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Co-expression analysis reveals interpretable gene modules controlled by trans-acting genetic variants.

ABSTRACT: Understanding the causal processes that contribute to disease onset and progression is essential for developing novel therapies. Although trans-acting expression quantitative trait loci (trans-eQTLs) can directly reveal cellular processes modulated by disease variants, detecting trans-eQTLs remains challenging due to their small effect sizes. Here, we analysed gene expression and genotype data from six blood cell types from 226 to 710 individuals. We used co-expression modules inferred from gene expression data with five methods as traits in trans-eQTL analysis to limit multiple testing and improve interpretability. In addition to replicating three established associations, we discovered a novel trans-eQTL near SLC39A8 regulating a module of metallothionein genes in LPS-stimulated monocytes. Interestingly, this effect was mediated by a transient cis-eQTL present only in early LPS response and lost before the trans effect appeared. Our analyses highlight how co-expression combined with functional enrichment analysis improves the identification and prioritisation of trans-eQTLs when applied to emerging cell-type-specific datasets.

SUBMITTER: Kolberg L

PROVIDER: S-EPMC7470823 | biostudies-literature | 2020 Sep

REPOSITORIES: biostudies-literature

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Co-expression analysis reveals interpretable gene modules controlled by trans-acting genetic variants.

Kolberg Liis L Kerimov Nurlan N Peterson Hedi H Alasoo Kaur K

eLife 20200903

Understanding the causal processes that contribute to disease onset and progression is essential for developing novel therapies. Although trans-acting expression quantitative trait loci (trans-eQTLs) can directly reveal cellular processes modulated by disease variants, detecting trans-eQTLs remains challenging due to their small effect sizes. Here, we analysed gene expression and genotype data from six blood cell types from 226 to 710 individuals. We used co-expression modul ...[more]

PMID: 32880574

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Project description:Genetic variation in gene expression makes an important contribution to phenotypic variation and susceptibility to disease. Recently, a subset of cis-acting expression quantitative loci (eQTLs) has been found to result from polymorphisms that affect RNA stability. Here we carried out a search for trans-acting variants that influence RNA stability. We first demonstrate that differences in the activity of trans-acting factors that stabilize RNA can be detected by comparing the expression levels of long-lived (stable) and short-lived (unstable) transcripts in high-throughput gene expression experiments. Using gene expression microarray data generated from eight HapMap3 populations, we calculated the relative expression ranks of long-lived transcripts versus short-lived transcripts in each sample. Treating this as a quantitative trait, we applied genome-wide association and identified a single nucleotide polymorphism (SNP), rs6137010, on chromosome 20p13 with which it is strongly associated in two Asian populations (p?=?4×10(-10) in CHB - Han Chinese from Beijing; p?=?1×10(-4) in JPT - Japanese from Tokyo). This SNP is a cis-eQTL for SNRPB in CHB and JPT but not in the other six HapMap3 populations. SNRPB is a core component of the spliceosome, and has previously been shown to affect the expression of many RNA processing factors. We propose that a cis-eQTL of SNRPB may be directly responsible for inter-individual variation in relative expression of long-lived versus short-lived transcript in Asian populations. In support of this hypothesis, knockdown of SNRPB results in a significant reduction in the relative expression of long-lived versus short-lived transcripts. Samples with higher relative expression of long-lived transcripts also had higher relative expression of coding compared to non-coding RNA and of RNA from housekeeping compared to non-housekeeping genes, due to the lower decay rates of coding RNAs, particularly those that perform housekeeping functions, compared to non-coding RNAs.

Project description:One major expectation from the transcriptome in humans is to characterize the biological basis of associations identified by genome-wide association studies. So far, few cis expression quantitative trait loci (eQTLs) have been reliably related to disease susceptibility. Trans-regulating mechanisms may play a more prominent role in disease susceptibility. We analyzed 12,808 genes detected in at least 5% of circulating monocyte samples from a population-based sample of 1,490 European unrelated subjects. We applied a method of extraction of expression patterns-independent component analysis-to identify sets of co-regulated genes. These patterns were then related to 675,350 SNPs to identify major trans-acting regulators. We detected three genomic regions significantly associated with co-regulated gene modules. Association of these loci with multiple expression traits was replicated in Cardiogenics, an independent study in which expression profiles of monocytes were available in 758 subjects. The locus 12q13 (lead SNP rs11171739), previously identified as a type 1 diabetes locus, was associated with a pattern including two cis eQTLs, RPS26 and SUOX, and 5 trans eQTLs, one of which (MADCAM1) is a potential candidate for mediating T1D susceptibility. The locus 12q24 (lead SNP rs653178), which has demonstrated extensive disease pleiotropy, including type 1 diabetes, hypertension, and celiac disease, was associated to a pattern strongly correlating to blood pressure level. The strongest trans eQTL in this pattern was CRIP1, a known marker of cellular proliferation in cancer. The locus 12q15 (lead SNP rs11177644) was associated with a pattern driven by two cis eQTLs, LYZ and YEATS4, and including 34 trans eQTLs, several of them tumor-related genes. This study shows that a method exploiting the structure of co-expressions among genes can help identify genomic regions involved in trans regulation of sets of genes and can provide clues for understanding the mechanisms linking genome-wide association loci to disease.

Project description:BackgroundThe heterogenous cytogenetic and molecular variations were harbored by AML patients, some of which are related with AML pathogenesis and clinical outcomes. We aimed to uncover the intrinsic expression profiles correlating with prognostic genetic abnormalities by WGCNA.MethodsWe downloaded the clinical and expression dataset from BeatAML, TCGA and GEO database. Using R (version 4.0.2) and 'WGCNA' package, the co-expression modules correlating with the ELN2017 prognostic markers were identified (R2 ≥ 0.4, p < 0.01). ORA detected the enriched pathways for the key co-expression modules. The patients in TCGA cohort were randomly assigned into the training set (50%) and testing set (50%). The LASSO penalized regression analysis was employed to build the prediction model, fitting OS to the expression level of hub genes by 'glmnet' package. Then the testing and 2 independent validation sets (GSE12417 and GSE37642) were used to validate the diagnostic utility and accuracy of the model.ResultsA total of 37 gene co-expression modules and 973 hub genes were identified for the BeatAML cohort. We found that 3 modules were significantly correlated with genetic markers (the 'lightyellow' module for NPM1 mutation, the 'saddlebrown' module for RUNX1 mutation, the 'lightgreen' module for TP53 mutation). ORA revealed that the 'lightyellow' module was mainly enriched in DNA-binding transcription factor activity and activation of HOX genes. The 'saddlebrown' module was enriched in immune response process. And the 'lightgreen' module was predominantly enriched in mitosis cell cycle process. The LASSO- regression analysis identified 6 genes (NFKB2, NEK9, HOXA7, APRC5L, FAM30A and LOC105371592) with non-zero coefficients. The risk score generated from the 6-gene model, was associated with ELN2017 risk stratification, relapsed disease, and prior MDS history. The 5-year AUC for the model was 0.822 and 0.824 in the training and testing sets, respectively. Moreover, the diagnostic utility of the model was robust when it was employed in 2 validation sets (5-year AUC 0.743-0.79).ConclusionsWe established the co-expression network signature correlated with the ELN2017 recommended prognostic genetic abnormalities in AML. The 6-gene prediction model for AML survival was developed and validated by multiple datasets.

Project description:Both cis- and trans-regulatory mutations contribute to gene expression divergence within and between species. To estimate their relative contributions, we examined two yeast strains, BY4741 (a laboratory strain) and RM11-1a (a wild strain), for their gene expression divergence first by microarray. By incorporating the obtained array data with ChIP-chip data, we generated a set of single-regulator-regulated genes that showed expression divergence between BY4741 and RM11-1a. We randomly selected 50 of these genes for further study. We found substantial variations in the DNA sequences of the promoter regions of these genes and in their regulators between the two strains, which are potential sources of expression divergence. We then developed a step-by-step approach to assess the relative contributions of cis- and trans-variations to expression divergence by using pyrosequencing to quantify the mRNA levels of the two alleles of BY4741 and RM11-1a in co-cultures and in hybrid diploids. Among the 50 genes studied, 4 genes showed no significant expression difference when the two strains were grown in separate cultures or in the same culture (co-culture) and 6 genes showed significant expression difference in separate cultures but not in co-culture. The remaining 40 genes showed expression divergence between the two strains in co-culture. Among these 40 genes, pyrosequencing of diploids showed that 18 cases of expression divergence (45%=18/40) can be attributed to differences in trans-acting factors alone, 7 cases (17.5%) mainly to trans-variations, 8 cases (20%) to both cis- and trans-acting factors, 3 cases (7.5%) mainly to cis-variations, and 4 cases (10%) to cis-acting factors alone. In addition, we replaced the BY promoter by the RM promoter in each of 10 BY genes that were found from our microarray data to have expression divergence between BY and RM, and in each case our quantitative PCR analysis revealed the cis effect of the promoter replacement on gene expression. In summary, our study suggests that trans-acting factors play the major role in expression evolution between yeast strains, but the role of cis variation is also important. Keywords: comparative gene expression between strains

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Co-expression analysis reveals interpretable gene modules controlled by trans-acting genetic variants.

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Co-expression analysis reveals interpretable gene modules controlled by <i>trans</i>-acting genetic variants.

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