eQTLMAPT: Fast and Accurate eQTL Mediation Analysis With Efficient Permutation Testing Approaches

Wang, Tao; Peng, Qidi; Liu, Bo; Liu, Xiaoli; Liu, Yongzhuang; Peng, Jiajie; Wang, Yadong

doi:10.3389/fgene.2019.01309

Cited by 15 publications

(15 citation statements)

References 47 publications

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“…To explore the mediation of trans-meQTL by cis-eQTL (e.g. of potential transcription factors), mediation analysis was performed by applying eQTLMAPT 39 to the primary melanocytes' meQTL data. Only trios with evidence of both cis-eQTL and trans-meQTL association were included.…”

Section: Identifying Cis-mediators For Trans-meqtlsmentioning

confidence: 99%

“…We then asked if any cis-eQTL variant is driving trans-meQTL (i.e. via allelic expression of transcription factors and the subsequent effect on methylation of downstream targets) by performing mediation analysis using eQTLMAPT 39 . For this, we tested 152 cis-eQTL variant:cis-eQTL gene:trans-meQTL probe trios (FDR < 0.05 for cis-meQTL and < 0.01 for trans-meQTL), of which 24 trios displayed significant mediation at P < 0.05 (Table S15; Figure S11).…”

Section: Melanocyte Trans-meqtls Highlight An Irf4 Transcriptional Regulatory Networkmentioning

confidence: 99%

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Cell-type-specific meQTL extends melanoma GWAS annotation beyond eQTL and informs melanocyte gene regulatory mechanisms

Zhang

Choi

Dilshat

et al. 2021

Preprint

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While expression quantitative trait loci (eQTL) have been powerful in identifying susceptibility genes from genome-wide association studies (GWAS) findings, most trait-associated loci are not explained by eQTL alone. Alternative QTLs including DNA methylation QTL (meQTL) are emerging, but cell-type-specific meQTL using cells of disease origin has been lacking. Here we established an meQTL dataset using primary melanocytes from 106 individuals and identified 1,497,502 significant cis-meQTLs. Multi-QTL colocalization using meQTL, eQTL, and mRNA splice-junction QTL from the same individuals together with imputed methylome-wide and transcriptome-wide association studies identified susceptibility genes at 63% of melanoma GWAS loci. Among three molecular QTLs, meQTLs were the single largest contributor. To compare melanocyte meQTLs with those from malignant melanomas, we performed meQTL analysis on skin cutaneous melanomas from The Cancer Genome Atlas (n = 444). A substantial proportion of meQTL probes (45.9%) in primary melanocytes are preserved in melanomas, while a smaller fraction of eQTL genes is preserved (12.7%). Integration of melanocyte multi-QTL and melanoma meQTL identified candidate susceptibility genes at 72% of melanoma GWAS loci. Beyond GWAS annotation, meQTL-eQTL colocalization in melanocytes suggested that 841 unique genes potentially share a causal variant with a nearby methylation probe in melanocytes. Finally, melanocyte trans-meQTL identified a hotspot for rs12203592, a cis-eQTL of a transcription factor, IRF4, with 131 candidate target CpGs. Motif enrichment and IRF4 ChIPseq analysis demonstrated that these target CpGs are enriched in IRF4 binding sites, suggesting an IRF4-mediated regulatory network. Our study highlights the utility of cell-type-specific meQTL.

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Section: Identifying Cis-mediators For Trans-meqtlsmentioning

confidence: 99%

Section: Melanocyte Trans-meqtls Highlight An Irf4 Transcriptional Regulatory Networkmentioning

confidence: 99%

Cell-type-specific meQTL extends melanoma GWAS annotation beyond eQTL and informs melanocyte gene regulatory mechanisms

Zhang

Choi

Dilshat

et al. 2021

Preprint

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“…The eQTL analysis aims to associate genetic variants with the variation of gene expression levels. The eQTL summaries have been widely applied in interpreting GWAS and in mendelian randomization studies [ 6 , 7 ].…”

Section: Introductionmentioning

confidence: 99%

A pipeline for RNA-seq based eQTL analysis with automated quality control procedures

et al. 2021

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Background Advances in the expression quantitative trait loci (eQTL) studies have provided valuable insights into the mechanism of diseases and traits-associated genetic variants. However, it remains challenging to evaluate and control the quality of multi-source heterogeneous eQTL raw data for researchers with limited computational background. There is an urgent need to develop a powerful and user-friendly tool to automatically process the raw datasets in various formats and perform the eQTL mapping afterward. Results In this work, we present a pipeline for eQTL analysis, termed eQTLQC, featured with automated data preprocessing for both genotype data and gene expression data. Our pipeline provides a set of quality control and normalization approaches, and utilizes automated techniques to reduce manual intervention. We demonstrate the utility and robustness of this pipeline by performing eQTL case studies using multiple independent real-world datasets with RNA-seq data and whole genome sequencing (WGS) based genotype data. Conclusions eQTLQC provides a reliable computational workflow for eQTL analysis. It provides standard quality control and normalization as well as eQTL mapping procedures for eQTL raw data in multiple formats. The source code, demo data, and instructions are freely available at https://github.com/stormlovetao/eQTLQC.

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“…Advancements in next-generation sequencing technologies open opportunities to various biological analyses, such as de novo assemblies of bacterial and eukaryotic genomes, and species classification based on metagenomics studies [ 1 ]. Short read alignment is a common first step of various downstream analyses, such as variant calling [ 2 ], RNA abundance quantification [ 3 ], and expression quantitative trait locus (eQTL) analysis [ 4 ].…”

Section: Introductionmentioning

confidence: 99%

Fast and SNP-aware short read alignment with SALT

2021

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Background DNA sequence alignment is a common first step in most applications of high-throughput sequencing technologies. The accuracy of sequence alignments directly affects the accuracy of downstream analyses, such as variant calling and quantitative analysis of transcriptome; therefore, rapidly and accurately mapping reads to a reference genome is a significant topic in bioinformatics. Conventional DNA read aligners map reads to a linear reference genome (such as the GRCh38 primary assembly). However, such a linear reference genome represents the genome of only one or a few individuals and thus lacks information on variations in the population. This limitation can introduce bias and impact the sensitivity and accuracy of mapping. Recently, a number of aligners have begun to map reads to populations of genomes, which can be represented by a reference genome and a large number of genetic variants. However, compared to linear reference aligners, an aligner that can store and index all genetic variants has a high cost in memory (RAM) space and leads to extremely long run time. Aligning reads to a graph-model-based index that includes all types of variants is ultimately an NP-hard problem in theory. By contrast, considering only single nucleotide polymorphism (SNP) information will reduce the complexity of the index and improve the speed of sequence alignment. Results The SNP-aware alignment tool (SALT) is a fast, memory-efficient, and SNP-aware short read alignment tool. SALT uses 5.8 GB of RAM to index a human reference genome (GRCh38) and incorporates 12.8M UCSC common SNPs. Compared with a state-of-the-art aligner, SALT has a similar speed but higher accuracy. Conclusions Herein, we present an SNP-aware alignment tool (SALT) that aligns reads to a reference genome that incorporates an SNP database. We benchmarked SALT using simulated and real datasets. The results demonstrate that SALT can efficiently map reads to the reference genome with significantly improved accuracy. Incorporating SNP information can improve the accuracy of read alignment and can reveal novel variants. The source code is freely available at https://github.com/weiquan/SALT.

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eQTLMAPT: Fast and Accurate eQTL Mediation Analysis With Efficient Permutation Testing Approaches

Cited by 15 publications

References 47 publications

Cell-type-specific meQTL extends melanoma GWAS annotation beyond eQTL and informs melanocyte gene regulatory mechanisms

Cell-type-specific meQTL extends melanoma GWAS annotation beyond eQTL and informs melanocyte gene regulatory mechanisms

A pipeline for RNA-seq based eQTL analysis with automated quality control procedures

Fast and SNP-aware short read alignment with SALT

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