Human genetic clustering
Human genetic clustering
Human genetic clustering refers to a wide range of scientific and statistical methods often used to characterize patterns and subgroups within studies 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 personalized medicine. Since the mapping of the human genome, and with the availability of increasingly powerful analytic tools, cluster analyses have revealed a range of ancestral and migratory trends among human populations and individuals.[1] Humans tend to cluster together by geographic ancestry, with divisions between clusters aligning largely with geographic barriers such as oceans or mountain ranges. But the practice of defining clusters among modern human populations is largely arbitrary and variable, and there are no genetic markers that have been found to completely distinguish between groups.[2]
Studies of human genetic clustering have been implicated in discussions of race, ethnicity, and scientific racism, as some have controversially suggested that genetically derived clusters may be understood as genetically determined races.[3][4] 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 restriction site polymorphisms, short tandem repeat polymorphisms, and single nucleotide polymorphisms (SNPs) among others.[5] Models for 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.[6][7] 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.[7]
Model-based clustering
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 "admixture inference," as individual genomes (or individuals within populations) can be characterized by the proportions of alleles linked to each cluster.[1] Of note, algorithms like STRUCTURE have required that populations are chosen for samples before running the cluster analysis.
Multidimensional summary statistics
Where model-based clustering characterizes populations using proportions of discrete 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 assessing the distribution of data; with larger samples of human genotypes, data tends to cluster in discrete groups as well as admixed position between groups.[1][7]
Caveats and drawbacks
There are many caveats and drawbacks to genetic clustering methods of any type, given the degree of admixture and relative similarity within the human population. All genetic cluster findings are 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.[1] STRUCTURE in particular may be misleading by requiring the data to be sorted into a predetermined number of clusters which may or may not reflect the actual population's distribution.[8] 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.[1][8]
Applications to human genetic data
Application of genetic clustering methods to a large human dataset was first marked by studies associated with the Human Genome Diversity Project (HGDP) data.[1] These early HGDP studies, such as those by Rosenberg and colleagues,[9][10] contributed to theories of the serial founder effect and early human migration out of Africa.
###PROBABLY NEED TO WRAP THIS SECTION INTO ANOTHER SECTION, i DON'T REALLY WANT TO GET INTO IT
Genetic clustering and race
Text of this section.
(Maglo et al 2016, Jorde & Wooding 2004; Bamshad articles)
Related issues
Clusters vs. clines
Brief summary of human genetic variation?
Possibly this is a "see also" section?
- ^ a b c d e f Novembre, John; Ramachandran, Sohini (2011-09-22). "Perspectives on Human Population Structure at the Cusp of the Sequencing Era". Annual Review of Genomics and Human Genetics. 12 (1): 245–274. doi:10.1146/annurev-genom-090810-183123. ISSN 1527-8204.
- ^ Bamshad, Michael J.; Olson, Steve E. (2003-12). "Does Race Exist?". Scientific American. 289 (6): 78–85. doi:10.1038/scientificamerican1203-78. ISSN 0036-8733.
{{cite journal}}: Check date values in:|date=(help) - ^ Jorde, Lynn B; Wooding, Stephen P (2004-10-26). "Genetic variation, classification and 'race'". Nature Genetics. 36 (S11): S28 – S33. doi:10.1038/ng1435. ISSN 1061-4036.
- ^ Verfasser., Marks, Jonathan (Jonathan M.), 1955-. Is science racist?. ISBN 978-0-7456-8925-8. OCLC 1037867598.
{{cite book}}:|last=has generic name (help)CS1 maint: multiple names: authors list (link) CS1 maint: numeric names: authors list (link) - ^ Bamshad, Michael; Wooding, Stephen; Salisbury, Benjamin A.; Stephens, J. Claiborne (2004-08). "Deconstructing the relationship between genetics and race". Nature Reviews Genetics. 5 (8): 598–609. doi:10.1038/nrg1401. ISSN 1471-0056.
{{cite journal}}: Check date values in:|date=(help) - ^ Novembre, John; Ramachandran, Sohini (2011-09-22). "Perspectives on Human Population Structure at the Cusp of the Sequencing Era". Annual Review of Genomics and Human Genetics. 12 (1): 245–274. doi:10.1146/annurev-genom-090810-183123. ISSN 1527-8204.
- ^ a b c Lawson, Daniel John; Falush, Daniel (2012-09-22). "Population Identification Using Genetic Data". Annual Review of Genomics and Human Genetics. 13 (1): 337–361. doi:10.1146/annurev-genom-082410-101510. ISSN 1527-8204.
- ^ a b Kalinowski, S T (2010-08-04). "The computer program STRUCTURE does not reliably identify the main genetic clusters within species: simulations and implications for human population structure". Heredity. 106 (4): 625–632. doi:10.1038/hdy.2010.95. ISSN 0018-067X.
- ^ Rosenberg, N. A. (2002-12-20). "Genetic Structure of Human Populations". Science. 298 (5602): 2381–2385. doi:10.1126/science.1078311. ISSN 0036-8075.
- ^ Rosenberg, Noah A; Mahajan, Saurabh; Ramachandran, Sohini; Zhao, Chengfeng; Pritchard, Jonathan K; Feldman, Marcus W (2005-12-09). "Clines, Clusters, and the Effect of Study Design on the Inference of Human Population Structure". PLoS Genetics. 1 (6): e70. doi:10.1371/journal.pgen.0010070. ISSN 1553-7404.
{{cite journal}}: CS1 maint: unflagged free DOI (link)