Additional file 2 of High performance imputation of structural and single nucleotide variants using low-coverage whole genome sequencing
Creators: name: Gundappa, Manu Kumar
name: Robledo, Diego
name: Hamilton, Alastair
name: Houston, Ross D.
name: Prendergast, James G. D.
name: Macqueen, Daniel J.
Publisher: name: figshare
Year: 2025
Subjects: subject: Genetics
subject: FOS: Biological sciences

description: Additional file 2: Figure S1. SNV imputation results for Southern Norway samples (n=49). PPV (a) and recall rate (b) for SNVs across tested WGS depths and PP cut-offs. Figure S2. SNV imputation results for Northern Norway samples (n=49). PPV (a) and recall rate (b) for SNVs across tested WGS depths and PP cut-offs. Figure S3. Performance of Southern Norway sample (n=49) deletion SV imputation. PPV (a) and recall rate (b) for across different PP cut-offs, sequencing depths and strategies used. Red and blue plots indicate PPVs when the pipeline was supplied with SNP GLs only, or both SNP and SV GLs, respectively. Figure S4. Performance of Northern Norway sample (n=49) deletion SV imputation. PPV (a) and recall rate (b) for across different PP cut-offs, sequencing depths and strategies used. Red and blue plots indicate PPVs when the pipeline was supplied with SNP GLs only, or both SNP and SV GLs, respectively. Figure S5. Performance of in-panel sample (n=98) duplication SV imputation. PPV (a) and recall rate (b) for across different PP cut-offs, sequencing depths and strategies used. Red and blue plots indicate PPVs when the pipeline was supplied with SNP GLs only, or both SNP and SV GLs, respectively. Figure S6. Performance of in-panel sample (n=98) inversion SV imputation. PPV (a) and recall rate (b) for across different PP cut-offs, sequencing depths and strategies used. Red and blue plots indicate PPVs when the pipeline was supplied with SNP GLs only, or both SNP and SV GLs, respectively. Figure S7. Performance of Southern Norway sample (n=49) duplication SV imputation. PPV (a) and recall rate (b) for across different PP cut-offs, sequencing depths and strategies used. Red and blue plots indicate PPVs when the pipeline was supplied with SNP GLs only, or both SNP and SV GLs, respectively. Figure S8. Performance of Northern Norway sample (n=49) duplication SV imputation. PPV (a) and recall rate (b) for across different PP cut-offs, sequencing depths and strategies used. Red and blue plots indicate PPVs when the pipeline was supplied with SNP GLs only, or both SNP and SV GLs, respectively. Figure S9. Performance of Southern Norway sample (n=49) inversion SV imputation. PPV (a) and recall rate (b) for across different PP cut-offs, sequencing depths and strategies used. Red and blue plots indicate PPVs when the pipeline was supplied with SNP GLs only, or both SNP and SV GLs, respectively. Figure S10. Performance of Northern Norway sample (n=49) inversion SV imputation. PPV (a) and recall rate (b) for across different PP cut-offs, sequencing depths and strategies used. Red and blue plots indicate PPVs when the pipeline was supplied with SNP GLs only, or both SNP and SV GLs, respectively. Figure S11. Impact of SV length on imputation accuracy of in-panel samples (N=98). PPVs for SV imputation at different SV length cut-offs are shown. The x-axis of each plot represents the length bins into which SVs were classified. Blue plots represent WGS depth of 1x, and green plots represent WGS depth of 4x. DEL-PPV, DUP-PPV, and INV-PPV correspond to the PPVs for deletions, duplications, and inversions, respectively. The numbers above each box plot indicate the mean PPV, while the numbers in red below the boxplots represent the total number of SVs in each length category. Figure S12. Merged SV imputation results for Southern Norway samples (n=49). PPV (a) and recall rate (b) for SV imputation across different SV classes, WGS depths and strategies at a PP cut-off of 0.9. Green plots indicate PPVs for the ‘logical’ and brown for ‘non-logical’ SV filtering approaches (see Methods). Figure S13. Merged SV imputation results for Northern Norway samples (n=49). PPV (a) and recall rate (b) for SV imputation across different SV classes, WGS depths and strategies at a PP cut-off of 0.9. Green plots indicate PPVs for the ‘logical’ and brown for ‘non-logical’ SV filtering approaches (see Methods). Figure S14. Allele frequency distribution of overlapping and non-overlapping SVs. Allele frequency distributions are shown for overlapping (g) and non-overlapping (h) homozygous (i.e. 1/1) SVs, as well as for overlapping (i) and non-overlapping (j) heterozygous (0/1) SVs, in the out-panel samples. Figure S15. Performance of out-panel sample (n=20) duplication SV imputation. PPV (a) and recall rate (b) for across different PP cut-offs, sequencing depths and strategies used. Red and blue plots indicate PPVs when the pipeline was supplied with SNP GLs only, or both SNP and SV GLs, respectively. Figure S16. Performance of out-panel sample (n=20) inversion SV imputation. PPV (a) and recall rate (b) for across different PP cut-offs, sequencing depths and strategies used. Red and blue plots indicate PPVs when the pipeline was supplied with SNP GLs only, or both SNP and SV GLs, respectively.