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ARS Home » Northeast Area » Beltsville, Maryland (BARC) » Beltsville Agricultural Research Center » Animal Genomics and Improvement Laboratory » Research » Publications at this Location » Publication #390975
Research Project: Improving Dairy Animals by Increasing Accuracy of Genomic Prediction, Evaluating New Traits, and Redefining Selection Goals

Location: Animal Genomics and Improvement Laboratory

Title: Recent advances and future needs in genotype imputation

Author
item Vanraden, Paul
item Null, Daniel
item Al-Khudhair, Ahmed

Submitted to: World Congress of Genetics Applied in Livestock Production
Publication Type: Proceedings
Publication Acceptance Date: 2/18/2022
Publication Date: 7/7/2022
Citation: Van Raden, P.M., Null, D.J., Al-Khudhair, A.S. 2022. Recent advances and future needs in genotype imputation. World Congress of Genetics Applied in Livestock Production. Front Matter, Commun. 349, pp. 1463-1466. https://doi.org/10.3920/978-90-8686-940-4_349.
DOI: https://doi.org/10.3920/978-90-8686-940-4_349

Interpretive Summary:

Technical Abstract: Genotype imputation is a useful tool in genomic prediction. Imputation enabled selecting and using many more loci for a much lesser cost than genotyping all animals with the same array density or technology. The discovered higher-effect markers in the imputation process are routinely applied in future genotyping arrays, also added to the genotype data of previously genotyped animals without re-genotyping all. The genomic prediction applications may require imputing new loci from less-dense to higher-density arrays and from descendant genotypes to ancestors in the opposite direction of normal reference and target populations. Routine expansion and recycling of haplotype libraries and updating only individuals expected to change would speed the imputation for routine predictions. This study compared imputation strategies for 78,964 loci of 4.6 million Holsteins. Sequence imputation is more challenging due to higher error rates and more complex variants, but new techniques could make sequencing cost-competitive with arrays for routine predictions.