Evidence map›Paper›PMID 41784739›Full record

ArticlePlant molecular biology2026

Rapid selection of early-maturing and low-ear position maize hybrids suitable for China using genomic prediction.

Ping Luo, Wenzhe Li, Lin Zhang, Jie Yang, Houwen Wang, Fei Wang, Ruisi Yang, Xinlong Xu, Ke Yang, Hongjun Yong and 10 more

Abstract read
PubMed Publisher
In one paragraph

Article in Plant molecular biology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

20 authors.

Ping LuoState Key Laboratory of Crop Gene Resources and Breeding, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100081, China.
Wenzhe LiState Key Laboratory of Crop Gene Resources and Breeding, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100081, China.
Lin ZhangCollege of Agronomy, Northeast Agricultural University, Heilongjiang, 150030, China.
Jie YangFood Crops Research Institute, Xinjiang Academy of Agricultural Science, Urumqi, 830052, China.
Houwen WangState Key Laboratory of Crop Gene Resources and Breeding, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100081, China.
Fei WangState Key Laboratory of Crop Gene Resources and Breeding, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100081, China.
Ruisi YangState Key Laboratory of Crop Gene Resources and Breeding, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100081, China.
Xinlong XuState Key Laboratory of Crop Gene Resources and Breeding, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100081, China.
Ke YangState Key Laboratory of Crop Gene Resources and Breeding, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100081, China.
Hongjun YongState Key Laboratory of Crop Gene Resources and Breeding, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100081, China.
Mingshun LiState Key Laboratory of Crop Gene Resources and Breeding, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100081, China.
Jianfeng WengState Key Laboratory of Crop Gene Resources and Breeding, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100081, China.
Degui ZhangState Key Laboratory of Crop Gene Resources and Breeding, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100081, China.
Zhiqiang ZhouState Key Laboratory of Crop Gene Resources and Breeding, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100081, China.
Jienan HanState Key Laboratory of Crop Gene Resources and Breeding, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100081, China.
Wenwei GaoCollege of Agriculture, Xinjiang Agricultural University, Urumqi, 830052, China.
Junjie FuState Key Laboratory of Crop Gene Resources and Breeding, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100081, China.
Xinhai LiState Key Laboratory of Crop Gene Resources and Breeding, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100081, China.
Zhiyong NiCollege of Agriculture, Xinjiang Agricultural University, Urumqi, 830052, China.
Zhuanfang HaoState Key Laboratory of Crop Gene Resources and Breeding, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100081, China. haozhuanfang@163.com.

Funding

Provincial Key Research and Development Program 2024B02008-2
6 · The paper itself

Abstract

Earlymaturity and lowear position traits are important selective traits in maize breeding procedures, especially in northern China. To construct a genomic prediction model for selecting early-maturity and low-ear-position hybrids adapted to different environments, 34elite inbred lines were selected to produce 285 single-cross hybrids using a partial diallel cross design. These inbred lines constituted a mini-core collection of Chinese maize germplasm, comprising 18 inbred lines from the Stiff Stalk heterotic group and 16 inbred lines from the Non-Stiff Stalk heterotic group. The parental inbred lines were genotyped by sequencing, and the 285 hybrids were phenotyped for early-maturity and low-ear-position-related traits at seven locations in China. The results revealed significant variations in early maturity and low ear position traits among seven locations. These traits were highly correlated across locations, with high heritability ranging from 0.89 to 0.94, indicating that genetic factors primarily govern early maturity and ear position. The population structure showed that the hybrids were mainly composed of four subgroups. Among them, the Reid × Iodent subgroup exhibited the shortest growth period and the lowest ear position across the seven locations and showed relatively high genetic diversity. Correspondingly, the low general combining abilities of the inbred lines in both subgroups across the locations were also identified. The genomic prediction accuracy of these agronomic traits ranged from 0.26 to 0.89. Accuracy across seven locations ranged from 0.37 to 0.89. We also found that effective genomic prediction models can be set and developed using a training set comprising less than 16% of the entire dataset, covering both breeding aims. Genomic prediction achieved moderate to high accuracy with a small training set, demonstrating clear potential to streamline hybrid selection and enhance prediction stability.

Indexed as

Genome, PlantGenomicsSelection, GeneticZea maysChinaGenetic VariationGenotypeHybridization, GeneticPhenotypePlant BreedingEarly maturingGenomic predictionLow ear positionMaizeTraining set optimization

Identifiers

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.