Incorporating external information to improve sparse signal detection in rare-variant gene-set-based analyses.

Area of Science:

• Genetics
• Bioinformatics
• Computational Biology

Background:

• Gene-set analyses assess disease associations within biologically related gene groups.
• Current methods treat all genes within a set equally, ignoring external importance information.
• Genes under purifying selection or showing genic constraint may have higher relevance to trait association.

Purpose of the Study:

• To improve gene-set analyses by incorporating external gene importance data.
• To enhance signal detection in genetic association studies using a higher-criticism approach.
• To increase statistical power, particularly for identifying sparse disease-gene associations.

Main Methods:

• Developed a novel gene-set analysis incorporating external gene importance information.
• Utilized a higher-criticism-based signal detection framework.
• Implemented the approach in the R package wHC.

Main Results:

• The enhanced method significantly increases power when external gene information is predictive of disease association.
• The approach is particularly effective for detecting sparse signals where few genes in a set are associated with the trait.
• Applied to amyotrophic lateral sclerosis (ALS), the method identified relevant gene sets including those with SOD1 and NEK1, and showed enrichment of small p-values for known ALS genes.

Conclusions:

• Incorporating external gene importance data into gene-set analysis provides a powerful enhancement.
• The method offers improved sensitivity for detecting genetic associations, especially in complex diseases like ALS.
• The wHC R package provides a tool for researchers to apply this advanced gene-set analysis technique.