Geographic patterns of genetic diversity allow us to make inferences about
population histories and the evolution of inherited disease. The statistica
l methods describing genetic variation in space, such as estimation of gene
tic variances, mapping of allele frequencies, and principal components anal
ysis, have opened up the possibility to reconstruct demographic processes w
hose effects have been tested by a variety of approaches, including spatial
autocorrelation, cladistic analyses, and simulations. These studies have s
ignificantly contributed to our understanding of human genetic variation; h
owever, the molecular data that have accumulated since the mid-1980s have a
lso created new complications. Reasons include the generally Limited sample
sizes, but, more generally, it is the nature of molecular variation itself
that makes it necessary to develop and apply specific models and methods f
or the treatment of DNA data. The foreseeable diffusion of laboratory techn
iques for the rapid typing of many DNA markers will force us to change our
approach to the study of human variation anyway, moving from the gene level
toward the genome level. Because extensive variation among loci is the rul
e rather than the exception, an important practical tip is to be skeptical
of inferences based on single-locus diversity.