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Missing data were multiple times imputed, using the 'Multivariate Imputation by Chained Equations' (MICE) procedure [ 13, 14], that runs under the statistical program R version 2.5.0 [ 15].
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One approach in dealing with missing data is multiple imputation (MI) (Rubin 2004).
The reasons for the presence of incomplete data are multiple.
Two approaches commonly used to deal with missing data are multiple imputation (MI) and inverse-probability weighting (IPW).
If ≥10 % of data were missing, multiple imputations were performed, assuming they were missing at random [36].
Data were from multiple breeds, the main breeds being Merino, White Suffolk, Border Leicester and Poll Dorset.
Coded data were reviewed multiple times by the primary author (CH).
Some, such as laboratory data, may be multiple measurements from the same source.
For example, electrocardiogram data rate is multiple times higher in comparison with body temperature.
Missing data will be multiple imputed.
The main challenge in mQTL data modeling is multiple correlation testing.
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