Identification and Quantification of Splicing Quantitative Trait Loci

Methods Mol Biol. 2020:2082:51-62. doi: 10.1007/978-1-0716-0026-9_4.

Abstract

Most complex traits, including diseases, have a large genetic component. Identifying the genetic variants and genes underlying phenotypic variation remains one of the most important objectives of current biomedical research. Unlike Mendelian or familial diseases, which are usually caused by mutations in the coding regions of individual genes, complex diseases are thought to result from the cumulative effects of a large number of variants, of which, the vast majority are noncoding. Therefore, to discern the genetic underpinnings of a complex trait, we must first understand the impact of noncoding variation, which presumably affects gene regulation. In this chapter, we outline the recent progress made and methods used to discover putative regulatory regions associated with complex traits. We will specifically focus on mapping splicing quantitative trait loci (sQTL) using Yoruba samples from GEUVADIS as a motivating example.

Keywords: Complex traits; Gene expression; Genetic variation; Quantitative trait loci (QTL); Splicing; Splicing QTL (sQTL).

MeSH terms

  • Alternative Splicing
  • Chromosome Mapping*
  • Computational Biology / methods
  • Gene Expression
  • Genetic Variation
  • Humans
  • Multifactorial Inheritance
  • Quantitative Trait Loci*
  • Quantitative Trait, Heritable
  • RNA Splicing*
  • Software