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Pre-Implantation Genetic Testing for Aneuploidy on a Semiconductor Based Next-Generation Sequencing Platform
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FamSeq: a variant calling program for family-based sequencing data using graphics processing units.

Gang Peng1, Yu Fan1, Wenyi Wang1

  • 1Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, Texas, United States of America.

Plos Computational Biology
|October 31, 2014
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Summary
This summary is machine-generated.

FamSeq reduces errors in genetic variant calling by using family pedigree data and Mendelian genetics. This novel approach improves accuracy for whole-genome sequencing in families.

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Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Variant calling from next-generation sequencing data is crucial for genetic studies.
  • Existing methods often do not leverage available pedigree information for family-based sequencing data.
  • High false positive and false negative rates remain a challenge in variant calling.

Purpose of the Study:

  • To develop a novel program, FamSeq, that integrates pedigree information and Mendelian genetic models to enhance variant calling accuracy.
  • To reduce both false positive and false negative rates in genetic variant detection using family data.
  • To provide efficient and scalable solutions for variant calling in large families.

Main Methods:

  • FamSeq incorporates pedigree information using four implementations of the Mendelian genetic model: Bayesian network, GPU-accelerated Bayesian network, Elston-Stewart, and Markov Chain Monte Carlo algorithms.
  • The Bayesian network algorithm was parallelized on a graphics processing unit (GPU) to handle pedigrees with inbreeding loops efficiently.
  • Performance was evaluated using pedigree sequencing data from families ranging from 7 to 12 individuals.

Main Results:

  • The Elston-Stewart algorithm provides rapid analytical results for pedigrees without inbreeding loops.
  • The Bayesian network method offers exact answers for pedigrees with inbreeding loops.
  • GPU parallelization of the Bayesian network algorithm achieved a 10-fold reduction in computation time for whole-genome sequencing data of a 12-person family, completing the analysis in two days.

Conclusions:

  • FamSeq effectively reduces false positive and false negative rates in variant calling by utilizing pedigree information.
  • The GPU-accelerated Bayesian network algorithm provides an efficient and precise method for variant calling in complex pedigrees, including those with inbreeding.
  • FamSeq offers a valuable tool for accurate genetic variant analysis in family-based sequencing studies.