Seed Weight as a Covariate in Association and Prediction Studies for Biomass Traits in Maize Seedlings

Abstract

Background: The seedling stage has received little attention in maize breeding to identify genotypes tolerant to water deficit. The aim of this study is to evaluate incorporation of seed weight (expressed as hundred kernel weight, HKW) as a covariate into genomic association and prediction studies for three biomass traits in a panel of elite inbred lines challenged by water withholding at seedling stage. Methods: 109 genotyped-by-sequencing (GBS) elite maize inbreds were phenotyped for HKW and planted in controlled conditions (16/8 day/night, 25 °C, 50% RH, 200 µMol/m2/s) in trays filled with soil. Plants in control (C) were watered every two days, while watering was stopped for 10 days in water withholding (WW). Fresh weight (FW), dry weight (DW), and dry matter content (DMC) were measured. Results: Adding HKW as a covariate increased the power of detection of associations in FW and DW by 44% and increased genomic prediction accuracy in C and decreased in WW. Conclusions: Seed weight was effectively incorporated into association studies for biomass traits in maize seedlings, whereas the incorporation into genomic predictions, particularly in water-stressed plants, was not worthwhile.

Keywords

• maize
• association mapping
• kernel weight
• water deficit
• genomic prediction