title: Bayesian statistics in genetics: a guide for the uninitiated.
abstract: Statistical analyses are used in many fields of genetic research. Most geneticists are taught classical statistics, which includes hypothesis testing, estimation and the construction of confidence intervals; this framework has proved more than satisfactory in many ways. What does a Bayesian framework have to offer geneticists? Its utility lies in offering a more direct approach to some questions and the incorporation of prior information. It can also provide a more straightforward interpretation of results. The utility of a Bayesian perspective, especially for complex problems, is becoming increasingly clear to the statistics community; geneticists are also finding this framework useful and are increasingly utilizing the power of this approach.
authors.affiliation: The Cancer Prevention, Detection, Control Research Program, Duke Medical Center, Box 2949, Durham, NC 27710, USA. shoem003@mc.duke.edu
authors.forename: J S
authors.initials: JS
authors.lastname: Shoemaker
authors.forename: I S
authors.initials: IS
authors.lastname: Painter
authors.forename: B S
authors.initials: BS
authors.lastname: Weir
mesh_terms: D001499:Bayes Theorem; D001699:Biometry; D005823:Genetics / Q000706:statistics & numerical data*; D008957:Models, Genetic; D011336:Probability; D019655:Quantitative Trait, Heritable