Another issue that may produce confounding effects is if data from different populations or geographical areas is handled differently (which may be inevitable, especially in the initial processing); then, in principle, this may induce artifacts that mimic real population structural differences. Even restricting analysis to markers with no missing data, apart from an inevitable power loss, does not necessarily eliminate the problems. After all, if a subset (the missing data) is chosen in a biased way, then the complementary subset must also be biased.
