eQTL Mapping using RNA-seq data

Department of Biostatistics, Department of Genetics, Carolina Center of Genome Science, UNC Chapel Hill, Chapel Hill, NC 27599, USA.
Statistics in Biosciences 05/2013; 5(1):198-219. DOI: 10.1007/s12561-012-9068-3
Source: PubMed


As RNA-seq is replacing gene expression microarrays to assess genome-wide transcription abundance, gene expression Quantitative Trait Locus (eQTL) studies using RNA-seq have emerged. RNA-seq delivers two novel features that are important for eQTL studies. First, it provides information on allele-specific expression (ASE), which is not available from gene expression microarrays. Second, it generates unprecedentedly rich data to study RNA-isoform expression. In this paper, we review current methods for eQTL mapping using ASE and discuss some future directions. We also review existing works that use RNA-seq data to study RNA-isoform expression and we discuss the gaps between these works and isoform-specific eQTL mapping.

  • [Show abstract] [Hide abstract]
    ABSTRACT: Transcriptome studies have revealed a surprisingly high level of variation among individuals in expression of key genes in the CNS under both normal and experimental conditions. Ten-fold variation is common, yet the specific causes and consequences of this variation are largely unknown. By combining classic gene mapping methods-family linkage studies and genomewide association-with high-throughput genomics, it is now possible to define quantitative trait loci (QTLs), single-gene variants, and even single SNPs and indels that control gene expression in different brain regions and cells. This review considers some of the major technical and conceptual challenges in analyzing variation in expression in the CNS with a focus on mRNAs, rather than noncoding RNAs or proteins. At one level of analysis, this work has been highly successful, and we finally have techniques that can be used to track down small numbers of loci that control expression in the CNS. But at a higher level of analysis, we still do not understand the genetic architecture of gene expression in brain, the consequences of expression QTLs on protein levels or on cell function, or the combined impact of expression differences on behavior and disease risk. These important gaps are likely to be bridged over the next several decades using (1) much larger sample sizes, (2) more powerful RNA sequencing and proteomic methods, and (3) novel statistical and computational models to predict genome-to-phenome relations.
    No preview · Article · Aug 2014 · International Review of Neurobiology
  • [Show abstract] [Hide abstract]
    ABSTRACT: Expression quantitative trait loci (eQTLs) are currently the most abundant and systematically-surveyed class of functional consequence for genetic variation. Recent genetic studies of gene expression have identified thousands of eQTLs in diverse tissue types for the majority of human genes. Application of this large eQTL catalog provides an important resource for understanding the molecular basis of common genetic diseases. However, only now has both the availability of individuals with full genomes and corresponding advances in functional genomics provided the opportunity to dissect eQTLs to identify causal regulatory variants. Resolving the properties of such causal regulatory variants is improving understanding of the molecular mechanisms that influence traits and guiding the development of new genome-scale approaches to variant interpretation. In this review, we provide an overview of current computational and experimental methods for identifying causal regulatory variants and predicting their phenotypic consequences.
    No preview · Article · Apr 2014 · Human Genetics
  • [Show abstract] [Hide abstract]
    ABSTRACT: Genome-wide association studies (GWAS) have shown a large number of genetic variants to be associated with complex diseases. The identification of the causal variant within an associated locus can sometimes be difficult because of linkage disequilibrium between the associated variants and because most GWAS loci contain multiple genes, or no genes at all. Expression quantitative trait locus (eQTL) mapping is a method to determine the effects of genetic variants on gene expression levels. eQTL mapping studies have enabled the prioritization of genetic variants within GWAS loci, and have shown that trait-associated SNPs often function in a tissue- or cell type-specific manner, sometimes having downstream effects on completely different chromosomes. Furthermore, recent RNA-seq studies have shown that a large repertoire of transcripts is available in cells, which are actively regulated by (trait-associated) variants. Future eQTL mapping studies will focus on broadening the range of available tissues and cell types, in order to determine the key tissues and cell types involved in complex traits. Finally, large meta-analyses will be able to pinpoint the causal variants within the trait-associated loci and determine their downstream effects in greater detail. This article is part of a Special Issue entitled: From Genome to Function.
    No preview · Article · May 2014 · Biochimica et Biophysica Acta
Show more