Content deleted Content added
m Open access bot: doi updated in citation with #oabot. |
m →Model-based clustering: links to main model-based clustering page added |
||
Line 8:
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|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]] ===
[[File:Rosenberg_1048people_993markers.jpg|thumb|Human population structure has been inferred from multilocus DNA sequence data (Rosenberg et al. 2002, 2005). Individuals from 52 populations were examined at 993 DNA markers. This data was used to partition individuals into K = 2, 3, 4, 5, or 6 gene clusters. In this figure, the average fractional membership of individuals from each population is represented by horizontal bars partitioned into K colored segments.]]
Common [[model-based clustering]] algorithms include STRUCTURE, ADMIXTURE, and HAPMIX. These algorithms operate by finding the best fit for genetic data among an arbitrary or mathematically derived number of clusters, such that differences within clusters are minimized and differences between clusters are maximized. This clustering method is also referred to as "[[Genetic admixture|admixture]] inference," as individual genomes (or individuals within populations) can be characterized by the proportions of [[allele]]s linked to each cluster.<ref name=":02" /> In other words, algorithms like STRUCTURE generate results that assume the existence of discrete ancestral populations, operationalized through unique genetic markers, which have combined over time to form the admixed populations of the modern day.
=== Multidimensional summary statistics ===
| |||