Bayesian Corrections of a Selection Bias in Genetics.
When there is a rare disease in a population, it is inefficient to take a random sample to estimate a parameter. Instead one takes a random sample of all nuclear families with the disease by ascertaining at least one sibling (proband) of each family. In these studies,
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if the ascertainment bias is ignored, an estimate of the proportion of siblings with the disease will be inflated. The problem arises in population genetics, and it is analogous to the well-known selection bias problem in survey sampling. Sometimes the situation is even worse; the investigator takes all the families that appear. Thus, there is a selection bias.Here, scientists at the medical college of Georgia have developed a Bayesian analysis to estimate the segregation ratio in nuclear families when there is an ascertainment bias.
The Bayesian analysis is useful because we can obtain exact distributions under the specified model, and we can input important prior information (e.g., about the genetic features of cystic fibrosis).
Authors: Balgobin Nandram1 and Hongyan Xu2*
Source: Nandram and Xu J Biomet Biostat 2011, 2:2
DOI: Published March 10, 2011
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