Prediction of genetic variance in biparental maize populations: Genomewide marker effects versus mean genetic variance in prior populations

Lian Lian, Amy Jacobson, Shengqiang Zhong, Rex N Bernardo

Research output: Contribution to journalArticlepeer-review

13 Scopus citations

Abstract

Good methods are lacking for predicting the genetic variance (VG) in biparental populations. Our objective was to determine whether genomewide marker effects and related populations could be used to predict the VG when two parents (A and B) are crossed to form a segregating population. For each of 85 A/B populations, 2 to 23 maize (Zea mays L.) populations with A and B as one of the parents were used as the training population. In the genomewide selection model, the testcross VG in A/B was predicted as the variance among the predicted genotypic values of progeny from a simulated A/B population. In the mean variance model, VG in A/B was predicted as the mean of VG in a series of A/* populations and */B populations, where * denotes a random parent. The correlations between observed and predicted VG were significant (P = 0.05) for both the genomewide selection model (0.18 for yield, 0.49 for moisture, and 0.52 for test weight) and the mean variance model (0.26 for yield, 0.46 for moisture, and 0.50 for test weight). The percentages of bias in estimates of VG were −28 to −60% for the genomewide selection model, but were only −1 to 5% for the mean variance model. Our results indicated that the VG in an A/B population could be predicted as the mean variance among populations with A and B as one of the parents. The mean variance model should be practical in breeding programs because it simply uses phenotypic data from prior, related populations.

Original languageEnglish (US)
Pages (from-to)1181-1188
Number of pages8
JournalCrop Science
Volume55
Issue number3
DOIs
StatePublished - Jan 1 2015

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