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However, most studies evaluating patient-reported outcomes do not use a reference population or pre-injury values.
When the size of the reference population is very large, it is not necessary to use a reference population that combines the two breeds to impute the genotypes of purebred animals because a within-breed reference population can provide a very high level of imputation accuracy (correct rate ≥ 0.99, correlation ≥ 0.95).
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A potentially attractive alternative option would be to impute based on the 1000 Genomes Project; however, this has the drawbacks of using a reference population that does not necessarily match the disease status and LD pattern of the study population.
Imputation of crossbred animals using a reference population that included only Yorkshire pigs resulted in a larger decline in accuracies than using a reference population that included Landrace pigs only.
For patient diagnostic categorisation, lumbar spine and femoral neck BMD T-scores were calculated using a reference population previously described.
For across-population genomic prediction, breeding values are predicted for individuals using a reference population of individuals from one or more different populations.
For genomic prediction based on one population, breeding values are predicted for individuals using a reference population of individuals from the same population.
Effects of dense genetic markers are estimated using a reference population and these effects are used to predict genomic breeding values (GBV) of selection candidates [ 1].
Ding et al. [ 4] investigated the accuracy of genomic prediction using a reference population consisting of cows, and showed that genomic selection using cows is feasible.
For crossbred animals, a highly accurate imputed 60 K crossbred dataset can be achieved from 8 K by using a reference population that combines both parental breeds.
Accuracies for the base scenario, for which breeding values of HF individuals were predicted using a reference population of HF individuals, were very high (>0.9).
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