Protocol for finding genetic variation associated with unmeasured traits through GenomicSEM common-factor GWAS

Area of Science:

• Genetics
• Pain Research
• Statistical Genomics

Background:

• Identifying genetic underpinnings of complex traits, especially unmeasured ones, remains challenging.
• Nociplastic pain, a complex condition, likely involves shared genetic factors across different chronic pain types.

Purpose of the Study:

• To present a detailed protocol for common-factor genome-wide association study (GWAS) using GenomicSEM.
• To apply this protocol to identify genetic variations associated with nociplastic pain by leveraging existing GWAS data.

Main Methods:

• Utilized Genomic Structural Equation Modeling (GenomicSEM) for common-factor GWAS.
• Integrated summary statistics from existing GWAS of six chronic overlapping pain conditions.
• Detailed steps for data preparation, including computing environment setup and linkage disequilibrium score regression.

Main Results:

• The protocol enables the analysis of genetic associations with unmeasured traits using common genetic factors.
• Demonstrated application to nociplastic pain, integrating data from multiple chronic pain GWAS.
• The method allows for running common-factor GWAS with and without individual SNP effects.

Conclusions:

• The presented GenomicSEM protocol offers a robust framework for exploring genetic architecture of complex traits, including nociplastic pain.
• This approach facilitates the discovery of shared genetic influences across related phenotypes.
• The protocol provides a reproducible method for genetic variation analysis in unmeasured traits.