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== Genetic clustering algorithms and methods ==
A wide range of methods have been developed to assess the structure of human populations with the use of genetic data. Early studies of within and between-group genetic variation used physical phenotypes and blood groups, with modern genetic studies using genetic markers such as [[Alu element|Alu sequences]], [[Microsatellite|short tandem repeat polymorphisms]], and [[Single-nucleotide polymorphism|single nucleotide polymorphisms]] (SNPs), among others.<ref>{{Cite journal|last1=Bamshad|first1=Michael|last2=Wooding|first2=Stephen|last3=Salisbury|first3=Benjamin A.|last4=Stephens|first4=J. Claiborne|date=August 2004|title=Deconstructing the relationship between genetics and race|url=http://dx.doi.org/10.1038/nrg1401|journal=Nature Reviews Genetics|volume=5|issue=8|pages=598–609|doi=10.1038/nrg1401|pmid=15266342|s2cid=12378279|issn=1471-0056}}</ref> Models for genetic clustering also vary by algorithms and programs used to process the data. Most sophisticated methods for determining clusters can be categorized as '''model-based clustering methods''' (such as the algorithm STRUCTURE<ref name=":132">{{Cite journal|last1=Pritchard|first1=Jonathan K|last2=Stephens|first2=Matthew|last3=Donnelly|first3=Peter|date=2000-06-01|title=Inference of Population Structure Using Multilocus Genotype Data|journal=Genetics|volume=155|issue=2|pages=945–959|doi=10.1093/genetics/155.2.945|pmid=10835412|pmc=1461096|issn=1943-2631|doi-access=free}}</ref>) or '''multidimensional summaries''' (typically through principal component analysis).<ref name=":02" /><ref name=":14">{{Cite journal|last1=Lawson|first1=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|pmid=22703172|issn=1527-8204|doi-access=free}}</ref> By processing a large number of SNPs (or other genetic marker data) in different ways, both approaches to genetic clustering tend to converge on similar patterns by identifying similarities among SNPs and/or [[haplotype]] tracts to reveal ancestral genetic similarities.<ref name=":14" />
=== Model-based clustering ===
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