Genetic Dissection of Rice Ratooning Ability Using an Introgression Line Population and Substitution Mapping of a Pleiotropic Quantitative Trait Locus qRA5.

Ratooning ability is a key factor that influences ratoon rice yield, in the area where light and temperature are not enough for second season rice. In the present study, an introgression line population derived from Minghui 63 as the recipient parent and 02428 as the donor parent was developed, and a high-density bin map containing 4568 bins was constructed. Nine ratooning-ability-related traits were measured, including maximum tiller number, panicle number, and grain yield per plant in the first season and ratoon season, as well as three secondary traits, maximum tiller number ratio, panicle number ratio, and grain yield ratio. A total of 22 main-effect QTLs were identified and explained for 3.26-18.63% of the phenotypic variations in the introgression line population. Three genomic regions, including 14.12-14.65 Mb on chromosome 5, 4.64-5.76 Mb on chromosome 8, and 10.64-15.52 Mb on chromosome 11, were identified to simultaneously control different ratooning-ability-related traits. Among them, qRA5 in the region of 14.12-14.65 Mb on chromosome 5 was validated for its pleiotropic effects on maximum tiller number and panicle number in the first season, as well as its maximum tiller number ratio, panicle number ratio, and grain yield ratio. Moreover, qRA5 was independent of genetic background and delimited into a 311.16 kb region by a substitution mapping approach. These results will help us better understand the genetic basis of rice ratooning ability and provide a valuable gene resource for breeding high-yield ratoon rice varieties.

Five plants per RIL for phenotyping traits of high or moderate heritability ensure the power of QTL mapping in a rice MAGIC population.

Currently, the power of QTL mapping is mainly dependent on the quality of phenotypic data in a given population, regardless of the statistical method, as the quality of genotypic data is easily guaranteed in the laboratory. Increasing the sample size per line used for phenotyping is a good way to improve the quality of phenotypic data. However, accommodating a large-scale mapping population takes a large area of rice field, which frequently results in high costs and extra environmental noises. To acquire a reasonable small sample size without a penalty in mapping power, we conducted three experiments with a 4-way MAGIC population and measured phenotypes of 5, 10, and 20 plants per RIL. Three traits including heading date, plant height, and tillers per plant were focused. With SNP- and bin-based QTL mapping, 3 major and 3 minor QTLs for heading date with high heritability and 2 major QTLs for plant height with moderate heritability were commonly detected across the three experiments, but no QTL for tillers per plant with low heritability were commonly identified. In addition, bin-based QTL mapping was more powerful than SNP-based mapping and able to rank the genetic effects of parental alleles. Thus, 5 plants per RIL for phenotyping ensure the power of QTL mapping for traits of high or moderate heritability, and bin-based QTL mapping is recommended for multiparent populations.

Bin-based genome-wide association analyses improve power and resolution in QTL mapping and identify favorable alleles from multiple parents in a four-way MAGIC rice population.

KEY MESSAGE: A whole genome bin map was developed for a MAGIC population. Association studies for heading date at bin level exhibited powerful QTL mapping and identified favorable alleles. The presumed advantages of multiparent advanced generation intercross (MAGIC) population in quantitative trait locus (QTL) mapping were not fully utilized in the previous studies in which genome-wide association studies (GWAS) were conducted at only single nucleotide polymorphism level. In this study, we genotyped a rice four-way MAGIC population of 247 F7 lines and their parents by sequencing. A total of 5934 bins with an average length of 65 kb were constructed and covered 97% of the genome. The MAGIC population showed low population structure and balanced parental contributions. A bin-based GWAS for heading date identified 4 QTLs in three environments. Three major QTLs were mapped exactly to the bins where the major heading date genes DTH3, Ghd7.1 and Ghd8 were located. Multiple comparisons showed that different parental alleles had varied genetic effects. Like DTH3, the alleles of the Guichao 2/YJSM, IR34 and Cypress had larger, intermediate and no effects, respectively. Based on comparative sequencing of 8 known heading date genes undetected in this MAGIC population, only Ghd7 exhibited diverse function among parents. The failure in Ghd7 mapping was well explained by its interaction with Hd1 because Ghd7 had no effects on heading date when combined with the nonfunctional hd1 carried by all four parents. Overall, bin-based GWAS have more mapping power and higher resolution with a MAGIC population and provide favorable alleles to breeders. The use of more diversified parents is encouraged to develop a MAGIC population for detecting more QTLs for important agronomical traits.