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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 #312161

Research Project: Improving Genetic Predictions in Dairy Animals Using Phenotypic and Genomic Information

Location: Animal Genomics and Improvement Laboratory

Title: Effect of reference population size and available ancestor genotypes on imputation of Mexican Holstein genotypes

Author
item GARCIA-RUIZ, ADRIANA - Universidad Nacional Autonoma De Mexico
item RUIZ-LOPEZ, FELIPE - Universidad Nacional Autonoma De Mexico
item Wiggans, George
item Van Tassell, Curtis - Curt
item MONTALDO, HUGO - Universidad Nacional Autonoma De Mexico

Submitted to: Journal of Dairy Science
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 2/2/2015
Publication Date: 5/1/2015
Citation: Garcia-Ruiz, A., Ruiz-Lopez, F.J., Wiggans, G.R., Van Tassell, C.P., Montaldo, H.H. 2015. Effect of reference population size and available ancestor genotypes on imputation of Mexican Holstein genotypes. Journal of Dairy Science. 98(5):3478-3484.

Interpretive Summary: Genomic selection is a recent technology that has enhanced genetic improvement in dairy cattle, but its success relies partly on the number of genotyped animals in the reference population used to estimate the effects of genetic markers on animal performance. For developing countries such as Mexico, using genotype information from related animals in countries with large genotyped populations (such as the United States and Canada) could increase the accuracy of genomic evaluations. The effects of reference population size and the availability of information from genotyped ancestors on the accuracy of imputation of genetic markers were investigated for Mexican Holstein cattle. A larger reference population and the availability of genotyped ancestors improved imputation; animals with genotyped parents in a large reference population had higher imputation accuracy than those with no or few genotyped relatives in a small reference population. For small local populations, including genotypes from other related populations can be an important tool for improving the accuracy of genomic evaluations.

Technical Abstract: The effects of reference population size and the availability of information from genotyped ancestors on the accuracy of imputation of single nucleotide polymorphisms (SNPs) were investigated for Mexican Holstein cattle. Three scenarios for reference population size were examined: (1) a local population of 2,011 genotyped Mexican Holsteins, (2) animals in scenario 1 plus 866 Holsteins in the U.S. genotype database (GDB) with genotyped Mexican daughters, and (3) animals in scenario 1 and all U.S. GDB Holsteins (338,073). Genotypes from 4 different chip densities (2 low-density, 1 mid-density, and 1 high-density) were imputed to the 45,195 markers on the mid-density chip. Imputation success was determined by comparing the numbers of SNPs with 1 or 2 alleles missing and the numbers of differently predicted SNPs (conflicts) among the 3 scenarios. Imputation accuracy improved as chip density and numbers of genotyped ancestors increased, and the percentage of SNP with 1 missing allele was greater than that for 2 missing alleles for all scenarios. The largest numbers of conflicts were found between scenarios 1 and 3. The inclusion of information from direct ancestors (dam or sire) with U.S. GDB genotypes in the imputation of Mexican Holstein genotypes increased imputation accuracy by 1 percentage point for low-density genotypes and 0.5 percentage points for high-density genotypes, which was about half the gain found with information from all U.S. GDB Holsteins. A larger reference population and the availability of genotyped ancestors improved imputation; animals with genotyped parents in a large reference population had higher imputation accuracy than those with no or few genotyped relatives in a small reference population. For small local populations, including genotypes from other related populations can aid in improving imputation accuracy.