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

By Emily Combs, R. Bernardo. 2013

186 citations recorded by the source corpus.

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 (h2), and effective number of chromosome segments underlying the trait (Me). Our objectives were to (i) determine how the prediction accuracy of different traits responds to changes in N, h2, and number of markers (NM) and (ii) determine if prediction accuracy is equal across traits if N, h2, and NM 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 h2 to 0.50, 0.30 and 0.20. As expected, increasing N, h2, and NM increased prediction accuracy. For the same trait within the same population, prediction accuracy was constant for different combinations of N and h2 that led to the same Nh2. Different traits, however, varied in their prediction accuracy even when N, h2, and NM were constant. Yield traits had lower prediction accuracy than other traits despite the constant N, h2, and NM. Empirical evidence and experience on the predictability of different traits are needed in designing training populations.

Subfields: Genetics, Plant Science

View Paper: https://www.semanticscholar.org/paper/39ca7e9a0dd44cb52454f3a445286f50b2866416

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Genetics

In Plant Science

Citations in the corpus

Genomic Selection for Crop Improvement (2017, unknown): “The genomic prediction model can also be used to predict future selection cycles created by intermating selected individuals within the family (Bernardo and Yu 2007; Combs and Bernardo 2013; Massman et al. 2013; Lorenz 2013).”
Genomewide Prediction Accuracy within 969 Maize Biparental Populations (2014, background): “) Heynh, the observed rMG generally agreed with E(rMG) but uncertainty in Me made the comparisons difficult (Combs and Bernardo, 2013a).”
Adding Genetically Distant Individuals to Training Populations Reduces Genomic Prediction Accuracy in Barley (2015, background): “Many factors affect genomic prediction accuracy, including model, marker density, TP composition, trait complexity, and precision of phenotyping (Combs and Bernardo, 2013; Lorenz et al., 2012; Lorenz, 2013; Heffner et al., 2011).”