripts, functional elements and variants, understand population genetic history of molecular quantitative trait loci, and further resolve the genetic basis of multiple human traits and disease.
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Affiliation(s)
| | - Page C Goddard
- Department of Genetics, Stanford University, Stanford, CA
| | - Emre Karakoc
- Human Genetics, Wellcome Trust Sanger Institute, Hinxton, Cambridge, UK
| | - Soumya Kundu
- Department of Computer Science, Stanford University, Stanford CA
| | | | - Daniel Nachun
- Department of Pathology, Stanford University, Stanford, CA
| | - Nathan Abell
- Department of Genetics, Stanford University, Stanford, CA
| | - Matthew Aguirre
- Department of Biomedical Data Science, Stanford University, Stanford, CA
| | - Tommy Carstensen
- Human Genetics, Wellcome Trust Sanger Institute, Hinxton, Cambridge, UK
| | - Ziwei Chen
- Department of Computer Science, Stanford University, Stanford CA
| | | | | | - Karen Feng
- Department of Biomedical Data Science, Stanford University, Stanford, CA
| | | | - Naiomi Hunter
- Department of Genetics, Stanford University, Stanford, CA
| | | | - Cristina Pomilla
- Human Genetics, Wellcome Trust Sanger Institute, Hinxton, Cambridge, UK
| | | | | | - Kevin S Smith
- Department of Pathology, Stanford University, Stanford, CA
| | - Rachel A Ungar
- Department of Genetics, Stanford University, Stanford, CA
| | - Brunilda Balliu
- Department of Pathology and Laboratory Medicine, University of California Los Angeles, Los Angeles, CA and Department of Computational Medicine, University of California Los Angeles, Los Angeles, CA
| | - Jacques Fellay
- School of Life Sciences, Ecole Polytechnique Federale de Lausanne, Lausanne, Switzerland and Precision Medicine Unit, Biomedical Data Science Center, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Paul Flicek
- Department of Genetics, University of Cambridge, Cambridge, UK
| | - Paul J McLaren
- Sexually Transmitted and Blood-Borne Infections Division at JC Wilt Infectious Diseases Research Centre, National Microbiology Laboratory Branch, Public Health Agency of Canada, Winnipeg, Canada and Department of Medical Microbiology and Infectious Diseases, University of Manitoba, Winnipeg, Canada
| | - Brenna Henn
- Department of Anthropology, University of California Davis, Davis CA and Genome Center, University of California Davis, Davis CA
| | - Rajiv C McCoy
- Department of Biology, Johns Hopkins University, Baltimore
| | - Lauren Sugden
- Department of Mathematics and Computer Science, Dusquesne University, Pittsburgh, PA
| | - Anshul Kundaje
- Department of Genetics, Stanford University, Stanford, CA
- Department of Computer Science, Stanford University, Stanford CA
| | | | - Deepti Gurdasani
- William Harvey Research Institute, Queen Mary University of London, London, UK; Kirby Institute, University of New South Wales, Australia; School of Medicine, University of Western Australia, Australia
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25
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Van Horebeek L, David M, Dedoncker N, Mallants K, Bijnens B, Goris A, Dubois B. A targeted sequencing extension for transcript genotyping in single-cell transcriptomics. Life Sci Alliance 2023; 6:e202301971. [PMID: 37696578 PMCID: PMC10494938 DOI: 10.26508/lsa.202301971] [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: 02/05/2023] [Revised: 08/17/2023] [Accepted: 08/29/2023] [Indexed: 09/13/2023] Open
Abstract
As no existing methods within the single-cell RNA sequencing repertoire combine genotyping of specific genomic loci with high throughput, we evaluated a straightforward, targeted sequencing approach as an extension to high-throughput droplet-based single-cell RNA sequencing. Overlaying standard gene expression data with transcript level genotype information provides a strategy to study the impact of genetic variants. Here, we describe this targeted sequencing extension, explain how to process the data and evaluate how technical parameters such as amount of input cDNA, number of amplification rounds, and sequencing depth influence the number of transcripts detected. Finally, we demonstrate how targeted sequencing can be used in two contexts: (1) simultaneous investigation of the presence of a somatic variant and its potential impact on the transcriptome of affected cells and (2) evaluation of allele-specific expression of a germline variant in ad hoc cell subsets. Through these and other comparable applications, our targeted sequencing extension has the potential to improve our understanding of functional effects caused by genetic variation.
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Affiliation(s)
- Lies Van Horebeek
- https://ror.org/05f950310 Laboratory for Neuroimmunology, Department of Neurosciences, Leuven Brain Institute, KU Leuven, Leuven, Belgium
| | - Margaux David
- https://ror.org/05f950310 Laboratory for Neuroimmunology, Department of Neurosciences, Leuven Brain Institute, KU Leuven, Leuven, Belgium
| | - Nina Dedoncker
- https://ror.org/05f950310 Laboratory for Neuroimmunology, Department of Neurosciences, Leuven Brain Institute, KU Leuven, Leuven, Belgium
| | - Klara Mallants
- https://ror.org/05f950310 Laboratory for Neuroimmunology, Department of Neurosciences, Leuven Brain Institute, KU Leuven, Leuven, Belgium
| | - Baukje Bijnens
- https://ror.org/05f950310 Laboratory for Neuroimmunology, Department of Neurosciences, Leuven Brain Institute, KU Leuven, Leuven, Belgium
| | - An Goris
- https://ror.org/05f950310 Laboratory for Neuroimmunology, Department of Neurosciences, Leuven Brain Institute, KU Leuven, Leuven, Belgium
| | - Bénédicte Dubois
- https://ror.org/05f950310 Laboratory for Neuroimmunology, Department of Neurosciences, Leuven Brain Institute, KU Leuven, Leuven, Belgium
- Department of Neurology, University Hospitals Leuven, Leuven, Belgium
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26
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Xu M, Liu Q, Bi R, Li Y, Li H, Kang WB, Yan Z, Zheng Q, Sun C, Ye M, Xiang BL, Luo XJ, Li M, Zhang DF, Yao YG. Coexistence of Multiple Functional Variants and Genes Underlies Genetic Risk Locus 11p11.2 of Alzheimer's Disease. Biol Psychiatry 2023; 94:743-759. [PMID: 37290560 DOI: 10.1016/j.biopsych.2023.05.020] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/29/2022] [Revised: 05/25/2023] [Accepted: 05/26/2023] [Indexed: 06/10/2023]
Abstract
BACKGROUND Genome-wide association studies have identified dozens of genetic risk loci for Alzheimer's disease (AD), yet the underlying causal variants and biological mechanisms remain elusive, especially for loci with complex linkage disequilibrium and regulation. METHODS To fully untangle the causal signal at a single locus, we performed a functional genomic study of 11p11.2 (the CELF1/SPI1 locus). Genome-wide association study signals at 11p11.2 were integrated with datasets of histone modification, open chromatin, and transcription factor binding to distill potentially functional variants (fVars). Their allelic regulatory activities were confirmed by allele imbalance, reporter assays, and base editing. Expressional quantitative trait loci and chromatin interaction data were incorporated to assign target genes to fVars. The relevance of these genes to AD was assessed by convergent functional genomics using bulk brain and single-cell transcriptomic, epigenomic, and proteomic datasets of patients with AD and control individuals, followed by cellular assays. RESULTS We found that 24 potential fVars, rather than a single variant, were responsible for the risk of 11p11.2. These fVars modulated transcription factor binding and regulated multiple genes by long-range chromatin interactions. Besides SPI1, convergent evidence indicated that 6 target genes (MTCH2, ACP2, NDUFS3, PSMC3, C1QTNF4, and MADD) of fVars were likely to be involved in AD development. Disruption of each gene led to cellular amyloid-β and phosphorylated tau changes, supporting the existence of multiple likely causal genes at 11p11.2. CONCLUSIONS Multiple variants and genes at 11p11.2 may contribute to AD risk. This finding provides new insights into the mechanistic and therapeutic challenges of AD.
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Affiliation(s)
- Min Xu
- Key Laboratory of Animal Models and Human Disease Mechanisms of the Chinese Academy of Sciences & Yunnan Province and KIZ/CUHK Joint Laboratory of Bioresources and Molecular Research in Common Diseases, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, China; Kunming College of Life Science, University of Chinese Academy of Sciences, Kunming, China
| | - Qianjin Liu
- Key Laboratory of Animal Models and Human Disease Mechanisms of the Chinese Academy of Sciences & Yunnan Province and KIZ/CUHK Joint Laboratory of Bioresources and Molecular Research in Common Diseases, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, China; Kunming College of Life Science, University of Chinese Academy of Sciences, Kunming, China
| | - Rui Bi
- Key Laboratory of Animal Models and Human Disease Mechanisms of the Chinese Academy of Sciences & Yunnan Province and KIZ/CUHK Joint Laboratory of Bioresources and Molecular Research in Common Diseases, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, China; Kunming College of Life Science, University of Chinese Academy of Sciences, Kunming, China; National Resource Center for Non-Human Primates, National Research Facility for Phenotypic & Genetic Analysis of Model Animals (Primate Facility), Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, China; Center for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences, Kunming, China
| | - Yu Li
- Key Laboratory of Animal Models and Human Disease Mechanisms of the Chinese Academy of Sciences & Yunnan Province and KIZ/CUHK Joint Laboratory of Bioresources and Molecular Research in Common Diseases, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, China; Kunming College of Life Science, University of Chinese Academy of Sciences, Kunming, China
| | - Hongli Li
- Key Laboratory of Animal Models and Human Disease Mechanisms of the Chinese Academy of Sciences & Yunnan Province and KIZ/CUHK Joint Laboratory of Bioresources and Molecular Research in Common Diseases, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, China; Kunming College of Life Science, University of Chinese Academy of Sciences, Kunming, China
| | - Wei-Bo Kang
- Key Laboratory of Animal Models and Human Disease Mechanisms of the Chinese Academy of Sciences & Yunnan Province and KIZ/CUHK Joint Laboratory of Bioresources and Molecular Research in Common Diseases, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, China; Kunming College of Life Science, University of Chinese Academy of Sciences, Kunming, China
| | - Zhongjiang Yan
- Key Laboratory of Animal Models and Human Disease Mechanisms of the Chinese Academy of Sciences & Yunnan Province and KIZ/CUHK Joint Laboratory of Bioresources and Molecular Research in Common Diseases, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, China; Kunming College of Life Science, University of Chinese Academy of Sciences, Kunming, China
| | - Quanzhen Zheng
- Key Laboratory of Animal Models and Human Disease Mechanisms of the Chinese Academy of Sciences & Yunnan Province and KIZ/CUHK Joint Laboratory of Bioresources and Molecular Research in Common Diseases, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, China; Kunming College of Life Science, University of Chinese Academy of Sciences, Kunming, China
| | - Chunli Sun
- Key Laboratory of Animal Models and Human Disease Mechanisms of the Chinese Academy of Sciences & Yunnan Province and KIZ/CUHK Joint Laboratory of Bioresources and Molecular Research in Common Diseases, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, China; Kunming College of Life Science, University of Chinese Academy of Sciences, Kunming, China
| | - Maosen Ye
- Key Laboratory of Animal Models and Human Disease Mechanisms of the Chinese Academy of Sciences & Yunnan Province and KIZ/CUHK Joint Laboratory of Bioresources and Molecular Research in Common Diseases, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, China; Kunming College of Life Science, University of Chinese Academy of Sciences, Kunming, China
| | - Bo-Lin Xiang
- Key Laboratory of Animal Models and Human Disease Mechanisms of the Chinese Academy of Sciences & Yunnan Province and KIZ/CUHK Joint Laboratory of Bioresources and Molecular Research in Common Diseases, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, China; Kunming College of Life Science, University of Chinese Academy of Sciences, Kunming, China
| | - Xiong-Jian Luo
- Key Laboratory of Animal Models and Human Disease Mechanisms of the Chinese Academy of Sciences & Yunnan Province and KIZ/CUHK Joint Laboratory of Bioresources and Molecular Research in Common Diseases, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, China; Center for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences, Kunming, China
| | - Ming Li
- Key Laboratory of Animal Models and Human Disease Mechanisms of the Chinese Academy of Sciences & Yunnan Province and KIZ/CUHK Joint Laboratory of Bioresources and Molecular Research in Common Diseases, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, China; Kunming College of Life Science, University of Chinese Academy of Sciences, Kunming, China
| | - Deng-Feng Zhang
- Key Laboratory of Animal Models and Human Disease Mechanisms of the Chinese Academy of Sciences & Yunnan Province and KIZ/CUHK Joint Laboratory of Bioresources and Molecular Research in Common Diseases, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, China; Kunming College of Life Science, University of Chinese Academy of Sciences, Kunming, China; National Resource Center for Non-Human Primates, National Research Facility for Phenotypic & Genetic Analysis of Model Animals (Primate Facility), Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, China; Center for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences, Kunming, China.
| | - Yong-Gang Yao
- Key Laboratory of Animal Models and Human Disease Mechanisms of the Chinese Academy of Sciences & Yunnan Province and KIZ/CUHK Joint Laboratory of Bioresources and Molecular Research in Common Diseases, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, China; Kunming College of Life Science, University of Chinese Academy of Sciences, Kunming, China; National Resource Center for Non-Human Primates, National Research Facility for Phenotypic & Genetic Analysis of Model Animals (Primate Facility), Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, China; Center for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences, Kunming, China.
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27
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Mostafavi H, Spence JP, Naqvi S, Pritchard JK. Systematic differences in discovery of genetic effects on gene expression and complex traits. Nat Genet 2023; 55:1866-1875. [PMID: 37857933 DOI: 10.1038/s41588-023-01529-1] [Citation(s) in RCA: 58] [Impact Index Per Article: 58.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2022] [Accepted: 09/14/2023] [Indexed: 10/21/2023]
Abstract
Most signals in genome-wide association studies (GWAS) of complex traits implicate noncoding genetic variants with putative gene regulatory effects. However, currently identified regulatory variants, notably expression quantitative trait loci (eQTLs), explain only a small fraction of GWAS signals. Here, we show that GWAS and cis-eQTL hits are systematically different: eQTLs cluster strongly near transcription start sites, whereas GWAS hits do not. Genes near GWAS hits are enriched in key functional annotations, are under strong selective constraint and have complex regulatory landscapes across different tissue/cell types, whereas genes near eQTLs are depleted of most functional annotations, show relaxed constraint, and have simpler regulatory landscapes. We describe a model to understand these observations, including how natural selection on complex traits hinders discovery of functionally relevant eQTLs. Our results imply that GWAS and eQTL studies are systematically biased toward different types of variant, and support the use of complementary functional approaches alongside the next generation of eQTL studies.
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Affiliation(s)
| | | | - Sahin Naqvi
- Department of Genetics, Stanford University, Stanford, CA, USA
- Department of Chemical and Systems Biology, Stanford University, Stanford, CA, USA
| | - Jonathan K Pritchard
- Department of Genetics, Stanford University, Stanford, CA, USA.
- Department of Biology, Stanford University, Stanford, CA, USA.
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28
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Zhang X, Gomez L, Below J, Naj A, Martin E, Kunkle B, Bush WS. An X Chromosome Transcriptome Wide Association Study Implicates ARMCX6 in Alzheimer's Disease. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.06.06.543877. [PMID: 37333116 PMCID: PMC10274627 DOI: 10.1101/2023.06.06.543877] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/20/2023]
Abstract
Background The X chromosome is often omitted in disease association studies despite containing thousands of genes which may provide insight into well-known sex differences in the risk of Alzheimer's Disease. Objective To model the expression of X chromosome genes and evaluate their impact on Alzheimer's Disease risk in a sex-stratified manner. Methods Using elastic net, we evaluated multiple modeling strategies in a set of 175 whole blood samples and 126 brain cortex samples, with whole genome sequencing and RNA-seq data. SNPs (MAF>0.05) within the cis-regulatory window were used to train tissue-specific models of each gene. We apply the best models in both tissues to sex-stratified summary statistics from a meta-analysis of Alzheimer's disease Genetics Consortium (ADGC) studies to identify AD-related genes on the X chromosome. Results Across different model parameters, sample sex, and tissue types, we modeled the expression of 217 genes (95 genes in blood and 135 genes in brain cortex). The average model R2 was 0.12 (range from 0.03 to 0.34). We also compared sex-stratified and sex-combined models on the X chromosome. We further investigated genes that escaped X chromosome inactivation (XCI) to determine if their genetic regulation patterns were distinct. We found ten genes associated with AD at p 0.05, with only ARMCX6 in female brain cortex (p = 0.008) nearing the significance threshold after adjusting for multiple testing (α = 0.002). Conclusions We optimized the expression prediction of X chromosome genes, applied these models to sex-stratified AD GWAS summary statistics, and identified one putative AD risk gene, ARMCX6.
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Affiliation(s)
- Xueyi Zhang
- Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, 44106, USA
| | - Lissette Gomez
- John P. Hussman Institute for Human Genomics, Miller School of Medicine, University of Miami, Miami, FL, 33136, USA
| | - Jennifer Below
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, 37235, USA
| | - Adam Naj
- Department of Biostatistics, Epidemiology, and Informatics, Penn Neurodegeneration Genomics Center, University of Pennsylvania Perelman School of Medicine, Philadelphia, 19104, USA; Department of Pathology and Laboratory Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, 19104, USA
| | - Eden Martin
- John P. Hussman Institute for Human Genomics, University of Miami Miller School of Medicine, Miami, 33176, USA
| | - Brian Kunkle
- John P. Hussman Institute for Human Genomics, University of Miami Miller School of Medicine, Miami, 33176, USA
| | - William S Bush
- Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, 44106, USA
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29
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Tsouris A, Brach G, Friedrich A, Hou J, Schacherer J. Diallel panel reveals a significant impact of low-frequency genetic variants on gene expression variation in yeast. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.07.21.550015. [PMID: 37503053 PMCID: PMC10370210 DOI: 10.1101/2023.07.21.550015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/29/2023]
Abstract
Unraveling the genetic sources of gene expression variation is essential to better understand the origins of phenotypic diversity in natural populations. Genome-wide association studies identified thousands of variants involved in gene expression variation, however, variants detected only explain part of the heritability. In fact, variants such as low-frequency and structural variants (SVs) are poorly captured in association studies. To assess the impact of these variants on gene expression variation, we explored a half-diallel panel composed of 323 hybrids originated from pairwise crosses of 26 natural Saccharomyces cerevisiae isolates. Using short- and long-read sequencing strategies, we established an exhaustive catalog of single nucleotide polymorphisms (SNPs) and SVs for this panel. Combining this dataset with the transcriptomes of all hybrids, we comprehensively mapped SNPs and SVs associated with gene expression variation. While SVs impact gene expression variation, SNPs exhibit a higher effect size with an overrepresentation of low-frequency variants compared to common ones. These results reinforce the importance of dissecting the heritability of complex traits with a comprehensive catalog of genetic variants at the population level.
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Affiliation(s)
- Andreas Tsouris
- Université de Strasbourg, CNRS, GMGM UMR 7156, Strasbourg, France
| | - Gauthier Brach
- Université de Strasbourg, CNRS, GMGM UMR 7156, Strasbourg, France
| | - Anne Friedrich
- Université de Strasbourg, CNRS, GMGM UMR 7156, Strasbourg, France
| | - Jing Hou
- Université de Strasbourg, CNRS, GMGM UMR 7156, Strasbourg, France
| | - Joseph Schacherer
- Université de Strasbourg, CNRS, GMGM UMR 7156, Strasbourg, France
- Institut Universitaire de France (IUF), Paris, France
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30
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Tsouris A, Brach G, Schacherer J, Hou J. Non-additive genetic components contribute significantly to population-wide gene expression variation. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.07.21.550013. [PMID: 37546809 PMCID: PMC10401925 DOI: 10.1101/2023.07.21.550013] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/08/2023]
Abstract
Gene expression variation, an essential step between genomic variation and phenotypic landscape, is collectively controlled by local (cis) and distant (trans) regulatory changes. Nevertheless, how these regulatory elements differentially influence the heritability of expression traits remains unclear. Here, we bridge this gap by analyzing the transcriptomes of a large diallel panel consisting of 323 unique hybrids originated from genetically divergent yeast isolates. We estimated the broad- and narrow-sense heritability across 5,087 transcript abundance traits and showed that non-additive components account for 36% of the phenotypic variance on average. By comparing allelic expression ratios in the hybrid and the corresponding parental pair, we identified regulatory changes in 25% of all cases, with a majority acting in trans. We further showed that trans-regulation could underlie coordinated expression variation across highly connected genes, resulting in significantly higher non-additive variance and most likely in some of the missing heritability of gene expression traits.
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Affiliation(s)
- Andreas Tsouris
- Université de Strasbourg, CNRS, GMGM UMR 7156, Strasbourg, France
| | - Gauthier Brach
- Université de Strasbourg, CNRS, GMGM UMR 7156, Strasbourg, France
| | - Joseph Schacherer
- Université de Strasbourg, CNRS, GMGM UMR 7156, Strasbourg, France
- Institut Universitaire de France (IUF), Paris, France
| | - Jing Hou
- Université de Strasbourg, CNRS, GMGM UMR 7156, Strasbourg, France
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Pan L, Zheng C, Yang Z, Pawitan Y, Vu TN, Shen X. Hidden Genetic Regulation of Human Complex Traits via Brain Isoforms. PHENOMICS (CHAM, SWITZERLAND) 2023; 3:217-227. [PMID: 37325708 PMCID: PMC10260721 DOI: 10.1007/s43657-023-00100-6] [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: 08/17/2022] [Revised: 02/15/2023] [Accepted: 02/17/2023] [Indexed: 06/17/2023]
Abstract
Alternative splicing exists in most multi-exonic genes, and exploring these complex alternative splicing events and their resultant isoform expressions is essential. However, it has become conventional that RNA sequencing results have often been summarized into gene-level expression counts mainly due to the multiple ambiguous mapping of reads at highly similar regions. Transcript-level quantification and interpretation are often overlooked, and biological interpretations are often deduced based on combined transcript information at the gene level. Here, for the most variable tissue of alternative splicing, the brain, we estimate isoform expressions in 1,191 samples collected by the Genotype-Tissue Expression (GTEx) Consortium using a powerful method that we previously developed. We perform genome-wide association scans on the isoform ratios per gene and identify isoform-ratio quantitative trait loci (irQTL), which could not be detected by studying gene-level expressions alone. By analyzing the genetic architecture of the irQTL, we show that isoform ratios regulate educational attainment via multiple tissues including the frontal cortex (BA9), cortex, cervical spinal cord, and hippocampus. These tissues are also associated with different neuro-related traits, including Alzheimer's or dementia, mood swings, sleep duration, alcohol intake, intelligence, anxiety or depression, etc. Mendelian randomization (MR) analysis revealed 1,139 pairs of isoforms and neuro-related traits with plausible causal relationships, showing much stronger causal effects than on general diseases measured in the UK Biobank (UKB). Our results highlight essential transcript-level biomarkers in the human brain for neuro-related complex traits and diseases, which could be missed by merely investigating overall gene expressions. Supplementary Information The online version contains supplementary material available at 10.1007/s43657-023-00100-6.
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Affiliation(s)
- Lu Pan
- Biostatistics Group, School of Life Sciences, Sun Yat-Sen University, Guangzhou, 510006 China
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, 17177 Sweden
| | - Chenqing Zheng
- Biostatistics Group, School of Life Sciences, Sun Yat-Sen University, Guangzhou, 510006 China
| | - Zhijian Yang
- Biostatistics Group, School of Life Sciences, Sun Yat-Sen University, Guangzhou, 510006 China
| | - Yudi Pawitan
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, 17177 Sweden
| | - Trung Nghia Vu
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, 17177 Sweden
| | - Xia Shen
- Biostatistics Group, School of Life Sciences, Sun Yat-Sen University, Guangzhou, 510006 China
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, 17177 Sweden
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Fudan University, Shanghai, 200433 China
- Center for Intelligent Medicine Research, Greater Bay Area Institute of Precision Medicine (Guangzhou), Fudan University, Guangzhou, 511458 China
- Centre for Global Health Research, Usher Institute, University of Edinburgh, Edinburgh, EH8 9AG UK
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32
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Stokes T, Cen HH, Kapranov P, Gallagher IJ, Pitsillides AA, Volmar C, Kraus WE, Johnson JD, Phillips SM, Wahlestedt C, Timmons JA. Transcriptomics for Clinical and Experimental Biology Research: Hang on a Seq. ADVANCED GENETICS (HOBOKEN, N.J.) 2023; 4:2200024. [PMID: 37288167 PMCID: PMC10242409 DOI: 10.1002/ggn2.202200024] [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: 09/02/2022] [Indexed: 06/09/2023]
Abstract
Sequencing the human genome empowers translational medicine, facilitating transcriptome-wide molecular diagnosis, pathway biology, and drug repositioning. Initially, microarrays are used to study the bulk transcriptome; but now short-read RNA sequencing (RNA-seq) predominates. Positioned as a superior technology, that makes the discovery of novel transcripts routine, most RNA-seq analyses are in fact modeled on the known transcriptome. Limitations of the RNA-seq methodology have emerged, while the design of, and the analysis strategies applied to, arrays have matured. An equitable comparison between these technologies is provided, highlighting advantages that modern arrays hold over RNA-seq. Array protocols more accurately quantify constitutively expressed protein coding genes across tissue replicates, and are more reliable for studying lower expressed genes. Arrays reveal long noncoding RNAs (lncRNA) are neither sparsely nor lower expressed than protein coding genes. Heterogeneous coverage of constitutively expressed genes observed with RNA-seq, undermines the validity and reproducibility of pathway analyses. The factors driving these observations, many of which are relevant to long-read or single-cell sequencing are discussed. As proposed herein, a reappreciation of bulk transcriptomic methods is required, including wider use of the modern high-density array data-to urgently revise existing anatomical RNA reference atlases and assist with more accurate study of lncRNAs.
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Affiliation(s)
- Tanner Stokes
- Faculty of ScienceMcMaster UniversityHamiltonL8S 4L8Canada
| | - Haoning Howard Cen
- Life Sciences InstituteUniversity of British ColumbiaVancouverV6T 1Z3Canada
| | | | - Iain J Gallagher
- School of Applied SciencesEdinburgh Napier UniversityEdinburghEH11 4BNUK
| | | | | | | | - James D. Johnson
- Life Sciences InstituteUniversity of British ColumbiaVancouverV6T 1Z3Canada
| | | | | | - James A. Timmons
- Miller School of MedicineUniversity of MiamiMiamiFL33136USA
- William Harvey Research InstituteQueen Mary University LondonLondonEC1M 6BQUK
- Augur Precision Medicine LTDStirlingFK9 5NFUK
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33
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Ollila HM, Sharon E, Lin L, Sinnott-Armstrong N, Ambati A, Yogeshwar SM, Hillary RP, Jolanki O, Faraco J, Einen M, Luo G, Zhang J, Han F, Yan H, Dong XS, Li J, Zhang J, Hong SC, Kim TW, Dauvilliers Y, Barateau L, Lammers GJ, Fronczek R, Mayer G, Santamaria J, Arnulf I, Knudsen-Heier S, Bredahl MKL, Thorsby PM, Plazzi G, Pizza F, Moresco M, Crowe C, Van den Eeden SK, Lecendreux M, Bourgin P, Kanbayashi T, Martínez-Orozco FJ, Peraita-Adrados R, Benetó A, Montplaisir J, Desautels A, Huang YS, Jennum P, Nevsimalova S, Kemlink D, Iranzo A, Overeem S, Wierzbicka A, Geisler P, Sonka K, Honda M, Högl B, Stefani A, Coelho FM, Mantovani V, Feketeova E, Wadelius M, Eriksson N, Smedje H, Hallberg P, Hesla PE, Rye D, Pelin Z, Ferini-Strambi L, Bassetti CL, Mathis J, Khatami R, Aran A, Nampoothiri S, Olsson T, Kockum I, Partinen M, Perola M, Kornum BR, Rueger S, Winkelmann J, Miyagawa T, Toyoda H, Khor SS, Shimada M, Tokunaga K, Rivas M, Pritchard JK, Risch N, Kutalik Z, O'Hara R, Hallmayer J, Ye CJ, Mignot EJ. Narcolepsy risk loci outline role of T cell autoimmunity and infectious triggers in narcolepsy. Nat Commun 2023; 14:2709. [PMID: 37188663 DOI: 10.1038/s41467-023-36120-z] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2021] [Accepted: 01/17/2023] [Indexed: 05/17/2023] Open
Abstract
Narcolepsy type 1 (NT1) is caused by a loss of hypocretin/orexin transmission. Risk factors include pandemic 2009 H1N1 influenza A infection and immunization with Pandemrix®. Here, we dissect disease mechanisms and interactions with environmental triggers in a multi-ethnic sample of 6,073 cases and 84,856 controls. We fine-mapped GWAS signals within HLA (DQ0602, DQB1*03:01 and DPB1*04:02) and discovered seven novel associations (CD207, NAB1, IKZF4-ERBB3, CTSC, DENND1B, SIRPG, PRF1). Significant signals at TRA and DQB1*06:02 loci were found in 245 vaccination-related cases, who also shared polygenic risk. T cell receptor associations in NT1 modulated TRAJ*24, TRAJ*28 and TRBV*4-2 chain-usage. Partitioned heritability and immune cell enrichment analyses found genetic signals to be driven by dendritic and helper T cells. Lastly comorbidity analysis using data from FinnGen, suggests shared effects between NT1 and other autoimmune diseases. NT1 genetic variants shape autoimmunity and response to environmental triggers, including influenza A infection and immunization with Pandemrix®.
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Affiliation(s)
- Hanna M Ollila
- Stanford University, Center for Sleep Sciences and Medicine, Department of Psychiatry and Behavioral Sciences, Palo Alto, CA, 94304, USA
- Institute for Molecular Medicine Finland, HiLIFE, University of Helsinki, Helsinki, Finland
- Anesthesia, Critical Care, and Pain Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Eilon Sharon
- Department of Genetics, Stanford University, Stanford, CA, 94305, USA
- Department of Biology, Stanford University, Stanford, CA, 94305, USA
| | - Ling Lin
- Stanford University, Center for Sleep Sciences and Medicine, Department of Psychiatry and Behavioral Sciences, Palo Alto, CA, 94304, USA
| | - Nasa Sinnott-Armstrong
- Department of Genetics, Stanford University, Stanford, CA, 94305, USA
- Department of Biology, Stanford University, Stanford, CA, 94305, USA
| | - Aditya Ambati
- Stanford University, Center for Sleep Sciences and Medicine, Department of Psychiatry and Behavioral Sciences, Palo Alto, CA, 94304, USA
| | - Selina M Yogeshwar
- Stanford University, Center for Sleep Sciences and Medicine, Department of Psychiatry and Behavioral Sciences, Palo Alto, CA, 94304, USA
- Department of Neurology, Charité-Universitätsmedizin, 10117, Berlin, Germany
- Charité-Universitätsmedizin Berlin, Einstein Center for Neurosciences Berlin, 10117, Berlin, Germany
| | - Ryan P Hillary
- Stanford University, Center for Sleep Sciences and Medicine, Department of Psychiatry and Behavioral Sciences, Palo Alto, CA, 94304, USA
| | - Otto Jolanki
- Department of Genetics, Stanford University, Stanford, CA, 94305, USA
| | - Juliette Faraco
- Stanford University, Center for Sleep Sciences and Medicine, Department of Psychiatry and Behavioral Sciences, Palo Alto, CA, 94304, USA
| | - Mali Einen
- Stanford University, Center for Sleep Sciences and Medicine, Department of Psychiatry and Behavioral Sciences, Palo Alto, CA, 94304, USA
| | - Guo Luo
- Stanford University, Center for Sleep Sciences and Medicine, Department of Psychiatry and Behavioral Sciences, Palo Alto, CA, 94304, USA
| | - Jing Zhang
- Stanford University, Center for Sleep Sciences and Medicine, Department of Psychiatry and Behavioral Sciences, Palo Alto, CA, 94304, USA
| | - Fang Han
- Division of Sleep Medicine, The Peking University People's Hospital, Beijing, China
| | - Han Yan
- Division of Sleep Medicine, The Peking University People's Hospital, Beijing, China
| | - Xiao Song Dong
- Division of Sleep Medicine, The Peking University People's Hospital, Beijing, China
| | - Jing Li
- Division of Sleep Medicine, The Peking University People's Hospital, Beijing, China
| | - Jun Zhang
- Department of Neurology, The Peking University People's Hospital, Beijing, China
| | - Seung-Chul Hong
- Department of Psychiatry, St. Vincent's Hospital, The Catholic University of Korea, Suwon, Korea
| | - Tae Won Kim
- Department of Psychiatry, St. Vincent's Hospital, The Catholic University of Korea, Suwon, Korea
| | - Yves Dauvilliers
- Sleep-Wake Disorders Center, National Reference Network for Narcolepsy, Department of Neurology, Gui-de-Chauliac Hospital, CHU Montpellier; Institute for Neurosciences of Montpellier (INM), INSERM, Université Montpellier 1, Montpellier, France
| | - Lucie Barateau
- Sleep-Wake Disorders Center, National Reference Network for Narcolepsy, Department of Neurology, Gui-de-Chauliac Hospital, CHU Montpellier; Institute for Neurosciences of Montpellier (INM), INSERM, Université Montpellier 1, Montpellier, France
| | - Gert Jan Lammers
- Department of Neurology, Leiden University Medical Center, Leiden, The Netherlands
- Stichting Epilepsie Instellingen Nederland (SEIN), Sleep-Wake Centre, Heemstede, The Netherlands
| | - Rolf Fronczek
- Department of Neurology, Leiden University Medical Center, Leiden, The Netherlands
- Stichting Epilepsie Instellingen Nederland (SEIN), Sleep-Wake Centre, Heemstede, The Netherlands
| | - Geert Mayer
- Hephata Klinik, Schimmelpfengstr. 6, 34613, Schwalmstadt, Germany
- Philipps Universität Marburg, Baldinger Str., 35043, Marburg, Germany
| | - Joan Santamaria
- Neurology Service, Institut de Neurociències Hospital Clínic, University of Barcelona, Barcelona, Spain
| | - Isabelle Arnulf
- Sleep Disorder Unit, Pitié-Salpêtrière Hospital, Assistance Publique-Hopitaux de Paris, 75013, Paris, France
| | - Stine Knudsen-Heier
- Norwegian Centre of Expertise for Neurodevelopment Disorders and Hypersomnias (NevSom), Department of Rare Disorders, Oslo University Hospital and University of Oslo, Oslo, Norway
| | - May Kristin Lyamouri Bredahl
- Norwegian Centre of Expertise for Neurodevelopment Disorders and Hypersomnias (NevSom), Department of Rare Disorders, Oslo University Hospital and University of Oslo, Oslo, Norway
- Hormone Laboratory, Department of Medical Biochemistry, Oslo University Hospital, Oslo, Norway
| | - Per Medbøe Thorsby
- Hormone Laboratory, Department of Medical Biochemistry, Oslo University Hospital, Oslo, Norway
| | - Giuseppe Plazzi
- Department of Biomedical and Neuromotor Sciences (DIBINEM), University of Bologna, Via Ugo Foscolo 7, 40123, Bologna, Italy
- IRCCS Institute of Neurological Sciences, Bologna, Italy
| | - Fabio Pizza
- Department of Biomedical and Neuromotor Sciences (DIBINEM), University of Bologna, Via Ugo Foscolo 7, 40123, Bologna, Italy
- IRCCS Institute of Neurological Sciences, Bologna, Italy
| | - Monica Moresco
- Department of Biomedical and Neuromotor Sciences (DIBINEM), University of Bologna, Via Ugo Foscolo 7, 40123, Bologna, Italy
- IRCCS Institute of Neurological Sciences, Bologna, Italy
| | | | | | - Michel Lecendreux
- Pediatric Sleep Center and National Reference Center for Narcolepsy and Idiopathic Hypersomnia Hospital Robert Debre, Paris, France
| | - Patrice Bourgin
- Department of Sleep Medicine, Strasbourg University Hospital, Strasbourg University, Strasbourg, France
| | - Takashi Kanbayashi
- Department of Neuropsychiatry, Akita University Graduate School of Medicine, Akita, Japan
- International Institute for Integrative Sleep Medicine (WPI-IIIS), University of Tsukuba, Tsukuba, Japan
| | - Francisco J Martínez-Orozco
- Sleep Unit. Clinical Neurophysiology Service. San Carlos University Hospital. University Complutense of Madrid, Madrid, Spain
| | - Rosa Peraita-Adrados
- Sleep and Epilepsy Unit, Clinical Neurophysiology Service, Gregorio Marañón University General Hospital and Research Institute, University Complutense of Madrid (UCM), Madrid, Spain
| | | | - Jacques Montplaisir
- Center for Advanced Research in Sleep Medicine, Hôpital du Sacré-Coeur and Department of Neurosciences, University of Montréal, Montréal, QC, Canada
| | - Alex Desautels
- Center for Advanced Research in Sleep Medicine, Hôpital du Sacré-Coeur and Department of Neurosciences, University of Montréal, Montréal, QC, Canada
| | - Yu-Shu Huang
- Department of Child Psychiatry and Sleep Center, Chang Gung Memorial Hospital and University, Taoyuan, Taiwan
| | - Poul Jennum
- Danish Center for Sleep Medicine, Department of Clinical Neurophysiology, University of Copenhagen, Glostrup Hospital, Glostrup, Denmark
| | - Sona Nevsimalova
- Department of Neurology and Centre of Clinical Neurosciences, First Faculty of Medicine, Charles University and General University Hosptal, Prague, Czech Republic
| | - David Kemlink
- Department of Neurology and Centre of Clinical Neurosciences, First Faculty of Medicine, Charles University and General University Hosptal, Prague, Czech Republic
| | - Alex Iranzo
- Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain
- Department of Neurology, Barcelona, Spain
- Multidisciplinary Sleep Disorders Unit, Barcelona, Spain
| | - Sebastiaan Overeem
- Sleep Medicine Center Kempenhaeghe, P.O. Box 61, 5590 AB, Heeze, The Netherlands
- Eindhoven University of Technology, Eindhoven, The Netherlands
| | - Aleksandra Wierzbicka
- Department of Clinical Neurophysiology, Institute of Psychiatry and Neurology, Warsaw, Poland
| | - Peter Geisler
- Department of Psychiatry and Psychotherapy, University of Regensburg, Regensburg, Germany
| | - Karel Sonka
- Department of Neurology and Centre of Clinical Neurosciences, First Faculty of Medicine, Charles University and General University Hosptal, Prague, Czech Republic
| | - Makoto Honda
- Department of Psychiatry and Behavioral Sciences, Tokyo Metropolitan Institute of Medical Science, Tokyo, Japan
- Seiwa Hospital, Neuropsychiatric Research Institute, Tokyo, Japan
| | - Birgit Högl
- Department of Neurology, Medical University Innsbruck (MUI), Innsbruck, Austria
| | - Ambra Stefani
- Department of Neurology, Medical University Innsbruck (MUI), Innsbruck, Austria
| | | | - Vilma Mantovani
- Center for Applied Biomedical Research (CRBA), St. Orsola-Malpighi University Hospital, Bologna, Italy
| | - Eva Feketeova
- Neurology Department, Medical Faculty of P. J. Safarik University, University Hospital of L. Pasteur Kosice, Kosice, Slovak Republic
| | - Mia Wadelius
- Department of Medical Sciences and Science for Life Laboratory, Uppsala University, Uppsala, Sweden
| | - Niclas Eriksson
- Department of Medical Sciences and Science for Life Laboratory, Uppsala University, Uppsala, Sweden
- Uppsala Clinical Research Center, Uppsala, Sweden
| | - Hans Smedje
- Division of Child and Adolescent Psychiatry, Karolinska Institutet, Stockholm, Sweden
| | - Pär Hallberg
- Department of Medical Sciences and Science for Life Laboratory, Uppsala University, Uppsala, Sweden
| | | | - David Rye
- Department of Neurology, Emory University School of Medicine, Atlanta, GA, USA
| | - Zerrin Pelin
- Faculty of Health Sciences, Hasan Kalyoncu University, Gaziantep, Turkey
| | - Luigi Ferini-Strambi
- Sleep Disorders Center, Division of Neuroscience, Ospedale San Raffaele, Università Vita-Salute, Milan, Italy
| | - Claudio L Bassetti
- Neurology Department, EOC, Ospedale Regionale di Lugano, Lugano, Ticino, Switzerland
- Department of Neurology, Inselspital, Bern University Hospital, and University of Bern, Bern, Switzerland
| | - Johannes Mathis
- Department of Neurology, Inselspital, Bern University Hospital, and University of Bern, Bern, Switzerland
| | - Ramin Khatami
- Department of Neurology, Inselspital, Bern University Hospital, and University of Bern, Bern, Switzerland
- Center for Sleep Medicine and Sleep Research, Clinic Barmelweid AG, Barmelweid, Switzerland
| | - Adi Aran
- Shaare Zedek Medical Center, Jerusalem, Israel
| | - Sheela Nampoothiri
- Department of Pediatric Genetics, Amrita Institute of Medical Sciences & Research Centre, Kerala, India
| | - Tomas Olsson
- Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
| | - Ingrid Kockum
- Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
| | - Markku Partinen
- Helsinki Sleep Clinic, Vitalmed Research Centre, Helsinki, Finland
- Department of Clinical Neurosciences, University of Helsinki, Helsinki, Finland
| | - Markus Perola
- University of Helsinki, Institute for Molecular Medicine, Finland (FIMM) and Diabetes and Obesity Research Program. University of Tartu, Estonian Genome Center, Tartu, Estonia
| | - Birgitte R Kornum
- Department of Neuroscience, University of Copenhagen, Copenhagen, Denmark
| | - Sina Rueger
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Juliane Winkelmann
- Institute of Neurogenomics, Helmholtz Zentrum München, German Research Centre for Environmental Health, Neuherberg, Germany
- Munich Cluster for Systems Neurology (SyNergy), Munich, Germany
- Neurologische Klinik und Poliklinik, Klinikum rechts der Isar der Technischen Universität München, Munich, Germany
| | - Taku Miyagawa
- Department of Psychiatry and Behavioral Sciences, Tokyo Metropolitan Institute of Medical Science, Tokyo, Japan
- Department of Human Genetics, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Hiromi Toyoda
- Department of Psychiatry and Behavioral Sciences, Tokyo Metropolitan Institute of Medical Science, Tokyo, Japan
| | - Seik-Soon Khor
- Department of Human Genetics, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Mihoko Shimada
- Department of Human Genetics, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Katsushi Tokunaga
- Department of Human Genetics, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Manuel Rivas
- Department of Biomedical Data Science-Administration, Stanford University, Palo Alto, CA, USA
| | | | - Neil Risch
- Dept. Epidemiology and Biostatistics, UCSF, 513 Parnassus Avenue, San Francisco, CA, 94117, USA
| | - Zoltan Kutalik
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
- University Center for Primary Care and Public Health, University of Lausanne, Lausanne, Switzerland, Lausanne, 1010, Switzerland
| | - Ruth O'Hara
- Department of Psychiatry and Behavioral Sciences, Stanford University, Palo Alto, CA, USA
- Mental Illness Research Education Clinical Centers (MIRECC), VA Palo Alto, Palo Alto, CA, USA
| | - Joachim Hallmayer
- Department of Psychiatry and Behavioral Sciences, Stanford University, Palo Alto, CA, USA
- Mental Illness Research Education Clinical Centers (MIRECC), VA Palo Alto, Palo Alto, CA, USA
| | - Chun Jimmie Ye
- Department of Epidemiology & Biostatistics, Institute for Human Genetics, University of California San Francisco, San Francisco, CA, USA
| | - Emmanuel J Mignot
- Stanford University, Center for Sleep Sciences and Medicine, Department of Psychiatry and Behavioral Sciences, Palo Alto, CA, 94304, USA.
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34
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Pandey D, Onkara Perumal P. A scoping review on deep learning for next-generation RNA-Seq. data analysis. Funct Integr Genomics 2023; 23:134. [PMID: 37084004 DOI: 10.1007/s10142-023-01064-6] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2022] [Revised: 03/24/2023] [Accepted: 04/17/2023] [Indexed: 04/22/2023]
Abstract
In the last decade, transcriptome research adopting next-generation sequencing (NGS) technologies has gathered incredible momentum amongst functional genomics scientists, particularly amongst clinical/biomedical research groups. The progressive enfoldment/adoption of NGS technologies has incited an abundance of next-generation transcriptomic data harbouring an opulence of new knowledge in public databases. Nevertheless, knowledge discovery from these next-generation RNA-Seq. data analysis necessitates extensive bioinformatics know-how besides elaborate data analysis software packages consistent with the type and context of data analysis. Several reliability and reproducibility concerns continue to impede RNA-Seq. data analysis. Characteristic challenges comprise of data quality, hardware and networking provisions, selection and prioritisation of data analysis tools, and yet significantly implementing of robust machine learning algorithms for maximised exploitation of these experimental transcriptomic data. Over the years, numerous machine learning algorithms have been implemented for improved transcriptomic data analysis executing predominantly shallow learning approaches. More recently, deep learning algorithms are becoming more mainstream, and enactment for next-generation RNA-Seq. data analysis could be revolutionary in the coming years in the biomedical domain. In this scoping review, we attempt to determine the existing literature's size and potential nature in deep learning and NGS RNA-Seq. data analysis. An analysis of the contemporary topics of next-generation RNA-Seq. data analysis based on deep learning algorithms is critically reviewed, emphasising open-source resources.
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Affiliation(s)
- Diksha Pandey
- Department of Biotechnology, National Institute of Technology, Warangal, Telanga na, 506004, India
| | - P Onkara Perumal
- Department of Biotechnology, National Institute of Technology, Warangal, Telanga na, 506004, India.
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35
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Johnson JS, Cote AC, Dobbyn A, Sloofman LG, Xu J, Cotter L, Charney AW, Birgegård A, Jordan J, Kennedy M, Landén M, Maguire SL, Martin NG, Mortensen PB, Thornton LM, Bulik CM, Huckins LM. Mapping anorexia nervosa genes to clinical phenotypes. Psychol Med 2023; 53:2619-2633. [PMID: 35379376 PMCID: PMC10123844 DOI: 10.1017/s0033291721004554] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/21/2021] [Revised: 09/23/2021] [Accepted: 10/20/2021] [Indexed: 12/21/2022]
Abstract
BACKGROUND Anorexia nervosa (AN) is a psychiatric disorder with complex etiology, with a significant portion of disease risk imparted by genetics. Traditional genome-wide association studies (GWAS) produce principal evidence for the association of genetic variants with disease. Transcriptomic imputation (TI) allows for the translation of those variants into regulatory mechanisms, which can then be used to assess the functional outcome of genetically regulated gene expression (GReX) in a broader setting through the use of phenome-wide association studies (pheWASs) in large and diverse clinical biobank populations with electronic health record phenotypes. METHODS Here, we applied TI using S-PrediXcan to translate the most recent PGC-ED AN GWAS findings into AN-GReX. For significant genes, we imputed AN-GReX in the Mount Sinai BioMe™ Biobank and performed pheWASs on over 2000 outcomes to test the clinical consequences of aberrant expression of these genes. We performed a secondary analysis to assess the impact of body mass index (BMI) and sex on AN-GReX clinical associations. RESULTS Our S-PrediXcan analysis identified 53 genes associated with AN, including what is, to our knowledge, the first-genetic association of AN with the major histocompatibility complex. AN-GReX was associated with autoimmune, metabolic, and gastrointestinal diagnoses in our biobank cohort, as well as measures of cholesterol, medications, substance use, and pain. Additionally, our analyses showed moderation of AN-GReX associations with measures of cholesterol and substance use by BMI, and moderation of AN-GReX associations with celiac disease by sex. CONCLUSIONS Our BMI-stratified results provide potential avenues of functional mechanism for AN-genes to investigate further.
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Affiliation(s)
- Jessica S. Johnson
- Pamela Sklar Division of Psychiatric Genomics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Genetics and Genomics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Alanna C. Cote
- Pamela Sklar Division of Psychiatric Genomics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Genetics and Genomics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Amanda Dobbyn
- Pamela Sklar Division of Psychiatric Genomics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Genetics and Genomics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Laura G. Sloofman
- Seaver Autism Center for Research and Treatment, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Jiayi Xu
- Pamela Sklar Division of Psychiatric Genomics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Genetics and Genomics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Liam Cotter
- Pamela Sklar Division of Psychiatric Genomics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Genetics and Genomics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Alexander W. Charney
- Pamela Sklar Division of Psychiatric Genomics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Genetics and Genomics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- James J. Peters Department of Veterans Affairs Medical Center, Mental Illness Research, Education and Clinical Centers, Bronx, NY 14068, USA
| | | | - Andreas Birgegård
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Jennifer Jordan
- Department of Psychological Medicine, Christchurch School of Medicine & Health Sciences, University of Otago, 2 Riccarton Avenue, PO Box 4345, 8140 Christchurch, New Zealand
| | - Martin Kennedy
- Department of Psychological Medicine, Christchurch School of Medicine & Health Sciences, University of Otago, 2 Riccarton Avenue, PO Box 4345, 8140 Christchurch, New Zealand
| | - Mikaél Landén
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
- Institute of Neuroscience and Physiology, Sahlgrenska Academy at Gothenburg University, SE-413 45 Gothenburg, Sweden
| | - Sarah L. Maguire
- InsideOut Institute, University of Sydney, New South Wales 2006, Australia
| | - Nicholas G. Martin
- QIMR Berghofer Medical Research Institute, Locked Bag 2000, Royal Brisbane Hospital, Herston, QLD 4029, Australia
| | - Preben Bo Mortensen
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, Aarhus, Denmark
- National Centre for Register-Based Research, Aarhus University, Aarhus, Denmark
| | - Laura M. Thornton
- Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC 27517, USA
| | - Cynthia M. Bulik
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
- Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC 27517, USA
- Department of Nutrition, University of North Carolina at Chapel Hill, Chapel Hill, NC 27517, USA
| | - Laura M. Huckins
- Pamela Sklar Division of Psychiatric Genomics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Genetics and Genomics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Seaver Autism Center for Research and Treatment, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- James J. Peters Department of Veterans Affairs Medical Center, Mental Illness Research, Education and Clinical Centers, Bronx, NY 14068, USA
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Kuo HC, Yao CT, Liao BY, Weng MP, Dong F, Hsu YC, Hung CM. Weak gene-gene interaction facilitates the evolution of gene expression plasticity. BMC Biol 2023; 21:57. [PMID: 36941675 PMCID: PMC10029303 DOI: 10.1186/s12915-023-01558-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2021] [Accepted: 03/10/2023] [Indexed: 03/23/2023] Open
Abstract
BACKGROUND Individual organisms may exhibit phenotypic plasticity when they acclimate to different conditions. Such plastic responses may facilitate or constrain the adaptation of their descendant populations to new environments, complicating their evolutionary trajectories beyond the genetic blueprint. Intriguingly, phenotypic plasticity itself can evolve in terms of its direction and magnitude during adaptation. However, we know little about what determines the evolution of phenotypic plasticity, including gene expression plasticity. Recent laboratory-based studies suggest dominance of reversing gene expression plasticity-plastic responses that move the levels of gene expression away from the new optima. Nevertheless, evidence from natural populations is still limited. RESULTS Here, we studied gene expression plasticity and its evolution in the montane and lowland populations of an elevationally widespread songbird-the Rufous-capped Babbler (Cyanoderma ruficeps)-with reciprocal transplant experiments and transcriptomic analyses; we set common gardens at altitudes close to these populations' native ranges. We confirmed the prevalence of reversing plasticity in genes associated with altitudinal adaptation. Interestingly, we found a positive relationship between magnitude and degree of evolution in gene expression plasticity, which was pertinent to not only adaptation-associated genes but also the whole transcriptomes from multiple tissues. Furthermore, we revealed that genes with weaker expressional interactions with other genes tended to exhibit stronger plasticity and higher degree of plasticity evolution, which explains the positive magnitude-evolution relationship. CONCLUSIONS Our experimental evidence demonstrates that species may initiate their adaptation to new habitats with genes exhibiting strong expression plasticity. We also highlight the role of expression interdependence among genes in regulating the magnitude and evolution of expression plasticity. This study illuminates how the evolution of phenotypic plasticity in gene expression facilitates the adaptation of species to challenging environments in nature.
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Affiliation(s)
- Hao-Chih Kuo
- Biodiversity Research Center, Academia Sinica, Taipei, 11529, Taiwan
| | - Cheng-Te Yao
- Division of Zoology, Endemic Species Research Institute, Nantou, 55244, Taiwan
| | - Ben-Yang Liao
- Institute of Population Health Sciences, National Health Research Institutes, Miaoli, 35053, Taiwan
| | - Meng-Pin Weng
- Institute of Population Health Sciences, National Health Research Institutes, Miaoli, 35053, Taiwan
| | - Feng Dong
- Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, 650223, Yunnan, China
| | - Yu-Cheng Hsu
- Department of Natural Resources and Environmental Studies, National Dong Hwa University, Hualien, 97401, Taiwan
| | - Chih-Ming Hung
- Biodiversity Research Center, Academia Sinica, Taipei, 11529, Taiwan.
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Hamanaka K, Yamauchi D, Koshimizu E, Watase K, Mogushi K, Ishikawa K, Mizusawa H, Tsuchida N, Uchiyama Y, Fujita A, Misawa K, Mizuguchi T, Miyatake S, Matsumoto N. Genome-wide identification of tandem repeats associated with splicing variation across 49 tissues in humans. Genome Res 2023; 33:435-447. [PMID: 37307504 PMCID: PMC10078293 DOI: 10.1101/gr.277335.122] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2022] [Accepted: 02/22/2023] [Indexed: 03/29/2023]
Abstract
Tandem repeats (TRs) are one of the largest sources of polymorphism, and their length is associated with gene regulation. Although previous studies reported several tandem repeats regulating gene splicing in cis (spl-TRs), no large-scale study has been conducted. In this study, we established a genome-wide catalog of 9537 spl-TRs with a total of 58,290 significant TR-splicing associations across 49 tissues (false discovery rate 5%) by using Genotype-Tissue expression (GTex) Project data. Regression models explaining splicing variation by using spl-TRs and other flanking variants suggest that at least some of the spl-TRs directly modulate splicing. In our catalog, two spl-TRs are known loci for repeat expansion diseases, spinocerebellar ataxia 6 (SCA6) and 12 (SCA12). Splicing alterations by these spl-TRs were compatible with those observed in SCA6 and SCA12. Thus, our comprehensive spl-TR catalog may help elucidate the pathomechanism of genetic diseases.
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Affiliation(s)
- Kohei Hamanaka
- Department of Human Genetics, Yokohama City University Graduate School of Medicine, Yokohama, Kanagawa 236-0004, Japan
| | | | - Eriko Koshimizu
- Department of Human Genetics, Yokohama City University Graduate School of Medicine, Yokohama, Kanagawa 236-0004, Japan
| | - Kei Watase
- Center for Brain Integration Research, Tokyo Medical and Dental University, Tokyo 113-8510, Japan
| | - Kaoru Mogushi
- Intractable Disease Research Center, Juntendo University Graduate School of Medicine, Tokyo 113-8421, Japan
| | - Kinya Ishikawa
- The Center for Personalized Medicine for Healthy Aging, Tokyo Medical and Dental University, Bunkyo-ku, Tokyo 113-8510, Japan
| | - Hidehiro Mizusawa
- Department of Neurology, National Center of Neurology and Psychiatry, Kodaira, Tokyo 187-8551, Japan
| | - Naomi Tsuchida
- Department of Human Genetics, Yokohama City University Graduate School of Medicine, Yokohama, Kanagawa 236-0004, Japan
- Department of Rare Disease Genomics, Yokohama City University Hospital, Yokohama, Kanagawa 236-0004, Japan
| | - Yuri Uchiyama
- Department of Human Genetics, Yokohama City University Graduate School of Medicine, Yokohama, Kanagawa 236-0004, Japan
- Department of Rare Disease Genomics, Yokohama City University Hospital, Yokohama, Kanagawa 236-0004, Japan
| | - Atsushi Fujita
- Department of Human Genetics, Yokohama City University Graduate School of Medicine, Yokohama, Kanagawa 236-0004, Japan
| | - Kazuharu Misawa
- Department of Human Genetics, Yokohama City University Graduate School of Medicine, Yokohama, Kanagawa 236-0004, Japan
| | - Takeshi Mizuguchi
- Department of Human Genetics, Yokohama City University Graduate School of Medicine, Yokohama, Kanagawa 236-0004, Japan
| | - Satoko Miyatake
- Department of Human Genetics, Yokohama City University Graduate School of Medicine, Yokohama, Kanagawa 236-0004, Japan
- Clinical Genetics Department, Yokohama City University Hospital, Yokohama, Kanagawa 236-0004, Japan
| | - Naomichi Matsumoto
- Department of Human Genetics, Yokohama City University Graduate School of Medicine, Yokohama, Kanagawa 236-0004, Japan;
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Vásquez V, Orozco J. Detection of COVID-19-related biomarkers by electrochemical biosensors and potential for diagnosis, prognosis, and prediction of the course of the disease in the context of personalized medicine. Anal Bioanal Chem 2023; 415:1003-1031. [PMID: 35970970 PMCID: PMC9378265 DOI: 10.1007/s00216-022-04237-7] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2022] [Revised: 06/30/2022] [Accepted: 07/18/2022] [Indexed: 02/07/2023]
Abstract
As a more efficient and effective way to address disease diagnosis and intervention, cutting-edge technologies, devices, therapeutic approaches, and practices have emerged within the personalized medicine concept depending on the particular patient's biology and the molecular basis of the disease. Personalized medicine is expected to play a pivotal role in assessing disease risk or predicting response to treatment, understanding a person's health status, and, therefore, health care decision-making. This work discusses electrochemical biosensors for monitoring multiparametric biomarkers at different molecular levels and their potential to elucidate the health status of an individual in a personalized manner. In particular, and as an illustration, we discuss several aspects of the infection produced by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) as a current health care concern worldwide. This includes SARS-CoV-2 structure, mechanism of infection, biomarkers, and electrochemical biosensors most commonly explored for diagnostics, prognostics, and potentially assessing the risk of complications in patients in the context of personalized medicine. Finally, some concluding remarks and perspectives hint at the use of electrochemical biosensors in the frame of other cutting-edge converging/emerging technologies toward the inauguration of a new paradigm of personalized medicine.
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Affiliation(s)
- Viviana Vásquez
- Max Planck Tandem Group in Nanobioengineering, Institute of Chemistry, Faculty of Natural and Exact Sciences, University of Antioquia, Complejo Ruta N, Calle 67 N° 52-20, Medellín, 050010, Colombia
| | - Jahir Orozco
- Max Planck Tandem Group in Nanobioengineering, Institute of Chemistry, Faculty of Natural and Exact Sciences, University of Antioquia, Complejo Ruta N, Calle 67 N° 52-20, Medellín, 050010, Colombia.
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Kelly DE, Ramdas S, Ma R, Rawlings-Goss RA, Grant GR, Ranciaro A, Hirbo JB, Beggs W, Yeager M, Chanock S, Nyambo TB, Omar SA, Woldemeskel D, Belay G, Li H, Brown CD, Tishkoff SA. The genetic and evolutionary basis of gene expression variation in East Africans. Genome Biol 2023; 24:35. [PMID: 36829244 PMCID: PMC9951478 DOI: 10.1186/s13059-023-02874-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2022] [Accepted: 02/13/2023] [Indexed: 02/26/2023] Open
Abstract
BACKGROUND Mapping of quantitative trait loci (QTL) associated with molecular phenotypes is a powerful approach for identifying the genes and molecular mechanisms underlying human traits and diseases, though most studies have focused on individuals of European descent. While important progress has been made to study a greater diversity of human populations, many groups remain unstudied, particularly among indigenous populations within Africa. To better understand the genetics of gene regulation in East Africans, we perform expression and splicing QTL mapping in whole blood from a cohort of 162 diverse Africans from Ethiopia and Tanzania. We assess replication of these QTLs in cohorts of predominantly European ancestry and identify candidate genes under selection in human populations. RESULTS We find the gene regulatory architecture of African and non-African populations is broadly shared, though there is a considerable amount of variation at individual loci across populations. Comparing our analyses to an equivalently sized cohort of European Americans, we find that QTL mapping in Africans improves the detection of expression QTLs and fine-mapping of causal variation. Integrating our QTL scans with signatures of natural selection, we find several genes related to immunity and metabolism that are highly differentiated between Africans and non-Africans, as well as a gene associated with pigmentation. CONCLUSION Extending QTL mapping studies beyond European ancestry, particularly to diverse indigenous populations, is vital for a complete understanding of the genetic architecture of human traits and can reveal novel functional variation underlying human traits and disease.
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Affiliation(s)
- Derek E Kelly
- Genomics and Computational Biology, University of Pennsylvania, Philadelphia, PA, USA
- Genetics, University of Pennsylvania, Philadelphia, PA, USA
| | - Shweta Ramdas
- Genetics, University of Pennsylvania, Philadelphia, PA, USA
| | - Rong Ma
- Biostatistics and Epidemiology, University of Pennsylvania, Philadelphia, PA, USA
| | | | | | | | - Jibril B Hirbo
- Department of Medicine, Division of Genetic Medicine, Vanderbilt University School of Medicine, Nashville, TN, USA
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
| | - William Beggs
- Genetics, University of Pennsylvania, Philadelphia, PA, USA
| | - Meredith Yeager
- Frederick National Laboratory for Cancer Research, Frederick, MD, USA
| | - Stephen Chanock
- Division of Cancer Epidemiology and Genetics, National Institutes of Health, Rockville, MD, USA
| | - Thomas B Nyambo
- Department of Biochemistry, Kampala International University in Tanzania, Dar Es Salaam, Tanzania
| | - Sabah A Omar
- Center for Biotechnology Research and Development, Kenya Medical Research Institute, Nairobi, Kenya
| | - Dawit Woldemeskel
- Microbial Cellular and Molecular Biology Department, Addis Ababa University, Addis Ababa, Ethiopia
| | - Gurja Belay
- Microbial Cellular and Molecular Biology Department, Addis Ababa University, Addis Ababa, Ethiopia
| | - Hongzhe Li
- Biostatistics and Epidemiology, University of Pennsylvania, Philadelphia, PA, USA
| | - Christopher D Brown
- Genomics and Computational Biology, University of Pennsylvania, Philadelphia, PA, USA
- Genetics, University of Pennsylvania, Philadelphia, PA, USA
| | - Sarah A Tishkoff
- Genetics, University of Pennsylvania, Philadelphia, PA, USA.
- Department of Biology, University of Pennsylvania, Philadelphia, USA.
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40
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Khunsriraksakul C, Li Q, Markus H, Patrick MT, Sauteraud R, McGuire D, Wang X, Wang C, Wang L, Chen S, Shenoy G, Li B, Zhong X, Olsen NJ, Carrel L, Tsoi LC, Jiang B, Liu DJ. Multi-ancestry and multi-trait genome-wide association meta-analyses inform clinical risk prediction for systemic lupus erythematosus. Nat Commun 2023; 14:668. [PMID: 36750564 PMCID: PMC9905560 DOI: 10.1038/s41467-023-36306-5] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2022] [Accepted: 01/25/2023] [Indexed: 02/09/2023] Open
Abstract
Systemic lupus erythematosus is a heritable autoimmune disease that predominantly affects young women. To improve our understanding of genetic etiology, we conduct multi-ancestry and multi-trait meta-analysis of genome-wide association studies, encompassing 12 systemic lupus erythematosus cohorts from 3 different ancestries and 10 genetically correlated autoimmune diseases, and identify 16 novel loci. We also perform transcriptome-wide association studies, computational drug repurposing analysis, and cell type enrichment analysis. We discover putative drug classes, including a histone deacetylase inhibitor that could be repurposed to treat lupus. We also identify multiple cell types enriched with putative target genes, such as non-classical monocytes and B cells, which may be targeted for future therapeutics. Using this newly assembled result, we further construct polygenic risk score models and demonstrate that integrating polygenic risk score with clinical lab biomarkers improves the diagnostic accuracy of systemic lupus erythematosus using the Vanderbilt BioVU and Michigan Genomics Initiative biobanks.
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Affiliation(s)
- Chachrit Khunsriraksakul
- Program in Bioinformatics and Genomics, Pennsylvania State University College of Medicine, Hershey, PA, 17033, USA
- Institute for Personalized Medicine, Pennsylvania State University College of Medicine, Hershey, PA, 17033, USA
| | - Qinmengge Li
- Department of Dermatology, University of Michigan Medical School, Ann Arbor, MI, 48109, USA
| | - Havell Markus
- Program in Bioinformatics and Genomics, Pennsylvania State University College of Medicine, Hershey, PA, 17033, USA
- Institute for Personalized Medicine, Pennsylvania State University College of Medicine, Hershey, PA, 17033, USA
| | - Matthew T Patrick
- Department of Dermatology, University of Michigan Medical School, Ann Arbor, MI, 48109, USA
| | - Renan Sauteraud
- Department of Public Health Sciences, Pennsylvania State University College of Medicine, Hershey, PA, 17033, USA
| | - Daniel McGuire
- Department of Public Health Sciences, Pennsylvania State University College of Medicine, Hershey, PA, 17033, USA
| | - Xingyan Wang
- Department of Public Health Sciences, Pennsylvania State University College of Medicine, Hershey, PA, 17033, USA
| | - Chen Wang
- Program in Bioinformatics and Genomics, Pennsylvania State University College of Medicine, Hershey, PA, 17033, USA
| | - Lida Wang
- Department of Public Health Sciences, Pennsylvania State University College of Medicine, Hershey, PA, 17033, USA
| | - Siyuan Chen
- Department of Public Health Sciences, Pennsylvania State University College of Medicine, Hershey, PA, 17033, USA
| | - Ganesh Shenoy
- Department of Neurosurgery, Pennsylvania State University College of Medicine, Hershey, PA, 17033, USA
| | - Bingshan Li
- Department of Molecular Physiology & Biophysics, Vanderbilt University, Nashville, TN, 37235, USA
| | - Xue Zhong
- Department of Medicine, Division of Genetic Medicine, Vanderbilt University Medical Center, Nashville, TN, 37232, USA
| | - Nancy J Olsen
- Department of Medicine, Pennsylvania State University College of Medicine, Hershey, PA, 17033, USA
| | - Laura Carrel
- Department of Biochemistry and Molecular Biology, Pennsylvania State University College of Medicine, Hershey, PA, 17033, USA
| | - Lam C Tsoi
- Department of Dermatology, University of Michigan Medical School, Ann Arbor, MI, 48109, USA
| | - Bibo Jiang
- Department of Public Health Sciences, Pennsylvania State University College of Medicine, Hershey, PA, 17033, USA
| | - Dajiang J Liu
- Program in Bioinformatics and Genomics, Pennsylvania State University College of Medicine, Hershey, PA, 17033, USA.
- Institute for Personalized Medicine, Pennsylvania State University College of Medicine, Hershey, PA, 17033, USA.
- Department of Public Health Sciences, Pennsylvania State University College of Medicine, Hershey, PA, 17033, USA.
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41
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Lafferty MJ, Aygün N, Patel NK, Krupa O, Liang D, Wolter JM, Geschwind DH, de la Torre-Ubieta L, Stein JL. MicroRNA-eQTLs in the developing human neocortex link miR-4707-3p expression to brain size. eLife 2023; 12:e79488. [PMID: 36629315 PMCID: PMC9859047 DOI: 10.7554/elife.79488] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2022] [Accepted: 01/10/2023] [Indexed: 01/12/2023] Open
Abstract
Expression quantitative trait loci (eQTL) data have proven important for linking non-coding loci to protein-coding genes. But eQTL studies rarely measure microRNAs (miRNAs), small non-coding RNAs known to play a role in human brain development and neurogenesis. Here, we performed small-RNA sequencing across 212 mid-gestation human neocortical tissue samples, measured 907 expressed miRNAs, discovering 111 of which were novel, and identified 85 local-miRNA-eQTLs. Colocalization of miRNA-eQTLs with GWAS summary statistics yielded one robust colocalization of miR-4707-3p expression with educational attainment and brain size phenotypes, where the miRNA expression increasing allele was associated with decreased brain size. Exogenous expression of miR-4707-3p in primary human neural progenitor cells decreased expression of predicted targets and increased cell proliferation, indicating miR-4707-3p modulates progenitor gene regulation and cell fate decisions. Integrating miRNA-eQTLs with existing GWAS yielded evidence of a miRNA that may influence human brain size and function via modulation of neocortical brain development.
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Affiliation(s)
- Michael J Lafferty
- Department of Genetics, University of North Carolina at Chapel HillChapel HillUnited States
- UNC Neuroscience Center, University of North Carolina at Chapel HillChapel HillUnited States
| | - Nil Aygün
- Department of Genetics, University of North Carolina at Chapel HillChapel HillUnited States
- UNC Neuroscience Center, University of North Carolina at Chapel HillChapel HillUnited States
| | - Niyanta K Patel
- Department of Genetics, University of North Carolina at Chapel HillChapel HillUnited States
- UNC Neuroscience Center, University of North Carolina at Chapel HillChapel HillUnited States
| | - Oleh Krupa
- Department of Genetics, University of North Carolina at Chapel HillChapel HillUnited States
- UNC Neuroscience Center, University of North Carolina at Chapel HillChapel HillUnited States
| | - Dan Liang
- Department of Genetics, University of North Carolina at Chapel HillChapel HillUnited States
- UNC Neuroscience Center, University of North Carolina at Chapel HillChapel HillUnited States
| | - Justin M Wolter
- Department of Genetics, University of North Carolina at Chapel HillChapel HillUnited States
- UNC Neuroscience Center, University of North Carolina at Chapel HillChapel HillUnited States
- Department of Cell Biology and Physiology, The University of North Carolina at Chapel HillChapel HillUnited States
- Carolina Institute for Developmental Disabilities, The University of North Carolina at Chapel HillChapel HillUnited States
| | - Daniel H Geschwind
- Neurogenetics Program, Department of Neurology, David Geffen School of Medicine, University of California, Los AngelesLos AngelesUnited States
- Center for Autism Research and Treatment, Semel Institute, David Geffen School of Medicine, University of California, Los AngelesLos AngelesUnited States
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los AngelesLos AngelesUnited States
- Department of Psychiatry and Biobehavioral Sciences, Semel Institute, David Geffen School of Medicine, University of California, Los AngelesLos AngelesUnited States
| | - Luis de la Torre-Ubieta
- Department of Psychiatry and Biobehavioral Sciences, Semel Institute, David Geffen School of Medicine, University of California, Los AngelesLos AngelesUnited States
| | - Jason L Stein
- Department of Genetics, University of North Carolina at Chapel HillChapel HillUnited States
- UNC Neuroscience Center, University of North Carolina at Chapel HillChapel HillUnited States
- Carolina Institute for Developmental Disabilities, The University of North Carolina at Chapel HillChapel HillUnited States
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Gedik H, Peterson RE, Riley BP, Vladimirov VI, Bacanu SA. Integrative Post-Genome-Wide Association Study Analyses Relevant to Psychiatric Disorders: Imputing Transcriptome and Proteome Signals. Complex Psychiatry 2023; 9:130-144. [PMID: 37588130 PMCID: PMC10425719 DOI: 10.1159/000530223] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2022] [Accepted: 03/09/2023] [Indexed: 08/18/2023] Open
Abstract
Background The genome-wide association study (GWAS) is a common tool to identify genetic variants associated with complex traits, including psychiatric disorders (PDs). However, post-GWAS analyses are needed to extend the statistical inference to biologically relevant entities, e.g., genes, proteins, and pathways. To achieve this goal, researchers developed methods that incorporate biologically relevant intermediate molecular phenotypes, such as gene expression and protein abundance, which are posited to mediate the variant-trait association. Transcriptome-wide association study (TWAS) and proteome-wide association study (PWAS) are commonly used methods to test the association between these molecular mediators and the trait. Summary In this review, we discuss the most recent developments in TWAS and PWAS. These methods integrate existing "omic" information with the GWAS summary statistics for trait(s) of interest. Specifically, they impute transcript/protein data and test the association between imputed gene expression/protein level with phenotype of interest by using (i) GWAS summary statistics and (ii) reference transcriptomic/proteomic/genomic datasets. TWAS and PWAS are suitable as analysis tools for (i) primary association scan and (ii) fine-mapping to identify potentially causal genes for PDs. Key Messages As post-GWAS analyses, TWAS and PWAS have the potential to highlight causal genes for PDs. These prioritized genes could indicate targets for the development of novel drug therapies. For researchers attempting such analyses, we recommend Mendelian randomization tools that use GWAS statistics for both trait and reference datasets, e.g., summary Mendelian randomization (SMR). We base our recommendation on (i) being able to use the same tool for both TWAS and PWAS, (ii) not requiring the pre-computed weights (and thus easier to update for larger reference datasets), and (iii) most larger transcriptome reference datasets are publicly available and easy to transform into a compatible format for SMR analysis.
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Affiliation(s)
- Huseyin Gedik
- Integrative Life Sciences, Virginia Institute of Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond, VA, USA
| | - Roseann E. Peterson
- Institute for Genomics in Health, SUNY Downstate Health Sciences University, Brooklyn, NY, USA
| | - Brien P. Riley
- Institute for Genomics in Health, SUNY Downstate Health Sciences University, Brooklyn, NY, USA
| | - Vladimir I. Vladimirov
- Department of Psychiatry, College of Medicine-Phoenix, University of Arizona, Phoenix, AZ, USA
| | - Silviu-Alin Bacanu
- Institute for Genomics in Health, SUNY Downstate Health Sciences University, Brooklyn, NY, USA
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43
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Long-read transcriptome sequencing reveals allele-specific variants at high resolution. Trends Genet 2023; 39:31-33. [PMID: 36207147 DOI: 10.1016/j.tig.2022.09.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2022] [Revised: 09/11/2022] [Accepted: 09/21/2022] [Indexed: 11/05/2022]
Abstract
Disturbance in the regulation of transcript structure plays a crucial role in human disease. In a recent study, Glinos et al. characterized allele-specific transcript alterations in long-read RNA sequencing (RNA-seq) data derived from multiple human tissues and provide a high-resolution view of how disease-associated genetic variants affect transcript structure.
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Fernandez-Rozadilla C, Timofeeva M, Chen Z, Law P, Thomas M, Schmit S, Díez-Obrero V, Hsu L, Fernandez-Tajes J, Palles C, Sherwood K, Briggs S, Svinti V, Donnelly K, Farrington S, Blackmur J, Vaughan-Shaw P, Shu XO, Long J, Cai Q, Guo X, Lu Y, Broderick P, Studd J, Huyghe J, Harrison T, Conti D, Dampier C, Devall M, Schumacher F, Melas M, Rennert G, Obón-Santacana M, Martín-Sánchez V, Moratalla-Navarro F, Oh JH, Kim J, Jee SH, Jung KJ, Kweon SS, Shin MH, Shin A, Ahn YO, Kim DH, Oze I, Wen W, Matsuo K, Matsuda K, Tanikawa C, Ren Z, Gao YT, Jia WH, Hopper J, Jenkins M, Win AK, Pai R, Figueiredo J, Haile R, Gallinger S, Woods M, Newcomb P, Duggan D, Cheadle J, Kaplan R, Maughan T, Kerr R, Kerr D, Kirac I, Böhm J, Mecklin LP, Jousilahti P, Knekt P, Aaltonen L, Rissanen H, Pukkala E, Eriksson J, Cajuso T, Hänninen U, Kondelin J, Palin K, Tanskanen T, Renkonen-Sinisalo L, Zanke B, Männistö S, Albanes D, Weinstein S, Ruiz-Narvaez E, Palmer J, Buchanan D, Platz E, Visvanathan K, Ulrich C, Siegel E, Brezina S, Gsur A, Campbell P, Chang-Claude J, Hoffmeister M, Brenner H, Slattery M, Potter J, Tsilidis K, Schulze M, Gunter M, Murphy N, Castells A, Castellví-Bel S, Moreira L, Arndt V, Shcherbina A, Stern M, Pardamean B, Bishop T, Giles G, Southey M, Idos G, McDonnell K, Abu-Ful Z, Greenson J, Shulman K, Lejbkowicz F, Offit K, Su YR, Steinfelder R, Keku T, van Guelpen B, Hudson T, Hampel H, Pearlman R, Berndt S, Hayes R, Martinez ME, Thomas S, Corley D, Pharoah P, Larsson S, Yen Y, Lenz HJ, White E, Li L, Doheny K, Pugh E, Shelford T, Chan A, Cruz-Correa M, Lindblom A, Hunter D, Joshi A, Schafmayer C, Scacheri P, Kundaje A, Nickerson D, Schoen R, Hampe J, Stadler Z, Vodicka P, Vodickova L, Vymetalkova V, Papadopoulos N, Edlund C, Gauderman W, Thomas D, Shibata D, Toland A, Markowitz S, Kim A, Chanock S, van Duijnhoven F, Feskens E, Sakoda L, Gago-Dominguez M, Wolk A, Naccarati A, Pardini B, FitzGerald L, Lee SC, Ogino S, Bien S, Kooperberg C, Li C, Lin Y, Prentice R, Qu C, Bézieau S, Tangen C, Mardis E, Yamaji T, Sawada N, Iwasaki M, Haiman C, Le Marchand L, Wu A, Qu C, McNeil C, Coetzee G, Hayward C, Deary I, Harris S, Theodoratou E, Reid S, Walker M, Ooi LY, Moreno V, Casey G, Gruber S, Tomlinson I, Zheng W, Dunlop M, Houlston R, Peters U. Deciphering colorectal cancer genetics through multi-omic analysis of 100,204 cases and 154,587 controls of European and east Asian ancestries. Nat Genet 2023; 55:89-99. [PMID: 36539618 PMCID: PMC10094749 DOI: 10.1038/s41588-022-01222-9] [Citation(s) in RCA: 57] [Impact Index Per Article: 57.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2021] [Accepted: 10/09/2022] [Indexed: 12/24/2022]
Abstract
Colorectal cancer (CRC) is a leading cause of mortality worldwide. We conducted a genome-wide association study meta-analysis of 100,204 CRC cases and 154,587 controls of European and east Asian ancestry, identifying 205 independent risk associations, of which 50 were unreported. We performed integrative genomic, transcriptomic and methylomic analyses across large bowel mucosa and other tissues. Transcriptome- and methylome-wide association studies revealed an additional 53 risk associations. We identified 155 high-confidence effector genes functionally linked to CRC risk, many of which had no previously established role in CRC. These have multiple different functions and specifically indicate that variation in normal colorectal homeostasis, proliferation, cell adhesion, migration, immunity and microbial interactions determines CRC risk. Crosstissue analyses indicated that over a third of effector genes most probably act outside the colonic mucosa. Our findings provide insights into colorectal oncogenesis and highlight potential targets across tissues for new CRC treatment and chemoprevention strategies.
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Affiliation(s)
- Ceres Fernandez-Rozadilla
- Edinburgh Cancer Research Centre, Institute of Genomics and Cancer, University of Edinburgh, Edinburgh, UK
- Genomic Medicine Group, Instituto de Investigacion Sanitaria de Santiago, Santiago de Compostela, Spain
| | - Maria Timofeeva
- Colon Cancer Genetics Group, Medical Research Council Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
- Danish Institute for Advanced Study, Department of Public Health, University of Southern Denmark, Odense, Denmark
| | - Zhishan Chen
- Division of Epidemiology, Department of Medicine, Vanderbilt-Ingram Cancer Center, Vanderbilt Epidemiology Center, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Philip Law
- Division of Genetics and Epidemiology, Institute of Cancer Research, London, UK
| | - Minta Thomas
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Stephanie Schmit
- Genomic Medicine Institute, Cleveland Clinic, Cleveland, OH, USA
- Population and Cancer Prevention Program, Case Comprehensive Cancer Center, Cleveland, OH, USA
| | - Virginia Díez-Obrero
- Colorectal Cancer Group, ONCOBELL Program, Bellvitge Biomedical Research Institute, Barcelona, Spain
- Oncology Data Analytics Program, Catalan Institute of Oncology, Barcelona, Spain
- Consortium for Biomedical Research in Epidemiology and Public Health, Madrid, Madrid, Spain
- Department of Clinical Sciences, Faculty of Medicine, University of Barcelona, Barcelona, Spain
| | - Li Hsu
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
- Department of Biostatistics, School of Public Health, University of Washington, Seattle, WA, USA
| | - Juan Fernandez-Tajes
- Edinburgh Cancer Research Centre, Institute of Genomics and Cancer, University of Edinburgh, Edinburgh, UK
| | - Claire Palles
- Institute of Cancer and Genomic Sciences, College of Medical and Dental Sciences, University of Birmingham, Birmingham, UK
| | - Kitty Sherwood
- Edinburgh Cancer Research Centre, Institute of Genomics and Cancer, University of Edinburgh, Edinburgh, UK
| | - Sarah Briggs
- Department of Public Health, Richard Doll Building, University of Oxford, Oxford, UK
| | - Victoria Svinti
- Colon Cancer Genetics Group, Medical Research Council Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
| | - Kevin Donnelly
- Colon Cancer Genetics Group, Medical Research Council Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
| | - Susan Farrington
- Colon Cancer Genetics Group, Medical Research Council Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
| | - James Blackmur
- Colon Cancer Genetics Group, Medical Research Council Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
| | - Peter Vaughan-Shaw
- Colon Cancer Genetics Group, Medical Research Council Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
| | - Xiao-Ou Shu
- Division of Epidemiology, Department of Medicine, Vanderbilt-Ingram Cancer Center, Vanderbilt Epidemiology Center, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Jirong Long
- Division of Epidemiology, Department of Medicine, Vanderbilt-Ingram Cancer Center, Vanderbilt Epidemiology Center, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Qiuyin Cai
- Division of Epidemiology, Department of Medicine, Vanderbilt-Ingram Cancer Center, Vanderbilt Epidemiology Center, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Xingyi Guo
- Division of Epidemiology, Department of Medicine, Vanderbilt-Ingram Cancer Center, Vanderbilt Epidemiology Center, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Biomedical Informatics, Vanderbilt University School of Medicine, Nashville, TN, USA
| | - Yingchang Lu
- Division of Epidemiology, Department of Medicine, Vanderbilt-Ingram Cancer Center, Vanderbilt Epidemiology Center, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Peter Broderick
- Division of Genetics and Epidemiology, Institute of Cancer Research, London, UK
| | - James Studd
- Division of Genetics and Epidemiology, Institute of Cancer Research, London, UK
| | - Jeroen Huyghe
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Tabitha Harrison
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - David Conti
- Department of Preventive Medicine, USC Norris Comprehensive Cancer Center, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Christopher Dampier
- Center for Public Health Genomics, Department of Public Health Sciences, University of Virginia, Charlottesville, VA, USA
| | - Mathew Devall
- Center for Public Health Genomics, Department of Public Health Sciences, University of Virginia, Charlottesville, VA, USA
| | - Fredrick Schumacher
- Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, OH, USA
- Case Comprehensive Cancer Center, Case Western Reserve University, Cleveland, OH, USA
| | - Marilena Melas
- The Steve and Cindy Rasmussen Institute for Genomic Medicine, Nationwide Children's Hospital, Columbus, OH, USA
| | - Gad Rennert
- Department of Community Medicine and Epidemiology, Lady Davis Carmel Medical Center, Haifa, Israel
- Ruth and Bruce Rappaport Faculty of Medicine, Technion-Israel Institute of Technology, Haifa, Israel
- Clalit National Cancer Control Center, Haifa, Israel
| | - Mireia Obón-Santacana
- Colorectal Cancer Group, ONCOBELL Program, Bellvitge Biomedical Research Institute, Barcelona, Spain
- Oncology Data Analytics Program, Catalan Institute of Oncology, Barcelona, Spain
- Consortium for Biomedical Research in Epidemiology and Public Health, Madrid, Spain
| | - Vicente Martín-Sánchez
- Consortium for Biomedical Research in Epidemiology and Public Health, Madrid, Madrid, Spain
- Biomedicine Institute, University of León, León, Spain
| | - Ferran Moratalla-Navarro
- Colorectal Cancer Group, ONCOBELL Program, Bellvitge Biomedical Research Institute, Barcelona, Spain
- Oncology Data Analytics Program, Catalan Institute of Oncology, Barcelona, Spain
- Consortium for Biomedical Research in Epidemiology and Public Health, Madrid, Madrid, Spain
- Department of Clinical Sciences, Faculty of Medicine, University of Barcelona, Barcelona, Spain
| | - Jae Hwan Oh
- Center for Colorectal Cancer, National Cancer Center Hospital, National Cancer Center, Gyeonggi-do, South Korea
| | - Jeongseon Kim
- Department of Cancer Biomedical Science, Graduate School of Cancer Science and Policy, National Cancer Center, Gyeonggi-do, South Korea
| | - Sun Ha Jee
- Department of Epidemiology and Health Promotion, Graduate School of Public Health, Yonsei University, Seoul, South Korea
| | - Keum Ji Jung
- Department of Epidemiology and Health Promotion, Graduate School of Public Health, Yonsei University, Seoul, South Korea
| | - Sun-Seog Kweon
- Department of Preventive Medicine, Chonnam National University Medical School, Gwangju, South Korea
| | - Min-Ho Shin
- Department of Preventive Medicine, Chonnam National University Medical School, Gwangju, South Korea
| | - Aesun Shin
- Department of Preventive Medicine, Seoul National University College of Medicine, Seoul, South Korea
- Cancer Research Institute, Seoul National University, Seoul, South Korea
| | - Yoon-Ok Ahn
- Department of Preventive Medicine, Seoul National University College of Medicine, Seoul, South Korea
| | - Dong-Hyun Kim
- Department of Social and Preventive Medicine, Hallym University College of Medicine, Okcheon-dong, South Korea
| | - Isao Oze
- Division of Cancer Epidemiology and Prevention, Aichi Cancer Center Research Institute, Nagoya, Japan
| | - Wanqing Wen
- Division of Epidemiology, Department of Medicine, Vanderbilt-Ingram Cancer Center, Vanderbilt Epidemiology Center, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Keitaro Matsuo
- Division of Molecular and Clinical Epidemiology, Aichi Cancer Center Research Institute, Nagoya, Japan
- Department of Epidemiology, Nagoya University Graduate School of Medicine, Nagoya, Japan
| | - Koichi Matsuda
- Laboratory of Clinical Genome Sequencing, Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, University of Tokyo, Tokyo, Japan
| | - Chizu Tanikawa
- Laboratory of Genome Technology, Human Genome Center, Institute of Medical Science, University of Tokyo, Tokyo, Japan
| | - Zefang Ren
- School of Public Health, Sun Yat-sen University, Guangzhou, China
| | - Yu-Tang Gao
- State Key Laboratory of Oncogenes and Related Genes and Department of Epidemiology, Shanghai Cancer Institute, Renji Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China
| | - Wei-Hua Jia
- State Key Laboratory of Oncology in South China, Cancer Center, Sun Yat-sen University, Guangzhou, China
| | - John Hopper
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Melbourne, Victoria, Australia
- Department of Epidemiology, School of Public Health and Institute of Health and Environment, Seoul National University, Seoul, South Korea
| | - Mark Jenkins
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Melbourne, Victoria, Australia
| | - Aung Ko Win
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Melbourne, Victoria, Australia
| | - Rish Pai
- Department of Laboratory Medicine and Pathology, Mayo Clinic Arizona, Scottsdale, AZ, USA
| | - Jane Figueiredo
- Department of Preventive Medicine, USC Norris Comprehensive Cancer Center, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- Department of Medicine, Samuel Oschin Comprehensive Cancer Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Robert Haile
- Division of Oncology, Department of Medicine, Cedars-Sinai Cancer Research Center for Health Equity, Los Angeles, CA, USA
| | - Steven Gallinger
- Lunenfeld Tanenbaum Research Institute, Mount Sinai Hospital, University of Toronto, Toronto, Ontario, Canada
| | - Michael Woods
- Division of Biomedical Sciences, Memorial University of Newfoundland, St. John, Ontario, Canada
| | - Polly Newcomb
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
- School of Public Health, University of Washington, Seattle, WA, USA
| | - David Duggan
- City of Hope National Medical Center, Translational Genomics Research Institute, Phoenix, AZ, USA
| | - Jeremy Cheadle
- Institute of Medical Genetics, Cardiff University, Cardiff, UK
| | - Richard Kaplan
- MRC Clinical Trials Unit, Medical Research Council, Cardiff, UK
| | - Timothy Maughan
- MRC Institute for Radiation Oncology, University of Oxford, Oxford, UK
| | - Rachel Kerr
- Department of Oncology, University of Oxford, Oxford, UK
| | - David Kerr
- Radcliffe Department of Medicine, University of Oxford, Oxford, UK
| | - Iva Kirac
- Department of Surgical Oncology, University Hospital for Tumors, Sestre milosrdnice University Hospital Center, Zagreb, Croatia
| | - Jan Böhm
- Department of Pathology, Central Finland Health Care District, Jyväskylä, Finland
| | | | - Pekka Jousilahti
- Department of Health and Welfare, Finnish Institute for Health and Welfare, Helsinki, Finland
| | - Paul Knekt
- Department of Health and Welfare, Finnish Institute for Health and Welfare, Helsinki, Finland
| | - Lauri Aaltonen
- Department of Medical and Clinical Genetics, University of Helsinki, Helsinki, Finland
- Genome-Scale Biology Research Program, University of Helsinki, Helsinki, Finland
| | - Harri Rissanen
- Department of Public Health and Welfare, Finnish Institute for Health and Welfare, Helsinki, Finland
| | - Eero Pukkala
- Finnish Cancer Registry, Institute for Statistical and Epidemiological Cancer Research, Helsinki, Finland
- Faculty of Social Sciences, Tampere University, Tampere, Finland
| | - Johan Eriksson
- Folkhälsan Research Centre, University of Helsinki, Helsinki, Finland
- Human Potential Translational Research Programme, National University of Singapore, Singapore, Singapore
- Unit of General Practice and Primary Health Care, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
| | - Tatiana Cajuso
- Department of Medical and Clinical Genetics, University of Helsinki, Helsinki, Finland
- Genome-Scale Biology Research Program, University of Helsinki, Helsinki, Finland
| | - Ulrika Hänninen
- Department of Medical and Clinical Genetics, University of Helsinki, Helsinki, Finland
- Genome-Scale Biology Research Program, University of Helsinki, Helsinki, Finland
| | - Johanna Kondelin
- Department of Medical and Clinical Genetics, University of Helsinki, Helsinki, Finland
- Genome-Scale Biology Research Program, University of Helsinki, Helsinki, Finland
| | - Kimmo Palin
- Department of Medical and Clinical Genetics, University of Helsinki, Helsinki, Finland
- Genome-Scale Biology Research Program, University of Helsinki, Helsinki, Finland
| | - Tomas Tanskanen
- Department of Medical and Clinical Genetics, University of Helsinki, Helsinki, Finland
- Genome-Scale Biology Research Program, University of Helsinki, Helsinki, Finland
| | | | - Brent Zanke
- Department of Oncology, University of Toronto, Toronto, Ontario, Canada
| | - Satu Männistö
- Department of Public Health and Welfare, Finnish Institute for Health and Welfare, Helsinki, Finland
| | - Demetrius Albanes
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Stephanie Weinstein
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Edward Ruiz-Narvaez
- Department of Nutritional Sciences, School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - Julie Palmer
- Slone Epidemiology Center at Boston University, Boston, MA, USA
- Department of Medicine, Boston University School of Medicine, Boston, MA, USA
| | - Daniel Buchanan
- Colorectal Oncogenomics Group, Department of Clinical Pathology, University of Melbourne, Parkville, Victoria, Australia
- University of Melbourne Centre for Cancer Research, Victorian Comprehensive Cancer Centre, Parkville, Victoria, Australia
- Genomic Medicine and Family Cancer Clinic, Royal Melbourne Hospital, Parkville, Victoria, Australia
| | - Elizabeth Platz
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Kala Visvanathan
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Cornelia Ulrich
- Huntsman Cancer Institute and Department of Population Health Sciences, University of Utah, Salt Lake City, UT, USA
| | - Erin Siegel
- Cancer Epidemiology Program, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA
| | - Stefanie Brezina
- Institute of Cancer Research, Department of Medicine I, Medical University Vienna, Vienna, Austria
| | - Andrea Gsur
- Institute of Cancer Research, Department of Medicine I, Medical University Vienna, Vienna, Austria
| | - Peter Campbell
- Department of Epidemiology and Population Health, Albert Einstein College of Medicine, New York, NY, USA
| | - Jenny Chang-Claude
- Division of Cancer Epidemiology, German Cancer Research Center, Heidelberg, Germany
- University Medical Centre Hamburg-Eppendorf, University Cancer Centre Hamburg, Hamburg, Germany
| | - Michael Hoffmeister
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center, Heidelberg, Germany
| | - Hermann Brenner
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center, Heidelberg, Germany
- Division of Preventive Oncology, German Cancer Research Center and National Center for Tumor Diseases, Heidelberg, Germany
- German Cancer Consortium, German Cancer Research Center, Heidelberg, Germany
| | - Martha Slattery
- Department of Internal Medicine, University of Utah, Salt Lake City, UT, USA
| | - John Potter
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
- Research Centre for Hauora and Health, Massey University, Wellington, New Zealand
| | - Konstantinos Tsilidis
- Department of Hygiene and Epidemiology, University of Ioannina School of Medicine, Ioannina, Greece
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK
| | - Matthias Schulze
- Department of Molecular Epidemiology, German Institute of Human Nutrition Potsdam-Rehbruecke, Nuthetal, Germany
- Institute of Nutritional Science, University of Potsdam, Potsdam, Germany
| | - Marc Gunter
- Nutrition and Metabolism Branch, International Agency for Research on Cancer, World Health Organization, Lyon, France
| | - Neil Murphy
- Nutrition and Metabolism Branch, International Agency for Research on Cancer, World Health Organization, Lyon, France
| | - Antoni Castells
- Gastroenterology Department, Hospital Clínic, Institut d'Investigacions Biomèdiques August Pi i Sunyer, Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas, University of Barcelona, Barcelona, Spain
| | - Sergi Castellví-Bel
- Gastroenterology Department, Hospital Clínic, Institut d'Investigacions Biomèdiques August Pi i Sunyer, Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas, University of Barcelona, Barcelona, Spain
| | - Leticia Moreira
- Gastroenterology Department, Hospital Clínic, Institut d'Investigacions Biomèdiques August Pi i Sunyer, Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas, University of Barcelona, Barcelona, Spain
| | - Volker Arndt
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center, Heidelberg, Germany
| | - Anna Shcherbina
- Department of Genetics, Stanford University, Stanford, CA, USA
| | - Mariana Stern
- Department of Population and Public Health Sciences, USC Norris Comprehensive Cancer Center, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- Jeonnam Regional Cancer Center, Chonnam National University Hwasun Hospital, Hwasun, South Korea
| | - Bens Pardamean
- Bioinformatics and Data Science Research Center, Bina Nusantara University, Jakarta, Indonesia
| | - Timothy Bishop
- Leeds Institute of Medical Research at St James's, University of Leeds, Leeds, UK
| | - Graham Giles
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Melbourne, Victoria, Australia
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Victoria, Australia
| | - Melissa Southey
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Victoria, Australia
- Department of Clinical Pathology, University of Melbourne, Melbourne, Victoria, Australia
| | - Gregory Idos
- Department of Medical Oncology and Center For Precision Medicine, City of Hope National Medical Center, Duarte, CA, USA
| | - Kevin McDonnell
- Ruth and Bruce Rappaport Faculty of Medicine, Technion-Israel Institute of Technology, Haifa, Israel
- Clalit National Cancer Control Center, Haifa, Israel
- Department of Medical Oncology and Center For Precision Medicine, City of Hope National Medical Center, Duarte, CA, USA
| | - Zomoroda Abu-Ful
- Department of Community Medicine and Epidemiology, Lady Davis Carmel Medical Center, Haifa, Israel
| | - Joel Greenson
- Ruth and Bruce Rappaport Faculty of Medicine, Technion-Israel Institute of Technology, Haifa, Israel
- Clalit National Cancer Control Center, Haifa, Israel
- Department of Pathology, University of Michigan, Ann Arbor, MI, USA
| | - Katerina Shulman
- Department of Community Medicine and Epidemiology, Lady Davis Carmel Medical Center, Haifa, Israel
| | - Flavio Lejbkowicz
- Department of Community Medicine and Epidemiology, Lady Davis Carmel Medical Center, Haifa, Israel
- Clalit National Cancer Control Center, Haifa, Israel
- Clalit Health Services, Personalized Genomic Service, Lady Davis Carmel Medical Center, Haifa, Israel
| | - Kenneth Offit
- Clinical Genetics Service, Department of Medicine, Memorial Sloan-Kettering Cancer Center, New York, NY, USA
- Department of Medicine, Weill Cornell Medical College, New York, NY, USA
| | - Yu-Ru Su
- Kaiser Permanente Washington Health Research Institute, Seattle, WA, USA
| | - Robert Steinfelder
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Temitope Keku
- Center for Gastrointestinal Biology and Disease, University of North Carolina, Chapel Hill, NC, USA
| | - Bethany van Guelpen
- Department of Radiation Sciences, Oncology Unit, Umeå University, Umeå, Sweden
- Wallenberg Centre for Molecular Medicine, Umeå University, Umeå, Sweden
| | - Thomas Hudson
- Ontario Institute for Cancer Research, Toronto, Ontario, Canada
| | - Heather Hampel
- Division of Human Genetics, Department of Internal Medicine, Ohio State University Comprehensive Cancer Center, Columbus, OH, USA
| | - Rachel Pearlman
- Division of Human Genetics, Department of Internal Medicine, Ohio State University Comprehensive Cancer Center, Columbus, OH, USA
| | - Sonja Berndt
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Richard Hayes
- Division of Epidemiology, Department of Population Health, New York University School of Medicine, New York, NY, USA
| | - Marie Elena Martinez
- Population Sciences, Disparities and Community Engagement, University of California San Diego Moores Cancer Center, La Jolla, CA, USA
- Department of Family Medicine and Public Health, University of California San Diego, La Jolla, CA, USA
| | - Sushma Thomas
- Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Douglas Corley
- Division of Research, Kaiser Permanente Northern California, Oakland, CA, USA
- Department of Gastroenterology, Kaiser Permanente Medical Center, San Francisco, CA, USA
| | - Paul Pharoah
- Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Susanna Larsson
- Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden
| | - Yun Yen
- Taipei Medical University, Taipei, Taiwan
| | - Heinz-Josef Lenz
- Department of Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Emily White
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
- Department of Epidemiology, University of Washington School of Public Health, Seattle, WA, USA
| | - Li Li
- Case Comprehensive Cancer Center, Case Western Reserve University, Cleveland, OH, USA
| | - Kimberly Doheny
- Center for Inherited Disease Research, Department of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Elizabeth Pugh
- Center for Inherited Disease Research, Department of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Tameka Shelford
- Center for Inherited Disease Research, Department of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Andrew Chan
- Clinical and Translational Epidemiology Unit, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
- Division of Gastroenterology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Harvard University, Boston, MA, USA
- Department of Immunology and Infectious Diseases, Harvard T.H. Chan School of Public Health, Harvard University, Boston, MA, USA
| | - Marcia Cruz-Correa
- Comprehensive Cancer Center, University of Puerto Rico, San Juan, Puerto Rico
| | - Annika Lindblom
- Department of Clinical Genetics, Karolinska University Hospital, Stockholm, Sweden
- Department of Molecular Medicine and Surgery, Karolinska Institutet, Stockholm, Sweden
| | - David Hunter
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Harvard University, Boston, MA, USA
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Amit Joshi
- Clinical and Translational Epidemiology Unit, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Harvard University, Boston, MA, USA
| | - Clemens Schafmayer
- Department of General Surgery, University Hospital Rostock, Rostock, Germany
| | - Peter Scacheri
- Department of Genetics and Genome Sciences, Case Western Reserve University, Cleveland, OH, USA
| | - Anshul Kundaje
- Department of Genetics, Stanford University, Stanford, CA, USA
- Department of Computer Science, Stanford University, Stanford, CA, USA
| | - Deborah Nickerson
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
| | - Robert Schoen
- Department of Medicine and Epidemiology, University of Pittsburgh Medical Center, Pittsburgh, PA, USA
| | - Jochen Hampe
- Department of Medicine I, University Hospital Dresden, Technische Universität Dresden, Dresden, Germany
| | - Zsofia Stadler
- Department of Medicine, Weill Cornell Medical College, New York, NY, USA
- Department of Medicine, Memorial Sloan-Kettering Cancer Center, New York, NY, USA
| | - Pavel Vodicka
- Department of Molecular Biology of Cancer, Institute of Experimental Medicine of the Czech Academy of Sciences, Prague, Czech Republic
- Institute of Biology and Medical Genetics, First Faculty of Medicine, Charles University, Prague, Czech Republic
- Faculty of Medicine and Biomedical Center in Pilsen, Charles University, Pilsen, Czech Republic
| | - Ludmila Vodickova
- Department of Molecular Biology of Cancer, Institute of Experimental Medicine of the Czech Academy of Sciences, Prague, Czech Republic
- Institute of Biology and Medical Genetics, First Faculty of Medicine, Charles University, Prague, Czech Republic
- Faculty of Medicine and Biomedical Center in Pilsen, Charles University, Pilsen, Czech Republic
| | - Veronika Vymetalkova
- Department of Molecular Biology of Cancer, Institute of Experimental Medicine of the Czech Academy of Sciences, Prague, Czech Republic
- Institute of Biology and Medical Genetics, First Faculty of Medicine, Charles University, Prague, Czech Republic
- Faculty of Medicine and Biomedical Center in Pilsen, Charles University, Pilsen, Czech Republic
| | - Nickolas Papadopoulos
- Department of Oncology Ludwig Center at the Sidney Kimmel Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Sol Goldman Pancreatic Cancer Research Center, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Department of Pathology, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - Chistopher Edlund
- Department of Preventive Medicine, USC Norris Comprehensive Cancer Center, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - William Gauderman
- Department of Preventive Medicine, USC Norris Comprehensive Cancer Center, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Duncan Thomas
- Department of Preventive Medicine, USC Norris Comprehensive Cancer Center, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - David Shibata
- Department of Surgery, University of Tennessee Health Science Center, Memphis, TN, USA
| | - Amanda Toland
- Departments of Cancer Biology and Genetics and Internal Medicine, Comprehensive Cancer Center, Ohio State University, Columbus, OH, USA
| | - Sanford Markowitz
- Departments of Medicine and Genetics, Case Comprehensive Cancer Center, Case Western Reserve University and University Hospitals of Cleveland, Cleveland, OH, USA
| | - Andre Kim
- Department of Preventive Medicine, USC Norris Comprehensive Cancer Center, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Stephen Chanock
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Franzel van Duijnhoven
- Division of Human Nutrition and Health, Wageningen University and Research, Wageningen, The Netherlands
| | - Edith Feskens
- Division of Human Nutrition, Wageningen University and Research, Wageningen, The Netherlands
| | - Lori Sakoda
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
- Division of Research, Kaiser Permanente Northern California, Oakland, CA, USA
| | - Manuela Gago-Dominguez
- Genomic Medicine Group, Galician Public Foundation of Genomic Medicine, Servicio Galego de Saude, Santiago de Compostela, Spain
- Instituto de Investigación Sanitaria de Santiago de Compostela, Santiago de Compostela, Spain
| | - Alicja Wolk
- Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden
| | - Alessio Naccarati
- Italian Institute for Genomic Medicine, Candiolo Cancer Institute FPO-IRCCS, Candiolo (TO), Italy
- Candiolo Cancer Institute FPO-IRCCS, Candiolo (TO), Italy
| | - Barbara Pardini
- Italian Institute for Genomic Medicine, Candiolo Cancer Institute FPO-IRCCS, Candiolo (TO), Italy
- Candiolo Cancer Institute FPO-IRCCS, Candiolo (TO), Italy
| | - Liesel FitzGerald
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia
- Menzies Institute for Medical Research, University of Tasmania, Hobart, Tasmania, Australia
| | - Soo Chin Lee
- National University Cancer Institute, Singapore, Cancer Science Institute of Singapore, National University of Singapore, Singapore, Singapore
| | - Shuji Ogino
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Harvard University, Boston, MA, USA
- Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Cancer Immunology Program, Dana-Farber Harvard Cancer Center, Boston, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Stephanie Bien
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Charles Kooperberg
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Christopher Li
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Yi Lin
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Ross Prentice
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
- Department of Biostatistics, University of Washington, Seattle, WA, USA
| | - Conghui Qu
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Stéphane Bézieau
- Service de Génétique Médicale, Centre Hospitalier Universitaire Nantes, Nantes, France
| | - Catherine Tangen
- SWOG Statistical Center, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Elaine Mardis
- Department of Pediatrics, Nationwide Children's Hospital, The Steve and Cindy Rasmussen Institute for Genomic Medicine, Columbus, OH, USA
| | - Taiki Yamaji
- Division of Epidemiology, National Cancer Center Institute for Cancer Control, National Cancer Center, Tokyo, Japan
| | - Norie Sawada
- Division of Cohort Research, National Cancer Center Institute for Cancer Control, National Cancer Center, Tokyo, Japan
| | - Motoki Iwasaki
- Division of Epidemiology, National Cancer Center Institute for Cancer Control, National Cancer Center, Tokyo, Japan
- Division of Cohort Research, National Cancer Center Institute for Cancer Control, National Cancer Center, Tokyo, Japan
| | - Christopher Haiman
- Department of Preventive Medicine, Center for Genetic Epidemiology, University of Southern California, Los Angeles, CA, USA
| | | | - Anna Wu
- Preventative Medicine, University of Southern California, Los Angeles, CA, USA
| | - Chenxu Qu
- USC Norris Comprehensive Cancer Center, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Caroline McNeil
- USC Norris Comprehensive Cancer Center, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | | | - Caroline Hayward
- MRC Human Genetics Unit, Institute of Genomics and Cancer, University of Edinburgh, Edinburgh, UK
| | - Ian Deary
- Lothian Birth Cohorts group, Department of Psychology, University of Edinburgh, Edinburgh, UK
| | - Sarah Harris
- Lothian Birth Cohorts group, Department of Psychology, University of Edinburgh, Edinburgh, UK
| | - Evropi Theodoratou
- Centre for Global Health, Usher Institute, University of Edinburgh, Edinburgh, UK
| | - Stuart Reid
- Colon Cancer Genetics Group, Medical Research Council Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
| | - Marion Walker
- Colon Cancer Genetics Group, Medical Research Council Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
| | - Li Yin Ooi
- Colon Cancer Genetics Group, Medical Research Council Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
- Department of Pathology, National University Hospital, National University Health System, Singapore, Singapore
| | - Victor Moreno
- Colorectal Cancer Group, ONCOBELL Program, Bellvitge Biomedical Research Institute, Barcelona, Spain
- Oncology Data Analytics Program, Catalan Institute of Oncology, Barcelona, Spain
- Consortium for Biomedical Research in Epidemiology and Public Health, Madrid, Madrid, Spain
- Department of Clinical Sciences, Faculty of Medicine, University of Barcelona, Barcelona, Spain
| | - Graham Casey
- Center for Public Health Genomics, Department of Public Health Sciences, University of Virginia, Charlottesville, VA, USA
| | - Stephen Gruber
- Department of Medical Oncology and Center For Precision Medicine, City of Hope National Medical Center, Duarte, CA, USA
| | - Ian Tomlinson
- Edinburgh Cancer Research Centre, Institute of Genomics and Cancer, University of Edinburgh, Edinburgh, UK.
- Institute of Cancer and Genomic Sciences, College of Medical and Dental Sciences, University of Birmingham, Birmingham, UK.
| | - Wei Zheng
- Division of Epidemiology, Department of Medicine, Vanderbilt-Ingram Cancer Center, Vanderbilt Epidemiology Center, Vanderbilt University Medical Center, Nashville, TN, USA.
| | - Malcolm Dunlop
- Colon Cancer Genetics Group, Medical Research Council Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK.
| | - Richard Houlston
- Division of Genetics and Epidemiology, Institute of Cancer Research, London, UK.
| | - Ulrike Peters
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA.
- Department of Epidemiology, University of Washington, Seattle, WA, USA.
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Curti N, Levi G, Giampieri E, Castellani G, Remondini D. A network approach for low dimensional signatures from high throughput data. Sci Rep 2022; 12:22253. [PMID: 36564421 PMCID: PMC9789141 DOI: 10.1038/s41598-022-25549-9] [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: 06/14/2022] [Accepted: 11/30/2022] [Indexed: 12/24/2022] Open
Abstract
One of the main objectives of high-throughput genomics studies is to obtain a low-dimensional set of observables-a signature-for sample classification purposes (diagnosis, prognosis, stratification). Biological data, such as gene or protein expression, are commonly characterized by an up/down regulation behavior, for which discriminant-based methods could perform with high accuracy and easy interpretability. To obtain the most out of these methods features selection is even more critical, but it is known to be a NP-hard problem, and thus most feature selection approaches focuses on one feature at the time (k-best, Sequential Feature Selection, recursive feature elimination). We propose DNetPRO, Discriminant Analysis with Network PROcessing, a supervised network-based signature identification method. This method implements a network-based heuristic to generate one or more signatures out of the best performing feature pairs. The algorithm is easily scalable, allowing efficient computing for high number of observables ([Formula: see text]-[Formula: see text]). We show applications on real high-throughput genomic datasets in which our method outperforms existing results, or is compatible with them but with a smaller number of selected features. Moreover, the geometrical simplicity of the resulting class-separation surfaces allows a clearer interpretation of the obtained signatures in comparison to nonlinear classification models.
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Affiliation(s)
- Nico Curti
- grid.6292.f0000 0004 1757 1758Department of Physics and Astronomy, University of Bologna, Bologna, Italy ,grid.470193.80000 0004 8343 7610INFN Bologna, Bologna, Italy
| | - Giuseppe Levi
- grid.6292.f0000 0004 1757 1758Department of Physics and Astronomy, University of Bologna, Bologna, Italy ,grid.470193.80000 0004 8343 7610INFN Bologna, Bologna, Italy
| | - Enrico Giampieri
- grid.470193.80000 0004 8343 7610INFN Bologna, Bologna, Italy ,grid.6292.f0000 0004 1757 1758Department of Experimental, Diagnostic and Specialty Medicine, University of Bologna, Bologna, Italy
| | - Gastone Castellani
- grid.470193.80000 0004 8343 7610INFN Bologna, Bologna, Italy ,grid.6292.f0000 0004 1757 1758Department of Experimental, Diagnostic and Specialty Medicine, University of Bologna, Bologna, Italy
| | - Daniel Remondini
- grid.6292.f0000 0004 1757 1758Department of Physics and Astronomy, University of Bologna, Bologna, Italy ,grid.470193.80000 0004 8343 7610INFN Bologna, Bologna, Italy
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46
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Vlk D, Trněný O, Řepková J. Genes Associated with Biological Nitrogen Fixation Efficiency Identified Using RNA Sequencing in Red Clover ( Trifolium pratense L.). LIFE (BASEL, SWITZERLAND) 2022; 12:life12121975. [PMID: 36556339 PMCID: PMC9785344 DOI: 10.3390/life12121975] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/07/2022] [Revised: 11/22/2022] [Accepted: 11/22/2022] [Indexed: 11/29/2022]
Abstract
Commonly studied in the context of legume-rhizobia symbiosis, biological nitrogen fixation (BNF) is a key component of the nitrogen cycle in nature. Despite its potential in plant breeding and many years of research, information is still lacking as to the regulation of hundreds of genes connected with plant-bacteria interaction, nodulation, and nitrogen fixation. Here, we compared root nodule transcriptomes of red clover (Trifolium pratense L.) genotypes with contrasting nitrogen fixation efficiency, and we found 491 differentially expressed genes (DEGs) between plants with high and low BNF efficiency. The annotation of genes expressed in nodules revealed more than 800 genes not yet experimentally confirmed. Among genes mediating nodule development, four nod-ule-specific cysteine-rich (NCR) peptides were confirmed in the nodule transcriptome. Gene duplication analyses revealed that genes originating from tandem and dispersed duplication are significantly over-represented among DEGs. Weighted correlation network analysis (WGCNA) organized expression profiles of the transcripts into 16 modules linked to the analyzed traits, such as nitrogen fixation efficiency or sample-specific modules. Overall, the results obtained broaden our knowledge about transcriptomic landscapes of red clover's root nodules and shift the phenotypic description of BNF efficiency on the level of gene expression in situ.
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Affiliation(s)
- David Vlk
- Department of Experimental Biology, Faculty of Sciences, Masaryk University, 611 37 Brno, Czech Republic
| | - Oldřich Trněný
- Agricultural Research, Ltd., Zahradní 1, 664 41 Troubsko, Czech Republic
| | - Jana Řepková
- Department of Experimental Biology, Faculty of Sciences, Masaryk University, 611 37 Brno, Czech Republic
- Correspondence: ; Tel.: +420-549-49-6895
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47
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Liu S, Won H, Clarke D, Matoba N, Khullar S, Mu Y, Wang D, Gerstein M. Illuminating links between cis-regulators and trans-acting variants in the human prefrontal cortex. Genome Med 2022; 14:133. [PMID: 36424644 PMCID: PMC9685876 DOI: 10.1186/s13073-022-01133-8] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2021] [Accepted: 10/25/2022] [Indexed: 11/25/2022] Open
Abstract
BACKGROUND Neuropsychiatric disorders afflict a large portion of the global population and constitute a significant source of disability worldwide. Although Genome-wide Association Studies (GWAS) have identified many disorder-associated variants, the underlying regulatory mechanisms linking them to disorders remain elusive, especially those involving distant genomic elements. Expression quantitative trait loci (eQTLs) constitute a powerful means of providing this missing link. However, most eQTL studies in human brains have focused exclusively on cis-eQTLs, which link variants to nearby genes (i.e., those within 1 Mb of a variant). A complete understanding of disease etiology requires a clearer understanding of trans-regulatory mechanisms, which, in turn, entails a detailed analysis of the relationships between variants and expression changes in distant genes. METHODS By leveraging large datasets from the PsychENCODE consortium, we conducted a genome-wide survey of trans-eQTLs in the human dorsolateral prefrontal cortex. We also performed colocalization and mediation analyses to identify mediators in trans-regulation and use trans-eQTLs to link GWAS loci to schizophrenia risk genes. RESULTS We identified ~80,000 candidate trans-eQTLs (at FDR<0.25) that influence the expression of ~10K target genes (i.e., "trans-eGenes"). We found that many variants associated with these candidate trans-eQTLs overlap with known cis-eQTLs. Moreover, for >60% of these variants (by colocalization), the cis-eQTL's target gene acts as a mediator for the trans-eQTL SNP's effect on the trans-eGene, highlighting examples of cis-mediation as essential for trans-regulation. Furthermore, many of these colocalized variants fall into a discernable pattern wherein cis-eQTL's target is a transcription factor or RNA-binding protein, which, in turn, targets the gene associated with the candidate trans-eQTL. Finally, we show that trans-regulatory mechanisms provide valuable insights into psychiatric disorders: beyond what had been possible using only cis-eQTLs, we link an additional 23 GWAS loci and 90 risk genes (using colocalization between candidate trans-eQTLs and schizophrenia GWAS loci). CONCLUSIONS We demonstrate that the transcriptional architecture of the human brain is orchestrated by both cis- and trans-regulatory variants and found that trans-eQTLs provide insights into brain-disease biology.
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Affiliation(s)
- Shuang Liu
- Waisman Center, University of Wisconsin - Madison, Madison, WI, 53705, USA
| | - Hyejung Won
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA.,Neuroscience Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
| | - Declan Clarke
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, 06520, USA
| | - Nana Matoba
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA.,Neuroscience Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
| | - Saniya Khullar
- Waisman Center, University of Wisconsin - Madison, Madison, WI, 53705, USA.,Department of Biostatistics and Medical Informatics, University of Wisconsin - Madison, Madison, WI, 53706, USA
| | - Yudi Mu
- Department of Statistics, University of Wisconsin - Madison, Madison, WI, 53706, USA
| | - Daifeng Wang
- Waisman Center, University of Wisconsin - Madison, Madison, WI, 53705, USA. .,Department of Biostatistics and Medical Informatics, University of Wisconsin - Madison, Madison, WI, 53706, USA. .,Department of Computer Sciences, University of Wisconsin - Madison, Madison, WI, 53706, USA.
| | - Mark Gerstein
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, 06520, USA. .,Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, 06520, USA. .,Department of Computer Science, Yale University, New Haven, CT, 06520, USA. .,Department of Statistics and Data Science, Yale University, New Haven, CT, 06520, USA.
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48
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Whole genome DNA and RNA sequencing of whole blood elucidates the genetic architecture of gene expression underlying a wide range of diseases. Sci Rep 2022; 12:20167. [PMID: 36424512 PMCID: PMC9686236 DOI: 10.1038/s41598-022-24611-w] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2022] [Accepted: 11/17/2022] [Indexed: 11/27/2022] Open
Abstract
To create a scientific resource of expression quantitative trail loci (eQTL), we conducted a genome-wide association study (GWAS) using genotypes obtained from whole genome sequencing (WGS) of DNA and gene expression levels from RNA sequencing (RNA-seq) of whole blood in 2622 participants in Framingham Heart Study. We identified 6,778,286 cis-eQTL variant-gene transcript (eGene) pairs at p < 5 × 10-8 (2,855,111 unique cis-eQTL variants and 15,982 unique eGenes) and 1,469,754 trans-eQTL variant-eGene pairs at p < 1e-12 (526,056 unique trans-eQTL variants and 7233 unique eGenes). In addition, 442,379 cis-eQTL variants were associated with expression of 1518 long non-protein coding RNAs (lncRNAs). Gene Ontology (GO) analyses revealed that the top GO terms for cis-eGenes are enriched for immune functions (FDR < 0.05). The cis-eQTL variants are enriched for SNPs reported to be associated with 815 traits in prior GWAS, including cardiovascular disease risk factors. As proof of concept, we used this eQTL resource in conjunction with genetic variants from public GWAS databases in causal inference testing (e.g., COVID-19 severity). After Bonferroni correction, Mendelian randomization analyses identified putative causal associations of 60 eGenes with systolic blood pressure, 13 genes with coronary artery disease, and seven genes with COVID-19 severity. This study created a comprehensive eQTL resource via BioData Catalyst that will be made available to the scientific community. This will advance understanding of the genetic architecture of gene expression underlying a wide range of diseases.
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Sauerwald N, Zhang Z, Ramos I, Nair VD, Soares-Schanoski A, Ge Y, Mao W, Alshammary H, Gonzalez-Reiche AS, van de Guchte A, Goforth CW, Lizewski RA, Lizewski SE, Amper MAS, Vasoya M, Seenarine N, Guevara K, Marjanovic N, Miller CM, Nudelman G, Schilling MA, Sealfon RSG, Termini MS, Vangeti S, Weir DL, Zaslavsky E, Chikina M, Wu YN, Van Bakel H, Letizia AG, Sealfon SC, Troyanskaya OG. Pre-infection antiviral innate immunity contributes to sex differences in SARS-CoV-2 infection. Cell Syst 2022; 13:924-931.e4. [PMID: 36323307 PMCID: PMC9623453 DOI: 10.1016/j.cels.2022.10.005] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2022] [Revised: 07/21/2022] [Accepted: 10/18/2022] [Indexed: 11/05/2022]
Abstract
Male sex is a major risk factor for SARS-CoV-2 infection severity. To understand the basis for this sex difference, we studied SARS-CoV-2 infection in a young adult cohort of United States Marine recruits. Among 2,641 male and 244 female unvaccinated and seronegative recruits studied longitudinally, SARS-CoV-2 infections occurred in 1,033 males and 137 females. We identified sex differences in symptoms, viral load, blood transcriptome, RNA splicing, and proteomic signatures. Females had higher pre-infection expression of antiviral interferon-stimulated gene (ISG) programs. Causal mediation analysis implicated ISG differences in number of symptoms, levels of ISGs, and differential splicing of CD45 lymphocyte phosphatase during infection. Our results indicate that the antiviral innate immunity set point causally contributes to sex differences in response to SARS-CoV-2 infection. A record of this paper's transparent peer review process is included in the supplemental information.
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Affiliation(s)
- Natalie Sauerwald
- Center for Computational Biology, Flatiron Institute, New York, NY 10010, USA
| | - Zijun Zhang
- Center for Computational Biology, Flatiron Institute, New York, NY 10010, USA
| | - Irene Ramos
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Venugopalan D Nair
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | | | - Yongchao Ge
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Weiguang Mao
- Department of Computational and Systems Biology, School of Medicine, University of Pittsburgh, Pittsburgh, PA 15213, USA
| | - Hala Alshammary
- Department of Microbiology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Ana S Gonzalez-Reiche
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Adriana van de Guchte
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Carl W Goforth
- Naval Medical Research Center, Silver Spring, MD 20910, USA
| | | | | | - Mary Anne S Amper
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Mital Vasoya
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Nitish Seenarine
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Kristy Guevara
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Nada Marjanovic
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Clare M Miller
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - German Nudelman
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | | | - Rachel S G Sealfon
- Center for Computational Biology, Flatiron Institute, New York, NY 10010, USA
| | - Michael S Termini
- Navy Medicine Readiness and Training Command Beaufort, Beaufort, SC 29902, USA
| | - Sindhu Vangeti
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Dawn L Weir
- Naval Medical Research Center, Silver Spring, MD 20910, USA
| | - Elena Zaslavsky
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Maria Chikina
- Department of Computational and Systems Biology, School of Medicine, University of Pittsburgh, Pittsburgh, PA 15213, USA
| | - Ying Nian Wu
- Department of Statistics, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Harm Van Bakel
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | | | - Stuart C Sealfon
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA.
| | - Olga G Troyanskaya
- Center for Computational Biology, Flatiron Institute, New York, NY 10010, USA; Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ 08540, USA; Department of Computer Science, Princeton University, Princeton, NJ 08540, USA.
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50
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Bruscadin JJ, Cardoso TF, da Silva Diniz WJ, Afonso J, de Souza MM, Petrini J, Nascimento Andrade BG, da Silva VH, Ferraz JBS, Zerlotini A, Mourão GB, Coutinho LL, de Almeida Regitano LC. Allele-specific expression reveals functional SNPs affecting muscle-related genes in bovine. BIOCHIMICA ET BIOPHYSICA ACTA (BBA) - GENE REGULATORY MECHANISMS 2022; 1865:194886. [DOI: 10.1016/j.bbagrm.2022.194886] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/13/2022] [Revised: 09/27/2022] [Accepted: 10/12/2022] [Indexed: 11/09/2022]
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