U.S. flag

An official website of the United States government

Dot gov

Official websites use .gov
A .gov website belongs to an official government organization in the United States.


Secure .gov websites use HTTPS
A lock ( ) or https:// means you’ve safely connected to the .gov website. Share sensitive information only on official, secure websites.


Main content area

Comparing the Predictive Abilities of Phenotypic and Marker‐Assisted Selection Methods in a Biparental Lettuce Population

Steffen Hadasch, Ivan Simko, Ryan J. Hayes, Joseph O. Ogutu, Hans‐Peter Piepho
The plant genome 2016 v.9 no.1 pp. eplantgenome2015.03.0014
Lactuca sativa, amplified fragment length polymorphism, data collection, disease resistance, downy mildew, genetic markers, genetic traits, genotype, genotyping, heritability, lettuce, marker-assisted selection, models, phenotype, polygenic inheritance, prediction, quantitative trait loci, shelf life, single nucleotide polymorphism
Breeding for traits with polygenic inheritance is a challenging task that can be done by phenotypic selection, marker‐assisted selection (MAS) or genome‐wide selection. We comparatively evaluated the predictive abilities of four selection models on a biparental lettuce (Lactuca sativa L.) population genotyped with 95 single nucleotide polymorphisms and 205 amplified fragment length polymorphism markers. These models were based on (i) phenotypic selection, (ii) MAS (with quantitative trait locus (QTL)‐linked markers), (iii) genomic prediction using all the available molecular markers, and (iv) genomic prediction using molecular markers plus QTL‐linked markers as fixed covariates. Each model's performance was assessed using data on the field resistance to downy mildew (DMR, mean heritability ∼0.71) and the quality of shelf life (SL, mean heritability ∼0.91) of lettuce in multiple environments. The predictive ability of each selection model was computed under three cross‐validation (CV) schemes based on sampling genotypes, environments, or both. For the DMR dataset, the predictive ability of the MAS model was significantly lower than that of the genomic prediction model. For the SL dataset, the predictive ability of the genomic prediction model was significantly lower than that for the model using QTL‐linked markers under two of the three CV schemes. Our results show that the predictive ability of the selection models depends strongly on the CV scheme used for prediction and the heritability of the target trait. Our study also shows that molecular markers can be used to predict DMR and SL for individuals from this cross that were genotyped but not phenotyped.