Impact of Phenotypic Correction Method and Missing Phenotypic Data on Genomic Prediction of Maize Hybrids

A - Papers appearing in refereed journals

Galli, G, Lyra, D. H., Alves, F. C, Granato, I. S. C., Bandeira-Sousa, M. and Fritsche-Neto, R 2018. Impact of Phenotypic Correction Method and Missing Phenotypic Data on Genomic Prediction of Maize Hybrids. Crop Science. 58 (4), pp. 1481-1491. https://doi.org/10.2135/cropsci2017.07.0459

AuthorsGalli, G, Lyra, D. H., Alves, F. C, Granato, I. S. C., Bandeira-Sousa, M. and Fritsche-Neto, R
Abstract

Phenotypic datasets in plant breeding are commonly incomplete due to missing phenotypic information. The best approach for correcting these datasets for a stage-wise genomic prediction (GP) is not unanimous in the scientific community. Therefore, this study evaluates a two-step GP based on different methods of phenotypic correction considering complete and incomplete datasets of maize (Zea mays L.) single crosses. The dataset consists of 325 hybrids evaluated for grain yield and plant height in four sites. Sequential levels of data loss were simulated to the original dataset (from 0 to 30%) to assess the impact of missing information. The prediction was performed by an additive genomic best linear unbiased prediction model (GBLUP) using best linear unbiased estimations (BLUEs), best linear unbiased predictions (BLUPs), and deregressed BLUPs as the response variable. Mean reliability and predictive ability slightly decreased as missing phenotypic information increased, irrespective of the response variable. Regarding phenotypic correction, all methods yielded similar results for these parameters over most missing information percentages. The coincidence of selection between single- and two-stage GP was not systematically affected by response variable across multiple selection intensities, and missing data only led to a minor decrease in coincidence. Therefore, from a breeding standpoint, regardless of phenotypic correction method and missing data level, a similar set of genotypes tend to be selected.

Year of Publication2018
JournalCrop Science
Journal citation58 (4), pp. 1481-1491
Digital Object Identifier (DOI)https://doi.org/10.2135/cropsci2017.07.0459
Web address (URL)https://dl.sciencesocieties.org/publications/cs/abstracts/58/4/1481
Open accessPublished as non-open access
Output statusPublished
Publication dates
Online14 Jun 2018
Publication process dates
Accepted07 May 2018
Copyright licensePublisher copyright
PublisherCrop Science Soc Amer
ISSN0011-183X

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