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PedBLIMP: extending linear predictors to impute genotypes in pedigrees.

Wenan Chen1, Daniel J Schaid

  • 1Division of Biomedical Statistics and Informatics, Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota, United States of America.

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|July 22, 2014
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Summary

PedBLIMP improves genotype imputation accuracy by integrating pedigree and linkage disequilibrium information. This method enhances rare variant imputation and outperforms existing tools like IMPUTE2 and GIGI under specific marker densities.

Keywords:
genotype imputationidentity by descentlinear predictorlinkage disequilibrium

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Area of Science:

  • Genetics
  • Statistical Genetics
  • Bioinformatics

Background:

  • Genotype imputation is crucial for genomic studies.
  • Existing methods like BLIMP use multivariate normal moments and population genetics models.
  • Accurate imputation is challenging for rare variants and in pedigrees.

Purpose of the Study:

  • To extend multivariate moments for genotype imputation in pedigrees.
  • To develop a method, PedBLIMP, that leverages both linkage disequilibrium (LD) and pedigree/identity-by-descent (IBD) information.
  • To evaluate PedBLIMP's imputation accuracy compared to existing methods.

Main Methods:

  • Extended conditional multivariate normal moments to incorporate pedigree/IBD information.
  • Utilized external panel data for LD estimation.
  • Evaluated performance on a pedigree design with varying marker densities (dense and sparse).
  • Compared PedBLIMP against BLIMP, IMPUTE2, and GIGI.

Main Results:

  • PedBLIMP demonstrated improved genotype imputation accuracy compared to BLIMP by incorporating pedigree/IBD information.
  • Imputation accuracy for rare variants was significantly enhanced due to the inclusion of pedigree/IBD data.
  • PedBLIMP outperformed IMPUTE2 and GIGI within a specific sparse marker density range.

Conclusions:

  • Integrating pedigree/IBD information with LD data improves genotype imputation accuracy.
  • PedBLIMP offers a robust approach for genotype imputation, particularly beneficial for rare variants.
  • The method shows competitive or superior performance against state-of-the-art imputation tools in certain scenarios.