An Updated Polygenic Index Repository: Expanded Phenotypes, New Cohorts, and Improved Causal Inference

Area of Science:

• Genetics
• Bioinformatics
• Biomedical Sciences

Background:

• Polygenic indexes (PGIs) are crucial DNA-based tools for predicting individual phenotypes.
• Existing repositories require updates to expand scope and methodology for broader application.

Purpose of the Study:

• To introduce Version 2 of the Polygenic Index Repository, enhancing its utility in biomedical and social sciences.
• To expand the number of phenotypes and datasets, and refine PGI construction methodology.

Main Methods:

• Expanded the repository to 61 phenotypes and 20 datasets.
• Utilized updated Genome-Wide Association Study (GWAS) meta-analysis summary statistics for 16 phenotypes.
• Incorporated imputed parental PGIs for family-based analyses and developed interpretation frameworks.

Main Results:

• Improved PGI predictive power through updated GWAS data.
• Increased coverage of phenotypes and participant cohorts.
• Enhanced tools for reducing confounding bias and improving interpretability in genetic analyses.

Conclusions:

• Version 2 of the Polygenic Index Repository offers significant advancements in PGI predictive accuracy and scope.
• The updates facilitate more robust and interpretable genetic association studies.
• This enhanced resource supports broader applications of polygenic indexes in research.