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. 2018 May 1;34(9):1591-1593.
doi: 10.1093/bioinformatics/btx782.

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

Khalid Kunji  1 , Ehsan Ullah  1 , Alejandro Q Nato Jr  2 , Ellen M Wijsman  2 , Mohamad Saad  1
Affiliations

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

Khalid Kunji et al. Bioinformatics. .

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.

Contact: msaad@hbku.edu.qa.

Supplementary information: Supplementary data are available at Bioinformatics online.

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Figures

Fig. 1.
Fig. 1.
GIGI-Quick when run on a pedigree of 189 individuals for chromosome 22 split into different numbers of chunks on a Ryzen 1800X. This is expressed as a percentage of the maximum to show scaling. 117 sparse markers (GWAS SNPs) and 377 953 dense markers (sequenced SNPs) were used. The speedup is obtained from the longest running chunk
See this image and copyright information in PMC

References

    1. Abecasis G.R. et al. (2002) Merlin – rapid analysis of dense genetic maps using sparse gene flow trees. Nat. Genet., 30, 97–101. - PubMed
    1. Bansal V. et al. (2010) Statistical analysis strategies for association studies involving rare variants. Nat. Rev. Genet., 11, 773–785. - PMC - PubMed
    1. Chen M.-H. et al. (2012) Using family-based imputation in genome-wide association studies with large complex pedigrees: The Framingham Heart Study. PLoS ONE, 7, e51589.. - PMC - PubMed
    1. Cheung C.Y. et al. (2013) GIGI: an approach to effective imputation of dense genotypes on large pedigrees. Am. J. Hum. Genet., 92, 504–516. - PMC - PubMed
    1. Evangelou E., Ioannidis J.P. (2013) Meta-analysis methods for genome-wide association studies and beyond. Nat. Rev. Genet., 14, 379–389. - PubMed

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