A complete tool set for molecular QTL discovery and analysis

By Olivier Delaneau, Halit Ongen, Andrew Anand Brown, A. Fort, N. Panousis, Emmanouil T. Dermitzakis. Nature Communications, 2016

326 citations recorded by the source corpus.

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/. Analysis of molecular quantitative trait loci (molQTL) can help interpret genome-wide association studies and requires efficient approaches to correct for multiple testing. This study describes a bioinformatics toolkit called QTLtool that can handle large data sets and quickly perform multiple types of molQTL analyses.

Subfields: Genetics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/414aea5ee7be76e6fb69b50a47e5cff1bb5bbb27