Evidence map›Paper›PMID 30456522›Full record

ArticleGenes & genomics2019

Identifying natural genotypes of grain number per panicle in rice (Oryza sativa L.) by association mapping.

Jianyin Xie, Fengmei Li, Najeeb Ullah Khan, Xiaoyang Zhu, Xueqiang Wang, Zhifang Zhang, Xiaoqian Ma, Yan Zhao, Quan Zhang, Shuyang Zhang and 4 more

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In one paragraph

Article in Genes & genomics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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0cells of the map it votes in
1citing papers in PubMed
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1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
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

14 authors.

Jianyin XieKey Lab of Crop Heterosis and Utilization of Ministry of Education and Beijing Key Lab of Crop Genetic Improvement, China Agricultural University, Beijing, 100193, China.
Fengmei LiKey Lab of Crop Heterosis and Utilization of Ministry of Education and Beijing Key Lab of Crop Genetic Improvement, China Agricultural University, Beijing, 100193, China.
Najeeb Ullah KhanKey Lab of Crop Heterosis and Utilization of Ministry of Education and Beijing Key Lab of Crop Genetic Improvement, China Agricultural University, Beijing, 100193, China.
Xiaoyang ZhuKey Lab of Crop Heterosis and Utilization of Ministry of Education and Beijing Key Lab of Crop Genetic Improvement, China Agricultural University, Beijing, 100193, China.
Xueqiang WangKey Lab of Crop Heterosis and Utilization of Ministry of Education and Beijing Key Lab of Crop Genetic Improvement, China Agricultural University, Beijing, 100193, China.
Zhifang ZhangKey Lab of Crop Heterosis and Utilization of Ministry of Education and Beijing Key Lab of Crop Genetic Improvement, China Agricultural University, Beijing, 100193, China.
Xiaoqian MaKey Lab of Crop Heterosis and Utilization of Ministry of Education and Beijing Key Lab of Crop Genetic Improvement, China Agricultural University, Beijing, 100193, China.
Yan ZhaoKey Lab of Crop Heterosis and Utilization of Ministry of Education and Beijing Key Lab of Crop Genetic Improvement, China Agricultural University, Beijing, 100193, China.
Quan ZhangKey Lab of Crop Heterosis and Utilization of Ministry of Education and Beijing Key Lab of Crop Genetic Improvement, China Agricultural University, Beijing, 100193, China.
Shuyang ZhangKey Lab of Crop Heterosis and Utilization of Ministry of Education and Beijing Key Lab of Crop Genetic Improvement, China Agricultural University, Beijing, 100193, China.
Zhanying ZhangKey Lab of Crop Heterosis and Utilization of Ministry of Education and Beijing Key Lab of Crop Genetic Improvement, China Agricultural University, Beijing, 100193, China.
Jinjie LiKey Lab of Crop Heterosis and Utilization of Ministry of Education and Beijing Key Lab of Crop Genetic Improvement, China Agricultural University, Beijing, 100193, China.
Zichao LiKey Lab of Crop Heterosis and Utilization of Ministry of Education and Beijing Key Lab of Crop Genetic Improvement, China Agricultural University, Beijing, 100193, China.
Hongliang ZhangKey Lab of Crop Heterosis and Utilization of Ministry of Education and Beijing Key Lab of Crop Genetic Improvement, China Agricultural University, Beijing, 100193, China. zhangl@cau.edu.cn.ORCID 0000-0002-7551-2583

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionAs one of the main yield components, grain number per panicle (GNP) played critical role in the rice yield improvement. The identification of natural advantageous variations under different situations will promote the sustainable genetic improvement in rice yield.

objectivesThis study was designed to identify natural genotypes in a rice mini-core collection, to examine the genotypic effects across the indica and japonica genetic background in different environments, and excavating the superior genotypes that had drove the modern genetic improvement.

methodsThe association mapping of GNP was carried out using a mini-core collection including 154 indica and 119 japonica accessions in seven different environments. Genotypic effects of each genotype for each QTL were calculated and genotype frequency distortion between the commercial rice cultivars and landraces was screened by χ

resultsIn total, 74 QTLs containing stable and sensitive QTLs in various environments were detected. Within them, 20 positive and 24 negative genotypes in indica, and 24 positive and 16 negative genotypes in japonica were identified. When checking the accumulation of positive genotypes identified in indica across cultivars in each of the two subspecies, it indicated that increased number of positive genotypes identified in indica results in the substantially increased GNP in both indica and japonica across all of the environments, while this trend was not obvious for the positive genotypes identified in japonica especially in short day environments. Moreover, the positive and negative genotype frequency distortion between the landraces and commercial rice cultivars indicated that both positive selection of positive genotypes and negative selection of negative genotypes had driven the genetic improvement on GNP.

conclusionOur findings suggested that the accumulation of positive genotypes and purifying negative genotypes played equivalently important roles in the improvement of rice yield, but the efficient use for some QTLs or genotypes depends on the comprehensive evaluation of their effect under diverse genetic backgrounds and environments.

Indexed as

GenotypeQuantitative Trait LociEcosystemEdible GrainGenome-Wide Association StudyOryzaPlant BreedingAssociation mappingGrain number per panicleOryza sativa L.Superior genotype

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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.