About this Research Topic
The current explosion of GWAS, driven in part by falling genotyping costs, has revealed some causal variants and others that have failed to replicate. In some cases, the observed linkage of associated SNPs to coding genes suggests that the associated variant might alter risk by regulating gene expression. cis-linked noncoding sequences often contain consensus binding sites for transcription factors [ENCODE Project Consortium, 2007], and recently, a causal variant for colorectal cancer was shown to be a cis-eQTL for SMAD7 expression [Pittman et al., 2009], suggesting that other associated variants might alter disease predisposition by affecting expression levels of key signaling molecules.
Although not all causal variants are likely to regulate expression levels of protein-coding genes, such evidence for an associated SNP can be useful information. Assuming that the associated variant somehow regulates the expression levels of a coding gene, existing prior biological knowledge for the coding gene can help validate the top GWAS hits, suggest a biological mechanism, and potentially give targets for future experimentation. A new technology, RNA-Seq, can produce sequences and digital expression measurements of mRNA molecules from samples.
Pioneering work provided direct evidence for the role of heritable genetic variation in gene regulation and gene co-regulation [Jansen and Nap, 2001; Brem et al., 2002; Yvert et al., 2003; Schadt et al., 2003]. More recent work has expanded upon this with Bayesian methods to predict transcriptional networks from these data, inferring causal relationships that were subsequently biologically validated [e.g. Lee et al., 2006, 2009]. With the challenge that these newer approaches generally require substantial computation, this is a rapidly expanding field that draws on biology, applied mathematics, and computer science to develop new methods.
This Research Topic solicits new methodologies for the genetic dissection of gene regulation and welcomes contributions from all areas of this field, including, but not limited to, methodologies for network prediction from expression and genotyping data, inferring epistatic interactions between eQTLs, species- or tissue-specificity of eQTLs, population genetic aspects of eQTLs, analysis of RNA-Seq data, and statistical models that incorporate expression and sequence data. Submissions describing methods for the integration of expression data and genotypes from disjoint individuals [e.g. Sillanpää and Noykova, 2008] are also welcome.
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