Second, the number of trans-eQTL associations is an order of magnitude more than cis-eQTL associations, which brings heavy computational burdens. First, trans-acting effects are usually weaker than cis-acting, which requires a large sample size to detect the weak signals ( Yao et al., 2017). However, very little knowledge is known for trans-eQTLs due to multifaceted reasons.
For example, cis-eQTLs usually reside close to transcription starting sites (TSS) and might affect the gene expression directly through affecting transcription factor (TF) binding process ( Nica and Dermitzakis, 2013). cis-acting or trans-acting may reflect different underlying regulation natures. eQTLs can exert their regulatory effects on local gene transcriptions ( cis-acting) and distant gene transcriptions ( trans-acting), defined by the physical distance between an eQTL and a gene, usually using 1 Mb as a threshold or on different chromosomes for trans-acting associations ( Ongen et al., 2015 GTEx Consortium, 2017). And eQTL summary statistics have been widely used in the interpretation of GWAS results and Mendelian randomization studies ( Cheng et al., 2018b Peng et al., 2019a). Most eQTL mapping studies access the eQTL effects through association tests between the genotypes of a variant and expression profiles of a gene using regression models ( Shabalin, 2012 Ongen et al., 2015).
The expression quantitative trait loci (eQTL) analysis has been proven a powerful tool in achieving this goal.Īn eQTL is essentially a variant at a specific genome location with its genetic variance associates with gene expression variation in a population. To understand the complex regulatory natures of genomic variants, one of the fundamental tasks is to discover target genes which can be regulated by variants in the cell. However, most of those traits-associated variants localize in non-coding regions, intergenic, or intronic regions, indicating that genomic variants are likely to be involved in gene regulation instead of exerting their effects through altering the protein sequence directly ( Gallagher and Chen-Plotkin, 2018). Vast genome variants relevant to disease risks and other traits have been unequivocally identified by genome-wide association studies (GWAS) ( Visscher et al., 2017). Understanding the complex functional natures of genome variants has been the focus of many studies in recent years, which provides us with advanced insights into phenotype variability and disease susceptibility ( Cheng et al., 2017 Watanabe et al., 2017 Gallagher and Chen-Plotkin, 2018). Experiments on real eQTL dataset demonstrate that eQTLMAPT provides higher resolution of estimated significance of mediation effects and is an order of magnitude faster than compared methods with similar accuracy. Third, eQTLMAPT provides flexible interfaces for users to combine various permutation schemes with different confounding adjustment methods. Second, it can efficiently and accurately estimate the significance level of mediation effects by modeling the null distribution with generalized Pareto distribution (GPD) trained from a few permutation statistics. First, it accelerates mediation analysis by effectively pruning the permutation process through adaptive permutation scheme.
Here, we present eQTLMAPT, an R package aiming to perform eQTL mediation analysis with implementation of efficient permutation procedures in multiple testing correction. To discover the genome-wide eQTL mediation effects combing genomic and transcriptomic profiles, it is necessary to develop novel computational methods to rapidly scan large number of candidate associations while controlling for multiple testing appropriately. Emerging evidence has shown that trans-eQTL effects on remote gene expression could be mediated by local transcripts, which is known as the mediation effects. 3School of Computer Science, Northwestern Polytechnical University, Xi’an, ChinaĮxpression quantitative trait locus (eQTL) analyses are critical in understanding the complex functional regulatory natures of genetic variation and have been widely used in the interpretation of disease-associated variants identified by genome-wide association studies (GWAS).
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