We can maybe forget about high read coverage. There is almost no accuracy gain in going from 5x to 30x coverage. This might be because imputation is such a big part of the prediction model, meaning that 5x is more than enough to guide the model in the right direction.

Pangenome size matters -> we should as a community invest in making larger pangenomes. This is maybe somewhat obvious, but nice to get it confirmed. X-axis is number of individuals in pangenome.

SNPs/indels are important when genotyping SVs. Our experiments show that SV genotyping accuracy drastically increases when we add more SNPs/indels to the pangenome. The x-axis in the plot below is allele frequency - SNPs/indels with freq lower than x-axis value are filtered away.

Genotyping SVs from reads alone yields much lower accuracy than when combined with imputation. Even KAGE/PanGenie with very few reads (0.5x) perform much better than e.g. BayesTyper (30x coverage) that does not do imputation.