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A statistical framework to guide sequencing choices in pedigrees.

Charles Y K Cheung1, Elizabeth Marchani Blue2, Ellen M Wijsman3

  • 1Division of Medical Genetics, Department of Medicine, University of Washington, Seattle, WA 98195, USA; Department of Biostatistics, University of Washington, Seattle, WA 98195, USA.

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Summary
This summary is machine-generated.

Prioritizing subjects for sequencing in large pedigrees improves the cost-effectiveness of genetic studies. A new method, GIGI-Pick, enhances genotype imputation accuracy for identifying rare functional variants.

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Area of Science:

  • Genetics
  • Bioinformatics
  • Computational Biology

Background:

  • Large pedigrees are valuable for identifying rare functional variants influencing heritable traits.
  • Pedigree-based genotype imputation offers a cost-effective approach to sequence data analysis by inferring missing genotypes.
  • Careful subject selection for sequencing is crucial due to high costs.

Purpose of the Study:

  • To introduce a statistical framework for systematic comparison of subject-selection strategies for sequencing.
  • To develop a method for identifying efficient subject selections to maximize genotype imputation accuracy.
  • To improve the cost-effectiveness of genetic studies by optimizing sequencing efforts.

Main Methods:

  • Introduction of "local coverage" and "genome-wide coverage" metrics for assessing genotype imputation ability.
  • Development of the GIGI-Pick method for subject selection based on coverage metrics.
  • Comparison of GIGI-Pick with existing methods (PRIMUS, ExomePicks, ad hoc) using simulations and a real pedigree.

Main Results:

  • GIGI-Pick substantially outperformed PRIMUS, ExomePicks, and ad hoc methods in genotype imputation accuracy for both common and rare alleles.
  • GIGI-Pick demonstrated the advantage of incorporating prior linkage information.
  • The method was successfully applied to a real pedigree for identifying causal mutations.

Conclusions:

  • The developed statistical framework and GIGI-Pick method enable efficient subject prioritization for sequencing in pedigree studies.
  • This approach facilitates the dissection of the genetic basis of heritable traits by improving the identification of causal variants.
  • GIGI-Pick offers a flexible and powerful tool for optimizing genetic research designs.