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

Frontiers in Plant Science

卷:13

Pengzun Ni¹  Shu Wang¹  Nicolas Morales²  Kelly R. Robbins²  Mahlet Teka Anche²  Yanye Ruan³  Lingyue Li³  Dongdong Dang³  Meiling Liu³ 

[1] College of Agronomy, Shenyang Agricultural University, Shenyang, China;

[2] Section of Plant Breeding and Genetics, School of Integrative Plant Sciences, Cornell University, Ithaca, NY, United States;

[3] 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;

关键词: kernel water content;    dry-down rate;    genomic prediction;    MT-GBLUP;    correlated traits;   

DOI  :  10.3389/fpls.2022.930429

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown