Systems biology approach to understand lipid metabolism in high oil maize lines

Together with the groups of Prof. Zoran Nikoloski (University of Potsdam, Germany), Prof. Yan Jianbing and Prof. Weiwei Wen (Huazhong Agricultural University, Wuhan, China), we are employing an approach that integrates the time-resolved  metabolomics and transcriptomics joint analysis with mQTL genetic mapping of maize to elucidate the association between genes and acyl-lipids.

Maize oil is an invaluable food and energy resource. Our goal is to identify and characterize novel lipid genes and provide means to further improve existing high-oil maize varieties. For this purpose, we i) jointly analyze the time-resolved leaf transcriptome and lipidome of B73 and the high-oil line By804, grown in two distinct trials, and ii) integrate the resulting transcript-lipid associations with the mQTL mapped in a bi-parental B73 × By804 recombinant inbred population.

In the first step, we employ the multivariate analysis of acyl-lipid levels under a sophisticated predictive regularized model, the graph-fused LASSO1,2, based on the transcriptome. Next, we examine the co-expression network topology of lipid-associated transcripts and compare with co-expression from the kernel3. In the second step, we co-localize selected candidates with the mQTL and bring together the genetics of lipid metabolism, with the transcriptome as an intermediate. Finally, candidates will be selected for experimental validation.

(Studied by Francisco Lima)

References

1. Kim, S., Sohn, K. A. & Xing, E. P. A multivariate regression approach to association analysis of a quantitative trait network. Bioinformatics 25 (2009).

2. Tibshirani, R. Regression Shrinkage and Selection via the Lasso. Journal of the Royal Statistical Society. Series B (Methodological) 58, 267-288 (1996).

3. Li, H. et al. Genome-wide association study dissects the genetic architecture of oil biosynthesis in maize kernels. Nature Genetics 45, 43-50, doi:10.1038/ng.2484 (2013)

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