Genome-Wide Association Study of Grain Appearance and Milling Quality in a Worldwide Collection of Indica Rice Germplasm

Abstract

Grain appearance quality and milling quality are the main determinants of market value of rice. Breeding for improved grain quality is a major objective of rice breeding worldwide. Identification of genes/QTL controlling quality traits is the prerequisite for increasing breeding efficiency through marker-assisted selection. Here, we reported a genome-wide association study in indica rice to identify QTL associated with 10 appearance and milling quality related traits, including grain length, grain width, grain length to width ratio, grain thickness, thousand grain weight, degree of endosperm chalkiness, percentage of grains with chalkiness, brown rice rate, milled rice rate and head milled rice rate. A diversity panel consisting of 272 indica accessions collected worldwide was evaluated in four locations including Hangzhou, Jingzhou, Sanya and Shenzhen representing indica rice production environments in China and genotyped using genotyping-by-sequencing and Diversity Arrays Technology based on next-generation sequencing technique called DArTseq™. A wide range of variation was observed for all traits in all environments. A total of 16 different association analysis models were compared to determine the best model for each trait-environment combination. Association mapping based on 18,824 high quality markers yielded 38 QTL for the 10 traits. Five of the detected QTL corresponded to known genes or fine mapped QTL. Among the 33 novel QTL identified, qDEC1.1 (qGLWR1.1), qBRR2.2 (qGL2.1), qTGW2.1 (qGL2.2), qGW11.1 (qMRR11.1) and qGL7.1 affected multiple traits with relatively large effects and/or were detected in multiple environments. The research provided an insight of the genetic architecture of rice grain quality and important information for mining genes/QTL with large effects within indica accessions for rice breeding.

Fig 1. Frequency of markers in different MAF classes.

SNP with MAF < 0.05 were excluded.

Fig 2. Box plot of 10 rice grain appearance and milling quality traits in four environments.

HZ: Hangzhou. JZ: Jingzhou. SY: Sanya. SZ: Shenzhen. GL: Grain Length. GW: Grain Width. GLWR: Grain Length and Width Ratio. GT: Grain Thickness. TGW: Thousand Grain Weight. BRR: Brown Rice Rate. MRR: Milled Rice Rate. HMRR: Head Milled Rice Rate. PGWC: Percentage of Grains With Chalkiness. DEC: Degree of Endosperm chalkiness.

Fig 3. Correlations between 10 gain quality traits measured in HZ, JZ, SY and SZ.

The values given were correlation coefficients multiplied by 100. The values without glyphs indicated insignificant at the level of 0.05.

Fig 4. LD decay plot in the whole population and three subpopulations inferred by STRUCTURE.

Y axis is the average r² value of each 10 kb region and X axis is physical distance between markers in unit of Mb. A power law curve (y = ax^k) was fitted to determine the physical position (x) corresponding to a given r² value (y)

Fig 5. Quantile-quantile plots of 16 models for GL measured in HZ.

The horizontal and vertical axes are -log10 transformed expected p-values and observed p-values, respectively. Model with more uniformly distributed p-values is better.