{"abstract":[{"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.","lang":"eng"}],"alternative_title":["ISTA Thesis"],"department":[{"_id":"GradSch"},{"_id":"NiBa"}],"citation":{"ista":"Matejovicova L. 2022. Genetic basis of flower colour as a model for adaptive evolution. Institute of Science and Technology Austria.","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>","short":"L. Matejovicova, Genetic Basis of Flower Colour as a Model for Adaptive Evolution, Institute of Science and Technology Austria, 2022.","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>.","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>.","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>","ieee":"L. Matejovicova, “Genetic basis of flower colour as a model for adaptive evolution,” Institute of Science and Technology Austria, 2022."},"author":[{"id":"2DFDEC72-F248-11E8-B48F-1D18A9856A87","first_name":"Lenka","last_name":"Matejovicova","full_name":"Matejovicova, Lenka"}],"degree_awarded":"PhD","tmp":{"legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","short":"CC BY (4.0)","image":"/images/cc_by.png","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)"},"ddc":["576","582"],"title":"Genetic basis of flower colour as a model for adaptive evolution","status":"public","acknowledged_ssus":[{"_id":"ScienComp"},{"_id":"Bio"}],"has_accepted_license":"1","user_id":"8b945eb4-e2f2-11eb-945a-df72226e66a9","article_processing_charge":"No","file_date_updated":"2022-04-07T08:11:51Z","date_published":"2022-04-06T00:00:00Z","oa_version":"Published Version","date_created":"2022-04-07T08:19:54Z","month":"04","publication_identifier":{"isbn":["978-3-99078-016-9"],"issn":["2663-337X"]},"type":"dissertation","supervisor":[{"last_name":"Barton","full_name":"Barton, Nicholas H","orcid":"0000-0002-8548-5240","id":"4880FE40-F248-11E8-B48F-1D18A9856A87","first_name":"Nicholas H"}],"_id":"11128","date_updated":"2023-06-23T06:26:41Z","file":[{"relation":"main_file","file_id":"11129","access_level":"open_access","file_name":"LenkaPhD_Official_PDFA.pdf","creator":"cchlebak","checksum":"e9609bc4e8f8e20146fc1125fd4f1bf7","date_updated":"2022-04-07T08:11:34Z","date_created":"2022-04-07T08:11:34Z","file_size":11906472,"content_type":"application/pdf"},{"file_id":"11130","relation":"source_file","creator":"cchlebak","access_level":"closed","file_name":"LenkaPhD Official_source.zip","date_updated":"2022-04-07T08:11:51Z","checksum":"99d67040432fd07a225643a212ee8588","content_type":"application/x-zip-compressed","file_size":23036766,"date_created":"2022-04-07T08:11:51Z"}],"page":"112","doi":"10.15479/at:ista:11128","publication_status":"published","license":"https://creativecommons.org/licenses/by/4.0/","oa":1,"language":[{"iso":"eng"}],"day":"06","year":"2022","publisher":"Institute of Science and Technology Austria"}