Evidence map›Paper›PMID 37402793›Full record

ArticleNature communications2023

Joint analysis of phenotype-effect-generation identifies loci associated with grain quality traits in rice hybrids.

Lanzhi Li, Xingfei Zheng, Jiabo Wang, Xueli Zhang, Xiaogang He, Liwen Xiong, Shufeng Song, Jing Su, Ying Diao, Zheming Yuan and 2 more

Abstract read
In one paragraph

Article in Nature communications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing 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

9 citing papers in PubMed.

  1. Review
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  8. Exploration of quality variation and stability of hybrid rice under multi-environments.Molecular breeding : new strategies in plant improvement · 2024
    Article
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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

12 authors.

Lanzhi Li *Hunan Engineering & Technology Research Center for Agricultural Big Data Analysis & Decision-making, College of Plant Protection, Hunan Agricultural University, 410128, Changsha, Hunan, China.ORCID 0000-0002-1531-8500
Xingfei Zheng *Hubei Key Laboratory of Food Crop Germplasm and Genetic Improvement, Food Crop Institute, Hubei Academy of Agricultural Sciences, 430064, Wuhan, Hubei, China.
Jiabo Wang *Key Laboratory of Qinghai-Tibetan Plateau Animal Genetic Resource Reservation and Utilization of Ministry of Education and Sichuan province, Southwest Minzu University, 610041, Chengdu, Sichuan, China.
Xueli ZhangHunan Engineering & Technology Research Center for Agricultural Big Data Analysis & Decision-making, College of Plant Protection, Hunan Agricultural University, 410128, Changsha, Hunan, China.
Xiaogang HeHunan Engineering & Technology Research Center for Agricultural Big Data Analysis & Decision-making, College of Plant Protection, Hunan Agricultural University, 410128, Changsha, Hunan, China.
Liwen XiongHunan Engineering & Technology Research Center for Agricultural Big Data Analysis & Decision-making, College of Plant Protection, Hunan Agricultural University, 410128, Changsha, Hunan, China.
Shufeng SongState Key Laboratory of Hybrid Rice, Hunan Hybrid Rice Research Center, Hunan Academy of Agricultural Sciences, 410125, Changsha, Hunan, China.ORCID 0000-0002-9068-9512
Jing SuHunan Engineering & Technology Research Center for Agricultural Big Data Analysis & Decision-making, College of Plant Protection, Hunan Agricultural University, 410128, Changsha, Hunan, China.
Ying DiaoSchool of Life Science and Technology, Wuhan Polytechnic University, 430023, Wuhan, Hubei, China.
Zheming YuanHunan Engineering & Technology Research Center for Agricultural Big Data Analysis & Decision-making, College of Plant Protection, Hunan Agricultural University, 410128, Changsha, Hunan, China.
Zhiwu ZhangDepartment of Crop and Soil Sciences, Washington State University, Pullman, WA, 99164, USA. Zhiwu.Zhang@WSU.edu.ORCID 0000-0002-5784-9684
Zhongli HuState Key Laboratory of Hybrid Rice, College of Life Science, Wuhan University, 430072, Wuhan, Hubei, China. huzhongli@whu.edu.cn.ORCID 0000-0002-7258-3829

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Genetic improvement of grain quality is more challenging in hybrid rice than in inbred rice due to additional nonadditive effects such as dominance. Here, we describe a pipeline developed for joint analysis of phenotypes, effects, and generations (JPEG). As a demonstration, we analyze 12 grain quality traits of 113 inbred lines (male parents), five tester lines (female parents), and 565 (113×5) of their hybrids. We sequence the parents for single nucleotide polymorphisms calling and infer the genotypes of the hybrids. Genome-wide association studies with JPEG identify 128 loci associated with at least one of the 12 traits, including 44, 97, and 13 loci with additive effects, dominant effects, and both additive and dominant effects, respectively. These loci together explain more than 30% of the genetic variation in hybrid performance for each of the traits. The JEPG statistical pipeline can help to identify superior crosses for breeding rice hybrids with improved grain quality.

Indexed as

OryzaEdible GrainGenome-Wide Association StudyGenotypePhenotypePlant Breeding

Identifiers

PMID37402793
PMCPMC10319794

What Socratic holds

Textmetadata
LicenceCC BY
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 Socratic graph.