Annual Scientific Report 2015

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
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2015 EMBL-EBI Annual Scientific Report