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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 #338535
Title: Genotype imputation in a tropical crossbred dairy cattle population

Author
item OLIVEIRA JR, GERSON - Universidad De Sao Paulo
item CHUD, TATIANE - Universidade Estadual Paulista (UNESP)
item VENTURA, RICARDO - University Of Guelph
item GARRICK, DORIAN - Iowa State University
item Cole, John
item MUNARI, DANISIO - Universidade Estadual Paulista (UNESP)
item FERRAZ, JOSE BENTO - Universidad De Sao Paulo
item MULLART, ERIK - Collaborator
item DENISE, SUE - Zoetis
item SMITH, SHANNON - Zoetis
item DA SILVA, MARCO - Embrapa

Submitted to: Journal of Dairy Science
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 8/16/2017
Publication Date: 12/1/2017
Citation: Oliveira Jr, G., Chud, T., Ventura, R., Garrick, D., Cole, J.B., Munari, D., Ferraz, J., Mullart, E., Denise, S., Smith, S., Da Silva, M. 2017. Genotype imputation in a tropical crossbred dairy cattle population. Journal of Dairy Science. 100(12):9623-9634. https://doi.org/10.3168/jds.2017-12732.
DOI: https://doi.org/10.3168/jds.2017-12732

Interpretive Summary: The application of genomic selection and genotype imputation presents challenges in crossbred populations because relationships of causal variants with markers may vary across breeds. In order to make genomic selection more cost effective, cheap low-density genotyping chips are often used in combination with imputation. Genotype imputation is a statistical technique that permits the inference of high-density, ungenotyped markers for an individual genotyped at low-density. It relies on a high-density genotyped reference population and offers substantial reductions in the costs of genotyping which allows for the genotyping of more animals and, thereby increasing the accuracy of genomic selection for a given investment in genotyping. The objective of this study was to investigate different strategies for genotype imputation in a population of crossbred Girolando (Gyr x Holstein) dairy cattle. The accuracy of imputation from low- (20K) and medium-densities (50 and 70K) to the HD panel density and from low to 50K density were investigated. The highest imputation accuracies were observed for scenarios including Girolando animals in the reference population, whereas using only Gyr animals resulted in low imputation accuracies, suggesting that the haplotypes segregating in the Girolando population had higher impact in accuracy than the pure breed haplotypes.

Technical Abstract: The application of new tools, such as genomic selection and genotype imputation, still presents challenges in crossbred populations because relationships of causal variants with markers may vary across breeds. In order to make genomic selection more cost effective, cheap low density chips are often used in combination with imputation. Genotype imputation is a statistical technique that permits the inference of high-density, ungenotyped markers for an individual genotyped at low-density. It relies on a high-density genotyped reference population and offers substantial reductions in the costs of genotyping which allows for the genotyping of more animals and, thereby increasing the accuracy of genomic selection for a given investment in genotyping. The objective of this study was to investigate different strategies for genotype imputation in a population of crossbred Girolando (Gyr x Holstein) dairy cattle. The dataset consisted of 478 Girolando, 583 Gyr and 1,198 Holstein sires genotyped at high-density with the Illumina BovineHD panel, which includes ~777K markers. The accuracy of imputation from low- (20K) and medium-densities (50 38 and 70K) to the HD panel density and from low to 50K density were investigated. Seven scenarios with 29 variations were tested for imputing genotypes of 166 randomly chosen Girolando animals. Imputation accuracy was measured as the correlation between observed and imputed genotypes (CORR) and also as the proportion of genotypes that were imputed correctly (CR). This is the first paper to check imputation accuracy in a Girolando population. The sample-specific imputation accuracies ranged from 0.38 to 0.97 (CORR) and from 0.49 to 0.96 (CR) imputing from low- and medium-densities to HD, and 0.41 to 0.95 (CORR) and from 0.50 to 0.94 (CR) for imputation from 20K to 50K. The highest imputation accuracies were observed for scenarios including Girolando animals in the reference population, whereas using only Gyr animals resulted in low imputation accuracies, suggesting that the haplotypes segregating in the Girolando population had higher impact in accuracy than the pure breed haplotypes. Crossbred animals (Girolando) must be included in the reference population to provide the best imputation accuracies, mainly when imputing from low density (20K) to high density panels. The results obtained in this work provide information to more cost-effectively implement genomic selection and will assist future studies involving genomic analysis in crossbred animals.