A complete tool set for molecular QTL discovery and analysis

Olivier Delaneau; Halit Ongen; Andrew A. Brown; Alexandre Fort; Nikolaos I. Panousis; Emmanouil T. Dermitzakis

doi:10.1038/ncomms15452

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)

Population Health and Genomics

Research output: Contribution to journal › Article › peer-review

173 Citations (Scopus)

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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 language	English
Article number	15452
Pages (from-to)	1-7
Number of pages	7
Journal	Nature Communications
Volume	8
DOIs	https://doi.org/10.1038/ncomms15452
Publication status	Published - 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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Final published version, 660 KBLicence: CC BY

Cite this

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title = "A complete tool set for molecular QTL discovery and analysis",

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/.",

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AU - Delaneau, Olivier

AU - Ongen, Halit

AU - Brown, Andrew A.

AU - Fort, Alexandre

AU - Panousis, Nikolaos I.

AU - Dermitzakis, Emmanouil T.

PY - 2017/5/18

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N2 - 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/.

AB - 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/.

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KW - Chromosome Mapping/methods

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KW - Genome-Wide Association Study

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KW - High-Throughput Nucleotide Sequencing/statistics & numerical data

KW - Humans

KW - Phenotype

KW - Polymorphism, Single Nucleotide

KW - Quality Control

KW - Quantitative Trait Loci

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A complete tool set for molecular QTL discovery and analysis

Abstract

Keywords

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