Methyl-TWAS: A powerful method for in silico transcriptome-wide association studies (TWAS) using long-range DNA...

Area of Science:

• Genomics
• Epigenetics
• Computational Biology

Background:

• In silico transcriptome-wide association studies (TWAS) traditionally use genotype data to link gene expression to complex traits.
• Genotype-based TWAS methods like PrediXcan have limited prediction accuracy due to lack of tissue specificity and environmental influence.
• DNA methylation is tissue-specific and influenced by environmental factors, making it a potentially better predictor of gene expression.

Approach:

• Methyl-TWAS is a novel penalized regression approach for in silico TWAS that utilizes long-range methylation markers.
• It predicts epigenetically regulated/associated expression (eGReX), integrating genetic (GReX) and environmental factors affecting gene expression.
• Methyl-TWAS incorporates both cis- and trans- CpGs across regulatory regions to enhance DEG identification.

Key Points:

• Methyl-TWAS significantly outperforms PrediXcan and other methods in imputing gene expression, especially in nasal epithelium and white blood cells.
• The method successfully identified a high proportion of differentially expressed genes (DEGs) in atopic asthma that were missed by genotype-based approaches.
• It demonstrates superior performance for immunity-related genes and DEGs in complex diseases like atopic asthma.

Conclusions:

• Methyl-TWAS is a powerful new tool for in silico TWAS, enhancing the ability to identify DEGs by leveraging genome-wide DNA methylation data.
• The approach offers improved accuracy and broader discovery of disease-associated genes compared to existing genotype-based methods.
• This method capitalizes on the increasing availability of public DNA methylation datasets across diverse human tissues.