Scatter plot of the mean allele frequencies per genome (n = 50) vs. age of the genomes (calibrated years before present).

Red line represents the linear model. We found that there is no significant correlation between the age of the individuals and the mean allele frequency (Spearman’s rank correlation ρ = -0.12, P = 0.41). (TIF)</p

Performance comparison of multiple tools and CONGA performance analysis.

Table A shows the CNV predictions of CONGA, GenomeSTRiP, FREEC, CNVnator and mrCaNaVaR on simulated genomes at depths 0.05×, 0.1×, 0.5×, 1× and 5× for deletions and duplications of multiple CNV size intervals including 100 bps–1 kbps (small), 1 kbps–10 kbps (medium) and 10 kbps–100 kbps (large). Here, “T” and “F” refer to correct and incorrect predictions respectively, “Miss” is the number of missed true events, “Recall” (TPR) is the true positive rate, and “FDR” is false discovery rate (1—“Precision”) for each run. The F-Score is calculated as (2 * Precision * Recall) / (Precision + Recall). Note that for CONGA, we included the performance for both C-score Table B shows a comparison between CONGA and GenomeSTRiP predictions on simulated genomes at depths 0.05×, 0.1×, 0.5×, 1× and 5× for deletions and duplications of 1 kbps–10 kbps (medium) and 10 kbps–100 kbps (large) CNV size intervals. Table C shows the copy-number (homozygous or heterozygous) predictions of CONGA on simulated genomes at depths 0.05×, 0.1×, 0.5×, 1× and 5× for deletions and duplications of multiple CNV size intervals including 100 bps–1 kbps (small), 1 kbps–10 kbps (medium) and 10 kbps–100 kbps (large). Here, “T” and “F” refer to correct and incorrect predictions respectively, “Miss” is the number of missed true events, “Recall” (TPR) is the true positive rate, and “FDR” is false discovery rate (1—“Precision”) for each run. The F-Score is calculated as (2 * Precision * Recall) / (Precision + Recall). Note that for CONGA, we included the performance for both C-score Table D shows deletion and duplication predictions of CONGA using Mota, Saqqaq and Yamnaya genomes down-sampled to various depths from their original coverages of 9.6×, 13.1× and 23.3×, respectively. Here, “T” and “F” refer to correct and incorrect predictions respectively, “Miss” is the number of missed true events, “Recall” (TPR) is the true positive rate, and “FDR” is false discovery rate (1—“Precision”) for each run. The F-Score is calculated as (2 * Precision * Recall) / (Precision + Recall). We calculated “True”, “False”, “Miss”, “Recall”, “Precision”, FDR and F-Score of down-sampled genomes assuming that our CONGA-based predictions with the original genomes (full data) reflect the ground truth. These predictions, in turn, were made using modern-day CNVs as candidate CNV list. The purpose of the experiment was to evaluate accuracy at lower coverage relative to the full data. Table E shows CONGA’s running time and memory consumption on genomes of various depths of coverage calculated using the down-sampled 23× Yamnaya genome (with coverages between 23× and 0.07×) as well as a comparison of CONGA, GenomeSTRiP, FREEC and CNVnator using a 5× simulated genome. Table F shows the results of CONGA runs on simulated genomes at a range of parameters, performed in order to determine the optimum parameters to be used. We tested multiple parameter combinations of C-Score, minimum read-pair support, mappability and minimum mapping quality (MAPQ) using simulated genomes. (XLSX)</p

A sample deletion missed due to poor signal.

An inserted deletion in a simulated ancient genome at 1× depth of coverage. The event breakpoint is chr22:39,386,521-39,391,930. CONGA missed this deletion due to the poor signal. (TIF)</p

IGV visualization of two high scoring (i.e., high likelihood) deletions and duplications predicted by CONGA.

The events displayed in the upper panels were detected in a modern-day human genome (NA07051: an ∼8 kbp deletion within chr7:16,169,440-16,177,556 and a ∼4 kbp duplication within chr7:22,496-26,553) and those in the lower panels in an ancient genome (RISE98: an ∼17 kbp deletion within chr6:32,506,809-32,524,264 and a ∼6 kbp duplication within chr1:1,520,604-1,526,959). The candidate CNV list used for genotyping was the long read CNV dataset described in Methods. Deducing the CNVs is straightforward with the modern-day genome data, however, it is less straightforward to distinguish these variations in ancient read data, especially for duplications. Note that this is one of the sample scenarios and we emphasize that a large number of CNVs identified in ancient genomes suffer from the same issue. (TIF)</p

Deleterious load estimates among 50 ancient genomes.

In all three panels, the x-axis represents a deleterious load-related statistic and the y-axis shows the ancient individuals. (A) Deleterious load based on SIFT-estimated SNP effects per individual. The x-axis represents the number of “deleterious” SNPs over the number of “tolerated” SNPs. (B) CONGA-estimated total deletion length in kb per individual, using the Final CNV call-set. (C) The number of genes that overlap with CONGA-estimated deletions. In panels B and C, heterozygous and homozygous calls were counted once. In panel C, the most affected individuals in terms of the number of gene overlaps are RISE497 (Russia, 2nd millennium BCE), DA380 (Turkmenistan, 4th millennium BCE), RISE675 (Russia, 3rd millennium BCE), and Chan (Iberia, 8th millennium BCE). We observed that these individuals were around 50% more affected than the rest. (TIF)</p

TPR vs FDR curves for deletion and duplication predictions of CONGA.

Here, we use Mota, Saqqaq and Yamnaya genomes down-sampled to various depths from their original coverages of 9.6×, 13.1× and 23.3×, respectively. The numbers inside boxes show the down-sampled coverage values. We calculated TPR and FDR for down-sampled genomes assuming that our CONGA-based predictions with the original genomes (full data) reflect the ground truth. These predictions, in turn, were made using modern-day CNVs as candidate CNV list. The purpose of the experiment was to evaluate accuracy at lower coverage relative to the full data, as well as to compare performance across different real genomes (Methods).</p

Site-frequency spectra of deletions genotyped in low and high coverage genomes.

Left panel represents the SFS of n = 25 below-median coverage genomes and right panel shows the SFS of n = 25 above-median coverage genomes. The median coverage value was 3.98×. We found no significant difference between the two SFS distributions (Kolmogorov-Smirnov test ρ = 0.27). (TIF)</p

General characteristics of deletions in the refined dataset (n = 50 genomes and n = 8,780 deletions, obtained after applying ancestry state filters and removing outlier genomes).

(A) Size distribution of the deletions in logarithmic scale. (B) The distribution of the deletion allele frequency (i.e. the proportion of deletion alleles across the 8,780 loci per genome) among the 50 genomes. (C) The distribution of the relative frequency of observed heterozygous (0/1) deletions over homozygous (1/1) deletions observed in our dataset. (D) The plot of relative deletion frequencies called heterozygous (red lines) and homozygous (blue lines) among 8,780 deletions, for each of the n = 50 ancient genomes in the refined dataset (after applying additional ancestry state filters and removing outlier genomes). (TIF)</p

Length distribution of CNVs inserted into the simulated genomes.

The total number of CNVs inserted into a genome (“counts”) is shown at the top of each graph. We used Varsim to insert these CNVs into each genome, yielding three genomes in total (for short, medium and large CNVs). (TIF)</p

Heatmaps of CONGA-genotyped n = 10,002 human-derived deletions across ancient genomes.

The color key includes 0 (gray) for reference allele, 1 (green) for heterozygous, 2 (magenta) for homozygous state and NA (white) for missing value. (A) Heatmap of deletions per genome on the raw dataset (n = 71 genomes). (B) Heatmap of deletions per genome on the refined dataset (with n = 50 genomes after removing divergent genomes). (TIF)</p