[From genome analysis to construction of an integrated omics knowledgebase for crops].

The advances in high-throughput technologies have enabled high-speed accumulation of omics data, which contain a large amount of genetic variations and their functional information. The integration and deep utilization of those data will be a long-term and difficult task, which requires highly efficient data storage and powerful data analysis and mining tools. In the past several years, our group has conducted multi-level genomic analyses in several plants, including genome assembly and annotation, comparative and population genomic studies, through collaboration with other labs inside and outside of our institution. Meanwhile, we have integrated a large amount of rice germplasm information and omics data into a structural database and developed related data query, visual display and mining web tools. Here, we summarize some of those results and discuss our next goal to construct an integrated omics knowledgebase for crops to support functional genomics and molecular design breeding.

Joint Exploration of Favorable Haplotypes for Mineral Concentrations in Milled Grains of Rice (Oryza sativa L.).

Grain minerals in rice, especially those in milled grains, are important sources of micro-nutrition elements, such as iron (Fe), zinc (Zn), manganese (Mn), copper (Cu), and selenium (Se), and of toxic heavy metal elements, especially cadmium (Cd), for populations consuming a rice diet. To date, the genetic mechanism underlying grain mineral concentrations (GMCs) in milled grain remains largely unknown. In this report, we adopted a set of 698 germplasms consisting of two subsets [indica/Xian (X-set) and japonica/Geng (G-set)], to detect quantitative trait loci (QTL) affecting GMC traits of Fe, Zn, Cd, Mn, Cu, and Se in milled grains. A total of 47 QTL regions, including 18 loci and 29 clusters (covering 62 Cd loci), responsible for the GMCs in milled grains were detected throughout the genome. A joint exploration of favorable haplotypes of candidate genes was carried out as follows: (1) By comparative mapping, 10 chromosome regions were found to be consistent with our previously detected QTL from linkage mapping. (2) Within eight of these regions on chromosomes 1, 4, 6, 7, and 8, candidate genes were identified in the genome annotation database. (3) A total of 192 candidate genes were then submitted to further haplotype analysis using million-scale single nucleotide polymorphisms (SNPs) from the X-set and the G-set. (4) Finally, 37 genes (19.3%) were found to be significant in the association between the QTL targeting traits and the haplotype variations by pair-wise comparison. (5) The phenotypic values for the haplotypes of each candidate were plotted. Three zinc finger (like) genes within two candidate QTL regions (qFe6-2 and qZn7), and three major GMC traits (Fe, Zn, and Cd) were picked as sample cases, in addition to non-exhausted cross validations, to elucidate this kind of association by trait value plotting. Taken together, our results, especially the 37 genes with favorable haplotype variations, will be useful for rice biofortification molecular breeding.