New Approaches to Population Genetics in the Light of Load at Many Loci
AuthorMatheson, Joseph Daniel
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PublisherThe University of Arizona.
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AbstractHermann Muller first used the term ‘genetic load’ in 1950 to refer to the burden imposed on populations by deleterious genetic variants, but fears about heritable deleterious traits proliferating have haunted the newborn field of population genetics from the beginning. Classical population genetic theory seemed to suggest that load could build up to dangerous levels in a variety of situations: rapid adaptation, high mutation rates, or sufficiently weak natural selection. Different formulations of load problems have been proposed and then apparently solved, but the solutions have not all stood the test of time. In this dissertation, I will highlight some places where established population genetic theory fails to sufficiently capture the effects of load, especially mildly deleterious load dispersed across many linked loci.I first investigate Haldane’s Dilemma, an implausibly strict speed limit on adaptation proposed by Haldane based on his analysis of the load that builds up during the process of substituting one allele for another. This dilemma was apparently solved by noting that Haldane’s calculations used the wrong type of load, but substantial confusion in the literature on the topic has left a more accurate formulation of the dilemma still unsolved. I apply this formulation to an Arabidopsis dataset tracking seed production, seed survival, and juvenile plant survival across eight different environments. In this dataset, the dilemma poses no real threat because a much higher fraction of deaths contribute to adaptation than had been anticipated by previous estimates. I then investigate the effects of removing deleterious load on neutral variation, a phenomenon known as background selection. The prevailing analytical approach assumes no linkage between deleterious mutations as a simplification. There are theoretical reasons to expect that this assumption will be wrong, and given the sheer quantity of deleterious mutations entering populations, violations of this assumption are likely not trivial. I find that when simulating a genome with realistic degrees of mutation and recombination, background selection has much stronger effects on neutral diversity than the prevailing theory predicts. I last investigate accumulation of small-effect deleterious load, a load problem which Kondrashov (1995) suggests should have killed us ‘one hundred times over’. One standard answer to this problem — synergistic epistasis between deleterious mutations — has been contradicted by empirical evidence. The second answer — beneficial mutations counteracting deleterious load — is promising, but previous theoretical descriptions again ignored the effects of linkage. I built a novel simulation model to investigate accumulating load in the presence of rare beneficial mutations. I find that rare large-effect beneficial mutations can asymmetrically counteract deleterious load. However, whether there are enough beneficial mutations left over for populations to adapt depends on the value of beneficial parameters for which we still lack good empirical estimates. I show in a variety of cases that new approaches which account for the effects of deleterious load across many linked loci are able to contribute new answers to foundational questions in population genetics.
Degree ProgramGraduate College
Ecology & Evolutionary Biology