Genomewide prediction accuracy within 969 maize biparental populations

Lian Lian, Amy Jacobson, Shengqiang Zhong, Rex Bernardo

Research output: Contribution to journalArticlepeer-review

47 Scopus citations

Abstract

In genomewide selection, the expected correlation between predicted and true genotypic values (rMG) has been previously derived as a function of the training population size (N), heritability (h2), and effective number of chromosome segments (Me) affecting the trait. Our objectives were to determine: (i) the mean and variability of rMG in 969 biparental maize (Zea mays L.) breeding populations for seven traits, (ii) if rMG can be predicted in advance, and (iii) how N, h2, and number of markers (NM) affect rMG. We modified a previous equation for expected rMG to account for linkage disequilibrium (r2) between a marker and a quantitative trait locus (QTL). Across the 969 populations, the mean and range (in parentheses) of observed rMG was 0.45 (-0.59, 1.03) for grain yield, 0.59 (-0.34, 0.96) for moisture, 0.55 (-0.24, 1.10) for test weight, 0.49 (-0.22, 1.04) for stalk lodging, 0.41 (-0.30, 0.93) for root lodging, 0.47 (-0.45, 0.97) for plant height, and 0.42 (-0.43, 0.94) for ear height. The observed rMG values were centered around the expected rMG when r2 was accounted for, but the observed rMG had a large spread around the expected rMG. The r2(Nh2)1/2 had the strongest association with observed rMG. When r2(Nh2)1/2 exceeded 8, the proportion of rMG equal to or larger than 0.50 reached 90% among all the population- trait combinations. We conclude it is difficult to predict rMG in advance, but that rules of thumb based on r2(Nh2)1/2 can help achieve a high rMG.

Original languageEnglish (US)
Pages (from-to)1514-1522
Number of pages9
JournalCrop Science
Volume54
Issue number4
DOIs
StatePublished - 2014

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