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BMC Proc. 2016 Oct 18;10(Suppl 7):295-301. eCollection 2016.

Identity-by-descent estimation with population- and pedigree-based imputation in admixed family data.

Author information

1
Department of Biostatistics, University of Washington, Seattle, WA USA ; Division of Medical Genetics, Department of Medicine, University of Washington, Seattle, WA USA.
2
Division of Medical Genetics, Department of Medicine, University of Washington, Seattle, WA USA.
3
Department of Statistics, University of Washington, Seattle, WA USA.
4
Department of Biostatistics, University of Washington, Seattle, WA USA.
5
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.

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.

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