JASPER: fast, powerful, multitrait association testing in structured samples gives insight on pleiotropy in gene...

Area of Science:

• Genetics and Bioinformatics
• Statistical Genomics

Background:

• Joint association analysis of multiple traits with genetic variants offers insights into genetic architecture and pleiotropy.
• Assessing multitrait genetic association significance is challenging in samples with population substructure or relatedness, often leading to power loss and inflated type 1 error.

Approach:

• We developed JASPER (Joint Association Significance testing in PopuLation-structured samples), a fast, powerful, and robust method for multitrait association significance testing.
• JASPER is designed for samples with population substructure, admixture, and/or relatedness, addressing limitations of existing methods.

Key Points:

• JASPER demonstrates higher power, better type 1 error control, and faster computation compared to existing methods, with advantages increasing with the number of traits.
• The method is applicable to diverse high-dimensional traits (e.g., gene expression, imaging, microbiome data) and accommodates covariates, ascertainment, and rare variants.
• JASPER is robust to phenotype model misspecification.

Conclusions:

• Application of JASPER to gene expression data in the Framingham Heart Study identified more significant associations, including novel pleiotropic effects.
• JASPER shows significant promise for powerful multitrait genetic association analysis in complex, structured samples.