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.
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Claims 4
Claim · model-written summary
Prediction reliability and ability slightly decreased with more missing data.
- Treatment
- missing phenotypic informatione0
- Outcome
- Mean reliability and predictive abilitye0
- Direction
- decreasede0
Zea maysNot reportedComparator
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“Mean reliability and predictive ability slightly decreased as missing phenotypic information increased, irrespective of the response variable.”
t0“Maize Hybrids”
Claim · model-written summary
Phenotypic correction methods gave similar prediction reliability and ability.
Not reportedValue
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“all methods yielded similar results for these parameters over most missing information percentages.”
t0“Maize Hybrids”
Claim · model-written summary
Response variable did not systematically affect single versus two stage selection coincidence.
- Outcome
- coincidence of selectione0
- Direction
- not systematically affectede0
- Comparator
- single‐ and two‐stage GPe0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“The coincidence of selection between single‐ and two‐stage GP was not systematically affected by response variable across multiple selection intensities”
t0“Maize Hybrids”
Claim · model-written summary
Missing data caused only a minor decrease in selection coincidence.
Not reportedComparatorValue
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“missing data only led to a minor decrease in coincidence.”
t0“Maize Hybrids”