Am J Phys Anthropol doi:10.1002/ajpa.21252
Mitochondrial DNA patterns in the Iberian Northern plateau: Population dynamics and substructure of the Zamora province
Luis Alvarez et al.
ABSTRACT
Several studies have shown the importance of recent events in the configuration of the genetic landscape of a specific territory. In this context, due to the phenomena of repopulation and demographic fluctuations that took place in recent centuries, the Iberian Northern plateau is a very interesting case study. The main aim of this work is to check if recent population movements together with existing boundaries (geographical and administrative) have influenced the current genetic composition of the area. To accomplish this general purpose, mitochondrial DNA variations of 214 individuals from a population located in the Western region of the Iberian Northern plateau (the province of Zamora) were analyzed. Results showed a typical Western European mitochondrial DNA haplogroup composition. However, unexpected high frequencies of U5, HV0, and L haplogroups were found in some regions. The analyses of microdifferentiation showed that there are differences between regions, but no geographic substructure organization can be noticed. It can be stated that the differences observed in the genetic pool of the sampled area at regional level results from the mixture of different populations carrying new lineages into this area at different points in history.
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My mtDNA haplogroup is U5b2:
ReplyDeleteHVR1: 16270T & 16519C. HVR2: 73G,150T,228A,263G,309.1C,315.1C. You can see near matches from northern Spain, that might be from a KGM refuge connection. Otherwise, my maternal line comes from the UK.
" The analyses of microdifferentiation showed that there are differences between regions, but no geographic substructure organization can be noticed. It can be stated that the differences observed in the genetic pool of the sampled area at regional level results from the mixture of different populations carrying new lineages into this area at different points in history."
ReplyDeleteThis problem, a signal detection in noise problem, has some similarities with detecting a wired or wireless communication signal.
The signal is buried in noise, sometimes at or even below the noise floor.
The signal, here, the genetic signature of an ancient geographically-in-place population, has some structure.
The noise also has some structure. In this case, there are likely multiple sources of noise, each with there own genetic structure. These sources of noise are the result of geographic spreading of populations into the geographic region.
In communication theory, the method of recovery and reconstruction of a signal is often done by making some assumptions about the structure of the signal and the structure of noise. Using such assumptions, one attempts to break the signal out of the noise. (That's how DSL works.)
Usually, signal recovery in noise techniques involve some statistical assumptions, as well. Some of the signal is lost in the noise, but over many samples, it is possible to gain a recognizeable picture of the original signal.
Signal recovery also uses adaptation.
In looking at the genetic substructure problem, I would think that there is some optimum geographic bound for sampling in order to maximize the signal and minimize the noise. Too far in, and you don't have enough of the signal. Too far out and your signal to noise ratio is going to drop.
One could try to use adaptive signally techniques to set the geographic bound on genetic samples in order to maximize the signal/noise ratio of the genetic substructure.
One other thought is that presumably, the more of the genome that is used for analysis, the better the chance of having more signal and less noise.
I guess the genetic statistics guys must be having a field day.
"In the province of Zamora, if the total number of African lineages are taken into account (Hgs L1b, L2b, L3, M1, and U6a1a), the contribution represents 5.7% of the total Hg composition"
ReplyDelete% L = 10/214 = 4.67%
% M1 and U6a1a = 2/214 = 0.93%