Pedigree relationship errors often occur in family data collected for genetic studies, and unidentified errors can lead to either increased false positives or decreased power in both linkage and association analyses. Here, we review several allele sharing as well as likelihood-based statistics that were proposed to efficiently extract genealogical information from available genome-wide marker data, and the software package PREST that implements these methods. We provide the detailed analytical steps involved using two application examples, and we discuss various practical issues, including result interpretation.
Keywords: Allele sharing; Association; EM algorithm; Hidden Markov model; IBD; IBS; IIS; Likelihood; Linkage; Multiple hypothesis testing; PREST; PREST-plus; Pedigree error; Relationship estimation; Robustness; Simulation; Software.