Exploiting genetic information to understand and manage natural populations.

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Copyright: O'Reilly, Gabe
Abstract
Population genetics is a continuously advancing field of study. Population wide datasets, often with large numbers of genetic loci typed, are becoming increasingly more common due to advances in sequencing technologies making large scale data collection economically feasible. While large investment accelerates practical data collection, theoretical methods to make use of this growing pool of data should also be developed. The aim of this thesis is to develop several such methods, for use on population wide datasets, each to answer different questions a researcher might have about their study populations. Each chapter broadens the scope of analysis, with the second chapter investigating variation between the two genomes within each individual at one time in a single population, Chapter 3 will investigate variation of one population over generations, and Chapter 4 will look at variation between potentially subdivided populations over generations. In Chapter 2 I develop an analogue to traditional inbreeding measures, that works with data from multi-locus gene families typed by next generation sequencing. This type of data is currently not amenable to traditional inbreeding measurements. In Chapter 3, I developed predictive equations to Shannon’s Information (a measure of genetic diversity) to see if they could accurately predict how Shannon’s Information declines over time in a population, because a decline in genetic diversity is often linked with a decline in fitness of individuals in a population. In Chapter 4, I develop a method to detect whether a potential split event has actually led to genetic subdivision, preferably as early as possible, and possibly without genetic data from before the event. These processes analysed in these three chapters (inbreeding, genetic drift, and subdivision) are all often deleterious to populations by lowering the fitness of individuals in that population. The deleterious nature of these processes makes them of great interest for study, giving great utility to the methods proposed in this thesis.
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Publication Year
2020
Resource Type
Thesis
Degree Type
PhD Doctorate
UNSW Faculty