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

García-Ruiz, Adriana; Ruiz-López, F.J.; Wiggans, G.R.; Tassell, C. P. van; Montaldo, Hugo H.

doi:10.3168/jds.2014-9132

Cited by 15 publications

(14 citation statements)

References 18 publications

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“…The average length of the genome covered by ROH was 276.89 Mb for the population in this study, similar to that of Holstein cattle in the USA, where two studies demonstrated a covered genome’s length of 290.6 Mb and 299.6 Mb [ 30 , 31 ], which could be the reason for a higher estimation of the inbreeding degree for animals descending from dams of USA origin. This may be because the registered Holstein population of Mexico presents a high genetic relationship with the population in the USA [ 32 ].…”

Section: Resultsmentioning

confidence: 99%

Correlation of Genomic and Pedigree Inbreeding Coefficients in Small Cattle Populations

Cortes-Hernández

García-Ruiz

Vásquez-Peláez

et al. 2021

Animals

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This study aimed to identify inbreeding coefficient (F) estimators useful for improvement programs in a small Holstein population through the evaluation of different methodologies in the Mexican Holstein population. F was estimated as follows: (a) from pedigree information (Fped); (b) through runs of homozygosity (Froh); (c) from the number of observed and expected homozygotic SNP in the individuals (Fgeno); (d) through the genomic relationship matrix (Fmg). The study included information from 4277 animals with pedigree records and 100,806 SNP. The average and standard deviation values of F were 3.11 ± 2.30 for Fped, −0.02 ± 3.55 for Fgeno, 2.77 ± 0.71 for Froh and 3.03 ± 3.05 for Fmg. The correlations between coefficients varied from 0.30 between Fped and Froh, to 0.96 between Fgeno and Fmg. Differences in the level of inbreeding among the parent’s country of origin were found regardless of the method used. The correlations among genomic inbreeding coefficients were high; however, they were low with Fped, so further research on this topic is required.

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Section: Resultsmentioning

confidence: 99%

Correlation of Genomic and Pedigree Inbreeding Coefficients in Small Cattle Populations

Cortes-Hernández

García-Ruiz

Vásquez-Peláez

et al. 2021

Animals

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“…It is well established that reference population size is one of the primary factors affecting the quality of imputation [ 22 – 24 ]. Although the FImpute imputation algorithm works based primarily on long haplotype sharing, in the absence of that, there is an inherent probabilistic nature to imputation.…”

Section: Discussionmentioning

confidence: 99%

Novel methods for genotype imputation to whole-genome sequence and a simple linear model to predict imputation accuracy

et al. 2017

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BackgroundAccurate imputation plays a major role in genomic studies of livestock industries, where the number of genotyped or sequenced animals is limited by costs. This study explored methods to create an ideal reference population for imputation to Next Generation Sequencing data in cattle.MethodsMethods for clustering of animals for imputation were explored, using 1000 Bull Genomes Project sequence data on 1146 animals from a variety of beef and dairy breeds. Imputation from 50 K to 777 K was first carried out to choose an ideal clustering method, using ADMIXTURE or PLINK clustering algorithms with either genotypes or reconstructed haplotypes.ResultsDue to efficiency, accuracy and ease of use, clustering with PLINK using haplotypes as quasi-genotypes was chosen as the most advantageous grouping method. It was found that using a clustered population slightly decreased computing time, while maintaining accuracy across the population. Although overall accuracy remained the same, a slight increase in accuracy was observed for groups of animals in some breeds (primarily purebred beef cattle from breeds with fewer sequenced animals) and for other groups, primarily crossbreed animals, a slight decrease in accuracy was observed. However, it was noted that some animals in each breed were poorly imputed across all methods. When imputed sequences were included in the reference population to aid imputation of poorly imputed animals, a small increase in overall accuracy was observed for nearly every individual in the population. Two models were created to predict imputation accuracy, a complete model using all information available including Euclidean distances from genotypes and haplotypes, pedigree information, and clustering groups and a simple model using only breed and an Euclidean distance matrix as predictors. Both models were successful in predicting imputation accuracy, with correlations between predicted and true imputation accuracy as measured by concordance rate of 0.87 and 0.83, respectively.ConclusionsA clustering methodology can be very useful to subgroup cattle for efficient genotype imputation. In addition, accuracy of genotype imputation from medium to high-density Single Nucleotide Polymorphisms (SNP) chip panels to whole-genome sequence can be predicted well using a simple linear model defined in this study.

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“…If high-density genotypes can be imputed reliably from low-density SNP arrays with sufficient accuracy, this would allow for the opportunity to genotype more animals in a more affordable way (García-Ruiz et al, 2015). Imputation is therefore an important statistical tool for enriching applications such as GWAS and GS that require higher or more evenly distributed marker densities (Marchini et al, 2007).…”

Section: Genotype Imputationmentioning

confidence: 99%

Genotype imputation as a cost-saving genomic strategy for South African Sanga cattle: A review

Lashmar¹,

Muchadeyi²,

Visser³

2019

SA J. An. Sci.

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The South African beef cattle population is heterogeneous and consists of a variety of breeds, production systems and breeding goals. Indigenous cattle breeds are uniquely adapted to their native surroundings, necessitating conservation of these breeds as usable genetic resources to sustain efficient production of beef. Current projections indicate positive growth in human population size, with parallel growth in nutritional demand, in the midst of intensifying environmental conditions. Sanga cattle, therefore, are invaluable assets to the South African beef industry. Modern genomic methodologies allow for an extensive insight into the genome architecture of local breeds. The evolution of these methodologies has also provided opportunities to incorporate deoxyribonucleic acid (DNA) information into breed improvement programs in the form of genomic selection (GS). Certain challenges, such as the high cost of generating adequate numbers of dense genotypic profiles and the introduction of ascertainment bias when non-commercial breeds are genotyped with commercial single nucleotide polymorphism (SNP) panels, have caused a lag in progress on the genomics front in South Africa. Genotype imputation is a statistical method that infers unavailable or missing genotypic data based on shared haplotypes within a population using a population or breed representative reference sample. Genotypes are generated in silico, providing an animal with genotypic information for SNP markers that were not genotyped, based on predictive model-based algorithms. The validation of this method for indigenous breeds will enable the development of cost-effective low-density bead chips, allowing more animals to be genotyped, and imputation to high-density information. The improvement in SNP densities, at lower cost, will allow enhanced power in genome-wide association studies (GWAS) and genomic estimated breeding value (GEBV)-based selection for these breeds. To fully reap the benefits of this methodology, however, will require the setting up of accurate and reliable frameworks that are optimized for its application in Sanga breeds. This review paper aims, first, to identify the challenges that have been impeding genomic applications for Sanga cattle and second, to outline the advantages that a method such as genotype imputation might provide.

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

Cited by 15 publications

References 18 publications

Correlation of Genomic and Pedigree Inbreeding Coefficients in Small Cattle Populations

Correlation of Genomic and Pedigree Inbreeding Coefficients in Small Cattle Populations

Novel methods for genotype imputation to whole-genome sequence and a simple linear model to predict imputation accuracy

Genotype imputation as a cost-saving genomic strategy for South African Sanga cattle: A review

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