SumStatsRehab: an efficient algorithm for GWAS summary statistics assessment and restoration.

Area of Science:

• Bioinformatics
• Genetics
• Computational Biology

Background:

• High-quality Genome-Wide Association Studies (GWAS) summary statistics are crucial for polygenic risk scores.
• Incomplete or unshared GWAS data limits the generation of accurate risk scores.
• Existing bioinformatics tools for restoring missing GWAS data are limited.

Purpose of the Study:

• To develop and evaluate a novel bioinformatics tool, SumStatsRehab, for restoring missing identification and association data in GWAS summary statistics files.
• To enhance the usability of incomplete GWAS datasets for downstream analyses, including polygenic risk score generation.

Main Methods:

• Utilized functional programming and a pipeline-like architecture.
• Developed algorithms to restore key genetic data columns including rsID, alleles, chromosomal position, and association statistics (beta, standard error, p-value).

Main Results:

• SumStatsRehab successfully restored rsID, effect/other alleles, chromosome, base pair position, effect allele frequencies, beta, standard error, and p-values.
• Achieved superior restoration accuracy compared to existing tools with minimal data loss.

Conclusions:

• SumStatsRehab provides an effective solution for data restoration in incomplete GWAS summary statistics.
• Increases the number of usable GWAS datasets, particularly valuable for under-researched complex traits and diseases.
• Facilitates more robust polygenic risk score calculations and genetic association studies.