Abstract
The driver tissues or cell types in which susceptibility genes initiate diseases remain elusive. We develop a unified framework to detect the causal tissues of complex diseases or traits according to selective expression of disease-associated genes in genome-wide association studies (GWASs). This framework consists of three components which run iteratively to produce a converged prioritization list of driver tissues. Additionally, this framework also outputs a list of prioritized genes as a byproduct. We apply the framework to six representative complex diseases or traits with GWAS summary statistics, which leads to the estimation of the lung as an associated tissue of rheumatoid arthritis.
Highlights
Tissue selectivity is an important characteristic of many complex diseases or traits [1]
We proposed a unified framework to estimate driver tissues or cell types of complex diseases or traits based on selective expression of phenotypeassociated genes of genome-wide association studies (GWASs)
The assumption is that the tissue-selective expression of causal or susceptibility genes indicates the tissues where complex phenotypes happen primarily [1], which are called driver or causal tissues
Summary
Tissue selectivity is an important characteristic of many complex diseases or traits [1]. As human brains consist of multiple heterogeneous regions, it is crucial to know which regions are the actual drivers [3]. It is generally known that cell proliferation in multiple tissues (e.g., skeletal and cardiac muscle) may contribute to the development of human height [4]. It is unclear which tissues are primarily more important for the development of height. For most of human diseases and traits, the primary driver tissues remain elusive [5]
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