Robust methods for expression quantitative trait loci mapping

Wei Cheng, Xiang Zhang, Wei Wang

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

As a promising tool for dissecting the genetic basis of common diseases, expression quantitative trait loci (eQTL) study has attracted increasing research interest. The traditional eQTL methods focus on testing the associations between individual single-nucleotide polymorphisms (SNPs) and gene expression traits. A major drawback of this approach is that it cannot model the joint effect of a set of SNPs on a set of genes, which may correspond to biological pathways. In this chapter, we study the problem of identifying group-wise associations in eQTL mapping. Based on the intuition of group-wise association, we examine how the integration of heterogeneous prior knowledge on the correlation structures between SNPs, and between genes can improve the robustness and the interpretability of eQTL mapping.

Original languageEnglish (US)
Title of host publicationBig Data Analytics in Genomics
PublisherSpringer International Publishing
Pages25-88
Number of pages64
ISBN (Electronic)9783319412795
ISBN (Print)9783319412788
DOIs
StatePublished - Jan 1 2016

    Fingerprint

All Science Journal Classification (ASJC) codes

  • Computer Science(all)
  • Medicine(all)
  • Mathematics(all)

Cite this

Cheng, W., Zhang, X., & Wang, W. (2016). Robust methods for expression quantitative trait loci mapping. In Big Data Analytics in Genomics (pp. 25-88). Springer International Publishing. https://doi.org/10.1007/978-3-319-41279-5_2