Human genetic clustering

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 sequences, short tandem repeat polymorphisms, and single nucleotide polymorphisms (SNPs), among others.[11] 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[12]) or multidimensional summaries (typically through principal component analysis).[1][13] 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.[13]

Model-based clustering

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 "admixture inference," as individual genomes (or individuals within populations) can be characterized by the proportions of alleles linked to each cluster.[1] 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.

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.[1] These early HGDP studies, such as those by Rosenberg et al. (2002),[4][16] 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.[5][6][17] Genetic clustering and HGDP studies have also contributed to methods for, and criticisms of, the genetic ancestry consumer testing industry.[18]

A number of landmark genetic cluster studies have been conducted on global human populations since 2002, including the following:

Clusters of individuals are often geographically structured. For example, when clustering a population of East Asians and Europeans, each group will likely form its own respective cluster based on similar allele frequencies. In this way, clusters can have a correlation with traditional concepts of race and self-identified ancestry; in some cases, such as medical questionnaires, the latter variables can be used as a proxy for genetic ancestry where genetic data is unavailable.[9][4] However, genetic variation is distributed in a complex, continuous, and overlapping manner, so this correlation is imperfect and the use of racial categories in medicine can introduce additional hazards.[9]

Some 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 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. Due to migration, gene flow, and baseline homogeneity, features between groups are extensively overlapping and intermixed.[2][9] 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.[7] 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 or sickle cell disease.[3][25] 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.[25] 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.[2][25]