Tejaas: reverse regression increases power for detecting trans-eQTLs.
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ABSTRACT:
SUBMITTER: Banerjee S
PROVIDER: S-EPMC8101255 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Trans-ethnic meta-analysis of genome-wide association studies (GWAS) across diverse populations can increase power to detect complex trait loci when the underlying causal variants are shared between ancestry groups. However, heterogeneity in allelic effects between GWAS at these loci can occur that is correlated with ancestry. Here, a novel approach is presented to detect SNP association and quantify the extent of heterogeneity in allelic effects that is correlated with ancestry. We employ trans-ethnic meta-regression to model allelic effects as a function of axes of genetic variation, derived from a matrix of mean pairwise allele frequency differences between GWAS, and implemented in the MR-MEGA software. Through detailed simulations, we demonstrate increased power to detect association for MR-MEGA over fixed- and random-effects meta-analysis across a range of scenarios of heterogeneity in allelic effects between ethnic groups. We also demonstrate improved fine-mapping resolution, in loci containing a single causal variant, compared to these meta-analysis approaches and PAINTOR, and equivalent performance to MANTRA at reduced computational cost. Application of MR-MEGA to trans-ethnic GWAS of kidney function in 71,461 individuals indicates stronger signals of association than fixed-effects meta-analysis when heterogeneity in allelic effects is correlated with ancestry. Application of MR-MEGA to fine-mapping four type 2 diabetes susceptibility loci in 22,086 cases and 42,539 controls highlights: (i) strong evidence for heterogeneity in allelic effects that is correlated with ancestry only at the index SNP for the association signal at the CDKAL1 locus; and (ii) 99% credible sets with six or fewer variants for five distinct association signals.
| S-EPMC5755684 | biostudies-literature
Project description:Expression quantitative trait locus (eQTL) analysis, which links variations in gene expression to genotypes, is essential to understanding gene regulation and to interpreting disease-associated loci. Currently identified eQTLs are mainly in samples of blood and other normal tissues. However, no database comprehensively provides eQTLs in large number of cancer samples. Using the genotype and expression data of 9196 tumor samples in 33 cancer types from The Cancer Genome Atlas (TCGA), we identified 5 606 570 eQTL-gene pairs in the cis-eQTL analysis and 231 210 eQTL-gene pairs in the trans-eQTL analysis. We further performed survival analysis and identified 22 212 eQTLs associated with patient overall survival. Furthermore, we linked the eQTLs to genome-wide association studies (GWAS) data and identified 337 131 eQTLs that overlap with existing GWAS loci. We developed PancanQTL, a user-friendly database (http://bioinfo.life.hust.edu.cn/PancanQTL/), to store cis-eQTLs, trans-eQTLs, survival-associated eQTLs and GWAS-related eQTLs to enable searching, browsing and downloading. PancanQTL could help the research community understand the effects of inherited variants in tumorigenesis and development.
| S-EPMC5753226 | biostudies-literature
Project description:BackgroundMapping expression quantitative trait loci (eQTLs) has provided insight into gene regulation. Compared to cis-eQTLs, the regulatory mechanisms of trans-eQTLs are less known. Previous studies suggest that trans-eQTLs may regulate expression of remote genes by altering the expression of nearby genes. Trans-association has been studied in the mediation analysis with a single mediator. However, prior applications with one mediator are prone to model misspecification due to correlations between genes. Motivated from the observation that trans-eQTLs are more likely to associate with more than one cis-gene than randomly selected SNPs in the GTEx dataset, we developed a computational method to identify trans-eQTLs that are mediated by multiple mediators.ResultsWe proposed two hypothesis tests for testing the total mediation effect (TME) and the component-wise mediation effects (CME), respectively. We demonstrated in simulation studies that the type I error rates were controlled in both tests despite model misspecification. The TME test was more powerful than the CME test when the two mediation effects are in the same direction, while the CME test was more powerful than the TME test when the two mediation effects are in opposite direction. Multiple mediator analysis had increased power to detect mediated trans-eQTLs, especially in large samples. In the HapMap3 data, we identified 11 mediated trans-eQTLs that were not detected by the single mediator analysis in the combined samples of African populations. Moreover, the mediated trans-eQTLs in the HapMap3 samples are more likely to be trait-associated SNPs. In terms of computation, although there is no limit in the number of mediators in our model, analysis takes more time when adding additional mediators. In the analysis of the HapMap3 samples, we included at most 5 cis-gene mediators. Majority of the trios we considered have one or two mediators.ConclusionsTrans-eQTLs are more likely to associate with multiple cis-genes than randomly selected SNPs. Mediation analysis with multiple mediators improves power of identification of mediated trans-eQTLs, especially in large samples.
| S-EPMC6440281 | biostudies-literature
Project description:Expression quantitative trait loci (eQTL) mapping is a widely used tool to study the genetics of gene expression. Confounding factors and the burden of multiple testing limit the ability to map distal trans eQTLs, which is important to understand downstream genetic effects on genes and pathways. We propose a two-stage linear mixed model that first learns local directed gene-regulatory networks to then condition on the expression levels of selected genes. We show that this covariate selection approach controls for confounding factors and regulatory context, thereby increasing eQTL detection power and improving the consistency between studies. GNet-LMM is available at: https://github.com/PMBio/GNetLMM.
| S-EPMC4765046 | biostudies-literature
Project description:Identifying the downstream effects of disease-associated SNPs is challenging. To help overcome this problem, we performed expression quantitative trait locus (eQTL) meta-analysis in non-transformed peripheral blood samples from 5,311 individuals with replication in 2,775 individuals. We identified and replicated trans eQTLs for 233 SNPs (reflecting 103 independent loci) that were previously associated with complex traits at genome-wide significance. Some of these SNPs affect multiple genes in trans that are known to be altered in individuals with disease: rs4917014, previously associated with systemic lupus erythematosus (SLE), altered gene expression of C1QB and five type I interferon response genes, both hallmarks of SLE. DeepSAGE RNA sequencing showed that rs4917014 strongly alters the 3' UTR levels of IKZF1 in cis, and chromatin immunoprecipitation and sequencing analysis of the trans-regulated genes implicated IKZF1 as the causal gene. Variants associated with cholesterol metabolism and type 1 diabetes showed similar phenomena, indicating that large-scale eQTL mapping provides insight into the downstream effects of many trait-associated variants.
| S-EPMC3991562 | biostudies-literature
Project description:BackgroundThere is a growing awareness that interaction between multiple genes play an important role in the risk of common, complex multi-factorial diseases. Many common diseases are affected by certain genotype combinations (associated with some genes and their interactions). The identification and characterization of these susceptibility genes and gene-gene interaction have been limited by small sample size and large number of potential interactions between genes. Several methods have been proposed to detect gene-gene interaction in a case control study. The penalized logistic regression (PLR), a variant of logistic regression with L2 regularization, is a parametric approach to detect gene-gene interaction. On the other hand, the Multifactor Dimensionality Reduction (MDR) is a nonparametric and genetic model-free approach to detect genotype combinations associated with disease risk.MethodsWe compared the power of MDR and PLR for detecting two-way and three-way interactions in a case-control study through extensive simulations. We generated several interaction models with different magnitudes of interaction effect. For each model, we simulated 100 datasets, each with 200 cases and 200 controls and 20 SNPs. We considered a wide variety of models such as models with just main effects, models with only interaction effects or models with both main and interaction effects. We also compared the performance of MDR and PLR to detect gene-gene interaction associated with acute rejection(AR) in kidney transplant patients.ResultsIn this paper, we have studied the power of MDR and PLR for detecting gene-gene interaction in a case-control study through extensive simulation. We have compared their performances for different two-way and three-way interaction models. We have studied the effect of different allele frequencies on these methods. We have also implemented their performance on a real dataset. As expected, none of these methods were consistently better for all data scenarios, but, generally MDR outperformed PLR for more complex models. The ROC analysis on the real dataset suggests that MDR outperforms PLR in detecting gene-gene interaction on the real dataset.ConclusionAs one might expect, the relative success of each method is context dependent. This study demonstrates the strengths and weaknesses of the methods to detect gene-gene interaction.
| S-EPMC2800840 | biostudies-literature
Project description:The goal was to study the effects of lead exposure on gene expression and identify the lead-responsive genes. After detecting 1,536 cis-eQTLs (FDR ≤ 10%) and 952 trans-eQTLs, we focused our analysis on Pb-sensitive “trans-eQTL hotspots”.
2016-06-10 | GSE83141 | GEO
Project description:BACKGROUND:The growth of DNA biobanks linked to data from electronic health records (EHRs) has enabled the discovery of numerous associations between genomic variants and clinical phenotypes. Nonetheless, although clinical data are generally longitudinal, standard approaches for detecting genotype-phenotype associations in such linked data, notably logistic regression, do not naturally account for variation in the period of follow-up or the time at which an event occurs. Here we explored the advantages of quantifying associations using Cox proportional hazards regression, which can account for the age at which a patient first visited the healthcare system (left truncation) and the age at which a patient either last visited the healthcare system or acquired a particular phenotype (right censoring). RESULTS:In comprehensive simulations, we found that, compared to logistic regression, Cox regression had greater power at equivalent Type I error. We then scanned for genotype-phenotype associations using logistic regression and Cox regression on 50 phenotypes derived from the EHRs of 49,792 genotyped individuals. Consistent with the findings from our simulations, Cox regression had approximately 10% greater relative sensitivity for detecting known associations from the NHGRI-EBI GWAS Catalog. In terms of effect sizes, the hazard ratios estimated by Cox regression were strongly correlated with the odds ratios estimated by logistic regression. CONCLUSIONS:As longitudinal health-related data continue to grow, Cox regression may improve our ability to identify the genetic basis for a wide range of human phenotypes.
| S-EPMC6829851 | biostudies-other
Project description:Trans-eQTLs have been implicated in complex traits and common diseases, but many were initially identified on the basis of having an effect in cis, and there has been no assessment of the significance of the overlap in relation to chance expectations. Here, we investigated whether trans-expression quantitative trait loci (eQTL) associations identified in whole blood contribute to variance in complex traits by determining (1) whether genome-wide significant (GWS) single-nucleotide polymorphisms (SNPs) were enriched for trans-eQTL (including trans-only eQTL), and (2) whether the genomic regions surrounding associated trans-genes were enriched for statistical associations in the relevant GWAS. On average for a given phenotype, we identify 4.8% of GWS SNPs overlapping with trans-eQTL present in blood, and show that for the majority of these phenotypes, this observation does not exceed that expected by chance. Likewise, we observe no enrichment for genetic associations with the GWAS phenotype in the regions surrounding the linked trans-genes, with the exception of rheumatoid arthritis. Interestingly, the GWS SNPs for each phenotype were consistently more enriched for unique trans-eQTL SNPs than trans-eQTL SNP-probe pairs (p = 4 × 10-7), with schizophrenia the only exception. This relative enrichment for trans-eQTL SNPs over trans-eQTL SNP-probe pairs implies that trait-associated trans-eQTL SNPs in whole blood are less likely to be 'master regulators' than random trans-eQTL SNPs. Taken together, these results suggest little evidence for the role of blood-based trans-eQTL in complex traits and disease, although this may reflect the finite size of currently available data sets and our findings may not hold for trans-eQTLs in more trait-relevant tissues. All software is publically available at https://github.com/IMB-Computational-Genomics-Lab/eqtlOverlapper .
| S-EPMC6117341 | biostudies-literature
Project description:This SuperSeries is composed of the SubSeries listed below. Refer to individual Series
2013-06-08 | E-GEOD-47729 | biostudies-arrayexpress