Identification of reliable QTLs and designed QTL breeding for grain shape and milling quality in the reciprocal introgression lines in rice.

Milling quality (MQ) and grain shape (GS) of rice (Oryza sativa L.) are correlated traits, both determine farmers' final profit. More than one population under multiple environments may provide valuable information for breeding selection on these MQ-GS correlations. However, suitable analytical methods for reciprocal introgression lines with linkage map for this kind of correlation remains unclear. In this study, our major tasks were (1) to provide a set of reciprocal introgression lines (composed of two BC2RIL populations) suitable for mapping by linkage mapping using markers/bins with physical positions; (2) to test the mapping effects of different methods by using MQ-GS correlation dissection as sample case; (3) to perform genetic and breeding simulation on pyramiding favorite alleles of QTLs for representative MQ-GS traits. Finally, with four analysis methods and data collected under five environments, we identified about 28.4 loci on average for MQ-GS traits. Notably, 52.3% of these loci were commonly detected by different methods and eight loci were novel. There were also nine regions harboring loci for different MQ-GS traits which may be underlying the MQ-GS correlations. Background independent (BI) loci were also found for each MQ and GS trait. All these information may provide useful resources for rice molecular breeding.

Pedigree genome data of an early-matured Geng/japonica glutinous rice mega variety Longgeng 57.

By using PacBio HiFi technology, we produced over 700 Gb of long-read sequencing (LRS) raw data; and by using Illumina paired-end whole-genome shotgun (WGS) sequencing technology, we generated more than 70 Gb of short-read sequencing (SRS) data. With LRS data, we assembled one genome and then generate a set of annotation data for an early-matured Geng/japonica glutinous rice mega variety genome, Longgeng 57 (LG57), which carries multiple elite traits including good grain quality and wide adaptability. Together with the SRS data from three parents of LG57, pedigree genome variations were called for three representative types of genes. These data sets can be used for deep variation mining, aid in the discovery of new insights into genome structure, function, and evolution, and help to provide essential support to biological research in general.