Working group session:
Comparative Genomics and Bioinformatics
Presentation type:
oral
Authors:
Zhu, Jun; Zhu, Jun
Presenter:
Correspondent:
Zhu, Jun; Zhu, Jun
Abstract:
New methods of mapping QTX based on omics data and their applications in crop breeding Jun Zhu Institute of Bioinformatics, Zhejiang University Hangzhou 310058, PR China Corresponding author: jzhu@zju.edu.cn It is a challenge to develop efficient statistical methods and computing software for mapping QTX underlying complex traits based on omics data. Recently, we have developed new mapping approaches of detecting gene-to-gene interaction and gene-to-environment interaction by GWAS of quantitative trait with markers (QTLs), SNPs (QTSs), transcripts (QTTs), proteins (QTPs), and metabolites (QTMs). The genetic model of complex traits can include cofactors (i.e. block, sex, age), genetic main effects (additive A, dominance D), epistasis effects (additive by additive AA, additive by dominance AD, dominance by dominance DD), and gene-to-environment interaction (AE, DE, AAE, ADE, and DDE). Mixed linear model approaches are used for unbiased prediction of all these genetic main effects, epistasis effects, and gene-to-environment interaction effects. The heritability of individual effects is estimated. Mapping software GMDR-GPU and QTXNetwork have also been developed, which can be used under different operating systems (Windows, Unix, Mac). The software use GPU computation technology and can be 250X faster than suing CPU computation. Worked examples and crop breeding applications will be demonstrated for mapping QTSs in corn NAM population, QTTs in cotton diallel crosses, and QTXs in tobacco variety test.