Genomic Prediction for Germplasm Improvement Through Inter-Heterotic-Group Line Crossing in Maize

Germplasm improvement is essential for maize breeding. Currently, intra-heterotic-group crossing is the major method for germplasm improvement, while inter-heterotic-group crossing is also used in breeding but not in a systematic way. In this study, five inbred lines from four heterotic groups were used to develop a connected segregating population through inter-heterotic-group line crossing (CSPIC), which comprised 5 subpopulations with 535 doubled haploid (DH) lines and 15 related test-cross populations including 1568 hybrids. Significant genetic variation was observed in most subpopulations, with several DH populations exhibiting superior phenotypes regarding traits such as plant height (PH), ear height (EH), days to anthesis (DTA), and days to silking (DTS). Notably, 10.8% of hybrids in the population POP5/C229 surpassed the high-yielding hybrid ND678 (CK). To reduce field planting costs and quickly screen for the best inter-heterotic-group DH lines and test-cross hybrids, we assessed the accuracy of genomic selection (GS) for within- and between-population predictions in the DH populations and the test-cross populations. Within the DH or the hybrid population, the prediction accuracy varied across populations and traits, with an average hybrid yield prediction accuracy of 0.41, reaching 0.54 in POP5/Z58. In the cross DH population predictions, the prediction accuracy of the half-sib population exceeded that of the non-sib cross population prediction, with the highest accuracy observed when the non-shared parents were from the same heterotic group, and the average phenotypic prediction accuracies of POP3 predicting POP2 and POP2 predicting POP3 were 0.54 and 0.45, respectively. In the cross hybrid population predictions, the accuracy was highest when both the training and the test sets came from the same DH populations, with an average accuracy of 0.43. The proportion of shared polymorphisms with respect to SNPs between the training and the test sets (PSP) exhibited a significant and strong correlation with the prediction accuracy of cross population prediction. This study demonstrates the feasibility of creating new heterotic groups through inter-heterotic-group crossing in germplasm improvement, and some cross population prediction patterns exhibited excellent prediction accuracy.

Authors

Institutions

Publication Details

Journal
International Journal of Molecular Sciences
Published
2025-03-15
DOI
https://doi.org/10.3390/ijms26062662
Citations
1
Primary Topic
Genetic and phenotypic traits in livestock
Type
article
Field-Weighted Citation Impact
1.82
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Genomic Prediction for Germplasm Improvement Through Inter-Heterotic-Group Line Crossing in Maize

Shizhong Xu, Shaojiang Chen, Shuwei Guo, Jinlong Li et al.
1 citations
International Journal of Molecular Sciences
Genetic and phenotypic traits in livestock
1.82
article

Genomic Prediction for Germplasm Improvement Through Inter-Heterotic-Group Line Crossing in Maize

Shizhong Xu, Shaojiang Chen, Shuwei Guo, Jinlong Li, Wenxin Liu, Yuandong Wang, Dehe Cheng
article en
1 citations

Abstract

Germplasm improvement is essential for maize breeding. Currently, intra-heterotic-group crossing is the major method for germplasm improvement, while inter-heterotic-group crossing is also used in breeding but not in a systematic way. In this study, five inbred lines from four heterotic groups were used to develop a connected segregating population through inter-heterotic-group line crossing (CSPIC), which comprised 5 subpopulations with 535 doubled haploid (DH) lines and 15 related test-cross populations including 1568 hybrids. Significant genetic variation was observed in most subpopulations, with several DH populations exhibiting superior phenotypes regarding traits such as plant height (PH), ear height (EH), days to anthesis (DTA), and days to silking (DTS). Notably, 10.8% of hybrids in the population POP5/C229 surpassed the high-yielding hybrid ND678 (CK). To reduce field planting costs and quickly screen for the best inter-heterotic-group DH lines and test-cross hybrids, we assessed the accuracy of genomic selection (GS) for within- and between-population predictions in the DH populations and the test-cross populations. Within the DH or the hybrid population, the prediction accuracy varied across populations and traits, with an average hybrid yield prediction accuracy of 0.41, reaching 0.54 in POP5/Z58. In the cross DH population predictions, the prediction accuracy of the half-sib population exceeded that of the non-sib cross population prediction, with the highest accuracy observed when the non-shared parents were from the same heterotic group, and the average phenotypic prediction accuracies of POP3 predicting POP2 and POP2 predicting POP3 were 0.54 and 0.45, respectively. In the cross hybrid population predictions, the accuracy was highest when both the training and the test sets came from the same DH populations, with an average accuracy of 0.43. The proportion of shared polymorphisms with respect to SNPs between the training and the test sets (PSP) exhibited a significant and strong correlation with the prediction accuracy of cross population prediction. This study demonstrates the feasibility of creating new heterotic groups through inter-heterotic-group crossing in germplasm improvement, and some cross population prediction patterns exhibited excellent prediction accuracy.

International Journal of Molecular SciencesVol. 26(6)
University of California, Riverside (US), Beijing Academy of Agricultural and Forestry Sciences (CN), China Agricultural University (CN)
Openalex Percentile: Top 18%
Genetic and phenotypic traits in livestock
1.82
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.