Fast and efficient QTL mapper for thousands of molecular phenotypes

Halit Ongen; Alfonso Buil; Andrew Anand Brown; Emmanouil T. Dermitzakis; Olivier Delaneau

doi:10.1093/bioinformatics/btv722

Fast and efficient QTL mapper for thousands of molecular phenotypes

Halit Ongen, Alfonso Buil, Andrew Anand Brown, Emmanouil T. Dermitzakis (Lead / Corresponding author), Olivier Delaneau (Lead / Corresponding author)

Population Health and Genomics

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325 Citations (Scopus)

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Abstract

MOTIVATION: In order to discover quantitative trait loci, multi-dimensional genomic datasets combining DNA-seq and ChiP-/RNA-seq require methods that rapidly correlate tens of thousands of molecular phenotypes with millions of genetic variants while appropriately controlling for multiple testing.

RESULTS: We have developed FastQTL, a method that implements a popular cis-QTL mapping strategy in a user- and cluster-friendly tool. FastQTL also proposes an efficient permutation procedure to control for multiple testing. The outcome of permutations is modeled using beta distributions trained from a few permutations and from which adjusted P-values can be estimated at any level of significance with little computational cost. The Geuvadis & GTEx pilot datasets can be now easily analyzed an order of magnitude faster than previous approaches.

AVAILABILITY AND IMPLEMENTATION: Source code, binaries and comprehensive documentation of FastQTL are freely available to download at http://fastqtl.sourceforge.net/

CONTACT: [email protected] or [email protected]

SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Original language	English
Pages (from-to)	1479-1485
Number of pages	7
Journal	Bioinformatics
Volume	32
Issue number	10
Early online date	26 Dec 2015
DOIs	https://doi.org/10.1093/bioinformatics/btv722
Publication status	Published - 15 May 2016

Keywords

Genomics
Phenotype
Quantitative Trait Loci
Software
Statistical Distributions

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10.1093/bioinformatics/btv722Licence: CC BY

Final published version
© The Author 2015. Published by Oxford University Press. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
Final published version, 520 KBLicence: CC BY

Cite this

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title = "Fast and efficient QTL mapper for thousands of molecular phenotypes",

abstract = "MOTIVATION: In order to discover quantitative trait loci, multi-dimensional genomic datasets combining DNA-seq and ChiP-/RNA-seq require methods that rapidly correlate tens of thousands of molecular phenotypes with millions of genetic variants while appropriately controlling for multiple testing.RESULTS: We have developed FastQTL, a method that implements a popular cis-QTL mapping strategy in a user- and cluster-friendly tool. FastQTL also proposes an efficient permutation procedure to control for multiple testing. The outcome of permutations is modeled using beta distributions trained from a few permutations and from which adjusted P-values can be estimated at any level of significance with little computational cost. The Geuvadis & GTEx pilot datasets can be now easily analyzed an order of magnitude faster than previous approaches.AVAILABILITY AND IMPLEMENTATION: Source code, binaries and comprehensive documentation of FastQTL are freely available to download at http://fastqtl.sourceforge.net/CONTACT: [email protected] or [email protected] INFORMATION: Supplementary data are available at Bioinformatics online.",

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author = "Halit Ongen and Alfonso Buil and Brown, {Andrew Anand} and Dermitzakis, {Emmanouil T.} and Olivier Delaneau",

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T1 - Fast and efficient QTL mapper for thousands of molecular phenotypes

AU - Ongen, Halit

AU - Buil, Alfonso

AU - Brown, Andrew Anand

AU - Dermitzakis, Emmanouil T.

AU - Delaneau, Olivier

PY - 2016/5/15

Y1 - 2016/5/15

N2 - MOTIVATION: In order to discover quantitative trait loci, multi-dimensional genomic datasets combining DNA-seq and ChiP-/RNA-seq require methods that rapidly correlate tens of thousands of molecular phenotypes with millions of genetic variants while appropriately controlling for multiple testing.RESULTS: We have developed FastQTL, a method that implements a popular cis-QTL mapping strategy in a user- and cluster-friendly tool. FastQTL also proposes an efficient permutation procedure to control for multiple testing. The outcome of permutations is modeled using beta distributions trained from a few permutations and from which adjusted P-values can be estimated at any level of significance with little computational cost. The Geuvadis & GTEx pilot datasets can be now easily analyzed an order of magnitude faster than previous approaches.AVAILABILITY AND IMPLEMENTATION: Source code, binaries and comprehensive documentation of FastQTL are freely available to download at http://fastqtl.sourceforge.net/CONTACT: [email protected] or [email protected] INFORMATION: Supplementary data are available at Bioinformatics online.

AB - MOTIVATION: In order to discover quantitative trait loci, multi-dimensional genomic datasets combining DNA-seq and ChiP-/RNA-seq require methods that rapidly correlate tens of thousands of molecular phenotypes with millions of genetic variants while appropriately controlling for multiple testing.RESULTS: We have developed FastQTL, a method that implements a popular cis-QTL mapping strategy in a user- and cluster-friendly tool. FastQTL also proposes an efficient permutation procedure to control for multiple testing. The outcome of permutations is modeled using beta distributions trained from a few permutations and from which adjusted P-values can be estimated at any level of significance with little computational cost. The Geuvadis & GTEx pilot datasets can be now easily analyzed an order of magnitude faster than previous approaches.AVAILABILITY AND IMPLEMENTATION: Source code, binaries and comprehensive documentation of FastQTL are freely available to download at http://fastqtl.sourceforge.net/CONTACT: [email protected] or [email protected] INFORMATION: Supplementary data are available at Bioinformatics online.

KW - Genomics

KW - Phenotype

KW - Quantitative Trait Loci

KW - Software

KW - Statistical Distributions

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DO - 10.1093/bioinformatics/btv722

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VL - 32

SP - 1479

EP - 1485

JO - Bioinformatics

JF - Bioinformatics

IS - 10

ER -

Fast and efficient QTL mapper for thousands of molecular phenotypes

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Keywords

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