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Favorable Alleles Mining for Gelatinization Temperature, Gel Consistency and Amylose Content in Oryza Sativa by Association Mapping


Favorable Alleles Mining for Gelatinization Temperature, Gel Consistency and Amylose Content in Oryza Sativa by Association Mapping

Hui Wang et al. BMC Genet.


Background: Improving the gelatinization temperature (GT), gel consistency (GC) and amylose content (AC) for parental grain eating and cooking qualities (ECQs) are key factors for enhancing average grain ECQs for hybrid japonica rice.

Results: In this study, a genome-wide association mapping (GWAS) for ECQs was performed on a selected sample of 462 rice accessions in 5 environments using 262 simple sequence repeat markers. We identified 10 loci and 27 favorable alleles for GT, GC and AC, and some of these loci were overlapped with starch synthesis-related genes. Four SSR loci for the GT trait were distributed on chromosomes 3, 5, 8, and 9, of which two SSR loci were novel. Two SSR loci associated with the GC trait were distributed on chromosomes 3 and 6, although only one SSR locus was novel. Four SSR loci associated with the AC trait were distributed on chromosomes 3, 6, 10, and 11, of which three SSR loci were novel. The novel loci RM6712 and RM6327 were simultaneously identified in more than 2 environments and were potentially reliable QTLs for ECQs, with 15 parental combinations being predicted. These QTLs and parental combinations should be used in molecular breeding to improve japonica rice average ECQs.

Conclusions: Among the 10 SSR loci associated with GT, GC and AC for grain ECQs detected in 27 favorable alleles, the favorable allele RM3600-90bp on chromosome 9 could significantly reduce GT, RM5753-115bp on chromosome 6 could significantly increase GC, and RM6327-230bp on chromosome 11 could significantly reduce AC in hybrid japonica rice mixed rice samples.

Keywords: Amylose content; Gel consistency; Gelatinization temperature; Linkage disequilibrium,·Genome-wide association mapping; Oryza sativa; Phenotypic and genetic diversities.

Conflict of interest statement

Ethics approval and consent to participate

All the rice seeds used in this research were collected and maintained in our laboratory during long-term rice science studies. Accession numbers 1-148 were obtained from Dr. Weidong Jin, the former PhD student guided by the corresponding author (Rf. Doi:10.1360/biodiv.060189). Accession numbers 149-177 were obtained from Mr. Nguyen Phuong Tung, the former international student from Vietnam studying in Nanjing Agricultural University for MS degree guided by the corresponding author (Rf. Doi:10.3969/J.issn.1001-7216.2014.03.004).

Consent for publication

Not applicable.

Competing interests

The authors declare that the research was conducted in the absence of any commercial or financial relationship that could be construed as a potential conflict of interest.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.


Fig. 1
Fig. 1
Graphical representation of different typical variety of rice showing the highest and the lowest values in gelatinization temperature which measured by the alkali spreading score (ASS) and gel consistency trait. a Rice of the lowest ASS named ‘Yuedao 70’. b The highest ASS named ‘Longdun 105’. c Summary statistics of gelatinization temperature of 462 rice accessions in five environments. GT values based on the ASS (1-7 grade). d The shortest length of gel consistent named ‘Haobuka’. e The longest length of gel consistent named ‘Hongnong 5’. f-g Summary statistics of gel consistency and amylose content of 462 rice accessions in five environments. Box plots span the 95th–fifth percentiles. Bar = 10mm
Fig. 2
Fig. 2
Population structure analysis in 262 rice accessions. a Changes in the △k value. b) Posterior probability of 262 accessions belonging to five subpopulations calculated by STRUCRURE software. The colored subsections within each vertical bar indicate membership coefficient (Q) of the accession to different clusters. Identified subpopulations are SP1 (red color), SP2 (green color), SP3 (navy blue color), SP4 (yellow color), SP5 (purple color). c Neighbor-joining tree for the 262 accessions based on Nei’s genetic distance. d Principal components analysis (PCA) for 462 accessions and referance accessions genotyped with 262 SSR markers
Fig. 3
Fig. 3
Genetic diversity analysis for the five subpopulations. a Number of alleles. b Number of alleles/locus in the five subpopulations. c Genetic diversity and PIC value in the five subpopulations
Fig. 4
Fig. 4
Manhattan and quantile-quantile plots of GWAS studies for GT, GC and AC with mixed linear model (MLM) in the five environments. a GT. b GC. c AC
Fig. 5
Fig. 5
The geographic distribution of accessions carrying favorable alleles (The source map was taken from a Geographic distribution and the relative frequencies of SSR marker RM3600 alleles. b Geographic distribution and the relative frequencies of SSR marker RM6327 alleles. The color in the pie charts indicates the marker alleles within each locus status and geographic provenance of the germplasm category

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