title: Geographic patterns: how to identify them and why.
abstract: Geographic patterns of genetic diversity allow us to make inferences about population histories and the evolution of inherited disease. The statistical methods describing genetic variation in space, such as estimation of genetic variances, mapping of allele frequencies, and principal components analysis, have opened up the possibility to reconstruct demographic processes whose effects have been tested by a variety of approaches, including spatial autocorrelation, cladistic analyses, and simulations. These studies have significantly contributed to our understanding of human genetic variation; however, the molecular data that have accumulated since the mid-1980s have also 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 for the treatment of DNA data. The foreseeable diffusion of laboratory techniques 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 rule rather than the exception, an important practical tip is to be skeptical of inferences based on single-locus diversity.
authors.affiliation: Dipartimento di Biologia, Università di Ferrara, Italy. bjg@dns.unife.it
authors.forename: G
authors.initials: G
authors.lastname: Barbujani
mesh_terms: D005075:Biological Evolution; D002874:Chromosome Mapping / Q000379:methods* / Q000639:trends; D016000:Cluster Analysis; D004059:Diffusion of Innovation; D004641:Emigration and Immigration / Q000706:statistics & numerical data*; D005163:Factor Analysis, Statistical; D005544:Forecasting; D005787:Gene Frequency / Q000235:genetics*; D014644:Genetic Variation / Q000235:genetics*; D006801:Humans; D008957:Models, Genetic; D017720:Molecular Epidemiology; D009154:Mutation / Q000235:genetics; D012111:Residence Characteristics / Q000706:statistics & numerical data; D019371:Topography, Medical / Q000379:methods*