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United States Department of Agriculture

Agricultural Research Service

Research Project: IMPROVING GENETIC PREDICTIONS FOR DAIRY ANIMALS USING PHENOTYPIC AND GENOMIC INFORMATION Title: Imputation of Missing Genotypes From Sparse to High Density Using Long-Range Phasing

Authors
item Daetwyler, H -
item Wiggans, George
item Hayes, B -
item Wolliams, J -
item Goddard, M -

Submitted to: World Congress of Genetics Applied in Livestock Production
Publication Type: Proceedings
Publication Acceptance Date: June 1, 2010
Publication Date: August 1, 2010
Citation: Daetwyler, H.D., Wiggans, G.R., Hayes, B.J., Wolliams, J.A., Goddard, M.E. 2010. Imputation of Missing Genotypes From Sparse to High Density Using Long-Range Phasing. World Congress of Genetics Applied in Livestock Production. Proc. 9th World Congr. Genet. Appl. Livest. Prod., Leipzig, Germany, Aug. 1–6, 4 pp.

Interpretive Summary: High density genotypes can be imputed from low density ones by determining haplotypes and tracing inheritance of long chromosome segments from common ancestors. A long-range phasing algorithm was developed to phase whole chromosomes and simultaneously impute a large number of missing markers. It was tested by imputing markers in sparsely genotyped individuals with many missing genotypes. The reduction in accuracy of genomic evaluation with imperfectly imputed and sparse data was moderate when compared to accuracy from full density. Imputation of missing genotypes from sparse to high density is feasible.

Technical Abstract: Related individuals in a population share long chromosome segments which trace to a common ancestor. We describe a long-range phasing algorithm that makes use of this property to phase whole chromosomes and simultaneously impute a large number of missing markers. We test our method by imputing markers in sparsely genotyped individuals with many missing genotypes. Futhermore, we investigate the reduction in accuracy of genomic evaluation with imperfectly imputed and sparse data. We show that imputation of missing genotypes from sparse to high density is feasible. Our results demonstrate that the reduction in genomic evaluation accuracy from imperfectly imputed marker data is moderate when compared to accuracy from full density.

Last Modified: 10/31/2014
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