Abstract
BackgroundReconstructability of population history, from genetic information of extant individuals, is studied under a simulation setting. We do not address the issue of accuracy of the reconstruction algorithms: we assume the availability of the theoretical best algorithm. On the other hand, we focus on the fraction (1 - f) of the common genetic history that is irreconstructible or impenetrable. Thus the fraction, f, gives an upper bound on the extent of estimability. In other words, there exists no method that can reconstruct a fraction larger than f of the entire common genetic history. For the realization of such a study, we first define a natural measure of the amount of genetic history. Next, we use a population simulator (from literature) that has at least two features. Firstly, it has the capability of providing samples from different demographies, to effectively reflect reality. Secondly, it also provides the underlying relevant genetic history, captured in its entirety, where such a measure is applicable. Finally, to compute f, we use an information content measure of the relevant genetic history. The simulator of choice provided the following demographies: Africans, Europeans, Asians and Afro-Americans.ResultsWe observe that higher the rate of recombination, lower the value of f, while f is invariant over varying mutation rates, in each of the demographies. The value of f increases with the number of samples, reaching a plateau and suggesting that in all the demographies at least about one-third of the relevant genetic history is impenetrable. The most surprising observation is that the the sum of the reconstructible history of the subsegments is indeed larger than the reconstructible history of the whole segment. In particular, longer the chromosomal segment, smaller the value of f, in all the demographies.ConclusionsWe present the very first framework for measuring the fraction of the relevant genetic history of a population that is mathematically elusive. Our observed results on the tested demographies suggest that it may be better to aggregate the analysis of smaller chunks of chromosomal segments than fewer large chunks. Also, no matter the richness of samples in a population, at least one-third of the population genetic history is impenetrable. The framework also opens up possible new lines of investigation along the following. Given the characteristics of a population, possibly derived from observed extant individuals, to estimate the (1) optimal sample size and (2) optimal sequence length for the most informative analysis.
Highlights
Reconstructability of population history, from genetic information of extant individuals, is studied under a simulation setting
Given the genetic landscape of some extant samples, its underlying Ancestral Recombinations Graph (ARG) is a plausible explanation of the observation, since it is the annotated topological structure that captures the genetic history in its totality, that is relevant to the extant samples
It can be viewed as a generator that faithfully produces the genetic landscape of the different demographies and since it is a random graph [13], we use multiple replicates to study its the characteristics
Summary
Reconstructability of population history, from genetic information of extant individuals, is studied under a simulation setting. We use a population simulator (from literature) that has at least two features It has the capability of providing samples from different demographies, to effectively reflect reality. It provides the underlying relevant genetic history, captured in its entirety, where such a measure is applicable. Every genetic event that is consequential to the genetic landscape of a population is captured in a topological structure called the Ancestral Recombinations Graph (ARG) [1]. The topology is not necessarily a tree, due to genetic exchange events such as recombinations, gene duplications and so on These are represented as nodes with multiple incoming edges in the ARG. The reader is directed to [2] for an exposition on random graph representation of the ARG
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