Title
Pedigree Error Detection and Relationship Estimation for Genetic Linkage Studies
Speaker
Lei Sun, Department of Statistics, University of Chicago
Abstract
Accurate information on the relationships among individuals is critical for valid genetic linkage studies, because the presence of pedigree errors can either reduce the power or increase the false positive evidence for linkage. Genome-screen data collected for linkage studies can provide considerable power to detect misspecified relationships and valuable information to infer the correct relationships.
Mathematical models for the underlying segregation and transmission of the chromosomes will be described. Under these models, all the crossover processes in a pedigree can be viewed jointly as a continuous-time Markov random walk on the vertices of a hypercube. In practice, only limited information on this Markov process can be observed and the dimension of the hypercube is generally large. To circumvent the computational difficulties, we construct augmented Markov processes that have substantially reduced numbers of states, and we use a hidden Markov method to calculate the likelihood of observed genotype data for specific pairs of individuals. This allows us to perform hypothesis tests for detection of misspecified relationships. For complex pedigrees, the likelihood calculations become infeasible. As an alternative, we propose some new statistics that are computationally simpler, yet result in powerful hypothesis tests for detection of pedigree errors. We also discuss their extensions to inbred pedigrees. To infer the true relationships for suspect pairs, we introduce a simple method for relationship estimation.
We will also discuss the implementation of the methods, with applications to several data sets.
