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Bien SA, Su YR, Conti DV, Harrison TA, Qu C, Guo X, Lu Y, Albanes D, Auer PL, Banbury BL, Berndt SI, Bézieau S, Brenner H, Buchanan DD, Caan BJ, Campbell PT, Carlson CS, Chan AT, Chang-Claude J, Chen S, Connolly CM, Easton DF, Feskens EJM, Gallinger S, Giles GG, Gunter MJ, Hampe J, Huyghe JR, Hoffmeister M, Hudson TJ, Jacobs EJ, Jenkins MA, Kampman E, Kang HM, Kühn T, Küry S, Lejbkowicz F, Le Marchand L, Milne RL, Li L, Li CI, Lindblom A, Lindor NM, Martín V, McNeil CE, Melas M, Moreno V, Newcomb PA, Offit K, Pharaoh PDP, Potter JD, Qu C, Riboli E, Rennert G, Sala N, Schafmayer C, Scacheri PC, Schmit SL, Severi G, Slattery ML, Smith JD, Trichopoulou A, Tumino R, Ulrich CM, van Duijnhoven FJB, Van Guelpen B, Weinstein SJ, White E, Wolk A, Woods MO, Wu AH, Abecasis GR, Casey G, Nickerson DA, Gruber SB, Hsu L, Zheng W, Peters U. Genetic variant predictors of gene expression provide new insight into risk of colorectal cancer. Hum Genet 2019; 138:307-326. [PMID: 30820706 PMCID: PMC6483948 DOI: 10.1007/s00439-019-01989-8] [Citation(s) in RCA: 32] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2018] [Accepted: 02/20/2019] [Indexed: 02/02/2023]
Abstract
Genome-wide association studies have reported 56 independently associated colorectal cancer (CRC) risk variants, most of which are non-coding and believed to exert their effects by modulating gene expression. The computational method PrediXcan uses cis-regulatory variant predictors to impute expression and perform gene-level association tests in GWAS without directly measured transcriptomes. In this study, we used reference datasets from colon (n = 169) and whole blood (n = 922) transcriptomes to test CRC association with genetically determined expression levels in a genome-wide analysis of 12,186 cases and 14,718 controls. Three novel associations were discovered from colon transverse models at FDR ≤ 0.2 and further evaluated in an independent replication including 32,825 cases and 39,933 controls. After adjusting for multiple comparisons, we found statistically significant associations using colon transcriptome models with TRIM4 (discovery P = 2.2 × 10- 4, replication P = 0.01), and PYGL (discovery P = 2.3 × 10- 4, replication P = 6.7 × 10- 4). Interestingly, both genes encode proteins that influence redox homeostasis and are related to cellular metabolic reprogramming in tumors, implicating a novel CRC pathway linked to cell growth and proliferation. Defining CRC risk regions as one megabase up- and downstream of one of the 56 independent risk variants, we defined 44 non-overlapping CRC-risk regions. Among these risk regions, we identified genes associated with CRC (P < 0.05) in 34/44 CRC-risk regions. Importantly, CRC association was found for two genes in the previously reported 2q25 locus, CXCR1 and CXCR2, which are potential cancer therapeutic targets. These findings provide strong candidate genes to prioritize for subsequent laboratory follow-up of GWAS loci. This study is the first to implement PrediXcan in a large colorectal cancer study and findings highlight the utility of integrating transcriptome data in GWAS for discovery of, and biological insight into, risk loci.
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Affiliation(s)
- Stephanie A Bien
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, 98109, USA.
- Department of Public Health Sciences, University of Virginia School of Medicine, Charlottesville, VA, 22908, USA.
| | - Yu-Ru Su
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, 98109, USA
- Department of Public Health Sciences, University of Virginia School of Medicine, Charlottesville, VA, 22908, USA
| | - David V Conti
- USC Norris Comprehensive Cancer Center, University of Southern California, Los Angeles, CA, 90089, USA
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA, 90033, USA
- Department of Public Health Sciences, University of Virginia School of Medicine, Charlottesville, VA, 22908, USA
| | - Tabitha A Harrison
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, 98109, USA
- Department of Public Health Sciences, University of Virginia School of Medicine, Charlottesville, VA, 22908, USA
| | - Conghui Qu
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, 98109, USA
- Department of Public Health Sciences, University of Virginia School of Medicine, Charlottesville, VA, 22908, USA
| | - Xingyi Guo
- Division of Epidemiology, Vanderbilt University School of Medicine, Nashville, TN, 37232, USA
- Department of Public Health Sciences, University of Virginia School of Medicine, Charlottesville, VA, 22908, USA
| | - Yingchang Lu
- Division of Epidemiology, Vanderbilt University School of Medicine, Nashville, TN, 37232, USA
- Department of Public Health Sciences, University of Virginia School of Medicine, Charlottesville, VA, 22908, USA
| | - Demetrius Albanes
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, 20892, USA
- Department of Public Health Sciences, University of Virginia School of Medicine, Charlottesville, VA, 22908, USA
| | - Paul L Auer
- Joseph J. Zilber School of Public Health, University of Wisconsin-Milwaukee, Milwaukee, WI, 53205, USA
- Department of Public Health Sciences, University of Virginia School of Medicine, Charlottesville, VA, 22908, USA
| | - Barbara L Banbury
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, 98109, USA
- Department of Public Health Sciences, University of Virginia School of Medicine, Charlottesville, VA, 22908, USA
| | - Sonja I Berndt
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, 20892, USA
- Department of Public Health Sciences, University of Virginia School of Medicine, Charlottesville, VA, 22908, USA
| | - Stéphane Bézieau
- Centre Hospitalier Universitaire Hotel-Dieu, 44093, Nantes, France
- Service de Génétique Médiczle, Centre Hospitalier Universitaire (CHU), 44093, Nantes, France
- Department of Public Health Sciences, University of Virginia School of Medicine, Charlottesville, VA, 22908, USA
| | - Hermann Brenner
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), 69120, Heidelberg, Germany
- Division of Preventive Oncology, German Cancer Research Center (DKFZ) and National Center for Tumor Diseases (NCT), 69120, Heidelberg, Germany
- German Cancer Consortium (DKTK), 69120, Heidelberg, Germany
- Department of Public Health Sciences, University of Virginia School of Medicine, Charlottesville, VA, 22908, USA
| | - Daniel D Buchanan
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, VIC, 3010, Australia
- Colorectal Oncogenomics Group, Department of Pathology, University of Melbourne, Melbourne, VIC, 3010, Australia
- Genetic Medicine and Familial Cancer Centre, The Royal Melbourne Hospital, Parkville, VIC, 3010, Australia
- Department of Public Health Sciences, University of Virginia School of Medicine, Charlottesville, VA, 22908, USA
| | - Bette J Caan
- Division of Research, Kaiser Permanente Medical Care Program of Northern California, Oakland, CA, 94612, USA
- Department of Public Health Sciences, University of Virginia School of Medicine, Charlottesville, VA, 22908, USA
| | - Peter T Campbell
- Epidemiology Research Program, American Cancer Society, Atlanta, GA, 30329-4251, USA
- Department of Public Health Sciences, University of Virginia School of Medicine, Charlottesville, VA, 22908, USA
| | - Christopher S Carlson
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, 98109, USA
- Department of Public Health Sciences, University of Virginia School of Medicine, Charlottesville, VA, 22908, USA
| | - Andrew T Chan
- Division of Gastroenterology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, 02114, USA
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, 02115, USA
- Department of Public Health Sciences, University of Virginia School of Medicine, Charlottesville, VA, 22908, USA
| | - Jenny Chang-Claude
- Unit of Genetic Epidemiology, Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), 69120, Heidelberg, Germany
- Genetic Tumour Epidemiology Group, University Medical Center Hamburg-Eppendorf, University Cancer Center Hamburg, 20246, Hamburg, Germany
- Department of Public Health Sciences, University of Virginia School of Medicine, Charlottesville, VA, 22908, USA
| | - Sai Chen
- Department of Biostatistics, University of Michigan, Ann Arbor, MI, 48109, USA
- Department of Public Health Sciences, University of Virginia School of Medicine, Charlottesville, VA, 22908, USA
| | - Charles M Connolly
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, 98109, USA
- Department of Public Health Sciences, University of Virginia School of Medicine, Charlottesville, VA, 22908, USA
| | - Douglas F Easton
- Department of Public Health and Primary Care School of Clinical Medicine, University of Cambridge, Cambridge, England, 01223, UK
- Department of Public Health Sciences, University of Virginia School of Medicine, Charlottesville, VA, 22908, USA
| | - Edith J M Feskens
- Division of Human Nutrition, Wageningen University & Research, Wageningen, The Netherlands
- Department of Public Health Sciences, University of Virginia School of Medicine, Charlottesville, VA, 22908, USA
| | - Steven Gallinger
- Lunenfeld Tanenbaum Research Institute, Mount Sinai Hospital, University of Toronto, Toronto, ON, 1X5, Canada
- Department of Public Health Sciences, University of Virginia School of Medicine, Charlottesville, VA, 22908, USA
| | - Graham G Giles
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, VIC, 3010, Australia
- Cancer Epidemiology & Intelligence Division, Cancer Council Victoria, Melbourne, 3004, Australia
- Department of Public Health Sciences, University of Virginia School of Medicine, Charlottesville, VA, 22908, USA
| | - Marc J Gunter
- Section for Epidemiology, Department of Public Health, Aarhus University, Aarhus, Denmark
- Department of Public Health Sciences, University of Virginia School of Medicine, Charlottesville, VA, 22908, USA
| | - Jochen Hampe
- Medical Department 1, University Hospital Dresden, TU Dresden, 01307, Dresden, Germany
- Department of Public Health Sciences, University of Virginia School of Medicine, Charlottesville, VA, 22908, USA
| | - Jeroen R Huyghe
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, 98109, USA
- Department of Public Health Sciences, University of Virginia School of Medicine, Charlottesville, VA, 22908, USA
| | - Michael Hoffmeister
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), 69120, Heidelberg, Germany
- Department of Public Health Sciences, University of Virginia School of Medicine, Charlottesville, VA, 22908, USA
| | - Thomas J Hudson
- Ontario Institute for Cancer Research, Toronto, ON, Canada
- AbbVie Inc, 1500 Seaport Blvd, Redwood City, CA, 94063, USA
- Department of Public Health Sciences, University of Virginia School of Medicine, Charlottesville, VA, 22908, USA
| | - Eric J Jacobs
- Epidemiology Research Program, American Cancer Society, Atlanta, GA, 30329-4251, USA
- Department of Public Health Sciences, University of Virginia School of Medicine, Charlottesville, VA, 22908, USA
| | - Mark A Jenkins
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, VIC, 3010, Australia
- Department of Public Health Sciences, University of Virginia School of Medicine, Charlottesville, VA, 22908, USA
| | - Ellen Kampman
- Division of Human Nutrition, Wageningen University & Research, Wageningen, The Netherlands
- Department of Public Health Sciences, University of Virginia School of Medicine, Charlottesville, VA, 22908, USA
| | - Hyun Min Kang
- Department of Biostatistics, University of Michigan, Ann Arbor, MI, 48109, USA
- Department of Public Health Sciences, University of Virginia School of Medicine, Charlottesville, VA, 22908, USA
| | - Tilman Kühn
- Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
- Department of Public Health Sciences, University of Virginia School of Medicine, Charlottesville, VA, 22908, USA
| | - Sébastien Küry
- Centre Hospitalier Universitaire Hotel-Dieu, 44093, Nantes, France
- Service de Génétique Médiczle, Centre Hospitalier Universitaire (CHU), 44093, Nantes, France
- Department of Public Health Sciences, University of Virginia School of Medicine, Charlottesville, VA, 22908, USA
| | - Flavio Lejbkowicz
- Clalit Health Services National Israeli Cancer Control Center, 34361, Haifa, Israel
- Department of Community Medicine and Epidemiology, Carmel Medical Center, 34361, Haifa, Israel
- Department of Public Health Sciences, University of Virginia School of Medicine, Charlottesville, VA, 22908, USA
| | - Loic Le Marchand
- University of Hawai'i Cancer Center, Honolulu, Hawaii, 96813, USA
- Department of Public Health Sciences, University of Virginia School of Medicine, Charlottesville, VA, 22908, USA
| | - Roger L Milne
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, VIC, 3010, Australia
- Cancer Epidemiology & Intelligence Division, Cancer Council Victoria, Melbourne, 3004, Australia
- Department of Public Health Sciences, University of Virginia School of Medicine, Charlottesville, VA, 22908, USA
| | - Li Li
- Department of Family Medicine and Community Health, Case Western Reserve University, Cleveland, OH, 44106, USA
- Department of Public Health Sciences, University of Virginia School of Medicine, Charlottesville, VA, 22908, USA
| | - Christopher I Li
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, 98109, USA
- Department of Public Health Sciences, University of Virginia School of Medicine, Charlottesville, VA, 22908, USA
| | - Annika Lindblom
- Department of Clinical Genetics, Karolinska University Hospital Solna, 171 77, Stockholm, Sweden
- Department of Molecular Medicine and Surgery, Karolinska Institutet Solna, 171 77, Stockholm, Sweden
- Department of Public Health Sciences, University of Virginia School of Medicine, Charlottesville, VA, 22908, USA
| | - Noralane M Lindor
- Department of Health Science Research, Mayo Clinic Arizona, Scottsdale, AZ, 85259, USA
- Department of Public Health Sciences, University of Virginia School of Medicine, Charlottesville, VA, 22908, USA
| | - Vicente Martín
- Biomedicine Institute (IBIOMED), University of León, León, Spain
- CIBER Epidemiología y Salud Pública (CIBERESP), 28029, Madrid, Spain
- Department of Public Health Sciences, University of Virginia School of Medicine, Charlottesville, VA, 22908, USA
| | - Caroline E McNeil
- USC Norris Comprehensive Cancer Center, University of Southern California, Los Angeles, CA, 90089, USA
- Department of Public Health Sciences, University of Virginia School of Medicine, Charlottesville, VA, 22908, USA
| | - Marilena Melas
- USC Norris Comprehensive Cancer Center, University of Southern California, Los Angeles, CA, 90089, USA
- Department of Public Health Sciences, University of Virginia School of Medicine, Charlottesville, VA, 22908, USA
| | - Victor Moreno
- CIBER Epidemiología y Salud Pública (CIBERESP), 28029, Madrid, Spain
- Catalan Institute of Oncology, Bellvitge Biomedical Research Institute (IDIBELL), 08028, Barcelona, Spain
- University of Barcelona, 08007, Barcelona, Spain
- Department of Public Health Sciences, University of Virginia School of Medicine, Charlottesville, VA, 22908, USA
| | - Polly A Newcomb
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, 98109, USA
- Department of Public Health Sciences, University of Virginia School of Medicine, Charlottesville, VA, 22908, USA
| | - Kenneth Offit
- Department of Medicine, Clinical Genetics Service, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA
- Department of Public Health Sciences, University of Virginia School of Medicine, Charlottesville, VA, 22908, USA
| | - Paul D P Pharaoh
- Department of Public Health and Primary Care, Centre for Cancer Genetic Epidemiology, University of Cambridge, Cambridge, CB2 1TN, UK
- Department of Public Health Sciences, University of Virginia School of Medicine, Charlottesville, VA, 22908, USA
| | - John D Potter
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, 98109, USA
- Department of Public Health Sciences, University of Virginia School of Medicine, Charlottesville, VA, 22908, USA
| | - Chenxu Qu
- USC Norris Comprehensive Cancer Center, University of Southern California, Los Angeles, CA, 90089, USA
- Department of Public Health Sciences, University of Virginia School of Medicine, Charlottesville, VA, 22908, USA
| | - Elio Riboli
- School of Public Health, Imperial College London, London, UK
- Department of Public Health Sciences, University of Virginia School of Medicine, Charlottesville, VA, 22908, USA
| | - Gad Rennert
- Clalit Health Services National Israeli Cancer Control Center, 34361, Haifa, Israel
- Department of Community Medicine and Epidemiology, Carmel Medical Center, 34361, Haifa, Israel
- Department of Public Health Sciences, University of Virginia School of Medicine, Charlottesville, VA, 22908, USA
| | - Núria Sala
- Unit of Nutrition and Cancer, Cancer Epidemiology Research Program, Catalan Institute of Oncology-IDIBELL, L'Hospitalet de Llobregat, 08908, Barcelona, Spain
- Molecular Epidemiology Group, Translational Research Laboratory, Catalan Institute of Oncology-IDIBELL, L'Hospitalet de Llobregat, 08908, Barcelona, Spain
- Department of Public Health Sciences, University of Virginia School of Medicine, Charlottesville, VA, 22908, USA
| | - Clemens Schafmayer
- Department of General and Thoracic Surgery, University Hospital Schleswig-Holstein, Campus Kiel, 24118, Kiel, Germany
- Department of Public Health Sciences, University of Virginia School of Medicine, Charlottesville, VA, 22908, USA
| | - Peter C Scacheri
- Department of Genetics and Genome Sciences, Case Western Reserve University, Cleveland, OH, 44106, USA
- Department of Public Health Sciences, University of Virginia School of Medicine, Charlottesville, VA, 22908, USA
| | - Stephanie L Schmit
- Department of Cancer Epidemiology, H. Lee Moffitt Cancer Center and Research Institute, Inc, Tampa, FL, 33612, USA
- Department of Gastrointestinal Oncology, H. Lee Moffitt Cancer Center and Research Institute, Inc, Tampa, FL, 33612, USA
- Department of Public Health Sciences, University of Virginia School of Medicine, Charlottesville, VA, 22908, USA
| | - Gianluca Severi
- Centre for Research in Epidemiology and Population Health, Institut de Cancérologie Gustave Roussy, Villejuif, France
- Department of Public Health Sciences, University of Virginia School of Medicine, Charlottesville, VA, 22908, USA
| | - Martha L Slattery
- Department of Internal Medicine, University of Utah, Salt Lake City, UT, USA
- Department of Public Health Sciences, University of Virginia School of Medicine, Charlottesville, VA, 22908, USA
| | - Joshua D Smith
- Department Genome Sciences, University of Washington, 98195, Seattle, WA, USA
- Department of Public Health Sciences, University of Virginia School of Medicine, Charlottesville, VA, 22908, USA
| | - Antonia Trichopoulou
- Hellenic Health Foundation, 13 Kaisareias & Alexandroupoleos, 115 27, Athens, Greece
- WHO Collaborating Center for Nutrition and Health, Unit of Nutritional Epidemiology and Nutrition in Public Health, Department of Hygiene, Epidemiology and Medical Statistics, Medical School, National and Kapodistrian University of Athens, Mikras Asias 75, 115 27, Athens, Greece
- Department of Public Health Sciences, University of Virginia School of Medicine, Charlottesville, VA, 22908, USA
| | - Rosario Tumino
- Affiliation Cancer Registry, Department of Prevention, Azienda Sanitaria Provinciale di Ragusa, Ragusa, Italy
- Department of Public Health Sciences, University of Virginia School of Medicine, Charlottesville, VA, 22908, USA
| | - Cornelia M Ulrich
- Population Sciences, Huntsman Cancer Institute, Salt Lake City, UT, 84112, USA
- Department of Public Health Sciences, University of Virginia School of Medicine, Charlottesville, VA, 22908, USA
| | - Fränzel J B van Duijnhoven
- Division of Human Nutrition, Wageningen University & Research, Wageningen, The Netherlands
- Department of Public Health Sciences, University of Virginia School of Medicine, Charlottesville, VA, 22908, USA
| | - Bethany Van Guelpen
- Department of Medical Biosciences, Pathology, Umeå University, Umeå, Sweden
- Department of Public Health Sciences, University of Virginia School of Medicine, Charlottesville, VA, 22908, USA
| | - Stephanie J Weinstein
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, 20892, USA
- Department of Public Health Sciences, University of Virginia School of Medicine, Charlottesville, VA, 22908, USA
| | - Emily White
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, 98109, USA
- Department of Public Health Sciences, University of Virginia School of Medicine, Charlottesville, VA, 22908, USA
| | - Alicja Wolk
- Institute of Environmental Medicine, Karolinska Institutet Solna, 17177, Stockholm, Sweden
- Department of Surgical Sciences, Uppsala University, 75121, Uppsala, Sweden
- Department of Public Health Sciences, University of Virginia School of Medicine, Charlottesville, VA, 22908, USA
| | - Michael O Woods
- Discipline of Genetics, Faculty of Medicine, Memorial University of Newfoundland, Saint John's, NL, A1B 3V6, Canada
- Department of Public Health Sciences, University of Virginia School of Medicine, Charlottesville, VA, 22908, USA
| | - Anna H Wu
- USC Norris Comprehensive Cancer Center, University of Southern California, Los Angeles, CA, 90089, USA
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA, 90033, USA
- Department of Public Health Sciences, University of Virginia School of Medicine, Charlottesville, VA, 22908, USA
| | - Goncalo R Abecasis
- Department of Biostatistics, University of Michigan, Ann Arbor, MI, 48109, USA
- Department of Public Health Sciences, University of Virginia School of Medicine, Charlottesville, VA, 22908, USA
| | - Graham Casey
- Centre for Research in Epidemiology and Population Health, Institut de Cancérologie Gustave Roussy, Villejuif, France
- Department of Public Health Sciences, University of Virginia School of Medicine, Charlottesville, VA, 22908, USA
| | - Deborah A Nickerson
- Department Genome Sciences, University of Washington, 98195, Seattle, WA, USA
- Department of Public Health Sciences, University of Virginia School of Medicine, Charlottesville, VA, 22908, USA
| | - Stephen B Gruber
- USC Norris Comprehensive Cancer Center, University of Southern California, Los Angeles, CA, 90089, USA
- Department of Public Health Sciences, University of Virginia School of Medicine, Charlottesville, VA, 22908, USA
| | - Li Hsu
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, 98109, USA
- Department of Public Health Sciences, University of Virginia School of Medicine, Charlottesville, VA, 22908, USA
| | - Wei Zheng
- Division of Epidemiology, Vanderbilt University School of Medicine, Nashville, TN, 37232, USA
- Department of Public Health Sciences, University of Virginia School of Medicine, Charlottesville, VA, 22908, USA
- Vanderbilt-Ingram Cancer Center, Vanderbilt University, Nashville, TN, 37232, USA
| | - Ulrike Peters
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, 98109, USA
- Department of Public Health Sciences, University of Virginia School of Medicine, Charlottesville, VA, 22908, USA
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Mancuso N, Freund MK, Johnson R, Shi H, Kichaev G, Gusev A, Pasaniuc B. Probabilistic fine-mapping of transcriptome-wide association studies. Nat Genet 2019; 51:675-682. [PMID: 30926970 DOI: 10.1038/s41588-019-0367-1] [Citation(s) in RCA: 258] [Impact Index Per Article: 43.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2018] [Accepted: 02/01/2019] [Indexed: 11/09/2022]
Abstract
Transcriptome-wide association studies using predicted expression have identified thousands of genes whose locally regulated expression is associated with complex traits and diseases. In this work, we show that linkage disequilibrium induces significant gene-trait associations at non-causal genes as a function of the expression quantitative trait loci weights used in expression prediction. We introduce a probabilistic framework that models correlation among transcriptome-wide association study signals to assign a probability for every gene in the risk region to explain the observed association signal. Importantly, our approach remains accurate when expression data for causal genes are not available in the causal tissue by leveraging expression prediction from other tissues. Our approach yields credible sets of genes containing the causal gene at a nominal confidence level (for example, 90%) that can be used to prioritize genes for functional assays. We illustrate our approach by using an integrative analysis of lipid traits, where our approach prioritizes genes with strong evidence for causality.
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Affiliation(s)
- Nicholas Mancuso
- Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA.
| | - Malika K Freund
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - Ruth Johnson
- Department of Computer Science, University of California, Los Angeles, Los Angeles, CA, USA
| | - Huwenbo Shi
- Bioinformatics Interdepartmental Program, University of California, Los Angeles, Los Angeles, CA, USA
| | - Gleb Kichaev
- Bioinformatics Interdepartmental Program, University of California, Los Angeles, Los Angeles, CA, USA
| | | | - Bogdan Pasaniuc
- Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA. .,Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA. .,Bioinformatics Interdepartmental Program, University of California, Los Angeles, Los Angeles, CA, USA.
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203
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Gaspar HA, Gerring Z, Hübel C, Middeldorp CM, Derks EM, Breen G. Using genetic drug-target networks to develop new drug hypotheses for major depressive disorder. Transl Psychiatry 2019; 9:117. [PMID: 30877270 PMCID: PMC6420656 DOI: 10.1038/s41398-019-0451-4] [Citation(s) in RCA: 31] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/05/2018] [Revised: 01/28/2019] [Accepted: 02/12/2019] [Indexed: 12/25/2022] Open
Abstract
The major depressive disorder (MDD) working group of the Psychiatric Genomics Consortium (PGC) has published a genome-wide association study (GWAS) for MDD in 130,664 cases, identifying 44 risk variants. We used these results to investigate potential drug targets and repurposing opportunities. We built easily interpretable bipartite drug-target networks integrating interactions between drugs and their targets, genome-wide association statistics, and genetically predicted expression levels in different tissues, using the online tool Drug Targetor ( drugtargetor.com ). We also investigated drug-target relationships that could be impacting MDD. MAGMA was used to perform pathway analyses and S-PrediXcan to investigate the directionality of tissue-specific expression levels in patients vs. controls. Outside the major histocompatibility complex (MHC) region, 153 protein-coding genes are significantly associated with MDD in MAGMA after multiple testing correction; among these, five are predicted to be down or upregulated in brain regions and 24 are known druggable genes. Several drug classes were significantly enriched, including monoamine reuptake inhibitors, sex hormones, antipsychotics, and antihistamines, indicating an effect on MDD and potential repurposing opportunities. These findings not only require validation in model systems and clinical examination, but also show that GWAS may become a rich source of new therapeutic hypotheses for MDD and other psychiatric disorders that need new-and better-treatment options.
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Affiliation(s)
- Héléna A Gaspar
- King's College London, Institute of Psychiatry, Psychology and Neuroscience, Social, Genetic and Developmental Psychiatry (SGDP) Centre, London, SE5 8AF, UK.
- National Institute for Health Research Biomedical Research Centre, South London and Maudsley National Health Service Trust, London, EC1V 2PD, UK.
| | - Zachary Gerring
- Translational Neurogenomics Laboratory, QIMR Berghofer Institute of Medical Research, Brisbane City, QLD 4006, Australia
| | - Christopher Hübel
- King's College London, Institute of Psychiatry, Psychology and Neuroscience, Social, Genetic and Developmental Psychiatry (SGDP) Centre, London, SE5 8AF, UK
- National Institute for Health Research Biomedical Research Centre, South London and Maudsley National Health Service Trust, London, EC1V 2PD, UK
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Christel M Middeldorp
- Child Health Research Centre, University of Queensland, South Brisbane, QLD 4072, Australia
- Child and Youth Mental Health Service, Children's Health Queensland Hospital and Health Service, South Brisbane, QLD 4101, Australia
- Biological Psychology, Vrije Universiteit Amsterdam, 1081 HV, Amsterdam, Netherlands
| | - Eske M Derks
- Translational Neurogenomics Laboratory, QIMR Berghofer Institute of Medical Research, Brisbane City, QLD 4006, Australia
| | - Gerome Breen
- King's College London, Institute of Psychiatry, Psychology and Neuroscience, Social, Genetic and Developmental Psychiatry (SGDP) Centre, London, SE5 8AF, UK
- National Institute for Health Research Biomedical Research Centre, South London and Maudsley National Health Service Trust, London, EC1V 2PD, UK
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204
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Unlu G, Gamazon ER, Qi X, Levic DS, Bastarache L, Denny JC, Roden DM, Mayzus I, Breyer M, Zhong X, Konkashbaev AI, Rzhetsky A, Knapik EW, Cox NJ. GRIK5 Genetically Regulated Expression Associated with Eye and Vascular Phenomes: Discovery through Iteration among Biobanks, Electronic Health Records, and Zebrafish. Am J Hum Genet 2019; 104:503-519. [PMID: 30827500 PMCID: PMC6407495 DOI: 10.1016/j.ajhg.2019.01.017] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2018] [Accepted: 01/29/2019] [Indexed: 12/15/2022] Open
Abstract
Although the use of model systems for studying the mechanism of mutations that have a large effect is common, we highlight here the ways that zebrafish-model-system studies of a gene, GRIK5, that contributes to the polygenic liability to develop eye diseases have helped to illuminate a mechanism that implicates vascular biology in eye disease. A gene-expression prediction derived from a reference transcriptome panel applied to BioVU, a large electronic health record (EHR)-linked biobank at Vanderbilt University Medical Center, implicated reduced GRIK5 expression in diverse eye diseases. We tested the function of GRIK5 by depletion of its ortholog in zebrafish, and we observed reduced blood vessel numbers and integrity in the eye and increased vascular permeability. Analyses of EHRs in >2.6 million Vanderbilt subjects revealed significant comorbidity of eye and vascular diseases (relative risks 2-15); this comorbidity was confirmed in 150 million individuals from a large insurance claims dataset. Subsequent studies in >60,000 genotyped BioVU participants confirmed the association of reduced genetically predicted expression of GRIK5 with comorbid vascular and eye diseases. Our studies pioneer an approach that allows a rapid iteration of the discovery of gene-phenotype relationships to the primary genetic mechanism contributing to the pathophysiology of human disease. Our findings also add dimension to the understanding of the biology driven by glutamate receptors such as GRIK5 (also referred to as GLUK5 in protein form) and to mechanisms contributing to human eye diseases.
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Affiliation(s)
- Gokhan Unlu
- Department of Medicine, Division of Genetic Medicine, Vanderbilt University Medical Center, Nashville, TN 37232, USA; Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN 37232, USA; Department of Cell and Developmental Biology, Vanderbilt University, Nashville, TN 37232, USA
| | - Eric R Gamazon
- Department of Medicine, Division of Genetic Medicine, Vanderbilt University Medical Center, Nashville, TN 37232, USA; Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN 37232, USA; Data Science Institute, Vanderbilt University, Nashville, TN 37232, USA; Clare Hall, University of Cambridge, Cambridge CB3 9AL, UK
| | - Xinzi Qi
- Department of Medicine, Division of Genetic Medicine, Vanderbilt University Medical Center, Nashville, TN 37232, USA; Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - Daniel S Levic
- Department of Medicine, Division of Genetic Medicine, Vanderbilt University Medical Center, Nashville, TN 37232, USA; Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN 37232, USA; Department of Cell and Developmental Biology, Vanderbilt University, Nashville, TN 37232, USA
| | - Lisa Bastarache
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN 37232, USA; Departments of Medicine and Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - Joshua C Denny
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN 37232, USA; Departments of Medicine and Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - Dan M Roden
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN 37232, USA; Departments of Medicine and Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN 37232, USA; Department of Pharmacology, Vanderbilt University, Nashville, TN 37232, USA
| | - Ilya Mayzus
- Departments of Medicine and Human Genetics, the University of Chicago, Chicago, IL 60637, USA
| | - Max Breyer
- Department of Medicine, Division of Genetic Medicine, Vanderbilt University Medical Center, Nashville, TN 37232, USA; Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - Xue Zhong
- Department of Medicine, Division of Genetic Medicine, Vanderbilt University Medical Center, Nashville, TN 37232, USA; Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - Anuar I Konkashbaev
- Department of Medicine, Division of Genetic Medicine, Vanderbilt University Medical Center, Nashville, TN 37232, USA; Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - Andrey Rzhetsky
- Departments of Medicine and Human Genetics, the University of Chicago, Chicago, IL 60637, USA
| | - Ela W Knapik
- Department of Medicine, Division of Genetic Medicine, Vanderbilt University Medical Center, Nashville, TN 37232, USA; Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN 37232, USA; Department of Cell and Developmental Biology, Vanderbilt University, Nashville, TN 37232, USA
| | - Nancy J Cox
- Department of Medicine, Division of Genetic Medicine, Vanderbilt University Medical Center, Nashville, TN 37232, USA; Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN 37232, USA; Data Science Institute, Vanderbilt University, Nashville, TN 37232, USA.
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205
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Vilgalys TP, Rogers J, Jolly CJ, Baboon Genome Analysis, Mukherjee S, Tung J. Evolution of DNA Methylation in Papio Baboons. Mol Biol Evol 2019; 36:527-540. [PMID: 30521003 PMCID: PMC6389319 DOI: 10.1093/molbev/msy227] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023] Open
Abstract
Changes in gene regulation have long been thought to play an important role in primate evolution. However, although a number of studies have compared genome-wide gene expression patterns across primate species, fewer have investigated the gene regulatory mechanisms that underlie such patterns, or the relative contribution of drift versus selection. Here, we profiled genome-scale DNA methylation levels in blood samples from five of the six extant species of the baboon genus Papio (4-14 individuals per species). This radiation presents the opportunity to investigate DNA methylation divergence at both shallow and deeper timescales (0.380-1.4 My). In contrast to studies in human populations, but similar to studies in great apes, DNA methylation profiles clearly mirror genetic and geographic structure. Divergence in DNA methylation proceeds fastest in unannotated regions of the genome and slowest in regions of the genome that are likely more constrained at the sequence level (e.g., gene exons). Both heuristic approaches and Ornstein-Uhlenbeck models suggest that DNA methylation levels at a small set of sites have been affected by positive selection, and that this class is enriched in functionally relevant contexts, including promoters, enhancers, and CpG islands. Our results thus indicate that the rate and distribution of DNA methylation changes across the genome largely mirror genetic structure. However, at some CpG sites, DNA methylation levels themselves may have been a target of positive selection, pointing to loci that could be important in connecting sequence variation to fitness-related traits.
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Affiliation(s)
- Tauras P Vilgalys
- Department of Evolutionary Anthropology, Duke University, Durham, NC
| | - Jeffrey Rogers
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX
| | - Clifford J Jolly
- Department of Anthropology, New York University, New York, NY
- Center for the Study of Human Origins, New York University, New York, NY
- New York Consortium for Evolutionary Primatology, New York, NY
| | | | - Sayan Mukherjee
- Department of Statistical Science, Duke University, Durham, NC
- Department of Mathematics, Duke University, Durham, NC
- Department of Computer Science, Duke University, Durham, NC
| | - Jenny Tung
- Department of Evolutionary Anthropology, Duke University, Durham, NC
- Department of Biology, Duke University, Durham, NC
- Duke University Population Research Institute, Duke University, Durham, NC
- Institute of Primate Research, National Museums of Kenya, Karen, Nairobi, Kenya
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206
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A Multivariate Genome-Wide Association Study of Wing Shape in Drosophila melanogaster. Genetics 2019; 211:1429-1447. [PMID: 30792267 DOI: 10.1534/genetics.118.301342] [Citation(s) in RCA: 34] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2018] [Accepted: 02/03/2019] [Indexed: 02/02/2023] Open
Abstract
Due to the complexity of genotype-phenotype relationships, simultaneous analyses of genomic associations with multiple traits will be more powerful and informative than a series of univariate analyses. However, in most cases, studies of genotype-phenotype relationships have been analyzed only one trait at a time. Here, we report the results of a fully integrated multivariate genome-wide association analysis of the shape of the Drosophila melanogaster wing in the Drosophila Genetic Reference Panel. Genotypic effects on wing shape were highly correlated between two different laboratories. We found 2396 significant SNPs using a 5% false discovery rate cutoff in the multivariate analyses, but just four significant SNPs in univariate analyses of scores on the first 20 principal component axes. One quarter of these initially significant SNPs retain their effects in regularized models that take into account population structure and linkage disequilibrium. A key advantage of multivariate analysis is that the direction of the estimated phenotypic effect is much more informative than a univariate one. We exploit this fact to show that the effects of knockdowns of genes implicated in the initial screen were on average more similar than expected under a null model. A subset of SNP effects were replicable in an unrelated panel of inbred lines. Association studies that take a phenomic approach, considering many traits simultaneously, are an important complement to the power of genomics.
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207
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Ali AT, Boehme L, Carbajosa G, Seitan VC, Small KS, Hodgkinson A. Nuclear genetic regulation of the human mitochondrial transcriptome. eLife 2019; 8:e41927. [PMID: 30775970 PMCID: PMC6420317 DOI: 10.7554/elife.41927] [Citation(s) in RCA: 51] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2018] [Accepted: 02/14/2019] [Indexed: 12/21/2022] Open
Abstract
Mitochondria play important roles in cellular processes and disease, yet little is known about how the transcriptional regime of the mitochondrial genome varies across individuals and tissues. By analyzing >11,000 RNA-sequencing libraries across 36 tissue/cell types, we find considerable variation in mitochondrial-encoded gene expression along the mitochondrial transcriptome, across tissues and between individuals, highlighting the importance of cell-type specific and post-transcriptional processes in shaping mitochondrial-encoded RNA levels. Using whole-genome genetic data we identify 64 nuclear loci associated with expression levels of 14 genes encoded in the mitochondrial genome, including missense variants within genes involved in mitochondrial function (TBRG4, MTPAP and LONP1), implicating genetic mechanisms that act in trans across the two genomes. We replicate ~21% of associations with independent tissue-matched datasets and find genetic variants linked to these nuclear loci that are associated with cardio-metabolic phenotypes and Vitiligo, supporting a potential role for variable mitochondrial-encoded gene expression in complex disease.
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Affiliation(s)
- Aminah T Ali
- Department of Medical and Molecular Genetics, School of Basic and Medical BiosciencesKing’s College LondonLondonUnited Kingdom
| | - Lena Boehme
- Department of Medical and Molecular Genetics, School of Basic and Medical BiosciencesKing’s College LondonLondonUnited Kingdom
| | - Guillermo Carbajosa
- Department of Medical and Molecular Genetics, School of Basic and Medical BiosciencesKing’s College LondonLondonUnited Kingdom
| | - Vlad C Seitan
- Department of Medical and Molecular Genetics, School of Basic and Medical BiosciencesKing’s College LondonLondonUnited Kingdom
| | - Kerrin S Small
- Department of Twin Research and Genetic Epidemiology, School of Life Course SciencesKing’s College LondonLondonUnited Kingdom
| | - Alan Hodgkinson
- Department of Medical and Molecular Genetics, School of Basic and Medical BiosciencesKing’s College LondonLondonUnited Kingdom
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208
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Yao Y, Liu Z, Wei Q, Ramsey SA. CERENKOV2: improved detection of functional noncoding SNPs using data-space geometric features. BMC Bioinformatics 2019; 20:63. [PMID: 30727967 PMCID: PMC6364436 DOI: 10.1186/s12859-019-2637-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2018] [Accepted: 01/18/2019] [Indexed: 02/07/2023] Open
Abstract
BACKGROUND We previously reported on CERENKOV, an approach for identifying regulatory single nucleotide polymorphisms (rSNPs) that is based on 246 annotation features. CERENKOV uses the xgboost classifier and is designed to be used to find causal noncoding SNPs in loci identified by genome-wide association studies (GWAS). We reported that CERENKOV has state-of-the-art performance (by two traditional measures and a novel GWAS-oriented measure, AVGRANK) in a comparison to nine other tools for identifying functional noncoding SNPs, using a comprehensive reference SNP set (OSU17, 15,331 SNPs). Given that SNPs are grouped within loci in the reference SNP set and given the importance of the data-space manifold geometry for machine-learning model selection, we hypothesized that within-locus inter-SNP distances would have class-based distributional biases that could be exploited to improve rSNP recognition accuracy. We thus defined an intralocus SNP "radius" as the average data-space distance from a SNP to the other intralocus neighbors, and explored radius likelihoods for five distance measures. RESULTS We expanded the set of reference SNPs to 39,083 (the OSU18 set) and extracted CERENKOV SNP feature data. We computed radius empirical likelihoods and likelihood densities for rSNPs and control SNPs, and found significant likelihood differences between rSNPs and control SNPs. We fit parametric models of likelihood distributions for five different distance measures to obtain ten log-likelihood features that we combined with the 248-dimensional CERENKOV feature matrix. On the OSU18 SNP set, we measured the classification accuracy of CERENKOV with and without the new distance-based features, and found that the addition of distance-based features significantly improves rSNP recognition performance as measured by AUPVR, AUROC, and AVGRANK. Along with feature data for the OSU18 set, the software code for extracting the base feature matrix, estimating ten distance-based likelihood ratio features, and scoring candidate causal SNPs, are released as open-source software CERENKOV2. CONCLUSIONS Accounting for the locus-specific geometry of SNPs in data-space significantly improved the accuracy with which noncoding rSNPs can be computationally identified.
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Affiliation(s)
- Yao Yao
- School of Electrical Engineering and Computer Science, Oregon State University, Corvallis, 97330 OR USA
- Department of Biomedical Sciences, Oregon State University, 106 Dryden Hall, Corvallis, 97330 OR USA
| | - Zheng Liu
- School of Electrical Engineering and Computer Science, Oregon State University, Corvallis, 97330 OR USA
- Department of Biomedical Sciences, Oregon State University, 106 Dryden Hall, Corvallis, 97330 OR USA
| | - Qi Wei
- School of Electrical Engineering and Computer Science, Oregon State University, Corvallis, 97330 OR USA
- Department of Biomedical Sciences, Oregon State University, 106 Dryden Hall, Corvallis, 97330 OR USA
| | - Stephen A. Ramsey
- School of Electrical Engineering and Computer Science, Oregon State University, Corvallis, 97330 OR USA
- Department of Biomedical Sciences, Oregon State University, 106 Dryden Hall, Corvallis, 97330 OR USA
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209
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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: 46] [Impact Index Per Article: 7.7] [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.
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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
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210
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Hu Y, Zhao T, Zang T, Zhang Y, Cheng L. Identification of Alzheimer's Disease-Related Genes Based on Data Integration Method. Front Genet 2019; 9:703. [PMID: 30740125 PMCID: PMC6355707 DOI: 10.3389/fgene.2018.00703] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2018] [Accepted: 12/14/2018] [Indexed: 01/18/2023] Open
Abstract
Alzheimer disease (AD) is the fourth major cause of death in the elderly following cancer, heart disease and cerebrovascular disease. Finding candidate causal genes can help in the design of Gene targeted drugs and effectively reduce the risk of the disease. Complex diseases such as AD are usually caused by multiple genes. The Genome-wide association study (GWAS), has identified the potential genetic variants for most diseases. However, because of linkage disequilibrium (LD), it is difficult to identify the causative mutations that directly cause diseases. In this study, we combined expression quantitative trait locus (eQTL) studies with the GWAS, to comprehensively define the genes that cause Alzheimer disease. The method used was the Summary Mendelian randomization (SMR), which is a novel method to integrate summarized data. Two GWAS studies and five eQTL studies were referenced in this paper. We found several candidate SNPs that have a strong relationship with AD. Most of these SNPs overlap in different data sets, providing relatively strong reliability. We also explain the function of the novel AD-related genes we have discovered.
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Affiliation(s)
- Yang Hu
- Department of Computer Science and Technology, School of Life Science and Technology, Harbin Institute of Technology, Harbin, China
| | - Tianyi Zhao
- Department of Computer Science and Technology, School of Life Science and Technology, Harbin Institute of Technology, Harbin, China
| | - Tianyi Zang
- Department of Computer Science and Technology, School of Life Science and Technology, Harbin Institute of Technology, Harbin, China
| | - Ying Zhang
- Department of Rehabilitation, Heilongjiang Province Land Reclamation Headquarters General Hospital, Harbin, China
| | - Liang Cheng
- Department of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
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211
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Alasoo K, Rodrigues J, Danesh J, Freitag DF, Paul DS, Gaffney DJ. Genetic effects on promoter usage are highly context-specific and contribute to complex traits. eLife 2019; 8:e41673. [PMID: 30618377 PMCID: PMC6349408 DOI: 10.7554/elife.41673] [Citation(s) in RCA: 44] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2018] [Accepted: 01/08/2019] [Indexed: 12/12/2022] Open
Abstract
Genetic variants regulating RNA splicing and transcript usage have been implicated in both common and rare diseases. Although transcript usage quantitative trait loci (tuQTLs) have been mapped across multiple cell types and contexts, it is challenging to distinguish between the main molecular mechanisms controlling transcript usage: promoter choice, splicing and 3' end choice. Here, we analysed RNA-seq data from human macrophages exposed to three inflammatory and one metabolic stimulus. In addition to conventional gene-level and transcript-level analyses, we also directly quantified promoter usage, splicing and 3' end usage. We found that promoters, splicing and 3' ends were predominantly controlled by independent genetic variants enriched in distinct genomic features. Promoter usage QTLs were also 50% more likely to be context-specific than other tuQTLs and constituted 25% of the transcript-level colocalisations with complex traits. Thus, promoter usage might be an underappreciated molecular mechanism mediating complex trait associations in a context-specific manner.
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Affiliation(s)
- Kaur Alasoo
- Institute of Computer ScienceUniversity of TartuTartuEstonia
- Wellcome Sanger Institute, Wellcome Genome CampusHinxtonUnited Kingdom
| | - Julia Rodrigues
- Wellcome Sanger Institute, Wellcome Genome CampusHinxtonUnited Kingdom
| | - John Danesh
- Wellcome Sanger Institute, Wellcome Genome CampusHinxtonUnited Kingdom
- BHF Cardiovascular Epidemiology Unit, Department of Public Health and Primary CareUniversity of CambridgeCambridgeUnited Kingdom
- British Heart Foundation Centre of Excellence, Division of Cardiovascular MedicineAddenbrooke’s HospitalCambridgeUnited Kingdom
- National Institute for Health Research Blood and Transplant Unit (NIHR BTRU) in Donor Health and Genomics, Department of Public Health and Primary CareUniversity of CambridgeCambridgeUnited Kingdom
| | - Daniel F Freitag
- Wellcome Sanger Institute, Wellcome Genome CampusHinxtonUnited Kingdom
- British Heart Foundation Centre of Excellence, Division of Cardiovascular MedicineAddenbrooke’s HospitalCambridgeUnited Kingdom
| | - Dirk S Paul
- Wellcome Sanger Institute, Wellcome Genome CampusHinxtonUnited Kingdom
- British Heart Foundation Centre of Excellence, Division of Cardiovascular MedicineAddenbrooke’s HospitalCambridgeUnited Kingdom
| | - Daniel J Gaffney
- Wellcome Sanger Institute, Wellcome Genome CampusHinxtonUnited Kingdom
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212
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Zhu S, Qian T, Hoshida Y, Shen Y, Yu J, Hao K. GIGSEA: genotype imputed gene set enrichment analysis using GWAS summary level data. Bioinformatics 2019; 35:160-163. [PMID: 30010968 PMCID: PMC6298047 DOI: 10.1093/bioinformatics/bty529] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2017] [Revised: 05/11/2018] [Accepted: 07/11/2018] [Indexed: 11/12/2022] Open
Abstract
Summary level data of GWAS becomes increasingly important in post-GWAS data mining. Here, we present GIGSEA (Genotype Imputed Gene Set Enrichment Analysis), a novel method that uses GWAS summary statistics and eQTL to infer differential gene expression and interrogate gene set enrichment for the trait-associated SNPs. By incorporating empirical eQTL of the disease relevant tissue, GIGSEA naturally accounts for factors such as gene size, gene boundary, SNP distal regulation and multiple-marker regulation. The weighted linear regression model was used to perform the enrichment test, properly adjusting for imputation accuracy, model incompleteness and redundancy in different gene sets. The significance level of enrichment is assessed by the permutation test, where matrix operation was employed to dramatically improve computation speed. GIGSEA has appropriate type I error rates, and discovers the plausible biological findings on the real data set. Availability and implementation GIGSEA is implemented in R, and freely available at www.github.com/zhushijia/GIGSEA. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Shijia Zhu
- Department of Genetics and Genomic Sciences and Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Tongqi Qian
- Department of Genetics and Genomic Sciences and Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Yujin Hoshida
- Liver Tumor Translational Research Program, Simmons Comprehensive Cancer Center, Division of Digestive and Liver Diseases, Department of Internal Medicine, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Yuan Shen
- Department of Psychiatry, Shanghai Tenth People's Hospital, Tongji University, Shanghai, China
| | - Jing Yu
- Department of Ophthalmology, Shanghai Tenth People's Hospital, Tongji University, Shanghai, China
| | - Ke Hao
- Department of Genetics and Genomic Sciences and Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Respiratory Medicine, Shanghai Tenth People's Hospital, Tongji University, Shanghai, China
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213
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Gene expression variation and parental allele inheritance in a Xiphophorus interspecies hybridization model. PLoS Genet 2018; 14:e1007875. [PMID: 30586357 PMCID: PMC6324826 DOI: 10.1371/journal.pgen.1007875] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2018] [Revised: 01/08/2019] [Accepted: 12/04/2018] [Indexed: 01/06/2023] Open
Abstract
Understanding the genetic mechanisms underlying segregation of phenotypic variation through successive generations is important for understanding physiological changes and disease risk. Tracing the etiology of variation in gene expression enables identification of genetic interactions, and may uncover molecular mechanisms leading to the phenotypic expression of a trait, especially when utilizing model organisms that have well-defined genetic lineages. There are a plethora of studies that describe relationships between gene expression and genotype, however, the idea that global variations in gene expression are also controlled by genotype remains novel. Despite the identification of loci that control gene expression variation, the global understanding of how genome constitution affects trait variability is unknown. To study this question, we utilized Xiphophorus fish of different, but tractable genetic backgrounds (inbred, F1 interspecies hybrids, and backcross hybrid progeny), and measured each individual’s gene expression concurrent with the degrees of inter-individual expression variation. We found, (a) F1 interspecies hybrids exhibited less variability than inbred animals, indicting gene expression variation is not affected by the fraction of heterozygous loci within an individual genome, and (b), that mixing genotypes in backcross populations led to higher levels of gene expression variability, supporting the idea that expression variability is caused by heterogeneity of genotypes of cis or trans loci. In conclusion, heterogeneity of genotype, introduced by inheritance of different alleles, accounts for the largest effects on global phenotypical variability. Phenotypical variability is a multi-factorial phenomenon. Although it has been shown that inheriting certain gene is associated with lower phenotypical variability, how genome complexity affect phenotypical variability is still unclear. To study this question, we used inbred Xiphophorus fish, backcross interspecies hybrids, and F1 interspecies hybrids between select Xiphophorus species to model genetic composition with minimum, medium, and maximum heterozygosity respectively, and measured their global gene expression variability. We found gene expression variation is not affected by the percentage of heterozygous loci in individual genome, but instead related to heterogeneity of genotype at local or remote loci.
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214
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van der Wijst MGP, de Vries DH, Brugge H, Westra HJ, Franke L. An integrative approach for building personalized gene regulatory networks for precision medicine. Genome Med 2018; 10:96. [PMID: 30567569 PMCID: PMC6299585 DOI: 10.1186/s13073-018-0608-4] [Citation(s) in RCA: 37] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/01/2023] Open
Abstract
Only a small fraction of patients respond to the drug prescribed to treat their disease, which means that most are at risk of unnecessary exposure to side effects through ineffective drugs. This inter-individual variation in drug response is driven by differences in gene interactions caused by each patient's genetic background, environmental exposures, and the proportions of specific cell types involved in disease. These gene interactions can now be captured by building gene regulatory networks, by taking advantage of RNA velocity (the time derivative of the gene expression state), the ability to study hundreds of thousands of cells simultaneously, and the falling price of single-cell sequencing. Here, we propose an integrative approach that leverages these recent advances in single-cell data with the sensitivity of bulk data to enable the reconstruction of personalized, cell-type- and context-specific gene regulatory networks. We expect this approach will allow the prioritization of key driver genes for specific diseases and will provide knowledge that opens new avenues towards improved personalized healthcare.
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Affiliation(s)
- Monique G P van der Wijst
- Department of Genetics, 5th floor ERIBA building, Antonius Deusinglaan 1, 9713AV Groningen, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Dylan H de Vries
- Department of Genetics, 5th floor ERIBA building, Antonius Deusinglaan 1, 9713AV Groningen, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Harm Brugge
- Department of Genetics, 5th floor ERIBA building, Antonius Deusinglaan 1, 9713AV Groningen, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Harm-Jan Westra
- Department of Genetics, 5th floor ERIBA building, Antonius Deusinglaan 1, 9713AV Groningen, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Lude Franke
- Department of Genetics, 5th floor ERIBA building, Antonius Deusinglaan 1, 9713AV Groningen, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands.
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Evidence for Weak Selective Constraint on Human Gene Expression. Genetics 2018; 211:757-772. [PMID: 30554168 PMCID: PMC6366908 DOI: 10.1534/genetics.118.301833] [Citation(s) in RCA: 42] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2018] [Accepted: 12/01/2018] [Indexed: 01/01/2023] Open
Abstract
Variation in human complex traits is connected to variation in gene expression, and selection on complex traits can be reflected in selection on gene expression. Here, Glassberg and Gao et al. analyze polymorphic.... Gene expression variation is a major contributor to phenotypic variation in human complex traits. Selection on complex traits may therefore be reflected in constraint on gene expression. Here, we explore the effects of stabilizing selection on cis-regulatory genetic variation in humans. We analyze patterns of expression variation at copy number variants and find evidence for selection against large increases in gene expression. Using allele-specific expression (ASE) data, we further show evidence of selection against smaller-effect variants. We estimate that, across all genes, singletons in a sample of 122 individuals have ∼2.2× greater effects on expression variation than the average variant across allele frequencies. Despite their increased effect size relative to common variants, we estimate that singletons in the sample studied explain, on average, only 5% of the heritability of gene expression from cis-regulatory variants. Finally, we show that genes depleted for loss-of-function variants are also depleted for cis-eQTLs and have low levels of allelic imbalance, confirming tighter constraint on the expression levels of these genes. We conclude that constraint on gene expression is present, but has relatively weak effects on most cis-regulatory variants, thus permitting high levels of gene-regulatory genetic variation.
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216
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Saha A, Battle A. False positives in trans-eQTL and co-expression analyses arising from RNA-sequencing alignment errors. F1000Res 2018; 7:1860. [PMID: 30613398 PMCID: PMC6305209 DOI: 10.12688/f1000research.17145.2] [Citation(s) in RCA: 34] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 03/29/2019] [Indexed: 12/29/2022] Open
Abstract
Sequence similarity among distinct genomic regions can lead to errors in alignment of short reads from next-generation sequencing. While this is well known, the downstream consequences of misalignment have not been fully characterized. We assessed the potential for incorrect alignment of RNA-sequencing reads to cause false positives in both gene expression quantitative trait locus (eQTL) and co-expression analyses. Trans-eQTLs identified from human RNA-sequencing studies appeared to be particularly affected by this phenomenon, even when only uniquely aligned reads are considered. Over 75% of trans-eQTLs using a standard pipeline occurred between regions of sequence similarity and therefore could be due to alignment errors. Further, associations due to mapping errors are likely to misleadingly replicate between studies. To help address this problem, we quantified the potential for "cross-mapping'' to occur between every pair of annotated genes in the human genome. Such cross-mapping data can be used to filter or flag potential false positives in both trans-eQTL and co-expression analyses. Such filtering substantially alters the detection of significant associations and can have an impact on the assessment of false discovery rate, functional enrichment, and replication for RNA-sequencing association studies.
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Affiliation(s)
- Ashis Saha
- Department of Computer Science, Johns Hopkins University, Baltimore, Maryland, 21218, USA
| | - Alexis Battle
- Department of Computer Science, Johns Hopkins University, Baltimore, Maryland, 21218, USA
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland, 21218, USA
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217
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Saha A, Battle A. False positives in trans-eQTL and co-expression analyses arising from RNA-sequencing alignment errors. F1000Res 2018; 7:1860. [PMID: 30613398 PMCID: PMC6305209 DOI: 10.12688/f1000research.17145.1] [Citation(s) in RCA: 33] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 11/21/2018] [Indexed: 12/19/2022] Open
Abstract
Sequence similarity among distinct genomic regions can lead to errors in alignment of short reads from next-generation sequencing. While this is well known, the downstream consequences of misalignment have not been fully characterized. We assessed the potential for incorrect alignment of RNA-sequencing reads to cause false positives in both gene expression quantitative trait locus (eQTL) and co-expression analyses. Trans-eQTLs identified from human RNA-sequencing studies appeared to be particularly affected by this phenomenon, even when only uniquely aligned reads are considered. Over 75\% of trans-eQTLs using a standard pipeline occurred between regions of sequence similarity and therefore could be due to alignment errors. Further, associations due to mapping errors are likely to misleadingly replicate between studies. To help address this problem, we quantified the potential for "cross-mapping'' to occur between every pair of annotated genes in the human genome. Such cross-mapping data can be used to filter or flag potential false positives in both trans-eQTL and co-expression analyses. Such filtering substantially alters the detection of significant associations and can have an impact on the assessment of false discovery rate, functional enrichment, and replication for RNA-sequencing association studies.
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Affiliation(s)
- Ashis Saha
- Department of Computer Science, Johns Hopkins University, Baltimore, Maryland, 21218, USA
| | - Alexis Battle
- Department of Computer Science, Johns Hopkins University, Baltimore, Maryland, 21218, USA
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland, 21218, USA
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218
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High-resolution genetic mapping of putative causal interactions between regions of open chromatin. Nat Genet 2018; 51:128-137. [PMID: 30478436 PMCID: PMC6330062 DOI: 10.1038/s41588-018-0278-6] [Citation(s) in RCA: 75] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2017] [Accepted: 10/15/2018] [Indexed: 01/04/2023]
Abstract
Physical interaction of regulatory elements in three-dimensional space poses a challenge for studies of disease because non-coding risk variants may be great distances from the genes they regulate. Experimental methods to capture these interactions, such as chromosome conformation capture, usually cannot assign causal direction of effect between regulatory elements, an important component of fine-mapping studies. We developed a Bayesian hierarchical approach that uses two-stage least squares and applied it to an ATAC-seq (assay for transposase-accessible chromatin using sequencing) data set from 100 individuals, to identify over 15,000 high-confidence causal interactions. Most (60%) interactions occurred over <20 kb, where chromosome conformation capture-based methods perform poorly. For a fraction of loci, we identified a single variant that alters accessibility across multiple regions, and experimentally validated the BLK locus, which is associated with multiple autoimmune diseases, using CRISPR genome editing. Our study highlights how association genetics of chromatin state is a powerful approach for identifying interactions between regulatory elements.
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219
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Salisbury-Ruf CT, Bertram CC, Vergeade A, Lark DS, Shi Q, Heberling ML, Fortune NL, Okoye GD, Jerome WG, Wells QS, Fessel J, Moslehi J, Chen H, Roberts LJ, Boutaud O, Gamazon ER, Zinkel SS. Bid maintains mitochondrial cristae structure and function and protects against cardiac disease in an integrative genomics study. eLife 2018; 7:40907. [PMID: 30281024 PMCID: PMC6234033 DOI: 10.7554/elife.40907] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2018] [Accepted: 09/27/2018] [Indexed: 01/07/2023] Open
Abstract
Bcl-2 family proteins reorganize mitochondrial membranes during apoptosis, to form pores and rearrange cristae. In vitro and in vivo analysis integrated with human genetics reveals a novel homeostatic mitochondrial function for Bcl-2 family protein Bid. Loss of full-length Bid results in apoptosis-independent, irregular cristae with decreased respiration. Bid-/- mice display stress-induced myocardial dysfunction and damage. A gene-based approach applied to a biobank, validated in two independent GWAS studies, reveals that decreased genetically determined BID expression associates with myocardial infarction (MI) susceptibility. Patients in the bottom 5% of the expression distribution exhibit >4 fold increased MI risk. Carrier status with nonsynonymous variation in Bid’s membrane binding domain, BidM148T, associates with MI predisposition. Furthermore, Bid but not BidM148T associates with Mcl-1Matrix, previously implicated in cristae stability; decreased MCL-1 expression associates with MI. Our results identify a role for Bid in homeostatic mitochondrial cristae reorganization, that we link to human cardiac disease. Cells contain specialized structures called mitochondria, which help to convert fuel into energy. These tiny energy factories have a unique double membrane, with a smooth outer and a folded inner lining. The folds, called cristae, provide a scaffold for the molecular machinery that produces chemical energy that the cell can use. The cristae are dynamic, and can change shape, condensing to increase energy output. Mitochondria also play a role in cell death. In certain situations, cristae can widen and release the proteins held within their folds. This can trigger a program of self-destruction in the cell. A family of proteins called Bcl-2 control such a ‘programmed cell death’ through the release of mitochondrial proteins. Some family members, including a protein called Bid, can reorganize cristae to regulate this cell-death program. When cells die, Bid proteins that had been split move to the mitochondria. But, even when cells are healthy, Bid molecules that are intact are always there, suggesting that this form of the protein may have another purpose. To investigate this further, Salisbury-Ruf, Bertram et al. used mice with Bid, and mice that lacked the protein. Without Bid, cells – including heart cells – struggled to work properly and used less oxygen than their normal counterparts. A closer look using electron microscopy revealed abnormalities in the cristae. However, adding ‘intact’ Bid proteins back in to the deficient cells restored them to normal. Moreover, without Bid, the mice hearts were less able to respond to an increased demand for energy. This decreased their performance and caused the formation of scars in the heart muscle called fibrosis, similar to a pattern observed in human patients following a heart attack. DNA data from an electronic health record database revealed a link between low levels of Bid genes and heart attack in humans, which was confirmed in further studies. In addition, a specific mutation in the Bid gene was found to affect its ability to regulate the formation of proper cristae. Combining evidence from mice with human genetics revealed new information about heart diseases. Mitochondrial health may be affected by a combination of specific variations in genes and changes in the Bid protein, which could affect heart attack risk. Understanding more about this association could help to identify and potentially reduce certain risk factors for heart attack.
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Affiliation(s)
- Christi T Salisbury-Ruf
- Department of Cell and Developmental Biology, Vanderbilt University, Nashville, United States
| | - Clinton C Bertram
- Department of Cell and Developmental Biology, Vanderbilt University, Nashville, United States
| | - Aurelia Vergeade
- Department of Pharmacology, Vanderbilt University, Nashville, United States
| | - Daniel S Lark
- Molecular Physiology and Biophysics, Vanderbilt University, Nashville, United States
| | - Qiong Shi
- Department of Medicine, Vanderbilt University Medical Center, Nashville, United States
| | - Marlene L Heberling
- Department of Biological Sciences, Vanderbilt University, Nashville, United States
| | - Niki L Fortune
- Department of Medicine, Vanderbilt University Medical Center, Nashville, United States
| | - G Donald Okoye
- Division of Cardiovascular Medicine and Cardio-oncology Program, Vanderbilt University Medical Center, Nashville, United States
| | - W Gray Jerome
- Department of Pathology, Microbiology, and Immunology, Vanderbilt University Medical Center, Nashville, United States
| | - Quinn S Wells
- Department of Medicine, Vanderbilt University Medical Center, Nashville, United States
| | - Josh Fessel
- Department of Medicine, Vanderbilt University Medical Center, Nashville, United States
| | - Javid Moslehi
- Division of Cardiovascular Medicine and Cardio-oncology Program, Vanderbilt University Medical Center, Nashville, United States
| | - Heidi Chen
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, United States
| | - L Jackson Roberts
- Department of Pharmacology, Vanderbilt University, Nashville, United States.,Department of Medicine, Vanderbilt University Medical Center, Nashville, United States
| | - Olivier Boutaud
- Department of Pharmacology, Vanderbilt University, Nashville, United States
| | - Eric R Gamazon
- Division of Genetic Medicine, Vanderbilt University Medical Center, Nashville, United States.,Clare Hall, University of Cambridge, Cambridge, United Kingdom
| | - Sandra S Zinkel
- Department of Cell and Developmental Biology, Vanderbilt University, Nashville, United States.,Department of Medicine, Vanderbilt University Medical Center, Nashville, United States
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220
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Ioannidis NM, Davis JR, DeGorter MK, Larson NB, McDonnell SK, French AJ, Battle AJ, Hastie TJ, Thibodeau SN, Montgomery SB, Bustamante CD, Sieh W, Whittemore AS. FIRE: functional inference of genetic variants that regulate gene expression. Bioinformatics 2018; 33:3895-3901. [PMID: 28961785 DOI: 10.1093/bioinformatics/btx534] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2017] [Accepted: 08/23/2017] [Indexed: 12/18/2022] Open
Abstract
Motivation Interpreting genetic variation in noncoding regions of the genome is an important challenge for personal genome analysis. One mechanism by which noncoding single nucleotide variants (SNVs) influence downstream phenotypes is through the regulation of gene expression. Methods to predict whether or not individual SNVs are likely to regulate gene expression would aid interpretation of variants of unknown significance identified in whole-genome sequencing studies. Results We developed FIRE (Functional Inference of Regulators of Expression), a tool to score both noncoding and coding SNVs based on their potential to regulate the expression levels of nearby genes. FIRE consists of 23 random forests trained to recognize SNVs in cis-expression quantitative trait loci (cis-eQTLs) using a set of 92 genomic annotations as predictive features. FIRE scores discriminate cis-eQTL SNVs from non-eQTL SNVs in the training set with a cross-validated area under the receiver operating characteristic curve (AUC) of 0.807, and discriminate cis-eQTL SNVs shared across six populations of different ancestry from non-eQTL SNVs with an AUC of 0.939. FIRE scores are also predictive of cis-eQTL SNVs across a variety of tissue types. Availability and implementation FIRE scores for genome-wide SNVs in hg19/GRCh37 are available for download at https://sites.google.com/site/fireregulatoryvariation/. Contact nilah@stanford.edu. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
| | | | - Marianne K DeGorter
- Department of Genetics
- Department of Pathology, Stanford University School of Medicine, Stanford, CA 94305, USA
| | | | | | - Amy J French
- Department of Laboratory Medicine & Pathology, Mayo Clinic, Rochester, MN 55905, USA
| | - Alexis J Battle
- Department of Computer Science, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Trevor J Hastie
- Department of Statistics, Stanford University, Stanford, CA 94305, USA
- Department of Biomedical Data Science, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Stephen N Thibodeau
- Department of Laboratory Medicine & Pathology, Mayo Clinic, Rochester, MN 55905, USA
| | - Stephen B Montgomery
- Department of Genetics
- Department of Pathology, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Carlos D Bustamante
- Department of Genetics
- Department of Biomedical Data Science, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Weiva Sieh
- Department of Health Research & Policy
- Department of Population Health Science & Policy
- Department of Genetics & Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Alice S Whittemore
- Department of Health Research & Policy
- Department of Biomedical Data Science, Stanford University School of Medicine, Stanford, CA 94305, USA
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221
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Mogil LS, Andaleon A, Badalamenti A, Dickinson SP, Guo X, Rotter JI, Johnson WC, Im HK, Liu Y, Wheeler HE. Genetic architecture of gene expression traits across diverse populations. PLoS Genet 2018; 14:e1007586. [PMID: 30096133 PMCID: PMC6105030 DOI: 10.1371/journal.pgen.1007586] [Citation(s) in RCA: 92] [Impact Index Per Article: 13.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2018] [Revised: 08/22/2018] [Accepted: 07/24/2018] [Indexed: 01/14/2023] Open
Abstract
For many complex traits, gene regulation is likely to play a crucial mechanistic role. How the genetic architectures of complex traits vary between populations and subsequent effects on genetic prediction are not well understood, in part due to the historical paucity of GWAS in populations of non-European ancestry. We used data from the MESA (Multi-Ethnic Study of Atherosclerosis) cohort to characterize the genetic architecture of gene expression within and between diverse populations. Genotype and monocyte gene expression were available in individuals with African American (AFA, n = 233), Hispanic (HIS, n = 352), and European (CAU, n = 578) ancestry. We performed expression quantitative trait loci (eQTL) mapping in each population and show genetic correlation of gene expression depends on shared ancestry proportions. Using elastic net modeling with cross validation to optimize genotypic predictors of gene expression in each population, we show the genetic architecture of gene expression for most predictable genes is sparse. We found the best predicted gene in each population, TACSTD2 in AFA and CHURC1 in CAU and HIS, had similar prediction performance across populations with R2 > 0.8 in each population. However, we identified a subset of genes that are well-predicted in one population, but poorly predicted in another. We show these differences in predictive performance are due to allele frequency differences between populations. Using genotype weights trained in MESA to predict gene expression in independent populations showed that a training set with ancestry similar to the test set is better at predicting gene expression in test populations, demonstrating an urgent need for diverse population sampling in genomics. Our predictive models and performance statistics in diverse cohorts are made publicly available for use in transcriptome mapping methods at https://github.com/WheelerLab/DivPop.
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Affiliation(s)
- Lauren S. Mogil
- Department of Biology, Loyola University Chicago, Chicago, Illinois, United States of America
| | - Angela Andaleon
- Department of Biology, Loyola University Chicago, Chicago, Illinois, United States of America
- Program in Bioinformatics, Loyola University Chicago, Chicago, Illinois, United States of America
| | - Alexa Badalamenti
- Program in Bioinformatics, Loyola University Chicago, Chicago, Illinois, United States of America
| | - Scott P. Dickinson
- Section of Genetic Medicine, Department of Medicine, University of Chicago, Chicago, Illinois, United States of America
| | - Xiuqing Guo
- Institute for Translational Genomics and Population Sciences, Los Angeles Biomedical Research Institute and Department of Pediatrics at Harbor-UCLA Medical Center, Torrance, California, United States of America
| | - Jerome I. Rotter
- Institute for Translational Genomics and Population Sciences, Los Angeles Biomedical Research Institute and Department of Pediatrics at Harbor-UCLA Medical Center, Torrance, California, United States of America
| | - W. Craig Johnson
- Department of Biostatistics, University of Washington, Seattle, Washington, United States of America
| | - Hae Kyung Im
- Section of Genetic Medicine, Department of Medicine, University of Chicago, Chicago, Illinois, United States of America
| | - Yongmei Liu
- Department of Epidemiology & Prevention, Wake Forest School of Medicine, Winston-Salem, North Carolina, United States of America
| | - Heather E. Wheeler
- Department of Biology, Loyola University Chicago, Chicago, Illinois, United States of America
- Program in Bioinformatics, Loyola University Chicago, Chicago, Illinois, United States of America
- Department of Computer Science, Loyola University Chicago, Chicago, Illinois, United States of America
- Department of Public Health Sciences, Stritch School of Medicine, Loyola University Chicago, Maywood, Illinois, United States of America
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222
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Albert FW, Bloom JS, Siegel J, Day L, Kruglyak L. Genetics of trans-regulatory variation in gene expression. eLife 2018; 7:e35471. [PMID: 30014850 PMCID: PMC6072440 DOI: 10.7554/elife.35471] [Citation(s) in RCA: 105] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2018] [Accepted: 06/30/2018] [Indexed: 12/02/2022] Open
Abstract
Heritable variation in gene expression forms a crucial bridge between genomic variation and the biology of many traits. However, most expression quantitative trait loci (eQTLs) remain unidentified. We mapped eQTLs by transcriptome sequencing in 1012 yeast segregants. The resulting eQTLs accounted for over 70% of the heritability of mRNA levels, allowing comprehensive dissection of regulatory variation. Most genes had multiple eQTLs. Most expression variation arose from trans-acting eQTLs distant from their target genes. Nearly all trans-eQTLs clustered at 102 hotspot locations, some of which influenced the expression of thousands of genes. Fine-mapped hotspot regions were enriched for transcription factor genes. While most genes had a local eQTL, most of these had no detectable effects on the expression of other genes in trans. Hundreds of non-additive genetic interactions accounted for small fractions of expression variation. These results reveal the complexity of genetic influences on transcriptome variation in unprecedented depth and detail.
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Affiliation(s)
- Frank Wolfgang Albert
- Department of Genetics, Cell Biology and DevelopmentUniversity of MinnesotaMinneapolisUnited States
| | - Joshua S Bloom
- Department of Human GeneticsUniversity of California, Los AngelesLos AngelesUnited States
- Department of Biological ChemistryUniversity of California, Los AngelesLos AngelesUnited States
- Howard Hughes Medical InstituteLos AngelesUnited States
| | - Jake Siegel
- Department of Human GeneticsUniversity of California, Los AngelesLos AngelesUnited States
- Department of Biological ChemistryUniversity of California, Los AngelesLos AngelesUnited States
- Howard Hughes Medical InstituteLos AngelesUnited States
| | - Laura Day
- Department of Human GeneticsUniversity of California, Los AngelesLos AngelesUnited States
- Department of Biological ChemistryUniversity of California, Los AngelesLos AngelesUnited States
- Howard Hughes Medical InstituteLos AngelesUnited States
| | - Leonid Kruglyak
- Department of Human GeneticsUniversity of California, Los AngelesLos AngelesUnited States
- Department of Biological ChemistryUniversity of California, Los AngelesLos AngelesUnited States
- Howard Hughes Medical InstituteLos AngelesUnited States
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223
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Genetic determinants of co-accessible chromatin regions in activated T cells across humans. Nat Genet 2018; 50:1140-1150. [PMID: 29988122 PMCID: PMC6097927 DOI: 10.1038/s41588-018-0156-2] [Citation(s) in RCA: 118] [Impact Index Per Article: 16.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2017] [Accepted: 05/22/2018] [Indexed: 12/15/2022]
Abstract
Over 90% of genetic variants associated with complex human traits map to non-coding regions, but little is understood about how they modulate gene regulation in health and disease. One possible mechanism is that genetic variants affect the activity of one or more cis-regulatory elements leading to gene expression variation in specific cell types. To identify such cases, we analyzed ATAC-seq and RNA-seq profiles from stimulated primary CD4+ T cells in up to 105 healthy donors. We found that regions of accessible chromatin (ATAC-peaks) are co-accessible at kilobase and megabase resolution, consistent with the three-dimensional chromatin organization measured by in situ Hi-C in T cells. Fifteen percent of genetic variants located within ATAC-peaks affected the accessibility of the corresponding peak (local-ATAC-QTLs). Local-ATAC-QTLs have the largest effects on co-accessible peaks, are associated with gene expression and are enriched for autoimmune disease variants. Our results provide insights into how natural genetic variants modulate cis-regulatory elements, in isolation or in concert, to influence gene expression.
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224
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Gamazon ER, Segrè AV, van de Bunt M, Wen X, Xi HS, Hormozdiari F, Ongen H, Konkashbaev A, Derks EM, Aguet F, Quan J, Nicolae DL, Eskin E, Kellis M, Getz G, McCarthy MI, Dermitzakis ET, Cox NJ, Ardlie KG. Using an atlas of gene regulation across 44 human tissues to inform complex disease- and trait-associated variation. Nat Genet 2018; 50:956-967. [PMID: 29955180 PMCID: PMC6248311 DOI: 10.1038/s41588-018-0154-4] [Citation(s) in RCA: 293] [Impact Index Per Article: 41.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2017] [Accepted: 05/08/2018] [Indexed: 12/27/2022]
Abstract
We apply integrative approaches to expression quantitative loci (eQTLs) from 44 tissues from the Genotype-Tissue Expression project and genome-wide association study data. About 60% of known trait-associated loci are in linkage disequilibrium with a cis-eQTL, over half of which were not found in previous large-scale whole blood studies. Applying polygenic analyses to metabolic, cardiovascular, anthropometric, autoimmune, and neurodegenerative traits, we find that eQTLs are significantly enriched for trait associations in relevant pathogenic tissues and explain a substantial proportion of the heritability (40-80%). For most traits, tissue-shared eQTLs underlie a greater proportion of trait associations, although tissue-specific eQTLs have a greater contribution to some traits, such as blood pressure. By integrating information from biological pathways with eQTL target genes and applying a gene-based approach, we validate previously implicated causal genes and pathways, and propose new variant and gene associations for several complex traits, which we replicate in the UK BioBank and BioVU.
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Affiliation(s)
- Eric R Gamazon
- Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA.
- Clare Hall, University of Cambridge, Cambridge, UK.
| | - Ayellet V Segrè
- The Broad Institute of Massachusetts Institute of Technology and Harvard University, Cambridge, MA, USA.
- Department of Ophthalmology and Ocular Genomics Institute, Massachusetts Eye and Ear, Harvard Medical School, Boston, MA, USA.
| | - Martijn van de Bunt
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Churchill Hospital, Oxford, UK
| | - Xiaoquan Wen
- Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA
| | - Hualin S Xi
- Computational Sciences, Pfizer Inc, Cambridge, MA, USA
| | - Farhad Hormozdiari
- Department of Computer Science, University of California, Los Angeles, CA, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Halit Ongen
- Department of Genetic Medicine and Development, University of Geneva Medical School, Geneva, Switzerland
- Institute for Genetics and Genomics in Geneva (iG3), University of Geneva, Geneva, Switzerland
- Swiss Institute of Bioinformatics, Geneva, Switzerland
| | - Anuar Konkashbaev
- Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Eske M Derks
- Translational Neurogenomics Group, QIMR Berghofer, Brisbane, Queensland, Australia
| | - François Aguet
- The Broad Institute of Massachusetts Institute of Technology and Harvard University, Cambridge, MA, USA
| | - Jie Quan
- Computational Sciences, Pfizer Inc, Cambridge, MA, USA
| | - Dan L Nicolae
- Section of Genetic Medicine, Department of Medicine, The University of Chicago, Chicago, IL, USA
- Department of Statistics, The University of Chicago, Chicago, IL, USA
- Department of Human Genetics, The University of Chicago, Chicago, IL, USA
| | - Eleazar Eskin
- Department of Computer Science, University of California, Los Angeles, CA, USA
| | - Manolis Kellis
- The Broad Institute of Massachusetts Institute of Technology and Harvard University, Cambridge, MA, USA
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Gad Getz
- The Broad Institute of Massachusetts Institute of Technology and Harvard University, Cambridge, MA, USA
- Massachusetts General Hospital Cancer Center and Department of Pathology, Massachusetts General Hospital, Boston, MA, USA
| | - Mark I McCarthy
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Churchill Hospital, Oxford, UK
| | - Emmanouil T Dermitzakis
- Department of Genetic Medicine and Development, University of Geneva Medical School, Geneva, Switzerland
- Institute for Genetics and Genomics in Geneva (iG3), University of Geneva, Geneva, Switzerland
- Swiss Institute of Bioinformatics, Geneva, Switzerland
| | - Nancy J Cox
- Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Kristin G Ardlie
- The Broad Institute of Massachusetts Institute of Technology and Harvard University, Cambridge, MA, USA
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225
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Weissbrod O, Rothschild D, Barkan E, Segal E. Host genetics and microbiome associations through the lens of genome wide association studies. Curr Opin Microbiol 2018; 44:9-19. [PMID: 29909175 DOI: 10.1016/j.mib.2018.05.003] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2018] [Revised: 03/15/2018] [Accepted: 05/25/2018] [Indexed: 12/22/2022]
Abstract
Recent studies indicate that the gut microbiome is partially heritable, motivating the need to investigate microbiome-host genome associations via microbial genome-wide association studies (mGWAS). Existing mGWAS demonstrate that microbiome-host genotype associations are typically weak and are spread across multiple variants, similar to associations often observed in genome-wide association studies (GWAS) of complex traits. Here we reconsider mGWAS by viewing them through the lens of GWAS, and demonstrate that there are striking similarities between the challenges and pitfalls faced by the two study designs. We further advocate the mGWAS community to adopt three key lessons learned over the history of GWAS: firstly, adopting uniform data and reporting formats to facilitate replication and meta-analysis efforts; secondly, enforcing stringent statistical criteria to reduce the number of false positive findings; and thirdly, considering the microbiome and the host genome as distinct entities, rather than studying different taxa and single nucleotide polymorphism (SNPs) separately. Finally, we anticipate that mGWAS sample sizes will have to increase by orders of magnitude to reproducibly associate the host genome with the gut microbiome.
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Affiliation(s)
- Omer Weissbrod
- Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot 7610001, Israel; Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot 7610001, Israel
| | - Daphna Rothschild
- Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot 7610001, Israel; Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot 7610001, Israel
| | - Elad Barkan
- Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot 7610001, Israel; Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot 7610001, Israel
| | - Eran Segal
- Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot 7610001, Israel; Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot 7610001, Israel.
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226
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Hanson C, Cairns J, Wang L, Sinha S. Principled multi-omic analysis reveals gene regulatory mechanisms of phenotype variation. Genome Res 2018; 28:1207-1216. [PMID: 29898900 PMCID: PMC6071639 DOI: 10.1101/gr.227066.117] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2017] [Accepted: 05/31/2018] [Indexed: 12/12/2022]
Abstract
Recent studies have analyzed large-scale data sets of gene expression to identify genes associated with interindividual variation in phenotypes ranging from cancer subtypes to drug sensitivity, promising new avenues of research in personalized medicine. However, gene expression data alone is limited in its ability to reveal cis-regulatory mechanisms underlying phenotypic differences. In this study, we develop a new probabilistic model, called pGENMi, that integrates multi-omic data to investigate the transcriptional regulatory mechanisms underlying interindividual variation of a specific phenotype—that of cell line response to cytotoxic treatment. In particular, pGENMi simultaneously analyzes genotype, DNA methylation, gene expression, and transcription factor (TF)-DNA binding data, along with phenotypic measurements, to identify TFs regulating the phenotype. It does so by combining statistical information about expression quantitative trait loci (eQTLs) and expression-correlated methylation marks (eQTMs) located within TF binding sites, as well as observed correlations between gene expression and phenotype variation. Application of pGENMi to data from a panel of lymphoblastoid cell lines treated with 24 drugs, in conjunction with ENCODE TF ChIP data, yielded a number of known as well as novel (TF, Drug) associations. Experimental validations by TF knockdown confirmed 41% of the predicted and tested associations, compared to a 12% confirmation rate of tested nonassociations (controls). An extensive literature survey also corroborated 62% of the predicted associations above a stringent threshold. Moreover, associations predicted only when combining eQTL and eQTM data showed higher precision compared to an eQTL-only or eQTM-only analysis using pGENMi, further demonstrating the value of multi-omic integrative analysis.
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Affiliation(s)
- Casey Hanson
- Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, USA
| | - Junmei Cairns
- Division of Clinical Pharmacology, Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, Minnesota 55905, USA
| | - Liewei Wang
- Division of Clinical Pharmacology, Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, Minnesota 55905, USA
| | - Saurabh Sinha
- Department of Computer Science and Institute of Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, USA
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227
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Ferguson LB, Harris RA, Mayfield RD. From gene networks to drugs: systems pharmacology approaches for AUD. Psychopharmacology (Berl) 2018; 235:1635-1662. [PMID: 29497781 PMCID: PMC6298603 DOI: 10.1007/s00213-018-4855-2] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/02/2017] [Accepted: 02/06/2018] [Indexed: 12/29/2022]
Abstract
The alcohol research field has amassed an impressive number of gene expression datasets spanning key brain areas for addiction, species (humans as well as multiple animal models), and stages in the addiction cycle (binge/intoxication, withdrawal/negative effect, and preoccupation/anticipation). These data have improved our understanding of the molecular adaptations that eventually lead to dysregulation of brain function and the chronic, relapsing disorder of addiction. Identification of new medications to treat alcohol use disorder (AUD) will likely benefit from the integration of genetic, genomic, and behavioral information included in these important datasets. Systems pharmacology considers drug effects as the outcome of the complex network of interactions a drug has rather than a single drug-molecule interaction. Computational strategies based on this principle that integrate gene expression signatures of pharmaceuticals and disease states have shown promise for identifying treatments that ameliorate disease symptoms (called in silico gene mapping or connectivity mapping). In this review, we suggest that gene expression profiling for in silico mapping is critical to improve drug repurposing and discovery for AUD and other psychiatric illnesses. We highlight studies that successfully apply gene mapping computational approaches to identify or repurpose pharmaceutical treatments for psychiatric illnesses. Furthermore, we address important challenges that must be overcome to maximize the potential of these strategies to translate to the clinic and improve healthcare outcomes.
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Affiliation(s)
- Laura B Ferguson
- Waggoner Center for Alcohol and Addiction Research, University of Texas at Austin, 1 University Station A4800, Austin, TX, 78712, USA
- Intitute for Neuroscience, University of Texas at Austin, Austin, TX, 78712, USA
| | - R Adron Harris
- Waggoner Center for Alcohol and Addiction Research, University of Texas at Austin, 1 University Station A4800, Austin, TX, 78712, USA
| | - Roy Dayne Mayfield
- Waggoner Center for Alcohol and Addiction Research, University of Texas at Austin, 1 University Station A4800, Austin, TX, 78712, USA.
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228
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Koshiba S, Motoike I, Saigusa D, Inoue J, Shirota M, Katoh Y, Katsuoka F, Danjoh I, Hozawa A, Kuriyama S, Minegishi N, Nagasaki M, Takai-Igarashi T, Ogishima S, Fuse N, Kure S, Tamiya G, Tanabe O, Yasuda J, Kinoshita K, Yamamoto M. Omics research project on prospective cohort studies from the Tohoku Medical Megabank Project. Genes Cells 2018; 23:406-417. [PMID: 29701317 DOI: 10.1111/gtc.12588] [Citation(s) in RCA: 42] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2018] [Accepted: 03/22/2018] [Indexed: 01/05/2023]
Abstract
Population-based prospective cohort studies are indispensable for modern medical research as they provide important knowledge on the influences of many kinds of genetic and environmental factors on the cause of disease. Although traditional cohort studies are mainly conducted using questionnaires and physical examinations, modern cohort studies incorporate omics and genomic approaches to obtain comprehensive physical information, including genetic information. Here, we report the design and midterm results of multi-omics analysis on population-based prospective cohort studies from the Tohoku Medical Megabank (TMM) Project. We have incorporated genomic and metabolomic studies in the TMM cohort study as both metabolome and genome analyses are suitable for high-throughput analysis of large-scale cohort samples. Moreover, an association study between the metabolome and genome show that metabolites are an important intermediate phenotype connecting genetic and lifestyle factors to physical and pathologic phenotypes. We apply our metabolome and genome analyses to large-scale cohort samples in the following studies.
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Affiliation(s)
- Seizo Koshiba
- Tohoku Medical Megabank Organization, Tohoku University, Sendai, Japan
- Graduate School of Medicine, Tohoku University, Sendai, Japan
| | - Ikuko Motoike
- Tohoku Medical Megabank Organization, Tohoku University, Sendai, Japan
- Graduate School of Information Sciences, Tohoku University, Sendai, Japan
| | - Daisuke Saigusa
- Tohoku Medical Megabank Organization, Tohoku University, Sendai, Japan
- Graduate School of Medicine, Tohoku University, Sendai, Japan
| | - Jin Inoue
- Tohoku Medical Megabank Organization, Tohoku University, Sendai, Japan
- Graduate School of Medicine, Tohoku University, Sendai, Japan
| | - Matsuyuki Shirota
- Tohoku Medical Megabank Organization, Tohoku University, Sendai, Japan
- Graduate School of Medicine, Tohoku University, Sendai, Japan
| | - Yasutake Katoh
- Tohoku Medical Megabank Organization, Tohoku University, Sendai, Japan
| | - Fumiki Katsuoka
- Tohoku Medical Megabank Organization, Tohoku University, Sendai, Japan
- Graduate School of Medicine, Tohoku University, Sendai, Japan
| | - Inaho Danjoh
- Tohoku Medical Megabank Organization, Tohoku University, Sendai, Japan
- Graduate School of Medicine, Tohoku University, Sendai, Japan
| | - Atsushi Hozawa
- Tohoku Medical Megabank Organization, Tohoku University, Sendai, Japan
- Graduate School of Medicine, Tohoku University, Sendai, Japan
| | - Shinichi Kuriyama
- Tohoku Medical Megabank Organization, Tohoku University, Sendai, Japan
- Graduate School of Medicine, Tohoku University, Sendai, Japan
- International Research Institute of Disaster Science, Tohoku University, Sendai, Japan
| | - Naoko Minegishi
- Tohoku Medical Megabank Organization, Tohoku University, Sendai, Japan
- Graduate School of Medicine, Tohoku University, Sendai, Japan
| | - Masao Nagasaki
- Tohoku Medical Megabank Organization, Tohoku University, Sendai, Japan
- Graduate School of Medicine, Tohoku University, Sendai, Japan
- Graduate School of Information Sciences, Tohoku University, Sendai, Japan
| | - Takako Takai-Igarashi
- Tohoku Medical Megabank Organization, Tohoku University, Sendai, Japan
- Graduate School of Medicine, Tohoku University, Sendai, Japan
| | - Soichi Ogishima
- Tohoku Medical Megabank Organization, Tohoku University, Sendai, Japan
- Graduate School of Medicine, Tohoku University, Sendai, Japan
| | - Nobuo Fuse
- Tohoku Medical Megabank Organization, Tohoku University, Sendai, Japan
- Graduate School of Medicine, Tohoku University, Sendai, Japan
| | - Shigeo Kure
- Tohoku Medical Megabank Organization, Tohoku University, Sendai, Japan
- Graduate School of Medicine, Tohoku University, Sendai, Japan
| | - Gen Tamiya
- Tohoku Medical Megabank Organization, Tohoku University, Sendai, Japan
- Graduate School of Medicine, Tohoku University, Sendai, Japan
- RIKEN Center for Advanced Intelligence Project, Tokyo, Japan
| | - Osamu Tanabe
- Tohoku Medical Megabank Organization, Tohoku University, Sendai, Japan
- Graduate School of Medicine, Tohoku University, Sendai, Japan
| | - Jun Yasuda
- Tohoku Medical Megabank Organization, Tohoku University, Sendai, Japan
- Graduate School of Medicine, Tohoku University, Sendai, Japan
| | - Kengo Kinoshita
- Tohoku Medical Megabank Organization, Tohoku University, Sendai, Japan
- Graduate School of Information Sciences, Tohoku University, Sendai, Japan
- Institute of Development, Aging and Cancer, Tohoku University, Sendai, Japan
| | - Masayuki Yamamoto
- Tohoku Medical Megabank Organization, Tohoku University, Sendai, Japan
- Graduate School of Medicine, Tohoku University, Sendai, Japan
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229
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The dorsoventral patterning of Musca domestica embryos: insights into BMP/Dpp evolution from the base of the lower cyclorraphan flies. EvoDevo 2018; 9:13. [PMID: 29796243 PMCID: PMC5956798 DOI: 10.1186/s13227-018-0102-5] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2018] [Accepted: 05/06/2018] [Indexed: 01/09/2023] Open
Abstract
Background In the last few years, accumulated information has indicated that the evolution of an extra-embryonic membrane in dipterans was accompanied by changes in the gene regulatory network controlled by the BMP/Dpp pathway, which is responsible for dorsal patterning in these insects. However, only comparative analysis of gene expression levels between distant species with two extra-embryonic membranes, like A. gambiae or C. albipunctata, and D. melanogaster, has been conducted. Analysis of gene expression in ancestral species, which evolved closer to the amnioserosa origin, could provide new insights into the evolution of dorsoventral patterning in dipterans. Results Here we describe the spatial expression of several key and downstream elements of the Dpp pathway and show the compared patterns of expression between Musca and Drosophila embryos, both dipterans with amnioserosa. Most of the analyzed gene showed a high degree of expression conservation, however, we found several differences in the gene expression pattern of M. domestica orthologs for sog and tolloid. Bioinformatics analysis of the promoter of both genes indicated that the variations could be related to the gain of several binding sites for the transcriptional factor Dorsal in the Md.tld promoter and Snail in the Md.sog enhancer. These altered expressions could explain the unclear formation of the pMad gradient in the M. domestica embryo, compared to the formation of the gradient in D. melanogaster. Conclusion Gene expression changes during the dorsal–ventral patterning in insects contribute to the differentiation of extra-embryonic tissues as a consequence of changes in the gene regulatory network controlled by BMP/Dpp. In this work, in early M. domestica embryos, we identified the expression pattern of several genes members involved in the dorsoventral specification of the embryo. We believe that these data can contribute to understanding the evolution of the BMP/Dpp pathway, the regulation of BMP ligands, and the formation of a Dpp gradient in higher cyclorraphan flies. Electronic supplementary material The online version of this article (10.1186/s13227-018-0102-5) contains supplementary material, which is available to authorized users.
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230
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Mostafavi S, Gaiteri C, Sullivan SE, White CC, Tasaki S, Xu J, Taga M, Klein HU, Patrick E, Komashko V, McCabe C, Smith R, Bradshaw EM, Root DE, Regev A, Yu L, Chibnik LB, Schneider JA, Young-Pearse TL, Bennett DA, De Jager PL. A molecular network of the aging human brain provides insights into the pathology and cognitive decline of Alzheimer's disease. Nat Neurosci 2018; 21:811-819. [PMID: 29802388 PMCID: PMC6599633 DOI: 10.1038/s41593-018-0154-9] [Citation(s) in RCA: 359] [Impact Index Per Article: 51.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2016] [Accepted: 04/20/2018] [Indexed: 02/07/2023]
Abstract
There is a need for new therapeutic targets with which to prevent Alzheimer’s disease (AD), a major contributor to aging-related cognitive decline. Here, we report the construction and validation of a molecular network of the aging human frontal cortex. Using RNA sequence data from 478 individuals, we first build a molecular network using modules of coexpressed genes and then relate these modules to AD and its neuropathologic and cognitive endophenotypes. We confirm these associations in two independent AD datasets as well as in epigenomic data. We also illustrate the use of the network in prioritizing amyloid-associated genes for in vitro validation in human neurons and astrocytes. These analyses based on unique cohorts enable us to resolve the role of distinct cortical modules that have a direct effect on the accumulation of AD pathology from those that have a direct effect on cognitive decline, exemplifying a network approach to complex diseases. Systems biology analysis of RNA sequencing data from the aging human cortex identifies a molecular network which prioritizes groups of genes that influence cognitive decline or neuropathology in Alzheimer’s disease.
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Affiliation(s)
- Sara Mostafavi
- Department of Statistics, Department of Medical Genetics, University of British Columbia, Vancouver, BC, Canada.,Canadian Institute for Advanced Research, Toronto, ON, Canada
| | - Chris Gaiteri
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, USA
| | - Sarah E Sullivan
- Department of Neurology, Brigham and Women's Hospital, Boston, MA, USA
| | | | - Shinya Tasaki
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, USA
| | - Jishu Xu
- Broad Institute, Cambridge, MA, USA
| | - Mariko Taga
- Broad Institute, Cambridge, MA, USA.,Center for Translational & Computational Neuroimmunology, Department of Neurology, Columbia University Medical Center, New York, NY, USA
| | - Hans-Ulrich Klein
- Broad Institute, Cambridge, MA, USA.,Center for Translational & Computational Neuroimmunology, Department of Neurology, Columbia University Medical Center, New York, NY, USA
| | | | - Vitalina Komashko
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, USA
| | | | - Robert Smith
- Department of Neurology, Brigham and Women's Hospital, Boston, MA, USA
| | - Elizabeth M Bradshaw
- Broad Institute, Cambridge, MA, USA.,Center for Translational & Computational Neuroimmunology, Department of Neurology, Columbia University Medical Center, New York, NY, USA
| | | | | | - Lei Yu
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, USA
| | - Lori B Chibnik
- Broad Institute, Cambridge, MA, USA.,Harvard Medical School, Boston, MA, USA.,Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Julie A Schneider
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, USA
| | - Tracy L Young-Pearse
- Department of Neurology, Brigham and Women's Hospital, Boston, MA, USA.,Harvard Medical School, Boston, MA, USA
| | - David A Bennett
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, USA.
| | - Philip L De Jager
- Broad Institute, Cambridge, MA, USA. .,Center for Translational & Computational Neuroimmunology, Department of Neurology, Columbia University Medical Center, New York, NY, USA.
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231
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Wang M, Hancock TP, Chamberlain AJ, Vander Jagt CJ, Pryce JE, Cocks BG, Goddard ME, Hayes BJ. Putative bovine topological association domains and CTCF binding motifs can reduce the search space for causative regulatory variants of complex traits. BMC Genomics 2018; 19:395. [PMID: 29793448 PMCID: PMC5968476 DOI: 10.1186/s12864-018-4800-0] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2018] [Accepted: 05/17/2018] [Indexed: 12/18/2022] Open
Abstract
BACKGROUND Topological association domains (TADs) are chromosomal domains characterised by frequent internal DNA-DNA interactions. The transcription factor CTCF binds to conserved DNA sequence patterns called CTCF binding motifs to either prohibit or facilitate chromosomal interactions. TADs and CTCF binding motifs control gene expression, but they are not yet well defined in the bovine genome. In this paper, we sought to improve the annotation of bovine TADs and CTCF binding motifs, and assess whether the new annotation can reduce the search space for cis-regulatory variants. RESULTS We used genomic synteny to map TADs and CTCF binding motifs from humans, mice, dogs and macaques to the bovine genome. We found that our mapped TADs exhibited the same hallmark properties of those sourced from experimental data, such as housekeeping genes, transfer RNA genes, CTCF binding motifs, short interspersed elements, H3K4me3 and H3K27ac. We showed that runs of genes with the same pattern of allele-specific expression (ASE) (either favouring paternal or maternal allele) were often located in the same TAD or between the same conserved CTCF binding motifs. Analyses of variance showed that when averaged across all bovine tissues tested, TADs explained 14% of ASE variation (standard deviation, SD: 0.056), while CTCF explained 27% (SD: 0.078). Furthermore, we showed that the quantitative trait loci (QTLs) associated with gene expression variation (eQTLs) or ASE variation (aseQTLs), which were identified from mRNA transcripts from 141 lactating cows' white blood and milk cells, were highly enriched at putative bovine CTCF binding motifs. The linearly-furthermost, and most-significant aseQTL and eQTL for each genic target were located within the same TAD as the gene more often than expected (Chi-Squared test P-value < 0.001). CONCLUSIONS Our results suggest that genomic synteny can be used to functionally annotate conserved transcriptional components, and provides a tool to reduce the search space for causative regulatory variants in the bovine genome.
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Affiliation(s)
- Min Wang
- AgriBio, Centre for AgriBioscience, Agriculture Victoria, Melbourne, VIC Australia
- School of Applied Systems Biology, La Trobe University, Melbourne, VIC Australia
| | | | | | | | - Jennie E. Pryce
- AgriBio, Centre for AgriBioscience, Agriculture Victoria, Melbourne, VIC Australia
- School of Applied Systems Biology, La Trobe University, Melbourne, VIC Australia
- DataGene Ltd, Bundoora, VIC 3083 Australia
| | - Benjamin G. Cocks
- AgriBio, Centre for AgriBioscience, Agriculture Victoria, Melbourne, VIC Australia
- School of Applied Systems Biology, La Trobe University, Melbourne, VIC Australia
| | - Mike E. Goddard
- AgriBio, Centre for AgriBioscience, Agriculture Victoria, Melbourne, VIC Australia
- Faculty of Veterinary and Agricultural Sciences, University of Melbourne, Parkville, Melbourne, VIC Australia
| | - Benjamin J. Hayes
- AgriBio, Centre for AgriBioscience, Agriculture Victoria, Melbourne, VIC Australia
- Queensland Alliance for Agriculture and Food Innovation, University of Queensland, St Lucia, QLD Australia
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232
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Barbeira AN, Dickinson SP, Bonazzola R, Zheng J, Wheeler HE, Torres JM, Torstenson ES, Shah KP, Garcia T, Edwards TL, Stahl EA, Huckins LM, Nicolae DL, Cox NJ, Im HK. Exploring the phenotypic consequences of tissue specific gene expression variation inferred from GWAS summary statistics. Nat Commun 2018; 9:1825. [PMID: 29739930 PMCID: PMC5940825 DOI: 10.1038/s41467-018-03621-1] [Citation(s) in RCA: 698] [Impact Index Per Article: 99.7] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2017] [Accepted: 12/27/2017] [Indexed: 12/25/2022] Open
Abstract
Scalable, integrative methods to understand mechanisms that link genetic variants with phenotypes are needed. Here we derive a mathematical expression to compute PrediXcan (a gene mapping approach) results using summary data (S-PrediXcan) and show its accuracy and general robustness to misspecified reference sets. We apply this framework to 44 GTEx tissues and 100+ phenotypes from GWAS and meta-analysis studies, creating a growing public catalog of associations that seeks to capture the effects of gene expression variation on human phenotypes. Replication in an independent cohort is shown. Most of the associations are tissue specific, suggesting context specificity of the trait etiology. Colocalized significant associations in unexpected tissues underscore the need for an agnostic scanning of multiple contexts to improve our ability to detect causal regulatory mechanisms. Monogenic disease genes are enriched among significant associations for related traits, suggesting that smaller alterations of these genes may cause a spectrum of milder phenotypes.
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Affiliation(s)
- Alvaro N Barbeira
- Section of Genetic Medicine, The University of Chicago, Chicago, IL, 60637, USA
| | - Scott P Dickinson
- Section of Genetic Medicine, The University of Chicago, Chicago, IL, 60637, USA
| | - Rodrigo Bonazzola
- Section of Genetic Medicine, The University of Chicago, Chicago, IL, 60637, USA
| | - Jiamao Zheng
- Section of Genetic Medicine, The University of Chicago, Chicago, IL, 60637, USA
| | - Heather E Wheeler
- Department of Biology, Loyola University Chicago, Chicago, IL, 60660, USA.,Department of Computer Science, Loyola University Chicago, Chicago, IL, 60660, USA
| | - Jason M Torres
- Committee on Molecular Metabolism and Nutrition, The University of Chicago, Chicago, IL, 60637, USA
| | - Eric S Torstenson
- Vanderbilt Genetic Institute, Vanderbilt University Medical Center, Nashville, TN, 37232, USA
| | - Kaanan P Shah
- Section of Genetic Medicine, The University of Chicago, Chicago, IL, 60637, USA
| | - Tzintzuni Garcia
- Center for Research Informatics, The University of Chicago, Chicago, IL, 60615, USA
| | - Todd L Edwards
- Division of Epidemiology, Department of Medicine, Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, 37232, USA
| | - Eli A Stahl
- Division of Psychiatric Genomics, Icahn School of Medicine at Mount Sinai, NYC, NY, 10029, USA.,Department of Genetics and Genomics, Icahn School of Medicine at Mount Sinai, NYC, NY, 10029, USA
| | - Laura M Huckins
- Division of Psychiatric Genomics, Icahn School of Medicine at Mount Sinai, NYC, NY, 10029, USA.,Department of Genetics and Genomics, Icahn School of Medicine at Mount Sinai, NYC, NY, 10029, USA
| | | | - Dan L Nicolae
- Section of Genetic Medicine, The University of Chicago, Chicago, IL, 60637, USA
| | - Nancy J Cox
- Vanderbilt Genetic Institute, Vanderbilt University Medical Center, Nashville, TN, 37232, USA
| | - Hae Kyung Im
- Section of Genetic Medicine, The University of Chicago, Chicago, IL, 60637, USA.
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233
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Chen XF, Zhu DL, Yang M, Hu WX, Duan YY, Lu BJ, Rong Y, Dong SS, Hao RH, Chen JB, Chen YX, Yao S, Thynn HN, Guo Y, Yang TL. An Osteoporosis Risk SNP at 1p36.12 Acts as an Allele-Specific Enhancer to Modulate LINC00339 Expression via Long-Range Loop Formation. Am J Hum Genet 2018; 102:776-793. [PMID: 29706346 PMCID: PMC5986728 DOI: 10.1016/j.ajhg.2018.03.001] [Citation(s) in RCA: 74] [Impact Index Per Article: 10.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2017] [Accepted: 02/28/2018] [Indexed: 01/10/2023] Open
Abstract
Genome-wide association studies (GWASs) have reproducibly associated variants within intergenic regions of 1p36.12 locus with osteoporosis, but the functional roles underlying these noncoding variants are unknown. Through an integrative functional genomic and epigenomic analyses, we prioritized rs6426749 as a potential causal SNP for osteoporosis at 1p36.12. Dual-luciferase assay and CRISPR/Cas9 experiments demonstrate that rs6426749 acts as a distal allele-specific enhancer regulating expression of a lncRNA (LINC00339) (∼360 kb) via long-range chromatin loop formation and that this loop is mediated by CTCF occupied near rs6426749 and LINC00339 promoter region. Specifically, rs6426749-G allele can bind transcription factor TFAP2A, which efficiently elevates the enhancer activity and increases LINC00339 expression. Downregulation of LINC00339 significantly increases the expression of CDC42 in osteoblast cells, which is a pivotal regulator involved in bone metabolism. Our study provides mechanistic insight into how a noncoding SNP affects osteoporosis by long-range interaction, a finding that could indicate promising therapeutic targets for osteoporosis.
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Affiliation(s)
- Xiao-Feng Chen
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, and Institute of Molecular Genetics, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an 710049, P. R. China
| | - Dong-Li Zhu
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, and Institute of Molecular Genetics, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an 710049, P. R. China
| | - Man Yang
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, and Institute of Molecular Genetics, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an 710049, P. R. China
| | - Wei-Xin Hu
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, and Institute of Molecular Genetics, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an 710049, P. R. China
| | - Yuan-Yuan Duan
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, and Institute of Molecular Genetics, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an 710049, P. R. China
| | - Bing-Jie Lu
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, and Institute of Molecular Genetics, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an 710049, P. R. China
| | - Yu Rong
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, and Institute of Molecular Genetics, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an 710049, P. R. China
| | - Shan-Shan Dong
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, and Institute of Molecular Genetics, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an 710049, P. R. China
| | - Ruo-Han Hao
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, and Institute of Molecular Genetics, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an 710049, P. R. China
| | - Jia-Bin Chen
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, and Institute of Molecular Genetics, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an 710049, P. R. China
| | - Yi-Xiao Chen
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, and Institute of Molecular Genetics, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an 710049, P. R. China
| | - Shi Yao
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, and Institute of Molecular Genetics, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an 710049, P. R. China
| | - Hlaing Nwe Thynn
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, and Institute of Molecular Genetics, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an 710049, P. R. China
| | - Yan Guo
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, and Institute of Molecular Genetics, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an 710049, P. R. China.
| | - Tie-Lin Yang
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, and Institute of Molecular Genetics, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an 710049, P. R. China.
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Abstract
Genome-wide association studies have discovered thousands of common alleles that associate with human phenotypes and disease. Many of these variants are in non-protein-coding (regulatory) regions and are believed to affect phenotypes by modifying gene expression. In any organism with a diploid genome, such as humans, measuring the expression of each allele of a gene provides a well-controlled way to identify allelic influences on that gene's expression. Here, we describe a protocol for precisely measuring the allele-specific expression of individual genes. This method targets the nucleotide differences between the two alleles of a gene within an individual and measures the "allelic skew," the extent to which one allele is expressed more than the other. We cover the design of effective assays, the optimization of reactions, and the interpretation of the resulting data.
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235
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Su YR, Di C, Bien S, Huang L, Dong X, Abecasis G, Berndt S, Bezieau S, Brenner H, Caan B, Casey G, Chang-Claude J, Chanock S, Chen S, Connolly C, Curtis K, Figueiredo J, Gala M, Gallinger S, Harrison T, Hoffmeister M, Hopper J, Huyghe JR, Jenkins M, Joshi A, Le Marchand L, Newcomb P, Nickerson D, Potter J, Schoen R, Slattery M, White E, Zanke B, Peters U, Hsu L. A Mixed-Effects Model for Powerful Association Tests in Integrative Functional Genomics. Am J Hum Genet 2018; 102:904-919. [PMID: 29727690 PMCID: PMC5986723 DOI: 10.1016/j.ajhg.2018.03.019] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2017] [Accepted: 03/15/2018] [Indexed: 01/05/2023] Open
Abstract
Genome-wide association studies (GWASs) have successfully identified thousands of genetic variants for many complex diseases; however, these variants explain only a small fraction of the heritability. Recently, genetic association studies that leverage external transcriptome data have received much attention and shown promise for discovering novel variants. One such approach, PrediXcan, is to use predicted gene expression through genetic regulation. However, there are limitations in this approach. The predicted gene expression may be biased, resulting from regularized regression applied to moderately sample-sized reference studies. Further, some variants can individually influence disease risk through alternative functional mechanisms besides expression. Thus, testing only the association of predicted gene expression as proposed in PrediXcan will potentially lose power. To tackle these challenges, we consider a unified mixed effects model that formulates the association of intermediate phenotypes such as imputed gene expression through fixed effects, while allowing residual effects of individual variants to be random. We consider a set-based score testing framework, MiST (mixed effects score test), and propose two data-driven combination approaches to jointly test for the fixed and random effects. We establish the asymptotic distributions, which enable rapid calculation of p values for genome-wide analyses, and provide p values for fixed and random effects separately to enhance interpretability over GWASs. Extensive simulations demonstrate that our approaches are more powerful than existing ones. We apply our approach to a large-scale GWAS of colorectal cancer and identify two genes, POU5F1B and ATF1, which would have otherwise been missed by PrediXcan, after adjusting for all known loci.
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Affiliation(s)
- Yu-Ru Su
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA.
| | - Chongzhi Di
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA
| | - Stephanie Bien
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA
| | - Licai Huang
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA
| | - Xinyuan Dong
- Department of Biostatistics, University of Washington, Seattle, WA 98195, USA
| | - Goncalo Abecasis
- Biostatistics, School of Public Health, University of Michigan, Ann Arbor, MI 48109, USA
| | - Sonja Berndt
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Rockville, MD 20850, USA
| | - Stephane Bezieau
- Service de Génétique Médicale Centre Hospitalier Universitaire (CHU) Nantes, Nantes 44093, France
| | - Hermann Brenner
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg 69120, Germany
| | - Bette Caan
- Division of Research, Kaiser Permanente Medical Care Program of Northern California, Oakland, CA 94612, USA
| | - Graham Casey
- Public Health Sciences Division, University of Virginia, Charlottesville, VA 22908, USA
| | - Jenny Chang-Claude
- Division of Cancer Epidemiology, German Cancer Research Center, Heidelberg 69009, Germany
| | - Stephen Chanock
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Rockville, MD 20850, USA
| | - Sai Chen
- Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA
| | - Charles Connolly
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA
| | - Keith Curtis
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA
| | - Jane Figueiredo
- Samuel Oschin Comprehensive Cancer Institute, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA
| | - Manish Gala
- Division of Gastroenterology, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Steven Gallinger
- Department of Surgery, Mount Sinai Hospital, Toronto, ON M5G 1X5, Canada
| | - Tabitha Harrison
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA
| | - Michael Hoffmeister
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg 69120, Germany
| | - John Hopper
- Melborne School of Population Health, The University of Melborne, Carlton, VIC 3010, Australia
| | - Jeroen R Huyghe
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA
| | - Mark Jenkins
- Melborne School of Population Health, The University of Melborne, Carlton, VIC 3010, Australia
| | - Amit Joshi
- Department of Epidemiology, Harvard School of Public Health, Boston, MA 02115, USA
| | - Loic Le Marchand
- Epidemiology Program, University of Hawaii Cancer Center, Honolulu, HI 96813, USA
| | - Polly Newcomb
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA; Department of Epidemiology, University of Washington School of Public Health, Seattle, WA 98109, USA
| | | | - John Potter
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA; Department of Epidemiology, University of Washington School of Public Health, Seattle, WA 98109, USA
| | - Robert Schoen
- Department of Medicine and Epidemiology, University of Pittsburgh Medical Center, Pittsburgh, PA 15213, USA
| | - Martha Slattery
- Department of Internal Medicine, University of Utah Health Sciences Center, Salt Lake City, UT 84132, USA
| | - Emily White
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA; Department of Epidemiology, University of Washington School of Public Health, Seattle, WA 98109, USA
| | - Brent Zanke
- Division of Hematology, Faculty of Medicine, The University of Ottawa, Ottawa, ON K1Y 4E9, USA
| | - Ulrike Peters
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA; Department of Epidemiology, University of Washington School of Public Health, Seattle, WA 98109, USA
| | - Li Hsu
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA; Department of Biostatistics, University of Washington, Seattle, WA 98195, USA.
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236
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Aguiar D, Cheng LF, Dumitrascu B, Mordelet F, Pai AA, Engelhardt BE. Bayesian nonparametric discovery of isoforms and individual specific quantification. Nat Commun 2018; 9:1681. [PMID: 29703885 PMCID: PMC5923247 DOI: 10.1038/s41467-018-03402-w] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2017] [Accepted: 02/11/2018] [Indexed: 12/18/2022] Open
Abstract
Most human protein-coding genes can be transcribed into multiple distinct mRNA isoforms. These alternative splicing patterns encourage molecular diversity, and dysregulation of isoform expression plays an important role in disease etiology. However, isoforms are difficult to characterize from short-read RNA-seq data because they share identical subsequences and occur in different frequencies across tissues and samples. Here, we develop biisq, a Bayesian nonparametric model for isoform discovery and individual specific quantification from short-read RNA-seq data. biisq does not require isoform reference sequences but instead estimates an isoform catalog shared across samples. We use stochastic variational inference for efficient posterior estimates and demonstrate superior precision and recall for simulations compared to state-of-the-art isoform reconstruction methods. biisq shows the most gains for low abundance isoforms, with 36% more isoforms correctly inferred at low coverage versus a multi-sample method and 170% more versus single-sample methods. We estimate isoforms in the GEUVADIS RNA-seq data and validate inferred isoforms by associating genetic variants with isoform ratios. Alternative splicing leads to transcript isoform diversity. Here, Aguiar et al. develop biisq, a Bayesian nonparametric approach to discover and quantify isoforms from RNA-seq data.
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Affiliation(s)
- Derek Aguiar
- Department of Computer Science, Princeton University, Princeton, NJ, 08540, USA.
| | - Li-Fang Cheng
- Department of Electrical Engineering, Princeton University, Princeton, NJ, 08540, USA
| | - Bianca Dumitrascu
- Lewis-Sigler Institute, Princeton University, Princeton, NJ, 08544, USA
| | - Fantine Mordelet
- Institute for Genome Sciences and Policy, Duke University, Durham, NC, 27708, USA
| | - Athma A Pai
- Department of Biology, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA.,RNA Therapeutics Institute, University of Massachusetts Medical School, Worcester, MA, 01605, USA
| | - Barbara E Engelhardt
- Department of Computer Science, Princeton University, Princeton, NJ, 08540, USA. .,Center for Statistics and Machine Learning, Princeton University, Princeton, NJ, 08540, USA.
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237
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Kycia I, Wolford BN, Huyghe JR, Fuchsberger C, Vadlamudi S, Kursawe R, Welch RP, Albanus RD, Uyar A, Khetan S, Lawlor N, Bolisetty M, Mathur A, Kuusisto J, Laakso M, Ucar D, Mohlke KL, Boehnke M, Collins FS, Parker SCJ, Stitzel ML. A Common Type 2 Diabetes Risk Variant Potentiates Activity of an Evolutionarily Conserved Islet Stretch Enhancer and Increases C2CD4A and C2CD4B Expression. Am J Hum Genet 2018; 102:620-635. [PMID: 29625024 DOI: 10.1016/j.ajhg.2018.02.020] [Citation(s) in RCA: 39] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2017] [Accepted: 02/22/2018] [Indexed: 01/17/2023] Open
Abstract
Genome-wide association studies (GWASs) and functional genomics approaches implicate enhancer disruption in islet dysfunction and type 2 diabetes (T2D) risk. We applied genetic fine-mapping and functional (epi)genomic approaches to a T2D- and proinsulin-associated 15q22.2 locus to identify a most likely causal variant, determine its direction of effect, and elucidate plausible target genes. Fine-mapping and conditional analyses of proinsulin levels of 8,635 non-diabetic individuals from the METSIM study support a single association signal represented by a cluster of 16 strongly associated (p < 10-17) variants in high linkage disequilibrium (r2 > 0.8) with the GWAS index SNP rs7172432. These variants reside in an evolutionarily and functionally conserved islet and β cell stretch or super enhancer; the most strongly associated variant (rs7163757, p = 3 × 10-19) overlaps a conserved islet open chromatin site. DNA sequence containing the rs7163757 risk allele displayed 2-fold higher enhancer activity than the non-risk allele in reporter assays (p < 0.01) and was differentially bound by β cell nuclear extract proteins. Transcription factor NFAT specifically potentiated risk-allele enhancer activity and altered patterns of nuclear protein binding to the risk allele in vitro, suggesting that it could be a factor mediating risk-allele effects. Finally, the rs7163757 proinsulin-raising and T2D risk allele (C) was associated with increased expression of C2CD4B, and possibly C2CD4A, both of which were induced by inflammatory cytokines, in human islets. Together, these data suggest that rs7163757 contributes to genetic risk of islet dysfunction and T2D by increasing NFAT-mediated islet enhancer activity and modulating C2CD4B, and possibly C2CD4A, expression in (patho)physiologic states.
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Affiliation(s)
- Ina Kycia
- Jackson Laboratory for Genomic Medicine, Farmington, CT 06032, USA
| | - Brooke N Wolford
- National Human Genome Research Institute, NIH, Bethesda, MD 20892, USA
| | - Jeroen R Huyghe
- Department of Biostatistics and Center for Statistical Genetics, School of Public Health, University of Michigan, Ann Arbor, MI 48109, USA
| | - Christian Fuchsberger
- Department of Biostatistics and Center for Statistical Genetics, School of Public Health, University of Michigan, Ann Arbor, MI 48109, USA
| | | | - Romy Kursawe
- Jackson Laboratory for Genomic Medicine, Farmington, CT 06032, USA
| | - Ryan P Welch
- Department of Biostatistics and Center for Statistical Genetics, School of Public Health, University of Michigan, Ann Arbor, MI 48109, USA
| | - Ricardo d'Oliveira Albanus
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA
| | - Asli Uyar
- Jackson Laboratory for Genomic Medicine, Farmington, CT 06032, USA
| | - Shubham Khetan
- Jackson Laboratory for Genomic Medicine, Farmington, CT 06032, USA
| | - Nathan Lawlor
- Jackson Laboratory for Genomic Medicine, Farmington, CT 06032, USA
| | - Mohan Bolisetty
- Jackson Laboratory for Genomic Medicine, Farmington, CT 06032, USA
| | - Anubhuti Mathur
- Jackson Laboratory for Genomic Medicine, Farmington, CT 06032, USA
| | - Johanna Kuusisto
- Department of Medicine, University of Eastern Finland and Kuopio University Hospital, 70210 Kuopio, Finland
| | - Markku Laakso
- Department of Medicine, University of Eastern Finland and Kuopio University Hospital, 70210 Kuopio, Finland
| | - Duygu Ucar
- Jackson Laboratory for Genomic Medicine, Farmington, CT 06032, USA; Institute of Systems Genomics, University of Connecticut Health Center, Farmington, CT 06032, USA
| | - Karen L Mohlke
- Department of Genetics, University of North Carolina, Chapel Hill, NC 27599, USA
| | - Michael Boehnke
- Department of Biostatistics and Center for Statistical Genetics, School of Public Health, University of Michigan, Ann Arbor, MI 48109, USA
| | - Francis S Collins
- National Human Genome Research Institute, NIH, Bethesda, MD 20892, USA
| | - Stephen C J Parker
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA; Department of Human Genetics, University of Michigan, Ann Arbor, MI 48109, USA
| | - Michael L Stitzel
- Jackson Laboratory for Genomic Medicine, Farmington, CT 06032, USA; Institute of Systems Genomics, University of Connecticut Health Center, Farmington, CT 06032, USA.
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238
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Wanke KA, Devanna P, Vernes SC. Understanding Neurodevelopmental Disorders: The Promise of Regulatory Variation in the 3'UTRome. Biol Psychiatry 2018; 83:548-557. [PMID: 29289333 DOI: 10.1016/j.biopsych.2017.11.006] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/07/2017] [Revised: 11/02/2017] [Accepted: 11/02/2017] [Indexed: 01/28/2023]
Abstract
Neurodevelopmental disorders have a strong genetic component, but despite widespread efforts, the specific genetic factors underlying these disorders remain undefined for a large proportion of affected individuals. Given the accessibility of exome sequencing, this problem has thus far been addressed from a protein-centric standpoint; however, protein-coding regions only make up ∼1% to 2% of the human genome. With the advent of whole genome sequencing we are in the midst of a paradigm shift as it is now possible to interrogate the entire sequence of the human genome (coding and noncoding) to fill in the missing heritability of complex disorders. These new technologies bring new challenges, as the number of noncoding variants identified per individual can be overwhelming, making it prudent to focus on noncoding regions of known function, for which the effects of variation can be predicted and directly tested to assess pathogenicity. The 3'UTRome is a region of the noncoding genome that perfectly fulfills these criteria and is of high interest when searching for pathogenic variation related to complex neurodevelopmental disorders. Herein, we review the regulatory roles of the 3'UTRome as binding sites for microRNAs or RNA binding proteins, or during alternative polyadenylation. We detail existing evidence that these regions contribute to neurodevelopmental disorders and outline strategies for identification and validation of novel putatively pathogenic variation in these regions. This evidence suggests that studying the 3'UTRome will lead to the identification of new risk factors, new candidate disease genes, and a better understanding of the molecular mechanisms contributing to neurodevelopmental disorders.
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Affiliation(s)
- Kai A Wanke
- Neurogenetics of Vocal Communication Group, Max Planck Institute for Psycholinguistics, Nijmegen, the Netherlands; Language and Genetics Department, Max Planck Institute for Psycholinguistics, Nijmegen, the Netherlands
| | - Paolo Devanna
- Neurogenetics of Vocal Communication Group, Max Planck Institute for Psycholinguistics, Nijmegen, the Netherlands
| | - Sonja C Vernes
- Neurogenetics of Vocal Communication Group, Max Planck Institute for Psycholinguistics, Nijmegen, the Netherlands; Donders Institute for Brain, Cognition and Behaviour, Nijmegen, the Netherlands.
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239
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Gene-by-environment interactions in urban populations modulate risk phenotypes. Nat Commun 2018; 9:827. [PMID: 29511166 PMCID: PMC5840419 DOI: 10.1038/s41467-018-03202-2] [Citation(s) in RCA: 66] [Impact Index Per Article: 9.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2017] [Accepted: 01/26/2018] [Indexed: 01/21/2023] Open
Abstract
Uncovering the interaction between genomes and the environment is a principal challenge of modern genomics and preventive medicine. While theoretical models are well defined, little is known of the G × E interactions in humans. We used an integrative approach to comprehensively assess the interactions between 1.6 million data points, encompassing a range of environmental exposures, health, and gene expression levels, coupled with whole-genome genetic variation. From ∼1000 individuals of a founder population in Quebec, we reveal a substantial impact of the environment on the transcriptome and clinical endophenotypes, overpowering that of genetic ancestry. Air pollution impacts gene expression and pathways affecting cardio-metabolic and respiratory traits, when controlling for genetic ancestry. Finally, we capture four expression quantitative trait loci that interact with the environment (air pollution). Our findings demonstrate how the local environment directly affects disease risk phenotypes and that genetic variation, including less common variants, can modulate individual’s response to environmental challenges. Individuals with different genotypes may respond differently to environmental variation. Here, Favé et al. find substantial impacts of different environment exposures on the transcriptome and clinical endophenotypes when controlling for genetic ancestry by analyzing data from ∼1000 individuals from a founder population in Quebec.
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240
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Zhang M, Lykke-Andersen S, Zhu B, Xiao W, Hoskins JW, Zhang X, Rost LM, Collins I, van de Bunt M, Jia J, Parikh H, Zhang T, Song L, Jermusyk A, Chung CC, Zhu B, Zhou W, Matters GL, Kurtz RC, Yeager M, Jensen TH, Brown KM, Ongen H, Bamlet WR, Murray BA, McCarthy MI, Chanock SJ, Chatterjee N, Wolpin BM, Smith JP, Olson SH, Petersen GM, Shi J, Amundadottir LT. Characterising cis-regulatory variation in the transcriptome of histologically normal and tumour-derived pancreatic tissues. Gut 2018; 67:521-533. [PMID: 28634199 PMCID: PMC5762429 DOI: 10.1136/gutjnl-2016-313146] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/30/2016] [Revised: 04/07/2017] [Accepted: 04/11/2017] [Indexed: 02/06/2023]
Abstract
OBJECTIVE To elucidate the genetic architecture of gene expression in pancreatic tissues. DESIGN We performed expression quantitative trait locus (eQTL) analysis in histologically normal pancreatic tissue samples (n=95) using RNA sequencing and the corresponding 1000 genomes imputed germline genotypes. Data from pancreatic tumour-derived tissue samples (n=115) from The Cancer Genome Atlas were included for comparison. RESULTS We identified 38 615 cis-eQTLs (in 484 genes) in histologically normal tissues and 39 713 cis-eQTL (in 237 genes) in tumour-derived tissues (false discovery rate <0.1), with the strongest effects seen near transcriptional start sites. Approximately 23% and 42% of genes with significant cis-eQTLs appeared to be specific for tumour-derived and normal-derived tissues, respectively. Significant enrichment of cis-eQTL variants was noted in non-coding regulatory regions, in particular for pancreatic tissues (1.53-fold to 3.12-fold, p≤0.0001), indicating tissue-specific functional relevance. A common pancreatic cancer risk locus on 9q34.2 (rs687289) was associated with ABO expression in histologically normal (p=5.8×10-8) and tumour-derived (p=8.3×10-5) tissues. The high linkage disequilibrium between this variant and the O blood group generating deletion variant in ABO (exon 6) suggested that nonsense-mediated decay (NMD) of the 'O' mRNA might explain this finding. However, knockdown of crucial NMD regulators did not influence decay of the ABO 'O' mRNA, indicating that a gene regulatory element influenced by pancreatic cancer risk alleles may underlie the eQTL. CONCLUSIONS We have identified cis-eQTLs representing potential functional regulatory variants in the pancreas and generated a rich data set for further studies on gene expression and its regulation in pancreatic tissues.
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Affiliation(s)
- Mingfeng Zhang
- Laboratory of Translational Genomics, Division of Cancer Epidemiology and Genetics, National Cancer Institute, NIH, Bethesda, Maryland 20892, USA
| | - Soren Lykke-Andersen
- Department of Molecular Biology and Genetics, Aarhus University, DK-8000 Aarhus, Denmark
| | - Bin Zhu
- Biostatistics Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, NIH, Bethesda, Maryland 20892, USA
| | - Wenming Xiao
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, FDA, Jefferson, AR 72079, USA
| | - Jason W. Hoskins
- Laboratory of Translational Genomics, Division of Cancer Epidemiology and Genetics, National Cancer Institute, NIH, Bethesda, Maryland 20892, USA
| | - Xijun Zhang
- Cancer Genomics Research Laboratory, Frederick National Laboratory for Cancer Research, Leidos Biomedical Research, Inc., Frederick, Maryland, 20892, USA
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, NIH, Bethesda, Maryland 20892, USA
| | - Lauren M. Rost
- Laboratory of Translational Genomics, Division of Cancer Epidemiology and Genetics, National Cancer Institute, NIH, Bethesda, Maryland 20892, USA
| | - Irene Collins
- Laboratory of Translational Genomics, Division of Cancer Epidemiology and Genetics, National Cancer Institute, NIH, Bethesda, Maryland 20892, USA
| | - Martijn van de Bunt
- Wellcome Trust Centre for Human Genetics, University of Oxford, Roosevelt Drive, Oxford OX3 7BN, UK
- Oxford Centre for Diabetes, Endocrinology & Metabolism, University of Oxford, Oxford OX3 7LJ, UK
| | - Jinping Jia
- Laboratory of Translational Genomics, Division of Cancer Epidemiology and Genetics, National Cancer Institute, NIH, Bethesda, Maryland 20892, USA
| | - Hemang Parikh
- Laboratory of Translational Genomics, Division of Cancer Epidemiology and Genetics, National Cancer Institute, NIH, Bethesda, Maryland 20892, USA
- Health Informatics Institute, Morsani College of Medicine, University of South Florida, Tampa, Florida 33612, USA
| | - Tongwu Zhang
- Laboratory of Translational Genomics, Division of Cancer Epidemiology and Genetics, National Cancer Institute, NIH, Bethesda, Maryland 20892, USA
| | - Lei Song
- Biostatistics Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, NIH, Bethesda, Maryland 20892, USA
| | - Ashley Jermusyk
- Laboratory of Translational Genomics, Division of Cancer Epidemiology and Genetics, National Cancer Institute, NIH, Bethesda, Maryland 20892, USA
| | - Charles C. Chung
- Cancer Genomics Research Laboratory, Frederick National Laboratory for Cancer Research, Leidos Biomedical Research, Inc., Frederick, Maryland, 20892, USA
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, NIH, Bethesda, Maryland 20892, USA
| | - Bin Zhu
- Cancer Genomics Research Laboratory, Frederick National Laboratory for Cancer Research, Leidos Biomedical Research, Inc., Frederick, Maryland, 20892, USA
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, NIH, Bethesda, Maryland 20892, USA
| | - Weiyin Zhou
- Cancer Genomics Research Laboratory, Frederick National Laboratory for Cancer Research, Leidos Biomedical Research, Inc., Frederick, Maryland, 20892, USA
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, NIH, Bethesda, Maryland 20892, USA
| | - Gail L. Matters
- Department of Biochemistry and Molecular Biology, The Pennsylvania State University College of Medicine, Hershey, PA 17033, USA
| | - Robert C. Kurtz
- Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, New York 10065, USA
| | - Meredith Yeager
- Cancer Genomics Research Laboratory, Frederick National Laboratory for Cancer Research, Leidos Biomedical Research, Inc., Frederick, Maryland, 20892, USA
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, NIH, Bethesda, Maryland 20892, USA
| | - Torben Heick Jensen
- Department of Molecular Biology and Genetics, Aarhus University, DK-8000 Aarhus, Denmark
| | - Kevin M. Brown
- Laboratory of Translational Genomics, Division of Cancer Epidemiology and Genetics, National Cancer Institute, NIH, Bethesda, Maryland 20892, USA
| | - Halit Ongen
- Department of Genetic Medicine and Development, University of Geneva Medical School, Geneva, Switzerland
| | - William R. Bamlet
- Division of Epidemiology, Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota 55905, USA
| | - Bradley A. Murray
- The Eli and Edythe L. Broad Institute of Massachusetts Institute of Technology and Harvard University Cambridge, Massachusetts 02142, USA
| | - Mark I. McCarthy
- Wellcome Trust Centre for Human Genetics, University of Oxford, Roosevelt Drive, Oxford OX3 7BN, UK
- Oxford Centre for Diabetes, Endocrinology & Metabolism, University of Oxford, Oxford OX3 7LJ, UK
- Oxford NIHR Biomedical Research Centre, Churchill Hospital, Old Road, Headington, Oxford OX3 7LE, UK
| | - Stephen J. Chanock
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, NIH, Bethesda, Maryland 20892, USA
| | - Nilanjan Chatterjee
- Biostatistics Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, NIH, Bethesda, Maryland 20892, USA
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland 21205, USA
| | - Brian M. Wolpin
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts, USA
| | - Jill P. Smith
- Division of Gastroenterology & Hepatology, Georgetown University Hospital, Washington, DC 20007, USA
| | - Sara H. Olson
- Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, New York 10065, USA
| | - Gloria M. Petersen
- Division of Epidemiology, Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota 55905, USA
| | - Jianxin Shi
- Biostatistics Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, NIH, Bethesda, Maryland 20892, USA
| | - Laufey T. Amundadottir
- Laboratory of Translational Genomics, Division of Cancer Epidemiology and Genetics, National Cancer Institute, NIH, Bethesda, Maryland 20892, USA
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241
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Ge Y, Concannon P. Molecular-genetic characterization of common, noncoding UBASH3A variants associated with type 1 diabetes. Eur J Hum Genet 2018; 26:1060-1064. [PMID: 29491471 DOI: 10.1038/s41431-018-0123-5] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2017] [Revised: 01/27/2018] [Accepted: 02/06/2018] [Indexed: 11/09/2022] Open
Abstract
Genome-wide association and fine-mapping studies have identified over 40 susceptibility regions for type 1 diabetes (T1D), a common autoimmune disease; however, most of the disease-associated variants are noncoding, and it remains a challenge to understand their biological contributions to T1D pathogenesis. One identified T1D risk locus is located at chromosome 21q22.3 where the most likely candidate gene is UBASH3A, a negative regulator of NF-κB signaling. Various noncoding variants in UBASH3A have been shown to be associated with T1D or other autoimmune diseases. Here we investigated four such SNPs-rs11203202, rs80054410, rs11203203, and rs1893592. We discovered a novel role for rs1893592 in T1D and showed that its minor allele protects against T1D. Our haplotype analysis identified three T1D-associated UBASH3A haplotypes, and revealed that risk for T1D is affected by additive effects of these four UBASH3A variants. In human primary CD4+ T cells, upon T-cell receptor stimulation, the minor allele of rs1893592 was associated with both a significant reduction in the overall mRNA levels of UBASH3A, and an increase in the proportion of a normally occurring, but low-abundant, UBASH3A transcript that retains intron-9 sequences and cannot produce full-length UBASH3A protein. This reduction in UBASH3A, as a consequence of the minor allele at rs1893592, resulted in increased secretion of IL-2, a key cytokine that is required for T-cell activation and function but is deficient in some T1D subjects. Our study provides new mechanistic insights into how rs1893592 affects T1D and autoimmunity, and how interactions between multiple T1D-associated, noncoding variants influence the disease risk.
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Affiliation(s)
- Yan Ge
- Department of Pathology, Immunology and Laboratory Medicine, University of Florida, Gainesville, FL, USA.,Genetics Institute, University of Florida, Gainesville, FL, USA
| | - Patrick Concannon
- Department of Pathology, Immunology and Laboratory Medicine, University of Florida, Gainesville, FL, USA. .,Genetics Institute, University of Florida, Gainesville, FL, USA.
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242
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Tian L, Khan A, Ning Z, Yuan K, Zhang C, Lou H, Yuan Y, Xu S. Genome-wide comparison of allele-specific gene expression between African and European populations. Hum Mol Genet 2018; 27:1067-1077. [DOI: 10.1093/hmg/ddy027] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2017] [Accepted: 01/05/2018] [Indexed: 11/12/2022] Open
Affiliation(s)
- Lei Tian
- Chinese Academy of Sciences Key Laboratory of Computational Biology, Max Planck Independent Research Group on Population Genomics, CAS-MPG Partner Institute for Computational Biology, Shanghai Institutes for Biological Sciences, CAS, Shanghai 200031, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Asifullah Khan
- Chinese Academy of Sciences Key Laboratory of Computational Biology, Max Planck Independent Research Group on Population Genomics, CAS-MPG Partner Institute for Computational Biology, Shanghai Institutes for Biological Sciences, CAS, Shanghai 200031, China
- Department of Biochemistry, Abdul Wali Khan University Mardan, Mardan-23200 KP, Pakistan
| | - Zhilin Ning
- Chinese Academy of Sciences Key Laboratory of Computational Biology, Max Planck Independent Research Group on Population Genomics, CAS-MPG Partner Institute for Computational Biology, Shanghai Institutes for Biological Sciences, CAS, Shanghai 200031, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Kai Yuan
- Chinese Academy of Sciences Key Laboratory of Computational Biology, Max Planck Independent Research Group on Population Genomics, CAS-MPG Partner Institute for Computational Biology, Shanghai Institutes for Biological Sciences, CAS, Shanghai 200031, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Chao Zhang
- Chinese Academy of Sciences Key Laboratory of Computational Biology, Max Planck Independent Research Group on Population Genomics, CAS-MPG Partner Institute for Computational Biology, Shanghai Institutes for Biological Sciences, CAS, Shanghai 200031, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Haiyi Lou
- Chinese Academy of Sciences Key Laboratory of Computational Biology, Max Planck Independent Research Group on Population Genomics, CAS-MPG Partner Institute for Computational Biology, Shanghai Institutes for Biological Sciences, CAS, Shanghai 200031, China
| | - Yuan Yuan
- Chinese Academy of Sciences Key Laboratory of Computational Biology, Max Planck Independent Research Group on Population Genomics, CAS-MPG Partner Institute for Computational Biology, Shanghai Institutes for Biological Sciences, CAS, Shanghai 200031, China
| | - Shuhua Xu
- Chinese Academy of Sciences Key Laboratory of Computational Biology, Max Planck Independent Research Group on Population Genomics, CAS-MPG Partner Institute for Computational Biology, Shanghai Institutes for Biological Sciences, CAS, Shanghai 200031, China
- University of Chinese Academy of Sciences, Beijing 100049, China
- School of Life Science and Technology, Shanghai Tech University, Shanghai 201210, China
- Collaborative Innovation Center of Genetics and Development, Shanghai 200438, China
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243
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Park E, Pan Z, Zhang Z, Lin L, Xing Y. The Expanding Landscape of Alternative Splicing Variation in Human Populations. Am J Hum Genet 2018; 102:11-26. [PMID: 29304370 PMCID: PMC5777382 DOI: 10.1016/j.ajhg.2017.11.002] [Citation(s) in RCA: 246] [Impact Index Per Article: 35.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2017] [Accepted: 11/03/2017] [Indexed: 12/16/2022] Open
Abstract
Alternative splicing is a tightly regulated biological process by which the number of gene products for any given gene can be greatly expanded. Genomic variants in splicing regulatory sequences can disrupt splicing and cause disease. Recent developments in sequencing technologies and computational biology have allowed researchers to investigate alternative splicing at an unprecedented scale and resolution. Population-scale transcriptome studies have revealed many naturally occurring genetic variants that modulate alternative splicing and consequently influence phenotypic variability and disease susceptibility in human populations. Innovations in experimental and computational tools such as massively parallel reporter assays and deep learning have enabled the rapid screening of genomic variants for their causal impacts on splicing. In this review, we describe technological advances that have greatly increased the speed and scale at which discoveries are made about the genetic variation of alternative splicing. We summarize major findings from population transcriptomic studies of alternative splicing and discuss the implications of these findings for human genetics and medicine.
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Affiliation(s)
- Eddie Park
- Department of Microbiology, Immunology, & Molecular Genetics, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Zhicheng Pan
- Bioinformatics Interdepartmental Graduate Program, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Zijun Zhang
- Bioinformatics Interdepartmental Graduate Program, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Lan Lin
- Department of Microbiology, Immunology, & Molecular Genetics, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Yi Xing
- Department of Microbiology, Immunology, & Molecular Genetics, University of California, Los Angeles, Los Angeles, CA 90095, USA; Bioinformatics Interdepartmental Graduate Program, University of California, Los Angeles, Los Angeles, CA 90095, USA.
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244
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Li B, Verma SS, Veturi YC, Verma A, Bradford Y, Haas DW, Ritchie MD. Evaluation of PrediXcan for prioritizing GWAS associations and predicting gene expression. PACIFIC SYMPOSIUM ON BIOCOMPUTING. PACIFIC SYMPOSIUM ON BIOCOMPUTING 2018; 23:448-459. [PMID: 29218904 PMCID: PMC5749400] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Abstract
Genome-wide association studies (GWAS) have been successful in facilitating the understanding of genetic architecture behind human diseases, but this approach faces many challenges. To identify disease-related loci with modest to weak effect size, GWAS requires very large sample sizes, which can be computational burdensome. In addition, the interpretation of discovered associations remains difficult. PrediXcan was developed to help address these issues. With built in SNP-expression models, PrediXcan is able to predict the expression of genes that are regulated by putative expression quantitative trait loci (eQTLs), and these predicted expression levels can then be used to perform gene-based association studies. This approach reduces the multiple testing burden from millions of variants down to several thousand genes. But most importantly, the identified associations can reveal the genes that are under regulation of eQTLs and consequently involved in disease pathogenesis. In this study, two of the most practical functions of PrediXcan were tested: 1) predicting gene expression, and 2) prioritizing GWAS results. We tested the prediction accuracy of PrediXcan by comparing the predicted and observed gene expression levels, and also looked into some potential influential factors and a filter criterion with the aim of improving PrediXcan performance. As for GWAS prioritization, predicted gene expression levels were used to obtain gene-trait associations, and background regions of significant associations were examined to decrease the likelihood of false positives. Our results showed that 1) PrediXcan predicted gene expression levels accurately for some but not all genes; 2) including more putative eQTLs into prediction did not improve the prediction accuracy; and 3) integrating predicted gene expression levels from the two PrediXcan whole blood models did not eliminate false positives. Still, PrediXcan was able to prioritize GWAS associations that were below the genome-wide significance threshold in GWAS, while retaining GWAS significant results. This study suggests several ways to consider PrediXcan's performance that will be of value to eQTL and complex human disease research.
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Affiliation(s)
- Binglan Li
- The Huck Institutes of the Life Sciences, The Pennsylvania State University, University Park, PA, United States
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245
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Veturi Y, Ritchie MD. How powerful are summary-based methods for identifying expression-trait associations under different genetic architectures? PACIFIC SYMPOSIUM ON BIOCOMPUTING. PACIFIC SYMPOSIUM ON BIOCOMPUTING 2018; 23:228-239. [PMID: 29218884 PMCID: PMC5785784] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
Transcriptome-wide association studies (TWAS) have recently been employed as an approach that can draw upon the advantages of genome-wide association studies (GWAS) and gene expression studies to identify genes associated with complex traits. Unlike standard GWAS, summary level data suffices for TWAS and offers improved statistical power. Two popular TWAS methods include either (a) imputing the cis genetic component of gene expression from smaller sized studies (using multi-SNP prediction or MP) into much larger effective sample sizes afforded by GWAS - TWAS-MP or (b) using summary-based Mendelian randomization - TWAS-SMR. Although these methods have been effective at detecting functional variants, it remains unclear how extensive variability in the genetic architecture of complex traits and diseases impacts TWAS results. Our goal was to investigate the different scenarios under which these methods yielded enough power to detect significant expression-trait associations. In this study, we conducted extensive simulations based on 6000 randomly chosen, unrelated Caucasian males from Geisinger's MyCode population to compare the power to detect cis expression-trait associations (within 500 kb of a gene) using the above-described approaches. To test TWAS across varying genetic backgrounds we simulated gene expression and phenotype using different quantitative trait loci per gene and cis-expression /trait heritability under genetic models that differentiate the effect of causality from that of pleiotropy. For each gene, on a training set ranging from 100 to 1000 individuals, we either (a) estimated regression coefficients with gene expression as the response using five different methods: LASSO, elastic net, Bayesian LASSO, Bayesian spike-slab, and Bayesian ridge regression or (b) performed eQTL analysis. We then sampled with replacement 50,000, 150,000, and 300,000 individuals respectively from the testing set of the remaining 5000 individuals and conducted GWAS on each set. Subsequently, we integrated the GWAS summary statistics derived from the testing set with the weights (or eQTLs) derived from the training set to identify expression-trait associations using (a) TWAS-MP (b) TWAS-SMR (c) eQTL-based GWAS, or (d) standalone GWAS. Finally, we examined the power to detect functionally relevant genes using the different approaches under the considered simulation scenarios. In general, we observed great similarities among TWAS-MP methods although the Bayesian methods resulted in improved power in comparison to LASSO and elastic net as the trait architecture grew more complex while training sample sizes and expression heritability remained small. Finally, we observed high power under causality but very low to moderate power under pleiotropy.
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Affiliation(s)
- Yogasudha Veturi
- Biomedical and Translational Informatics Institute, Geisinger Danville, PA
| | - Marylyn D. Ritchie
- Biomedical and Translational Informatics Institute, Geisinger Danville, PA
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246
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Lee PH, Lee C, Li X, Wee B, Dwivedi T, Daly M. Principles and methods of in-silico prioritization of non-coding regulatory variants. Hum Genet 2018; 137:15-30. [PMID: 29288389 PMCID: PMC5892192 DOI: 10.1007/s00439-017-1861-0] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2017] [Accepted: 12/14/2017] [Indexed: 12/13/2022]
Abstract
Over a decade of genome-wide association, studies have made great strides toward the detection of genes and genetic mechanisms underlying complex traits. However, the majority of associated loci reside in non-coding regions that are functionally uncharacterized in general. Now, the availability of large-scale tissue and cell type-specific transcriptome and epigenome data enables us to elucidate how non-coding genetic variants can affect gene expressions and are associated with phenotypic changes. Here, we provide an overview of this emerging field in human genomics, summarizing available data resources and state-of-the-art analytic methods to facilitate in-silico prioritization of non-coding regulatory mutations. We also highlight the limitations of current approaches and discuss the direction of much-needed future research.
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Affiliation(s)
- Phil H Lee
- Center for Genomic Medicine, Massachusetts General Hospital and Harvard Medical School, Simches Research Building, 185 Cambridge St, Boston, MA, 02114, USA.
- Quantitative Genomics Program, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
| | - Christian Lee
- Center for Genomic Medicine, Massachusetts General Hospital and Harvard Medical School, Simches Research Building, 185 Cambridge St, Boston, MA, 02114, USA
- Department of Life Sciences, Harvard University, Cambridge, MA, USA
| | - Xihao Li
- Quantitative Genomics Program, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Brian Wee
- Center for Genomic Medicine, Massachusetts General Hospital and Harvard Medical School, Simches Research Building, 185 Cambridge St, Boston, MA, 02114, USA
| | - Tushar Dwivedi
- Center for Genomic Medicine, Massachusetts General Hospital and Harvard Medical School, Simches Research Building, 185 Cambridge St, Boston, MA, 02114, USA
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA
| | - Mark Daly
- Center for Genomic Medicine, Massachusetts General Hospital and Harvard Medical School, Simches Research Building, 185 Cambridge St, Boston, MA, 02114, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, 02142, USA
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247
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Meyer D, C Aguiar VR, Bitarello BD, C Brandt DY, Nunes K. A genomic perspective on HLA evolution. Immunogenetics 2018; 70:5-27. [PMID: 28687858 PMCID: PMC5748415 DOI: 10.1007/s00251-017-1017-3] [Citation(s) in RCA: 123] [Impact Index Per Article: 17.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2016] [Accepted: 06/16/2017] [Indexed: 12/20/2022]
Abstract
Several decades of research have convincingly shown that classical human leukocyte antigen (HLA) loci bear signatures of natural selection. Despite this conclusion, many questions remain regarding the type of selective regime acting on these loci, the time frame at which selection acts, and the functional connections between genetic variability and natural selection. In this review, we argue that genomic datasets, in particular those generated by next-generation sequencing (NGS) at the population scale, are transforming our understanding of HLA evolution. We show that genomewide data can be used to perform robust and powerful tests for selection, capable of identifying both positive and balancing selection at HLA genes. Importantly, these tests have shown that natural selection can be identified at both recent and ancient timescales. We discuss how findings from genomewide association studies impact the evolutionary study of HLA genes, and how genomic data can be used to survey adaptive change involving interaction at multiple loci. We discuss the methodological developments which are necessary to correctly interpret genomic analyses involving the HLA region. These developments include adapting the NGS analysis framework so as to deal with the highly polymorphic HLA data, as well as developing tools and theory to search for signatures of selection, quantify differentiation, and measure admixture within the HLA region. Finally, we show that high throughput analysis of molecular phenotypes for HLA genes-namely transcription levels-is now a feasible approach and can add another dimension to the study of genetic variation.
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Affiliation(s)
- Diogo Meyer
- Department of Genetics and Evolutionary Biology, University of São Paulo, 05508-090, São Paulo, SP, Brazil.
| | - Vitor R C Aguiar
- Department of Genetics and Evolutionary Biology, University of São Paulo, 05508-090, São Paulo, SP, Brazil
| | - Bárbara D Bitarello
- Department of Genetics and Evolutionary Biology, University of São Paulo, 05508-090, São Paulo, SP, Brazil
- Department of Evolutionary Genetics, Max Planck Institute for Evolutionary Anthropology, Leipzig, Germany
| | - Débora Y C Brandt
- Department of Genetics and Evolutionary Biology, University of São Paulo, 05508-090, São Paulo, SP, Brazil
- Department of Integrative Biology, University of California, Berkeley, CA, USA
| | - Kelly Nunes
- Department of Genetics and Evolutionary Biology, University of São Paulo, 05508-090, São Paulo, SP, Brazil
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248
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CRAWFORD DANAC, MORGAN ALEXANDERA, DENNY JOSHUAC, ARONOW BRUCEJ, BRENNER STEVENE. PRECISION MEDICINE: FROM DIPLOTYPES TO DISPARITIES TOWARDS IMPROVED HEALTH AND THERAPIES. PACIFIC SYMPOSIUM ON BIOCOMPUTING. PACIFIC SYMPOSIUM ON BIOCOMPUTING 2018; 23:389-399. [PMID: 29218899 PMCID: PMC6182117] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Abstract
Precision medicine research efforts both in basic science discovery and clinical implementation are well underway and promise to provide individualized preventions and treatments, improving overall health care delivery. To achieve these goals, advances in data capture and analysis are needed spanning different types of 'omic and clinical data. The efforts to enhance precise treatments for all may accentuate healthcare disparities unless specific challenges are identified and addressed. This session of the 2018 Pacific Symposium on Biocomputing presents the latest developments in this transdisciplinary research space of genomics, medicine, and population health.
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Affiliation(s)
- DANA C. CRAWFORD
- Population and Quantitative Health Sciences, Institute for Computational Biology, Case Western Reserve University, Cleveland, OH, 44106 USA,
| | | | - JOSHUA C. DENNY
- Vanderbilt University Medical Center, Nashville, TN 37203 USA,
| | - BRUCE J. ARONOW
- Center for Computational Medicine, Cincinnati Children’s Hospital Medical Center and the University of Cincinnati, Cincinnati, OH 45229 USA,
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249
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Dendrou CA, Cortes A, Shipman L, Evans HG, Attfield KE, Jostins L, Barber T, Kaur G, Kuttikkatte SB, Leach OA, Desel C, Faergeman SL, Cheeseman J, Neville MJ, Sawcer S, Compston A, Johnson AR, Everett C, Bell JI, Karpe F, Ultsch M, Eigenbrot C, McVean G, Fugger L. Resolving TYK2 locus genotype-to-phenotype differences in autoimmunity. Sci Transl Med 2017; 8:363ra149. [PMID: 27807284 DOI: 10.1126/scitranslmed.aag1974] [Citation(s) in RCA: 188] [Impact Index Per Article: 23.5] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2016] [Accepted: 10/14/2016] [Indexed: 01/08/2023]
Abstract
Thousands of genetic variants have been identified, which contribute to the development of complex diseases, but determining how to elucidate their biological consequences for translation into clinical benefit is challenging. Conflicting evidence regarding the functional impact of genetic variants in the tyrosine kinase 2 (TYK2) gene, which is differentially associated with common autoimmune diseases, currently obscures the potential of TYK2 as a therapeutic target. We aimed to resolve this conflict by performing genetic meta-analysis across disorders; subsequent molecular, cellular, in vivo, and structural functional follow-up; and epidemiological studies. Our data revealed a protective homozygous effect that defined a signaling optimum between autoimmunity and immunodeficiency and identified TYK2 as a potential drug target for certain common autoimmune disorders.
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Affiliation(s)
- Calliope A Dendrou
- Oxford Centre for Neuroinflammation, Nuffield Department of Clinical Neurosciences, Division of Clinical Neurology, John Radcliffe Hospital, University of Oxford, Oxford OX3 9DS, UK
| | - Adrian Cortes
- Oxford Centre for Neuroinflammation, Nuffield Department of Clinical Neurosciences, Division of Clinical Neurology, John Radcliffe Hospital, University of Oxford, Oxford OX3 9DS, UK.,Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford OX3 7BN, UK
| | - Lydia Shipman
- Oxford Centre for Neuroinflammation, Nuffield Department of Clinical Neurosciences, Division of Clinical Neurology, John Radcliffe Hospital, University of Oxford, Oxford OX3 9DS, UK
| | - Hayley G Evans
- Oxford Centre for Neuroinflammation, Nuffield Department of Clinical Neurosciences, Division of Clinical Neurology, John Radcliffe Hospital, University of Oxford, Oxford OX3 9DS, UK
| | - Kathrine E Attfield
- Medical Research Council Human Immunology Unit, Weatherall Institute of Molecular Medicine, John Radcliffe Hospital, University of Oxford, Oxford OX3 9DS, UK
| | - Luke Jostins
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford OX3 7BN, UK
| | - Thomas Barber
- Oxford Centre for Neuroinflammation, Nuffield Department of Clinical Neurosciences, Division of Clinical Neurology, John Radcliffe Hospital, University of Oxford, Oxford OX3 9DS, UK
| | - Gurman Kaur
- Medical Research Council Human Immunology Unit, Weatherall Institute of Molecular Medicine, John Radcliffe Hospital, University of Oxford, Oxford OX3 9DS, UK
| | - Subita Balaram Kuttikkatte
- Medical Research Council Human Immunology Unit, Weatherall Institute of Molecular Medicine, John Radcliffe Hospital, University of Oxford, Oxford OX3 9DS, UK
| | - Oliver A Leach
- Oxford Centre for Neuroinflammation, Nuffield Department of Clinical Neurosciences, Division of Clinical Neurology, John Radcliffe Hospital, University of Oxford, Oxford OX3 9DS, UK
| | - Christiane Desel
- Oxford Centre for Neuroinflammation, Nuffield Department of Clinical Neurosciences, Division of Clinical Neurology, John Radcliffe Hospital, University of Oxford, Oxford OX3 9DS, UK
| | - Soren L Faergeman
- Oxford Centre for Neuroinflammation, Nuffield Department of Clinical Neurosciences, Division of Clinical Neurology, John Radcliffe Hospital, University of Oxford, Oxford OX3 9DS, UK.,Department of Clinical Medicine, Aarhus University Hospital, 8200 Aarhus N, Denmark
| | - Jane Cheeseman
- Oxford Centre for Diabetes, Endocrinology, and Metabolism, University of Oxford, Oxford OX3 7LE, UK
| | - Matt J Neville
- Oxford Centre for Diabetes, Endocrinology, and Metabolism, University of Oxford, Oxford OX3 7LE, UK.,National Institute for Health Research Oxford Biomedical Research Centre, Oxford University Hospitals Trust, Churchill Hospital, Oxford OX3 7LE, UK
| | - Stephen Sawcer
- Department of Clinical Neurosciences, University of Cambridge, Cambridge CB2 0QQ, UK
| | - Alastair Compston
- Department of Clinical Neurosciences, University of Cambridge, Cambridge CB2 0QQ, UK
| | - Adam R Johnson
- Structural Biology and Biochemical Pharmacology, Genentech Inc., 1 DNA Way, South San Francisco, CA 94080, USA
| | - Christine Everett
- Structural Biology and Biochemical Pharmacology, Genentech Inc., 1 DNA Way, South San Francisco, CA 94080, USA
| | - John I Bell
- University of Oxford, Richard Doll Building, Roosevelt Drive, Oxford OX3 7DG, UK
| | - Fredrik Karpe
- Oxford Centre for Diabetes, Endocrinology, and Metabolism, University of Oxford, Oxford OX3 7LE, UK.,National Institute for Health Research Oxford Biomedical Research Centre, Oxford University Hospitals Trust, Churchill Hospital, Oxford OX3 7LE, UK
| | - Mark Ultsch
- Structural Biology and Biochemical Pharmacology, Genentech Inc., 1 DNA Way, South San Francisco, CA 94080, USA
| | - Charles Eigenbrot
- Structural Biology and Biochemical Pharmacology, Genentech Inc., 1 DNA Way, South San Francisco, CA 94080, USA
| | - Gil McVean
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford OX3 7BN, UK
| | - Lars Fugger
- Oxford Centre for Neuroinflammation, Nuffield Department of Clinical Neurosciences, Division of Clinical Neurology, John Radcliffe Hospital, University of Oxford, Oxford OX3 9DS, UK. .,Medical Research Council Human Immunology Unit, Weatherall Institute of Molecular Medicine, John Radcliffe Hospital, University of Oxford, Oxford OX3 9DS, UK.,Department of Clinical Medicine, Aarhus University Hospital, 8200 Aarhus N, Denmark
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250
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Ecker S, Pancaldi V, Valencia A, Beck S, Paul DS. Epigenetic and Transcriptional Variability Shape Phenotypic Plasticity. Bioessays 2017; 40. [PMID: 29251357 DOI: 10.1002/bies.201700148] [Citation(s) in RCA: 45] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2017] [Revised: 10/31/2017] [Indexed: 12/15/2022]
Abstract
Epigenetic and transcriptional variability contribute to the vast diversity of cellular and organismal phenotypes and are key in human health and disease. In this review, we describe different types, sources, and determinants of epigenetic and transcriptional variability, enabling cells and organisms to adapt and evolve to a changing environment. We highlight the latest research and hypotheses on how chromatin structure and the epigenome influence gene expression variability. Further, we provide an overview of challenges in the analysis of biological variability. An improved understanding of the molecular mechanisms underlying epigenetic and transcriptional variability, at both the intra- and inter-individual level, provides great opportunity for disease prevention, better therapeutic approaches, and personalized medicine.
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Affiliation(s)
- Simone Ecker
- UCL Cancer Institute, University College London, 72 Huntley Street, London, WC1E 6BT, UK
| | - Vera Pancaldi
- Barcelona Supercomputing Center (BSC), C/ Jordi Girona 39-31, 08034, Barcelona, Spain
| | - Alfonso Valencia
- Barcelona Supercomputing Center (BSC), C/ Jordi Girona 39-31, 08034, Barcelona, Spain.,ICREA, Pg. Lluís Companys 23, 08010, Barcelona, Spain
| | - Stephan Beck
- UCL Cancer Institute, University College London, 72 Huntley Street, London, WC1E 6BT, UK
| | - Dirk S Paul
- MRC/BHF Cardiovascular Epidemiology Unit Department of Public Health and Primary Care, University of Cambridge, Cambridge, CB1 8RN, UK.,Department of Human Genetics Wellcome Trust Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge, CB10 1HH, UK
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