Advertisement

Bayesian Analysis

  • Shuji Ogino
  • Robert B. Wilson
Chapter
  • 1.7k Downloads

Abstract

The purpose of this chapter is to describe basic and general principles of Bayesian analysis for molecular pathologists. Thomas Bayes first described the theorem named after him in an essay on “the doctrine of chances,” published posthumously in 1763, and republished in 1958.1 Analyses based on Bayes’ theorem are routinely applied to calculate probabilities in a wide variety of circumstances, not limited to medicine or genetics. In molecular pathology, Bayesian analysis is commonly used to calculate genetic risk, incorporating population data, pedigree information, and genetic testing results. First, Bayesian analysis will be introduced with two simple, concrete examples. In subsequent sections, the general principles illustrated by these examples are discussed and applied to more complex scenarios. For more in-depth treatments, the reader is referred to Introduction to Risk Calculation in Genetic Counseling by Young2 and The Calculation of Genetic Risks by Bridge3 as well as several articles on genetic risk assessment that include advanced Bayesian analyses, particularly for spinal muscular atrophy (SMA)4,5 and cystic fibrosis (CF). 6, 7, 8, 9

Keywords

Conditional Probability Prior Probability Bayesian Analysis Joint Probability Spinal Muscular Atrophy 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Bayes T. An essay towards solving a problem in the doctrine of chances. Biometrika. 1958;45:296–315.CrossRefGoogle Scholar
  2. 2.
    Young I. Introduction to Risk Calculation in Genetic Counseling. Oxford: Oxford University Press; 1999.Google Scholar
  3. 3.
    Bridge P. The Calculation of Genetic Risks: Worked Examples in DNA Diagnostics. Baltimore: Johns Hopkins University Press; 1997.Google Scholar
  4. 4.
    Ogino S, Leonard DGB, Rennert H, Ewens WJ, Wilson RB. Genetic risk assessment in carrier testing for spinal muscular atrophy. Am J Med Genet. 2002;110:301–307.PubMedCrossRefGoogle Scholar
  5. 5.
    Ogino S, Wilson RB. Genetic testing and risk assessment for spinal muscular atrophy (SMA). Hum Genet. 2002;111:477–500.PubMedCrossRefGoogle Scholar
  6. 6.
    Ogino S, Wilson RB. Bayesian analysis and risk assessment in genetic counseling and testing. J Mol Diagn. 2004;6:1–9.PubMedGoogle Scholar
  7. 7.
    Ogino S, Wilson RB, Gold B, Hawley P, Grody WW. Bayesian analysis for cystic fibrosis risks in prenatal and carrier screening. Genet Med. 2004;6:439–449.PubMedGoogle Scholar
  8. 8.
    Ogino S, wilson RB, Grody WW. Bayesian risk assessment for autosomal recessive diseases: fetal echogenic bowel with one or no detectable CFTR mutation. J Med Genet. 2004;41:e70.PubMedCrossRefGoogle Scholar
  9. 9.
    Ogino S, Flodman P, Wilson RB, Gold B, Grody WW. Risk calculations for cystic fibrosis in neonatal screening by immunoreactive trypsinogen and CFTR mutation tests. Genet Med. 2005;7:317–327.PubMedGoogle Scholar
  10. 10.
    Grody WW, Cutting GR, Klinger KW, Richards CS, Watson MS, Desnick RJ. Laboratory standards and guidelines for populationbased cystic fibrosis carrier screening. Genet Med. 2001;3:149–154.PubMedCrossRefGoogle Scholar
  11. 11.
    Richards CS, Bradley LA, Amos J, et al. Standards and guidelines for CFTR mutation testing. Genet Med. 2002;4:379–391.PubMedGoogle Scholar
  12. 12.
    Watson MS, Cutting GR, Desnick RJ, et al. Cystic fibrosis population carrier screening: 2004 revision of American College of Medical Genetics mutation panel. Genet Med. 2004;6:387–391.PubMedGoogle Scholar
  13. 13.
    Wirth B, Herz M, Wetter A, et al. Quantitative analysis of survival motor neuron copies: identification of subtle SMN1 mutations in patients with spinal muscular atrophy, genotype-phenotype correlation, and implications for genetic counseling. Am J Hum Genet. 1999;64:1340–1356.PubMedCrossRefGoogle Scholar
  14. 14.
    Ogino S, Wilson RB, Gold B. New insights on the evolution of the SMN1 and SMN2 region: simulation and meta-analysis for allele and haplotype frequency calculations. Eur J Hum Genet. 2004;12:1015–1023.PubMedCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2007

Authors and Affiliations

  • Shuji Ogino
    • 1
    • 2
    • 3
  • Robert B. Wilson
    • 4
  1. 1.Department of PathologyBrigham and Women’s HospitalBoston
  2. 2.Department of Medical OncologyDana-Farber Cancer InstituteBoston
  3. 3.Harvard Medical SchoolBostonUSA
  4. 4.Department of Pathology and Laboratory MedicineUniversity of PennsylvaniaPhiladelphiaUSA

Personalised recommendations