Genomic Prediction Strategies for Dry-Down-Related Traits in Maize

Pengzun Ni; Mahlet Teka Anche; Yanye Ruan; Dongdong Dang; Nicolas Morales; Lingyue Li; Meiling Liu; Shu Wang; Kelly R Robbins

doi:10.3389/fpls.2022.930429

Genomic Prediction Strategies for Dry-Down-Related Traits in Maize

Front Plant Sci. 2022 Jun 30:13:930429. doi: 10.3389/fpls.2022.930429. eCollection 2022.

Authors

Pengzun Ni^{1

2

3}, Mahlet Teka Anche², Yanye Ruan¹, Dongdong Dang¹, Nicolas Morales², Lingyue Li¹, Meiling Liu¹, Shu Wang³, Kelly R Robbins²

Affiliations

¹ Shenyang Key Laboratory of Maize Genomic Selection Breeding, Liaoning Province Research Center of Plant Genetic Engineering Technology, College of Biological Science and Technology, Shenyang Agricultural University, Shenyang, China.
² Section of Plant Breeding and Genetics, School of Integrative Plant Sciences, Cornell University, Ithaca, NY, United States.
³ College of Agronomy, Shenyang Agricultural University, Shenyang, China.

Abstract

For efficient mechanical harvesting, low grain moisture content at harvest time is essential. Dry-down rate (DR), which refers to the reduction in grain moisture content after the plants enter physiological maturity, is one of the main factors affecting the amount of moisture in the kernels. Dry-down rate is estimated using kernel moisture content at physiological maturity and at harvest time; however, measuring kernel water content at physiological maturity, which is sometimes referred as kernel water content at black layer formation (BWC), is time-consuming and resource-demanding. Therefore, inferring BWC from other correlated and easier to measure traits could improve the efficiency of breeding efforts for dry-down-related traits. In this study, multi-trait genomic prediction models were used to estimate genetic correlations between BWC and water content at harvest time (HWC) and flowering time (FT). The results show there is moderate-to-high genetic correlation between the traits (0.24-0.66), which supports the use of multi-trait genomic prediction models. To investigate genomic prediction strategies, several cross-validation scenarios representing possible implementations of genomic prediction were evaluated. The results indicate that, in most scenarios, the use of multi-trait genomic prediction models substantially increases prediction accuracy. Furthermore, the inclusion of historical records for correlated traits can improve prediction accuracy, even when the target trait is not measured on all the plots in the training set.

Keywords: MT-GBLUP; correlated traits; dry-down rate; genomic prediction; kernel water content.