Gene regulation

From genotype to phenotype

To understand how genetic, epigenetic and gene expression variation cause variation in health and disease traits in human, data-driven biology seeks to learn molecular mechanisms from interconnected omics data.

How does information flow from the genotype to the phenotype?

Gene expression determines cellular states

• Genes are transcribed into mRNA and translated into protein.
• Every step in this process can be regulated by genetic or environmental factors.
• The repertoire and relative levels of proteins expressed determines cellular identity, fate, cell-to-cell communication, etc.
• Variation in cellular properties causes variation in phenotypes.

Genetic variation causes phenotype variation by affecting gene expression.

How does genetic variation affect gene expression?

Genetic variation influences gene regulation

Genetic variation directly affects DNA methylation, binding of epigenetic and transcription factors and 3D chromatin organization, leading to altered transcription.

Genetic variation informs on local regulatory mechanisms

Coupling genetic variation data with information on TF binding events allows to predict functional effects of motif disruption and cooperative and collaborative TF binding mechanisms in a locus-specific manner.

Genetic variation informs on distal regulatory mechanisms

Genotypes of regulatory variants are fixed from birth and act as experimental instruments to inhibit or promote expression of regulatory factors independent of environmental confounders.

Genes are organized in hierarchical, multi-tissue causal networks

• Variation in expression of one gene has downstream consequences on expression of other genes.
• Example: Introduction of just 4 TFs (“Yamanaka factors”) converts adult cells into stem cells.
• Hundreds to thousands of genes are differentially expressed between cellular states (e.g. healthy vs. disease).
• Gene expression in one tissue can affect gene expression in other tissues.
• Phenotype variation also causes gene expression variation (“reverse causation”).

Reconstruction of causal pathwas and gene networks is essential to understand how the genotype determines the phenotype.

Systems genetics in human requires large data and new analytical methods

Instead of direct experimental manipulation of DNA elements or regulatory factors, the relation between genetic variation, gene regulation, gene expression, and (disease) phenotypes in human must be studied by:

• Statistical sampling of the causes (genotypes) and consequences (gene expression) of gene regulation using genome-wide measurements in unrelated individuals.
• Mapping associations between data measured at multiple scales.
• Using statistical and machine learning approaches to reconstruct models of molecular mechanisms.