Predicting Genetic Variance from Genomewide Marker Effects Estimated from a Diverse Panel of Maize Inbreds

Abstract

Predicting the genetic variance (VG) in a biparental population has been difficult. Our objective was to determine whether the population mean, VG, and mean of the top 10% of progeny in a cross can be predicted effectively from genomewide marker effects estimated from a diverse panel of inbreds. Eight maize (Zea mays L.) crosses that differed in their predicted mean and VG were evaluated for plant and ear height and growing degree days to silking. Each cross was represented by 120 to 144 random F3 lines that were evaluated in balanced experiments at three locations in Minnesota in 2017. Correlations between the observed and predicted means of each breeding population were significant (r ≥ 0.80, P = 0.05) for all three traits. However, correlations between the observed and predicted VG were nonsignificant (−0.24 to 0.14) for the three traits. Correlations between the observed and predicted mean of the top 10% of progeny in each cross were significant (P = 0.05) for plant height (0.72), but not for ear height and silking date. These results for predicting the mean of the top 10% of progeny reflected the ability to predict the mean but not VG. We concluded that the mean, but not the VG, of biparental crosses can be effectively predicted from genomewide marker effects estimated from a diverse panel of inbreds.

Multi-trait Improvement by Predicting Genetic Correlations in Breeding Crosses

“…, mean, genetic variance, and genetic correlation) are attributable to the nature of each statistic and have practical implications. Greater accuracy when predicting the cross mean vs. genetic variance was predicted by theory (Zhong and Jannink 2007) and has been observed empirically (Adeyemo and Bernardo 2019; Neyhart and Smith 2019); this trend is expected because the genetic variance, a second-order statistic, will be more adversely impacted by error in marker effect estimates. Similarly, the accuracy of the genetic correlation, a ratio of second-order statistics with large sampling variance (Robertson 1959), will be even more adversely affected.…”

Improvement of key agronomical traits in soybean through genomic prediction of superior crosses

“…It would also allow predictions to be made for crosses involving germplasm untested in a specific environment. Recent studies indeed indicate a moderate to high accuracy of progeny means estimated as midparental genomic estimated breeding value (Abed & Belzile, 2019;Adeyemo & Bernardo, 2019;. By contrast, only a low to moderate accuracy has been found in empirical studies of predicted variance and within-family correlation between traits (Adeyemo & Bernardo, 2019;Lado et al, 2017;Zhong & Jannink 2007).…”

A connected half‐sib family training population for genomic prediction in barley

“…A better option for increasing PA with CV A or CV W would be selection of parents that maximize within‐family genetic variance for all traits under selection. Predicting genetic variance of a cross has historically been a difficult prediction problem, but recent studies have described several methods to improve prediction of progeny variance based on genome‐wide molecular markers (Lehermeier, Teyssèdre, & Schön, 2017; Mohammadi, Tiede, & Smith, 2015; Osthushenrich et al., 2018) although variable results have been reported (Adeyemo & Bernardo, 2019; Lado et al., 2017; Neyhart & Smith, 2019). These methods require estimation of marker effects to predict progeny variance, meaning potential parents must be genotyped and phenotyped in a sufficiently large field experiment to accurately estimate marker effects.…”