AUTHOR=Liu Yuncan , Ao Man , Lu Ming , Zheng Shubo , Zhu Fangbo , Ruan Yanye , Guan Yixin , Zhang Ao , Cui Zhenhai 

TITLE=Genomic selection to improve husk tightness based on genomic molecular markers in maize

JOURNAL=Frontiers in Plant Science

VOLUME=Volume 14 - 2023

YEAR=2023

URL=https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2023.1252298

DOI=10.3389/fpls.2023.1252298

ISSN=1664-462X

ABSTRACT=The husk tightness (HTI) in maize plays a crucial role in regulating the water content of ears during the maturity stage, thereby influencing the quality of mechanical grain harvesting in China. Genomic selection (GS), which employs molecular markers, offers a promising approach for identifying and selecting inbred lines with the desired HTI trait in maize breeding. However, the effectiveness of GS is contingent upon various factors, including the genetic architecture of breeding populations, sequencing platforms, and statistical models. In this study, we sought to examine the impact of these factors on the predictive capacity of GS for HTI. Over a period of two years, an association panel of maize inbred lines was cultivated across three sites, divided into four subgroups. To predict HTI, marker data from three sequencing platforms -Genotyping by sequencing (GBS), RNA sequencing (RNA-seq), and chips (50 K) -were utilized through six statistical models: ridge regression best linear unbiased prediction (rrBLUP), BayesA, BayesB, BayesC, Bayesian LASSO, and Bayesian ridge regression. The findings indicate that the RNA-seq sequencing platform and rrBLUP model demonstrated the highest accuracy in predicting HTI across all subgroups. To mitigate the significant variability in the predictive capacity range and its impact on practical production implementation, we opted to employ 70% of the lines as the training set, with the remaining 30% serving as the testing set. Furthermore, our findings indicate that the predictive capacity for husk tightness can be substantially enhanced by selecting a specific SS subgroup for sampling the testing set. Several factors such as sequencing price and prediction ability were used to evaluate the optimal HTI prediction method, which laid the foundation for the breeding of great HTI varieties and provided a new idea for the GS breeding strategy of other agronomic traits in maize.