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Title: Using rice grain ionomic data to explore methods of genomic-wide association for multiple environments and multiple traits
Author: Ruangareerate, Panthita
ISNI:       0000 0004 7972 5441
Awarding Body: University of Aberdeen
Current Institution: University of Aberdeen
Date of Award: 2019
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Rice (Oryza sativa L.) is an important crop in terms of contribution of calories to the global human diet. Manganese (Mn) and potassium (K) are essential elements for rice plants and commonly contribute to human health via consumption; however, the understanding of the genes controlling natural variation in crop plants is limited. Genome-wide association study (GWAS) is a statistically powerful approach to identify genetic loci and genes related to complex traits. In this study, the integration of different methods of GWAS analysis were used to increase the power of the analysis for identifying valuable quantitative trait loci (QTL) and potential candidate genes responsible for the accumulation of grain Mn and K as well as their correlated traits including iron (Fe), zinc (Zn), cadmium (Cd), rubidium (Rb) and sodium (Na) across 389 diverse rice cultivars grown in Arkansas/Texas under multiple years. Single-trait analysis, which is a common approach, was initially performed to identify significant loci in order to assess the impact of marker density on GWAS. As a result, significant loci could be detected using the high-density SNP dataset. Based on the 5.2M SNP dataset, major QTLs across environments were obviously located on chromosomes 3 and 7 for Mn, and chromosomes 3, 4, 6 and 11 for K. To increase the power of QTL detection, the phenotypic data of grain Mn and K concentrations was combined from three flooded-field experiments using multi-experiment analysis based on the 5.2M SNP dataset. New QTLs were identified that were not found in individual experiments, on chromosomes 2, 8, 9 and 12 for Mn and chromosomes 2, 3, 8 and 9 for K. There were twelve and thirteen potential candidate genes underlying these QTLs related to grain Mn and K concentrations, respectively, indicating complex traits with multiple genes controlling. Multi-trait analysis was conducted for two correlated element sets, Na-K and Mn-Fe-Zn-Cd, in each experiment based on the 5.2M SNP dataset to identify pleiotropic genes. Putative QTLs associated with multiple correlated traits were identified from six chromosomes for K-Na (1, 2, 3, 4, 7 and 8) and five chromosomes for Mn-Fe-Zn-Cd (1, 2, 5, 6 and 7) including eight and nine candidate genes, respectively. This work is the first utilisation of different GWAS analyses based on high SNP density of the rice natural population to identify a large number of QTLs and potential candidate genes associated with the ionomic traits. The novel constitutive QTLs across environments and co-localised QTLs were identified using multipleexperiment and multi-trait analyses, respectively. Hence, both approaches should be advantage to facilitate genomic breeding programs in rice and other crops for considering QTLs and genes associated with single or multiple complex traits in natural populations.
Supervisor: Price, Adam ; Douglas, Alex ; Norton, Gareth Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available
Keywords: Rice ; Crops