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Expression Quantitative Trait Loci Information Improves Predictive Modeling of Disease Relevance of Non-Coding Genetic Variation.

Croteau-Chonka DC, Rogers AJ, Raj T, McGeachie MJ, Qiu W, Ziniti JP, Stubbs BJ, Liang L, Martinez FD, Strunk RC, Lemanske RF, Liu AH, Stranger BE, Carey VJ, Raby BA - PLoS ONE (2015)

Bottom Line: Disease-associated loci identified through genome-wide association studies (GWAS) frequently localize to non-coding sequence.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.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.

View Article: PubMed Central - PubMed

Affiliation: Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, United States of America.

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.

No MeSH data available.


Related in: MedlinePlus

Genes associated with inflammatory and other categories of disease traits enriched for meta-analysis eQTL genes.In each histogram, the observed number of genes in the given category harboring at least one significant eQTL SNP (meta-analysis FDR < 5%) is marked with a dashed vertical line. The  distributions derived from 10,000 permutations are shown with gray bars.
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pone.0140758.g003: Genes associated with inflammatory and other categories of disease traits enriched for meta-analysis eQTL genes.In each histogram, the observed number of genes in the given category harboring at least one significant eQTL SNP (meta-analysis FDR < 5%) is marked with a dashed vertical line. The distributions derived from 10,000 permutations are shown with gray bars.

Mentions: Given the reported enrichments of disease-associated variants for eQTLs [2, 3, 9] and for other functional sequence annotations [11, 12], we next assessed the relationship of the eQTL genes detected in the meta-analysis with disease-associated genes by comparing their representation in the NHGRI GWAS Catalog. Among the 6,535 eQTL genes detected by meta-analysis, 950 (14.5%) were previously reported as GWAS loci, representing a significant enrichment among GWAS genes those that harbor at least one eQTL (P < 10−04) (Fig 3). This enrichment appeared to be most robust for genes associated with inflammatory, metabolic and mental health traits. Though we have previously reported associations with height- and cancer-associated regulatory variants detected in CD4+ lymphocytes [3], there were no significant enrichment for meta-analysis eQTLs for these disease categories (P ≥ 0.05), possibly reflecting the larger proportion of samples from peripheral blood contributing to the meta-analysis.


Expression Quantitative Trait Loci Information Improves Predictive Modeling of Disease Relevance of Non-Coding Genetic Variation.

Croteau-Chonka DC, Rogers AJ, Raj T, McGeachie MJ, Qiu W, Ziniti JP, Stubbs BJ, Liang L, Martinez FD, Strunk RC, Lemanske RF, Liu AH, Stranger BE, Carey VJ, Raby BA - PLoS ONE (2015)

Genes associated with inflammatory and other categories of disease traits enriched for meta-analysis eQTL genes.In each histogram, the observed number of genes in the given category harboring at least one significant eQTL SNP (meta-analysis FDR < 5%) is marked with a dashed vertical line. The  distributions derived from 10,000 permutations are shown with gray bars.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4608673&req=5

pone.0140758.g003: Genes associated with inflammatory and other categories of disease traits enriched for meta-analysis eQTL genes.In each histogram, the observed number of genes in the given category harboring at least one significant eQTL SNP (meta-analysis FDR < 5%) is marked with a dashed vertical line. The distributions derived from 10,000 permutations are shown with gray bars.
Mentions: Given the reported enrichments of disease-associated variants for eQTLs [2, 3, 9] and for other functional sequence annotations [11, 12], we next assessed the relationship of the eQTL genes detected in the meta-analysis with disease-associated genes by comparing their representation in the NHGRI GWAS Catalog. Among the 6,535 eQTL genes detected by meta-analysis, 950 (14.5%) were previously reported as GWAS loci, representing a significant enrichment among GWAS genes those that harbor at least one eQTL (P < 10−04) (Fig 3). This enrichment appeared to be most robust for genes associated with inflammatory, metabolic and mental health traits. Though we have previously reported associations with height- and cancer-associated regulatory variants detected in CD4+ lymphocytes [3], there were no significant enrichment for meta-analysis eQTLs for these disease categories (P ≥ 0.05), possibly reflecting the larger proportion of samples from peripheral blood contributing to the meta-analysis.

Bottom Line: Disease-associated loci identified through genome-wide association studies (GWAS) frequently localize to non-coding sequence.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.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.

View Article: PubMed Central - PubMed

Affiliation: Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, United States of America.

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.

No MeSH data available.


Related in: MedlinePlus