Question

In three or fewer sentences, explain how clusters identified from ADMIXTURE analysis of a few populations from each continent might misrepresent the complete pattern of human genetic variation.

Answer 1

The human genetic variability among the human species is known to be relatively low compared to other primate species. There are paradoxically more genetic differences between Western and Eastern chimpanzee individuals sampled in the African continent than in any genome of two human individuals sampled in different continents.Human genetic diversity also tends to be positively correlated with the geographic distance between the sampled individuals, which is mainly a result from isolation by distance.A basic assumption is that any current individual genome or population is a mixture of ancestries from past populations.

Method for estimating ancestry have traditionally focused on populations their main interests are to establish the relationship among populations and to quantify the admixture proportions in the admixed populations. Admixture proportions are computed from the amount of loci that can be traced back to a certain ancestral population.Ancestry methods is to describe the relationship between individuals in terms of genetic ancestry. This can either mean the identification of the a priori unknown ancestry components, the quantification of the proportions of these components, or the identification of the assumed population of an individual.Individual ancestry methods can be classified depending on the assumptions of the method, the scope of the algorithms and the use of prior ancestry information. From a technical point of view, there is large variation in the speed and computational requirements of the different methods. Speed depends on the computational complexity of each method, for example, is O(n m K2) for ADMIXTURE and O(n m K) for sNMF, as well as the possibility to apply divide-and-conquer computational approaches such as multithreading. The multithreading can only provide a linear time factor gain, which in the case of higher polynomial complexities does not have a strong computational impact.