A complete tool set for molecular QTL discovery and analysis

Olivier Delaneau (Lead / Corresponding author), Halit Ongen, Andrew A. Brown, Alexandre Fort, Nikolaos I. Panousis, Emmanouil T. Dermitzakis (Lead / Corresponding author)

Research output: Contribution to journalArticlepeer-review

35 Citations (Scopus)
121 Downloads (Pure)

Abstract

Population scale studies combining genetic information with molecular phenotypes (for example, gene expression) have become a standard to dissect the effects of genetic variants onto organismal phenotypes. These kinds of data sets require powerful, fast and versatile methods able to discover molecular Quantitative Trait Loci (molQTL). Here we propose such a solution, QTLtools, a modular framework that contains multiple new and well-established methods to prepare the data, to discover proximal and distal molQTLs and, finally, to integrate them with GWAS variants and functional annotations of the genome. We demonstrate its utility by performing a complete expression QTL study in a few easy-to-perform steps. QTLtools is open source and available at https://qtltools.github.io/qtltools/.

Original languageEnglish
Article number15452
Pages (from-to)1-7
Number of pages7
JournalNature Communications
Volume8
DOIs
Publication statusPublished - 18 May 2017

Keywords

  • Algorithms
  • Chromosome Mapping/methods
  • Genome, Human
  • Genome-Wide Association Study
  • Genotype
  • High-Throughput Nucleotide Sequencing/statistics & numerical data
  • Humans
  • Phenotype
  • Polymorphism, Single Nucleotide
  • Quality Control
  • Quantitative Trait Loci

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