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High-resolution QTL mapping for grain appearance traits and co-localization of chalkiness-associated differentially expressed candidate genes in rice

Overview of attention for article published in Rice, September 2016
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Title
High-resolution QTL mapping for grain appearance traits and co-localization of chalkiness-associated differentially expressed candidate genes in rice
Published in
Rice, September 2016
DOI 10.1186/s12284-016-0121-6
Pubmed ID
Authors

Likai Chen, Weiwei Gao, Siping Chen, Liping Wang, Jiyong Zou, Yongzhu Liu, Hui Wang, Zhiqiang Chen, Tao Guo

Abstract

Grain appearance quality is a main determinant of market value in rice and one of the highly important traits requiring improvement in breeding programs. The genetic basis of grain shape and endosperm chalkiness have been given significant attention because of their importance in affecting grain quality. Meanwhile, the introduction of NGS (Next Generation Sequencing) has a significant part to play in the area of genomics, and offers the possibility for high-resolution genetic map construction, population genetics analysis and systematic expression profile study. A RIL population derived from an inter-subspecific cross between indica rice PYZX and japonica rice P02428 was generated, based on the significant variations for the grain morphology and cytological structure between these two parents. Using the Genotyping-By-Sequencing (GBS) approach, 2711 recombination bin markers with an average physical length of 137.68 kb were obtained, and a high-density genetic map was constructed. Global genetic mapping of QTLs affecting grain shape and chalkiness traits was performed across four environments and the newly identified stable loci were obtained. Twelve important QTL clusters were detected, four of which were coincident with the genomic regions of cloned genes or fine mapped QTL reported. Eight novel QTL clusters (including six for grain shape, one for chalkiness, and one for both grain shape and chalkiness) were firstly obtained and highlighted the value and reliability of the QTL analysis. The important QTL cluster on chromosome 5 affects multiple traits including circularity (CS), grain width (GW), area size of grain (AS), percentage of grains with chalkiness (PGWC) and degree of endosperm chalkiness (DEC), indicating some potentially pleiotropic effects. The transcriptome analysis demonstrated an available gene expression profile responsible for the development of chalkiness, and several DEGs (differentially expressed genes) were co-located nearby the three chalkiness-related QTL regions on chromosomes 5, 7, and 8. Candidate genes were extrapolated, which were suitable for functional validation and breeding utilization. QTLs affecting grain shape (grain width, grain length, length-width ratio, circularity, area size of grain, and perimeter length of grain) and chalkiness traits (percentage of grains with chalkiness and degree of endosperm chalkiness) were mapped with the high-density GBS-SNP based markers. The important differentially expressed genes (DEGs) were co-located in the QTL cluster regions on chromosomes 5, 7 and 8 affecting PGWC and DEC parameters. Our research provides a crucial insight into the genetic architecture of rice grain shape and chalkiness, and acquired potential candidate loci for molecular cloning and grain quality improvement.

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Geographical breakdown

Country Count As %
Benin 1 2%
Italy 1 2%
Unknown 57 97%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 18 31%
Researcher 15 25%
Student > Master 3 5%
Professor 3 5%
Other 2 3%
Other 6 10%
Unknown 12 20%
Readers by discipline Count As %
Agricultural and Biological Sciences 33 56%
Biochemistry, Genetics and Molecular Biology 11 19%
Arts and Humanities 1 2%
Philosophy 1 2%
Environmental Science 1 2%
Other 0 0%
Unknown 12 20%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 02 October 2016.
All research outputs
#18,472,072
of 22,889,074 outputs
Outputs from Rice
#249
of 387 outputs
Outputs of similar age
#243,786
of 321,009 outputs
Outputs of similar age from Rice
#8
of 9 outputs
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