Accuracy of Genomewide Selection for Different Traits with Constant Population Size, Heritability, and Number of Markers

In genomewide selection, the expected correlation between predicted performance and true genotypic value is a function of the training population size ( N ), heritability on an entry‐mean basis ( h 2 ), and effective number of chromosome segments underlying the trait ( M e ). Our objectives were to (i) determine how the prediction accuracy of different traits responds to changes in N , h 2 , and number of markers ( N M ) and (ii) determine if prediction accuracy is equal across traits if N , h 2 , and N M are kept constant. In a simulated population and four empirical populations in maize ( Zea mays L.), barley ( Hordeum vulgare L.), and wheat ( Triticum aestivum L.), we added random nongenetic effects to the phenotypic data to reduce h 2 to 0.50, 0.30 and 0.20. As expected, increasing N , h 2 , and N M increased prediction accuracy. For the same trait within the same population, prediction accuracy was constant for different combinations of N and h 2 that led to the same Nh 2 . Different traits, however, varied in their prediction accuracy even when N , h 2 , and N M were constant. Yield traits had lower prediction accuracy than other traits despite the constant N , h 2 , and N M . Empirical evidence and experience on the predictability of different traits are needed in designing training populations.