Ancient DNA from South-East Europe Reveals Different Events during Early and Middle Neolithic Influencing the European Genetic Heritage

The importance of the process of Neolithization for the genetic make-up of European populations has been hotly debated, with shifting hypotheses from a demic diffusion (DD) to a cultural diffusion (CD) model. In this regard, ancient DNA data from the Balkan Peninsula, which is an important source of information to assess the process of Neolithization in Europe, is however missing. In the present study we show genetic information on ancient populations of the South-East of Europe. We assessed mtDNA from ten sites from the current territory of Romania, spanning a time-period from the Early Neolithic to the Late Bronze Age. mtDNA data from Early Neolithic farmers of the Starcevo Cris culture in Romania (Carcea, Gura Baciului and Negrilesti sites), confirm their genetic relationship with those of the LBK culture (Linienbandkeramik Kultur) in Central Europe, and they show little genetic continuity with modern European populations. On the other hand, populations of the Middle-Late Neolithic (Boian, Zau and Gumelnita cultures), supposedly a second wave of Neolithic migration from Anatolia, had a much stronger effect on the genetic heritage of the European populations. In contrast, we find a smaller contribution of Late Bronze Age migrations to the genetic composition of Europeans. Based on these findings, we propose that permeation of mtDNA lineages from a second wave of Middle-Late Neolithic migration from North-West Anatolia into the Balkan Peninsula and Central Europe represent an important contribution to the genetic shift between Early and Late Neolithic populations in Europe, and consequently to the genetic make-up of modern European populations.

Preprocess and data analysis techniques for affymetrix DNA microarrays using bioconductor: a case study in Alzheimer disease

DNA microarray, or DNA chip, is a technology that allows us to obtain the expression level of many genes in a single experiment. The fact that numerical expression values can be easily obtained gives us the possibility to use multiple statistical techniques of data analysis. In this project microarray data is obtained from Gene Expression Omnibus, the repository of National Center for Biotechnology Information (NCBI). Then, the noise is removed and data is normalized, also we use hypothesis tests to find the most relevant genes that may be involved in a disease and use machine learning methods like KNN, Random Forest or Kmeans. For performing the analysis we use Bioconductor, packages in R for the analysis of biological data, and we conduct a case study in Alzheimer disease. The complete code can be found in https://github.com/alberto-poncelas/ bioc-alzheimer

Ancient DNA from Hunter-Gatherer and Farmer Groups from Northern Spain Supports a Random Dispersion Model for the Neolithic Expansion into Europe

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