Skip to main content
eScholarship
Open Access Publications from the University of California

UC Davis

UC Davis Electronic Theses and Dissertations bannerUC Davis

Constraints to Adaptation in Maize: Environmental Trade-offs and Deleterious Alleles

Abstract

Selection can result in populations becoming more adapted, yet even in the presence of selection other effects can constrain adaptation. Two of the effects that can cause this discrepancy are genotype by environment interactions (GxE) and the accumulation of deleterious alleles. GxE is observed when an allele has different effects depending on the environment and can result in the maintenance of genetic variation, particularly when no genotype is best adapted to all environments. Deleterious alleles are generally considered as those which are deleterious across the conditions members of a species might encounter. Although they experience negative selection, they can still contribute a substantial proportion of genetic variance.

In this dissertation, I analyzed GxE and deleterious alleles in maize. First, I investigated GxE in a maize mapping population. We find that GxE contributes a substantial amount to the phenotypic variance for many traits. While we identify loci contributing to GxE, overall most of the GxE variance may be due to unidentified polygenic effects. Estimating the genetic covariances between traits in each environment reveals large differences in the genetic variance-covariance matrix between environments and in particular shows that differences in selection on flowering time may be contributing to the observed GxE for yield. Second, we analyzed the distribution of structural variants in maize inbred lines along the genome. We find that structural variants are more depleted in constrained regions of the genome than single nucleotide polymorphisms, possibly indicating that structural variants are more likely to be deleterious. Finally, we apply a machine learning method to identify constrained regions of the maize genome based on population genetic data. These predictions allow us to assess more recent evolutionary constraint in maize and find regions where mutations are more likely to be deleterious.

Main Content
For improved accessibility of PDF content, download the file to your device.
Current View