page_title: Race and genetics
section_title: Number of clusters
hierarchical_section_title: Race and genetics / Research methods / Number of clusters
caption_attribution_description: From: Low Levels of Genetic Divergence across Geographically and Linguistically Diverse Populations from India Rosenberg NA, Mahajan S, Gonzalez-Quevedo C, Blum MGB, Nino-Rosales L, et al. PLoS Genetics Vol. 2, No. 12, e215 doi:10.1371/journal.pgen.0020215 [1] (fig. 2A) original caption: " Representative estimate of population structure for 1,384 individuals from worldwide populations, including 432 individuals from India. The plot represents the highest-likelihood run among ten STRUCTURE runs with K = 7 clusters. Eight of the other nine runs identified a cluster largely corresponding to India, and five of these eight produced plots nearly identical to the one shown."
context_page_description: The relationship between race and genetics is relevant to the controversy concerning race classification. In everyday life, many societies classify populations into groups based on phenotypical traits and impressions of probable geographic ancestry and cultural identity—these are the groups usually called "races" in countries like the United States, Brazil, and South Africa. Patterns of variation of human genetic traits are generally clinal, with more abrupt shifts at places where steady gene flow is interrupted. The pattern of genetic variants tend to form larger regional clusters. Such a pattern can be explained by the expansion of the human population from Africa and serial genetic bottlenecks. This causes genetic clusters to correlate statistically with population groups when a number of alleles are evaluated.
  Genetic analysis enables scientists to estimate the geographic ancestry of a person by using ancestry-informative markers, and by inference the probable racial category into which they will be classified in a given society. In that way there is a distinct statistical correlation between gene frequencies and racial categories.
context_section_description: Cluster analysis has been criticized because the number of clusters to search for is decided in advance, with different values possible (although with varying degrees of probability). Principal component analysis does not decide in advance how many components for which to search.
  The 2002 study by Rosenberg et al. exemplifies why meanings of these clusterings are disputable. The study shows that at the K=5 cluster analysis, genetic clusterings roughly map onto each of the five major geographical regions. Similar results were gathered in further studies in 2005.
  However, in addition to the five main supposedly geographical clusters, a sixth group, the Kalash, a minority ethnic group in Pakistan, began to appear starting at K=6. The racial naturalist Nicholas Wade considers that the results “make no genetic or geographic sense”. They are therefore omitted in his book A Troublesome Inheritance in favour of the K=5 cluster analysis.
  This bias, however, is reflective of how the research is inherently flawed. The sample population is chosen with geographical representation and folk concepts of race in mind, instead of accounting for the genetic diversity within the different geographical regions. The Kalash did not fit into the general pattern for it had been a genetically isolated population that happened to be reflected in this study. Potentially numerous genetically drifted groups, such as the uncontacted Sentinelese, are not represented in the study.
caption: Gene clusters from Rosenberg (2006) for K=7 clusters. (Cluster analysis divides a dataset into any prespecified number of clusters.) Individuals have genes from multiple clusters. The cluster prevalent only among the Kalash people (yellow) only splits off at K=7 and greater.
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