Ensuring food security in a world with an increasing population and demand on natural resources is becoming ever more pertinent. Plant breeders are using an increasingly diverse range of data types such as phenotypic and genotypic data to identify plant lines with desirable characteristics suitable to be taken forward in plant breeding programmes. These characteristics include a number of key morphological and physiological traits, such as disease resistance and yield that need to be maintained and improved upon if a commercial plant variety is to be successful. The ability to predict and understand the inheritance of alleles that facilitate resistance to pathogens or any other commercially important characteristic is crucially important to experimental plant genetics and commercial plant breeding programmes. However, derivation of the inheritance of such traits by traditional molecular techniques is expensive and time consuming, even with recent developments in high-throughput technologies. This is especially true in industrial settings where, due to time constraints relating to growing seasons, many thousands of plant lines may need to be screened quickly, efficiently and economically every year. Thus, computational tools that provide the ability to integrate and visualize diverse data types with an associated plant pedigree structure will enable breeders to make more informed and subsequently better decisions on the plant lines that are used in crossings. This will help meet both the demands for increased yield and production and adaptation to climate change. Traditional family tree style layouts are commonly used and simple to understand but are unsuitable for the data densities that are now commonplace in large breeding programmes. The size and complexity of plant pedigrees means that there is a cognitive limitation in conceptualising large plant pedigree structures, therefore novel techniques and tools are required by geneticists and plant breeders to improve pedigree comprehension. Taking a user-centred, iterative approach to design, a pedigree visualization system was developed for exploring a large and unique set of experimental barley (H. vulgare) data. This work progressed from the development of a static pedigree visualization to interactive prototypes and finally the Helium pedigree visualization software. At each stage of the development process, user feedback in the form of informal and more structured user evaluation from domain experts guided the development lifecycle with users' concerns addressed and additional functionality added. Plant pedigrees are very different to those from humans and farmed animals and consequently the development of the pedigree visualizations described in this work focussed on implementing currently accepted techniques used in pedigree visualization and adapting them to meet the specific demands of plant pedigrees. Helium includes techniques to aid problems with user understanding identified through user testing; examples of these include difficulties where crosses between varieties are situated in different regions of the pedigree layout. There are good biological reasons why this happens but it has been shown, through testing, that it leads to problems with users' comprehension of the relatedness of individuals in the pedigree. The inclusion of visual cues and the use of localised layouts have allowed complications like these to be reduced. Other examples include the use of sizing of nodes to show the frequency of usage of specific plant lines which have been shown to act as positional reference points to users, and subsequently bringing a secondary level of structure to the pedigree layout. The use of these novel techniques has allowed the classification of three main types of plant line, which have been coined: principal, flanking and terminal plant lines. This technique has also shown visually the most frequently used plant lines, which while previously known in text records, were never quantified. Helium's main contributions are two-fold. Firstly it has applied visualization techniques used in traditional pedigrees and applied them to the domain of plant pedigrees; this has addressed problems with handling large experimental plant pedigrees. The scale, complexity and diversity of data and the number of plant lines that Helium can handle exceed other currently available plant pedigree visualization tools. These techniques (including layout, phenotypic and genotypic encoding) have been improved to deal with the differences that exist between human/mammalian pedigrees which take account of problems such as the complexity of crosses and routine inbreeding. Secondly, the verification of the effectiveness of the visualizations has been demonstrated by performing user testing on a group of 28 domain experts. The improvements have advanced both user understanding of pedigrees and allowed a much greater density and scale of data to be visualized. User testing has shown that the implementation and extensions to visualization techniques has improved user comprehension of plant pedigrees when asked to perform real-life tasks with barley datasets. Results have shown an increase in correct responses between the prototype interface and Helium. A SUS analysis has sown a high acceptance rate for Helium.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:725114 |
| Date | January 2016 |
| Creators | Shaw, Paul David |
| Contributors | Kennedy, Jessie |
| Publisher | Edinburgh Napier University |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Source | http://researchrepository.napier.ac.uk/Output/463271 |
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