Table 4.
Top-five (the most cited between January 2020 and September 2021 in PubMed) tools for the study of PRS with their characteristics
| Tool | Type | Availability | Input data | Algorithm | Characteristics | Year | Reference |
|---|---|---|---|---|---|---|---|
| PRSice | Command-line (C++, Compiled, R for plotting) | Free | Binary PLINK BED/BIM/FAM) or imputed (Oxford .bgen) | Pruning and Thresholding (P + T) | Visualization options with R | 2015 | [72, 73] |
| PRS-CS | Command-line (Python) | Free | GWAS summary statistics External LD reference panel | Continuous shrinkage (CS) on SNP effect sizes + High-dimensional Bayesian regression framework | External LD reference panel | 2019 | [84] |
| SBLUP/BLUP GCTA | Command-line (C++, Compiled) | Free | Binary PLINK BED/BIM/FAM) or imputed (Oxford .bgen v1.2) | Linear mixed-effects model | Analyses individual chromosomes | 2020 v1.93.2beta | [75, 76] |
| SBayesR GCTB | Command-line (C++, Compiled) | Free | Binary PLINK BED/BIM/FAM) | Bayesian mixture model | Uses low computational resources | 2019 | [77] |
| lassosum | R Package bigstatsr | Free | Binary PLINK BED/BIM/FAM) | Regularized regression model | External LD reference panel Pseudovalidation | 2017 | [81] |