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GIGI-Quick: a fast approach to impute missing genotypes in genome-wide association family data

Abstract Summary Genome-wide association studies have become common over the last ten years, with a shift towards targeting rare variants, especially in pedigree-data. Despite lower costs, sequencing for rare variants still remains expensive. To have a relatively large sample with acceptable cost, i... Full description

Journal Title: Bioinformatics 2018, Vol. 34(9), pp.1591-1593
Main Author: Kunji, Khalid
Other Authors: Ullah, Ehsan , Nato, Alejandro Q , Wijsman, Ellen M , Saad, Mohamad
Format: Electronic Article Electronic Article
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ID: ISSN: 1367-4803 ; E-ISSN: 1460-2059 ; DOI: 10.1093/bioinformatics/btx782
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recordid: oxford10.1093/bioinformatics/btx782
title: GIGI-Quick: a fast approach to impute missing genotypes in genome-wide association family data
format: Article
creator:
  • Kunji, Khalid
  • Ullah, Ehsan
  • Nato, Alejandro Q
  • Wijsman, Ellen M
  • Saad, Mohamad
subjects:
  • Biology
ispartof: Bioinformatics, 2018, Vol. 34(9), pp.1591-1593
description: Abstract Summary Genome-wide association studies have become common over the last ten years, with a shift towards targeting rare variants, especially in pedigree-data. Despite lower costs, sequencing for rare variants still remains expensive. To have a relatively large sample with acceptable cost, imputation approaches may be used, such as GIGI for pedigree data. GIGI is an imputation method that handles large pedigrees and is particularly good for rare variant imputation. GIGI requires a subset of individuals in a pedigree to be fully sequenced, while other individuals are sequenced only at relevant markers. The imputation will infer the missing genotypes at untyped markers. Running GIGI on large pedigrees for large numbers of markers can be very time consuming. We present GIGI-Quick as a method to efficiently split GIGI's input, run GIGI in parallel and efficiently merge the output to reduce the runtime with the number of cores. This allows obtaining imputation results faster, and therefore all subsequent association analyses. Availability and and implementation GIGI-Quick is open source and publicly available via: https://cse-git.qcri.org/Imputation/GIGI-Quick. Supplementary information Supplementary data are available at Bioinformatics online.
language:
source:
identifier: ISSN: 1367-4803 ; E-ISSN: 1460-2059 ; DOI: 10.1093/bioinformatics/btx782
fulltext: fulltext
issn:
  • 1367-4803
  • 13674803
  • 1460-2059
  • 14602059
url: Link


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titleGIGI-Quick: a fast approach to impute missing genotypes in genome-wide association family data
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descriptionAbstract Summary Genome-wide association studies have become common over the last ten years, with a shift towards targeting rare variants, especially in pedigree-data. Despite lower costs, sequencing for rare variants still remains expensive. To have a relatively large sample with acceptable cost, imputation approaches may be used, such as GIGI for pedigree data. GIGI is an imputation method that handles large pedigrees and is particularly good for rare variant imputation. GIGI requires a subset of individuals in a pedigree to be fully sequenced, while other individuals are sequenced only at relevant markers. The imputation will infer the missing genotypes at untyped markers. Running GIGI on large pedigrees for large numbers of markers can be very time consuming. We present GIGI-Quick as a method to efficiently split GIGI's input, run GIGI in parallel and efficiently merge the output to reduce the runtime with the number of cores. This allows obtaining imputation results faster, and therefore all subsequent association analyses. Availability and and implementation GIGI-Quick is open source and publicly available via: https://cse-git.qcri.org/Imputation/GIGI-Quick. Supplementary information Supplementary data are available at Bioinformatics online.
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