Genetic Networks, Adaptation, and the Evolution of Genomic Islands of Divergence Thesis uri icon



  • Thesis (Ph.D., Bioinformatics & Computational Biology) -- University of Idaho, 2016 | Genetics has made great strides in identifying specific genes that ašect traits of biomedical and basic biological importance, and modern genomic technology has greatly increased our power to detect even genes with small ešects on phenotype (Yang et al., 2010). However, genes underlying important phenotypes don’t exist in isolation; rather, they interact in two important ways that are now amenable to direct observation with genomic techniques. First, genes interact within genetic regulatory networks to produce complex quantitative phenotypes. For example, in many gene regulatory cascades a given gene product may interact with a number of other genes and proteins. e incorporation of a network-level functional view of genetic interactions into models of multivariate phenotypic evolution represents a new synthesis in biology, enabled by the wealth of empirical genomic data (Zhu et al., 2009; O’Malley, 2012). By modeling relative simple gene regulatory networks, I found that the direction of new phenotypic (co)variation that is supplied to a population from new mutation (the M-matrix) depends on a given network topology. Such mutational (co)variation directly contributes to the shape and orientation of additive genetic (co)variation (the G-matrix) which ašects how quickly populations can adapt to a new environment. When letting the network topology itself evolve, I found that populations can quickly explore phenotype space and, as such, can get closer to new phenotypic optima than without mutations in the network topology. Moreover, the adaptive trajectories taken later during the adaptive walk directly depend on historical contingencies (i.e., which networks were selected for in the past). Lastly, when network topology evolves, reproductive isolation can evolve too as a result of persistent overdominance. Second, genes exist in physical locations along chromosomes so that the action of evolutionary forces like mutation and selection on single loci has impacts on patterns of variation at neighboring loci. Meiotic recombination is central in connecting physical genetic elements to population genetic theory as well as quantitative trait loci. As such, I have created an R package that uses a Hidden Markov Model (HMM) approach to identify recombination hotspots, coldspots, crossovers and non-crossover gene conversion tracts from low-coverage whole genome or reduced representation (e.g., RADseq) data. is approach is applicable for any haploid or diploid organisms with a reference genome.

publication date

  • June 1, 2016