They can't possibly be working with genomes spanning the past 250k years--- the oldest known human remains are only estimated to be ~230k years old, and I doubt they have parent/child trios nicely spanning the intervening few hundred thousand years. So they have to be working on mutation rate inferences.
From a non-expert reading of the article, their pipeline is more like: modern human population genomes -> estimates of modern mutations' ages -> estimate of average historical parental ages.
The first estimation (genome -> mutation ages) was carried out in a prior study "GEVA" [1], and this paper's contribution seems to be estimating the average parental ages based on those previously estimated historical mutation rates.
I couldn't find any mention of using old DNA samples. The GEVA study pulls from two genome databases, TGP [2] and SGDP [3], both of which seem to be entirely modern genomes. I'm not an expert in the field, so maybe it's obvious to a population geneticist that these databases do include old genomes.
Given that they're only using modern (surviving) genomes, the critique of survivorship bias seems valid.
From a non-expert reading of the article, their pipeline is more like: modern human population genomes -> estimates of modern mutations' ages -> estimate of average historical parental ages.
The first estimation (genome -> mutation ages) was carried out in a prior study "GEVA" [1], and this paper's contribution seems to be estimating the average parental ages based on those previously estimated historical mutation rates.
I couldn't find any mention of using old DNA samples. The GEVA study pulls from two genome databases, TGP [2] and SGDP [3], both of which seem to be entirely modern genomes. I'm not an expert in the field, so maybe it's obvious to a population geneticist that these databases do include old genomes.
Given that they're only using modern (surviving) genomes, the critique of survivorship bias seems valid.
[1] https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6992231/
[2] https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4750478/
[3] https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5161557/