As populations with multilinear transmission (i.e., mixing of genetic material from two parents, say) evolve over generations, the genetic transmission lines constitute complicated networks. In contrast, unilinear transmission leads to simpler network structures (trees). The genetic exchange in multilinear transmission is further influenced by migration, incubation, mixing and so on. The task we address in the paper is to tease apart subtle admixtures from the usual interrelationships of related populations. We present a combinatorial approach based on persistence in topology to detect admixture in populations. We show, based on controlled simulations, that topological characteristics have the potential for detecting subtle admixture in related populations. We then apply the technique successfully to a set of avocado germplasm data indicating that the approach has the potential for novel characterizations of relatedness in populations. We believe that this approach also has the potential for not only detecting but also discriminating ancient from recent admixture.
Parida, L., Utro, F., Yorukoglu, D., Carrieri, A., Kuhn, D., Basu, S. (2015). Topological signatures for population admixture. In 19th Annual International Conference on Research in Computational Molecular Biology, RECOMB 2015; Warsaw; Poland; 12 April 2015 through 15 April 2015 (pp.261-275). Springer Verlag [10.1007/978-3-319-16706-0_27].
Topological signatures for population admixture
CARRIERI, ANNA PAOLAPenultimo
;
2015
Abstract
As populations with multilinear transmission (i.e., mixing of genetic material from two parents, say) evolve over generations, the genetic transmission lines constitute complicated networks. In contrast, unilinear transmission leads to simpler network structures (trees). The genetic exchange in multilinear transmission is further influenced by migration, incubation, mixing and so on. The task we address in the paper is to tease apart subtle admixtures from the usual interrelationships of related populations. We present a combinatorial approach based on persistence in topology to detect admixture in populations. We show, based on controlled simulations, that topological characteristics have the potential for detecting subtle admixture in related populations. We then apply the technique successfully to a set of avocado germplasm data indicating that the approach has the potential for novel characterizations of relatedness in populations. We believe that this approach also has the potential for not only detecting but also discriminating ancient from recent admixture.
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