Since at least 2001, a wide range of methods have been developed to assess the structure of human populations with the use of genetic data. MethodsMost dependcommonly, ongenetic theclusters can be derived by analysis of [[Single-nucleotide polymorphism|single nucleotide polymorphisms]] (SNPs), although other genetic data usedcan tobe determineinput clustersand analyzed as well. asModels thefor genetic clustering also vary by algorithms and programs used to process the data. Most methods for determining clusters can be categorized as '''model-based clustering methods''' or '''multidimensional summaries'''.<ref>{{Cite journal|last=Novembre|first=John|last2=Ramachandran|first2=Sohini|date=2011-09-22|title=Perspectives on Human Population Structure at the Cusp of the Sequencing Era|url=http://dx.doi.org/10.1146/annurev-genom-090810-183123|journal=Annual Review of Genomics and Human Genetics|volume=12|issue=1|pages=245–274|doi=10.1146/annurev-genom-090810-183123|issn=1527-8204}}</ref><ref>{{Cite journal|last=Lawson|first=Daniel John|last2=Falush|first2=Daniel|date=2012-09-22|title=Population Identification Using Genetic Data|url=http://dx.doi.org/10.1146/annurev-genom-082410-101510|journal=Annual Review of Genomics and Human Genetics|volume=13|issue=1|pages=337–361|doi=10.1146/annurev-genom-082410-101510|issn=1527-8204}}</ref>
=== Model-based clustering ===
Common model-based clustering algorithms include STRUCTURE, ADMIXTURE, and HAPMIX. These algorithms typically establish an arbitrary number of clusters and calculate the best fit for the data, placing individuals into groups with maximally similar genotypes within clusters and maximally different between clusters.
Model-based clustering approaches use a variety of algorithms to assume
Most commonly, population clusters have been determined by analysis of [[Single-nucleotide polymorphism|single nucleotide polymorphisms]] (SNPs)
