A Bayesian approach to copy-number-polymorphism analysis in nuclear pedigrees

Abstract

Segmental copy-number polymorphisms (CNPs) represent a significant component of human genetic variation and are likely to contribute to disease susceptibility. These potentially multiallelic and highly polymorphic systems present new challenges to family-based genetic-analysis tools that commonly assume codominant markers and allow for no genotyping error. The copy-number quantitation (CNP phenotype) represents the total number of segmental copies present in an individual and provides a means to infer, rather than to observe, the underlying allele segregation. We present an integrated approach to meet these challenges, in the form of a graphical model in which we infer the underlying CNP phenotype from the (single or replicate) quantitative measure within the analysis while assuming an allele-based system segregating through the pedigree. This approach can be readily applied to the study of any form of genetic measure, and the construction permits extension to a wide variety of hypothesis tests. We have implemented the basic model for use with nuclear families, and we illustrate its application through an analysis of the CNP located in gene CCL3L1 in 201 families with asthma.

Figure 1.

Model represented as a directed acyclic graph. Ovals represent stochastic nodes, rectangles represent logical nodes, and shaded rectangles summarize repeated structures.

Figure 2.

One selected U.K. family. Blackened symbols represent individuals affected with asthma. The column of numbers below each individual lists the replicate CNP assay measure for each. The number to the left of the column represents the integer (closest to the mean) assignment. The numbers to the right in parentheses reflect the most likely (in larger, bold type) and the next most likely underlying phenotype taken from the marginal posterior probability for each individual.