Human population genetic structure and inference of group membership

Abstract

A major goal of biomedical research is to develop the capability to provide highly personalized health care. To do so, it is necessary to understand the distribution of interindividual genetic variation at loci underlying physical characteristics, disease susceptibility, and response to treatment. Variation at these loci commonly exhibits geographic structuring and may contribute to phenotypic differences between groups. Thus, in some situations, it may be important to consider these groups separately. Membership in these groups is commonly inferred by use of a proxy such as place-of-origin or ethnic affiliation. These inferences are frequently weakened, however, by use of surrogates, such as skin color, for these proxies, the distribution of which bears little resemblance to the distribution of neutral genetic variation. Consequently, it has become increasingly controversial whether proxies are sufficient and accurate representations of groups inferred from neutral genetic variation. This raises three questions: how many data are required to identify population structure at a meaningful level of resolution, to what level can population structure be resolved, and do some proxies represent population structure accurately? We assayed 100 Alu insertion polymorphisms in a heterogeneous collection of approximately 565 individuals, approximately 200 of whom were also typed for 60 microsatellites. Stripped of identifying information, correct assignment to the continent of origin (Africa, Asia, or Europe) with a mean accuracy of at least 90% required a minimum of 60 Alu markers or microsatellites and reached 99%-100% when >/=100 loci were used. Less accurate assignment (87%) to the appropriate genetic cluster was possible for a historically admixed sample from southern India. These results set a minimum for the number of markers that must be tested to make strong inferences about detecting population structure among Old World populations under ideal experimental conditions. We note that, whereas some proxies correspond crudely, if at all, to population structure, the heuristic value of others is much higher. This suggests that a more flexible framework is needed for making inferences about population structure and the utility of proxies.

Figure 1

Predicted origin versus known origin for Africans, East Asians, and Europeans, estimated from 1–100 Alu insertion polymorphisms, bounded by 95% CIs.

Figure 2

Predicted origin versus known origin for Africans, East Asians, and Europeans, estimated from 1–60 microsatellite loci, bounded by 95% CIs.

Figure 3

Predicted origin vs. known origin for Africans, East Asians, and Europeans, estimated from 1–160 loci including both 100 Alu and 60 microsatellite loci, bounded by 95% CIs.

Figure 4

Assignment of samples from 23 ethnic groups from Africa, Asia, and Europe, to genetic clusters inferred from the analysis of 100 Alu insertion polymorphisms for K=2, 3, and 4. Sample sizes for each population are in parentheses.

Figure 5

Proportion of ancestry for individual samples from Africa, Asia, and Europe for K=3, using 20, 60, and 100 Alu insertion polymorphisms; 20 and 60 microsatellite loci; and all 160 loci. The proportion of ancestry increases toward each apex.

Figure 6

Proportion of ancestry for individual samples from Asia, Europe, and India, for K=3, using 100 Alu loci (left), compared with the proportion of ancestry for individual samples from Africa, Asia, Europe, and India, for K=3, using 100 Alu loci (right). The proportion of ancestry increases toward each apex.

Figure A

Distribution of F_ST estimates among sub-Saharan Africans, East Asians, and Europeans, for each Alu marker (blue) and each microsatellite locus (yellow).