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

Area of Science:

• Genetics
• Statistical genetics
• Bioinformatics

Background:

• Joint association analysis of multiple traits and genetic variants offers insights into genetic architecture and pleiotropy.
• High-dimensional traits (e.g., images, longitudinal data) present unique challenges for genetic association studies.
• Assessing multitrait genetic association significance is difficult in samples with population substructure or relatedness, leading to power loss and inflated type 1 error.

Purpose of the Study:

• To develop a fast, powerful, and robust method for assessing multitrait genetic association significance.
• To address challenges posed by population substructure, admixture, and relatedness in genetic samples.
• To improve the detection of genetic associations and pleiotropic effects in complex datasets.

Main Methods:

• Developed JASPER (Joint Association Significance Testing in Population-stratified Samples), a novel statistical method.
• Evaluated JASPER's performance using simulations, comparing it against existing methods.
• Applied JASPER to analyze gene expression data from the Framingham Heart Study.

Main Results:

• JASPER demonstrated higher statistical power and better type 1 error control compared to existing methods in simulations.
• The computational speed and power advantages of JASPER increased with the number of traits analyzed.
• Application to the Framingham Heart Study identified more significant gene expression associations, including novel pleiotropic effects.

Conclusions:

• JASPER is a powerful and efficient tool for multitrait genetic association analysis in structured samples.
• The method is robust to phenotype model misspecification and accommodates covariates, ascertainment, and rare variants.
• JASPER shows significant promise for advancing genetic discovery in diverse biological and clinical applications.