@phdthesis{11128,
  abstract     = {Although we often see studies focusing on simple or even discrete traits in studies of colouration,
the variation of “appearance” phenotypes found in nature is often more complex, continuous
and high-dimensional. Therefore, we developed automated methods suitable for large datasets
of genomes and images, striving to account for their complex nature, while minimising human
bias. We used these methods on a dataset of more than 20, 000 plant SNP genomes and
corresponding fower images from a hybrid zone of two subspecies of Antirrhinum majus with
distinctly coloured fowers to improve our understanding of the genetic nature of the fower
colour in our study system.
Firstly, we use the advantage of large numbers of genotyped plants to estimate the haplotypes in
the main fower colour regulating region. We study colour- and geography-related characteristics
of the estimated haplotypes and how they connect to their relatedness. We show discrepancies
from the expected fower colour distributions given the genotype and identify particular
haplotypes leading to unexpected phenotypes. We also confrm a signifcant defcit of the
double recessive recombinant and quite surprisingly, we show that haplotypes of the most
frequent parental type are much less variable than others.
Secondly, we introduce our pipeline capable of processing tens of thousands of full fower
images without human interaction and summarising each image into a set of informative scores.
We show the compatibility of these machine-measured fower colour scores with the previously
used manual scores and study impact of external efect on the resulting scores. Finally, we use
the machine-measured fower colour scores to ft and examine a phenotype cline across the
hybrid zone in Planoles using full fower images as opposed to discrete, manual scores and
compare it with the genotypic cline.},
  author       = {Matejovicova, Lenka},
  isbn         = {978-3-99078-016-9},
  issn         = {2663-337X},
  pages        = {112},
  publisher    = {Institute of Science and Technology Austria},
  title        = {{Genetic basis of flower colour as a model for adaptive evolution}},
  doi          = {10.15479/at:ista:11128},
  year         = {2022},
}