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QTL Mapping and CRISPR/Cas9 Editing to Identify a Drug Resistance Gene in Toxoplasma gondii
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A Bayesian approach to detect QTL affecting a simulated binary and quantitative trait.

Aniek C Bouwman1, Luc Lg Janss, Henri Cm Heuven

  • 1Animal Breeding and Genomics Centre, Wageningen University, P,O, Box 338, 6700AH Wageningen, The Netherlands. Aniek.Bouwman@wur.nl.

BMC Proceedings
|June 1, 2011
PubMed
Summary
This summary is machine-generated.

This study demonstrates that a Bayesian variable selection method is effective for genome-wide association studies (GWAS). The approach successfully mapped quantitative trait loci (QTL) for both quantitative and binary traits using dense marker data.

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Area of Science:

  • Genetics
  • Bioinformatics
  • Statistical genomics

Background:

  • Analysis of simulated data from the 14th QTL-MAS workshop.
  • Utilized a Bayesian approach implemented in the program iBay.
  • Data included genotypes for 10,031 single nucleotide polymorphisms (SNPs) and phenotypes for quantitative and binary traits.

Purpose of the Study:

  • To evaluate the efficacy of a Bayesian variable selection method for genome-wide association.
  • To assess the method's performance in mapping quantitative trait loci (QTL) for different trait types.

Main Methods:

  • Bayesian variable selection approach.
  • Implementation using the iBay program.
  • Analysis of simulated genotype and phenotype data.

Main Results:

  • Successfully mapped 8/30 additive QTL, 1/3 imprinted QTL, and 2 epistatic QTL for a quantitative trait.
  • Successfully mapped 11/22 additive QTL for a binary trait.
  • Detected 4/22 pleiotropic QTL.

Conclusions:

  • The Bayesian variable selection method proved successful for genome-wide association.
  • The method demonstrated reasonable speed with dense marker maps.
  • This approach is a viable tool for genetic studies involving complex traits.