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Large-Scale Multi-Omics Genome-Wide Association Studies (Mo-GWAS): Guidelines for Sample Preparation and Normalization
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Advances in Bayesian multiple quantitative trait loci mapping in experimental crosses.

N Yi1, D Shriner

  • 1Section on Statistical Genetics, Department of Biostatistics, University of Alabama at Birmingham, Birmingham, AL 35294-0022, USA. nyi@ms.soph.uab.edu

Heredity
|November 8, 2007
PubMed
Summary

Bayesian methods offer advanced tools for mapping multiple quantitative trait loci (QTL) in complex genetic studies. This review highlights recent Bayesian approaches for QTL mapping and their applications in understanding disease genetics.

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Area of Science:

  • Genetics
  • Bioinformatics
  • Statistical Genetics

Background:

  • Complex human diseases and traits are influenced by multiple interacting quantitative trait loci (QTL) and environmental factors.
  • Mapping QTL is essential for understanding the genetic basis of complex traits and identifying responsible genes.

Purpose of the Study:

  • To review recently developed and emerging Bayesian methods for multiple QTL mapping in experimental crosses.
  • To compare and contrast different Bayesian approaches and associated software.
  • To identify future research directions in Bayesian QTL mapping.

Main Methods:

  • Review of advanced statistical methods, focusing on Bayesian approaches for multiple QTL mapping.
  • Comparative analysis of various Bayesian methods and their computational software.
  • Discussion of the joint inference capabilities of Bayesian methods for QTL number, position, and effects.

Main Results:

  • Bayesian methods have significantly advanced the field of multiple QTL mapping over the past decade.
  • These methods enable joint inference of QTL number, genomic positions, and genetic effects.
  • A range of recently developed Bayesian methods and software are available for experimental crosses.

Conclusions:

  • Bayesian approaches represent a remarkable evolution in multiple QTL mapping.
  • Further research is needed to refine and expand upon existing Bayesian methods.
  • Continued development of Bayesian software will enhance the study of complex genetic traits.