Abstract
This thesis consists on three chapters that investigate the genetic past of Iberia using modern and ancient DNA.The first part offers a snapshot of the current mitochondrial diversity in the Iberian peninsula based on a newly generated dataset with over one thousand fully sequenced mitochondrial genomes. The genetic depth and resolution of this dataset allowed to date the arrival of the vast majority of mitochondrial lineages to Iberia at the time of the Neolithic. It also made possible to describe patterns in some lineages, like U6, that were shaped by Medieval and later population movements which were not considered significant until now.
The second part explores the evolution and transformations of the population in the east of Iberia from the late Neolithic to the Middle Ages through the genomes of twenty ancient individuals sequenced to varying depths. The prehistoric individuals indicate little genomic contribution from local Iberian hunter-gatherers by the end of the Neolithic and beginning of the Copper Age. I also found evidence for two important admixture events whose genetic legacy has been lost. The first event is evidence of genomic influx of ancestry from North African and eastern Mediterranean sources into the local late Roman population. The second admixture event I detected is heavy and widespread admixture with North African migrants that settled in the region during the Islamic period.
The last part focuses on how ancient genomes can be interrogated in a comprehensive way using modern machine learning techniques to better understand what ancient individuals looked like. For this purpose I developed an alternative method using pseudo-phenotypic data to recreate, in an comprehensive way, a polygenic trait: skin pigmentation. My results confirmed that indigenous European hunter-gatherers had darker skin tones than later European populations while also explaining the different polygenic base of similar phenotypes.
| Date of Award | 17 Dec 2020 |
|---|---|
| Original language | English |
| Supervisor | Martin Richards (Main Supervisor), Maria Pala (Co-Supervisor) & Ceiridwen Edwards (Co-Supervisor) |