Exploiting NGS technologies for efficient and accurate genotype to phenotype 
mapping in plant systems

Stegle, O

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Exploiting NGS technologies for efficient and accurate genotype to phenotype mapping in plant systems

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Stegle, O
Former Research Group Machine Learning and Computational Biology, Max Planck Institute for Intelligent Systems, Max Planck Society;

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https://cropscience.uni-hohenheim.de/fileadmin/einrichtungen/cropscience/pdf_Dateien/Programheft_2_EucarpiaAR_26.7.12.pdf
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Stegle, O. (2012). Exploiting NGS technologies for efficient and accurate genotype to phenotype mapping in plant systems. Talk presented at XVth Meeting of the EUCARPIA Section: Biometrics in Plant Breeding. Stuttgart-Hohenheim, Germany.

Cite as: https://hdl.handle.net/21.11116/0000-0001-AA03-C

Abstract

Combining NGS technology and modelling at a systems level to dissect the causes of transcriptome variability. Molecular traits vary between samples and dissecting the precise origin of this variation is an important step towards understanding the implications of cellular variation for higher-level traits. We have studied the mRNA variability in 19 accessions of Arabidopsis thaliana at an unprecedented level of detail (Gan et al., 2011) and have extended this analysis to larger samples sets of over 200 lines. As a first step, accurate quantification of molecular
phenotypes is needed. To this end, we will discuss computational approaches to quantify whole-gene expression levels and splicing phenotypes from RNA-Seq datasets.
Second, building on quantitative readouts of the molecular state, we discuss novel statistical approaches to dissect the causes of molecular variability. These models (Stegle and Parts, 2012) allow for attributing the overall gene expression variability to genetic factors, environmental
effects and their interactions. Genetic factors may either act in cis or trans and differ in effect sizes for single locus effects and larger indels. Finally, population structure and subtle environmental factors may confound such analysis if not appropriately taken into account within the model. By means of this tight integration of computational genomics and statistics analyses, we are able to derive a comprehensive picture of the heritable and environmental component of molecular traits, attributing more than 50% of gene expression variation to individual causes.