Annual Scientific Report 2015

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Stegle Group Statistical Genomics & Systems Genetics We use computational approaches to map genotype to phenotype on a genome-wide scale. Using statistics, we seek to understand how genetic background and environment jointly shape phenotypic traits or cause diseases, how genetic and external factors are integrated at different molecular layers, and how molecular signatures vary between individual cells. To make accurate inferences from high-dimensional ‘omics datasets, it is essential to account for biological and technical noise and to propagate evidence strength between the different steps of a given analysis. To address these needs, we develop statistical analysis methods in the areas of gene regulation, genome wide association studies (GWAS) and causal reasoning in molecular systems. Our methodological work ties in with experimental collaborations, and we actively develop methods to fully exploit large-scale datasets that are obtained using the most recent technologies. In doing so, we derive computational methods to dissect phenotypic variability at the level of the transcriptome, epigenome and the proteome, and derive advanced statistical methods for the emerging field of single-cell biology. Major achievements In 2015 we developed and applied methods for linking genetic variation data and phenotype. We derived a new statistical model that allows studying genetic associations between sets of genetic variants and multiple correlated phenotypes (Casale et al. 2015). The model makes it possible to interrogate very large cohorts with hundreds of thousands of samples, increases statistical power and clarifies the genetic basis of phenotypic correlation between genetically diverse individuals. In addition to deriving new statistical pools, we actively applied these methods to study the regulatory consequence of copy-number changes and other structural variants in the human genome on geneexpression levels. In a collaboration with Korbel team at EMBL Heidelberg, we surveyed the effect of structural variants on gene expression at a genome-wide scale using the data from the final release of the 1000 Genomes Project (Sudmant et al., 2015). In parallel to our efforts in population genomics, we extended our methodological work to the field of single-cell genomics. In collaboration with the Marioni and Teichmann groups at EMBL-EBI we devised new ways to dissect transcriptional heterogeneity between single cells (Buettner et al., 2015). Our approach, for the first time, enables modelling both known and unknown factors that underlie single-cell transcriptome variation. This method has already helped identify new sub-clusters of cells in single-cell RNAseq studies of differentiating T-cells and will be an important building block for our future aims. Future plans In 2016 we will continue to develop innovative statistical approaches to analyse data from high-throughput genetic and molecular profiling studies. Our on-going efforts are motivated by single-cell genomics data, in particular using new assays that allow to prolife multiple molecular layers in the same sets of cells in parallel. By linking these layers, we hope to gain new insights into gene regulation, the sources of transcriptome heterogeneity and, ultimately, cell-fate decisions in development and cell differentiation. Selected publications Buettner F, et al. (2015) Computational analysis of cell-to-cell heterogeneity in single-cell RNA-sequencing data reveals hidden subpopulations of cells. Nature Biotechnol. 33:155-160 Casale FP et al. (2015) Efficient set tests for the genetic analysis of correlated traits. Nature Methods 12: 755-758 Stegle O, Teichmann SA and Marioni JC (2015) Computational and analytical challenges in single-cell transcriptomics. Nature Rev. Genet. 16:133-145 Stephan J, Stegle O and Beyer A (2015) A random forest approach to capture genetic effects in the presence of population structure. Nature Commun. 6:7432 Sudmant PH, et al. (2015) An integrated map of structural variation in 2,504 human genomes. Nature 526: 75-81 143 2015 EMBL-EBI Annual Scientific Report
Oliver Stegle PhD in Physics, University of Cambridge, 2009. Postdoctoral Fellow, Max Planck Institutes Tübingen, 2009–2012. At EMBL-EBI since November 2012 Statistical methodology to dissect transcriptional heterogenetiy in single-cell RNA-Seq datasets (adapted from Buettner et al., 2015). Left: underlying source of variation in single-cell transcriptome data. Right: Our scLVM approach to identify and account for such factors. 2015 EMBL-EBI Annual Scientific Report 144
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Annual Scientific Report 2015

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