Genomewide predictions as a substitute for a portion of phenotyping in maize

Nicholas C. Ames, Rex Bernardo

Research output: Contribution to journalArticlepeer-review

2 Scopus citations


When genomewide predictions are available, maize (Zea mays L.) breeders may consider foregoing first-year phenotyping of testcrosses or, at the very least, reducing the number of locations used in phenotyping. Our objectives were to determine the equivalency between genomewide predictions and the number of locations used in phenotyping, and the extent to which genomewide predictions can reduce subsequent phenotyping in maize. For each of 21 test populations, we constructed half-sib training populations from prior biparental populations evaluated in multiple environments. Marker data were available for 2911 single nucleotide polymorphism markers. We estimated the number of locations (LEq) for which the response to phenotypic selection was equal to the response to genomewide selection. The median analytical estimate of LEq (cross-validation estimate of LEq in parentheses) was 1.1 (1) for yield, 1.8 (2) for moisture, and 3.0 (3) for test weight. The estimates of LEq varied widely among the test populations. We estimated the response to selection for an index that combined genomewide predictions and phenotypic data from different numbers of predictor locations (LP). The improvement in the response when LP increased from 0 (genomewide selection) to 1 was greater than the improvement in the response when LP increased from 1 to 2. This result suggested that phenotyping even at a single location captured signals that genomewide prediction did not capture. The analysis herein is helpful in designing breeding schemes that achieve a balance between the amount of genetic gain and the time and cost required to achieve such gain.

Original languageEnglish (US)
Pages (from-to)181-189
Number of pages9
JournalCrop Science
Issue number1
StatePublished - Jan 1 2020

Bibliographical note

Funding Information:
Nick Ames was supported by a Ph.D. fellowship from Bayer. We thank Drs. Sam Eathington, Mike Lohuis, David Butruille, and Shengqiang Zhong of Bayer for allowing us access to the datasets used in this study.

Publisher Copyright:
© 2020 The Authors. Crop Science © 2020 Crop Science Society of America


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