WINC

Supervised by Dr. Montinaro (University of Bari + University of Tartu) & Prof. Pagani (University of Padua + University of Tartu)

Why is this project cool?

Contemporary individuals are the combination of genetic fragments inherited from ancestors belonging to multiple populations, as the result of migration and admixture. Isolating and characterizing these layers are crucial to the understanding of the genetic history of a given population. Ancestry deconvolution approaches make use of a large amount of source individuals, therefore constraining the performance of Local Ancestry Inferences when only few genomes are available from a given population. Here we present WINC, a local ancestry framework derived from the combination of ChromoPainter and NNLS approaches, as a method to retrieve local genetic assignments when only a few reference individuals are available. The framework is aided by a score assignment based on source differentiation to maximize the amount of sequences retrieved and is capable of retrieving accurate ancestry assignments when only two individuals for source populations are used.

Schematic representation of WINC approach. WINC is based on the ChromoPainter/NNLS framework, with the additional step of splitting the copying vectors resulting from the ChromoPainter (CP) run before analyzing them through the NNLS step. First step: ChromoPainter run. CP identifies the closest neighbor “donor” for any “recipient” individual haplotype. ChromoPainter then reconstructs the recipient individuals as a combination of genomic segments, or chunks, “donated” by any other individual in the data set. The information is then stored in copying vectors, where, for each recipient haplotype, it is indicated which donor individual is the closest neighbour. In this way, we obtain the copying vectors of our target populations: both the sources and the admixed individuals. Second step: splitting copying vectors. We then split the copying vectors in genomic windows of the same length. Window size depends on the ancestry chunks, which in turn depends on the amount of generations since the admixture. Third step: performing NNLS analyses on the copying vector’s genomic windows obtained from the previous step. The NNLS step assigns a window to a specific ancestry, by reconstructing the painting profile of a given individual as a combination (or proportion) of copying vectors from the source individuals.

Simulated samples and FST between simulated groups

References

2021