text: Stone Age and Bronze Age Europe13–16 (*F*ST = 5–9% (refs. 13,17)). Modern 
  populations provide more power to detect differences, but their genetic 
  affinity to ancient individuals may be confounded by later gene flow, 
  that is, after the time of the ancient individual(s)18. The most principled 
  approach is thus to build ancestry models in which source and ‘out- 
  group/reference’ populations are older than, or at least contemporary 
  with, the target genome or group that we are trying to model18. However, 
  this has been challenging, due to the limited statistical power offered 
  by the thousands-fold lower sample sizes and reduced sequence qual- 
  ity of ancient genomes. 
  
  Reconstructing genetic histories and ancestry models from ancient 
  DNA (aDNA) data commonly uses methods based on*f*-statistics13,19–22. 
  Their popularity is rooted in a number of favourable properties, such 
  as enabling analyses of lower-quality aDNA data, relative robustness 
  to ascertainment and theoretical guarantees of unbiasedness, includ- 
  ing in the presence of population bottlenecks21,23. Approaches derived 
  from*f*-statistics, such as qpAdm13, are close to unique in enabling the 
  unbiased fitting of admixture models, including identifying the num- 
  ber of such events and the closest representatives of sources13,14,23. 
  However,*f*-statistics have not always had sufficient power to recon- 
  struct events that involve closely related ancestries, despite increas- 
  ing sample sizes6,24. Methods that identify haplotypes, or shared 
  segments of DNA that are not broken down by recombination, have 
  previously been shown to have more power than those using individual 