title: Identity-by-descent estimation with population- and pedigree-based imputation in admixed family data.
abstract: BACKGROUND
  In the past few years, imputation approaches have been mainly used in population-based designs of genome-wide association studies, although both family- and population-based imputation methods have been proposed. With the recent surge of family-based designs, family-based imputation has become more important. Imputation methods for both designs are based on identity-by-descent (IBD) information. Apart from imputation, the use of IBD information is also common for several types of genetic analysis, including pedigree-based linkage analysis.
  
  
  METHODS
  We compared the performance of several family- and population-based imputation methods in large pedigrees provided by Genetic Analysis Workshop 19 (GAW19). We also evaluated the performance of a new IBD mapping approach that we propose, which combines IBD information from known pedigrees with information from unrelated individuals.
  
  
  RESULTS
  Different combinations of the imputation methods have varied imputation accuracies. Moreover, we showed gains from the use of both known pedigrees and unrelated individuals with our IBD mapping approach over the use of known pedigrees only.
  
  
  CONCLUSIONS
  Our results represent accuracies of different combinations of imputation methods that may be useful for data sets similar to the GAW19 pedigree data. Our IBD mapping approach, which uses both known pedigree and unrelated individuals, performed better than classical linkage analysis.
authors.affiliation: Department of Biostatistics, University of Washington, Seattle, WA USA ; Division of Medical Genetics, Department of Medicine, University of Washington, Seattle, WA USA.
authors.forename: Mohamad
authors.initials: M
authors.lastname: Saad
authors.affiliation: Division of Medical Genetics, Department of Medicine, University of Washington, Seattle, WA USA.
authors.forename: Alejandro Q
authors.initials: AQ
authors.lastname: Nato
authors.affiliation: Department of Statistics, University of Washington, Seattle, WA USA.
authors.forename: Fiona L
authors.initials: FL
authors.lastname: Grimson
authors.affiliation: Department of Statistics, University of Washington, Seattle, WA USA.
authors.forename: Steven M
authors.initials: SM
authors.lastname: Lewis
authors.affiliation: Department of Biostatistics, University of Washington, Seattle, WA USA.
authors.forename: Lisa A
authors.initials: LA
authors.lastname: Brown
authors.affiliation: Division of Medical Genetics, Department of Medicine, University of Washington, Seattle, WA USA.
authors.forename: Elizabeth M
authors.initials: EM
authors.lastname: Blue
authors.affiliation: Department of Biostatistics, University of Washington, Seattle, WA USA.
authors.forename: Timothy A
authors.initials: TA
authors.lastname: Thornton
authors.affiliation: Department of Statistics, University of Washington, Seattle, WA USA.
authors.forename: Elizabeth A
authors.initials: EA
authors.lastname: Thompson
authors.affiliation: Department of Biostatistics, University of Washington, Seattle, WA USA ; Division of Medical Genetics, Department of Medicine, University of Washington, Seattle, WA USA ; Department of Genome Sciences, University of Washington, Seattle, WA USA.
authors.forename: Ellen M
authors.initials: EM
authors.lastname: Wijsman