Dataset Information

Colon-specific eQTL analysis to inform on functional SNPs.

ABSTRACT: BACKGROUND:Genome-wide association studies on colorectal cancer have identified more than 60 susceptibility loci, but for most of them there is no clear knowledge of functionality or the underlying gene responsible for the risk modification. Expression quantitative trail loci (eQTL) may provide functional information for such single nucleotide polymorphisms (SNPs). METHODS:We have performed detailed eQTL analysis specific for colon tissue on a series of 97 colon tumours, their paired adjacent normal mucosa and 47 colon mucosa samples donated by healthy individuals. R package MatrixEQTL was used to search for genome-wide cis-eQTL and trans-eQTL fitting linear models adjusted for age, gender and tissue type to rank transformed expression data. RESULTS:The cis-eQTL analyses has revealed 29,073 SNP-gene associations with permutation-adjusted P-values?

SUBMITTER: Moreno V

PROVIDER: S-EPMC6203735 | biostudies-literature | 2018 Oct

REPOSITORIES: biostudies-literature

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Colon-specific eQTL analysis to inform on functional SNPs.

Moreno Victor V Alonso M Henar MH Closa Adrià A Vallés Xavier X Diez-Villanueva Anna A Valle Laura L Castellví-Bel Sergi S Sanz-Pamplona Rebeca R Lopez-Doriga Adriana A Cordero David D Solé Xavier X

British journal of cancer 20181004 8

<h4>Background</h4>Genome-wide association studies on colorectal cancer have identified more than 60 susceptibility loci, but for most of them there is no clear knowledge of functionality or the underlying gene responsible for the risk modification. Expression quantitative trail loci (eQTL) may provide functional information for such single nucleotide polymorphisms (SNPs).<h4>Methods</h4>We have performed detailed eQTL analysis specific for colon tissue on a series of 97 colon tumours, their pai ...[more]

PMID: 30283144

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Project description:Epistasis plays an essential rule in understanding the regulation mechanisms and is an essential component of the genetic architecture of the gene expressions. However, interaction analysis of gene expressions remains fundamentally unexplored due to great computational challenges and data availability. Due to variation in splicing, transcription start sites, polyadenylation sites, post-transcriptional RNA editing across the entire gene, and transcription rates of the cells, RNA-seq measurements generate large expression variability and collectively create the observed position level read count curves. A single number for measuring gene expression which is widely used for microarray measured gene expression analysis is highly unlikely to sufficiently account for large expression variation across the gene. Simultaneously analyzing epistatic architecture using the RNA-seq and whole genome sequencing (WGS) data poses enormous challenges.We develop a nonlinear functional regression model (FRGM) with functional responses where the position-level read counts within a gene are taken as a function of genomic position, and functional predictors where genotype profiles are viewed as a function of genomic position, for epistasis analysis with RNA-seq data. Instead of testing the interaction of all possible pair-wises SNPs, the FRGM takes a gene as a basic unit for epistasis analysis, which tests for the interaction of all possible pairs of genes and use all the information that can be accessed to collectively test interaction between all possible pairs of SNPs within two genome regions.By large-scale simulations, we demonstrate that the proposed FRGM for epistasis analysis can achieve the correct type 1 error and has higher power to detect the interactions between genes than the existing methods. The proposed methods are applied to the RNA-seq and WGS data from the 1000 Genome Project. The numbers of pairs of significantly interacting genes after Bonferroni correction identified using FRGM, RPKM and DESeq were 16,2361, 260 and 51, respectively, from the 350 European samples.The proposed FRGM for epistasis analysis of RNA-seq can capture isoform and position-level information and will have a broad application. Both simulations and real data analysis highlight the potential for the FRGM to be a good choice of the epistatic analysis with sequencing data.

Project description:BACKGROUND:Severe asthma is a chronic disease contributing to disproportionate disease morbidity and mortality. From the year of 2007, many genome-wide association studies (GWAS) have documented a large number of asthma-associated genetic variants and related genes. Nevertheless, the molecular mechanism of these identified variants involved in asthma or severe asthma risk remains largely unknown. METHODS:In the current study, we systematically integrated 3 independent expression quantitative trait loci (eQTL) data (N?=?1977) and a large-scale GWAS summary data of moderate-to-severe asthma (N?=?30,810) by using the Sherlock Bayesian analysis to identify whether expression-related variants contribute risk to severe asthma. Furthermore, we performed various bioinformatics analyses, including pathway enrichment analysis, PPI network enrichment analysis, in silico permutation analysis, DEG analysis and co-expression analysis, to prioritize important genes associated with severe asthma. RESULTS:In the discovery stage, we identified 1129 significant genes associated with moderate-to-severe asthma by using the Sherlock Bayesian analysis. Two hundred twenty-eight genes were prominently replicated by using MAGMA gene-based analysis. These 228 replicated genes were enriched in 17 biological pathways including antigen processing and presentation (Corrected P?=?4.30?×?10-?6), type I diabetes mellitus (Corrected P?=?7.09?×?10-?5), and asthma (Corrected P?=?1.72?×?10-?3). With the use of a series of bioinformatics analyses, we highlighted 11 important genes such as GNGT2, TLR6, and TTC19 as authentic risk genes associated with moderate-to-severe/severe asthma. With respect to GNGT2, there were 3 eSNPs of rs17637472 (PeQTL?=?2.98?×?10-?8 and PGWAS?=?3.40?×?10-?8), rs11265180 (PeQTL?=?6.0?×?10-?6 and PGWAS?=?1.99?×?10-?3), and rs1867087 (PeQTL?=?1.0?×?10-?4 and PGWAS?=?1.84?×?10-?5) identified. In addition, GNGT2 is significantly expressed in severe asthma compared with mild-moderate asthma (P?=?0.045), and Gngt2 shows significantly distinct expression patterns between vehicle and various glucocorticoids (Anova P?=?1.55?×?10-?6). CONCLUSIONS:Our current study provides multiple lines of evidence to support that these 11 identified genes as important candidates implicated in the pathogenesis of severe asthma.

Dataset Information

Colon-specific eQTL analysis to inform on functional SNPs.

Publications

Colon-specific eQTL analysis to inform on functional SNPs.

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