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).6-9
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