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Population genetics and Markov chains
Published in Henry C. Tuckwell, Elementary Applications of Probability Theory, 2018
In some reproductive processes (e.g. human) the chromosome pairs of the offspring contain one chromosome from each parent. Population genetics concerns itself with the numbers of genes of various types in populations, usually with a view to studying their changes from generation to generation.
Thirty years of conservation genetics in New Zealand: what have we learnt?
Published in Journal of the Royal Society of New Zealand, 2019
A holy grail of population genetics is the determination of the molecular basis of adaptation and fitness differences among individuals. There have been an enormous number of genotype-wide association studies (GWAS) of traits and disease in humans, livestock and crop plants over the last 5 years. Conservation genomic studies on NZ species are well underway, and will become more widespread as high-throughput sequencing becomes even cheaper. These types of studies may allow us to identify and target diversity important to long-term survival. It has recently been argued that progress could best be achieved through teaming up with primary industry to build on knowledge and advances, with mutually beneficial outcomes (Galla et al. 2016). A big limitation here is sample size: GWAS studies typically use 104–105 individuals, yet often fail to explain much of the heritability in a trait, so only genes of major effect are likely to be detectable when the sample size is low.
Approximate Bayesian computation for censored data and its application to reliability assessment
Published in IISE Transactions, 2018
Kristin McCullough, Nader Ebrahimi
ABC was first proposed in population genetics by Tavare et al. (1997). Since then, ABC has become popular in the biological sciences, where it is used for the analysis of complex problems in population genetics, ecology, epidemiology, and systems biology (Beaumont et al., 2002; Beaumont, 2010; Blum, 2010; Csillery et al., 2010). Recently, for example, ABC has been used for the estimation of sub-epidemic dynamics (Ibeh and Aris-Brosou, 2016); horizontal gene transfer in bacteria (Jarvenpaa et al., 2016); and number concentrations of monodisperse nanoparticles in suspension by optical microscopy (Roding et al., 2016). It has been used to model growth dynamics of repeated measurements of tumor volumes in mice (Picchini and Forman, 2017) and the occurrence and size of defects in advanced gas-cooled nuclear reactor boilers (Mason, 2016). For further examples, see Turner and Sederburg (2014), Grazian and Liseo (2015), Jasra (2015), Krishnanathan et al. (2015), and Mitrovic et al. (2016).
A dynamic optimization method for adaptive signal control in a connected vehicle environment
Published in Journal of Intelligent Transportation Systems, 2020
Zhihong Yao, Yangsheng Jiang, Bin Zhao, Xiaoling Luo, Bo Peng
A GA is a stochastic search algorithm based on population genetics and the theory of evolution (Whitley, 1994). It has been widely applied in transportation systems and other areas (Ceylan & Bell, 2004; Lee, Abdulhai, Shalaby, & Chung, 2005; Putha, Quadrifoglio, & Zechman, 2012; Zhang, Shang, & Chen, 2010). In this paper, a GA is applied to solve the optimization model, that is, Equation (29), presented in Section 3.1. The GA includes the following parts: encoding and decoding, selection rule, crossover rule, and mutation rule. The encoding and decoding of the solution should satisfy the constraints of the model. Subsequently, the corresponding operations are described in detail.