This documentation provides comprehensive information on the code, data, and methods used in the article.
Traditional GWAS and PGS studies utilize SNP arrays with genotype imputation; however, low-pass whole-genome sequencing (lpWGS) has emerged as a strong alternative.
General purpose: To compare the performance of genotyping arrays and low-pass WGS.
- Compared: 8 genotyping arrays vs. 6 lpWGS coverage levels (0.5× to 2×)
- Population: 2,504 individuals from five superpopulations in the 1000 Genomes Project.
- Methods: Applied 10-fold cross-validation to perform genotype imputation and evaluate polygenic scores (PGS) across 4 traits. Results were summarized and assessed for performance.
- lpWGS matched population-optimized arrays in imputation and PGS accuracy
- lpWGS outperformed arrays in underrepresented populations
- lpWGS was superior for rare and low-frequency variants
Low-pass WGS is a flexible and powerful alternative to genotyping arrays, especially valuable for studies involving diverse or underrepresented populations.
This study analyzes data from 2,504 unrelated individuals in the 1000 Genomes Project[@byrska2022high], which was sequenced at high coverage (30x) by the New York Genome Center (1KGPHC). Two main datasets were utilized:
- Mapped sequence data (CRAM format)
- Phased variant data (VCF format)
- Processing data:
- Cross-Validation Framework: A 10-fold stratified cross-validation ensures balanced population representation for imputation testing.
- Variant Filtering: VCF files were filtered to improve imputation accuracy.
- Data Simulation: Low-pass sequencing and eight SNP arrays data were simulated from high-coverage data.
- Genotype Imputation:
- lpWGS: GLIMPSE2 was used for lpWGS imputation.
- SNP arrays: undergo phasing with SHAPEIT5 and imputation with Minimac4.
- Evaluation:
- Restructure imputed data: Imputed data was merged by population
- lpWGS performance: compared to 30x WGS to assess accuracy and coverage performance, followed by visualization.
- PRS performance: We calculated PRS and compared it to 30× WGS to assess PRS performance and visualize the results.
- Available data: Information on the datasets used in this study.
- About: Acknowledging contributions and support.