1
|
Kukendrarajah K, Farmaki AE, Lambiase PD, Schilling R, Finan C, Floriaan Schmidt A, Providencia R. Advancing drug development for atrial fibrillation by prioritising findings from human genetic association studies. EBioMedicine 2024; 105:105194. [PMID: 38941956 PMCID: PMC11260865 DOI: 10.1016/j.ebiom.2024.105194] [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: 09/22/2023] [Revised: 05/14/2024] [Accepted: 05/28/2024] [Indexed: 06/30/2024] Open
Abstract
BACKGROUND Drug development for atrial fibrillation (AF) has failed to yield new approved compounds. We sought to identify and prioritise potential druggable targets with support from human genetics, by integrating the available evidence with bioinformatics sources relevant for AF drug development. METHODS Genetic hits for AF and related traits were identified through structured search of MEDLINE. Genes derived from each paper were cross-referenced with the OpenTargets platform for drug interactions. Confirmation/validation was demonstrated through structured searches and review of evidence on MEDLINE and ClinialTrials.gov for each drug and its association with AF. FINDINGS 613 unique drugs were identified, with 21 already included in AF Guidelines. Cardiovascular drugs from classes not currently used for AF (e.g. ranolazine and carperitide) and anti-inflammatory drugs (e.g. dexamethasone and mehylprednisolone) had evidence of potential benefit. Further targets were considered druggable but remain open for drug development. INTERPRETATION Our systematic approach, combining evidence from different bioinformatics platforms, identified drug repurposing opportunities and druggable targets for AF. FUNDING KK is supported by Barts Charity grant G-002089 and is mentored on the AFGen 2023-24 Fellowship funded by the AFGen NIH/NHLBI grant R01HL092577. RP is supported by the UCL BHF Research Accelerator AA/18/6/34223 and NIHR grant NIHR129463. AFS is supported by the BHF grants PG/18/5033837, PG/22/10989 and UCL BHF Accelerator AA/18/6/34223 as well as the UK Research and Innovation (UKRI) under the UK government's Horizon Europe funding guarantee EP/Z000211/1 and by the UKRI-NIHR grant MR/V033867/1 for the Multimorbidity Mechanism and Therapeutics Research Collaboration. AF is supported by UCL BHF Accelerator AA/18/6/34223. CF is supported by UCL BHF Accelerator AA/18/6/34223.
Collapse
Affiliation(s)
- Kishore Kukendrarajah
- Institute of Health Informatics, University College London, 222 Euston Road, NW1 2DA, United Kingdom; Barts Heart Centre, St Bartholomew's Hospital, West Smithfield, EC1A 7BE, United Kingdom.
| | - Aliki-Eleni Farmaki
- Institute of Health Informatics, University College London, 222 Euston Road, NW1 2DA, United Kingdom
| | - Pier D Lambiase
- Barts Heart Centre, St Bartholomew's Hospital, West Smithfield, EC1A 7BE, United Kingdom; Institute of Cardiovascular Science, University College London, Gower Street, WC1E 6HX, United Kingdom
| | - Richard Schilling
- Barts Heart Centre, St Bartholomew's Hospital, West Smithfield, EC1A 7BE, United Kingdom
| | - Chris Finan
- Institute of Cardiovascular Science, University College London, Gower Street, WC1E 6HX, United Kingdom; UCL British Heart Foundation Research Accelerator, United Kingdom; Division Heart and Lungs, Department of Cardiology, University Medical Center Utrecht, Utrecht University, the Netherlands
| | - Amand Floriaan Schmidt
- Institute of Cardiovascular Science, University College London, Gower Street, WC1E 6HX, United Kingdom; UCL British Heart Foundation Research Accelerator, United Kingdom; Division Heart and Lungs, Department of Cardiology, University Medical Center Utrecht, Utrecht University, the Netherlands; Department of Cardiology, Amsterdam Cardiovascular Sciences, Amsterdam University Medical Centres, University of Amsterdam, the Netherlands
| | - Rui Providencia
- Institute of Health Informatics, University College London, 222 Euston Road, NW1 2DA, United Kingdom; Barts Heart Centre, St Bartholomew's Hospital, West Smithfield, EC1A 7BE, United Kingdom
| |
Collapse
|
2
|
Wang X, Liu D, Luo J, Kong D, Zhang Y. Exploring the Role of Enhancer-Mediated Transcriptional Regulation in Precision Biology. Int J Mol Sci 2023; 24:10843. [PMID: 37446021 PMCID: PMC10342031 DOI: 10.3390/ijms241310843] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2023] [Revised: 06/18/2023] [Accepted: 06/25/2023] [Indexed: 07/15/2023] Open
Abstract
The emergence of precision biology has been driven by the development of advanced technologies and techniques in high-resolution biological research systems. Enhancer-mediated transcriptional regulation, a complex network of gene expression and regulation in eukaryotes, has attracted significant attention as a promising avenue for investigating the underlying mechanisms of biological processes and diseases. To address biological problems with precision, large amounts of data, functional information, and research on the mechanisms of action of biological molecules is required to address biological problems with precision. Enhancers, including typical enhancers and super enhancers, play a crucial role in gene expression and regulation within this network. The identification and targeting of disease-associated enhancers hold the potential to advance precision medicine. In this review, we present the concepts, progress, importance, and challenges in precision biology, transcription regulation, and enhancers. Furthermore, we propose a model of transcriptional regulation for multi-enhancers and provide examples of their mechanisms in mammalian cells, thereby enhancing our understanding of how enhancers achieve precise regulation of gene expression in life processes. Precision biology holds promise in providing new tools and platforms for discovering insights into gene expression and disease occurrence, ultimately benefiting individuals and society as a whole.
Collapse
Affiliation(s)
- Xueyan Wang
- College of Life Science and Technology, Huazhong Agricultural University, Wuhan 430070, China;
- Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Key Laboratory of Livestock and Poultry Multi-Omics of MARA, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen 518120, China; (D.L.); (J.L.); (D.K.)
| | - Danli Liu
- Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Key Laboratory of Livestock and Poultry Multi-Omics of MARA, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen 518120, China; (D.L.); (J.L.); (D.K.)
| | - Jing Luo
- Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Key Laboratory of Livestock and Poultry Multi-Omics of MARA, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen 518120, China; (D.L.); (J.L.); (D.K.)
| | - Dashuai Kong
- Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Key Laboratory of Livestock and Poultry Multi-Omics of MARA, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen 518120, China; (D.L.); (J.L.); (D.K.)
| | - Yubo Zhang
- College of Life Science and Technology, Huazhong Agricultural University, Wuhan 430070, China;
- Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Key Laboratory of Livestock and Poultry Multi-Omics of MARA, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen 518120, China; (D.L.); (J.L.); (D.K.)
| |
Collapse
|
3
|
Clay SM, Schoettler N, Goldstein AM, Carbonetto P, Dapas M, Altman MC, Rosasco MG, Gern JE, Jackson DJ, Im HK, Stephens M, Nicolae DL, Ober C. Fine-mapping studies distinguish genetic risks for childhood- and adult-onset asthma in the HLA region. Genome Med 2022; 14:55. [PMID: 35606880 PMCID: PMC9128203 DOI: 10.1186/s13073-022-01058-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2021] [Accepted: 05/12/2022] [Indexed: 02/02/2023] Open
Abstract
BACKGROUND Genome-wide association studies of asthma have revealed robust associations with variation across the human leukocyte antigen (HLA) complex with independent associations in the HLA class I and class II regions for both childhood-onset asthma (COA) and adult-onset asthma (AOA). However, the specific variants and genes contributing to risk are unknown. METHODS We used Bayesian approaches to perform genetic fine-mapping for COA and AOA (n=9432 and 21,556, respectively; n=318,167 shared controls) in White British individuals from the UK Biobank and to perform expression quantitative trait locus (eQTL) fine-mapping in immune (lymphoblastoid cell lines, n=398; peripheral blood mononuclear cells, n=132) and airway (nasal epithelial cells, n=188) cells from ethnically diverse individuals. We also examined putatively causal protein coding variation from protein crystal structures and conducted replication studies in independent multi-ethnic cohorts from the UK Biobank (COA n=1686; AOA n=3666; controls n=56,063). RESULTS Genetic fine-mapping revealed both shared and distinct causal variation between COA and AOA in the class I region but only distinct causal variation in the class II region. Both gene expression levels and amino acid variation contributed to risk. Our results from eQTL fine-mapping and amino acid visualization suggested that the HLA-DQA1*03:01 allele and variation associated with expression of the nonclassical HLA-DQA2 and HLA-DQB2 genes accounted entirely for the most significant association with AOA in GWAS. Our studies also suggested a potentially prominent role for HLA-C protein coding variation in the class I region in COA. We replicated putatively causal variant associations in a multi-ethnic cohort. CONCLUSIONS We highlight roles for both gene expression and protein coding variation in asthma risk and identified putatively causal variation and genes in the HLA region. A convergence of genomic, transcriptional, and protein coding evidence implicates the HLA-DQA2 and HLA-DQB2 genes and HLA-DQA1*03:01 allele in AOA.
Collapse
Affiliation(s)
- Selene M Clay
- Department of Human Genetics, University of Chicago, Chicago, IL, 60637, USA.
| | - Nathan Schoettler
- Section of Pulmonary and Critical Care, Department of Medicine, University of Chicago, Chicago, IL, 60637, USA
| | - Andrew M Goldstein
- Department of Statistics, University of Chicago, Chicago, IL, 60637, USA
| | - Peter Carbonetto
- Department of Human Genetics, University of Chicago, Chicago, IL, 60637, USA
| | - Matthew Dapas
- Department of Human Genetics, University of Chicago, Chicago, IL, 60637, USA
| | - Matthew C Altman
- Division of Allergy and Infectious Diseases, Department of Medicine, University of Washington, Seattle, WA, 98109, USA
- Systems Immunology Program, Benaroya Research Institute, Seattle, WA, 98101, USA
| | - Mario G Rosasco
- Systems Immunology Program, Benaroya Research Institute, Seattle, WA, 98101, USA
| | - James E Gern
- Department of Pediatrics, University of Wisconsin, School of Medicine and Public Health, Madison, WI, 53706, USA
| | - Daniel J Jackson
- Department of Pediatrics, University of Wisconsin, School of Medicine and Public Health, Madison, WI, 53706, USA
| | - Hae Kyung Im
- Section of Genetic Medicine, Department of Medicine, University of Chicago, Chicago, IL, 60637, USA
| | - Matthew Stephens
- Department of Statistics, University of Chicago, Chicago, IL, 60637, USA
| | - Dan L Nicolae
- Department of Human Genetics, University of Chicago, Chicago, IL, 60637, USA
- Department of Statistics, University of Chicago, Chicago, IL, 60637, USA
| | - Carole Ober
- Department of Human Genetics, University of Chicago, Chicago, IL, 60637, USA.
| |
Collapse
|
4
|
Sun S, Zhu J, Mozaffari S, Ober C, Chen M, Zhou X. Heritability estimation and differential analysis of count data with generalized linear mixed models in genomic sequencing studies. Bioinformatics 2019; 35:487-496. [PMID: 30020412 PMCID: PMC6361238 DOI: 10.1093/bioinformatics/bty644] [Citation(s) in RCA: 39] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2018] [Revised: 06/24/2018] [Accepted: 07/17/2018] [Indexed: 12/13/2022] Open
Abstract
Motivation Genomic sequencing studies, including RNA sequencing and bisulfite sequencing studies, are becoming increasingly common and increasingly large. Large genomic sequencing studies open doors for accurate molecular trait heritability estimation and powerful differential analysis. Heritability estimation and differential analysis in sequencing studies requires the development of statistical methods that can properly account for the count nature of the sequencing data and that are computationally efficient for large datasets. Results Here, we develop such a method, PQLseq (Penalized Quasi-Likelihood for sequencing count data), to enable effective and efficient heritability estimation and differential analysis using the generalized linear mixed model framework. With extensive simulations and comparisons to previous methods, we show that PQLseq is the only method currently available that can produce unbiased heritability estimates for sequencing count data. In addition, we show that PQLseq is well suited for differential analysis in large sequencing studies, providing calibrated type I error control and more power compared to the standard linear mixed model methods. Finally, we apply PQLseq to perform gene expression heritability estimation and differential expression analysis in a large RNA sequencing study in the Hutterites. Availability and implementation PQLseq is implemented as an R package with source code freely available at www.xzlab.org/software.html and https://cran.r-project.org/web/packages/PQLseq/index.html. Supplementary information Supplementary data are available at Bioinformatics online.
Collapse
Affiliation(s)
- Shiquan Sun
- Department of Computer Science, Northwestern Polytechnical University, Xi’an, Shaanxi, China
- Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA
| | - Jiaqiang Zhu
- Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA
| | - Sahar Mozaffari
- Department of Human Genetics, University of Chicago, Chicago, IL, USA
| | - Carole Ober
- Department of Human Genetics, University of Chicago, Chicago, IL, USA
| | - Mengjie Chen
- Department of Human Genetics, University of Chicago, Chicago, IL, USA
- Department of Medicine, University of Chicago, Chicago, IL, USA
| | - Xiang Zhou
- Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA
- Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA
| |
Collapse
|
5
|
Mozaffari SV, DeCara JM, Shah SJ, Sidore C, Fiorillo E, Cucca F, Lang RM, Nicolae DL, Ober C. Parent-of-origin effects on quantitative phenotypes in a large Hutterite pedigree. Commun Biol 2019; 2:28. [PMID: 30675526 PMCID: PMC6338666 DOI: 10.1038/s42003-018-0267-4] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2018] [Accepted: 12/14/2018] [Indexed: 12/22/2022] Open
Abstract
The impact of the parental origin of associated alleles in GWAS has been largely ignored. Yet sequence variants could affect traits differently depending on whether they are inherited from the mother or the father, as in imprinted regions, where identical inherited DNA sequences can have different effects based on the parental origin. To explore parent-of-origin effects (POEs), we studied 21 quantitative phenotypes in a large Hutterite pedigree to identify variants with single parent (maternal-only or paternal-only) effects, and then variants with opposite parental effects. Here we show that POEs, which can be opposite in direction, are relatively common in humans, have potentially important clinical effects, and will be missed in traditional GWAS. We identified POEs with 11 phenotypes, most of which are risk factors for cardiovascular disease. Many of the loci identified are characteristic of imprinted regions and are associated with the expression of nearby genes.
Collapse
Affiliation(s)
- Sahar V. Mozaffari
- Department of Human Genetics, University of Chicago, Chicago, IL 60637 USA
- Committee on Genetics, Genomics, and Systems Biology, University of Chicago, Chicago, IL 60637 USA
| | - Jeanne M. DeCara
- Department of Medicine, University of Chicago, Chicago, IL 60637 USA
| | - Sanjiv J. Shah
- Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL 60611 USA
| | - Carlo Sidore
- Istituto di Ricerca Genetica e Biomedica (IRGB), CNR, Monserrato, 09042 Italy
| | - Edoardo Fiorillo
- Istituto di Ricerca Genetica e Biomedica (IRGB), CNR, Monserrato, 09042 Italy
| | - Francesco Cucca
- Istituto di Ricerca Genetica e Biomedica (IRGB), CNR, Monserrato, 09042 Italy
- Dipartimento di Scienze Biomediche, Universita di Sassari, Sassari, 07100 Italy
| | - Roberto M. Lang
- Department of Medicine, University of Chicago, Chicago, IL 60637 USA
| | - Dan L. Nicolae
- Department of Human Genetics, University of Chicago, Chicago, IL 60637 USA
- Committee on Genetics, Genomics, and Systems Biology, University of Chicago, Chicago, IL 60637 USA
- Department of Medicine, University of Chicago, Chicago, IL 60637 USA
- Department of Statistics, University of Chicago, Chicago, IL 60637 USA
| | - Carole Ober
- Department of Human Genetics, University of Chicago, Chicago, IL 60637 USA
- Committee on Genetics, Genomics, and Systems Biology, University of Chicago, Chicago, IL 60637 USA
| |
Collapse
|
6
|
Prosperi M, Min JS, Bian J, Modave F. Big data hurdles in precision medicine and precision public health. BMC Med Inform Decis Mak 2018; 18:139. [PMID: 30594159 PMCID: PMC6311005 DOI: 10.1186/s12911-018-0719-2] [Citation(s) in RCA: 87] [Impact Index Per Article: 14.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2018] [Accepted: 12/04/2018] [Indexed: 12/18/2022] Open
Abstract
BACKGROUND Nowadays, trendy research in biomedical sciences juxtaposes the term 'precision' to medicine and public health with companion words like big data, data science, and deep learning. Technological advancements permit the collection and merging of large heterogeneous datasets from different sources, from genome sequences to social media posts or from electronic health records to wearables. Additionally, complex algorithms supported by high-performance computing allow one to transform these large datasets into knowledge. Despite such progress, many barriers still exist against achieving precision medicine and precision public health interventions for the benefit of the individual and the population. MAIN BODY The present work focuses on analyzing both the technical and societal hurdles related to the development of prediction models of health risks, diagnoses and outcomes from integrated biomedical databases. Methodological challenges that need to be addressed include improving semantics of study designs: medical record data are inherently biased, and even the most advanced deep learning's denoising autoencoders cannot overcome the bias if not handled a priori by design. Societal challenges to face include evaluation of ethically actionable risk factors at the individual and population level; for instance, usage of gender, race, or ethnicity as risk modifiers, not as biological variables, could be replaced by modifiable environmental proxies such as lifestyle and dietary habits, household income, or access to educational resources. CONCLUSIONS Data science for precision medicine and public health warrants an informatics-oriented formalization of the study design and interoperability throughout all levels of the knowledge inference process, from the research semantics, to model development, and ultimately to implementation.
Collapse
Affiliation(s)
- Mattia Prosperi
- Department of Epidemiology, College of Medicine & College of Public Health and Health Professions, University of Florida, Gainesville, FL, 32610, USA.
| | - Jae S Min
- Department of Epidemiology, College of Medicine & College of Public Health and Health Professions, University of Florida, Gainesville, FL, 32610, USA
| | - Jiang Bian
- Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, FL, 32610, USA
| | - François Modave
- Center for Health Outcomes and Informatics Research, Loyola University Chicago, Maywood, IL, 60153, USA
| |
Collapse
|
7
|
Cvetesic N, Leitch HG, Borkowska M, Müller F, Carninci P, Hajkova P, Lenhard B. SLIC-CAGE: high-resolution transcription start site mapping using nanogram-levels of total RNA. Genome Res 2018; 28:1943-1956. [PMID: 30404778 PMCID: PMC6280763 DOI: 10.1101/gr.235937.118] [Citation(s) in RCA: 25] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2018] [Accepted: 10/25/2018] [Indexed: 01/22/2023]
Abstract
Cap analysis of gene expression (CAGE) is a methodology for genome-wide quantitative mapping of mRNA 5′ ends to precisely capture transcription start sites at a single nucleotide resolution. In combination with high-throughput sequencing, CAGE has revolutionized our understanding of the rules of transcription initiation, led to discovery of new core promoter sequence features, and discovered transcription initiation at enhancers genome-wide. The biggest limitation of CAGE is that even the most recently improved version (nAnT-iCAGE) still requires large amounts of total cellular RNA (5 µg), preventing its application to scarce biological samples such as those from early embryonic development or rare cell types. Here, we present SLIC-CAGE, a Super-Low Input Carrier-CAGE approach to capture 5′ ends of RNA polymerase II transcripts from as little as 5–10 ng of total RNA. This dramatic increase in sensitivity is achieved by specially designed, selectively degradable carrier RNA. We demonstrate the ability of SLIC-CAGE to generate data for genome-wide promoterome with 1000-fold less material than required by existing CAGE methods, by generating a complex, high-quality library from mouse embryonic day 11.5 primordial germ cells.
Collapse
Affiliation(s)
- Nevena Cvetesic
- Institute of Clinical Sciences, Faculty of Medicine, Imperial College London, London W12 0NN, United Kingdom.,MRC London Institute of Medical Sciences, London W12 0NN, United Kingdom
| | - Harry G Leitch
- Institute of Clinical Sciences, Faculty of Medicine, Imperial College London, London W12 0NN, United Kingdom.,MRC London Institute of Medical Sciences, London W12 0NN, United Kingdom
| | - Malgorzata Borkowska
- Institute of Clinical Sciences, Faculty of Medicine, Imperial College London, London W12 0NN, United Kingdom.,MRC London Institute of Medical Sciences, London W12 0NN, United Kingdom
| | - Ferenc Müller
- Institute of Cancer and Genomic Sciences, University of Birmingham, Edgbaston B15 2TT, United Kingdom
| | - Piero Carninci
- RIKEN Center for Life Science Technologies, Division of Genomic Technologies, Yokohama City, Kanagawa 230-0045, Japan.,RIKEN Omics Science Center, Yokohama City, Kanagawa 230-0045, Japan
| | - Petra Hajkova
- Institute of Clinical Sciences, Faculty of Medicine, Imperial College London, London W12 0NN, United Kingdom.,MRC London Institute of Medical Sciences, London W12 0NN, United Kingdom
| | - Boris Lenhard
- Institute of Clinical Sciences, Faculty of Medicine, Imperial College London, London W12 0NN, United Kingdom.,MRC London Institute of Medical Sciences, London W12 0NN, United Kingdom.,Sars International Centre for Marine Molecular Biology, University of Bergen, N-5008 Bergen, Norway
| |
Collapse
|
8
|
Mozaffari SV, Stein MM, Magnaye KM, Nicolae DL, Ober C. Parent of origin gene expression in a founder population identifies two new candidate imprinted genes at known imprinted regions. PLoS One 2018; 13:e0203906. [PMID: 30204804 PMCID: PMC6133383 DOI: 10.1371/journal.pone.0203906] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2018] [Accepted: 08/29/2018] [Indexed: 11/18/2022] Open
Abstract
Genomic imprinting is the phenomena that leads to silencing of one copy of a gene inherited from a specific parent. Mutations in imprinted regions have been involved in diseases showing parent of origin effects. Identifying genes with evidence of parent of origin expression patterns in family studies allows the detection of more subtle imprinting. Here, we use allele specific expression in lymphoblastoid cell lines from 306 Hutterites related in a single pedigree to provide formal evidence for parent of origin effects. We take advantage of phased genotype data to assign parent of origin to RNA-seq reads in individuals with gene expression data. Our approach identified known imprinted genes, two putative novel imprinted genes, PXDC1 and PWAR6, and 14 genes with asymmetrical parent of origin gene expression. We used gene expression in peripheral blood leukocytes (PBL) to validate our findings, and then confirmed imprinting control regions (ICRs) using DNA methylation levels in the PBLs.
Collapse
Affiliation(s)
- Sahar V. Mozaffari
- Committee on Genetics, Genomics & Systems Biology, University of Chicago, Chicago, Illinois, United States of America
- Department of Human Genetics, University of Chicago, Chicago, Illinois, United States of America
| | - Michelle M. Stein
- Department of Human Genetics, University of Chicago, Chicago, Illinois, United States of America
| | - Kevin M. Magnaye
- Department of Human Genetics, University of Chicago, Chicago, Illinois, United States of America
| | - Dan L. Nicolae
- Committee on Genetics, Genomics & Systems Biology, University of Chicago, Chicago, Illinois, United States of America
- Department of Human Genetics, University of Chicago, Chicago, Illinois, United States of America
- Department of Statistics, University of Chicago, Chicago, Illinois, United States of America
| | - Carole Ober
- Committee on Genetics, Genomics & Systems Biology, University of Chicago, Chicago, Illinois, United States of America
- Department of Human Genetics, University of Chicago, Chicago, Illinois, United States of America
| |
Collapse
|
9
|
Knowles DA, Burrows CK, Blischak JD, Patterson KM, Serie DJ, Norton N, Ober C, Pritchard JK, Gilad Y. Determining the genetic basis of anthracycline-cardiotoxicity by molecular response QTL mapping in induced cardiomyocytes. eLife 2018; 7:e33480. [PMID: 29737278 PMCID: PMC6010343 DOI: 10.7554/elife.33480] [Citation(s) in RCA: 69] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2017] [Accepted: 04/30/2018] [Indexed: 12/17/2022] Open
Abstract
Anthracycline-induced cardiotoxicity (ACT) is a key limiting factor in setting optimal chemotherapy regimes, with almost half of patients expected to develop congestive heart failure given high doses. However, the genetic basis of sensitivity to anthracyclines remains unclear. We created a panel of iPSC-derived cardiomyocytes from 45 individuals and performed RNA-seq after 24 hr exposure to varying doxorubicin dosages. The transcriptomic response is substantial: the majority of genes are differentially expressed and over 6000 genes show evidence of differential splicing, the later driven by reduced splicing fidelity in the presence of doxorubicin. We show that inter-individual variation in transcriptional response is predictive of in vitro cell damage, which in turn is associated with in vivo ACT risk. We detect 447 response-expression quantitative trait loci (QTLs) and 42 response-splicing QTLs, which are enriched in lower ACT GWAS [Formula: see text]-values, supporting the in vivo relevance of our map of genetic regulation of cellular response to anthracyclines.
Collapse
Affiliation(s)
- David A Knowles
- Department of GeneticsStanford UniversityStanfordUnited States
- Department of RadiologyStanford UniversityStanfordUnited States
| | | | - John D Blischak
- Department of Human GeneticsUniversity of ChicagoChicagoUnited States
| | | | - Daniel J Serie
- Department of Health Sciences ResearchMayo ClinicJacksonvilleUnited States
| | - Nadine Norton
- Department of Cancer BiologyMayo ClinicJacksonvilleUnited States
| | - Carole Ober
- Department of Human GeneticsUniversity of ChicagoChicagoUnited States
| | - Jonathan K Pritchard
- Department of GeneticsStanford UniversityStanfordUnited States
- Department of BiologyStanford UniversityStanfordUnited States
- Howard Hughes Medical InstituteStanford UniversityStanfordUnited States
| | - Yoav Gilad
- Department of Human GeneticsUniversity of ChicagoChicagoUnited States
- Department of MedicineUniversity of ChicagoChicagoUnited States
| |
Collapse
|
10
|
Gershon ES, Pearlson G, Keshavan MS, Tamminga C, Clementz B, Buckley PF, Alliey-Rodriguez N, Liu C, Sweeney JA, Keedy S, Meda SA, Tandon N, Shafee R, Bishop JR, Ivleva EI. Genetic analysis of deep phenotyping projects in common disorders. Schizophr Res 2018; 195:51-57. [PMID: 29056493 PMCID: PMC5910299 DOI: 10.1016/j.schres.2017.09.031] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/02/2017] [Revised: 09/19/2017] [Accepted: 09/22/2017] [Indexed: 11/19/2022]
Abstract
Several studies of complex psychotic disorders with large numbers of neurobiological phenotypes are currently under way, in living patients and controls, and on assemblies of brain specimens. Genetic analyses of such data typically present challenges, because of the choice of underlying hypotheses on genetic architecture of the studied disorders and phenotypes, large numbers of phenotypes, the appropriate multiple testing corrections, limited numbers of subjects, imputations required on missing phenotypes and genotypes, and the cross-disciplinary nature of the phenotype measures. Advances in genotype and phenotype imputation, and in genome-wide association (GWAS) methods, are useful in dealing with these challenges. As compared with the more traditional single-trait analyses, deep phenotyping with simultaneous genome-wide analyses serves as a discovery tool for previously unsuspected relationships of phenotypic traits with each other, and with specific molecular involvements.
Collapse
Affiliation(s)
- Elliot S Gershon
- Department of Psychiatry, Department of Human Genetics, University of Chicago, United States.
| | - Godfrey Pearlson
- Yale University Departments of Psychiatry & Neuroscience, Hartford, CT, United States; Olin Neuropsychiatry Research Center, Institute of Living, Hartford, Connecticut, USA
| | | | - Carol Tamminga
- Department of Psychiatry, University of Texas Southwestern Medical Center, Dallas, TX, United States
| | - Brett Clementz
- Department of Psychology, University of Georgia, Athens, GA, United States
| | - Peter F Buckley
- School of Medicine Virginia Commonwealth University (VCU), Richmond, VA, United States
| | - Ney Alliey-Rodriguez
- University of Chicago, Department of Psychiatry and Behavioral Neurosciences, Chicago, IL, United States
| | - Chunyu Liu
- University of Illinois at Chicago, Chicago, IL, United States
| | - John A Sweeney
- Department of Psychiatry, University of Texas Southwestern Medical Center, Dallas, TX, United States; University of Cincinnati, Department of Psychiatry and Behavioral Neuroscience, Cincinnati, OH, United States
| | - Sarah Keedy
- University of Chicago, Department of Psychiatry and Behavioral Neurosciences, Chicago, IL, United States
| | - Shashwath A Meda
- Yale University Departments of Psychiatry & Neuroscience, Hartford, CT, United States
| | - Neeraj Tandon
- Beth Israel Deaconess Medical Center, Dept of Psychiatry, Harvard Medical School, United States
| | - Rebecca Shafee
- Broad Institute of MIT and Harvard, Cambridge, MA, United States; Department of Genetics, Harvard Medical School, United States
| | - Jeffrey R Bishop
- Department of Clinical and Experimental Pharmacology, University of Minnesota, Minneapolis, MN, United States
| | - Elena I Ivleva
- Department of Psychiatry, University of Texas Southwestern Medical Center, Dallas, TX, United States
| |
Collapse
|
11
|
DNA sequence-level analyses reveal potential phenotypic modifiers in a large family with psychiatric disorders. Mol Psychiatry 2018; 23:2254-2265. [PMID: 29880880 PMCID: PMC6294736 DOI: 10.1038/s41380-018-0087-4] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/02/2017] [Revised: 03/30/2018] [Accepted: 04/09/2018] [Indexed: 02/07/2023]
Abstract
Psychiatric disorders are a group of genetically related diseases with highly polygenic architectures. Genome-wide association analyses have made substantial progress towards understanding the genetic architecture of these disorders. More recently, exome- and whole-genome sequencing of cases and families have identified rare, high penetrant variants that provide direct functional insight. There remains, however, a gap in the heritability explained by these complementary approaches. To understand how multiple genetic variants combine to modify both severity and penetrance of a highly penetrant variant, we sequenced 48 whole genomes from a family with a high loading of psychiatric disorder linked to a balanced chromosomal translocation. The (1;11)(q42;q14.3) translocation directly disrupts three genes: DISC1, DISC2, DISC1FP and has been linked to multiple brain imaging and neurocognitive outcomes in the family. Using DNA sequence-level linkage analysis, functional annotation and population-based association, we identified common and rare variants in GRM5 (minor allele frequency (MAF) > 0.05), PDE4D (MAF > 0.2) and CNTN5 (MAF < 0.01) that may help explain the individual differences in phenotypic expression in the family. We suggest that whole-genome sequencing in large families will improve the understanding of the combined effects of the rare and common sequence variation underlying psychiatric phenotypes.
Collapse
|
12
|
Igartua C, Mozaffari SV, Nicolae DL, Ober C. Rare non-coding variants are associated with plasma lipid traits in a founder population. Sci Rep 2017; 7:16415. [PMID: 29180722 PMCID: PMC5704019 DOI: 10.1038/s41598-017-16550-8] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2017] [Accepted: 11/14/2017] [Indexed: 12/31/2022] Open
Abstract
Founder populations are ideally suited for studies on the clinical effects of alleles that are rare in general populations but occur at higher frequencies in these isolated populations. Whole genome sequencing in 98 Hutterites, a founder population of European descent, and subsequent imputation revealed 660,238 single nucleotide polymorphisms that are rare (<1%) or absent in European populations, but occur at frequencies >1% in the Hutterites. We examined the effects of these rare in European variants on plasma lipid levels in 828 Hutterites and applied a Bayesian hierarchical framework to prioritize potentially causal variants based on functional annotations. We identified two novel non-coding rare variants associated with LDL cholesterol (rs17242388 in LDLR) and HDL cholesterol (rs189679427 between GOT2 and APOOP5), and replicated previous associations of a splice variant in APOC3 (rs138326449) with triglycerides and HDL-C. All three variants are at well-replicated loci in GWAS but are independent from and have larger effect sizes than the known common variation in these regions. Candidate eQTL analyses in in LCLs in the Hutterites suggest that these rare non-coding variants are likely to mediate their effects on lipid traits by regulating gene expression.
Collapse
Affiliation(s)
- Catherine Igartua
- Department of Human Genetics, University of Chicago, Chicago, IL, 60637, USA.
| | - Sahar V Mozaffari
- Department of Human Genetics, University of Chicago, Chicago, IL, 60637, USA.,Committee of Genetics, Genomics and Systems Biology, University of Chicago, Chicago, IL, 60637, USA
| | - Dan L Nicolae
- Department of Human Genetics, University of Chicago, Chicago, IL, 60637, USA.,Department of Statistics, University of Chicago, Chicago, IL, 60637, USA.,Department of Medicine, University of Chicago, Chicago, IL, 60637, USA
| | - Carole Ober
- Department of Human Genetics, University of Chicago, Chicago, IL, 60637, USA.,Committee of Genetics, Genomics and Systems Biology, University of Chicago, Chicago, IL, 60637, USA
| |
Collapse
|