|
= Human genetic clustering =
'''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=":002">{{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=":332">{{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=":992">{{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=":10102">{{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=":11112">{{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=":12122">{{Cite journal|last=Jordan|first=I. King|last2=Rishishwar|first2=Lavanya|last3=Conley|first3=Andrew B.|date=2019-09|title=Native American admixture recapitulates population-specific migration and settlement of the continental United States|url=https://pubmed.ncbi.nlm.nih.gov/31545791/|journal=PLoS genetics|volume=15|issue=9|pages=e1008225|doi=10.1371/journal.pgen.1008225|issn=1553-7404|pmc=6756731|pmid=31545791}}</ref> Notably, the practice of defining clusters among modern human populations is largely arbitrary and variable due to the continuous nature of human genotypes; although individual genetic markers can be used to produce smaller groups, there are no models that produce completely distinct subgroups when larger numbers of genetic markers are used.<ref name=":332" /><ref name=":552">{{Cite journal|last=Bamshad|first=Michael J.|last2=Olson|first2=Steve E.|date=2003-12|title=Does Race Exist?|url=http://dx.doi.org/10.1038/scientificamerican1203-78|journal=Scientific American|volume=289|issue=6|pages=78–85|doi=10.1038/scientificamerican1203-78|issn=0036-8733}}</ref><ref name=":222">{{Cite journal|last=Kalinowski|first=S T|date=2010-08-04|title=The computer program STRUCTURE does not reliably identify the main genetic clusters within species: simulations and implications for human population structure|url=http://dx.doi.org/10.1038/hdy.2010.95|journal=Heredity|volume=106|issue=4|pages=625–632|doi=10.1038/hdy.2010.95|issn=0018-067X}}</ref>
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=":442">{{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=2004-08|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|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=":13132">{{Cite journal|last=Pritchard|first=Jonathan K|last2=Stephens|first2=Matthew|last3=Donnelly|first3=Peter|date=2000-06-01|title=Inference of Population Structure Using Multilocus Genotype Data|url=https://doi.org/10.1093/genetics/155.2.945|journal=Genetics|volume=155|issue=2|pages=945–959|doi=10.1093/genetics/155.2.945|issn=1943-2631}}</ref>) or '''multidimensional summaries''' (typically through principal component analysis).<ref name=":002" /><ref name=":114">{{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> 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=":114" />
=== Model-based clustering ===
[[File:Rosenberg 1048people 993markersRosenberg_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|alleles]] linked to each cluster.<ref name=":002" /> 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 ===
Where model-based clustering characterizes populations using proportions of presupposed ancestral clusters, multidimensional summary statistics characterize populations on a continuous spectrum. The most common multidimensional statistical method used for genetic clustering is [[principal component analysis]] (PCA), which plots individuals by two or more axes (their "principal components") that represent aggregations of genetic markers that account for the highest variance. Clusters can then be identified by visually assessing the distribution of data; with larger samples of human genotypes, data tends to cluster in distinct groups as well as admixed positions between groups.<ref name=":002" /><ref name=":114" />
=== 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=":002" /> 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=":002" /><ref name=":222" /> 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=":222">{{Cite journal|last=Kalinowski|first=S T|date=2010-08-04|title=The computer program STRUCTURE does not reliably identify the main genetic clusters within species: simulations and implications for human population structure|url=http://dx.doi.org/10.1038/hdy.2010.95|journal=Heredity|volume=106|issue=4|pages=625–632|doi=10.1038/hdy.2010.95|issn=0018-067X}}</ref><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=":13132" /><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=PMC5068833|pmid=27729489}}</ref>
== 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=":002" /> These early HGDP studies, such as those by Rosenberg et al. (2002),<ref name=":10102">{{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><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=":11112" /><ref name=":12122" /><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=2015-03|title=The fine-scale genetic structure of the British population|url=https://www.nature.com/articles/nature14230|journal=Nature|language=en|volume=519|issue=7543|pages=309–314|doi=10.1038/nature14230|issn=1476-4687}}</ref> Genetic clustering and HGDP studies have also contributed to methods for, and criticisms of, the [[Genealogical DNA test|genetic ancestry consumer testing]] industry.<ref>{{Cite journal|last=Royal|first=Charmaine D.|last2=Novembre|first2=John|last3=Fullerton|first3=Stephanie M.|last4=Goldstein|first4=David B.|last5=Long|first5=Jeffrey C.|last6=Bamshad|first6=Michael J.|last7=Clark|first7=Andrew G.|date=2010-05-14|title=Inferring Genetic Ancestry: Opportunities, Challenges, and Implications|url=https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2869013/|journal=American Journal of Human Genetics|volume=86|issue=5|pages=661–673|doi=10.1016/j.ajhg.2010.03.011|issn=0002-9297|pmc=2869013|pmid=20466090}}</ref>
A number of landmark genetic cluster studies have been conducted on global human populations since 2002, including the following:
|Rosenberg et al.
|2002
|Genetic Structure of Human Populations<ref name=":882">{{Cite journal|last1=Rosenberg|first1=Noah A.|last2=Pritchard|first2=Jonathan K.|last3=Weber|first3=James L.|last4=Cann|first4=Howard M.|last5=Kidd|first5=Kenneth K.|last6=Zhivotovsky|first6=Lev A.|last7=Feldman|first7=Marcus W.|date=2002-12-20|title=Genetic Structure of Human Populations|journal=Science|volume=298|issue=5602|pages=2381–2385|bibcode=2002Sci...298.2381R|doi=10.1126/science.1078311|issn=0036-8075|pmid=12493913|s2cid=8127224}}</ref>
|1056 / 52
|[[Human Genome Diversity Project]] (HGDP-CEPH)
|Rosenberg et al.
|2005
|Clines, Clusters, and the Effect of Study Design on the Inference of Human Population Structure<ref name="rosenberg2005rosenberg20052">{{cite journal|last1=Rosenberg|first1=NA|last2=Mahajan|first2=S|last3=Ramachandran|first3=S|last4=Zhao|first4=C|last5=Pritchard|first5=JK|display-authors=etal|year=2005|title=Clines, Clusters, and the Effect of Study Design on the Inference of Human Population Structure|url=|journal=PLOS Genet|volume=1|issue=6|page=e70|doi=10.1371/journal.pgen.0010070|pmc=1310579|pmid=16355252|authorlink5=Jonathan K. Pritchard}}</ref>
|1056 / 52
|[[Human Genome Diversity Project]] (HGDP-CEPH)
|Tishkoff et al.
|2009
|The Genetic Structure and History of Africans and African Americans<ref name=":62622">{{Cite journal|last1=Tishkoff|first1=Sarah A|last2=Reed|first2=Floyd A|last3=Friedlaender|first3=Françoise R|last4=Ehret|first4=Christopher|last5=Ranciaro|first5=Alessia|last6=Froment|first6=Alain|last7=Hirbo|first7=Jibril B|last8=Awomoyi|first8=Agnes A|last9=Bodo|first9=Jean-Marie|last10=Doumbo|first10=Ogobara|last11=Ibrahim|first11=Muntaser|date=2009-05-22|title=The Genetic Structure and History of Africans and African Americans|journal=Science|volume=324|issue=5930|pages=1035–1044|bibcode=2009Sci...324.1035T|doi=10.1126/science.1172257|issn=0036-8075|pmc=2947357|pmid=19407144|last20first12=Pretorius|first25=ScottAbdalla MT|last25last13=WilliamsKotze|first24first13=JamesMaritha LJ|last24last14=WeberLema|first23first14=CharlesGodfrey|last23last15=WambebeMoore|first22first15=MahamadouJason AH|last22last16=TheraMortensen|first21first16=MichaelHolly|first17=Thomas WB|last21last18=SmithOmar|first20first18=GideonSabah SA|last19last12=PowellJuma|first19=Kweli|last12last19=JumaPowell|first18first20=SabahGideon AS|last18last21=OmarSmith|first17first21=ThomasMichael BW|first16last22=HollyThera|last16=Mortensen|first15first22=JasonMahamadou HA|last15last23=MooreWambebe|first14first23=GodfreyCharles|last14last24=LemaWeber|first13first24=MarithaJames JL|last13last25=KotzeWilliams|first12first25=AbdallaScott TM|last20=Pretorius|last17=Nyambo}}</ref>
|~3400 / 185
|HGDP-CEPH ''plus'' 133 additional African populations and Indian individuals
|Xing et al.
|2010
|Toward a more uniform sampling of human genetic diversity: A survey of worldwide populations by high-density genotyping<ref name=":772">{{Cite journal|last1=Xing|first1=Jinchuan|last2=Watkins|first2=W. Scott|last3=Shlien|first3=Adam|last4=Walker|first4=Erin|last5=Huff|first5=Chad D.|last6=Witherspoon|first6=David J.|last7=Zhang|first7=Yuhua|last8=Simonson|first8=Tatum S.|last9=Weiss|first9=Robert B.|last10=Schiffman|first10=Joshua D.|last11=Malkin|first11=David|date=October 2010|title=Toward a more uniform sampling of human genetic diversity: A survey of worldwide populations by high-density genotyping|journal=Genomics|volume=96|issue=4|pages=199–210|doi=10.1016/j.ygeno.2010.07.004|issn=0888-7543|pmc=2945611|pmid=20643205|last12=Woodward|first12=Scott R.|last13=Jorde|first13=Lynn B.}}</ref>
|850 / 40
|HapMap ''plus'' 296 individuals
|250,000 SNPs
|}
== Genetic clustering and race ==
A plurality of human genetic clustering studies have produced clusters of individuals with similar geographic origins or ancestry, and these findings have been interpreted by some to suggest biological support for the concept of race. Clustering results have often, for example, shown distinct cluster assignments between individuals with African and non-African ancestry, and other levels of clustering have come close to placing individuals all within their corresponding continental populations (e.g., Europeans clustered together, East Asians clustered together).<ref name=":442" /> Rosenberg et al. (2002) suggested a division of human populations into five clusters that can be seen to resemble major geographic continental divisions, and concluded that self-identified ancestry (taken by many to mean race) may be an adequate proxy for ancestry. And the association between genetic clusters and race may be further confounded by false assumptions about racialized traits, such as skin color or temperament, having clear genetic roots.<ref name=":663">{{Cite book|last=1980-|first=Koenig, Barbara A. Lee, Sandra Soo-Jin, 1966- Richardson, Sarah S.,|url=http://worldcat.org/oclc/468194495|title=Revisiting race in a genomic age|date=2008|publisher=Rutgers University Press|isbn=978-0-8135-4323-9|oclc=468194495}}</ref> In these ways, aspects of genetic clusters may be seen to resemble the traditional notion of race, at least as understood in the United States.
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=":332" /><ref name=":442" /> 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=":552" /> 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=":992">{{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><ref name=":663" /> Researchers are careful to emphasize that ancestry--revealed in part through cluster analyses--plays an important role in understanding risk of disease. But racial or ethnic identity does not perfectly align with genetic ancestry, and so race and ethnicity do not reveal enough information to make a medical diagnosis.<ref name=":663" /> Race as a variable in medicine is more likely to reflect social factors, where ancestry information is more likely to be meaningful when considering genetic ancestry.<ref name=":332" /><ref name=":663" />
== References ==
| 