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Pudjihartono M, Golovina E, Fadason T, O'Sullivan JM, Schierding W. Links between melanoma germline risk loci, driver genes and comorbidities: insight from a tissue-specific multi-omic analysis. Mol Oncol 2024; 18:1031-1048. [PMID: 38308491 PMCID: PMC10994230 DOI: 10.1002/1878-0261.13599] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2023] [Revised: 11/15/2023] [Accepted: 01/22/2024] [Indexed: 02/04/2024] Open
Abstract
Genome-wide association studies (GWAS) have associated 76 loci with the risk of developing melanoma. However, understanding the molecular basis of such associations has remained a challenge because most of these loci are in non-coding regions of the genome. Here, we integrated data on epigenomic markers, three-dimensional (3D) genome organization, and expression quantitative trait loci (eQTL) from melanoma-relevant tissues and cell types to gain novel insights into the mechanisms underlying melanoma risk. This integrative approach revealed a total of 151 target genes, both near and far away from the risk loci in linear sequence, with known and novel roles in the etiology of melanoma. Using protein-protein interaction networks, we identified proteins that interact-directly or indirectly-with the products of the target genes. The interacting proteins were enriched for known melanoma driver genes. Further integration of these target genes into tissue-specific gene regulatory networks revealed patterns of gene regulation that connect melanoma to its comorbidities. Our study provides novel insights into the biological implications of genetic variants associated with melanoma risk.
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Affiliation(s)
| | | | | | - Justin M. O'Sullivan
- Liggins InstituteThe University of AucklandNew Zealand
- The Maurice Wilkins CentreThe University of AucklandNew Zealand
- Australian Parkinson's MissionGarvan Institute of Medical ResearchSydneyAustralia
- MRC Lifecourse Epidemiology UnitUniversity of SouthamptonUK
- Singapore Institute for Clinical SciencesAgency for Science, Technology and Research (A*STAR)Singapore CitySingapore
| | - William Schierding
- Liggins InstituteThe University of AucklandNew Zealand
- The Maurice Wilkins CentreThe University of AucklandNew Zealand
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2
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Everman ER, Macdonald SJ. Gene expression variation underlying tissue-specific responses to copper stress in Drosophila melanogaster. G3 (BETHESDA, MD.) 2024; 14:jkae015. [PMID: 38262701 PMCID: PMC11021028 DOI: 10.1093/g3journal/jkae015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/17/2023] [Revised: 01/04/2024] [Accepted: 01/08/2024] [Indexed: 01/25/2024]
Abstract
Copper is one of a handful of biologically necessary heavy metals that is also a common environmental pollutant. Under normal conditions, copper ions are required for many key physiological processes. However, in excess, copper results in cell and tissue damage ranging in severity from temporary injury to permanent neurological damage. Because of its biological relevance, and because many conserved copper-responsive genes respond to nonessential heavy metal pollutants, copper resistance in Drosophila melanogaster is a useful model system with which to investigate the genetic control of the heavy metal stress response. Because heavy metal toxicity has the potential to differently impact specific tissues, we genetically characterized the control of the gene expression response to copper stress in a tissue-specific manner in this study. We assessed the copper stress response in head and gut tissue of 96 inbred strains from the Drosophila Synthetic Population Resource using a combination of differential expression analysis and expression quantitative trait locus mapping. Differential expression analysis revealed clear patterns of tissue-specific expression. Tissue and treatment specific responses to copper stress were also detected using expression quantitative trait locus mapping. Expression quantitative trait locus associated with MtnA, Mdr49, Mdr50, and Sod3 exhibited both genotype-by-tissue and genotype-by-treatment effects on gene expression under copper stress, illuminating tissue- and treatment-specific patterns of gene expression control. Together, our data build a nuanced description of the roles and interactions between allelic and expression variation in copper-responsive genes, provide valuable insight into the genomic architecture of susceptibility to metal toxicity, and highlight candidate genes for future functional characterization.
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Affiliation(s)
- Elizabeth R Everman
- School of Biological Sciences, The University of Oklahoma, 730 Van Vleet Oval, Norman, OK 73019, USA
| | - Stuart J Macdonald
- Molecular Biosciences, University of Kansas, 1200 Sunnyside Ave, Lawrence, KS 66045, USA
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3
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Margaritte-Jeannin P, Vernet R, Budu-Aggrey A, Ege M, Madore AM, Linhard C, Mohamdi H, von Mutius E, Granell R, Demenais F, Laprise C, Bouzigon E, Dizier MH. TNS1 and NRXN1 Genes Interacting With Early-Life Smoking Exposure in Asthma-Plus-Eczema Susceptibility. ALLERGY, ASTHMA & IMMUNOLOGY RESEARCH 2023; 15:779-794. [PMID: 37957795 PMCID: PMC10643854 DOI: 10.4168/aair.2023.15.6.779] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/23/2022] [Revised: 05/15/2023] [Accepted: 06/13/2023] [Indexed: 11/15/2023]
Abstract
PURPOSE Numerous genes have been associated with allergic diseases (asthma, allergic rhinitis, and eczema), but they explain only part of their heritability. This is partly because most previous studies ignored complex mechanisms such as gene-environment (G-E) interactions and complex phenotypes such as co-morbidity. However, it was recently evidenced that the co-morbidity of asthma-plus-eczema appears as a sub-entity depending on specific genetic factors. Besides, evidence also suggest that gene-by-early life environmental tobacco smoke (ETS) exposure interactions play a role in asthma, but were never investigated for asthma-plus-eczema. To identify genetic variants interacting with ETS exposure that influence asthma-plus-eczema susceptibility. METHODS To conduct a genome-wide interaction study (GWIS) of asthma-plus-eczema according to ETS exposure, we applied a 2-stage strategy with a first selection of single nucleotide polymorphisms (SNPs) from genome-wide association meta-analysis to be tested at a second stage by interaction meta-analysis. All meta-analyses were conducted across 4 studies including a total of 5,516 European-ancestry individuals, of whom 1,164 had both asthma and eczema. RESULTS Two SNPs showed significant interactions with ETS exposure. They were located in 2 genes, NRXN1 (2p16) and TNS1 (2q35), never reported associated and/or interacting with ETS exposure for asthma, eczema or more generally for allergic diseases. TNS1 is a promising candidate gene because of its link to lung and skin diseases with possible interactive effect with tobacco smoke exposure. CONCLUSIONS This first GWIS of asthma-plus-eczema with ETS exposure underlines the importance of studying sub-phenotypes such as co-morbidities as well as G-E interactions to detect new susceptibility genes.
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Affiliation(s)
- Patricia Margaritte-Jeannin
- Université Paris Cité, UMRS 1124, INSERM, Genomic Epidemiology and Multifactorial Diseases Group, Paris, France
| | - Raphaël Vernet
- Université Paris Cité, UMRS 1124, INSERM, Genomic Epidemiology and Multifactorial Diseases Group, Paris, France
| | - Ashley Budu-Aggrey
- Medical Research Council (MRC) Integrative Epidemiology Unit, Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Markus Ege
- Dr von Hauner Children's Hospital, Ludwig Maximilian University; Institute of Asthma and Allergy prevention, Helmholtz Centre Munich; Comprehensive Pneumology Center Munich (CPC-M), German Center for Lung Research, Munich, Germany
| | - Anne-Marie Madore
- Département des sciences fondamentales, Centre intersectoriel en santé durable (CISD), Université du Québec à Chicoutimi, Saguenay, QC, Canada
| | - Christophe Linhard
- Université Paris Cité, UMRS 1124, INSERM, Genomic Epidemiology and Multifactorial Diseases Group, Paris, France
| | - Hamida Mohamdi
- Université Paris Cité, UMRS 1124, INSERM, Genomic Epidemiology and Multifactorial Diseases Group, Paris, France
| | - Erika von Mutius
- Dr von Hauner Children's Hospital, Ludwig Maximilian University; Institute of Asthma and Allergy prevention, Helmholtz Centre Munich; Comprehensive Pneumology Center Munich (CPC-M), German Center for Lung Research, Munich, Germany
| | - Raquell Granell
- Medical Research Council (MRC) Integrative Epidemiology Unit, Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Florence Demenais
- Université Paris Cité, UMRS 1124, INSERM, Genomic Epidemiology and Multifactorial Diseases Group, Paris, France
| | - Cathrine Laprise
- Département des sciences fondamentales, Centre intersectoriel en santé durable (CISD), Université du Québec à Chicoutimi, Saguenay, QC, Canada
| | - Emmanuelle Bouzigon
- Université Paris Cité, UMRS 1124, INSERM, Genomic Epidemiology and Multifactorial Diseases Group, Paris, France
| | - Marie-Hélène Dizier
- Université Paris Cité, UMRS 1124, INSERM, Genomic Epidemiology and Multifactorial Diseases Group, Paris, France.
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4
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Wang Y, Ding Y, Liu S, Wang C, Zhang E, Chen C, Zhu M, Zhang J, Zhu C, Ji M, Dai J, Jin G, Hu Z, Shen H, Ma H. Integrative splicing-quantitative-trait-locus analysis reveals risk loci for non-small-cell lung cancer. Am J Hum Genet 2023; 110:1574-1589. [PMID: 37562399 PMCID: PMC10502736 DOI: 10.1016/j.ajhg.2023.07.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2023] [Revised: 07/13/2023] [Accepted: 07/14/2023] [Indexed: 08/12/2023] Open
Abstract
Splicing quantitative trait loci (sQTLs) have been demonstrated to contribute to disease etiology by affecting alternative splicing. However, the role of sQTLs in the development of non-small-cell lung cancer (NSCLC) remains unknown. Thus, we performed a genome-wide sQTL study to identify genetic variants that affect alternative splicing in lung tissues from 116 individuals of Chinese ancestry, which resulted in the identification of 1,385 sQTL-harboring genes (sGenes) containing 378,210 significant variant-intron pairs. A comprehensive characterization of these sQTLs showed that they were enriched in actively transcribed regions, genetic regulatory elements, and splicing-factor-binding sites. Moreover, sQTLs were largely distinct from expression quantitative trait loci (eQTLs) and showed significant enrichment in potential risk loci of NSCLC. We also integrated sQTLs into NSCLC GWAS datasets (13,327 affected individuals and 13,328 control individuals) by using splice-transcriptome-wide association study (spTWAS) and identified alternative splicing events in 19 genes that were significantly associated with NSCLC risk. By using functional annotation and experiments, we confirmed an sQTL variant, rs35861926, that reduced the risk of lung adenocarcinoma (rs35861926-T, OR = 0.88, 95% confidence interval [CI]: 0.82-0.93, p = 1.87 × 10-5) by promoting FARP1 exon 20 skipping to downregulate the expression level of the long transcript FARP1-011. Transcript FARP1-011 promoted the migration and proliferation of lung adenocarcinoma cells. Overall, our study provided informative lung sQTL resources and insights into the molecular mechanisms linking sQTL variants to NSCLC risk.
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Affiliation(s)
- Yuzhuo Wang
- Department of Medical Informatics, School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, Jiangsu 211166, China; Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu 211166, China
| | - Yue Ding
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu 211166, China; Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing, Jiangsu 211166, China
| | - Su Liu
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu 211166, China; Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing, Jiangsu 211166, China
| | - Cheng Wang
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu 211166, China; Department of Bioinformatics, School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, Jiangsu 211166, China
| | - Erbao Zhang
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu 211166, China; Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing, Jiangsu 211166, China
| | - Congcong Chen
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu 211166, China; Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing, Jiangsu 211166, China
| | - Meng Zhu
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu 211166, China; Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing, Jiangsu 211166, China
| | - Jing Zhang
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu 211166, China; Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing, Jiangsu 211166, China
| | - Chen Zhu
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu 211166, China; Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing, Jiangsu 211166, China; Department of Cancer Prevention, The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Hangzhou, Zhejiang 310022, China
| | - Mengmeng Ji
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu 211166, China; Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing, Jiangsu 211166, China
| | - Juncheng Dai
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu 211166, China; Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing, Jiangsu 211166, China
| | - Guangfu Jin
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu 211166, China; Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing, Jiangsu 211166, China
| | - Zhibin Hu
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu 211166, China; Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing, Jiangsu 211166, China
| | - Hongbing Shen
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu 211166, China; Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing, Jiangsu 211166, China; Research Units of Cohort Study on Cardiovascular Diseases and Cancers, Chinese Academy of Medical Sciences, Beijing 100730, China.
| | - Hongxia Ma
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu 211166, China; Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing, Jiangsu 211166, China.
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5
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Ferguson R, Chat V, Morales L, Simpson D, Monson KR, Cohen E, Zusin S, Madonna G, Capone M, Simeone E, Pavlick A, Luke JJ, Gajewski TF, Osman I, Ascierto P, Weber J, Kirchhoff T. Germline immunomodulatory expression quantitative trait loci (ieQTLs) associated with immune-related toxicity from checkpoint inhibition. Eur J Cancer 2023; 189:112923. [PMID: 37301715 PMCID: PMC11000635 DOI: 10.1016/j.ejca.2023.05.011] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2023] [Revised: 05/11/2023] [Accepted: 05/13/2023] [Indexed: 06/12/2023]
Abstract
BACKGROUND Immune checkpoint inhibition (ICI) has improved clinical outcomes for metastatic melanoma patients; however, 65-80% of patients treated with ICI experience immune-related adverse events (irAEs). Given the plausible link of irAEs with underlying host immunity, we explored whether germline genetic variants controlling the expression of 42 immunomodulatory genes were associated with the risk of irAEs in melanoma patients treated with the single-agent anti-CTLA-4 antibody ipilimumab (IPI). METHODS We identified 42 immunomodulatory expression quantitative trait loci (ieQTLs) most significantly associated with the expression of 382 immune-related genes. These germline variants were genotyped in IPI-treated melanoma patients, collected as part of a multi-institutional collaboration. We tested the association of ieQTLs with irAEs in a discovery cohort of 95 patients, followed by validation in an additional 97 patients. RESULTS We found that the alternate allele of rs7036417, a variant linked to increased expression of SYK, was strongly associated with an increased risk of grade 3-4 toxicity [odds ratio (OR) = 7.46; 95% confidence interval (CI) = 2.65-21.03; p = 1.43E-04]. This variant was not associated with response (OR = 0.90; 95% CI = 0.37-2.21; p = 0.82). CONCLUSION We report that rs7036417 is associated with increased risk of severe irAEs, independent of IPI efficacy. SYK plays an important role in B-cell/T-cell expansion, and increased pSYK has been reported in patients with autoimmune disease. The association between rs7036417 and IPI irAEs in our data suggests a role of SYK overexpression in irAE development. These findings support the hypothesis that inherited variation in immune-related pathways modulates ICI toxicity and suggests SYK as a possible future target for therapies to reduce irAEs.
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Affiliation(s)
- Robert Ferguson
- Laura and Isaac Perlmutter Cancer Center, New York University Langone Health, New York, NY, USA; Departments of Population Health and Environmental Medicine, New York University-Grossman School of Medicine, New York, NY, USA; The Interdisciplinary Melanoma Cooperative Group, New York University-Grossman School of Medicine, New York, NY, USA
| | - Vylyny Chat
- Laura and Isaac Perlmutter Cancer Center, New York University Langone Health, New York, NY, USA; Departments of Population Health and Environmental Medicine, New York University-Grossman School of Medicine, New York, NY, USA; The Interdisciplinary Melanoma Cooperative Group, New York University-Grossman School of Medicine, New York, NY, USA
| | - Leah Morales
- Laura and Isaac Perlmutter Cancer Center, New York University Langone Health, New York, NY, USA; Departments of Population Health and Environmental Medicine, New York University-Grossman School of Medicine, New York, NY, USA; The Interdisciplinary Melanoma Cooperative Group, New York University-Grossman School of Medicine, New York, NY, USA
| | - Danny Simpson
- Laura and Isaac Perlmutter Cancer Center, New York University Langone Health, New York, NY, USA; Departments of Population Health and Environmental Medicine, New York University-Grossman School of Medicine, New York, NY, USA; The Interdisciplinary Melanoma Cooperative Group, New York University-Grossman School of Medicine, New York, NY, USA
| | - Kelsey R Monson
- Laura and Isaac Perlmutter Cancer Center, New York University Langone Health, New York, NY, USA; Departments of Population Health and Environmental Medicine, New York University-Grossman School of Medicine, New York, NY, USA; The Interdisciplinary Melanoma Cooperative Group, New York University-Grossman School of Medicine, New York, NY, USA
| | - Elisheva Cohen
- Laura and Isaac Perlmutter Cancer Center, New York University Langone Health, New York, NY, USA; Departments of Population Health and Environmental Medicine, New York University-Grossman School of Medicine, New York, NY, USA; The Interdisciplinary Melanoma Cooperative Group, New York University-Grossman School of Medicine, New York, NY, USA
| | - Sarah Zusin
- Laura and Isaac Perlmutter Cancer Center, New York University Langone Health, New York, NY, USA; Departments of Population Health and Environmental Medicine, New York University-Grossman School of Medicine, New York, NY, USA; The Interdisciplinary Melanoma Cooperative Group, New York University-Grossman School of Medicine, New York, NY, USA
| | - Gabriele Madonna
- Melanoma Cancer Immunotherapy and Innovative Therapy Unit, Istituto Nazionale Tumori IRCCS Fondazione G. Pascale, Napoli, Italy
| | - Mariaelena Capone
- Melanoma Cancer Immunotherapy and Innovative Therapy Unit, Istituto Nazionale Tumori IRCCS Fondazione G. Pascale, Napoli, Italy
| | - Ester Simeone
- Melanoma Cancer Immunotherapy and Innovative Therapy Unit, Istituto Nazionale Tumori IRCCS Fondazione G. Pascale, Napoli, Italy
| | - Anna Pavlick
- Division of Hematology & Medical Oncology, the Cutaneous Oncology Program, Weill Cornell Medicine and New York-Presbyterian, New York, USA
| | - Jason J Luke
- Department of Immunology, University of Pittsburgh, Pittsburgh, PA 15213, USA; UPMC Hillman Cancer Center, Pittsburgh, PA 15232, USA
| | - Thomas F Gajewski
- Department of Pathology, University of Chicago, Chicago, IL, USA; Section of Hematology/Oncology, Department of Medicine, University of Chicago, Chicago, IL, USA; Ben May Department for Cancer Research, University of Chicago, Chicago, IL, USA
| | - Iman Osman
- Laura and Isaac Perlmutter Cancer Center, New York University Langone Health, New York, NY, USA; The Interdisciplinary Melanoma Cooperative Group, New York University-Grossman School of Medicine, New York, NY, USA; Department of Medicine, New York University-Grossman School of Medicine, New York, NY, USA; Ronald O. Perelman Department of Dermatology, New York University-Grossman School of Medicine, New York, NY, USA
| | - Paolo Ascierto
- Melanoma Cancer Immunotherapy and Innovative Therapy Unit, Istituto Nazionale Tumori IRCCS Fondazione G. Pascale, Napoli, Italy
| | - Jeffrey Weber
- Laura and Isaac Perlmutter Cancer Center, New York University Langone Health, New York, NY, USA; The Interdisciplinary Melanoma Cooperative Group, New York University-Grossman School of Medicine, New York, NY, USA; Department of Medicine, New York University-Grossman School of Medicine, New York, NY, USA
| | - Tomas Kirchhoff
- Laura and Isaac Perlmutter Cancer Center, New York University Langone Health, New York, NY, USA; Departments of Population Health and Environmental Medicine, New York University-Grossman School of Medicine, New York, NY, USA; The Interdisciplinary Melanoma Cooperative Group, New York University-Grossman School of Medicine, New York, NY, USA.
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6
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Cabana-Domínguez J, Llonga N, Arribas L, Alemany S, Vilar-Ribó L, Demontis D, Fadeuilhe C, Corrales M, Richarte V, Børglum AD, Ramos-Quiroga JA, Soler Artigas M, Ribasés M. Transcriptomic risk scores for attention deficit/hyperactivity disorder. Mol Psychiatry 2023; 28:3493-3502. [PMID: 37537283 PMCID: PMC10618083 DOI: 10.1038/s41380-023-02200-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/07/2023] [Revised: 07/17/2023] [Accepted: 07/21/2023] [Indexed: 08/05/2023]
Abstract
Attention deficit/hyperactivity disorder (ADHD) is a highly heritable neurodevelopmental disorder. We performed a transcriptome-wide association study (TWAS) using the latest genome-wide association study (GWAS) meta-analysis, in 38,691 individuals with ADHD and 186,843 controls, and 14 gene-expression reference panels across multiple brain tissues and whole blood. Based on TWAS results, we selected subsets of genes and constructed transcriptomic risk scores (TRSs) for the disorder in peripheral blood mononuclear cells of individuals with ADHD and controls. We found evidence of association between ADHD and TRSs constructed using expression profiles from multiple brain areas, with individuals with ADHD carrying a higher burden of TRSs than controls. TRSs were uncorrelated with the polygenic risk score (PRS) for ADHD and, in combination with PRS, improved significantly the proportion of variance explained over the PRS-only model. These results support the complementary predictive potential of genetic and transcriptomic profiles in blood and underscore the potential utility of gene expression for risk prediction and deeper insight in molecular mechanisms underlying ADHD.
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Affiliation(s)
- Judit Cabana-Domínguez
- Psychiatric Genetics Unit, Group of Psychiatry, Mental Health and Addiction, Vall d'Hebron Research Institute (VHIR), Universitat Autònoma de Barcelona, Barcelona, Spain.
- Department of Mental Health, Hospital Universitari Vall d'Hebron, Barcelona, Spain.
- Biomedical Network Research Centre on Mental Health (CIBERSAM), Madrid, Spain.
- Department of Genetics, Microbiology, and Statistics, Faculty of Biology, Universitat de Barcelona, Barcelona, Spain.
| | - Natalia Llonga
- Psychiatric Genetics Unit, Group of Psychiatry, Mental Health and Addiction, Vall d'Hebron Research Institute (VHIR), Universitat Autònoma de Barcelona, Barcelona, Spain
- Department of Mental Health, Hospital Universitari Vall d'Hebron, Barcelona, Spain
| | - Lorena Arribas
- Psychiatric Genetics Unit, Group of Psychiatry, Mental Health and Addiction, Vall d'Hebron Research Institute (VHIR), Universitat Autònoma de Barcelona, Barcelona, Spain
- Department of Mental Health, Hospital Universitari Vall d'Hebron, Barcelona, Spain
| | - Silvia Alemany
- Psychiatric Genetics Unit, Group of Psychiatry, Mental Health and Addiction, Vall d'Hebron Research Institute (VHIR), Universitat Autònoma de Barcelona, Barcelona, Spain
- Department of Mental Health, Hospital Universitari Vall d'Hebron, Barcelona, Spain
| | - Laura Vilar-Ribó
- Psychiatric Genetics Unit, Group of Psychiatry, Mental Health and Addiction, Vall d'Hebron Research Institute (VHIR), Universitat Autònoma de Barcelona, Barcelona, Spain
- Department of Mental Health, Hospital Universitari Vall d'Hebron, Barcelona, Spain
- Biomedical Network Research Centre on Mental Health (CIBERSAM), Madrid, Spain
| | - Ditte Demontis
- Department of Biomedicine/Human Genetics, Aarhus University, Aarhus, Denmark
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Aarhus, Denmark
- Center for Genomics and Personalized Medicine, Aarhus, Denmark
- The Novo Nordisk Foundation Center for Genomic Mechanisms of Disease, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Christian Fadeuilhe
- Psychiatric Genetics Unit, Group of Psychiatry, Mental Health and Addiction, Vall d'Hebron Research Institute (VHIR), Universitat Autònoma de Barcelona, Barcelona, Spain
- Department of Mental Health, Hospital Universitari Vall d'Hebron, Barcelona, Spain
- Biomedical Network Research Centre on Mental Health (CIBERSAM), Madrid, Spain
- Department of Psychiatry and Forensic Medicine, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Montse Corrales
- Psychiatric Genetics Unit, Group of Psychiatry, Mental Health and Addiction, Vall d'Hebron Research Institute (VHIR), Universitat Autònoma de Barcelona, Barcelona, Spain
- Department of Mental Health, Hospital Universitari Vall d'Hebron, Barcelona, Spain
- Biomedical Network Research Centre on Mental Health (CIBERSAM), Madrid, Spain
- Department of Psychiatry and Forensic Medicine, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Vanesa Richarte
- Psychiatric Genetics Unit, Group of Psychiatry, Mental Health and Addiction, Vall d'Hebron Research Institute (VHIR), Universitat Autònoma de Barcelona, Barcelona, Spain
- Department of Mental Health, Hospital Universitari Vall d'Hebron, Barcelona, Spain
- Biomedical Network Research Centre on Mental Health (CIBERSAM), Madrid, Spain
- Department of Psychiatry and Forensic Medicine, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Anders D Børglum
- Department of Biomedicine/Human Genetics, Aarhus University, Aarhus, Denmark
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Aarhus, Denmark
- Center for Genomics and Personalized Medicine, Aarhus, Denmark
| | - Josep Antoni Ramos-Quiroga
- Psychiatric Genetics Unit, Group of Psychiatry, Mental Health and Addiction, Vall d'Hebron Research Institute (VHIR), Universitat Autònoma de Barcelona, Barcelona, Spain
- Department of Mental Health, Hospital Universitari Vall d'Hebron, Barcelona, Spain
- Biomedical Network Research Centre on Mental Health (CIBERSAM), Madrid, Spain
- Department of Psychiatry and Forensic Medicine, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - María Soler Artigas
- Psychiatric Genetics Unit, Group of Psychiatry, Mental Health and Addiction, Vall d'Hebron Research Institute (VHIR), Universitat Autònoma de Barcelona, Barcelona, Spain.
- Department of Mental Health, Hospital Universitari Vall d'Hebron, Barcelona, Spain.
- Biomedical Network Research Centre on Mental Health (CIBERSAM), Madrid, Spain.
- Department of Genetics, Microbiology, and Statistics, Faculty of Biology, Universitat de Barcelona, Barcelona, Spain.
| | - Marta Ribasés
- Psychiatric Genetics Unit, Group of Psychiatry, Mental Health and Addiction, Vall d'Hebron Research Institute (VHIR), Universitat Autònoma de Barcelona, Barcelona, Spain.
- Department of Mental Health, Hospital Universitari Vall d'Hebron, Barcelona, Spain.
- Biomedical Network Research Centre on Mental Health (CIBERSAM), Madrid, Spain.
- Department of Genetics, Microbiology, and Statistics, Faculty of Biology, Universitat de Barcelona, Barcelona, Spain.
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7
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Everman ER, Macdonald SJ. Gene expression variation underlying tissue-specific responses to copper stress in Drosophila melanogaster. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.07.12.548746. [PMID: 37503205 PMCID: PMC10370140 DOI: 10.1101/2023.07.12.548746] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/29/2023]
Abstract
Copper is one of a handful of biologically necessary heavy metals that is also a common environmental pollutant. Under normal conditions, copper ions are required for many key physiological processes. However, in excess, copper quickly results in cell and tissue damage that can range in severity from temporary injury to permanent neurological damage. Because of its biological relevance, and because many conserved copper-responsive genes also respond to other non-essential heavy metal pollutants, copper resistance in Drosophila melanogaster is a useful model system with which to investigate the genetic control of the response to heavy metal stress. Because heavy metal toxicity has the potential to differently impact specific tissues, we genetically characterized the control of the gene expression response to copper stress in a tissue-specific manner in this study. We assessed the copper stress response in head and gut tissue of 96 inbred strains from the Drosophila Synthetic Population Resource (DSPR) using a combination of differential expression analysis and expression quantitative trait locus (eQTL) mapping. Differential expression analysis revealed clear patterns of tissue-specific expression, primarily driven by a more pronounced gene expression response in gut tissue. eQTL mapping of gene expression under control and copper conditions as well as for the change in gene expression following copper exposure (copper response eQTL) revealed hundreds of genes with tissue-specific local cis-eQTL and many distant trans-eQTL. eQTL associated with MtnA, Mdr49, Mdr50, and Sod3 exhibited genotype by environment effects on gene expression under copper stress, illuminating several tissue- and treatment-specific patterns of gene expression control. Together, our data build a nuanced description of the roles and interactions between allelic and expression variation in copper-responsive genes, provide valuable insight into the genomic architecture of susceptibility to metal toxicity, and highlight many candidate genes for future functional characterization.
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Affiliation(s)
- Elizabeth R Everman
- 1200 Sunnyside Ave, University of Kansas, Molecular Biosciences, Lawrence, KS 66045, USA
- 730 Van Vleet Oval, University of Oklahoma, Biology, Norman, OK 73019, USA
| | - Stuart J Macdonald
- 1200 Sunnyside Ave, University of Kansas, Molecular Biosciences, Lawrence, KS 66045, USA
- 1200 Sunnyside Ave, University of Kansas, Center for Computational Biology, Lawrence, KS 66045, USA
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8
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Gedik H, Peterson RE, Riley BP, Vladimirov VI, Bacanu SA. Integrative Post-Genome-Wide Association Study Analyses Relevant to Psychiatric Disorders: Imputing Transcriptome and Proteome Signals. Complex Psychiatry 2023; 9:130-144. [PMID: 37588130 PMCID: PMC10425719 DOI: 10.1159/000530223] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2022] [Accepted: 03/09/2023] [Indexed: 08/18/2023] Open
Abstract
Background The genome-wide association study (GWAS) is a common tool to identify genetic variants associated with complex traits, including psychiatric disorders (PDs). However, post-GWAS analyses are needed to extend the statistical inference to biologically relevant entities, e.g., genes, proteins, and pathways. To achieve this goal, researchers developed methods that incorporate biologically relevant intermediate molecular phenotypes, such as gene expression and protein abundance, which are posited to mediate the variant-trait association. Transcriptome-wide association study (TWAS) and proteome-wide association study (PWAS) are commonly used methods to test the association between these molecular mediators and the trait. Summary In this review, we discuss the most recent developments in TWAS and PWAS. These methods integrate existing "omic" information with the GWAS summary statistics for trait(s) of interest. Specifically, they impute transcript/protein data and test the association between imputed gene expression/protein level with phenotype of interest by using (i) GWAS summary statistics and (ii) reference transcriptomic/proteomic/genomic datasets. TWAS and PWAS are suitable as analysis tools for (i) primary association scan and (ii) fine-mapping to identify potentially causal genes for PDs. Key Messages As post-GWAS analyses, TWAS and PWAS have the potential to highlight causal genes for PDs. These prioritized genes could indicate targets for the development of novel drug therapies. For researchers attempting such analyses, we recommend Mendelian randomization tools that use GWAS statistics for both trait and reference datasets, e.g., summary Mendelian randomization (SMR). We base our recommendation on (i) being able to use the same tool for both TWAS and PWAS, (ii) not requiring the pre-computed weights (and thus easier to update for larger reference datasets), and (iii) most larger transcriptome reference datasets are publicly available and easy to transform into a compatible format for SMR analysis.
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Affiliation(s)
- Huseyin Gedik
- Integrative Life Sciences, Virginia Institute of Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond, VA, USA
| | - Roseann E. Peterson
- Institute for Genomics in Health, SUNY Downstate Health Sciences University, Brooklyn, NY, USA
| | - Brien P. Riley
- Institute for Genomics in Health, SUNY Downstate Health Sciences University, Brooklyn, NY, USA
| | - Vladimir I. Vladimirov
- Department of Psychiatry, College of Medicine-Phoenix, University of Arizona, Phoenix, AZ, USA
| | - Silviu-Alin Bacanu
- Institute for Genomics in Health, SUNY Downstate Health Sciences University, Brooklyn, NY, USA
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9
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Pudjihartono M, Perry JK, Print C, O'Sullivan JM, Schierding W. Interpretation of the role of germline and somatic non-coding mutations in cancer: expression and chromatin conformation informed analysis. Clin Epigenetics 2022; 14:120. [PMID: 36171609 PMCID: PMC9520844 DOI: 10.1186/s13148-022-01342-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2022] [Accepted: 09/21/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND There has been extensive scrutiny of cancer driving mutations within the exome (especially amino acid altering mutations) as these are more likely to have a clear impact on protein functions, and thus on cell biology. However, this has come at the neglect of systematic identification of regulatory (non-coding) variants, which have recently been identified as putative somatic drivers and key germline risk factors for cancer development. Comprehensive understanding of non-coding mutations requires understanding their role in the disruption of regulatory elements, which then disrupt key biological functions such as gene expression. MAIN BODY We describe how advancements in sequencing technologies have led to the identification of a large number of non-coding mutations with uncharacterized biological significance. We summarize the strategies that have been developed to interpret and prioritize the biological mechanisms impacted by non-coding mutations, focusing on recent annotation of cancer non-coding variants utilizing chromatin states, eQTLs, and chromatin conformation data. CONCLUSION We believe that a better understanding of how to apply different regulatory data types into the study of non-coding mutations will enhance the discovery of novel mechanisms driving cancer.
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Affiliation(s)
| | - Jo K Perry
- Liggins Institute, The University of Auckland, Auckland, New Zealand
- The Maurice Wilkins Centre, The University of Auckland, Auckland, New Zealand
| | - Cris Print
- The Maurice Wilkins Centre, The University of Auckland, Auckland, New Zealand
- Department of Molecular Medicine and Pathology, School of Medical Sciences, University of Auckland, Auckland, 1142, New Zealand
| | - Justin M O'Sullivan
- Liggins Institute, The University of Auckland, Auckland, New Zealand
- The Maurice Wilkins Centre, The University of Auckland, Auckland, New Zealand
- Australian Parkinson's Mission, Garvan Institute of Medical Research, Sydney, NSW, Australia
- MRC Lifecourse Epidemiology Unit, University of Southampton, Southampton, UK
| | - William Schierding
- Liggins Institute, The University of Auckland, Auckland, New Zealand.
- The Maurice Wilkins Centre, The University of Auckland, Auckland, New Zealand.
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10
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Yang CH, Fagnocchi L, Apostle S, Wegert V, Casaní-Galdón S, Landgraf K, Panzeri I, Dror E, Heyne S, Wörpel T, Chandler DP, Lu D, Yang T, Gibbons E, Guerreiro R, Bras J, Thomasen M, Grunnet LG, Vaag AA, Gillberg L, Grundberg E, Conesa A, Körner A, Pospisilik JA. Independent phenotypic plasticity axes define distinct obesity sub-types. Nat Metab 2022; 4:1150-1165. [PMID: 36097183 PMCID: PMC9499872 DOI: 10.1038/s42255-022-00629-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/03/2022] [Accepted: 07/29/2022] [Indexed: 01/04/2023]
Abstract
Studies in genetically 'identical' individuals indicate that as much as 50% of complex trait variation cannot be traced to genetics or to the environment. The mechanisms that generate this 'unexplained' phenotypic variation (UPV) remain largely unknown. Here, we identify neuronatin (NNAT) as a conserved factor that buffers against UPV. We find that Nnat deficiency in isogenic mice triggers the emergence of a bi-stable polyphenism, where littermates emerge into adulthood either 'normal' or 'overgrown'. Mechanistically, this is mediated by an insulin-dependent overgrowth that arises from histone deacetylase (HDAC)-dependent β-cell hyperproliferation. A multi-dimensional analysis of monozygotic twin discordance reveals the existence of two patterns of human UPV, one of which (Type B) phenocopies the NNAT-buffered polyphenism identified in mice. Specifically, Type-B monozygotic co-twins exhibit coordinated increases in fat and lean mass across the body; decreased NNAT expression; increased HDAC-responsive gene signatures; and clinical outcomes linked to insulinemia. Critically, the Type-B UPV signature stratifies both childhood and adult cohorts into four metabolic states, including two phenotypically and molecularly distinct types of obesity.
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Affiliation(s)
- Chih-Hsiang Yang
- Van Andel Institute, Grand Rapids, MI, USA
- Max Planck Institute of Immunobiology and Epigenetics, Freiburg, Germany
| | | | | | - Vanessa Wegert
- Van Andel Institute, Grand Rapids, MI, USA
- Max Planck Institute of Immunobiology and Epigenetics, Freiburg, Germany
| | | | - Kathrin Landgraf
- Medical Faculty, University of Leipzig, University Hospital for Children & Adolescents, Center for Pediatric Research Leipzig, Leipzig, Germany
| | - Ilaria Panzeri
- Van Andel Institute, Grand Rapids, MI, USA
- Max Planck Institute of Immunobiology and Epigenetics, Freiburg, Germany
| | - Erez Dror
- Max Planck Institute of Immunobiology and Epigenetics, Freiburg, Germany
| | - Steffen Heyne
- Max Planck Institute of Immunobiology and Epigenetics, Freiburg, Germany
- Roche Diagnostics Deutschland, Mannheim, Germany
| | - Till Wörpel
- Max Planck Institute of Immunobiology and Epigenetics, Freiburg, Germany
| | | | - Di Lu
- Van Andel Institute, Grand Rapids, MI, USA
| | - Tao Yang
- Van Andel Institute, Grand Rapids, MI, USA
| | - Elizabeth Gibbons
- Department of Neurodegenerative Science, Van Andel Institute, Grand Rapids, MI, USA
| | - Rita Guerreiro
- Department of Neurodegenerative Science, Van Andel Institute, Grand Rapids, MI, USA
| | - Jose Bras
- Department of Neurodegenerative Science, Van Andel Institute, Grand Rapids, MI, USA
| | - Martin Thomasen
- Department of Endocrinology, Rigshospitalet, Copenhagen, Denmark
| | - Louise G Grunnet
- Department of Endocrinology, Rigshospitalet, Copenhagen, Denmark
- Steno Diabetes Center Copenhagen, Herlev, Denmark
| | - Allan A Vaag
- Department of Endocrinology, Rigshospitalet, Copenhagen, Denmark
- Steno Diabetes Center Copenhagen, Herlev, Denmark
- Lund University Diabetes Centre, Lund University, Malmö, Sweden
| | - Linn Gillberg
- Department of Biomedical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Elin Grundberg
- Genomic Medicine Center, Children's Mercy Research Institute, Children's Mercy Kansas City, MO, USA
| | - Ana Conesa
- Institute for Integrative Systems Biology, Spanish National Research Council (CSIC), Paterna, Valencia, Spain
- Microbiology and Cell Science Department, University of Florida, Gainesville, FL, USA
| | - Antje Körner
- Medical Faculty, University of Leipzig, University Hospital for Children & Adolescents, Center for Pediatric Research Leipzig, Leipzig, Germany
- Helmholtz Institute for Metabolic, Obesity and Vascular Research (HI-MAG) of the Helmholtz Zentrum München at the University of Leipzig and University Hospital Leipzig, Leipzig, Germany
| | - J Andrew Pospisilik
- Van Andel Institute, Grand Rapids, MI, USA.
- Max Planck Institute of Immunobiology and Epigenetics, Freiburg, Germany.
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11
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Cuomo ASE, Heinen T, Vagiaki D, Horta D, Marioni JC, Stegle O. CellRegMap: a statistical framework for mapping context-specific regulatory variants using scRNA-seq. Mol Syst Biol 2022; 18:e10663. [PMID: 35972065 PMCID: PMC9380406 DOI: 10.15252/msb.202110663] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2021] [Revised: 06/28/2022] [Accepted: 07/01/2022] [Indexed: 11/11/2022] Open
Abstract
Single-cell RNA sequencing (scRNA-seq) enables characterizing the cellular heterogeneity in human tissues. Recent technological advances have enabled the first population-scale scRNA-seq studies in hundreds of individuals, allowing to assay genetic effects with single-cell resolution. However, existing strategies to analyze these data remain based on principles established for the genetic analysis of bulk RNA-seq. In particular, current methods depend on a priori definitions of discrete cell types, and hence cannot assess allelic effects across subtle cell types and cell states. To address this, we propose the Cell Regulatory Map (CellRegMap), a statistical framework to test for and quantify genetic effects on gene expression in individual cells. CellRegMap provides a principled approach to identify and characterize genotype-context interactions of known eQTL variants using scRNA-seq data. This model-based approach resolves allelic effects across cellular contexts of different granularity, including genetic effects specific to cell subtypes and continuous cell transitions. We validate CellRegMap using simulated data and apply it to previously identified eQTL from two recent studies of differentiating iPSCs, where we uncover hundreds of eQTL displaying heterogeneity of genetic effects across cellular contexts. Finally, we identify fine-grained genetic regulation in neuronal subtypes for eQTL that are colocalized with human disease variants.
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Affiliation(s)
- Anna S E Cuomo
- European Bioinformatics Institute (EMBL‐EBI)CambridgeUK
- Wellcome Sanger InstituteCambridgeUK
- Present address:
Garvan Institute of Medical ScienceSydneyNSWAustralia
| | - Tobias Heinen
- Division of Computational Genomics and Systems GeneticsGerman Cancer Research Centre (DKFZ)HeidelbergGermany
- European Molecular Biology Laboratory (EMBL)Genome BiologyHeidelbergGermany
- Faculty of Mathematics and Computer ScienceHeidelberg UniversityHeidelbergGermany
| | - Danai Vagiaki
- Division of Computational Genomics and Systems GeneticsGerman Cancer Research Centre (DKFZ)HeidelbergGermany
- European Molecular Biology Laboratory (EMBL)Genome BiologyHeidelbergGermany
- Faculty of BiosciencesHeidelberg UniversityHeidelbergGermany
| | - Danilo Horta
- European Bioinformatics Institute (EMBL‐EBI)CambridgeUK
| | - John C Marioni
- European Bioinformatics Institute (EMBL‐EBI)CambridgeUK
- Wellcome Sanger InstituteCambridgeUK
- Cancer Research UKCambridge InstituteCambridgeUK
| | - Oliver Stegle
- European Bioinformatics Institute (EMBL‐EBI)CambridgeUK
- Wellcome Sanger InstituteCambridgeUK
- Division of Computational Genomics and Systems GeneticsGerman Cancer Research Centre (DKFZ)HeidelbergGermany
- European Molecular Biology Laboratory (EMBL)Genome BiologyHeidelbergGermany
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12
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Aggregative trans-eQTL analysis detects trait-specific target gene sets in whole blood. Nat Commun 2022; 13:4323. [PMID: 35882830 PMCID: PMC9325868 DOI: 10.1038/s41467-022-31845-9] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2021] [Accepted: 07/06/2022] [Indexed: 01/13/2023] Open
Abstract
Large scale genetic association studies have identified many trait-associated variants and understanding the role of these variants in the downstream regulation of gene-expressions can uncover important mediating biological mechanisms. Here we propose ARCHIE, a summary statistic based sparse canonical correlation analysis method to identify sets of gene-expressions trans-regulated by sets of known trait-related genetic variants. Simulation studies show that compared to standard methods, ARCHIE is better suited to identify "core"-like genes through which effects of many other genes may be mediated and can capture disease-specific patterns of genetic associations. By applying ARCHIE to publicly available summary statistics from the eQTLGen consortium, we identify gene sets which have significant evidence of trans-association with groups of known genetic variants across 29 complex traits. Around half (50.7%) of the selected genes do not have any strong trans-associations and are not detected by standard methods. We provide further evidence for causal basis of the target genes through a series of follow-up analyses. These results show ARCHIE is a powerful tool for identifying sets of genes whose trans-regulation may be related to specific complex traits.
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13
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Cote AC, Young HE, Huckins LM. Comparison of confound adjustment methods in the construction of gene co-expression networks. Genome Biol 2022; 23:44. [PMID: 35115012 PMCID: PMC8812044 DOI: 10.1186/s13059-022-02606-0] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2021] [Accepted: 01/03/2022] [Indexed: 11/23/2022] Open
Abstract
Adjustment for confounding sources of expression variation is an important preprocessing step in large gene expression studies, but the effect of confound adjustment on co-expression network analysis has not been well-characterized. Here, we demonstrate that the choice of confound adjustment method can have a considerable effect on the architecture of the resulting co-expression network. We compare standard and alternative confound adjustment methods and provide recommendations for their use in the construction of gene co-expression networks from bulk tissue RNA-seq datasets.
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Affiliation(s)
- Alanna C. Cote
- Pamela Sklar Division of Psychiatric Genomics, Icahn School of Medicine at Mount Sinai, New York, NY 10029 USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029 USA
- Department of Genetics and Genomics, Icahn School of Medicine at Mount Sinai, New York, NY 10029 USA
| | - Hannah E. Young
- Pamela Sklar Division of Psychiatric Genomics, Icahn School of Medicine at Mount Sinai, New York, NY 10029 USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029 USA
- Department of Genetics and Genomics, Icahn School of Medicine at Mount Sinai, New York, NY 10029 USA
| | - Laura M. Huckins
- Pamela Sklar Division of Psychiatric Genomics, Icahn School of Medicine at Mount Sinai, New York, NY 10029 USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029 USA
- Department of Genetics and Genomics, Icahn School of Medicine at Mount Sinai, New York, NY 10029 USA
- Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY 10029 USA
- Seaver Autism Center for Research and Treatment, Icahn School of Medicine at Mount Sinai, New York, NY 10029 USA
- Mental Illness Research, Education and Clinical Centers, James J. Peters Department of Veterans Affairs Medical Center, Bronx, NY 10468 USA
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14
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Elorbany R, Popp JM, Rhodes K, Strober BJ, Barr K, Qi G, Gilad Y, Battle A. Single-cell sequencing reveals lineage-specific dynamic genetic regulation of gene expression during human cardiomyocyte differentiation. PLoS Genet 2022; 18:e1009666. [PMID: 35061661 PMCID: PMC8809621 DOI: 10.1371/journal.pgen.1009666] [Citation(s) in RCA: 30] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2021] [Revised: 02/02/2022] [Accepted: 12/21/2021] [Indexed: 12/13/2022] Open
Abstract
Dynamic and temporally specific gene regulatory changes may underlie unexplained genetic associations with complex disease. During a dynamic process such as cellular differentiation, the overall cell type composition of a tissue (or an in vitro culture) and the gene regulatory profile of each cell can both experience significant changes over time. To identify these dynamic effects in high resolution, we collected single-cell RNA-sequencing data over a differentiation time course from induced pluripotent stem cells to cardiomyocytes, sampled at 7 unique time points in 19 human cell lines. We employed a flexible approach to map dynamic eQTLs whose effects vary significantly over the course of bifurcating differentiation trajectories, including many whose effects are specific to one of these two lineages. Our study design allowed us to distinguish true dynamic eQTLs affecting a specific cell lineage from expression changes driven by potentially non-genetic differences between cell lines such as cell composition. Additionally, we used the cell type profiles learned from single-cell data to deconvolve and re-analyze data from matched bulk RNA-seq samples. Using this approach, we were able to identify a large number of novel dynamic eQTLs in single cell data while also attributing dynamic effects in bulk to a particular lineage. Overall, we found that using single cell data to uncover dynamic eQTLs can provide new insight into the gene regulatory changes that occur among heterogeneous cell types during cardiomyocyte differentiation.
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Affiliation(s)
- Reem Elorbany
- Interdisciplinary Scientist Training Program, University of Chicago, Chicago, Illinois, United States of America
| | - Joshua M. Popp
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland, United States of America
| | - Katherine Rhodes
- Department of Human Genetics, University of Chicago, Chicago, Illinois, United States of America
| | - Benjamin J. Strober
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland, United States of America
| | - Kenneth Barr
- Department of Human Genetics, University of Chicago, Chicago, Illinois, United States of America
| | - Guanghao Qi
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland, United States of America
| | - Yoav Gilad
- Department of Human Genetics, University of Chicago, Chicago, Illinois, United States of America
- Department of Medicine, University of Chicago, Chicago, Illinois, United States of America
| | - Alexis Battle
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland, United States of America
- Department of Computer Science, Johns Hopkins University, Baltimore, Maryland, United States of America
- Department of Genetic Medicine, Johns Hopkins University, Baltimore, Maryland, United States of America
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15
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Lin SH, Thakur R, Machiela MJ. LDexpress: an online tool for integrating population-specific linkage disequilibrium patterns with tissue-specific expression data. BMC Bioinformatics 2021; 22:608. [PMID: 34930111 PMCID: PMC8686638 DOI: 10.1186/s12859-021-04531-8] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2021] [Accepted: 12/13/2021] [Indexed: 11/10/2022] Open
Abstract
Genome-wide association studies have identified thousands of genetic susceptibility loci associated with cancer as well as other traits and diseases. Mapping germline variation in identified genetic susceptibility regions to alterations in nearby gene expression nominates candidate genes potentially related to disease risk for further functional investigation. We developed LDexpress as an online resource that integrates population-specific linkage disequilibrium data from the 1000 Genomes (1000G) project and tissue-specific expression data from the Genotype-Tissue Expression project to better study regional germline variation impacting gene expression. LDexpress is a publicly available web tool designed to be easy to use, flexible to conduct a wide range of variant queries, and quick to efficiently investigate dozens of query variants across multiple tissue types. We demonstrate the utility of LDexpress using example genomic queries and anticipate this tool will accelerate understanding of disease etiology by uncovering associations of regional germline variation to nearby gene expression.
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Affiliation(s)
- Shu-Hong Lin
- Integrative Tumor Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, 20892, USA
| | - Rohit Thakur
- Integrative Tumor Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, 20892, USA.,Laboratory of Translational Genomics, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, 20892, USA
| | - Mitchell J Machiela
- Integrative Tumor Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, 20892, USA.
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16
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Li B, Ritchie MD. From GWAS to Gene: Transcriptome-Wide Association Studies and Other Methods to Functionally Understand GWAS Discoveries. Front Genet 2021; 12:713230. [PMID: 34659337 PMCID: PMC8515949 DOI: 10.3389/fgene.2021.713230] [Citation(s) in RCA: 50] [Impact Index Per Article: 16.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2021] [Accepted: 07/27/2021] [Indexed: 12/12/2022] Open
Abstract
Since their inception, genome-wide association studies (GWAS) have identified more than a hundred thousand single nucleotide polymorphism (SNP) loci that are associated with various complex human diseases or traits. The majority of GWAS discoveries are located in non-coding regions of the human genome and have unknown functions. The valley between non-coding GWAS discoveries and downstream affected genes hinders the investigation of complex disease mechanism and the utilization of human genetics for the improvement of clinical care. Meanwhile, advances in high-throughput sequencing technologies reveal important genomic regulatory roles that non-coding regions play in the transcriptional activities of genes. In this review, we focus on data integrative bioinformatics methods that combine GWAS with functional genomics knowledge to identify genetically regulated genes. We categorize and describe two types of data integrative methods. First, we describe fine-mapping methods. Fine-mapping is an exploratory approach that calibrates likely causal variants underneath GWAS signals. Fine-mapping methods connect GWAS signals to potentially causal genes through statistical methods and/or functional annotations. Second, we discuss gene-prioritization methods. These are hypothesis generating approaches that evaluate whether genetic variants regulate genes via certain genetic regulatory mechanisms to influence complex traits, including colocalization, mendelian randomization, and the transcriptome-wide association study (TWAS). TWAS is a gene-based association approach that investigates associations between genetically regulated gene expression and complex diseases or traits. TWAS has gained popularity over the years due to its ability to reduce multiple testing burden in comparison to other variant-based analytic approaches. Multiple types of TWAS methods have been developed with varied methodological designs and biological hypotheses over the past 5 years. We dive into discussions of how TWAS methods differ in many aspects and the challenges that different TWAS methods face. Overall, TWAS is a powerful tool for identifying complex trait-associated genes. With the advent of single-cell sequencing, chromosome conformation capture, gene editing technologies, and multiplexing reporter assays, we are expecting a more comprehensive understanding of genomic regulation and genetically regulated genes underlying complex human diseases and traits in the future.
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Affiliation(s)
- Binglan Li
- Department of Biomedical Data Science, Stanford University, Stanford, CA, United States
| | - Marylyn D Ritchie
- Department of Genetics, University of Pennsylvania, Philadelphia, PA, United States.,Institute for Biomedical Informatics, University of Pennsylvania, Philadelphia, PA, United States
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17
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BOGOMOLOV MARINA, PETERSON CHRISTINEB, BENJAMINI YOAV, SABATTI CHIARA. Hypotheses on a tree: new error rates and testing strategies. Biometrika 2021; 108:575-590. [PMID: 36825068 PMCID: PMC9945647 DOI: 10.1093/biomet/asaa086] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
We introduce a multiple testing procedure that controls global error rates at multiple levels of resolution. Conceptually, we frame this problem as the selection of hypotheses that are organized hierarchically in a tree structure. We describe a fast algorithm and prove that it controls relevant error rates given certain assumptions on the dependence between the p-values. Through simulations, we demonstrate that the proposed procedure provides the desired guarantees under a range of dependency structures and that it has the potential to gain power over alternative methods. Finally, we apply the method to studies on the genetic regulation of gene expression across multiple tissues and on the relation between the gut microbiome and colorectal cancer.
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Affiliation(s)
- MARINA BOGOMOLOV
- The William Davidson Faculty of Industrial Engineering and Management, Technion-Israel Institute of Technology, Technion City, Haifa 3200003, Israel
| | - CHRISTINE B. PETERSON
- Department of Biostatistics, Division of Basic Science Research, The University of Texas, MD Anderson Cancer Center, Houston, Texas 77030, U.S.A
| | - YOAV BENJAMINI
- Department of Statistics and Operations Research, Tel-Aviv University, P.O. Box 39040, Tel-Aviv 6997801, Israel
| | - CHIARA SABATTI
- Department of Statistics, Stanford University, 50 Governor’s Lane, Stanford, California 94305, U.S.A
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Margaritte-Jeannin P, Budu-Aggrey A, Ege M, Madore AM, Linhard C, Mohamdi H, von Mutius E, Granell R, Demenais F, Laprise C, Bouzigon E, Dizier MH. Identification of OCA2 as a novel locus for the co-morbidity of asthma-plus-eczema. Clin Exp Allergy 2021; 52:70-81. [PMID: 34155719 DOI: 10.1111/cea.13972] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2021] [Revised: 06/04/2021] [Accepted: 06/04/2021] [Indexed: 11/27/2022]
Abstract
BACKGROUND Numerous genes have been associated with the three most common allergic diseases (asthma, allergic rhinitis or eczema) but these genes explain only a part of the heritability. In the vast majority of genetic studies, complex phenotypes such as co-morbidity of two of these diseases, have not been considered. This may partly explain missing heritability. OBJECTIVE To identify genetic variants specifically associated with the co-morbidity of asthma-plus-eczema. METHODS We first conducted a meta-analysis of four GWAS (Genome-Wide Association Study) of the combined asthma-plus-eczema phenotype (total of 8807 European-ancestry subjects of whom 1208 subjects had both asthma and eczema). To assess whether the association with SNP(s) was specific to the co-morbidity, we also conducted a meta-analysis of homogeneity test of association according to disease status ("asthma-plus-eczema" vs. the presence of only one disease "asthma only or eczema only"). We then used a joint test by combining the two test statistics from the co-morbidity-SNP association and the phenotypic heterogeneity of SNP effect meta-analyses. RESULTS Seven SNPs were detected for specific association to the asthma-plus-eczema co-morbidity, two with significant and five with suggestive evidence using the joint test after correction for multiple testing. The two significant SNPs are located in the OCA2 gene (Oculocutaneous Albinism II), a new locus never detected for significant evidence of association with any allergic disease. This gene is a promising candidate gene, because of its link to skin and lung diseases, and to epithelial barrier and immune mechanisms. CONCLUSION Our study underlines the importance of studying sub-phenotypes as co-morbidities to detect new susceptibility genes.
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Affiliation(s)
| | - Ashley Budu-Aggrey
- Medical Research Council (MRC) Integrative Epidemiology Unit, Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Markus Ege
- Comprehensive Pneumology Center Munich (CPC-M), German Center for Lung Research, Dr von Hauner Children's Hospital, Ludwig Maximilian University, Munich, Germany
| | - Anne-Marie Madore
- Département des Sciences Fondamentales, Centre Intersectoriel en Santé Durable (CISD), Université du Québec à Chicoutimi, Saguenay, QC, Canada
| | | | | | - Erika von Mutius
- Comprehensive Pneumology Center Munich (CPC-M), German Center for Lung Research, Dr von Hauner Children's Hospital, Ludwig Maximilian University, Munich, Germany
| | - Raquel Granell
- Medical Research Council (MRC) Integrative Epidemiology Unit, Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | | | - Catherine Laprise
- Département des Sciences Fondamentales, Centre Intersectoriel en Santé Durable (CISD), Université du Québec à Chicoutimi, Saguenay, QC, Canada
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19
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Deep transcriptome sequencing of subgenual anterior cingulate cortex reveals cross-diagnostic and diagnosis-specific RNA expression changes in major psychiatric disorders. Neuropsychopharmacology 2021; 46:1364-1372. [PMID: 33558674 PMCID: PMC8134494 DOI: 10.1038/s41386-020-00949-5] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/29/2020] [Revised: 12/16/2020] [Accepted: 12/17/2020] [Indexed: 01/29/2023]
Abstract
Despite strong evidence of heritability and growing discovery of genetic markers for major mental illness, little is known about how gene expression in the brain differs across psychiatric diagnoses, or how known genetic risk factors shape these differences. Here we investigate expressed genes and gene transcripts in postmortem subgenual anterior cingulate cortex (sgACC), a key component of limbic circuits linked to mental illness. RNA obtained postmortem from 200 donors diagnosed with bipolar disorder, schizophrenia, major depression, or no psychiatric disorder was deeply sequenced to quantify expression of over 85,000 gene transcripts, many of which were rare. Case-control comparisons detected modest expression differences that were correlated across disorders. Case-case comparisons revealed greater expression differences, with some transcripts showing opposing patterns of expression between diagnostic groups, relative to controls. The ~250 rare transcripts that were differentially-expressed in one or more disorder groups were enriched for genes involved in synapse formation, cell junctions, and heterotrimeric G-protein complexes. Common genetic variants were associated with transcript expression (eQTL) or relative abundance of alternatively spliced transcripts (sQTL). Common genetic variants previously associated with disease risk were especially enriched for sQTLs, which together accounted for disproportionate fractions of diagnosis-specific heritability. Genetic risk factors that shape the brain transcriptome may contribute to diagnostic differences between broad classes of mental illness.
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20
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Umans BD, Battle A, Gilad Y. Where Are the Disease-Associated eQTLs? Trends Genet 2021; 37:109-124. [PMID: 32912663 PMCID: PMC8162831 DOI: 10.1016/j.tig.2020.08.009] [Citation(s) in RCA: 136] [Impact Index Per Article: 45.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2020] [Revised: 08/07/2020] [Accepted: 08/14/2020] [Indexed: 02/07/2023]
Abstract
Most disease-associated variants, although located in putatively regulatory regions, do not have detectable effects on gene expression. One explanation could be that we have not examined gene expression in the cell types or conditions that are most relevant for disease. Even large-scale efforts to study gene expression across tissues are limited to human samples obtained opportunistically or postmortem, mostly from adults. In this review we evaluate recent findings and suggest an alternative strategy, drawing on the dynamic and highly context-specific nature of gene regulation. We discuss new technologies that can extend the standard regulatory mapping framework to more diverse, disease-relevant cell types and states.
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Affiliation(s)
- Benjamin D Umans
- Department of Medicine, University of Chicago, Chicago, IL, USA.
| | - Alexis Battle
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA; Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA.
| | - Yoav Gilad
- Department of Medicine, University of Chicago, Chicago, IL, USA; Department of Human Genetics, University of Chicago, Chicago, IL, USA.
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21
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Lagou V, Mägi R, Hottenga JJ, Grallert H, Perry JRB, Bouatia-Naji N, Marullo L, Rybin D, Jansen R, Min JL, Dimas AS, Ulrich A, Zudina L, Gådin JR, Jiang L, Faggian A, Bonnefond A, Fadista J, Stathopoulou MG, Isaacs A, Willems SM, Navarro P, Tanaka T, Jackson AU, Montasser ME, O'Connell JR, Bielak LF, Webster RJ, Saxena R, Stafford JM, Pourcain BS, Timpson NJ, Salo P, Shin SY, Amin N, Smith AV, Li G, Verweij N, Goel A, Ford I, Johnson PCD, Johnson T, Kapur K, Thorleifsson G, Strawbridge RJ, Rasmussen-Torvik LJ, Esko T, Mihailov E, Fall T, Fraser RM, Mahajan A, Kanoni S, Giedraitis V, Kleber ME, Silbernagel G, Meyer J, Müller-Nurasyid M, Ganna A, Sarin AP, Yengo L, Shungin D, Luan J, Horikoshi M, An P, Sanna S, Boettcher Y, Rayner NW, Nolte IM, Zemunik T, Iperen EV, Kovacs P, Hastie ND, Wild SH, McLachlan S, Campbell S, Polasek O, Carlson O, Egan J, Kiess W, Willemsen G, Kuusisto J, Laakso M, Dimitriou M, Hicks AA, Rauramaa R, Bandinelli S, Thorand B, Liu Y, Miljkovic I, Lind L, Doney A, Perola M, Hingorani A, Kivimaki M, Kumari M, Bennett AJ, Groves CJ, Herder C, Koistinen HA, Kinnunen L, Faire UD, Bakker SJL, Uusitupa M, Palmer CNA, Jukema JW, Sattar N, Pouta A, Snieder H, Boerwinkle E, Pankow JS, Magnusson PK, Krus U, Scapoli C, de Geus EJCN, Blüher M, Wolffenbuttel BHR, Province MA, Abecasis GR, Meigs JB, Hovingh GK, Lindström J, Wilson JF, Wright AF, Dedoussis GV, Bornstein SR, Schwarz PEH, Tönjes A, Winkelmann BR, Boehm BO, März W, Metspalu A, Price JF, Deloukas P, Körner A, Lakka TA, Keinanen-Kiukaanniemi SM, Saaristo TE, Bergman RN, Tuomilehto J, Wareham NJ, Langenberg C, Männistö S, Franks PW, Hayward C, Vitart V, Kaprio J, Visvikis-Siest S, Balkau B, Altshuler D, Rudan I, Stumvoll M, Campbell H, van Duijn CM, Gieger C, Illig T, Ferrucci L, Pedersen NL, Pramstaller PP, Boehnke M, Frayling TM, Shuldiner AR, Peyser PA, Kardia SLR, Palmer LJ, Penninx BW, Meneton P, Harris TB, Navis G, Harst PVD, Smith GD, Forouhi NG, Loos RJF, Salomaa V, Soranzo N, Boomsma DI, Groop L, Tuomi T, Hofman A, Munroe PB, Gudnason V, Siscovick DS, Watkins H, Lecoeur C, Vollenweider P, Franco-Cereceda A, Eriksson P, Jarvelin MR, Stefansson K, Hamsten A, Nicholson G, Karpe F, Dermitzakis ET, Lindgren CM, McCarthy MI, Froguel P, Kaakinen MA, Lyssenko V, Watanabe RM, Ingelsson E, Florez JC, Dupuis J, Barroso I, Morris AP, Prokopenko I. Sex-dimorphic genetic effects and novel loci for fasting glucose and insulin variability. Nat Commun 2021; 12:24. [PMID: 33402679 PMCID: PMC7785747 DOI: 10.1038/s41467-020-19366-9] [Citation(s) in RCA: 71] [Impact Index Per Article: 23.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2020] [Accepted: 09/22/2020] [Indexed: 12/20/2022] Open
Abstract
Differences between sexes contribute to variation in the levels of fasting glucose and insulin. Epidemiological studies established a higher prevalence of impaired fasting glucose in men and impaired glucose tolerance in women, however, the genetic component underlying this phenomenon is not established. We assess sex-dimorphic (73,089/50,404 women and 67,506/47,806 men) and sex-combined (151,188/105,056 individuals) fasting glucose/fasting insulin genetic effects via genome-wide association study meta-analyses in individuals of European descent without diabetes. Here we report sex dimorphism in allelic effects on fasting insulin at IRS1 and ZNF12 loci, the latter showing higher RNA expression in whole blood in women compared to men. We also observe sex-homogeneous effects on fasting glucose at seven novel loci. Fasting insulin in women shows stronger genetic correlations than in men with waist-to-hip ratio and anorexia nervosa. Furthermore, waist-to-hip ratio is causally related to insulin resistance in women, but not in men. These results position dissection of metabolic and glycemic health sex dimorphism as a steppingstone for understanding differences in genetic effects between women and men in related phenotypes.
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Affiliation(s)
- Vasiliki Lagou
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, United Kingdom
- Department of Microbiology and Immunology, Laboratory of Adaptive Immunity, KU Leuven, Leuven, Belgium
- VIB-KU Leuven Center for Brain and Disease Research, Leuven, Belgium
| | - Reedik Mägi
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Jouke- Jan Hottenga
- Department of Biological Psychology, Vrije Universiteit, Amsterdam, the Netherlands
- Amsterdam Public Health Research Institute, VU University medical center, Amsterdam, the Netherlands
| | - Harald Grallert
- Research Unit of Molecular Epidemiology, Institute of Epidemiology, Helmholtz Zentrum München Research Center for Environmental Health, Neuherberg, Germany
- German Center for Diabetes Research (DZD, München-Neuherberg, Germany
| | - John R B Perry
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Cambridge, United Kingdom
| | - Nabila Bouatia-Naji
- University of Lille Nord de France, Lille, France
- CNRS UMR8199, Institut Pasteur de Lille, Lille, France
- INSERM U970, Paris Cardiovascular Research Center PARCC, 75006, Paris, France
| | - Letizia Marullo
- Department of Life Sciences and Biotechnology, University of Ferrara, Ferrara, Italy
| | - Denis Rybin
- Boston University Data Coordinating Center, Boston, MA, USA
| | - Rick Jansen
- Department of Psychiatry, VU University Medical Center, Amsterdam, the Netherlands
| | - Josine L Min
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Bristol Medical School, University of Bristol, Bristol, United Kingdom
| | - Antigone S Dimas
- Institute for Bioinnovation, Biomedical Sciences Research Center Al. Fleming, Vari, Greece
| | - Anna Ulrich
- Department of Medicine, Imperial College London, London, UK
| | | | - Jesper R Gådin
- Cardiovascular Medicine Unit, Center for Molecular Medicine, Department of Medicine, Karolinska Institutet, Stockholm, Karolinska University Hospital, Solna, Sweden
| | - Longda Jiang
- Department of Medicine, Imperial College London, London, UK
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, 4072, Australia
| | | | - Amélie Bonnefond
- University of Lille Nord de France, Lille, France
- CNRS UMR8199, Institut Pasteur de Lille, Lille, France
- Department of Medicine, Imperial College London, London, UK
| | - Joao Fadista
- Department of Epidemiology Research, Statens Serum Institut, Copenhagen, Denmark
| | | | - Aaron Isaacs
- Genetic Epidemiology Unit, Department of Epidemiology, Erasmus Medical Center, Rotterdam, the Netherlands
- CARIM School for Cardiovascular Diseases and Maastricht Centre for Systems Biology (MaCSBio, Maastricht University, Maastricht, the Netherlands
- Department of Physiology, Maastricht University, Maastricht, the Netherlands
| | - Sara M Willems
- Genetic Epidemiology Unit, Department of Epidemiology, Erasmus Medical Center, Rotterdam, the Netherlands
| | - Pau Navarro
- MRC Human Genetics Unit, MRC Institute of Genetics and Molecular Medicine, University of Edinburgh, Western General Hospital, Edinburgh, UK
| | - Toshiko Tanaka
- Translational Gerontology Branch, Longitudinal Study Section, National Institute on Aging, Baltimore, MD, USA
| | - Anne U Jackson
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA
| | - May E Montasser
- Division of Endocrinology, Diabetes, and Nutrition, Department of Medicine, University of Maryland, School of Medicine, Baltimore, MD, USA
| | - Jeff R O'Connell
- Division of Endocrinology, Diabetes, and Nutrition, Department of Medicine, University of Maryland, School of Medicine, Baltimore, MD, USA
| | - Lawrence F Bielak
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - Rebecca J Webster
- Laboratory for Cancer Medicine, Harry Perkins Institute of Medical Research, University of Western Australia Centre for Medical Research, Nedlands, WA, Australia
| | - Richa Saxena
- Broad Institute of Harvard and Massachusetts Institute of Technology (MIT), Cambridge, MA, USA
- Center for Human Genetic Research, Massachusetts General Hospital, Boston, MA, USA
- Department of Genetics, Harvard Medical School, Boston, MA, USA
- Departmentartment of Anesthesia, Critical Care and Pain Medicine, MGH, Boston, MA, USA
| | - Jeanette M Stafford
- Department of Biostatistics and Data Science, Division of Public Health Sciences, Wake Forest University School of Medicine, Winston-Salem, NC, USA
| | - Beate St Pourcain
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Bristol Medical School, University of Bristol, Bristol, United Kingdom
| | - Nicholas J Timpson
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Bristol Medical School, University of Bristol, Bristol, United Kingdom
| | - Perttu Salo
- Public Health Genomics Unit, Department of Chronic Disease Prevention, Finnish Institute for Health and Welfare, Helsinki, Finland
| | - So-Youn Shin
- Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, UK
| | - Najaf Amin
- Department of Epidemiology Erasmus MC, Rotterdam, the Netherlands
| | - Albert V Smith
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA
- Icelandic Heart Association, Kopavogur, Iceland
- Faculty of Medicine, University of Iceland, Reykjavik, Iceland
| | - Guo Li
- Cardiovascular Health Research Unit, University of Washington, Seattle, WA, USA
- Department of Medicine, University of Washington, Seattle, WA, USA
| | - Niek Verweij
- Department of Cardiology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Anuj Goel
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, United Kingdom
- Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
| | - Ian Ford
- Robertson Centre for Biostatistics, University of Glasgow, Glasgow, UK
| | - Paul C D Johnson
- Robertson Centre for Biostatistics, University of Glasgow, Glasgow, UK
- Institute of Biodiversity, Animal Health & Comparative Medicine, University of Glasgow, Glasgow, UK
| | - Toby Johnson
- William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, UK
- NIHR Barts Cardiovascular Biomedical Research Unit, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, UK
| | - Karen Kapur
- Department of Medical Genetics, University of Lausanne, Lausanne, Switzerland
| | | | - Rona J Strawbridge
- Cardiovascular Medicine Unit, Department of Medicine, Solna, Karolinska Institutet, Stockholm, Sweden
- Center for Molecular Medicine, Karolinska University Hospital Solna, Stockholm, Sweden
- Institute of Health and Wellbeing, University of Glasgow, Glasgow, UK
| | - Laura J Rasmussen-Torvik
- Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Tõnu Esko
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Evelin Mihailov
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Tove Fall
- Department of Medical Sciences, Molecular Epidemiology and Science for Life Laboratory, Uppsala University, Uppsala, Sweden
| | - Ross M Fraser
- Usher Institute, University of Edinburgh, Edinburgh, UK
- Synpromics Ltd, Roslin Innovation Centre, Easter Bush Campus, Edinburgh, EH25 9RG, UK
| | - Anubha Mahajan
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, United Kingdom
- Genentech, 340 Point San Bruno Boulevard, South San Francisco, CA, 94080, USA
| | - Stavroula Kanoni
- William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, UK
- Wellcome Trust Sanger Institute, Hinxton, UK
| | - Vilmantas Giedraitis
- Department of Public Health and Caring Sciences, Uppsala Universitet, Uppsala, Sweden
| | - Marcus E Kleber
- Vth Department of Medicine, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Günther Silbernagel
- Division of Angiology, Department of Internal Medicine, Medical University of Graz, Graz, Austria
| | - Julia Meyer
- Institute of Genetic Epidemiology,Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Martina Müller-Nurasyid
- Institute of Genetic Epidemiology,Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- Institute of Medical Informatics, Biometry and Epidemiology, Chair of Epidemiology and Chair of Genetic Epidemiology, Ludwig-Maximilians-Universität, Munich, Germany
- Department of Medicine I, University Hospital Grosshadern, Ludwig-Maximilians-University, Munich, Germany
- Institute of Medical Biostatistics, Epidemiology and Informatics (IMBEI, University Medical Center, Johannes Gutenberg University, 55101, Mainz, Germany
| | - Andrea Ganna
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Antti-Pekka Sarin
- Institute for Molecular Medicine Finland, FIMM, University of Helsinki, Helsinki, Finland
- Public Health Genomics Unit, Finnish Institute for Health and Welfare, Helsinki, Finland
| | - Loic Yengo
- University of Lille Nord de France, Lille, France
- CNRS UMR8199, Institut Pasteur de Lille, Lille, France
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, 4072, Australia
| | - Dmitry Shungin
- Department of Public Health & Clinical Medicine, Umeå University, Umeå, Sweden
- Department of Clinical Sciences, Genetic and Molecular Epidemiology Unit, Skåne University Hospital Malmö, Malmö, Sweden
- Department of Odontology, Umeå University, Umeå, Sweden
| | - Jian'an Luan
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Cambridge, United Kingdom
| | - Momoko Horikoshi
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, United Kingdom
- Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Oxford, UK
- RIKEN, Center for Integrative Medical Sciences, Laboratory for Endocrinology, Metabolism and Kidney Disease, Yokohama, Japan
| | - Ping An
- Division of Statistical Genomics, Washington University School of Medicine, St. Louis, MO, USA
| | - Serena Sanna
- Istituto di Ricerca Genetica e Biomedica, CNR, Monserrato, Italy
- Department of Genetics, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands
| | - Yvonne Boettcher
- Department of Medicine, University of Leipzig, Leipzig, Germany
- IFB AdiposityDiseases, University of Leipzig, Leipzig, Germany
| | - N William Rayner
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, United Kingdom
- Wellcome Trust Sanger Institute, Hinxton, UK
- Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Oxford, UK
| | - Ilja M Nolte
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | | | - Erik van Iperen
- Department of Clinical Epidemiology and Biostatistics, Academic Medical Center, University of Amsterdam, Amsterdam, the Netherlands
| | - Peter Kovacs
- IFB AdiposityDiseases, University of Leipzig, Leipzig, Germany
| | - Nicholas D Hastie
- MRC Human Genetics Unit, MRC Institute of Genetics and Molecular Medicine, University of Edinburgh, Western General Hospital, Edinburgh, UK
| | - Sarah H Wild
- Usher Institute, University of Edinburgh, Edinburgh, UK
| | | | - Susan Campbell
- MRC Human Genetics Unit, MRC Institute of Genetics and Molecular Medicine, University of Edinburgh, Western General Hospital, Edinburgh, UK
| | - Ozren Polasek
- Faculty of Medicine, University of Split, Split, Croatia
| | - Olga Carlson
- Laboratory of Clinical Investigation, National Institute of Aging, Baltimore, MD, USA
| | - Josephine Egan
- Laboratory of Clinical Investigation, National Institute of Aging, Baltimore, MD, USA
| | - Wieland Kiess
- IFB AdiposityDiseases, University of Leipzig, Leipzig, Germany
- Pediatric Research Center, Department of Women's & Child Health, University of Leipzig, Leipzig, Germany
| | - Gonneke Willemsen
- Department of Biological Psychology, Vrije Universiteit, Amsterdam, the Netherlands
| | - Johanna Kuusisto
- Department of Medicine, University of Eastern Finland and Kuopio University Hospital, Kuopio, Finland
| | - Markku Laakso
- Department of Medicine, University of Eastern Finland and Kuopio University Hospital, Kuopio, Finland
| | - Maria Dimitriou
- Department of Dietetics-Nutrition, Harokopio University, Athens, Greece
| | - Andrew A Hicks
- Center for Biomedicine, European Academy Bozen/Bolzano (EURAC) (Affiliated Institute of the University of LübeckLübeckGermany), Bolzano, Italy
| | - Rainer Rauramaa
- Kuopio Research Institute of Exercise Medicine, Kuopio, Finland
- Department of Clinical Physiology and Nuclear Medicine, Kuopio University Hospital, Kuopio, Finland
| | | | - Barbara Thorand
- German Center for Diabetes Research (DZD, München-Neuherberg, Germany
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Yongmei Liu
- Department of Epidemiology and Prevention, Division of Public Health Sciences, Wake Forest University School of Medicine, Winston-Salem, NC, USA
| | - Iva Miljkovic
- Department of Epidemiology, Center for Aging and Population Health, University of Pittsburgh, Pittsburgh, PA, USA
| | - Lars Lind
- Department of Medical Sciences, Uppsala University, Akademiska sjukhuset, Uppsala, Sweden
| | - Alex Doney
- Pat McPherson Centre for Pharmacogenetics and Pharmacogenomics, Division of Molecular and Clinical Medicine, Ninewells Hospital and Medical School, University of Dundee, Dundee, UK
| | - Markus Perola
- Public Health Genomics Unit, Department of Chronic Disease Prevention, Finnish Institute for Health and Welfare, Helsinki, Finland
- Institute for Molecular Medicine Finland, FIMM, University of Helsinki, Helsinki, Finland
- Estonian Genome Center, University of Tartu, Tartu, Estonia
| | - Aroon Hingorani
- Department of Epidemiology and Public Health, University College London, London, UK
| | - Mika Kivimaki
- Department of Epidemiology and Public Health, University College London, London, UK
| | - Meena Kumari
- Department of Epidemiology and Public Health, University College London, London, UK
- University of Essex, Wivenhoe Park, Colchester, Essex, UK
| | - Amanda J Bennett
- Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Oxford, UK
| | - Christopher J Groves
- Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Oxford, UK
| | - Christian Herder
- German Center for Diabetes Research (DZD, München-Neuherberg, Germany
- Institute of Clinical Diabetology, German Diabetes Center, Leibniz Center for Diabetes Research at Heinrich Heine University Düsseldorf, Düsseldorf, Germany
- Division of Endocrinology and Diabetology, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Heikki A Koistinen
- Department of Public Health Solutions, Finnish Institute for Health and Welfare, P.O. Box 30, Helsinki, FI-00271, Finland
- Department of Medicine, University of Helsinki and Helsinki University Central Hospital, P.O. Box 340, Haartmaninkatu 4, Helsinki, FI-00029, Finland
- Minerva Foundation Institute for Medical Research, Biomedicum 2U, Tukholmankatu 8, Helsinki, FI-00290, Finland
| | - Leena Kinnunen
- Department of Public Health Solutions, Finnish Institute for Health and Welfare, P.O. Box 30, Helsinki, FI-00271, Finland
| | - Ulf de Faire
- Division of Cardiovascular Epidemiology, Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden
| | - Stephan J L Bakker
- Department of Internal Medicine, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Matti Uusitupa
- Institute of Public Health and Clinical Nutrition, University of Eastern Finland, Kuopio, Finland
| | - Colin N A Palmer
- Pat McPherson Centre for Pharmacogenetics and Pharmacogenomics, Division of Molecular and Clinical Medicine, Ninewells Hospital and Medical School, University of Dundee, Dundee, UK
| | - J Wouter Jukema
- Dept of Cardiology, Leiden University Medical Center, Leiden, the Netherlands
- Netherlands Heart Institute, Utrecht, the Netherlands
| | - Naveed Sattar
- Institute of Cardiovascular and Medical Sciences, University of Glasgow, Glasgow, United Kingdom
| | - Anneli Pouta
- Department of Government Services, Finnish Institute for Health and Welfare, Helsinki, Finland
- PEDEGO Research Unit, Medical Research Center Oulu, Oulu University Hospital and University of Oulu, Oulu, Finland
| | - Harold Snieder
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Eric Boerwinkle
- IMM Center for Human Genetics, University of Texas Health Science Center at Houston, Houston, TX, USA
- Division of Epidemiology, School of Public Health, University of Texas Health Science Center at Houston, Houston, TX, USA
| | - James S Pankow
- Division of Epidemiology and Community Health, School of Public Health, University of Minnesota, Minneapolis, MiI, USA
| | - Patrik K Magnusson
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Ulrika Krus
- Department of Clinical Sciences, Diabetes and Endocrinology Research Unit, University Hospital Malmö, Lund University, Malmö, Sweden
| | - Chiara Scapoli
- Department of Life Sciences and Biotechnology, University of Ferrara, Ferrara, Italy
| | - Eco J C N de Geus
- Department of Biological Psychology, Vrije Universiteit, Amsterdam, the Netherlands
- Amsterdam Public Health Research Institute, VU University medical center, Amsterdam, the Netherlands
| | - Matthias Blüher
- Department of Medicine, University of Leipzig, Leipzig, Germany
- Department of Preventive Medicine, Keck School of Medicine of USC, Los Angeles, CA, USA
| | - Bruce H R Wolffenbuttel
- Department of Endocrinology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Michael A Province
- Division of Statistical Genomics, Washington University School of Medicine, St. Louis, MO, USA
| | - Goncalo R Abecasis
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA
| | - James B Meigs
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- General Medicine Division, Massachusetts General Hospital, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - G Kees Hovingh
- Department of Vascular Medicine, Amsterdam UMC, Amsterdam, the Netherlands
- Novo Nordisk A/S, Copenhagen, Denmark
| | - Jaana Lindström
- Finnish Institute for Health and Welfare, Diabetes Prevention Unit, Helsinki, Finland
| | - James F Wilson
- MRC Human Genetics Unit, MRC Institute of Genetics and Molecular Medicine, University of Edinburgh, Western General Hospital, Edinburgh, UK
- Usher Institute, University of Edinburgh, Edinburgh, UK
| | - Alan F Wright
- MRC Human Genetics Unit, MRC Institute of Genetics and Molecular Medicine, University of Edinburgh, Western General Hospital, Edinburgh, UK
| | | | - Stefan R Bornstein
- Department of Medicine, Division for Prevention and Care of Diabetes, Faculty of Medicine Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
| | - Peter E H Schwarz
- Department for Prevention and Care of Diabetes, Faculty of Medicine Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
- Paul Langerhans Institute Dresden of the Helmholtz Center Munich at University Hospital and Faculty of Medicine, TU Dresden, Dresden, Germany
- German Center for Diabetes Research (DZD e.V.), Neuherberg, Germany
| | - Anke Tönjes
- Department of Medicine, University of Leipzig, Leipzig, Germany
- IFB AdiposityDiseases, University of Leipzig, Leipzig, Germany
| | | | - Bernhard O Boehm
- Lee Kong Chian School of Medicine, Nanyang Technological University Singapore and Imperial College London, Singapore, Singapore
| | - Winfried März
- Vth Department of Medicine, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
- Synlab Academy, Synlab Holding Deutschland GmbH, Mannheim, Germany
| | - Andres Metspalu
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | | | - Panos Deloukas
- William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, UK
- Wellcome Trust Sanger Institute, Hinxton, UK
- Princess Al-Jawhara Al-Brahim Centre of Excellence in Research of Hereditary Disorders (PACER-HD, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Antje Körner
- IFB AdiposityDiseases, University of Leipzig, Leipzig, Germany
- Diabetes Research Center, Diabetes Unit, Massachusetts General Hospital, Boston, MA, USA
| | - Timo A Lakka
- Kuopio Research Institute of Exercise Medicine, Kuopio, Finland
- Institute of Biomedicine/Physiology, University of Eastern Finland, Kuopio Campus, Kuopio, Finland
| | - Sirkka M Keinanen-Kiukaanniemi
- Faculty of Medicine, Center for Life Course Health Research, University of Oulu, Oulu, Finland
- Unit of General Practice, Oulu University Hospital, Oulu, Finland
| | - Timo E Saaristo
- Finnish Diabetes Association, Tampere, Finland
- Pirkanmaa Hospital District, Tampere, Finland
| | - Richard N Bergman
- Diabetes and Obesity Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Jaakko Tuomilehto
- Department of Chronic Disease Prevention, Finnish Institute for Health and Welfare, Helsinki, Finland
- Department of Public Health, University of Helsinki, Helsinki, Finland
- Centre for Vascular Prevention, Danube-University Krems, Krems, Austria
- Diabetes Research Group, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Nicholas J Wareham
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Cambridge, United Kingdom
| | - Claudia Langenberg
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Cambridge, United Kingdom
| | - Satu Männistö
- Department of Public Health Solutions, Finnish Institute for Health and Welfare, P.O. Box 30, Helsinki, FI-00271, Finland
| | - Paul W Franks
- Department of Clinical Sciences, Genetic and Molecular Epidemiology Unit, Skåne University Hospital Malmö, Malmö, Sweden
- Department of Nutrition, Harvard School of Public Health, Boston, MA, USA
- Department of Public Health & Clinical Medicine, Units of Medicine and Nutritional Research, Umeå University, Umeå, Sweden
| | - Caroline Hayward
- MRC Human Genetics Unit, MRC Institute of Genetics and Molecular Medicine, University of Edinburgh, Western General Hospital, Edinburgh, UK
| | - Veronique Vitart
- MRC Human Genetics Unit, MRC Institute of Genetics and Molecular Medicine, University of Edinburgh, Western General Hospital, Edinburgh, UK
| | - Jaakko Kaprio
- Institute for Molecular Medicine Finland, FIMM, University of Helsinki, Helsinki, Finland
- Department of Public Health, University of Helsinki, Helsinki, Finland
| | | | - Beverley Balkau
- Inserm, CESP Center for Research in Epidemiology and Public Health, U1018, Villejuif, France
- Univ Paris-Saclay, Univ Paris Sud, UVSQ, UMRS 1018, UMRS 1018, Villejuif, France
| | - David Altshuler
- Broad Institute of Harvard and Massachusetts Institute of Technology (MIT), Cambridge, MA, USA
- Center for Human Genetic Research, Massachusetts General Hospital, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Igor Rudan
- Usher Institute, University of Edinburgh, Edinburgh, UK
| | - Michael Stumvoll
- Department of Medicine, University of Leipzig, Leipzig, Germany
- IFB AdiposityDiseases, University of Leipzig, Leipzig, Germany
| | | | - Cornelia M van Duijn
- Genetic Epidemiology Unit, Department of Epidemiology, Erasmus Medical Center, Rotterdam, the Netherlands
- Centre for Medical Systems Biology, Leiden, the Netherlands
| | - Christian Gieger
- Research Unit of Molecular Epidemiology, Institute of Epidemiology, Helmholtz Zentrum München Research Center for Environmental Health, Neuherberg, Germany
- German Center for Diabetes Research (DZD, München-Neuherberg, Germany
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Thomas Illig
- Research Unit of Molecular Epidemiology, Institute of Epidemiology, Helmholtz Zentrum München Research Center for Environmental Health, Neuherberg, Germany
- Hannover Unified Biobank, Hannover Medical School, Hannover, Germany
- Institute of Human Genetics, Hannover Medical School, Hannover, Germany
| | - Luigi Ferrucci
- Clinical Research Branch, National Institute on Aging, Baltimore, Maryland, USA
| | - Nancy L Pedersen
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Peter P Pramstaller
- Center for Biomedicine, European Academy Bozen/Bolzano (EURAC) (Affiliated Institute of the University of LübeckLübeckGermany), Bolzano, Italy
- Department of Neurology, General Central Hospital, Bolzano, Italy
- Department of Neurology, University of Lübeck, Lübeck, Germany
| | - Michael Boehnke
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA
| | - Timothy M Frayling
- Genetics of Complex Traits, Peninsula Medical School, University of Exeter, Exeter, UK
| | - Alan R Shuldiner
- Division of Endocrinology, Diabetes, and Nutrition, Department of Medicine, University of Maryland, School of Medicine, Baltimore, MD, USA
- The Regeneron Genetics Center, Regeneron Pharmaceuticals, Tarrytown, NY, USA
| | - Patricia A Peyser
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - Sharon L R Kardia
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - Lyle J Palmer
- School of Public Health, University of Adelaide, Adelaide, Australia
| | - Brenda W Penninx
- Department of Psychiatry, VU University Medical Center, Amsterdam, the Netherlands
| | - Pierre Meneton
- U872 Institut National de la Santé et de la Recherche Médicale, Centre de Recherche des Cordeliers, 75006, Paris, France
| | - Tamara B Harris
- Geriatric Epidemiology Section, Laboratory of Epidemiology, Demography, and Biometry, National Institute on Aging, Bethesda, MD, USA
| | - Gerjan Navis
- Department of Internal Medicine, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Pim van der Harst
- Department of Cardiology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
- Department of Genetics, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands
| | - George Davey Smith
- MRC Integrative Epidemiology Unit (IEU), University of Bristol, Bristol, UK
| | - Nita G Forouhi
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Cambridge, United Kingdom
| | - Ruth J F Loos
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Cambridge, United Kingdom
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Veikko Salomaa
- Finnish Institute for Health and Welfare, Helsinki, Finland
| | - Nicole Soranzo
- Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, UK
| | - Dorret I Boomsma
- Department of Biological Psychology, Vrije Universiteit, Amsterdam, the Netherlands
| | - Leif Groop
- Institute for Molecular Medicine Finland, FIMM, University of Helsinki, Helsinki, Finland
- Department of Clinical Sciences, Diabetes and Endocrinology Research Unit, University Hospital Malmö, Lund University, Malmö, Sweden
| | - Tiinamaija Tuomi
- Institute for Molecular Medicine Finland, FIMM, University of Helsinki, Helsinki, Finland
- Department of Clinical Sciences, Diabetes and Endocrinology Research Unit, University Hospital Malmö, Lund University, Malmö, Sweden
- Endocrinology, Abdominal Centre, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
- Research Program for Clinical and Molecular Metabolism, University of Helsinki and Folkhälsan Research Center, Helsinki, Finland
| | - Albert Hofman
- Department of Epidemiology Erasmus MC, Rotterdam, the Netherlands
- Netherlands Consortium for healthy ageing, the Hague, the Netherlands
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, USA
| | - Patricia B Munroe
- William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, UK
- NIHR Barts Cardiovascular Biomedical Research Unit, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, UK
| | - Vilmundur Gudnason
- Icelandic Heart Association, Kopavogur, Iceland
- Faculty of Medicine University of Iceland, Reykjavik, Iceland
| | - David S Siscovick
- Cardiovascular Health Research Unit, University of Washington, Seattle, WA, USA
- Department of Medicine, University of Washington, Seattle, WA, USA
- Department of Epidemiology, University of Washington, Seattle, WA, USA
| | - Hugh Watkins
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, United Kingdom
- Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
| | - Cecile Lecoeur
- University of Lille Nord de France, Lille, France
- CNRS UMR8199, Institut Pasteur de Lille, Lille, France
| | - Peter Vollenweider
- Department of Medicine, University Hospital Lausanne, Lausanne, Switzerland
| | - Anders Franco-Cereceda
- Cardiothoracic Surgery Unit, Department of Molecular Medicine and Surgery, Karolinska Institutet, Stockholm, Sweden
| | - Per Eriksson
- Cardiovascular Medicine Unit, Center for Molecular Medicine, Department of Medicine, Karolinska Institutet, Stockholm, Karolinska University Hospital, Solna, Sweden
| | - Marjo-Riitta Jarvelin
- Department of Epidemiology and Biostatistics and HPA-MRC Center, School of Public Health, Imperial College London, London, UK
- Institue of Health Sciences, University of Oulu, Oulu, Finland
| | - Kari Stefansson
- deCODE Genetics, Reykjavik, Iceland
- Faculty of Medicine, University of Iceland, Reykjavík, Iceland
| | - Anders Hamsten
- Cardiovascular Medicine Unit, Department of Medicine, Solna, Karolinska Institutet, Stockholm, Sweden
- Center for Molecular Medicine, Karolinska University Hospital Solna, Stockholm, Sweden
- Department of Cardiology, Karolinska University Hospital Solna, Stockholm, Sweden
| | | | - Fredrik Karpe
- Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Oxford, UK
- Oxford National Institute for Health Research Biomedical Research Centre, Churchill Hospital, Oxford, UK
| | - Emmanouil T Dermitzakis
- Department of Genetic Medicine and Development, University of Geneva Medical School, Geneva, Switzerland
| | - Cecilia M Lindgren
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, United Kingdom
- Broad Institute of Harvard and Massachusetts Institute of Technology (MIT), Cambridge, MA, USA
- Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Oxford, UK
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK
| | - Mark I McCarthy
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, United Kingdom
- Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Oxford, UK
- Oxford National Institute for Health Research Biomedical Research Centre, Churchill Hospital, Oxford, UK
- Genentech, 340 Point San Bruno Boulevard, South San Francisco, CA, 94080, USA
| | - Philippe Froguel
- University of Lille Nord de France, Lille, France
- CNRS UMR8199, Institut Pasteur de Lille, Lille, France
- Department of Medicine, Imperial College London, London, UK
| | - Marika A Kaakinen
- Department of Medicine, Imperial College London, London, UK
- School of Biosciences and Medicine, Department of Clinical and Experimental Medicine, University of Surrey, Guildford, UK
| | - Valeriya Lyssenko
- Department of Clinical Sciences, Diabetes and Endocrinology Research Unit, University Hospital Malmö, Lund University, Malmö, Sweden
- Department of Clinical Science, University of Bergen, Bergen, Norway
| | - Richard M Watanabe
- Department of Preventive Medicine, Keck School of Medicine of USC, Los Angeles, CA, USA
- Department of Physiology & Neuroscience, Keck School of Medicine of USC, Los Angeles, CA, USA
- USC Diabetes and Obesity Research Institute, Los Angeles, CA, USA
| | - Erik Ingelsson
- Department of Medical Sciences, Molecular Epidemiology and Science for Life Laboratory, Uppsala University, Uppsala, Sweden
- Department of Medicine, Division of Cardiovascular Medicine, Stanford University School of Medicine, Stanford, CA, USA
- Stanford Cardiovascular Institute, Stanford University, Stanford, CA, 94305, USA
| | - Jose C Florez
- Center for Human Genetic Research, Massachusetts General Hospital, Boston, MA, USA
- Diabetes Research Center, Diabetes Unit, Massachusetts General Hospital, Boston, MA, USA
| | - Josée Dupuis
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
| | - Inês Barroso
- Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, UK
- University of Cambridge Metabolic Research Laboratories and NIHR Cambridge Biomedical Research Centre, Wellcome Trust-MRC Institute of Metabolic Science, Cambridge, UK
- Exeter Centre of ExcEllence in Diabetes (ExCEED), University of Exeter Medical School, Exeter, UK
| | - Andrew P Morris
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, United Kingdom
- Estonian Genome Center, University of Tartu, Tartu, Estonia
- Department of Biostatistics, University of Liverpool, Liverpool, UK
- Centre for Genetics and Genomics Versus Arthritis, Centre for Musculoskeletal Research, The University of Manchester, Manchester, UK
| | - Inga Prokopenko
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, United Kingdom.
- Department of Medicine, Imperial College London, London, UK.
- Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Oxford, UK.
- School of Biosciences and Medicine, Department of Clinical and Experimental Medicine, University of Surrey, Guildford, UK.
- Institute of Biochemistry and Genetics, Ufa Federal Research Centre Russian Academy of Sciences, Ufa, Russian Federation.
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22
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Blencowe M, Ahn IS, Saleem Z, Luk H, Cely I, Mäkinen VP, Zhao Y, Yang X. Gene networks and pathways for plasma lipid traits via multitissue multiomics systems analysis. J Lipid Res 2021; 62:100019. [PMID: 33561811 PMCID: PMC7873371 DOI: 10.1194/jlr.ra120000713] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2020] [Revised: 12/04/2020] [Accepted: 12/23/2020] [Indexed: 12/13/2022] Open
Abstract
Genome-wide association studies (GWASs) have implicated ∼380 genetic loci for plasma lipid regulation. However, these loci only explain 17-27% of the trait variance, and a comprehensive understanding of the molecular mechanisms has not been achieved. In this study, we utilized an integrative genomics approach leveraging diverse genomic data from human populations to investigate whether genetic variants associated with various plasma lipid traits, namely, total cholesterol, high and low density lipoprotein cholesterol (HDL and LDL), and triglycerides, from GWASs were concentrated on specific parts of tissue-specific gene regulatory networks. In addition to the expected lipid metabolism pathways, gene subnetworks involved in "interferon signaling," "autoimmune/immune activation," "visual transduction," and "protein catabolism" were significantly associated with all lipid traits. In addition, we detected trait-specific subnetworks, including cadherin-associated subnetworks for LDL; glutathione metabolism for HDL; valine, leucine, and isoleucine biosynthesis for total cholesterol; and insulin signaling and complement pathways for triglyceride. Finally, by using gene-gene relations revealed by tissue-specific gene regulatory networks, we detected both known (e.g., APOH, APOA4, and ABCA1) and novel (e.g., F2 in adipose tissue) key regulator genes in these lipid-associated subnetworks. Knockdown of the F2 gene (coagulation factor II, thrombin) in 3T3-L1 and C3H10T1/2 adipocytes altered gene expression of Abcb11, Apoa5, Apof, Fabp1, Lipc, and Cd36; reduced intracellular adipocyte lipid content; and increased extracellular lipid content, supporting a link between adipose thrombin and lipid regulation. Our results shed light on the complex mechanisms underlying lipid metabolism and highlight potential novel targets for lipid regulation and lipid-associated diseases.
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Affiliation(s)
- Montgomery Blencowe
- Department of Integrative Biology and Physiology, University of California, Los Angeles, Los Angeles, CA, USA; Molecular, Cellular, and Integrative Physiology Interdepartmental Program, University of California, Los Angeles, Los Angeles, CA, USA
| | - In Sook Ahn
- Department of Integrative Biology and Physiology, University of California, Los Angeles, Los Angeles, CA, USA
| | - Zara Saleem
- Department of Integrative Biology and Physiology, University of California, Los Angeles, Los Angeles, CA, USA
| | - Helen Luk
- Department of Integrative Biology and Physiology, University of California, Los Angeles, Los Angeles, CA, USA
| | - Ingrid Cely
- Department of Integrative Biology and Physiology, University of California, Los Angeles, Los Angeles, CA, USA
| | - Ville-Petteri Mäkinen
- Department of Integrative Biology and Physiology, University of California, Los Angeles, Los Angeles, CA, USA; South Australian Health and Medical Research Institute, Adelaide, Australia
| | - Yuqi Zhao
- Department of Integrative Biology and Physiology, University of California, Los Angeles, Los Angeles, CA, USA.
| | - Xia Yang
- Department of Integrative Biology and Physiology, University of California, Los Angeles, Los Angeles, CA, USA; Molecular, Cellular, and Integrative Physiology Interdepartmental Program, University of California, Los Angeles, Los Angeles, CA, USA; Interdepartmental Program of Bioinformatics, University of California, Los Angeles, Los Angeles, CA, USA.
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23
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Finkbeiner S. Functional genomics, genetic risk profiling and cell phenotypes in neurodegenerative disease. Neurobiol Dis 2020; 146:105088. [PMID: 32977020 PMCID: PMC7686089 DOI: 10.1016/j.nbd.2020.105088] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2020] [Revised: 09/11/2020] [Accepted: 09/18/2020] [Indexed: 12/03/2022] Open
Abstract
Human genetics provides unbiased insights into the causes of human disease, which can be used to create a foundation for effective ways to more accurately diagnose patients, stratify patients for more successful clinical trials, discover and develop new therapies, and ultimately help patients choose the safest and most promising therapeutic option based on their risk profile. But the process for translating basic observations from human genetics studies into pathogenic disease mechanisms and treatments is laborious and complex, and this challenge has particularly slowed the development of interventions for neurodegenerative disease. In this review, we discuss the many steps in the process, the important considerations at each stage, and some of the latest tools and technologies that are available to help investigators translate insights from human genetics into diagnostic and therapeutic strategies that will lead to the sort of advances in clinical care that make a difference for patients.
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Affiliation(s)
- Steven Finkbeiner
- Center for Systems and Therapeutics, USA; Taube/Koret Center for Neurodegenerative Disease Research, Gladstone Institutes, San Francisco, CA 94158, USA; Departments of Neurology and Physiology, University of Califorina, San Francisco, CA 94158, USA.
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24
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Raymond B, Yengo L, Costilla R, Schrooten C, Bouwman AC, Hayes BJ, Veerkamp RF, Visscher PM. Using prior information from humans to prioritize genes and gene-associated variants for complex traits in livestock. PLoS Genet 2020; 16:e1008780. [PMID: 32925905 PMCID: PMC7514049 DOI: 10.1371/journal.pgen.1008780] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2020] [Revised: 09/24/2020] [Accepted: 07/21/2020] [Indexed: 01/13/2023] Open
Abstract
Genome-Wide Association Studies (GWAS) in large human cohorts have identified thousands of loci associated with complex traits and diseases. For identifying the genes and gene-associated variants that underlie complex traits in livestock, especially where sample sizes are limiting, it may help to integrate the results of GWAS for equivalent traits in humans as prior information. In this study, we sought to investigate the usefulness of results from a GWAS on human height as prior information for identifying the genes and gene-associated variants that affect stature in cattle, using GWAS summary data on samples sizes of 700,000 and 58,265 for humans and cattle, respectively. Using Fisher's exact test, we observed a significant proportion of cattle stature-associated genes (30/77) that are also associated with human height (odds ratio = 5.1, p = 3.1e-10). Result of randomized sampling tests showed that cattle orthologs of human height-associated genes, hereafter referred to as candidate genes (C-genes), were more enriched for cattle stature GWAS signals than random samples of genes in the cattle genome (p = 0.01). Randomly sampled SNPs within the C-genes also tend to explain more genetic variance for cattle stature (up to 13.2%) than randomly sampled SNPs within random cattle genes (p = 0.09). The most significant SNPs from a cattle GWAS for stature within the C-genes did not explain more genetic variance for cattle stature than the most significant SNPs within random cattle genes (p = 0.87). Altogether, our findings support previous studies that suggest a similarity in the genetic regulation of height across mammalian species. However, with the availability of a powerful GWAS for stature that combined data from 8 cattle breeds, prior information from human-height GWAS does not seem to provide any additional benefit with respect to the identification of genes and gene-associated variants that affect stature in cattle.
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Affiliation(s)
- Biaty Raymond
- Animal Breeding and Genomics, Wageningen University and Research, Wageningen, The Netherlands
- Biometris, Wageningen University and Research, Wageningen, The Netherlands
- * E-mail:
| | - Loic Yengo
- Institute for Molecular Bioscience, University of Queensland, St. Lucia, Australia
| | - Roy Costilla
- Queensland Alliance for Agriculture and Food Innovation, University of Queensland, St. Lucia, Australia
| | | | - Aniek C. Bouwman
- Animal Breeding and Genomics, Wageningen University and Research, Wageningen, The Netherlands
| | - Ben J. Hayes
- Queensland Alliance for Agriculture and Food Innovation, University of Queensland, St. Lucia, Australia
| | - Roel F. Veerkamp
- Animal Breeding and Genomics, Wageningen University and Research, Wageningen, The Netherlands
| | - Peter M. Visscher
- Institute for Molecular Bioscience, University of Queensland, St. Lucia, Australia
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25
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He Y, Chhetri SB, Arvanitis M, Srinivasan K, Aguet F, Ardlie KG, Barbeira AN, Bonazzola R, Im HK, Brown CD, Battle A. sn-spMF: matrix factorization informs tissue-specific genetic regulation of gene expression. Genome Biol 2020; 21:235. [PMID: 32912314 PMCID: PMC7488540 DOI: 10.1186/s13059-020-02129-6] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2019] [Accepted: 08/04/2020] [Indexed: 01/09/2023] Open
Abstract
Genetic regulation of gene expression, revealed by expression quantitative trait loci (eQTLs), exhibits complex patterns of tissue-specific effects. Characterization of these patterns may allow us to better understand mechanisms of gene regulation and disease etiology. We develop a constrained matrix factorization model, sn-spMF, to learn patterns of tissue-sharing and apply it to 49 human tissues from the Genotype-Tissue Expression (GTEx) project. The learned factors reflect tissues with known biological similarity and identify transcription factors that may mediate tissue-specific effects. sn-spMF, available at https://github.com/heyuan7676/ts_eQTLs , can be applied to learn biologically interpretable patterns of eQTL tissue-specificity and generate testable mechanistic hypotheses.
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Affiliation(s)
- Yuan He
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, 21218, MD, USA
| | - Surya B Chhetri
- HudsonAlpha Institute for Biotechnology, Huntsville, 35806, AL, USA
- Current Address: Department of Biomedical Engineering, Johns Hopkins University, Baltimore, 21218, MD, USA
| | - Marios Arvanitis
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, 21218, MD, USA
- Department of Medicine, Division of Cardiology, Johns Hopkins University, Baltimore, 21287, MD, USA
| | - Kaushik Srinivasan
- Department of Computer Science, Johns Hopkins University, Baltimore, 21218, MD, USA
| | - François Aguet
- The Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | | | - Alvaro N Barbeira
- Section of Genetic Medicine, Department of Medicine, The University of Chicago, Chicago, IL, USA
| | - Rodrigo Bonazzola
- Section of Genetic Medicine, Department of Medicine, The University of Chicago, Chicago, IL, USA
| | - Hae Kyung Im
- Section of Genetic Medicine, Department of Medicine, The University of Chicago, Chicago, IL, USA
| | - Christopher D Brown
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, 19104, PA, USA.
| | - Alexis Battle
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, 21218, MD, USA.
- Department of Computer Science, Johns Hopkins University, Baltimore, 21218, MD, USA.
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26
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Jakobson CM, Jarosz DF. What Has a Century of Quantitative Genetics Taught Us About Nature's Genetic Tool Kit? Annu Rev Genet 2020; 54:439-464. [PMID: 32897739 DOI: 10.1146/annurev-genet-021920-102037] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
The complexity of heredity has been appreciated for decades: Many traits are controlled not by a single genetic locus but instead by polymorphisms throughout the genome. The importance of complex traits in biology and medicine has motivated diverse approaches to understanding their detailed genetic bases. Here, we focus on recent systematic studies, many in budding yeast, which have revealed that large numbers of all kinds of molecular variation, from noncoding to synonymous variants, can make significant contributions to phenotype. Variants can affect different traits in opposing directions, and their contributions can be modified by both the environment and the epigenetic state of the cell. The integration of prospective (synthesizing and analyzing variants) and retrospective (examining standing variation) approaches promises to reveal how natural selection shapes quantitative traits. Only by comprehensively understanding nature's genetic tool kit can we predict how phenotypes arise from the complex ensembles of genetic variants in living organisms.
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Affiliation(s)
- Christopher M Jakobson
- Department of Chemical and Systems Biology, Stanford University School of Medicine, Stanford, California 94305, USA;
| | - Daniel F Jarosz
- Department of Chemical and Systems Biology, Stanford University School of Medicine, Stanford, California 94305, USA; .,Department of Developmental Biology, Stanford University School of Medicine, Stanford, California 94305, USA
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27
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Allum F, Grundberg E. Capturing functional epigenomes for insight into metabolic diseases. Mol Metab 2020; 38:100936. [PMID: 32199819 PMCID: PMC7300388 DOI: 10.1016/j.molmet.2019.12.016] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/27/2019] [Revised: 12/23/2019] [Accepted: 12/30/2019] [Indexed: 12/12/2022] Open
Abstract
BACKGROUND Metabolic diseases such as obesity are known to be driven by both environmental and genetic factors. Although genome-wide association studies of common variants and their impact on complex traits have provided some biological insight into disease etiology, identified genetic variants have been found to contribute only a small proportion to disease heritability, and to map mainly to non-coding regions of the genome. To link variants to function, association studies of cellular traits, such as epigenetic marks, in disease-relevant tissues are commonly applied. SCOPE OF THE REVIEW We review large-scale efforts to generate genome-wide maps of coordinated epigenetic marks and their utility in complex disease dissection with a focus on DNA methylation. We contrast DNA methylation profiling methods and discuss the advantages of using targeted methods for single-base resolution assessments of methylation levels across tissue-specific regulatory regions to deepen our understanding of contributing factors leading to complex diseases. MAJOR CONCLUSIONS Large-scale assessments of DNA methylation patterns in metabolic disease-linked study cohorts have provided insight into the impact of variable epigenetic variants in disease etiology. In-depth profiling of epigenetic marks at regulatory regions, particularly at tissue-specific elements, will be key to dissect the genetic and environmental components contributing to metabolic disease onset and progression.
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Affiliation(s)
- Fiona Allum
- Department of Human Genetics, McGill University, Montréal, Québec, H3A 0C7, Canada; McGill University and Genome Quebec Innovation Centre, Montréal, Québec, H3A 0G1, Canada
| | - Elin Grundberg
- Children's Mercy Kansas City, Kansas City, MO, 64108, United States.
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28
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Wu Y, Broadaway KA, Raulerson CK, Scott LJ, Pan C, Ko A, He A, Tilford C, Fuchsberger C, Locke AE, Stringham HM, Jackson AU, Narisu N, Kuusisto J, Pajukanta P, Collins FS, Boehnke M, Laakso M, Lusis AJ, Civelek M, Mohlke KL. Colocalization of GWAS and eQTL signals at loci with multiple signals identifies additional candidate genes for body fat distribution. Hum Mol Genet 2020; 28:4161-4172. [PMID: 31691812 DOI: 10.1093/hmg/ddz263] [Citation(s) in RCA: 32] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2019] [Revised: 10/23/2019] [Accepted: 10/30/2019] [Indexed: 12/22/2022] Open
Abstract
Integration of genome-wide association study (GWAS) signals with expression quantitative trait loci (eQTL) studies enables identification of candidate genes. However, evaluating whether nearby signals may share causal variants, termed colocalization, is affected by the presence of allelic heterogeneity, different variants at the same locus impacting the same phenotype. We previously identified eQTL in subcutaneous adipose tissue from 770 participants in the Metabolic Syndrome in Men (METSIM) study and detected 15 eQTL signals that colocalized with GWAS signals for waist-hip ratio adjusted for body mass index (WHRadjBMI) from the Genetic Investigation of Anthropometric Traits consortium. Here, we reevaluated evidence of colocalization using two approaches, conditional analysis and the Bayesian test COLOC, and show that providing COLOC with approximate conditional summary statistics at multi-signal GWAS loci can reconcile disagreements in colocalization classification between the two tests. Next, we performed conditional analysis on the METSIM subcutaneous adipose tissue data to identify conditionally distinct or secondary eQTL signals. We used the two approaches to test for colocalization with WHRadjBMI GWAS signals and evaluated the differences in colocalization classification between the two tests. Through these analyses, we identified four GWAS signals colocalized with secondary eQTL signals for FAM13A, SSR3, GRB14 and FMO1. Thus, at loci with multiple eQTL and/or GWAS signals, analyzing each signal independently enabled additional candidate genes to be identified.
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Affiliation(s)
- Ying Wu
- Department of Genetics, University of North Carolina, Chapel Hill, NC 27599, USA
| | - K Alaine Broadaway
- Department of Genetics, University of North Carolina, Chapel Hill, NC 27599, USA
| | - Chelsea K Raulerson
- Department of Genetics, University of North Carolina, Chapel Hill, NC 27599, USA
| | - Laura J Scott
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI 48109, USA
| | - Calvin Pan
- Department of Human Genetics, David Geffen School of Medicine at UCLA, Los Angeles, CA 90095, USA
| | - Arthur Ko
- Department of Human Genetics, David Geffen School of Medicine at UCLA, Los Angeles, CA 90095, USA
| | - Aiqing He
- Bristol-Myers Squibb, Pennington, NJ 08534, USA
| | | | - Christian Fuchsberger
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI 48109, USA.,Center for Biomedicine, European Academy of Bolzano/Bozen, University of Lübeck, Bolzano/Bozen, Italy
| | - Adam E Locke
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI 48109, USA.,McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO 63108, USA
| | - Heather M Stringham
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI 48109, USA
| | - Anne U Jackson
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI 48109, USA
| | - Narisu Narisu
- National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - Johanna Kuusisto
- Department of Medicine, University of Eastern Finland and Kuopio University Hospital, Kuopio 70210, Finland
| | - Päivi Pajukanta
- Department of Human Genetics, David Geffen School of Medicine at UCLA, Los Angeles, CA 90095, USA
| | - Francis S Collins
- National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - Michael Boehnke
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI 48109, USA
| | - Markku Laakso
- Department of Medicine, University of Eastern Finland and Kuopio University Hospital, Kuopio 70210, Finland
| | - Aldons J Lusis
- Department of Human Genetics, David Geffen School of Medicine at UCLA, Los Angeles, CA 90095, USA
| | - Mete Civelek
- Department of Human Genetics, David Geffen School of Medicine at UCLA, Los Angeles, CA 90095, USA.,Department of Biomedical Engineering, University of Virginia, Charlottesville, VA 22904, USA
| | - Karen L Mohlke
- Department of Genetics, University of North Carolina, Chapel Hill, NC 27599, USA
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Abstract
Cardiovascular diseases are the leading cause of death worldwide. Complex diseases with highly heterogenous disease progression among patient populations, cardiovascular diseases feature multifactorial contributions from both genetic and environmental stressors. Despite significant effort utilizing multiple approaches from molecular biology to genome-wide association studies, the genetic landscape of cardiovascular diseases, particularly for the nonfamilial forms of heart failure, is still poorly understood. In the past decade, systems-level approaches based on omics technologies have become an important approach for the study of complex traits in large populations. These advances create opportunities to integrate genetic variation with other biological layers to identify and prioritize candidate genes, understand pathogenic pathways, and elucidate gene-gene and gene-environment interactions. In this review, we will highlight some of the recent progress made using systems genetics approaches to uncover novel mechanisms and molecular bases of cardiovascular pathophysiological manifestations. The key technology and data analysis platforms necessary to implement systems genetics will be described, and the current major challenges and future directions will also be discussed. For complex cardiovascular diseases, such as heart failure, systems genetics represents a powerful strategy to obtain mechanistic insights and to develop individualized diagnostic and therapeutic regiments, paving the way for precision cardiovascular medicine.
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Affiliation(s)
- Christoph D. Rau
- Departments of Anesthesiology, Medicine, Physiology
- Current address: Department of Genetics, University of North Carolina School of Medicine, Chapel Hill, NC 27599
| | - Aldons J. Lusis
- Department of Human Genetics and Medicine, David Geffen School of Medicine, University of California, Los Angeles, CA 90095
| | - Yibin Wang
- Departments of Anesthesiology, Medicine, Physiology
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30
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Gene expression changes in lymphoblastoid cell lines and primary B cells by dexamethasone. Pharmacogenet Genomics 2020; 29:58-64. [PMID: 30562215 DOI: 10.1097/fpc.0000000000000365] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
BACKGROUND Human Epstein-Barr virus-transformed lymphoblastoid cell lines (LCLs) have been thought to be a useful model system for pharmacogenomics studies. The purpose of this study was to determine the effect of Epstein-Barr virus transformation on gene expression changes by dexamethasone (Dex) in LCLs and primary B cells (PBCs) derived from the same individuals. PATIENTS AND METHODS We prepared LCLs and purified PBCs from the same six male donors participating in the Childhood Asthma Management Program clinical trial, and compared mRNA profiles after 6 h incubation with Dex (10 mol/l) or sham buffer. We assessed differential expression and put the list of differentially expressed genes into the web interface of ConsensusPathDB to find the pathway-level interpretation of our genes specified. As a supplementary analysis, we looked at the expression of the Dex-regulated (inducing or repressing) genes in treatment-naive PBCs and LCLs (pre-Dex treatment) from the GSE30916 dataset. RESULTS By hierarchical clustering, we found clustering of probes by cell types but not by individuals irrespective of Dex treatment. We observed that the Dex-regulated genes significantly overlapped in PBCs and LCLs. In addition, the expression of these genes showed significant correlations between treatment-naive PBCs and LCLs. Common genes showing significantly decreased expressions by the Dex treatment in both cells were enriched in immune responses and proinflammatory signaling pathways. CONCLUSION Taken together, these results suggest the uses of LCLs are representative of the primary biologic effects of corticosteroids treatment.
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Association of complement C3d receptor 2 genotypes with the acquisition of HIV infection in a trial of recombinant glycoprotein 120 vaccine. AIDS 2020; 34:25-32. [PMID: 31634193 DOI: 10.1097/qad.0000000000002401] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
OBJECTIVES Complement C3d receptor 2 (CR2) is the main receptor for complement protein C3d and plays an important role in adaptive immune responses. CR2 genetic variants are associated with susceptibility to systemic lupus erythematosus as well as to HIV-1 infection. In addition, CR2 function can be subverted by HIV-1 for an efficient entry into target cells; in a process known as antibody-dependent enhancement of viral infection. We sought to determine the association between CR2 gene variants with HIV-1 acquisition after vaccination with recombinant gp120 protein (Vax004 clinical trial). DESIGN AND METHODS This is a retrospective cross-sectional study, comprising male volunteers of European ancestry including infected (n = 273) and uninfected (n = 402) vaccinees and placebo, who were genotyped for three single nucleotide polymorphisms (SNPs) in the CR2 gene region. RESULTS An interaction was observed between the baseline sexual behavior and the SNP rs3813946 for higher risk of infection in vacinees (interaction term P = 0.02). This SNP was associated with increased susceptibility to HIV-1 infection after vaccination in volunteers with low behavioral risk odds ratio (95% confidence interval): 5.5 (1.4-21.7) P = 0.006 but not vaccinees with high behavioral risk or volunteers given placebo (P = 0.7). Moreover, CR2 genotype was strongly associated with the rate of HIV-1 acquisition after vaccination in low-risk volunteers [hazard odds ratio (95% confidence interval): 3.3 (1.6-7.0), P = 0.001]. CONCLUSION The current study suggests that CR2 may play a role in HIV-1 acquisition after vaccination with rgp120 proteins.
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Abstract
Network-based approach is rapidly emerging as a promising strategy to integrate and interpret different -omics datasets, including metabolomics. The first section of this chapter introduces the current progresses and main concepts in multi-omics integration. The second section provides an overview of the public resources available for creation of biological networks. The third section describes three common application scenarios including subnetwork identification, network-based enrichment analysis, and systems metabolomics. The section four introduces the concept of hierarchical community network analysis. The section five discusses different tools for network visualization. The chapter ends with a future perspective on multi-omics integration.
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Affiliation(s)
- Guangyan Zhou
- Institute of Parasitology, McGill University, Montreal, QC, Canada
| | - Shuzhao Li
- Department of Medicine, Emory University School of Medicine, Atlanta, GA, USA
| | - Jianguo Xia
- Institute of Parasitology, McGill University, Montreal, QC, Canada. .,Department of Animal Science, McGill University, Montreal, QC, Canada. .,Department of Microbiology and Immunology, McGill University, Montreal, QC, Canada. .,Department of Human Genetics, McGill University, Montreal, QC, Canada.
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33
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Vijayakrishnan J, Qian M, Studd JB, Yang W, Kinnersley B, Law PJ, Broderick P, Raetz EA, Allan J, Pui CH, Vora A, Evans WE, Moorman A, Yeoh A, Yang W, Li C, Bartram CR, Mullighan CG, Zimmerman M, Hunger SP, Schrappe M, Relling MV, Stanulla M, Loh ML, Houlston RS, Yang JJ. Identification of four novel associations for B-cell acute lymphoblastic leukaemia risk. Nat Commun 2019; 10:5348. [PMID: 31767839 PMCID: PMC6877561 DOI: 10.1038/s41467-019-13069-6] [Citation(s) in RCA: 51] [Impact Index Per Article: 10.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2019] [Accepted: 10/17/2019] [Indexed: 12/12/2022] Open
Abstract
There is increasing evidence for a strong inherited genetic basis of susceptibility to acute lymphoblastic leukaemia (ALL) in children. To identify new risk variants for B-cell ALL (B-ALL) we conducted a meta-analysis with four GWAS (genome-wide association studies), totalling 5321 cases and 16,666 controls of European descent. We herein describe novel risk loci for B-ALL at 9q21.31 (rs76925697, P = 2.11 × 10-8), for high-hyperdiploid ALL at 5q31.1 (rs886285, P = 1.56 × 10-8) and 6p21.31 (rs210143 in BAK1, P = 2.21 × 10-8), and ETV6-RUNX1 ALL at 17q21.32 (rs10853104 in IGF2BP1, P = 1.82 × 10-8). Particularly notable are the pleiotropic effects of the BAK1 variant on multiple haematological malignancies and specific effects of IGF2BP1 on ETV6-RUNX1 ALL evidenced by both germline and somatic genomic analyses. Integration of GWAS signals with transcriptomic/epigenomic profiling and 3D chromatin interaction data for these leukaemia risk loci suggests deregulation of B-cell development and the cell cycle as central mechanisms governing genetic susceptibility to ALL.
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Affiliation(s)
- Jayaram Vijayakrishnan
- Division of Genetics and Epidemiology, The Institute of Cancer Research, 15 Cotswold Road, Sutton, Surrey, SM2 5NG, UK
| | - Maoxiang Qian
- Department of Pharmaceutical Sciences, St. Jude Children's Research Hospital, Memphis, Tennessee, USA
- Children's Hospital and Institutes of Biomedical Sciences, Fudan University, Shanghai, China
| | - James B Studd
- Division of Genetics and Epidemiology, The Institute of Cancer Research, 15 Cotswold Road, Sutton, Surrey, SM2 5NG, UK
| | - Wenjian Yang
- Department of Pharmaceutical Sciences, St. Jude Children's Research Hospital, Memphis, Tennessee, USA
| | - Ben Kinnersley
- Division of Genetics and Epidemiology, The Institute of Cancer Research, 15 Cotswold Road, Sutton, Surrey, SM2 5NG, UK
| | - Philip J Law
- Division of Genetics and Epidemiology, The Institute of Cancer Research, 15 Cotswold Road, Sutton, Surrey, SM2 5NG, UK
| | - Peter Broderick
- Division of Genetics and Epidemiology, The Institute of Cancer Research, 15 Cotswold Road, Sutton, Surrey, SM2 5NG, UK
| | - Elizabeth A Raetz
- Division of Pediatric Hematology and Oncology, New York University Langone Health, New York, New York, USA
| | - James Allan
- Northern Institute for Cancer Research, Newcastle University, Newcastle upon Tyne, NE2 4HH, UK
| | - Ching-Hon Pui
- Department of Oncology, St. Jude Children's Research Hospital, Memphis, Tennessee, USA
- Hematological Malignancies Program, St. Jude Children's Research Hospital, Memphis, Tennessee, USA
| | | | - William E Evans
- Department of Pharmaceutical Sciences, St. Jude Children's Research Hospital, Memphis, Tennessee, USA
- Hematological Malignancies Program, St. Jude Children's Research Hospital, Memphis, Tennessee, USA
| | - Anthony Moorman
- Wolfson Childhood Cancer Research Centre, Northern Institute for Cancer Research, Newcastle University, Newcastle upon Tyne, NE1 7RU, UK
| | - Allen Yeoh
- Centre for Translational Research in Acute Leukaemia, Department of Paediatrics, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- VIVA-University Children's Cancer Centre, Khoo Teck Puat-National University Children's Medical Institute, National University Hospital, National University Health System, Singapore, Singapore
| | - Wentao Yang
- Department of Pharmaceutical Sciences, St. Jude Children's Research Hospital, Memphis, Tennessee, USA
| | - Chunliang Li
- Department of Tumor Cell Biology, St. Jude Children's Research Hospital, Memphis, Tennessee, USA
| | - Claus R Bartram
- Institute of Human Genetics, University Hospital, Heidelberg, Germany
| | - Charles G Mullighan
- Department of Oncology, St. Jude Children's Research Hospital, Memphis, Tennessee, USA
- Hematological Malignancies Program, St. Jude Children's Research Hospital, Memphis, Tennessee, USA
- Department of Pathology, St. Jude Children's Research Hospital, Memphis, Tennessee, USA
| | - Martin Zimmerman
- Department of Paediatric Haematology and Oncology, Hannover Medical School, 30625, Hannover, Germany
| | - Stephen P Hunger
- Department of Paediatrics and Centre for Childhood Cancer Research, Children's Hospital of Philadelphia and the Perelman School of Medicine at The University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Martin Schrappe
- Department of Paediatrics, University Medical Centre Schleswig-Holstein, Kiel, Germany
| | - Mary V Relling
- Department of Pharmaceutical Sciences, St. Jude Children's Research Hospital, Memphis, Tennessee, USA
- Hematological Malignancies Program, St. Jude Children's Research Hospital, Memphis, Tennessee, USA
| | - Martin Stanulla
- Department of Paediatric Haematology and Oncology, Hannover Medical School, 30625, Hannover, Germany
| | - Mignon L Loh
- Department of Pediatrics, Benioff Children's Hospital and the Helen Diller Family Comprehensive Cancer Center, University of California San Francisco, San Francisco, California, USA
| | - Richard S Houlston
- Division of Genetics and Epidemiology, The Institute of Cancer Research, 15 Cotswold Road, Sutton, Surrey, SM2 5NG, UK.
| | - Jun J Yang
- Department of Pharmaceutical Sciences, St. Jude Children's Research Hospital, Memphis, Tennessee, USA.
- Department of Oncology, St. Jude Children's Research Hospital, Memphis, Tennessee, USA.
- Hematological Malignancies Program, St. Jude Children's Research Hospital, Memphis, Tennessee, USA.
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34
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Benson KK, Hu W, Weller AH, Bennett AH, Chen ER, Khetarpal SA, Yoshino S, Bone WP, Wang L, Rabinowitz JD, Voight BF, Soccio RE. Natural human genetic variation determines basal and inducible expression of PM20D1, an obesity-associated gene. Proc Natl Acad Sci U S A 2019; 116:23232-23242. [PMID: 31659023 PMCID: PMC6859347 DOI: 10.1073/pnas.1913199116] [Citation(s) in RCA: 26] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023] Open
Abstract
PM20D1 is a candidate thermogenic enzyme in mouse fat, with its expression cold-induced and enriched in brown versus white adipocytes. Thiazolidinedione (TZD) antidiabetic drugs, which activate the peroxisome proliferator-activated receptor-γ (PPARγ) nuclear receptor, are potent stimuli for adipocyte browning yet fail to induce Pm20d1 expression in mouse adipocytes. In contrast, PM20D1 is one of the most strongly TZD-induced transcripts in human adipocytes, although not in cells from all individuals. Two putative PPARγ binding sites exist near the gene's transcription start site (TSS) in human but not mouse adipocytes. The -4 kb upstream site falls in a segmental duplication of a nearly identical intronic region +2.5 kb downstream of the TSS, and this duplication occurred in the primate lineage and not in other mammals, like mice. PPARγ binding and gene activation occur via this upstream duplicated site, thus explaining the species difference. Furthermore, this functional upstream PPARγ site exhibits genetic variation among people, with 1 SNP allele disrupting a PPAR response element and giving less activation by PPARγ and TZDs. In addition to this upstream variant that determines PPARγ regulation of PM20D1 in adipocytes, distinct variants downstream of the TSS have strong effects on PM20D1 expression in human fat as well as other tissues. A haplotype of 7 tightly linked downstream SNP alleles is associated with very low PMD201 expression and correspondingly high DNA methylation at the TSS. These PM20D1 low-expression variants may account for human genetic associations in this region with obesity as well as neurodegenerative diseases.
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Affiliation(s)
- Kiara K Benson
- Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104
- Division of Endocrinology, Diabetes, and Metabolism, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104
- Institute for Diabetes, Obesity, and Metabolism, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104
| | - Wenxiang Hu
- Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104
- Division of Endocrinology, Diabetes, and Metabolism, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104
- Institute for Diabetes, Obesity, and Metabolism, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104
| | - Angela H Weller
- Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104
- Division of Endocrinology, Diabetes, and Metabolism, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104
- Institute for Diabetes, Obesity, and Metabolism, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104
| | - Alexis H Bennett
- Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104
- Division of Endocrinology, Diabetes, and Metabolism, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104
- Institute for Diabetes, Obesity, and Metabolism, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104
| | - Eric R Chen
- Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104
- Division of Endocrinology, Diabetes, and Metabolism, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104
- Institute for Diabetes, Obesity, and Metabolism, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104
| | - Sumeet A Khetarpal
- Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104
- Division of Endocrinology, Diabetes, and Metabolism, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104
- Institute for Diabetes, Obesity, and Metabolism, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104
| | - Satoshi Yoshino
- Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104
- Division of Endocrinology, Diabetes, and Metabolism, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104
- Institute for Diabetes, Obesity, and Metabolism, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104
| | - William P Bone
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104
- Department of Systems Pharmacology and Translational Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104
- Institute of Translational Medicine and Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104
| | - Lin Wang
- Department of Chemistry, Princeton University, Princeton, NJ 08544
- Lewis Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ 08544
| | - Joshua D Rabinowitz
- Department of Chemistry, Princeton University, Princeton, NJ 08544
- Lewis Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ 08544
| | - Benjamin F Voight
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104
- Department of Systems Pharmacology and Translational Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104
- Institute of Translational Medicine and Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104
| | - Raymond E Soccio
- Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104;
- Division of Endocrinology, Diabetes, and Metabolism, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104
- Institute for Diabetes, Obesity, and Metabolism, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104
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Walker RL, Ramaswami G, Hartl C, Mancuso N, Gandal MJ, de la Torre-Ubieta L, Pasaniuc B, Stein JL, Geschwind DH. Genetic Control of Expression and Splicing in Developing Human Brain Informs Disease Mechanisms. Cell 2019; 179:750-771.e22. [PMID: 31626773 PMCID: PMC8963725 DOI: 10.1016/j.cell.2019.09.021] [Citation(s) in RCA: 145] [Impact Index Per Article: 29.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2018] [Revised: 06/06/2019] [Accepted: 09/20/2019] [Indexed: 02/08/2023]
Abstract
Tissue-specific regulatory regions harbor substantial genetic risk for disease. Because brain development is a critical epoch for neuropsychiatric disease susceptibility, we characterized the genetic control of the transcriptome in 201 mid-gestational human brains, identifying 7,962 expression quantitative trait loci (eQTL) and 4,635 spliceQTL (sQTL), including several thousand prenatal-specific regulatory regions. We show that significant genetic liability for neuropsychiatric disease lies within prenatal eQTL and sQTL. Integration of eQTL and sQTL with genome-wide association studies (GWAS) via transcriptome-wide association identified dozens of novel candidate risk genes, highlighting shared and stage-specific mechanisms in schizophrenia (SCZ). Gene network analysis revealed that SCZ and autism spectrum disorder (ASD) affect distinct developmental gene co-expression modules. Yet, in each disorder, common and rare genetic variation converges within modules, which in ASD implicates superficial cortical neurons. More broadly, these data, available as a web browser and our analyses, demonstrate the genetic mechanisms by which developmental events have a widespread influence on adult anatomical and behavioral phenotypes.
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Affiliation(s)
- Rebecca L Walker
- Department of Neurology, Center for Autism Research and Treatment, Semel Institute, David Geffen School of Medicine, University of California, Los Angeles, 695 Charles E. Young Drive South, Los Angeles, CA 90095, USA; Program in Neurobehavioral Genetics, Semel Institute, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA; Interdepartmental Program in Bioinformatics, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Gokul Ramaswami
- Department of Neurology, Center for Autism Research and Treatment, Semel Institute, David Geffen School of Medicine, University of California, Los Angeles, 695 Charles E. Young Drive South, Los Angeles, CA 90095, USA
| | - Christopher Hartl
- Department of Neurology, Center for Autism Research and Treatment, Semel Institute, David Geffen School of Medicine, University of California, Los Angeles, 695 Charles E. Young Drive South, Los Angeles, CA 90095, USA; Interdepartmental Program in Bioinformatics, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Nicholas Mancuso
- Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90024, USA
| | - Michael J Gandal
- Department of Neurology, Center for Autism Research and Treatment, Semel Institute, David Geffen School of Medicine, University of California, Los Angeles, 695 Charles E. Young Drive South, Los Angeles, CA 90095, USA; Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA; Department of Psychiatry, Semel Institute, David Geffen School of Medicine, University of California, Los Angeles, 695 Charles E. Young Drive South, Los Angeles, CA 90095, USA
| | - Luis de la Torre-Ubieta
- Department of Neurology, Center for Autism Research and Treatment, Semel Institute, David Geffen School of Medicine, University of California, Los Angeles, 695 Charles E. Young Drive South, Los Angeles, CA 90095, USA; Department of Psychiatry, Semel Institute, David Geffen School of Medicine, University of California, Los Angeles, 695 Charles E. Young Drive South, Los Angeles, CA 90095, USA
| | - Bogdan Pasaniuc
- Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90024, USA; Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Jason L Stein
- Department of Genetics and UNC Neuroscience Center, University of North Carolina, Chapel Hill, NC 27599, USA
| | - Daniel H Geschwind
- Department of Neurology, Center for Autism Research and Treatment, Semel Institute, David Geffen School of Medicine, University of California, Los Angeles, 695 Charles E. Young Drive South, Los Angeles, CA 90095, USA; Program in Neurobehavioral Genetics, Semel Institute, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA; Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA; Department of Psychiatry, Semel Institute, David Geffen School of Medicine, University of California, Los Angeles, 695 Charles E. Young Drive South, Los Angeles, CA 90095, USA.
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36
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Vandiedonck C. Genetic association of molecular traits: A help to identify causative variants in complex diseases. Clin Genet 2019; 93:520-532. [PMID: 29194587 DOI: 10.1111/cge.13187] [Citation(s) in RCA: 34] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2017] [Revised: 11/24/2017] [Accepted: 11/27/2017] [Indexed: 12/14/2022]
Abstract
In the past 15 years, major progresses have been made in the understanding of the genetic basis of regulation of gene expression. These new insights have revolutionized our approach to resolve the genetic variation underlying complex diseases. Gene transcript levels were the first expression phenotypes that were studied. They are heritable and therefore amenable to genome-wide association studies. The genetic variants that modulate them are called expression quantitative trait loci. Their study has been extended to other molecular quantitative trait loci (molQTLs) that regulate gene expression at the various levels, from chromatin state to cellular responses. Altogether, these studies have generated a wealth of basic information on the genome-wide patterns of gene expression and their inter-individual variation. Most importantly, molQTLs have become an invaluable asset in the genetic study of complex diseases. Although the identification of the disease-causing variants on the basis of their overlap with molQTLs requires caution, molQTLs can help to prioritize the relevant candidate gene(s) in the disease-associated regions and bring a functional interpretation of the associated variants, therefore, bridging the gap between genotypes and clinical phenotypes.
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Affiliation(s)
- C Vandiedonck
- Univ Paris Diderot, Sorbonne Paris Cité, Paris, France
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Chen Z, Wen W, Beeghly-Fadiel A, Shu XO, Díez-Obrero V, Long J, Bao J, Wang J, Liu Q, Cai Q, Moreno V, Zheng W, Guo X. Identifying Putative Susceptibility Genes and Evaluating Their Associations with Somatic Mutations in Human Cancers. Am J Hum Genet 2019; 105:477-492. [PMID: 31402092 PMCID: PMC6731359 DOI: 10.1016/j.ajhg.2019.07.006] [Citation(s) in RCA: 29] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2019] [Accepted: 07/10/2019] [Indexed: 12/23/2022] Open
Abstract
Genome-wide association studies (GWASs) have identified hundreds of genetic risk variants for human cancers. However, target genes for the majority of risk loci remain largely unexplored. It is also unclear whether GWAS risk-loci-associated genes contribute to mutational signatures and tumor mutational burden (TMB) in cancer tissues. We systematically conducted cis-expression quantitative trait loci (cis-eQTL) analyses for 294 GWAS-identified variants for six major types of cancer-colorectal, lung, ovary, prostate, pancreas, and melanoma-by using transcriptome data from the Genotype-Tissue Expression (GTEx) Project, the Cancer Genome Atlas (TCGA), and other public data sources. By using integrative analysis strategies, we identified 270 candidate target genes, including 99 with previously unreported associations, for six cancer types. By analyzing functional genomic data, our results indicate that 180 genes (66.7% of 270) had evidence of cis-regulation by putative functional variants via proximal promoter or distal enhancer-promoter interactions. Together with our previously reported associations for breast cancer risk, our results show that 24 genes are shared by at least two cancer types, including four genes for both breast and ovarian cancer. By integrating mutation data from TCGA, we found that expression levels of 33 and 66 putative susceptibility genes were associated with specific mutational signatures and TMB of cancer-driver genes, respectively, at a Bonferroni-corrected p < 0.05. Together, these findings provide further insight into our understanding of how genetic risk variants might contribute to carcinogenesis through the regulation of susceptibility genes that are related to the biogenesis of somatic mutations.
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Affiliation(s)
- Zhishan Chen
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, and Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine, Nashville, TN 37203, USA
| | - Wanqing Wen
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, and Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine, Nashville, TN 37203, USA
| | - Alicia Beeghly-Fadiel
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, and Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine, Nashville, TN 37203, USA
| | - Xiao-Ou Shu
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, and Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine, Nashville, TN 37203, USA
| | - Virginia Díez-Obrero
- Unit of Biomarkers and Susceptibility, Oncology Data Analytics Program, Catalan Institute of Oncology, Barcelona 08908, Spain; Colorectal Cancer Group, ONCOBELL Program, Bellvitge Biomedical Research Institute, Barcelona 08908, Spain; Consortium for Biomedical Research in Epidemiology and Public Health, Barcelona 08908, Spain; Department of Clinical Sciences, Faculty of Medicine, University of Barcelona, Barcelona 08908, Spain
| | - Jirong Long
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, and Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine, Nashville, TN 37203, USA
| | - Jiandong Bao
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, and Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine, Nashville, TN 37203, USA; College of Life Sciences, Fujian Agriculture and Forestry University, Fuzhou 350002, Fujian, China
| | - Jing Wang
- Center for Quantitative Sciences, Vanderbilt University School of Medicine, Nashville, TN 37232, USA
| | - Qi Liu
- Center for Quantitative Sciences, Vanderbilt University School of Medicine, Nashville, TN 37232, USA
| | - Qiuyin Cai
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, and Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine, Nashville, TN 37203, USA
| | - Victor Moreno
- Unit of Biomarkers and Susceptibility, Oncology Data Analytics Program, Catalan Institute of Oncology, Barcelona 08908, Spain; Colorectal Cancer Group, ONCOBELL Program, Bellvitge Biomedical Research Institute, Barcelona 08908, Spain; Consortium for Biomedical Research in Epidemiology and Public Health, Barcelona 08908, Spain; Department of Clinical Sciences, Faculty of Medicine, University of Barcelona, Barcelona 08908, Spain
| | - Wei Zheng
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, and Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine, Nashville, TN 37203, USA
| | - Xingyi Guo
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, and Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine, Nashville, TN 37203, USA.
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Open Chromatin Profiling in Adipose Tissue Marks Genomic Regions with Functional Roles in Cardiometabolic Traits. G3-GENES GENOMES GENETICS 2019; 9:2521-2533. [PMID: 31186305 PMCID: PMC6686932 DOI: 10.1534/g3.119.400294] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Identifying the regulatory mechanisms of genome-wide association study (GWAS) loci affecting adipose tissue has been restricted due to limited characterization of adipose transcriptional regulatory elements. We profiled chromatin accessibility in three frozen human subcutaneous adipose tissue needle biopsies and preadipocytes and adipocytes from the Simpson Golabi-Behmel Syndrome (SGBS) cell strain using an assay for transposase-accessible chromatin (ATAC-seq). We identified 68,571 representative accessible chromatin regions (peaks) across adipose tissue samples (FDR < 5%). GWAS loci for eight cardiometabolic traits were enriched in these peaks (P < 0.005), with the strongest enrichment for waist-hip ratio. Of 110 recently described cardiometabolic GWAS loci colocalized with adipose tissue eQTLs, 59 loci had one or more variants overlapping an adipose tissue peak. Annotated variants at the SNX10 waist-hip ratio locus and the ATP2A1-SH2B1 body mass index locus showed allelic differences in regulatory assays. These adipose tissue accessible chromatin regions elucidate genetic variants that may alter adipose tissue function to impact cardiometabolic traits.
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Timasheva YR, Balkhiyarova ZR, Nasibullin TR, Avzaletdinova DS, Morugova TV, Mustafina OE, Prokopenko I. Multilocus associations of inflammatory genes with the risk of type 1 diabetes. Gene 2019; 707:1-8. [PMID: 31054364 DOI: 10.1016/j.gene.2019.04.085] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2018] [Revised: 04/10/2019] [Accepted: 04/30/2019] [Indexed: 10/26/2022]
Abstract
BACKGROUND Genome-wide association studies have captured a large proportion of genetic variation related to type 1 diabetes mellitus (T1D). However, most of these studies are performed in populations of European ancestry and therefore the disease risk estimations can be inaccurate when extrapolated to other world populations. METHODS We conducted a case-control study in 1866 individuals from the three major populations of the Republic of Bashkortostan (Russians, Tatars, and Bashkirs) in Russian Federation, using single-locus and multilocus approach to identify genetic predictors of T1D. RESULTS We found that LTA rs909253 and TNF rs1800629 polymorphisms were associated with T1D in the group of Tatars. Meta-analysis of the association study results in the three ethnic groups has confirmed the association between the T1D risk and LTA rs909253 genetic variant. LTA rs909253 and TNF rs1800629 loci were also featured in combinations most significantly associated with T1D. CONCLUSION Our findings suggest that LTA rs909253 and TNF rs1800629 polymorphisms are associated with the risk of T1D both independently and in combination with polymorphic markers in other inflammatory genes, and the analysis of multi-allelic combinations provides valuable insight in the study of polygenic traits.
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Affiliation(s)
- Yanina R Timasheva
- Institute of Biochemistry and Genetics of Ufa Federal Research Centre of Russian Academy of Sciences, 71 October Avenue, 450054 Ufa, Russian Federation; Section of Genomics of Common Disease, Department of Medicine, Imperial College London, Hammersmith Hospital Campus, Burlington Danes Building, Du Cane Road, London W12 0NN, United Kingdom; Bashkir State Medical University, 3 Lenin street, 450000 Ufa, Russian Federation.
| | - Zhanna R Balkhiyarova
- Section of Genomics of Common Disease, Department of Medicine, Imperial College London, Hammersmith Hospital Campus, Burlington Danes Building, Du Cane Road, London W12 0NN, United Kingdom; Bashkir State Medical University, 3 Lenin street, 450000 Ufa, Russian Federation
| | - Timur R Nasibullin
- Institute of Biochemistry and Genetics of Ufa Federal Research Centre of Russian Academy of Sciences, 71 October Avenue, 450054 Ufa, Russian Federation
| | | | - Tatiana V Morugova
- Bashkir State Medical University, 3 Lenin street, 450000 Ufa, Russian Federation
| | - Olga E Mustafina
- Institute of Biochemistry and Genetics of Ufa Federal Research Centre of Russian Academy of Sciences, 71 October Avenue, 450054 Ufa, Russian Federation
| | - Inga Prokopenko
- Section of Genomics of Common Disease, Department of Medicine, Imperial College London, Hammersmith Hospital Campus, Burlington Danes Building, Du Cane Road, London W12 0NN, United Kingdom
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40
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Immunomodulatory germline variation associated with the development of multiple primary melanoma (MPM). Sci Rep 2019; 9:10173. [PMID: 31308438 PMCID: PMC6629847 DOI: 10.1038/s41598-019-46665-z] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2019] [Accepted: 06/28/2019] [Indexed: 12/27/2022] Open
Abstract
Multiple primary melanoma (MPM) has been associated with a higher 10-year mortality risk compared to patients with single primary melanoma (SPM). Given that 3–8% of patients with SPM develop additional primary melanomas, new markers predictive of MPM risk are needed. Based on the evidence that the immune system may regulate melanoma progression, we explored whether germline genetic variants controlling the expression of 41 immunomodulatory genes modulate the risk of MPM compared to patients with SPM or healthy controls. By genotyping these 41 variants in 977 melanoma patients, we found that rs2071304, linked to the expression of SPI1, was strongly associated with MPM risk reduction (OR = 0.60; 95% CI = 0.45–0.81; p = 0.0007) when compared to patients with SPM. Furthermore, we showed that rs6695772, a variant affecting expression of BATF3, is also associated with MPM-specific survival (HR = 3.42; 95% CI = 1.57–7.42; p = 0.0019). These findings provide evidence that the genetic variation in immunomodulatory pathways may contribute to the development of secondary primary melanomas and also associates with MPM survival. The study suggests that inherited host immunity may play an important role in MPM development.
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41
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Sillo TO, Beggs AD, Morton DG, Middleton G. Mechanisms of immunogenicity in colorectal cancer. Br J Surg 2019; 106:1283-1297. [PMID: 31216061 PMCID: PMC6772007 DOI: 10.1002/bjs.11204] [Citation(s) in RCA: 28] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2019] [Revised: 03/06/2019] [Accepted: 03/12/2019] [Indexed: 12/24/2022]
Abstract
Background The immune response in cancer is increasingly understood to be important in determining clinical outcomes, including responses to cancer therapies. New insights into the mechanisms underpinning the immune microenvironment in colorectal cancer are helping to develop the role of immunotherapy and suggest targeted approaches to the management of colorectal cancer at all disease stages. Method A literature search was performed in PubMed, MEDLINE and Cochrane Library databases to identify relevant articles. This narrative review discusses the current understanding of the contributors to immunogenicity in colorectal cancer and potential applications for targeted therapies. Results Responsiveness to immunotherapy in colorectal cancer is non-uniform. Several factors, both germline and tumour-related, are potential determinants of immunogenicity in colorectal cancer. Current approaches target tumours with high immunogenicity driven by mutations in DNA mismatch repair genes. Recent work suggests a role for therapies that boost the immune response in tumours with low immunogenicity. Conclusion With the development of promising therapies to boost the innate immune response, there is significant potential for the expansion of the role of immunotherapy as an adjuvant to surgical treatment in colorectal cancer.
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Affiliation(s)
- T O Sillo
- Institute of Immunology and Immunotherapy, College of Medical and Dental Sciences, University of Birmingham, Birmingham, UK
| | - A D Beggs
- Institute of Cancer and Genomic Sciences, College of Medical and Dental Sciences, University of Birmingham, Birmingham, UK
| | - D G Morton
- Academic Department of Surgery, College of Medical and Dental Sciences, Queen Elizabeth Hospital, Birmingham, UK
| | - G Middleton
- Institute of Immunology and Immunotherapy, College of Medical and Dental Sciences, University of Birmingham, Birmingham, UK
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42
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Li G, Shabalin AA, Rusyn I, Wright FA, Nobel AB. An empirical Bayes approach for multiple tissue eQTL analysis. Biostatistics 2019; 19:391-406. [PMID: 29029013 DOI: 10.1093/biostatistics/kxx048] [Citation(s) in RCA: 33] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2016] [Accepted: 08/23/2017] [Indexed: 12/18/2022] Open
Abstract
Expression quantitative trait locus (eQTL) analyses identify genetic markers associated with the expression of a gene. Most up-to-date eQTL studies consider the connection between genetic variation and expression in a single tissue. Multi-tissue analyses have the potential to improve findings in a single tissue, and elucidate the genotypic basis of differences between tissues. In this article, we develop a hierarchical Bayesian model (MT-eQTL) for multi-tissue eQTL analysis. MT-eQTL explicitly captures patterns of variation in the presence or absence of eQTL, as well as the heterogeneity of effect sizes across tissues. We devise an efficient Expectation-Maximization (EM) algorithm for model fitting. Inferences concerning eQTL detection and the configuration of eQTL across tissues are derived from the adaptive thresholding of local false discovery rates, and maximum a posteriori estimation, respectively. We also provide theoretical justification of the adaptive procedure. We investigate the MT-eQTL model through an extensive analysis of a 9-tissue data set from the GTEx initiative.
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Affiliation(s)
- Gen Li
- Department of Biostatistics, Mailman School of Public Health, Columbia University, 722 W 168th St, New York, NY, 10032 USA
| | - Andrey A Shabalin
- Center for Biomarker Research and Personalized Medicine, Virginia Commonwealth University, 1112 East Clay Street, Richmond, VA, 23298 USA
| | - Ivan Rusyn
- Texas Veterinary Medical Center, Texas A & M University, 660 Raymond Stotzer Pkwy, College Station, TX, 77843 USA
| | - Fred A Wright
- Department of Statistics and Biological Sciences, North Carolina State University, 1 Lampe Drive, Raleigh, NC, 27695 USA
| | - Andrew B Nobel
- Department of Statistics and Operations Research, University of North Carolina at Chapel Hill, 318 E Cameron Ave, Chapel Hill, NC, 27599 USA
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43
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Zhao Y, Blencowe M, Shi X, Shu L, Levian C, Ahn IS, Kim SK, Huan T, Levy D, Yang X. Integrative Genomics Analysis Unravels Tissue-Specific Pathways, Networks, and Key Regulators of Blood Pressure Regulation. Front Cardiovasc Med 2019; 6:21. [PMID: 30931314 PMCID: PMC6423920 DOI: 10.3389/fcvm.2019.00021] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2018] [Accepted: 02/18/2019] [Indexed: 01/23/2023] Open
Abstract
Blood pressure (BP) is a highly heritable trait and a major cardiovascular disease risk factor. Genome wide association studies (GWAS) have implicated a number of susceptibility loci for systolic (SBP) and diastolic (DBP) blood pressure. However, a large portion of the heritability cannot be explained by the top GWAS loci and a comprehensive understanding of the underlying molecular mechanisms is still lacking. Here, we utilized an integrative genomics approach that leveraged multiple genetic and genomic datasets including (a) GWAS for SBP and DBP from the International Consortium for Blood Pressure (ICBP), (b) expression quantitative trait loci (eQTLs) from genetics of gene expression studies of human tissues related to BP, (c) knowledge-driven biological pathways, and (d) data-driven tissue-specific regulatory gene networks. Integration of these multidimensional datasets revealed tens of pathways and gene subnetworks in vascular tissues, liver, adipose, blood, and brain functionally associated with DBP and SBP. Diverse processes such as platelet production, insulin secretion/signaling, protein catabolism, cell adhesion and junction, immune and inflammation, and cardiac/smooth muscle contraction, were shared between DBP and SBP. Furthermore, "Wnt signaling" and "mammalian target of rapamycin (mTOR) signaling" pathways were found to be unique to SBP, while "cytokine network", and "tryptophan catabolism" to DBP. Incorporation of gene regulatory networks in our analysis informed on key regulator genes that orchestrate tissue-specific subnetworks of genes whose variants together explain ~20% of BP heritability. Our results shed light on the complex mechanisms underlying BP regulation and highlight potential novel targets and pathways for hypertension and cardiovascular diseases.
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Affiliation(s)
- Yuqi Zhao
- Department of Integrative Biology and Physiology, University of California, Los Angeles, Los Angeles, CA, United States
| | - Montgomery Blencowe
- Department of Integrative Biology and Physiology, University of California, Los Angeles, Los Angeles, CA, United States
| | - Xingyi Shi
- Department of Integrative Biology and Physiology, University of California, Los Angeles, Los Angeles, CA, United States
| | - Le Shu
- Department of Integrative Biology and Physiology, University of California, Los Angeles, Los Angeles, CA, United States
| | - Candace Levian
- Department of Integrative Biology and Physiology, University of California, Los Angeles, Los Angeles, CA, United States
| | - In Sook Ahn
- Department of Integrative Biology and Physiology, University of California, Los Angeles, Los Angeles, CA, United States
| | - Stuart K. Kim
- Department of Genetics, Department of Developmental Biology, Stanford University Medical Center, Stanford, CA, United States
| | - Tianxiao Huan
- The National Heart Lung and Blood Institute's Framingham Heart Study, Framingham, MA, United States
- The Population Sciences Branch and the Division of Intramural Research, National Heart, Lung and Blood Institute, Bethesda, MD, United States
| | - Daniel Levy
- The National Heart Lung and Blood Institute's Framingham Heart Study, Framingham, MA, United States
- The Population Sciences Branch and the Division of Intramural Research, National Heart, Lung and Blood Institute, Bethesda, MD, United States
| | - Xia Yang
- Department of Integrative Biology and Physiology, University of California, Los Angeles, Los Angeles, CA, United States
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Qin H, Niu T, Zhao J. Identifying Multi-Omics Causers and Causal Pathways for Complex Traits. Front Genet 2019; 10:110. [PMID: 30847004 PMCID: PMC6393387 DOI: 10.3389/fgene.2019.00110] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2018] [Accepted: 01/30/2019] [Indexed: 12/23/2022] Open
Abstract
The central dogma of molecular biology delineates a unidirectional causal flow, i.e., DNA → RNA → protein → trait. Genome-wide association studies, next-generation sequencing association studies, and their meta-analyses have successfully identified ~12,000 susceptibility genetic variants that are associated with a broad array of human physiological traits. However, such conventional association studies ignore the mediate causers (i.e., RNA, protein) and the unidirectional causal pathway. Such studies may not be ideally powerful; and the genetic variants identified may not necessarily be genuine causal variants. In this article, we model the central dogma by a mediate causal model and analytically prove that the more remote an omics level is from a physiological trait, the smaller the magnitude of their correlation is. Under both random and extreme sampling schemes, we numerically demonstrate that the proteome-trait correlation test is more powerful than the transcriptome-trait correlation test, which in turn is more powerful than the genotype-trait association test. In conclusion, integrating RNA and protein expressions with DNA data and causal inference are necessary to gain a full understanding of how genetic causal variants contribute to phenotype variations.
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Affiliation(s)
- Huaizhen Qin
- Department of Epidemiology, College of Public Health and Health Professions and College of Medicine, University of Florida, Gainesville, FL, United States
- Department of Global Biostatistics and Data Science, Tulane University, New Orleans, LA, United States
| | - Tianhua Niu
- Department of Global Biostatistics and Data Science, Tulane University, New Orleans, LA, United States
- Department of Biochemistry and Molecular Biology, Tulane University School Medicine, New Orleans, LA, United States
| | - Jinying Zhao
- Department of Epidemiology, College of Public Health and Health Professions and College of Medicine, University of Florida, Gainesville, FL, United States
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Farashi S, Kryza T, Clements J, Batra J. Post-GWAS in prostate cancer: from genetic association to biological contribution. Nat Rev Cancer 2019; 19:46-59. [PMID: 30538273 DOI: 10.1038/s41568-018-0087-3] [Citation(s) in RCA: 56] [Impact Index Per Article: 11.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
Genome-wide association studies (GWAS) have been successful in deciphering the genetic component of predisposition to many human complex diseases including prostate cancer. Germline variants identified by GWAS progressively unravelled the substantial knowledge gap concerning prostate cancer heritability. With the beginning of the post-GWAS era, more and more studies reveal that, in addition to their value as risk markers, germline variants can exert active roles in prostate oncogenesis. Consequently, current research efforts focus on exploring the biological mechanisms underlying specific susceptibility loci known as causal variants by applying novel and precise analytical methods to available GWAS data. Results obtained from these post-GWAS analyses have highlighted the potential of exploiting prostate cancer risk-associated germline variants to identify new gene networks and signalling pathways involved in prostate tumorigenesis. In this Review, we describe the molecular basis of several important prostate cancer-causal variants with an emphasis on using post-GWAS analysis to gain insight into cancer aetiology. In addition to discussing the current status of post-GWAS studies, we also summarize the main molecular mechanisms of potential causal variants at prostate cancer risk loci and explore the major challenges in moving from association to functional studies and their implication in clinical translation.
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Affiliation(s)
- Samaneh Farashi
- Cancer Program, School of Biomedical Sciences, Institute of Health and Biomedical Innovation, Queensland University of Technology, Brisbane, Queensland, Australia
- Australian Prostate Cancer Research Centre - Queensland, Queensland University of Technology, Translational Research Institute, Woolloongabba, Queensland, Australia
| | - Thomas Kryza
- Cancer Program, School of Biomedical Sciences, Institute of Health and Biomedical Innovation, Queensland University of Technology, Brisbane, Queensland, Australia
- Australian Prostate Cancer Research Centre - Queensland, Queensland University of Technology, Translational Research Institute, Woolloongabba, Queensland, Australia
| | - Judith Clements
- Cancer Program, School of Biomedical Sciences, Institute of Health and Biomedical Innovation, Queensland University of Technology, Brisbane, Queensland, Australia
- Australian Prostate Cancer Research Centre - Queensland, Queensland University of Technology, Translational Research Institute, Woolloongabba, Queensland, Australia
| | - Jyotsna Batra
- Cancer Program, School of Biomedical Sciences, Institute of Health and Biomedical Innovation, Queensland University of Technology, Brisbane, Queensland, Australia.
- Australian Prostate Cancer Research Centre - Queensland, Queensland University of Technology, Translational Research Institute, Woolloongabba, Queensland, Australia.
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Petridis C, Navarini AA, Dand N, Saklatvala J, Baudry D, Duckworth M, Allen MH, Curtis CJ, Lee SH, Burden AD, Layton A, Bataille V, Pink AE, Carlavan I, Voegel JJ, Spector TD, Trembath RC, McGrath JA, Smith CH, Barker JN, Simpson MA. Genome-wide meta-analysis implicates mediators of hair follicle development and morphogenesis in risk for severe acne. Nat Commun 2018; 9:5075. [PMID: 30542056 PMCID: PMC6290788 DOI: 10.1038/s41467-018-07459-5] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2018] [Accepted: 10/26/2018] [Indexed: 12/21/2022] Open
Abstract
Acne vulgaris is a highly heritable common, chronic inflammatory disease of the skin for which five genetic risk loci have so far been identified. Here, we perform a genome-wide association study of 3823 cases and 16,144 controls followed by meta-analysis with summary statistics from a previous study, with a total sample size of 26,722. We identify 20 independent association signals at 15 risk loci, 12 of which have not been previously implicated in the disease. Likely causal variants disrupt the coding region of WNT10A and a P63 transcription factor binding site in SEMA4B. Risk alleles at the 1q25 locus are associated with increased expression of LAMC2, in which biallelic loss-of-function mutations cause the blistering skin disease epidermolysis bullosa. These findings indicate that variation affecting the structure and maintenance of the skin, in particular the pilosebaceous unit, is a critical aspect of the genetic predisposition to severe acne. Acne vulgaris is a chronic inflammation of the skin, the genetic basis of which is incompletely understood. Here, Petridis et al. perform GWAS and meta-analysis for acne in 26,722 individuals and identify 12 novel risk loci that implicate structure and maintenance of the skin in severe acne risk.
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Affiliation(s)
- Christos Petridis
- Department of Medical and Molecular Genetics, School of Basic & Medical Biosciences, King's College London, London, SE1 9RT, UK
| | - Alexander A Navarini
- Department of Medical and Molecular Genetics, School of Basic & Medical Biosciences, King's College London, London, SE1 9RT, UK.,Departement of Dermatology, University Hospital of Zurich and University of Zurich, CH-8091, Zurich, Switzerland
| | - Nick Dand
- Department of Medical and Molecular Genetics, School of Basic & Medical Biosciences, King's College London, London, SE1 9RT, UK
| | - Jake Saklatvala
- Department of Medical and Molecular Genetics, School of Basic & Medical Biosciences, King's College London, London, SE1 9RT, UK
| | - David Baudry
- St John's Institute of Dermatology, School of Basic & Medical Biosciences, King's College London, London, SE1 9RT, UK
| | - Michael Duckworth
- St John's Institute of Dermatology, School of Basic & Medical Biosciences, King's College London, London, SE1 9RT, UK
| | - Michael H Allen
- St John's Institute of Dermatology, School of Basic & Medical Biosciences, King's College London, London, SE1 9RT, UK
| | - Charles J Curtis
- NIHR Maudsley Biomedical Research Centre (BRC) at South London and Maudsley NHS Foundation Trust (SLaM) & Institute of Psychiatry, Psychology and Neuroscience (IoPPN), King's College London, London, SE5 8AF, UK.,Social Genetic & Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience (IoPPN), King's College London, London, SE5 8AF, UK
| | - Sang Hyuck Lee
- NIHR Maudsley Biomedical Research Centre (BRC) at South London and Maudsley NHS Foundation Trust (SLaM) & Institute of Psychiatry, Psychology and Neuroscience (IoPPN), King's College London, London, SE5 8AF, UK.,Social Genetic & Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience (IoPPN), King's College London, London, SE5 8AF, UK
| | - A David Burden
- Institute of Infection, Immunity and Inflammation, University of Glasgow, Glasgow, G12 8TA, UK
| | - Alison Layton
- Department of Dermatology, Harrogate and District Foundation Trust, Harrogate, HG2 7SX, UK
| | - Veronique Bataille
- Twin Research and Genetic Epidemiology Unit, School of Basic & Medical Biosciences, King's College London, London, SE1 7EH, UK
| | - Andrew E Pink
- St John's Institute of Dermatology, School of Basic & Medical Biosciences, King's College London, London, SE1 9RT, UK
| | | | - Isabelle Carlavan
- Research Department, Galderma R&D, Sophia Antipolis, 06410 Biot, France
| | - Johannes J Voegel
- Research Department, Galderma R&D, Sophia Antipolis, 06410 Biot, France
| | - Timothy D Spector
- Twin Research and Genetic Epidemiology Unit, School of Basic & Medical Biosciences, King's College London, London, SE1 7EH, UK
| | - Richard C Trembath
- Department of Medical and Molecular Genetics, School of Basic & Medical Biosciences, King's College London, London, SE1 9RT, UK
| | - John A McGrath
- St John's Institute of Dermatology, School of Basic & Medical Biosciences, King's College London, London, SE1 9RT, UK
| | - Catherine H Smith
- St John's Institute of Dermatology, School of Basic & Medical Biosciences, King's College London, London, SE1 9RT, UK
| | - Jonathan N Barker
- St John's Institute of Dermatology, School of Basic & Medical Biosciences, King's College London, London, SE1 9RT, UK.
| | - Michael A Simpson
- Department of Medical and Molecular Genetics, School of Basic & Medical Biosciences, King's College London, London, SE1 9RT, UK.
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47
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Genome-wide study of hair colour in UK Biobank explains most of the SNP heritability. Nat Commun 2018; 9:5271. [PMID: 30531825 PMCID: PMC6288091 DOI: 10.1038/s41467-018-07691-z] [Citation(s) in RCA: 69] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2018] [Accepted: 11/14/2018] [Indexed: 01/22/2023] Open
Abstract
Natural hair colour within European populations is a complex genetic trait. Previous work has established that MC1R variants are the principal genetic cause of red hair colour, but with variable penetrance. Here, we have extensively mapped the genes responsible for hair colour in the white, British ancestry, participants in UK Biobank. MC1R only explains 73% of the SNP heritability for red hair in UK Biobank, and in fact most individuals with two MC1R variants have blonde or light brown hair. We identify other genes contributing to red hair, the combined effect of which accounts for ~90% of the SNP heritability. Blonde hair is associated with over 200 genetic variants and we find a continuum from black through dark and light brown to blonde and account for 73% of the SNP heritability of blonde hair. Many of the associated genes are involved in hair growth or texture, emphasising the cellular connections between keratinocytes and melanocytes in the determination of hair colour. Natural hair colour in Europeans is a complex genetic trait. Here, the authors carry out a genome-wide association study using UK BioBank data, suggesting that in combination with pigmentation genes, variants with roles in hair texture and growth can affect hair colouration or our perception of it.
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48
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Raj T, Li YI, Wong G, Humphrey J, Wang M, Ramdhani S, Wang YC, Ng B, Gupta I, Haroutunian V, Schadt EE, Young-Pearse T, Mostafavi S, Zhang B, Sklar P, Bennett DA, De Jager PL. Integrative transcriptome analyses of the aging brain implicate altered splicing in Alzheimer's disease susceptibility. Nat Genet 2018; 50:1584-1592. [PMID: 30297968 PMCID: PMC6354244 DOI: 10.1038/s41588-018-0238-1] [Citation(s) in RCA: 251] [Impact Index Per Article: 41.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2017] [Accepted: 08/16/2018] [Indexed: 12/12/2022]
Abstract
Here we use deep sequencing to identify sources of variation in mRNA splicing in the dorsolateral prefrontal cortex (DLPFC) of 450 subjects from two aging cohorts. Hundreds of aberrant pre-mRNA splicing events are reproducibly associated with Alzheimer's disease. We also generate a catalog of splicing quantitative trait loci (sQTL) effects: splicing of 3,006 genes is influenced by genetic variation. We report that altered splicing is the mechanism for the effects of the PICALM, CLU and PTK2B susceptibility alleles. Furthermore, we performed a transcriptome-wide association study and identified 21 genes with significant associations with Alzheimer's disease, many of which are found in known loci, whereas 8 are in novel loci. These results highlight the convergence of old and new genes associated with Alzheimer's disease in autophagy-lysosomal-related pathways. Overall, this study of the transcriptome of the aging brain provides evidence that dysregulation of mRNA splicing is a feature of Alzheimer's disease and is, in some cases, genetically driven.
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Affiliation(s)
- Towfique Raj
- Ronald M. Loeb Center for Alzheimer's Disease, Department of Neuroscience and Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
| | - Yang I Li
- Section of Genetic Medicine, University of Chicago, Chicago, IL, USA
| | - Garrett Wong
- Ronald M. Loeb Center for Alzheimer's Disease, Department of Neuroscience and Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Jack Humphrey
- Genetics Institute, University College London, London, UK
- Department of Neurodegenerative Disease, Institute of Neurology, University College London, London, UK
| | - Minghui Wang
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Satesh Ramdhani
- Ronald M. Loeb Center for Alzheimer's Disease, Department of Neuroscience and Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Ying-Chih Wang
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Bernard Ng
- Department of Statistics, University of British Columbia, Vancouver, British Columbia, Canada
- Department of Medical Genetics, University of British Columbia, Vancouver, British Columbia, Canada
| | - Ishaan Gupta
- Ronald M. Loeb Center for Alzheimer's Disease, Department of Neuroscience and Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Vahram Haroutunian
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- James J. Peters VA Medical Center, New York, NY, USA
| | - Eric E Schadt
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Tracy Young-Pearse
- Ann Romney Center for Neurologic Diseases, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Sara Mostafavi
- Department of Statistics, University of British Columbia, Vancouver, British Columbia, Canada
- Department of Medical Genetics, University of British Columbia, Vancouver, British Columbia, Canada
| | - Bin Zhang
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Pamela Sklar
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - David A Bennett
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, USA
| | - Philip L De Jager
- Center for Translational and Computational Neuroimmunology, Department of Neurology, Columbia University Medical Center, New York, NY, USA.
- The Broad Institute of MIT and Harvard, Cambridge, MA, USA.
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49
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Zhang T, Choi J, Kovacs MA, Shi J, Xu M, Goldstein AM, Trower AJ, Bishop DT, Iles MM, Duffy DL, MacGregor S, Amundadottir LT, Law MH, Loftus SK, Pavan WJ, Brown KM. Cell-type-specific eQTL of primary melanocytes facilitates identification of melanoma susceptibility genes. Genome Res 2018; 28:1621-1635. [PMID: 30333196 PMCID: PMC6211648 DOI: 10.1101/gr.233304.117] [Citation(s) in RCA: 53] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2017] [Accepted: 09/21/2018] [Indexed: 12/18/2022]
Abstract
Most expression quantitative trait locus (eQTL) studies to date have been performed in heterogeneous tissues as opposed to specific cell types. To better understand the cell-type-specific regulatory landscape of human melanocytes, which give rise to melanoma but account for <5% of typical human skin biopsies, we performed an eQTL analysis in primary melanocyte cultures from 106 newborn males. We identified 597,335 cis-eQTL SNPs prior to linkage disequilibrium (LD) pruning and 4997 eGenes (FDR < 0.05). Melanocyte eQTLs differed considerably from those identified in the 44 GTEx tissue types, including skin. Over a third of melanocyte eGenes, including key genes in melanin synthesis pathways, were unique to melanocytes compared to those of GTEx skin tissues or TCGA melanomas. The melanocyte data set also identified trans-eQTLs, including those connecting a pigmentation-associated functional SNP with four genes, likely through cis-regulation of IRF4 Melanocyte eQTLs are enriched in cis-regulatory signatures found in melanocytes as well as in melanoma-associated variants identified through genome-wide association studies. Melanocyte eQTLs also colocalized with melanoma GWAS variants in five known loci. Finally, a transcriptome-wide association study using melanocyte eQTLs uncovered four novel susceptibility loci, where imputed expression levels of five genes (ZFP90, HEBP1, MSC, CBWD1, and RP11-383H13.1) were associated with melanoma at genome-wide significant P-values. Our data highlight the utility of lineage-specific eQTL resources for annotating GWAS findings, and present a robust database for genomic research of melanoma risk and melanocyte biology.
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Affiliation(s)
- Tongwu Zhang
- Laboratory of Translational Genomics, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, Maryland 20892, USA
| | - Jiyeon Choi
- Laboratory of Translational Genomics, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, Maryland 20892, USA
| | - Michael A Kovacs
- Laboratory of Translational Genomics, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, Maryland 20892, USA
| | - Jianxin Shi
- Biostatistics Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, Maryland 20892, USA
| | - Mai Xu
- Laboratory of Translational Genomics, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, Maryland 20892, USA
| | - Alisa M Goldstein
- Clinical Genetics Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, Maryland 20892, USA
| | - Adam J Trower
- Section of Epidemiology and Biostatistics, Leeds Institute of Cancer and Pathology, University of Leeds, Leeds, LS9 7TF, United Kingdom
| | - D Timothy Bishop
- Section of Epidemiology and Biostatistics, Leeds Institute of Cancer and Pathology, University of Leeds, Leeds, LS9 7TF, United Kingdom
| | - Mark M Iles
- Section of Epidemiology and Biostatistics, Leeds Institute of Cancer and Pathology, University of Leeds, Leeds, LS9 7TF, United Kingdom
| | - David L Duffy
- Statistical Genetics, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, 4006, Australia
| | - Stuart MacGregor
- Statistical Genetics, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, 4006, Australia
| | - Laufey T Amundadottir
- Laboratory of Translational Genomics, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, Maryland 20892, USA
| | - Matthew H Law
- Statistical Genetics, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, 4006, Australia
| | - Stacie K Loftus
- Genetic Disease Research Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, Maryland 20892, USA
| | - William J Pavan
- Genetic Disease Research Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, Maryland 20892, USA
| | - Kevin M Brown
- Laboratory of Translational Genomics, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, Maryland 20892, USA
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50
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Davenport EE, Amariuta T, Gutierrez-Arcelus M, Slowikowski K, Westra HJ, Luo Y, Shen C, Rao DA, Zhang Y, Pearson S, von Schack D, Beebe JS, Bing N, John S, Vincent MS, Zhang B, Raychaudhuri S. Discovering in vivo cytokine-eQTL interactions from a lupus clinical trial. Genome Biol 2018; 19:168. [PMID: 30340504 PMCID: PMC6195724 DOI: 10.1186/s13059-018-1560-8] [Citation(s) in RCA: 25] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2018] [Accepted: 10/05/2018] [Indexed: 01/20/2023] Open
Abstract
BACKGROUND Cytokines are critical to human disease and are attractive therapeutic targets given their widespread influence on gene regulation and transcription. Defining the downstream regulatory mechanisms influenced by cytokines is central to defining drug and disease mechanisms. One promising strategy is to use interactions between expression quantitative trait loci (eQTLs) and cytokine levels to define target genes and mechanisms. RESULTS In a clinical trial for anti-IL-6 in patients with systemic lupus erythematosus, we measure interferon (IFN) status, anti-IL-6 drug exposure, and whole blood genome-wide gene expression at three time points. We show that repeat transcriptomic measurements increases the number of cis eQTLs identified compared to using a single time point. We observe a statistically significant enrichment of in vivo eQTL interactions with IFN status and anti-IL-6 drug exposure and find many novel interactions that have not been previously described. Finally, we find transcription factor binding motifs interrupted by eQTL interaction SNPs, which point to key regulatory mediators of these environmental stimuli and therefore potential therapeutic targets for autoimmune diseases. In particular, genes with IFN interactions are enriched for ISRE binding site motifs, while those with anti-IL-6 interactions are enriched for IRF4 motifs. CONCLUSIONS This study highlights the potential to exploit clinical trial data to discover in vivo eQTL interactions with therapeutically relevant environmental variables.
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Affiliation(s)
- Emma E Davenport
- Center for Data Sciences, Brigham and Women's Hospital, Boston, MA, 02115, USA
- Divisions of Genetics and Rheumatology, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA
- Partners Center for Personalized Genetic Medicine, Boston, MA, 02115, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, 02142, USA
| | - Tiffany Amariuta
- Center for Data Sciences, Brigham and Women's Hospital, Boston, MA, 02115, USA
- Divisions of Genetics and Rheumatology, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA
- Partners Center for Personalized Genetic Medicine, Boston, MA, 02115, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, 02142, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, 02115, USA
| | - Maria Gutierrez-Arcelus
- Center for Data Sciences, Brigham and Women's Hospital, Boston, MA, 02115, USA
- Divisions of Genetics and Rheumatology, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA
- Partners Center for Personalized Genetic Medicine, Boston, MA, 02115, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, 02142, USA
| | - Kamil Slowikowski
- Center for Data Sciences, Brigham and Women's Hospital, Boston, MA, 02115, USA
- Divisions of Genetics and Rheumatology, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA
- Partners Center for Personalized Genetic Medicine, Boston, MA, 02115, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, 02142, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, 02115, USA
| | - Harm-Jan Westra
- Center for Data Sciences, Brigham and Women's Hospital, Boston, MA, 02115, USA
- Divisions of Genetics and Rheumatology, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA
- Partners Center for Personalized Genetic Medicine, Boston, MA, 02115, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, 02142, USA
| | - Yang Luo
- Center for Data Sciences, Brigham and Women's Hospital, Boston, MA, 02115, USA
- Divisions of Genetics and Rheumatology, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA
- Partners Center for Personalized Genetic Medicine, Boston, MA, 02115, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, 02142, USA
| | - Ciyue Shen
- Department of Cell Biology, Harvard Medical School, Boston, MA, 02115, USA
| | - Deepak A Rao
- Division of Rheumatology, Allergy, Immunology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA
| | | | - Stephen Pearson
- Pfizer New Haven Clinical Research Unit, New Haven, CT, 06511, USA
| | | | | | - Nan Bing
- Pfizer Inc., Cambridge, MA, 02139, USA
| | | | | | | | - Soumya Raychaudhuri
- Center for Data Sciences, Brigham and Women's Hospital, Boston, MA, 02115, USA.
- Divisions of Genetics and Rheumatology, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA.
- Partners Center for Personalized Genetic Medicine, Boston, MA, 02115, USA.
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, 02142, USA.
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, 02115, USA.
- Faculty of Medical and Human Sciences, University of Manchester, M13 9PL, Manchester, UK.
- Harvard New Research Building, 77 Avenue Louis Pasteur, Suite 250D, Boston, MA, 02446, USA.
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