Title: Towards a deeper haplotype mining of complex traits in rice with RFGB v2.0

Biography:

Tian-Qing Zheng, is working on rice data mining and molecular breeding in the Institute of Crop Sciences (ICS), Chinese Academy of Agricultural Sciences (CAAS). He is now working as the Functional Unit Leader for Crop Breeding Design in an Crop Open Lab of CAAS. His research interests are focused around data-mining and improvement of complex traits in rice. He is leading the project for the development of the RFGB (Rice Functional Genomics Breeding) website, which has been now updated to version 2.0

Abstract

After big genotypic dataset such as the 3000-rice genome (3K-RG) are becoming open through various platforms, one-stand solutions which could offer user-friendly web services for users with overwhelming phenotyping data based on the sequenced genomes are sincerely desired. Here we introduce a new version of the Rice Functional Genomic and Breeding (RFGBv2.0). It includes five major modules, which are: Phenotype, Haplotype, SNP & InDel, Restore Sequence, and Germplasm. Their functions are described with the embedded 3K-RG data as example. Four tips of iceberg for user cases of RFGB v2.0 with corresponding technical routes were presented including: 1) exploring favorable donors for higher zinc concentration in milled grains, 2) shortlisting candidate genes for grain length with near isogenic lines, 3) mining favorable haplotypes for seedling vigor traits under paddy direct seeding system, and 4) variations and restore sequence seeking for a leaf rolling QTL region. RFGB v2.0 has offered a unique view on bridging the huge gaps between two big datasets of genome and phenome from 3K-RG, which will spark more ideas on deeper mining of complex traits in rice.