A Review of Statistical Methods for Identifying Trait-Relevant Tissues and Cell Types.

Area of Science:

• Genetics and Bioinformatics
• Genomic Data Analysis

Background:

• Genome-wide association studies (GWASs) identify genetic variants linked to complex traits and diseases, but underlying biological mechanisms are often unclear.
• Identifying tissue- and cell type-specific effects of genetic variants is essential for understanding disease etiology.
• Existing statistical methods for trait-tissue relevance inference vary in their use of genomic data and statistical models.

Approach:

• This technical review comprehensively summarizes ten existing statistical methods for inferring trait-tissue relevance.
• Methods reviewed integrate genomic data, including functional annotations, expression quantitative trait loci (eQTLs), genetically regulated gene expression (GRGE), and gene co-expression networks.
• Statistical models employed range from linear mixed models to covariance network models.

Key Points:

• A diverse set of genomic information is utilized by current trait-tissue relevance inference methods.
• Varied statistical modeling approaches are applied, highlighting the complexity of inferring tissue-specific genetic effects.
• The review provides a consolidated overview for researchers in the field.

Conclusions:

• This review serves as a valuable reference for both method developers and applied researchers.
• It facilitates the selection and application of appropriate methods for identifying trait-relevant tissues and cell types.
• Enhanced understanding of trait-tissue relevance can accelerate the discovery of biological mechanisms underlying complex diseases.