Expression Quantitative Trait Loci Information Improves Predictive Modeling of Disease Relevance of Non-Coding Genetic Variation
Disease-associated loci identified through genome-wide association studies (GWAS) frequently localize to non-coding sequence. We and others have demonstrated strong enrichment of such single nucleotide polymorphisms (SNPs) for expression quantitative trait loci (eQTLs), supporting an important role...
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| Published in: | PloS one Vol. 10; no. 10; p. e0140758 |
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| Main Authors: | , , , , , , , , , , , , , , |
| Format: | Journal Article |
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16-10-2015
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| Abstract | Disease-associated loci identified through genome-wide association studies (GWAS) frequently localize to non-coding sequence. We and others have demonstrated strong enrichment of such single nucleotide polymorphisms (SNPs) for expression quantitative trait loci (eQTLs), supporting an important role for regulatory genetic variation in complex disease pathogenesis. Herein we describe our initial efforts to develop a predictive model of disease-associated variants leveraging eQTL information. We first catalogued cis-acting eQTLs (SNPs within 100 kb of target gene transcripts) by meta-analyzing four studies of three blood-derived tissues (n = 586). At a false discovery rate < 5%, we mapped eQTLs for 6,535 genes; these were enriched for disease-associated genes (P < 10(-04)), particularly those related to immune diseases and metabolic traits. Based on eQTL information and other variant annotations (distance from target gene transcript, minor allele frequency, and chromatin state), we created multivariate logistic regression models to predict SNP membership in reported GWAS. The complete model revealed independent contributions of specific annotations as strong predictors, including evidence for an eQTL (odds ratio (OR) = 1.2-2.0, P < 10(-11)) and the chromatin states of active promoters, different classes of strong or weak enhancers, or transcriptionally active regions (OR = 1.5-2.3, P < 10(-11)). This complete prediction model including eQTL association information ultimately allowed for better discrimination of SNPs with higher probabilities of GWAS membership (6.3-10.0%, compared to 3.5% for a random SNP) than the other two models excluding eQTL information. This eQTL-based prediction model of disease relevance can help systematically prioritize non-coding GWAS SNPs for further functional characterization. |
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| AbstractList | Disease-associated loci identified through genome-wide association studies (GWAS) frequently localize to non-coding sequence. We and others have demonstrated strong enrichment of such single nucleotide polymorphisms (SNPs) for expression quantitative trait loci (eQTLs), supporting an important role for regulatory genetic variation in complex disease pathogenesis. Herein we describe our initial efforts to develop a predictive model of disease-associated variants leveraging eQTL information. We first catalogued cis-acting eQTLs (SNPs within 100kb of target gene transcripts) by meta-analyzing four studies of three blood-derived tissues (n = 586). At a false discovery rate < 5%, we mapped eQTLs for 6,535 genes; these were enriched for disease-associated genes (P < 10.sup.-04 ), particularly those related to immune diseases and metabolic traits. Based on eQTL information and other variant annotations (distance from target gene transcript, minor allele frequency, and chromatin state), we created multivariate logistic regression models to predict SNP membership in reported GWAS. The complete model revealed independent contributions of specific annotations as strong predictors, including evidence for an eQTL (odds ratio (OR) = 1.2-2.0, P < 10.sup.-11) and the chromatin states of active promoters, different classes of strong or weak enhancers, or transcriptionally active regions (OR = 1.5-2.3, P < 10.sup.-11). This complete prediction model including eQTL association information ultimately allowed for better discrimination of SNPs with higher probabilities of GWAS membership (6.3-10.0%, compared to 3.5% for a random SNP) than the other two models excluding eQTL information. This eQTL-based prediction model of disease relevance can help systematically prioritize non-coding GWAS SNPs for further functional characterization. Disease-associated loci identified through genome-wide association studies (GWAS) frequently localize to non-coding sequence. We and others have demonstrated strong enrichment of such single nucleotide polymorphisms (SNPs) for expression quantitative trait loci (eQTLs), supporting an important role for regulatory genetic variation in complex disease pathogenesis. Herein we describe our initial efforts to develop a predictive model of disease-associated variants leveraging eQTL information. We first catalogued cis -acting eQTLs (SNPs within 100kb of target gene transcripts) by meta-analyzing four studies of three blood-derived tissues ( n = 586). At a false discovery rate < 5%, we mapped eQTLs for 6,535 genes; these were enriched for disease-associated genes ( P < 10 −04 ), particularly those related to immune diseases and metabolic traits. Based on eQTL information and other variant annotations (distance from target gene transcript, minor allele frequency, and chromatin state), we created multivariate logistic regression models to predict SNP membership in reported GWAS. The complete model revealed independent contributions of specific annotations as strong predictors, including evidence for an eQTL (odds ratio (OR) = 1.2–2.0, P < 10 −11 ) and the chromatin states of active promoters, different classes of strong or weak enhancers, or transcriptionally active regions (OR = 1.5–2.3, P < 10 −11 ). This complete prediction model including eQTL association information ultimately allowed for better discrimination of SNPs with higher probabilities of GWAS membership (6.3–10.0%, compared to 3.5% for a random SNP) than the other two models excluding eQTL information. This eQTL-based prediction model of disease relevance can help systematically prioritize non-coding GWAS SNPs for further functional characterization. Disease-associated loci identified through genome-wide association studies (GWAS) frequently localize to non-coding sequence. We and others have demonstrated strong enrichment of such single nucleotide polymorphisms (SNPs) for expression quantitative trait loci (eQTLs), supporting an important role for regulatory genetic variation in complex disease pathogenesis. Herein we describe our initial efforts to develop a predictive model of disease-associated variants leveraging eQTL information. We first catalogued cis-acting eQTLs (SNPs within 100 kb of target gene transcripts) by meta-analyzing four studies of three blood-derived tissues (n = 586). At a false discovery rate < 5%, we mapped eQTLs for 6,535 genes; these were enriched for disease-associated genes (P < 10(-04)), particularly those related to immune diseases and metabolic traits. Based on eQTL information and other variant annotations (distance from target gene transcript, minor allele frequency, and chromatin state), we created multivariate logistic regression models to predict SNP membership in reported GWAS. The complete model revealed independent contributions of specific annotations as strong predictors, including evidence for an eQTL (odds ratio (OR) = 1.2-2.0, P < 10(-11)) and the chromatin states of active promoters, different classes of strong or weak enhancers, or transcriptionally active regions (OR = 1.5-2.3, P < 10(-11)). This complete prediction model including eQTL association information ultimately allowed for better discrimination of SNPs with higher probabilities of GWAS membership (6.3-10.0%, compared to 3.5% for a random SNP) than the other two models excluding eQTL information. This eQTL-based prediction model of disease relevance can help systematically prioritize non-coding GWAS SNPs for further functional characterization. Disease-associated loci identified through genome-wide association studies (GWAS) frequently localize to non-coding sequence. We and others have demonstrated strong enrichment of such single nucleotide polymorphisms (SNPs) for expression quantitative trait loci (eQTLs), supporting an important role for regulatory genetic variation in complex disease pathogenesis. Herein we describe our initial efforts to develop a predictive model of disease-associated variants leveraging eQTL information. We first catalogued cis -acting eQTLs (SNPs within 100kb of target gene transcripts) by meta-analyzing four studies of three blood-derived tissues ( n = 586). At a false discovery rate < 5%, we mapped eQTLs for 6,535 genes; these were enriched for disease-associated genes ( P < 10 −04 ), particularly those related to immune diseases and metabolic traits. Based on eQTL information and other variant annotations (distance from target gene transcript, minor allele frequency, and chromatin state), we created multivariate logistic regression models to predict SNP membership in reported GWAS. The complete model revealed independent contributions of specific annotations as strong predictors, including evidence for an eQTL (odds ratio (OR) = 1.2–2.0, P < 10 −11 ) and the chromatin states of active promoters, different classes of strong or weak enhancers, or transcriptionally active regions (OR = 1.5–2.3, P < 10 −11 ). This complete prediction model including eQTL association information ultimately allowed for better discrimination of SNPs with higher probabilities of GWAS membership (6.3–10.0%, compared to 3.5% for a random SNP) than the other two models excluding eQTL information. This eQTL-based prediction model of disease relevance can help systematically prioritize non-coding GWAS SNPs for further functional characterization. Disease-associated loci identified through genome-wide association studies (GWAS) frequently localize to non-coding sequence. We and others have demonstrated strong enrichment of such single nucleotide polymorphisms (SNPs) for expression quantitative trait loci (eQTLs), supporting an important role for regulatory genetic variation in complex disease pathogenesis. Herein we describe our initial efforts to develop a predictive model of disease-associated variants leveraging eQTL information. We first catalogued cis-acting eQTLs (SNPs within 100kb of target gene transcripts) by meta-analyzing four studies of three blood-derived tissues (n = 586). At a false discovery rate < 5%, we mapped eQTLs for 6,535 genes; these were enriched for disease-associated genes (P < 10−04), particularly those related to immune diseases and metabolic traits. Based on eQTL information and other variant annotations (distance from target gene transcript, minor allele frequency, and chromatin state), we created multivariate logistic regression models to predict SNP membership in reported GWAS. The complete model revealed independent contributions of specific annotations as strong predictors, including evidence for an eQTL (odds ratio (OR) = 1.2–2.0, P < 10−11) and the chromatin states of active promoters, different classes of strong or weak enhancers, or transcriptionally active regions (OR = 1.5–2.3, P < 10−11). This complete prediction model including eQTL association information ultimately allowed for better discrimination of SNPs with higher probabilities of GWAS membership (6.3–10.0%, compared to 3.5% for a random SNP) than the other two models excluding eQTL information. This eQTL-based prediction model of disease relevance can help systematically prioritize non-coding GWAS SNPs for further functional characterization. |
| Audience | Academic |
| Author | Croteau-Chonka, Damien C Stubbs, Benjamin J McGeachie, Michael J Qiu, Weiliang Carey, Vincent J Liang, Liming Raby, Benjamin A Stranger, Barbara E Liu, Andrew H Raj, Towfique Ziniti, John P Strunk, Robert C Rogers, Angela J Martinez, Fernando D Lemanske, Jr, Robert F |
| AuthorAffiliation | 11 Section of Genetic Medicine, Department of Medicine, University of Chicago, Chicago, Illinois, United States of America 10 Division of Allergy and Clinical Immunology, Department of Pediatrics, National Jewish Health and University of Colorado School of Medicine, Denver, Colorado, United States of America 2 Division of Pulmonary and Critical Care Medicine, School of Medicine, Stanford University, Stanford, California, United States of America 1 Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, Massachusetts, United States of America 12 BWH Pulmonary Genetics Center, Division of Pulmonary and Critical Care Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, United States of America 3 Program in Translational NeuroPsychiatric Genomics, Department of Neurology, Brigham and Women’s Hospital, Boston, Massachusetts, United States of America 9 University of |
| AuthorAffiliation_xml | – name: 12 BWH Pulmonary Genetics Center, Division of Pulmonary and Critical Care Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, United States of America – name: 2 Division of Pulmonary and Critical Care Medicine, School of Medicine, Stanford University, Stanford, California, United States of America – name: 6 Departments of Biostatistics and Epidemiology, Harvard School of Public Health, Boston, Massachusetts, United States of America – name: 3 Program in Translational NeuroPsychiatric Genomics, Department of Neurology, Brigham and Women’s Hospital, Boston, Massachusetts, United States of America – name: 4 Division of Genetics, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, Massachusetts, United States of America – name: 5 Program in Medical and Population Genetics, Broad Institute, Cambridge, Massachusetts, United States of America – name: 11 Section of Genetic Medicine, Department of Medicine, University of Chicago, Chicago, Illinois, United States of America – name: 7 Arizona Respiratory Center and BIO5 Institute, University of Arizona, Tucson, Arizona, United States of America – name: 8 Division of Allergy, Immunology and Pulmonary Medicine, Department of Pediatrics, Washington University School of Medicine, St. Louis, Missouri, United States of America – name: 1 Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, Massachusetts, United States of America – name: 9 University of Wisconsin School of Medicine and Public Health, Madison, WI, United States of America – name: Cincinnati Children's Hospital Medical center, UNITED STATES – name: 10 Division of Allergy and Clinical Immunology, Department of Pediatrics, National Jewish Health and University of Colorado School of Medicine, Denver, Colorado, United States of America |
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| Notes | ObjectType-Article-2 SourceType-Scholarly Journals-1 ObjectType-Feature-1 ObjectType-Review-3 content type line 23 Conceived and designed the experiments: DCCC AJR LL BES VJC BAR. Performed the experiments: DCCC AJR VJC. Analyzed the data: DCCC AJR TR MJM VJC. Contributed reagents/materials/analysis tools: FDM BJS RCS RFL AHL BES VJC BAR. Wrote the paper: DCCC AJR MJM VJC BAR. Assisted with data generation and quality control: WQ JPZ BJH. Competing Interests: The authors have declared that no competing interests exist. |
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| SubjectTerms | Alleles Annotations Asthma Bioinformatics Blood Chromatin Chromosomes Critical care Disease Enhancers Female Gene expression Gene Expression Regulation Gene Frequency Genes Genetic diversity Genetic variation Genome-wide association studies Genome-Wide Association Study Genomes Hospitals Humans Immune System Diseases - genetics Immunological diseases Immunology Male Medical schools Medicine Meta-analysis Metabolic Diseases - genetics Models, Genetic Nucleotide sequence Pathogenesis Pediatrics Polymorphism, Single Nucleotide Population genetics Prediction models Predictive Value of Tests Principal components analysis Public health Quantitative Trait Loci Regression analysis Regression models Single nucleotide polymorphisms Single-nucleotide polymorphism Studies Transcription Womens health |
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| Title | Expression Quantitative Trait Loci Information Improves Predictive Modeling of Disease Relevance of Non-Coding Genetic Variation |
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