Genome-based prediction of testcross values in maize

Abstract

This is the first large-scale experimental study on genome-based prediction of testcross values in an advanced cycle breeding population of maize. The study comprised testcross progenies of 1,380 doubled haploid lines of maize derived from 36 crosses and phenotyped for grain yield and grain dry matter content in seven locations. The lines were genotyped with 1,152 single nucleotide polymorphism markers. Pedigree data were available for three generations. We used best linear unbiased prediction and stratified cross-validation to evaluate the performance of prediction models differing in the modeling of relatedness between inbred lines and in the calculation of genome-based coefficients of similarity. The choice of similarity coefficient did not affect prediction accuracies. Models including genomic information yielded significantly higher prediction accuracies than the model based on pedigree information alone. Average prediction accuracies based on genomic data were high even for a complex trait like grain yield (0.72-0.74) when the cross-validation scheme allowed for a high degree of relatedness between the estimation and the test set. When predictions were performed across distantly related families, prediction accuracies decreased significantly (0.47-0.48). Prediction accuracies decreased with decreasing sample size but were still high when the population size was halved (0.67-0.69). The results from this study are encouraging with respect to genome-based prediction of the genetic value of untested lines in advanced cycle breeding populations and the implementation of genomic selection in the breeding process.

Applications of Population Genetics to Animal Breeding, from Wright, Fisher and Lush to Genomic Prediction

“…Theory/simulation studies (e.g., Jannink et al 2010), and also experimental trials, have been undertaken. Results to date for maize correspond to those in livestock, in that predictions are better within families (i.e., specific F2 of initial cross) than across families (0.72 vs. 0.47 for grain yield; Albrecht et al 2011), and in another data set predictions of cross performance had essentially zero accuracy (Windhausen et al 2012). Wimmer et al (2013) provide analyses in three species and find that methods involving marker selection (in contrast to BLUP employing ridge regression) can be unreliable unless data sets are large.…”

Genomic BLUP Decoded: A Look into the Black Box of Genomic Prediction

“…Two types of pedigrees were simulated as summarized in Table 2: one represents a cross-validation scenario from dairy cattle breeding, and the other one is a top-cross design (Falconer and Mackay 1996, p. 276) from corn breeding similar to those in Albrecht et al (2011). The dairy cattle pedigree consisted of 14, 143, or 285 families, each having 7 half sibs in training and 10 in validation.…”

Entering the second century of maize quantitative genetics

“…This model is then used to assign breeding values to new individuals based solely on their genotype. Simulation studies and early field studies indicate strong potential for GS in maize and other crop species (Bernardo and Yu, 2007;Bernardo, 2009;Albrecht et al, 2011;Heslot et al, 2012), while other studies have shown promise by predicting maize phenotypes from transcriptomes (Fu et al, 2012), metabolites (Riedelsheimer et al, 2012a, b) and spectral reflectance (Weber et al, 2012). The ultimate goal of all these techniques is to shorten the time per breeding cycle and/or reduce the need for phenotyping in order to make breeding both faster and more efficient.…”