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Carnes MU, Quach BC, Zhou L, Han S, Tao R, Mandal M, Deep-Soboslay A, Marks JA, Page GP, Maher BS, Jaffe AE, Won H, Bierut LJ, Hyde TM, Kleinman JE, Johnson EO, Hancock DB. Smoking-informed methylation and expression QTLs in human brain and colocalization with smoking-associated genetic loci. Neuropsychopharmacology 2024:10.1038/s41386-024-01885-4. [PMID: 38830989 DOI: 10.1038/s41386-024-01885-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/30/2023] [Revised: 04/19/2024] [Accepted: 05/06/2024] [Indexed: 06/05/2024]
Abstract
Smoking is a leading cause of preventable morbidity and mortality. Smoking is heritable, and genome-wide association studies (GWASs) of smoking behaviors have identified hundreds of significant loci. Most GWAS-identified variants are noncoding with unknown neurobiological effects. We used genome-wide genotype, DNA methylation, and RNA sequencing data in postmortem human nucleus accumbens (NAc) to identify cis-methylation/expression quantitative trait loci (meQTLs/eQTLs), investigate variant-by-cigarette smoking interactions across the genome, and overlay QTL evidence at smoking GWAS-identified loci to evaluate their regulatory potential. Active smokers (N = 52) and nonsmokers (N = 171) were defined based on cotinine biomarker levels and next-of-kin reporting. We simultaneously tested variant and variant-by-smoking interaction effects on methylation and expression, separately, adjusting for biological and technical covariates and correcting for multiple testing using a two-stage procedure. We found >2 million significant meQTL variants (padj < 0.05) corresponding to 41,695 unique CpGs. Results were largely driven by main effects, and five meQTLs, mapping to NUDT12, FAM53B, RNF39, and ADRA1B, showed a significant interaction with smoking. We found 57,683 significant eQTL variants for 958 unique eGenes (padj < 0.05) and no smoking interactions. Colocalization analyses identified loci with smoking-associated GWAS variants that overlapped meQTLs/eQTLs, suggesting that these heritable factors may influence smoking behaviors through functional effects on methylation/expression. One locus containing MUSTN1 and ITIH4 colocalized across all data types (GWAS, meQTL, and eQTL). In this first genome-wide meQTL map in the human NAc, the enriched overlap with smoking GWAS-identified genetic loci provides evidence that gene regulation in the brain helps explain the neurobiology of smoking behaviors.
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Affiliation(s)
- Megan Ulmer Carnes
- Genomics and Translational Research Center, RTI International, Research Triangle Park, NC, USA
| | - Bryan C Quach
- Genomics and Translational Research Center, RTI International, Research Triangle Park, NC, USA
| | - Linran Zhou
- Genomics and Translational Research Center, RTI International, Research Triangle Park, NC, USA
| | - Shizhong Han
- Lieber Institute for Brain Development (LIBD), Baltimore, MD, USA
| | - Ran Tao
- Lieber Institute for Brain Development (LIBD), Baltimore, MD, USA
| | - Meisha Mandal
- Genomics and Translational Research Center, RTI International, Research Triangle Park, NC, USA
| | | | - Jesse A Marks
- Genomics and Translational Research Center, RTI International, Research Triangle Park, NC, USA
| | - Grier P Page
- Genomics and Translational Research Center, RTI International, Research Triangle Park, NC, USA
- Fellow Program, RTI International, Research Triangle Park, NC, USA
| | - Brion S Maher
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University, Baltimore, MD, USA
| | - Andrew E Jaffe
- Lieber Institute for Brain Development (LIBD), Baltimore, MD, USA
| | - Hyejung Won
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Laura J Bierut
- Department of Psychiatry, Washington University in St. Louis, St. Louis, MO, USA
| | - Thomas M Hyde
- Lieber Institute for Brain Development (LIBD), Baltimore, MD, USA
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University, Baltimore, MD, USA
- Department of Neurology, Johns Hopkins University, Baltimore, MD, USA
| | - Joel E Kleinman
- Lieber Institute for Brain Development (LIBD), Baltimore, MD, USA
| | - Eric O Johnson
- Genomics and Translational Research Center, RTI International, Research Triangle Park, NC, USA
- Fellow Program, RTI International, Research Triangle Park, NC, USA
| | - Dana B Hancock
- Genomics and Translational Research Center, RTI International, Research Triangle Park, NC, USA.
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2
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Carnes MU, Quach BC, Zhou L, Han S, Tao R, Mandal M, Deep-Soboslay A, Marks JA, Page GP, Maher BS, Jaffe AE, Won H, Bierut LJ, Hyde TM, Kleinman JE, Johnson EO, Hancock DB. Smoking-informed methylation and expression QTLs in human brain and colocalization with smoking-associated genetic loci. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.09.18.23295431. [PMID: 37790540 PMCID: PMC10543041 DOI: 10.1101/2023.09.18.23295431] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/05/2023]
Abstract
Smoking is a leading cause of preventable morbidity and mortality. Smoking is heritable, and genome-wide association studies (GWAS) of smoking behaviors have identified hundreds of significant loci. Most GWAS-identified variants are noncoding with unknown neurobiological effects. We used genome-wide genotype, DNA methylation, and RNA sequencing data in postmortem human nucleus accumbens (NAc) to identify cis-methylation/expression quantitative trait loci (meQTLs/eQTLs), investigate variant-by-cigarette smoking interactions across the genome, and overlay QTL evidence at smoking GWAS-identified loci to evaluate their regulatory potential. Active smokers (N=52) and nonsmokers (N=171) were defined based on cotinine biomarker levels and next-of-kin reporting. We simultaneously tested variant and variant-by-smoking interaction effects on methylation and expression, separately, adjusting for biological and technical covariates and using a two-stage multiple testing approach with eigenMT and Bonferroni corrections. We found >2 million significant meQTL variants (padj<0.05) corresponding to 41,695 unique CpGs. Results were largely driven by main effects; five meQTLs, mapping to NUDT12, FAM53B, RNF39, and ADRA1B, showed a significant interaction with smoking. We found 57,683 significant eQTLs for 958 unique eGenes (padj<0.05) and no smoking interactions. Colocalization analyses identified loci with smoking-associated GWAS variants that overlapped meQTLs/eQTLs, suggesting that these heritable factors may influence smoking behaviors through functional effects on methylation/expression. One locus containing MUSTIN1 and ITIH4 colocalized across all data types (GWAS + meQTL + eQTL). In this first genome-wide meQTL map in the human NAc, the enriched overlap with smoking GWAS-identified genetic loci provides evidence that gene regulation in the brain helps explain the neurobiology of smoking behaviors.
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Affiliation(s)
- Megan Ulmer Carnes
- Genomics and Translational Research Center, RTI International, Research Triangle Park, North Carolina
| | - Bryan C. Quach
- Genomics and Translational Research Center, RTI International, Research Triangle Park, North Carolina
| | - Linran Zhou
- Genomics and Translational Research Center, RTI International, Research Triangle Park, North Carolina
| | - Shizhong Han
- Lieber Institute for Brain Development (LIBD), Baltimore, Maryland
| | - Ran Tao
- Lieber Institute for Brain Development (LIBD), Baltimore, Maryland
| | - Meisha Mandal
- Genomics and Translational Research Center, RTI International, Research Triangle Park, North Carolina
| | | | - Jesse A. Marks
- Genomics and Translational Research Center, RTI International, Research Triangle Park, North Carolina
| | - Grier P. Page
- Genomics and Translational Research Center, RTI International, Research Triangle Park, North Carolina
- Fellow Program, RTI International, Research Triangle Park, North Carolina
| | - Brion S. Maher
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University, Baltimore, Maryland
| | - Andrew E. Jaffe
- Lieber Institute for Brain Development (LIBD), Baltimore, Maryland
| | - Hyejung Won
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
| | - Laura J. Bierut
- Department of Psychiatry, Washington University in St. Louis, Missouri
| | - Thomas M. Hyde
- Lieber Institute for Brain Development (LIBD), Baltimore, Maryland
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University, Baltimore, Maryland
- Department of Neurology, Johns Hopkins University, Baltimore, Maryland
| | - Joel E. Kleinman
- Lieber Institute for Brain Development (LIBD), Baltimore, Maryland
| | - Eric O. Johnson
- Genomics and Translational Research Center, RTI International, Research Triangle Park, North Carolina
- Fellow Program, RTI International, Research Triangle Park, North Carolina
| | - Dana B. Hancock
- Genomics and Translational Research Center, RTI International, Research Triangle Park, North Carolina
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3
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Welzenbach J, Hammond NL, Nikolić M, Thieme F, Ishorst N, Leslie EJ, Weinberg SM, Beaty TH, Marazita ML, Mangold E, Knapp M, Cotney J, Rada-Iglesias A, Dixon MJ, Ludwig KU. Integrative approaches generate insights into the architecture of non-syndromic cleft lip with or without cleft palate. HGG ADVANCES 2021; 2:100038. [PMID: 35047836 PMCID: PMC8756534 DOI: 10.1016/j.xhgg.2021.100038] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2021] [Accepted: 05/27/2021] [Indexed: 12/15/2022] Open
Abstract
Non-syndromic cleft lip with or without cleft palate (nsCL/P) is a common congenital facial malformation with a multifactorial etiology. Genome-wide association studies (GWASs) have identified multiple genetic risk loci. However, functional interpretation of these loci is hampered by the underrepresentation in public resources of systematic functional maps representative of human embryonic facial development. To generate novel insights into the etiology of nsCL/P, we leveraged published GWAS data on nsCL/P as well as available chromatin modification and expression data on mid-facial development. Our analyses identified five novel risk loci, prioritized candidate target genes within associated regions, and highlighted distinct pathways. Furthermore, the results suggest the presence of distinct regulatory effects of nsCL/P risk variants throughout mid-facial development and shed light on its regulatory architecture. Our integrated data provide a platform to advance hypothesis-driven molecular investigations of nsCL/P and other human facial defects.
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Affiliation(s)
- Julia Welzenbach
- Institute of Human Genetics, University Hospital Bonn, Medical Faculty, Venusberg-Campus 1, 53127 Bonn, Germany
| | - Nigel L. Hammond
- Faculty of Biology, Medicine, and Health, Manchester Academic Health Sciences Centre, University of Manchester, Manchester M13 9PT, UK
| | - Miloš Nikolić
- Center for Molecular Medicine Cologne (CMMC), University of Cologne, Cologne, Germany
| | - Frederic Thieme
- Institute of Human Genetics, University Hospital Bonn, Medical Faculty, Venusberg-Campus 1, 53127 Bonn, Germany
| | - Nina Ishorst
- Institute of Human Genetics, University Hospital Bonn, Medical Faculty, Venusberg-Campus 1, 53127 Bonn, Germany
| | - Elizabeth J. Leslie
- Department of Human Genetics, School of Medicine, Emory University, Atlanta, GA, USA
| | - Seth M. Weinberg
- Center for Craniofacial and Dental Genetics, Department of Oral Biology, School of Dental Medicine, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Human Genetics, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA, USA
| | - Terri H. Beaty
- Department of Epidemiology, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA
| | - Mary L. Marazita
- Center for Craniofacial and Dental Genetics, Department of Oral Biology, School of Dental Medicine, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Human Genetics, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Psychiatry and Clinical and Translational Science Institute, School of Medicine, University of Pittsburgh, Pittsburgh, PA 15260, USA
| | - Elisabeth Mangold
- Institute of Human Genetics, University Hospital Bonn, Medical Faculty, Venusberg-Campus 1, 53127 Bonn, Germany
| | - Michael Knapp
- Institute of Medical Biometry, Informatics, and Epidemiology, University Hospital Bonn, Bonn, Germany
| | - Justin Cotney
- Department of Genetics and Genome Sciences, UConn Health, Farmington, CT, USA
- Institute for Systems Genomics, University of Connecticut, Storrs, CT, USA
| | - Alvaro Rada-Iglesias
- Center for Molecular Medicine Cologne (CMMC), University of Cologne, Cologne, Germany
- Cluster of Excellence Cellular Stress Responses in Aging-Associated Diseases (CECAD), University of Cologne, Cologne, Germany
- Institute of Biomedicine and Biotechnology of Cantabria (IBBTEC), University of Cantabria, Cantabria, Spain
| | - Michael J. Dixon
- Faculty of Biology, Medicine, and Health, Manchester Academic Health Sciences Centre, University of Manchester, Manchester M13 9PT, UK
| | - Kerstin U. Ludwig
- Institute of Human Genetics, University Hospital Bonn, Medical Faculty, Venusberg-Campus 1, 53127 Bonn, Germany
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4
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Palmer RHC, Johnson EC, Won H, Polimanti R, Kapoor M, Chitre A, Bogue MA, Benca‐Bachman CE, Parker CC, Verma A, Reynolds T, Ernst J, Bray M, Kwon SB, Lai D, Quach BC, Gaddis NC, Saba L, Chen H, Hawrylycz M, Zhang S, Zhou Y, Mahaffey S, Fischer C, Sanchez‐Roige S, Bandrowski A, Lu Q, Shen L, Philip V, Gelernter J, Bierut LJ, Hancock DB, Edenberg HJ, Johnson EO, Nestler EJ, Barr PB, Prins P, Smith DJ, Akbarian S, Thorgeirsson T, Walton D, Baker E, Jacobson D, Palmer AA, Miles M, Chesler EJ, Emerson J, Agrawal A, Martone M, Williams RW. Integration of evidence across human and model organism studies: A meeting report. GENES, BRAIN, AND BEHAVIOR 2021; 20:e12738. [PMID: 33893716 PMCID: PMC8365690 DOI: 10.1111/gbb.12738] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/20/2021] [Revised: 04/11/2021] [Accepted: 04/21/2021] [Indexed: 12/13/2022]
Abstract
The National Institute on Drug Abuse and Joint Institute for Biological Sciences at the Oak Ridge National Laboratory hosted a meeting attended by a diverse group of scientists with expertise in substance use disorders (SUDs), computational biology, and FAIR (Findability, Accessibility, Interoperability, and Reusability) data sharing. The meeting's objective was to discuss and evaluate better strategies to integrate genetic, epigenetic, and 'omics data across human and model organisms to achieve deeper mechanistic insight into SUDs. Specific topics were to (a) evaluate the current state of substance use genetics and genomics research and fundamental gaps, (b) identify opportunities and challenges of integration and sharing across species and data types, (c) identify current tools and resources for integration of genetic, epigenetic, and phenotypic data, (d) discuss steps and impediment related to data integration, and (e) outline future steps to support more effective collaboration-particularly between animal model research communities and human genetics and clinical research teams. This review summarizes key facets of this catalytic discussion with a focus on new opportunities and gaps in resources and knowledge on SUDs.
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Affiliation(s)
- Rohan H. C. Palmer
- Behavioral Genetics of Addiction Laboratory, Department of PsychologyEmory UniversityAtlantaGeorgiaUSA
| | - Emma C. Johnson
- Department of PsychiatryWashington University School of MedicineSt. LouisMissouriUSA
| | - Hyejung Won
- Department of Genetics and Neuroscience CenterUniversity of North Carolina at Chapel HillChapel HillNorth CarolinaUSA
| | - Renato Polimanti
- Department of PsychiatryYale University School of MedicineWest HavenConnecticutUSA
| | - Manav Kapoor
- Nash Family Department of Neuroscience and Friedman Brain InstituteIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
| | - Apurva Chitre
- Department of PsychiatryUniversity of California, San DiegoLa JollaCaliforniaUSA
| | | | - Chelsie E. Benca‐Bachman
- Behavioral Genetics of Addiction Laboratory, Department of PsychologyEmory UniversityAtlantaGeorgiaUSA
| | - Clarissa C. Parker
- Department of Psychology and Program in NeuroscienceMiddlebury CollegeMiddleburyVermontUSA
| | - Anurag Verma
- Biomedical and Translational Informatics LaboratoryUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | | | - Jason Ernst
- Department of Biological ChemistryUniversity of California Los AngelesLos AngelesCaliforniaUSA
| | - Michael Bray
- Department of PsychiatryWashington University School of MedicineSt. LouisMissouriUSA
| | - Soo Bin Kwon
- Department of Biological ChemistryUniversity of California Los AngelesLos AngelesCaliforniaUSA
| | - Dongbing Lai
- Department of Medical and Molecular GeneticsIndiana University School of MedicineIndianapolisIndianaUSA
| | - Bryan C. Quach
- GenOmics, Bioinformatics, and Translational Research Center, Biostatistics and Epidemiology DivisionRTI InternationalResearch Triangle ParkNorth CarolinaUSA
| | - Nathan C. Gaddis
- GenOmics, Bioinformatics, and Translational Research Center, Biostatistics and Epidemiology DivisionRTI InternationalResearch Triangle ParkNorth CarolinaUSA
| | - Laura Saba
- Department of Pharmaceutical SciencesUniversity of Colorado, Anschutz Medical CampusAuroraColoradoUSA
| | - Hao Chen
- Department of Pharmacology, Addiction Science, and ToxicologyUniversity of Tennessee Health Science CenterMemphisTennesseeUSA
| | | | - Shan Zhang
- Department of Statistics and ProbabilityMichigan State UniversityEast LansingMichiganUSA
| | - Yuan Zhou
- Department of Department of BiostatisticsUniversity of FloridaGainesvilleFloridaUSA
| | - Spencer Mahaffey
- Department of Pharmaceutical Sciences, School of PharmacyUniversity of Colorado DenverAuroraColoradoUSA
| | - Christian Fischer
- Department of Genetics, Genomics and InformaticsUniversity of Tennessee Health Science CenterMemphisTennesseeUSA
| | - Sandra Sanchez‐Roige
- Department of PsychiatryUniversity of California, San DiegoLa JollaCaliforniaUSA
| | - Anita Bandrowski
- Department of NeuroscienceUniversity of California, San DiegoLa JollaCaliforniaUSA
| | - Qing Lu
- Department of Department of BiostatisticsUniversity of FloridaGainesvilleFloridaUSA
| | - Li Shen
- Nash Family Department of Neuroscience and Friedman Brain InstituteIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
| | | | - Joel Gelernter
- Department of PsychiatryYale University School of MedicineWest HavenConnecticutUSA
| | - Laura J. Bierut
- Department of PsychiatryWashington University School of MedicineSt. LouisMissouriUSA
| | - Dana B. Hancock
- GenOmics, Bioinformatics, and Translational Research Center, Biostatistics and Epidemiology DivisionRTI InternationalResearch Triangle ParkNorth CarolinaUSA
| | - Howard J. Edenberg
- Department of Medical and Molecular GeneticsIndiana University School of MedicineIndianapolisIndianaUSA
- Department of Biochemistry and Molecular BiologyIndiana University School of MedicineIndianapolisIndianaUSA
| | - Eric O. Johnson
- GenOmics, Bioinformatics, and Translational Research Center, Biostatistics and Epidemiology DivisionRTI InternationalResearch Triangle ParkNorth CarolinaUSA
| | - Eric J. Nestler
- Nash Family Department of Neuroscience and Friedman Brain InstituteIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
| | - Peter B. Barr
- Department of PsychologyVirginia Commonwealth UniversityRichmondVirginiaUSA
| | - Pjotr Prins
- Department of Genetics, Genomics and InformaticsUniversity of Tennessee Health Science CenterMemphisTennesseeUSA
| | - Desmond J. Smith
- Department of Molecular and Medical PharmacologyDavid Geffen School of Medicine, UCLALos AngelesCaliforniaUSA
| | - Schahram Akbarian
- Friedman Brain Institute and Departments of Psychiatry and NeuroscienceIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
| | | | | | - Erich Baker
- Department of Computer ScienceBaylor UniversityWacoTexasUSA
| | - Daniel Jacobson
- Computational and Predictive Biology, BiosciencesOak Ridge National LaboratoryOak RidgeTennesseeUSA
- Department of PsychologyUniversity of Tennessee KnoxvilleKnoxvilleTennesseeUSA
| | - Abraham A. Palmer
- Department of PsychiatryUniversity of California, San DiegoLa JollaCaliforniaUSA
- Institute for Genomic Medicine, University of California San DiegoLa JollaCaliforniaUSA
| | - Michael Miles
- Department of Pharmacology and ToxicologyVirginia Commonwealth UniversityRichmondVirginiaUSA
| | | | | | - Arpana Agrawal
- Department of PsychiatryWashington University School of MedicineSt. LouisMissouriUSA
| | - Maryann Martone
- Department of NeuroscienceUniversity of California, San DiegoLa JollaCaliforniaUSA
| | - Robert W. Williams
- Department of Genetics, Genomics and InformaticsUniversity of Tennessee Health Science CenterMemphisTennesseeUSA
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Miller JE, Veturi Y, Ritchie MD. Innovative strategies for annotating the "relationSNP" between variants and molecular phenotypes. BioData Min 2019; 12:10. [PMID: 31114635 PMCID: PMC6518798 DOI: 10.1186/s13040-019-0197-9] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2018] [Accepted: 04/18/2019] [Indexed: 11/10/2022] Open
Abstract
Characterizing how variation at the level of individual nucleotides contributes to traits and diseases has been an area of growing interest since the completion of sequencing the first human genome. Our understanding of how a single nucleotide polymorphism (SNP) leads to a pathogenic phenotype on a genome-wide scale is a fruitful endeavor for anyone interested in developing diagnostic tests, therapeutics, or simply wanting to understand the etiology of a disease or trait. To this end, many datasets and algorithms have been developed as resources/tools to annotate SNPs. One of the most common practices is to annotate coding SNPs that affect the protein sequence. Synonymous variants are often grouped as one type of variant, however there are in fact many tools available to dissect their effects on gene expression. More recently, large consortiums like ENCODE and GTEx have made it possible to annotate non-coding regions. Although annotating variants is a common technique among human geneticists, the constant advances in tools and biology surrounding SNPs requires an updated summary of what is known and the trajectory of the field. This review will discuss the history behind SNP annotation, commonly used tools, and newer strategies for SNP annotation. Additionally, we will comment on the caveats that distinguish approaches from one another, along with gaps in the current state of knowledge, and potential future directions. We do not intend for this to be a comprehensive review for any specific area of SNP annotation, but rather it will be an excellent resource for those unfamiliar with computational tools used to functionally characterize SNPs. In summary, this review will help illustrate how each SNP annotation method impacts the way in which the genetic and molecular etiology of a disease is explored in-silico.
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Affiliation(s)
- Jason E. Miller
- Department of Genetics, Institute for Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania, 3400 Civic Center Blvd., Philadelphia, PA 19104 USA
| | - Yogasudha Veturi
- Department of Genetics, Institute for Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania, 3400 Civic Center Blvd., Philadelphia, PA 19104 USA
| | - Marylyn D. Ritchie
- Department of Genetics, Institute for Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania, 3400 Civic Center Blvd., Philadelphia, PA 19104 USA
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6
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Su YR, Di C, Bien S, Huang L, Dong X, Abecasis G, Berndt S, Bezieau S, Brenner H, Caan B, Casey G, Chang-Claude J, Chanock S, Chen S, Connolly C, Curtis K, Figueiredo J, Gala M, Gallinger S, Harrison T, Hoffmeister M, Hopper J, Huyghe JR, Jenkins M, Joshi A, Le Marchand L, Newcomb P, Nickerson D, Potter J, Schoen R, Slattery M, White E, Zanke B, Peters U, Hsu L. A Mixed-Effects Model for Powerful Association Tests in Integrative Functional Genomics. Am J Hum Genet 2018; 102:904-919. [PMID: 29727690 PMCID: PMC5986723 DOI: 10.1016/j.ajhg.2018.03.019] [Citation(s) in RCA: 25] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2017] [Accepted: 03/15/2018] [Indexed: 01/05/2023] Open
Abstract
Genome-wide association studies (GWASs) have successfully identified thousands of genetic variants for many complex diseases; however, these variants explain only a small fraction of the heritability. Recently, genetic association studies that leverage external transcriptome data have received much attention and shown promise for discovering novel variants. One such approach, PrediXcan, is to use predicted gene expression through genetic regulation. However, there are limitations in this approach. The predicted gene expression may be biased, resulting from regularized regression applied to moderately sample-sized reference studies. Further, some variants can individually influence disease risk through alternative functional mechanisms besides expression. Thus, testing only the association of predicted gene expression as proposed in PrediXcan will potentially lose power. To tackle these challenges, we consider a unified mixed effects model that formulates the association of intermediate phenotypes such as imputed gene expression through fixed effects, while allowing residual effects of individual variants to be random. We consider a set-based score testing framework, MiST (mixed effects score test), and propose two data-driven combination approaches to jointly test for the fixed and random effects. We establish the asymptotic distributions, which enable rapid calculation of p values for genome-wide analyses, and provide p values for fixed and random effects separately to enhance interpretability over GWASs. Extensive simulations demonstrate that our approaches are more powerful than existing ones. We apply our approach to a large-scale GWAS of colorectal cancer and identify two genes, POU5F1B and ATF1, which would have otherwise been missed by PrediXcan, after adjusting for all known loci.
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Affiliation(s)
- Yu-Ru Su
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA.
| | - Chongzhi Di
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA
| | - Stephanie Bien
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA
| | - Licai Huang
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA
| | - Xinyuan Dong
- Department of Biostatistics, University of Washington, Seattle, WA 98195, USA
| | - Goncalo Abecasis
- Biostatistics, School of Public Health, University of Michigan, Ann Arbor, MI 48109, USA
| | - Sonja Berndt
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Rockville, MD 20850, USA
| | - Stephane Bezieau
- Service de Génétique Médicale Centre Hospitalier Universitaire (CHU) Nantes, Nantes 44093, France
| | - Hermann Brenner
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg 69120, Germany
| | - Bette Caan
- Division of Research, Kaiser Permanente Medical Care Program of Northern California, Oakland, CA 94612, USA
| | - Graham Casey
- Public Health Sciences Division, University of Virginia, Charlottesville, VA 22908, USA
| | - Jenny Chang-Claude
- Division of Cancer Epidemiology, German Cancer Research Center, Heidelberg 69009, Germany
| | - Stephen Chanock
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Rockville, MD 20850, USA
| | - Sai Chen
- Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA
| | - Charles Connolly
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA
| | - Keith Curtis
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA
| | - Jane Figueiredo
- Samuel Oschin Comprehensive Cancer Institute, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA
| | - Manish Gala
- Division of Gastroenterology, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Steven Gallinger
- Department of Surgery, Mount Sinai Hospital, Toronto, ON M5G 1X5, Canada
| | - Tabitha Harrison
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA
| | - Michael Hoffmeister
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg 69120, Germany
| | - John Hopper
- Melborne School of Population Health, The University of Melborne, Carlton, VIC 3010, Australia
| | - Jeroen R Huyghe
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA
| | - Mark Jenkins
- Melborne School of Population Health, The University of Melborne, Carlton, VIC 3010, Australia
| | - Amit Joshi
- Department of Epidemiology, Harvard School of Public Health, Boston, MA 02115, USA
| | - Loic Le Marchand
- Epidemiology Program, University of Hawaii Cancer Center, Honolulu, HI 96813, USA
| | - Polly Newcomb
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA; Department of Epidemiology, University of Washington School of Public Health, Seattle, WA 98109, USA
| | | | - John Potter
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA; Department of Epidemiology, University of Washington School of Public Health, Seattle, WA 98109, USA
| | - Robert Schoen
- Department of Medicine and Epidemiology, University of Pittsburgh Medical Center, Pittsburgh, PA 15213, USA
| | - Martha Slattery
- Department of Internal Medicine, University of Utah Health Sciences Center, Salt Lake City, UT 84132, USA
| | - Emily White
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA; Department of Epidemiology, University of Washington School of Public Health, Seattle, WA 98109, USA
| | - Brent Zanke
- Division of Hematology, Faculty of Medicine, The University of Ottawa, Ottawa, ON K1Y 4E9, USA
| | - Ulrike Peters
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA; Department of Epidemiology, University of Washington School of Public Health, Seattle, WA 98109, USA
| | - Li Hsu
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA; Department of Biostatistics, University of Washington, Seattle, WA 98195, USA.
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Abstract
PURPOSE OF REVIEW With the advent of the genome-wide association study (GWAS), our understanding of the genetics of addiction has made significant strides forward. Here, we summarize genetic loci containing variants identified at genome-wide statistical significance (P < 5 × 10-8) and independently replicated, review evidence of functional or regulatory effects for GWAS-identified variants, and outline multi-omics approaches to enhance discovery and characterize addiction loci. RECENT FINDINGS Replicable GWAS findings span 11 genetic loci for smoking, eight loci for alcohol, and two loci for illicit drugs combined and include missense functional variants and noncoding variants with regulatory effects in human brain tissues traditionally viewed as addiction-relevant (e.g., prefrontal cortex [PFC]) and, more recently, tissues often overlooked (e.g., cerebellum). GWAS analyses have discovered several novel, replicable variants contributing to addiction. Using larger sample sizes from harmonized datasets and new approaches to integrate GWAS with multiple 'omics data across human brain tissues holds great promise to significantly advance our understanding of the biology underlying addiction.
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Affiliation(s)
- Dana B Hancock
- Behavioral and Urban Health Program, Behavioral Health and Criminal Justice Division, RTI International, 3040 East Cornwallis Road, P. O. Box 12194, Research Triangle Park, NC, 27709, USA.
| | - Christina A Markunas
- Behavioral and Urban Health Program, Behavioral Health and Criminal Justice Division, RTI International, 3040 East Cornwallis Road, P. O. Box 12194, Research Triangle Park, NC, 27709, USA
| | - Laura J Bierut
- Department of Psychiatry, Washington University, St. Louis, MO, USA
| | - Eric O Johnson
- Fellow Program and Behavioral Health and Criminal Justice Division, RTI International, Research Triangle Park, NC, USA
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Mei H, Li L, Griswold M, Mosley T. Gene Expression Meta-Analysis of Seven Candidate Gene Sets for Diabetes Traits Following a GWAS Pathway Study. Front Genet 2018; 9:52. [PMID: 29503662 PMCID: PMC5820295 DOI: 10.3389/fgene.2018.00052] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2017] [Accepted: 02/02/2018] [Indexed: 12/26/2022] Open
Abstract
Seven gene sets were significantly enriched for SNP associations with diabetes, and considered as potential diabetes pathways in a previous meta-analysis of diabetes GWAS. This study aims to examine if these gene sets also have expression associations with diabetes. The analysis was conducted using pooled data from 23 diabetes gene expression studies. Gene associations were examined using linear modeling with an empirical Bayes approach, and pathway associations were investigated by testing enrichment for significant genes. Meta-analyses were performed to investigate gene and pathway associations in all studies and tissue types. The analysis showed that six gene sets and three member genes of ACADSB, RASSF2, and KLF12 had significant associations with diabetes traits. The findings suggest that these gene sets are related to diabetes regulation, and their functions tend to be tissue non-specific.
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Affiliation(s)
- Hao Mei
- Department of Data Science, School of Population Health, University of Mississippi Medical Center, Jackson, MS, United States
- Shanghai Children’s Medical Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- *Correspondence: Hao Mei,
| | - Lianna Li
- Department of Biology, Tougaloo College, Jackson, MS, United States
| | - Michael Griswold
- Department of Data Science, School of Population Health, University of Mississippi Medical Center, Jackson, MS, United States
| | - Thomas Mosley
- Department of Neurology, University of Mississippi Medical Center, Jackson, MS, United States
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