Biology subjects

Vizzari, M. T.

Publications and source records attributed to Vizzari, M. T..

4 recordsLinked to original sources

bioRxiv2025

Inference of human pigmentation from ancient DNA by genotype likelihood

Light eyes, hair and skins probably evolved several times as Homo sapiens dispersed from Africa. In areas with lower UV radiation, light pigmentation alleles increased in frequency because of their adaptive advantage and of other contingent factors such as migration and drift. However, the tempo and mode of their spread is not known. Phenotypic inference from ancient DNA is complicated, both because these traits are polygenic, and because of low sequence depth. We evaluated the effects of the latter by randomly removing reads in two high-coverage ancient samples, the Paleolithic Ust-Ishim from Russia and the Mesolithic SF12 from Sweden. We could thus compare three approaches to pigmentation inference, concluding that, for suboptimal levels of coverage (<8x), a probabilistic method estimating genotype likelihoods leads to the most robust predictions. We then applied that protocol to 348 ancient genomes from Eurasia, describing how skin, eye and hair color evolved over the past 45,000 years. The shift towards lighter pigmentations turned out to be all but linear in time and place, and slower than expected, with half of the individuals showing dark or intermediate skin colors well into the Copper and Iron ages. We also observed a peak of light eye pigmentation in Mesolithic times, and an accelerated change during the spread of Neolithic farmers over Western Eurasia, although localized processes of gene flow and admixture, or lack thereof, also played a significant role.

genomics↗

bioRxiv2024

Low-ABC: a robust demographic Inference from low-coverage whole-genome data through ABC

The reconstruction of past demographic histories relies on the pattern of genetic variation shown by the sampled populations; this means that an accurate estimation of genotypes is crucial for a reliable inference of past processes. A commonly adopted approach to reconstruct complex demographic dynamics is the Approximate Bayesian Computation (ABC) framework. It exploits coalescent simulations to generate the expected level of variation under different evolutionary scenarios. Demographic inference is then performed by comparing the simulated data with the genotypes called in the sampled individuals. Low sequencing coverage drastically affects the ability to reliably call genotypes, thus making low-coverage data unsuitable for such powerful inferential approaches. Here, we present Low-ABC, a new ABC approach to infer past population processes using low-coverage whole-genome data. Under this framework, both observed and simulated genetic variation are not directly compared using called genotypes, but rather obtained using genotype likelihoods to consider the uncertainty caused by the low sequencing coverage. We first evaluated the inferential power of this procedure in distinguishing among different demographic models and in inferring model parameters under different experimental conditions, including a wide spectrum of sequencing coverage (1x to 30x), number of individuals, number, and size of genetic loci. We showed that the use of genotype likelihoods integrated into an ABC framework provides a reliable inference of past population dynamics, thus making possible the application of model-based inference also for low-coverage data. We then applied Low-ABC to shed light on the relationship between Mesolithic and Early Neolithic European populations.

genomics↗

bioRxiv2021

Climate and mountains shaped human ancestral genetic lineages

Extensive sequencing of modern and ancient human genomes has revealed that contemporary populations can be explained as the result of recent mixing of a few distinct ancestral genetic lineages1. But the small number of aDNA samples that predate the Last Glacial Maximum means that the origins of these lineages are not well understood. Here, we circumvent the limited sampling by modelling explicitly the effect of climatic changes and terrain on population demography and migrations through time and space, and show that these factors are sufficient to explain the divergence among ancestral lineages. Our reconstructions show that the sharp separation between African and Eurasian lineages is a consequence of only a few limited periods of connectivity through the arid Arabian peninsula, which acted as the gate out of the Arican continent. The subsequent spread across Eurasia was then mostly shaped by mountain ranges, and to a lesser extent deserts, leading to the split of European and Asians, and the further diversification of these two groups. A high tolerance to cold climates allowed the persistence at high latitudes even during the Last Glacial Maximum, maintaining a pocket in Beringia that led to the later, rapid colonisation of the Americas. The advent of food production was associated with an increase in movement2, but mountains and climate have been shown to still play a major role even in this latter period3,4, affecting the mixing of the ancestral lineages that we have shown to be shaped by those two factors in the first place.

genomics↗

bioRxiv2018

A new Approximate Bayesian Computation framework to distinguish among complex evolutionary models using whole-genome data

Inferring past demographic histories is crucial in population genetics, and the amount of complete genomes now available should in principle facilitate this inference. In practice, however, the available inferential methods suffer from severe limitations. Although hundreds complete genomes can be simultaneously analyzed, complex demographic processes can easily exceed computational constraints, and the procedures to evaluate the reliability of the estimates contribute to increase the computational effort. Here we present an Approximate Bayesian Computation (ABC) framework, based on the Random Forest algorithm, to infer complex past population processes using complete genomes. To do this, we propose to summarize the data by the full genomic distribution of the four mutually exclusive categories of segregating sites (FDSS), a statistic fast to compute from unphased genome data. We constructed an efficient ABC pipeline and tested how accurately it allows one to recognize the true model among models of increasing complexity, using simulated data and taking into account different sampling strategies in terms of number of individuals analyzed, number and size of the genetic loci considered. We tested the power of the FDSS to be informative about even complex evolutionary histories and compared the results with those obtained summarizing the data through the unfolded Site Frequency Spectrum, thus highlighting for both statistics the experimental conditions maximizing the inferential power. Finally, we analyzed two datasets, testing models (a) on the dispersal of anatomically modern humans out of Africa and (b) the evolutionary relationships of the three species of Orangutan inhabiting Borneo and Sumatra.

genetics↗