Haplotype Inference in Complex Pedigrees
Despite the desirable information contained in complex pedigree data sets, analysis methods struggle to efficiently process these data. The attractiveness of pedigree data is their power for detecting rare variants, particularly in comparison with studies of unrelated individuals. In addition, rather than assuming individuals in a study are unrelated, knowledge of their relationships can avoid spurious results due to confounding population structure effects. However, a major challenge for applying pedigree methods is difficulty in handling complex pedigrees having multiple founding lineages, inbreeding, and half-sibling relationships. A key ingredient in association studies is imputation and inference of haplotypes from genotype data. Existing haplotype inference methods either do not efficiently scale to complex pedigrees or are of limited accuracy. In this article, we present algorithms for efficient haplotype inference and imputation in complex pedigrees. Our method, PhyloPed, leverages the perfect phylogeny model, resulting in an efficient method with high accuracy. PhyloPed effectively combines the founder haplotype information from different lineages and is immune to inaccuracies in prior information about the founders. In addition, we demonstrate that inference of missing data, using PhyloPed, can substantially improve disease association. For Online Supplementary Material, see www.liebertonline.com.
algorithms combinatorial optimization genetic analysis genetic variation machine learning