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'''Human genetic clustering''' refers to patterns of relative genetic similarity among human individuals and populations, as well as the wide range of scientific and statistical methods used to study this aspect of [[human genetic variation]].
Clustering studies are thought to be valuable for characterizing the general structure of genetic variation among human populations, to contribute to the study of ancestral origins, evolutionary history, and precision medicine. Since the mapping of the human genome, and with the availability of increasingly powerful analytic tools, [[Cluster analysis|cluster analyses]] have revealed a range of ancestral and migratory trends among human populations and individuals.<ref name=":02">{{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> Human genetic clusters tend to be organized by geographic ancestry, with divisions between clusters aligning largely with geographic barriers such as oceans or mountain ranges.<ref name=":32">{{Cite journal|last=Maglo|first=Koffi N.|last2=Mersha|first2=Tesfaye B.|last3=Martin|first3=Lisa J.|date=2016-02-17|title=Population Genomics and the Statistical Values of Race: An Interdisciplinary Perspective on the Biological Classification of Human Populations and Implications for Clinical Genetic Epidemiological Research|url=http://dx.doi.org/10.3389/fgene.2016.00022|journal=Frontiers in Genetics|volume=7|doi=10.3389/fgene.2016.00022|issn=1664-8021}}</ref><ref name=":92">{{Cite journal|date=2012-10-29|editor-last=Goodman|editor-first=Alan H.|editor2-last=Moses|editor2-first=Yolanda T.|editor3-last=Jones|editor3-first=Joseph L.|title=Race|url=http://dx.doi.org/10.1002/9781118233023|doi=10.1002/9781118233023}}</ref> Clustering studies have been applied to global populations,<ref name=":102">{{Cite journal|last=Rosenberg|first=N. A.|date=2002-12-20|title=Genetic Structure of Human Populations|url=http://dx.doi.org/10.1126/science.1078311|journal=Science|volume=298|issue=5602|pages=2381–2385|doi=10.1126/science.1078311|issn=0036-8075}}</ref> as well as to population subsets like post-colonial North America.<ref name=":112">{{Cite journal|last=Han|first=Eunjung|last2=Carbonetto|first2=Peter|last3=Curtis|first3=Ross E.|last4=Wang|first4=Yong|last5=Granka|first5=Julie M.|last6=Byrnes|first6=Jake|last7=Noto|first7=Keith|last8=Kermany|first8=Amir R.|last9=Myres|first9=Natalie M.|last10=Barber|first10=Mathew J.|last11=Rand|first11=Kristin A.|date=2017-02-07|title=Clustering of 770,000 genomes reveals post-colonial population structure of North America|url=https://www.nature.com/articles/ncomms14238|journal=Nature Communications|language=en|volume=8|issue=1|pages=14238|doi=10.1038/ncomms14238|issn=2041-1723}}</ref><ref name=":122">{{Cite journal|last=Jordan|first=I. King|last2=Rishishwar|first2=Lavanya|last3=Conley|first3=Andrew B.|date=September 2019
Many studies of human genetic clustering have been implicated in discussions of [[Race (human categorization)|race]], [[Ethnic group|ethnicity]], and [[scientific racism]], as some have controversially suggested that genetically derived clusters may be understood as proof of genetically determined races.<ref name=":42">{{Cite journal|last=Jorde|first=Lynn B|last2=Wooding|first2=Stephen P|date=2004-10-26|title=Genetic variation, classification and 'race'|url=http://dx.doi.org/10.1038/ng1435|journal=Nature Genetics|volume=36|issue=S11|pages=S28–S33|doi=10.1038/ng1435|issn=1061-4036}}</ref><ref>{{Cite book|last=Verfasser.|first=Marks, Jonathan (Jonathan M.), 1955-|url=http://worldcat.org/oclc/1037867598|title=Is science racist?|isbn=978-0-7456-8925-8|oclc=1037867598}}</ref> Although cluster analyses invariably organize humans (or groups of humans) into subgroups, debate is ongoing on how to interpret these genetic clusters with respect to race and its social and phenotypic features. And, because there is such a small fraction of genetic variation between human genotypes overall, genetic clustering approaches are highly dependent on the sampled data, genetic markers, and statistical methods applied to their construction.
== 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|last=Bamshad|first=Michael|last2=Wooding|first2=Stephen|last3=Salisbury|first3=Benjamin A.|last4=Stephens|first4=J. Claiborne|date=August 2004
=== 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 [[
=== Multidimensional summary statistics ===
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=== Caveats and limitations ===
There are caveats and limitations to genetic clustering methods of any type, given the degree of admixture and relative similarity within the human population. All genetic cluster findings are [[Sampling bias|biased]] by the sampling process used to gather data, and by the quality and quantity of that data. For example, many clustering studies use data derived from populations that are geographically distinct and far apart from one another, which may present an illusion of discrete clusters where, in reality, populations are much more blended with one another when intermediary groups are included.<ref name=":02" /> Sample size also plays an important moderating role on cluster findings, as different sample size inputs can influence cluster assignment, and more subtle relationships between genotypes may only emerge with larger sample sizes.<ref name=":02" /><ref name=":22" /> In particular, the use of STRUCTURE has been widely criticized as being potentially misleading through requiring data to be sorted into a predetermined number of clusters which may or may not reflect the actual population's distribution.<ref name=":22" /><ref>{{Cite journal|last=Lawson|first=Daniel J.|last2=van Dorp|first2=Lucy|last3=Falush|first3=Daniel|date=2018-08-14|title=A tutorial on how not to over-interpret STRUCTURE and ADMIXTURE bar plots|url=https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6092366/|journal=Nature Communications|volume=9|doi=10.1038/s41467-018-05257-7|issn=2041-1723|pmc=6092366|pmid=30108219}}</ref> The creators of STRUCTURE originally described the algorithm as an "[[Exploratory data analysis|exploratory]]" method to be interpreted with caution and not as a test with statistically significant power.<ref name=":132" /><ref>{{Cite journal|last=Novembre|first=John|date=2016-10-01|title=Pritchard, Stephens, and Donnelly on Population Structure|url=https://doi.org/10.1534/genetics.116.195164|journal=Genetics|volume=204|issue=2|pages=391–393|doi=10.1534/genetics.116.195164|issn=1943-2631|pmc=
== Notable applications to human genetic data ==
Modern applications of genetic clustering methods to global-scale genetic data were first marked by studies associated with the [[Human Genome Diversity Project]] (HGDP) data.<ref name=":02" /> These early HGDP studies, such as those by Rosenberg et al. (2002),<ref name=":102" /><ref>{{Cite journal|last=Rosenberg|first=Noah A|last2=Mahajan|first2=Saurabh|last3=Ramachandran|first3=Sohini|last4=Zhao|first4=Chengfeng|last5=Pritchard|first5=Jonathan K|last6=Feldman|first6=Marcus W|date=2005-12-09|title=Clines, Clusters, and the Effect of Study Design on the Inference of Human Population Structure|url=http://dx.doi.org/10.1371/journal.pgen.0010070|journal=PLoS Genetics|volume=1|issue=6|pages=e70|doi=10.1371/journal.pgen.0010070|issn=1553-7404}}</ref> contributed to theories of the serial founder effect and early human migration out of Africa, and clustering methods have been notably applied to describe admixed continental populations.<ref name=":112" /><ref name=":122" /><ref>{{Cite journal|last=Leslie|first=Stephen|last2=Winney|first2=Bruce|last3=Hellenthal|first3=Garrett|last4=Davison|first4=Dan|last5=Boumertit|first5=Abdelhamid|last6=Day|first6=Tammy|last7=Hutnik|first7=Katarzyna|last8=Royrvik|first8=Ellen C.|last9=Cunliffe|first9=Barry|last10=Lawson|first10=Daniel J.|last11=Falush|first11=Daniel|date=March 2015
A number of landmark genetic cluster studies have been conducted on global human populations since 2002, including the following:
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Many other scholars have challenged the idea that race can be inferred by genetic clusters, drawing distinctions between arbitrarily assigned genetic clusters, ancestry, and race. One recurring caution against thinking of human populations in terms of clusters is the notion that genotypic variation and traits are distributed evenly between populations, along gradual [[Cline (biology)|clines]] rather than along discrete population boundaries; so although genetic similarities are usually organized geographically, their underlying populations have never been completely separated from one another. And due to migration, gene flow, and baseline homogeneity, features between groups are extensively overlapping and intermixed.<ref name=":32" /><ref name=":42" /> Moreover, genetic clusters do not typically match socially defined racial groups; many commonly understood races may not be sorted into the same genetic cluster, and many genetic clusters are made up of individuals who would have distinct racial identities.<ref name=":52" /> In general, clusters may most simply be understood as products of the methods used to sample and analyze genetic data; not without meaning for understanding ancestry and genetic characteristics, but inadequate to fully explaining the concept of race, which is more often described in terms of social and cultural forces.
In the related context of [[personalized medicine]], race is currently listed as a [[risk factor]] for a wide range of medical conditions with genetic and non-genetic causes. Questions have emerged regarding whether or not genetic clusters support the idea of race as a valid construct to apply to medical research and treatment of disease, because there are many diseases that correspond with specific genetic markers and/or with specific populations, as seen with [[Tay–Sachs disease|Tay-Sachs disease]] or [[sickle cell disease]].<ref name=":92" /><ref name=":63" /> Researchers are careful to emphasize that
== References ==
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