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

Khalid Kunji, Ehsan Ullah, Alejandro Q. Nato, Ellen M. Wijsman, Mohamad Saad

Research output: Contribution to journalArticle

1 Citation (Scopus)


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: Contact Supplementary informationSupplementary dataare available at Bioinformatics online.

Original languageEnglish
Pages (from-to)1591-1593
Number of pages3
Issue number9
Publication statusPublished - 1 May 2018


ASJC Scopus subject areas

  • Statistics and Probability
  • Biochemistry
  • Molecular Biology
  • Computer Science Applications
  • Computational Theory and Mathematics
  • Computational Mathematics

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