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Inferring the Demographic History of Iranian Populations from Whole Exome Sequencing Data

Heidari, Jalal | 2022

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  1. Type of Document: M.Sc. Thesis
  2. Language: Farsi
  3. Document No: 55740 (19)
  4. University: Sharif University of Technology
  5. Department: Computer Engineering
  6. Advisor(s): Motahari, Abolfazl; Khalaj, Babak
  7. Abstract:
  8. One of the cornerstones of population genetics is finding ancestral relations between people of a population. Mutations and recombinations are two major signals that can be exploited to infer the population ancestral relationships. Next Generation Sequencing (NGS) has paved the way for achieving this endeavor by providing massive data including single nucleotide polymorphisms, insertions, and deletions as well as structural variations between individuals. In this thesis, the goal is to infer the ancestral structure of a given gene from thousands of NGS datasets. We also attempt to decode times of important events including mutations and recombinations. To this end, we first split a gene into mosaics of continuous segments where recombination has not occurred within the population or a few samples showing signals of recombination. Within each mosaic, an ancestral tree can be reconstructed as no recombination is introduced within the region. The inferred trees are used to construct a panel of ancestral haplotype sequences. In the final stage, to discover the Ancestral Recombination Graph (ARG) a methodology similar to tsinfer is used. In fact, our strategy is a way to improve the method presented in tsinfer. Through simulated data which are fine tuned to represent human populations in the best way, we show that our strategy outperforms that of tsinfer. The evaluation is based on Kendal-Coljin distance which measures the difference between two trees.We also used the whole-exome sequencing data related to the X chromosome of Iranian samples, which are around 1000 samples. We obtained a coalescent tree for each gene of Iranian populations.
  9. Keywords:
  10. Demography ; Population Genetics ; Whole Exome Sequencing ; Coalescent Tree ; Next Generation of Sequencing (NGS)

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