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Description

Asthma is one of the most common childhood diseases, and acute asthma exacerbation is the most frequent
cause of hospitalization, but the disease mechanisms are poorly understood and current treatment options are
inadequate. The aim of this proposal is to understand the basic mechanisms of childhood asthma and mechanisms
responsible for severe exacerbations with a focus on genetic regulation and pathways.
The clinical backbone of the project consists of unique clinical and registry-based cohorts with potential for
discovery of novel genetic mechanisms as demonstrated by preliminary results. To learn as much as possible
from our data we will put an emphasis on the development of efficient and novel statistical methods, particularly
within the Bayesian paradigm, to capture properties of gene regulation not captured by traditional statistical
approaches in genetics. For example, we will explore the use of such methods for finding SNPs whose effects are
masked due to non-additive (epistatic) interactions with other SNPs. Bayesian methods are well suited for these
purposes due to the simplicity with which complex models can be constructed and fitted while using prior
regularisation to avoid the pitfalls of multiple testing artefacts. We will also explore the use of biologically relevant
biological information (via priors) for informing model building (e.g., knowledge of biochemical pathways).
Similarly, Bayesian methods can be used to efficiently and stringently integrate other data types and data from
previous analyses with our genotype data.
The project will increase our understanding of childhood asthma and its exacerbations with the potential for
personalized prevention and treatment of this clinically important disease entity. Furthermore, it will result in
novel analytical methods with expected value for genetic research in general.
StatusActive
Effective start/end date01/08/201831/07/2022
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ID: 159541123