---
_id: '11128'
abstract:
- lang: eng
  text: "Although we often see studies focusing on simple or even discrete traits
    in studies of colouration,\r\nthe variation of “appearance” phenotypes found in
    nature is often more complex, continuous\r\nand high-dimensional. Therefore, we
    developed automated methods suitable for large datasets\r\nof genomes and images,
    striving to account for their complex nature, while minimising human\r\nbias.
    We used these methods on a dataset of more than 20, 000 plant SNP genomes and\r\ncorresponding
    fower images from a hybrid zone of two subspecies of Antirrhinum majus with\r\ndistinctly
    coloured fowers to improve our understanding of the genetic nature of the fower\r\ncolour
    in our study system.\r\nFirstly, we use the advantage of large numbers of genotyped
    plants to estimate the haplotypes in\r\nthe main fower colour regulating region.
    We study colour- and geography-related characteristics\r\nof the estimated haplotypes
    and how they connect to their relatedness. We show discrepancies\r\nfrom the expected
    fower colour distributions given the genotype and identify particular\r\nhaplotypes
    leading to unexpected phenotypes. We also confrm a signifcant defcit of the\r\ndouble
    recessive recombinant and quite surprisingly, we show that haplotypes of the most\r\nfrequent
    parental type are much less variable than others.\r\nSecondly, we introduce our
    pipeline capable of processing tens of thousands of full fower\r\nimages without
    human interaction and summarising each image into a set of informative scores.\r\nWe
    show the compatibility of these machine-measured fower colour scores with the
    previously\r\nused manual scores and study impact of external efect on the resulting
    scores. Finally, we use\r\nthe machine-measured fower colour scores to ft and
    examine a phenotype cline across the\r\nhybrid zone in Planoles using full fower
    images as opposed to discrete, manual scores and\r\ncompare it with the genotypic
    cline."
acknowledged_ssus:
- _id: ScienComp
- _id: Bio
alternative_title:
- ISTA Thesis
article_processing_charge: No
author:
- first_name: Lenka
  full_name: Matejovicova, Lenka
  id: 2DFDEC72-F248-11E8-B48F-1D18A9856A87
  last_name: Matejovicova
citation:
  ama: Matejovicova L. Genetic basis of flower colour as a model for adaptive evolution.
    2022. doi:<a href="https://doi.org/10.15479/at:ista:11128">10.15479/at:ista:11128</a>
  apa: Matejovicova, L. (2022). <i>Genetic basis of flower colour as a model for adaptive
    evolution</i>. Institute of Science and Technology Austria. <a href="https://doi.org/10.15479/at:ista:11128">https://doi.org/10.15479/at:ista:11128</a>
  chicago: Matejovicova, Lenka. “Genetic Basis of Flower Colour as a Model for Adaptive
    Evolution.” Institute of Science and Technology Austria, 2022. <a href="https://doi.org/10.15479/at:ista:11128">https://doi.org/10.15479/at:ista:11128</a>.
  ieee: L. Matejovicova, “Genetic basis of flower colour as a model for adaptive evolution,”
    Institute of Science and Technology Austria, 2022.
  ista: Matejovicova L. 2022. Genetic basis of flower colour as a model for adaptive
    evolution. Institute of Science and Technology Austria.
  mla: Matejovicova, Lenka. <i>Genetic Basis of Flower Colour as a Model for Adaptive
    Evolution</i>. Institute of Science and Technology Austria, 2022, doi:<a href="https://doi.org/10.15479/at:ista:11128">10.15479/at:ista:11128</a>.
  short: L. Matejovicova, Genetic Basis of Flower Colour as a Model for Adaptive Evolution,
    Institute of Science and Technology Austria, 2022.
date_created: 2022-04-07T08:19:54Z
date_published: 2022-04-06T00:00:00Z
date_updated: 2023-06-23T06:26:41Z
day: '06'
ddc:
- '576'
- '582'
degree_awarded: PhD
department:
- _id: GradSch
- _id: NiBa
doi: 10.15479/at:ista:11128
file:
- access_level: open_access
  checksum: e9609bc4e8f8e20146fc1125fd4f1bf7
  content_type: application/pdf
  creator: cchlebak
  date_created: 2022-04-07T08:11:34Z
  date_updated: 2022-04-07T08:11:34Z
  file_id: '11129'
  file_name: LenkaPhD_Official_PDFA.pdf
  file_size: 11906472
  relation: main_file
- access_level: closed
  checksum: 99d67040432fd07a225643a212ee8588
  content_type: application/x-zip-compressed
  creator: cchlebak
  date_created: 2022-04-07T08:11:51Z
  date_updated: 2022-04-07T08:11:51Z
  file_id: '11130'
  file_name: LenkaPhD Official_source.zip
  file_size: 23036766
  relation: source_file
file_date_updated: 2022-04-07T08:11:51Z
has_accepted_license: '1'
language:
- iso: eng
month: '04'
oa: 1
oa_version: Published Version
page: '112'
publication_identifier:
  isbn:
  - 978-3-99078-016-9
  issn:
  - 2663-337X
publication_status: published
publisher: Institute of Science and Technology Austria
status: public
supervisor:
- first_name: Nicholas H
  full_name: Barton, Nicholas H
  id: 4880FE40-F248-11E8-B48F-1D18A9856A87
  last_name: Barton
  orcid: 0000-0002-8548-5240
title: Genetic basis of flower colour as a model for adaptive evolution
tmp:
  image: /images/cc_by.png
  legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode
  name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
  short: CC BY (4.0)
type: dissertation
user_id: 8b945eb4-e2f2-11eb-945a-df72226e66a9
year: '2022'
...