Enlarging a training set for genomic selection by imputation of un-genotyped animals in populations of varying genetic architecture

Abstract

BackgroundThe most common application of imputation is to infer genotypes of a high-density panel of markers on animals that are genotyped for a low-density panel. However, the increase in accuracy of genomic predictions resulting from an increase in the number of markers tends to reach a plateau beyond a certain density. Another application of imputation is to increase the size of the training set with un-genotyped animals. This strategy can be particularly successful when a set of closely related individuals are genotyped.MethodsImputation on completely un-genotyped dams was performed using known genotypes from the sire of each dam, one offspring and the offspring’s sire. Two methods were applied based on either allele or haplotype frequencies to infer genotypes at ambiguous loci. Results of these methods and of two available software packages were compared. Quality of imputation under different population structures was assessed. The impact of using imputed dams to enlarge training sets on the accuracy of genomic predictions was evaluated for different populations, heritabilities and sizes of training sets.ResultsImputation accuracy ranged from 0.52 to 0.93 depending on the population structure and the method used. The method that used allele frequencies performed better than the method based on haplotype frequencies. Accuracy of imputation was higher for populations with higher levels of linkage disequilibrium and with larger proportions of markers with more extreme allele frequencies. Inclusion of imputed dams in the training set increased the accuracy of genomic predictions. Gains in accuracy ranged from close to zero to 37.14%, depending on the simulated scenario. Generally, the larger the accuracy already obtained with the genotyped training set, the lower the increase in accuracy achieved by adding imputed dams.ConclusionsWhenever a reference population resembling the family configuration considered here is available, imputation can be used to achieve an extra increase in accuracy of genomic predictions by enlarging the training set with completely un-genotyped dams. This strategy was shown to be particularly useful for populations with lower levels of linkage disequilibrium, for genomic selection on traits with low heritability, and for species or breeds for which the size of the reference population is limited.

Review: Opportunities and challenges for small populations of dairy cattle in the era of genomics

“…Another application of imputation, usually referred to as population-based imputation, is the so-called pedigreebased imputation (Pimentel et al, 2013). Large half-sib families and sires with large number of progeny characterize the population structure in dairy cattle.…”

“…These circumstances allow inferring of the genotype of an un-genotyped animal with the help of the genotype information from its relatives. Imputation of completely un-genotyped animals has the potential to enlarge the reference population for genomic selection and thus to increase the reliability of genomic prediction Pimentel et al, 2013).…”

“…To pursue the pedigree-based imputation strategy with un-genotyped individuals requires a set of closely related genotyped animals. Pimentel et al (2013) derived an algorithm to impute un-genotyped dams combining genotype information from the sire of each dam, one offspring, and the offspring's sire. The addition of these dams to the reference population resulted in a significant increase of the genomic predictions accuracies (up to 37.2%).…”

“…The addition of these dams to the reference population resulted in a significant increase of the genomic predictions accuracies (up to 37.2%). This method revealed to be particularly beneficial for populations with lower LD level, for low heritability traits, and for species with a limited reference population size (Pimentel et al, 2013). Hickey et al (2012) developed an imputation method based on segregation analyses, phasing rules, long-range phasing and haplotype library information and found it to be an efficient and accurate method for data sets including pedigrees up to 25 000 animals.…”

Imputation of ungenotyped parental genotypes in dairy and beef cattle from progeny genotypes

“…Hence, a bull born in a foreign country may receive an estimated breeding value in all countries despite having no progeny in those countries and therefore once a genotype is available, the sire can be included in the reference population of the national genomic evaluations using his INTERBULL breeding value. Furthermore, accuracy of genomic prediction can be increased with the addition of (sometimes by then dead) female animals to the reference population (Pryce and Hayes, 2012); therefore imputing genotypes of influential females with no available biological sample could be useful to increase the accuracy of genomic predictions (Pimentel et al, 2013).…”