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Inference of Recombination Rate in Iranian Population Genetics

Ansari, Ehsan | 2023

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  1. Type of Document: M.Sc. Thesis
  2. Language: Farsi
  3. Document No: 56515 (19)
  4. University: Sharif University of Technology
  5. Department: Computer Engineering
  6. Advisor(s): Motahari, Abolfazl
  7. Abstract:
  8. Population genetics studies the distribution and changes in allele frequencies under the influence of five main evolutionary processes: natural selection, genetic drift, mutation, gene flow, and recombination. Among these, the recombination process can influence a wide range of biological processes by rearranging genes, repairing DNA structure, and participating actively in cell division mechanisms. Recombination has the ability to create genetic diversity through gene rearrangement, which is the main reason for creating diversity and evolution in organisms. Models such as Hill-Robertson have proven the influential role of recombination in accelerating evolutionary mechanisms. Also, recombination can play an influential role in many genetic abnormalities due to its active participation in cell division mechanisms. The occurrence of recombination, like many other biological processes, is a probabilistic phenomenon. As a result, its frequency has been reported to be different under different conditions and also throughout the genome. The recombination rate parameter is used to measure recombination frequency. Therefore, the recombination rate is the most important parameter that helps understand the recombination process and its effects and influences. This thesis aims to investigate methods for estimating the recombination rate and finding the best method to estimate it in the Iranian population. Methods for estimating the recombination rate are divided into two main categories, direct and analytical, which analytical methods have been investigated in this thesis. In analytical methods, the recombination rate is estimated through statistical analysis of population genetic data and based on patterns of haplotype shifts and allele abnormalities. Given the antiquity and importance of estimating the recombination rate, numerous algorithms have been proposed to estimate the recombination rate, including Bayesian statistical algorithms, maximum likelihood, Markov chain, linear regression, and more recently, machine learning algorithms and deep neural networks. Due to insufficient information about the structural complexities of human populations and the inability to model many civilizational phenomena in population genetics modeling, algorithms for estimating the recombination rate show unstable performance in human population data. In this thesis, by selecting an algorithm based on deep learning and using the transfer learning technique, we tried to overcome the problems that exist in estimating the recombination rate. Also, in the process of using this technique, the Bayesian approximation algorithm was used to constrain the input range and fine-tune the model. The results indicate that the proposed model has shown more stable performance in estimating the recombination rate compared to the base model. Overall, this thesis was an attempt to apply novel machine learning methods to estimate the recombination rate in the Iranian population, and it is hoped that its results can be useful in future studies
  9. Keywords:
  10. Recombination ; Population Genetics ; Evolutionary Model ; Machine Learning ; Transfer Learning ; Recombination Rate Estimation

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