Gareth John Norton

Advances in quantitative genetics have enabled researchers to identify genomic regions associated with changes in phenotype. However, these regions can contain hundreds to thousands of genes, and progressing from genomic regions to causative genes is still challenging. In genome-wide association studies (GWAS) measuring elemental accumulation (ionomic) traits, only 5% of loci contain orthologs of genes known to control the ionome—indicating that many causal genes are still unknown. To identify candidates for the remaining 95% of loci, we developed a method that uses GWAS studies across multiple species to identify conserved causative genes. By Filtering the Results of Multi-species, Analogous, GWAS Experiments (FiReMAGE), we processed the GWAS of 19 ionomic traits in Arabidopsis (Arabidopsis thaliana), soybean (Glycine max), rice (Oryza sativa), maize (Zea mays), and sorghum and identified alleles affecting trait variation at genes conserved across these 5 species. Permutation testing demonstrated that homologous genes were present in loci affecting element accumulation more often than expected. The repeated recovery of homologs at GWAS loci highlights the conservation of ionomic genetic regulators in flowering plants. FiReMAGE identified more candidate genes encoding proteins with known roles in regulating the ionome than expected by chance, validating the approach. These alleles are directly available for improving plant nutrition, food nutritional value, and food safety. FiReMAGE also identified conserved genes with no previously identified function. This provides a path to discover mechanisms of trait variation and conserved genes of previously unknown function via GWAS. The scripts to run FiReMAGE and adapt it to any trait are available on GitHub.

Rice is a globally important crop and is particularly efficient at assimilating arsenic (As). Identifying QTLs and genes associated with grain As is essential for breeding low‐As rice cultivars. In this study, data on As accumulation in grains of Rice Diversity Panel 1 in five field environments at four diverse geographic sites were reanalyzed to compare genome‐wide association (GWA) methods. Two single‐locus (EMMAX for single trait and GEMMA for multi‐experiments) and six multi‐locus (FASTmrEMMA, ISIS EM‐BLASSO, mrMLM, pKWmEB, pLARmEB, and FASTmrMLM) GWA methods were used. A total of 90 and 111 QTLs were detected using EMMAX and GEMMA, respectively. A total of 2, 11, 12, 19, 23, and 25 QTNs were identified by FASTmrEMMA, ISIS EM‐BLASSO, mrMLM, pKWmEB, pLARmEB, and FASTmrMLM, respectively. Among these, 22 QTLs/QTNs were co‐detected by single‐locus and multi‐locus GWAS methods. From these QTLs/QTNs, a total of 10 candidate genes were identified. Analysis of the haplotype variants of one candidate genes, OsABCC1 , and one cluster of the plasma membrane intrinsic proteins genes revealed that a greater than 10% reduction in grain As could be achieved. The QTLs/QTNs and candidate genes identified give insight into the molecular mechanisms regulating As accumulation in rice and serve as breeding targets for developing low grain As rice cultivars.

Resource-poor areas with moisture deficit lands following aerobic and direct seeded rice (DSR) methods of cultivation face severe problems of iron deficiency. In this study, Bengal and Assam Aus rice panel was phenotyped at the seedling stage using an iron-deprived hydroponic medium for various shoot and root traits. A novel iron deficiency scoring scale was used to classify the tolerance reaction and could range anywhere between 0 and 9, indicating the most tolerant and susceptible, respectively. The GWAS results identified four putative candidate genes; OsFLA for number of leaves and shoot length, OsBIDK1 for root traits; average diameter, volume, biomass, projected area, and surface area, OsHPL3 for chlorophyll index of the third leaf and AKR2B (XBOS252) was for Fe score, (which was earlier reported in relation to Xa21). The nsSNP (nsSNPs) variations in these gene sequences were used to group the panel and identify superior haplotypes and donors. BR16 was identified as a superior donor, with higher chlorophyll index and shoot length than RA23, also higher values for root traits like root average diameter, root volume, root projected area and root surface area followed by Shete Bhado. The impact of identified nsSNPs on protein structure and stability was investigated. The conserved domains detected in the mutated proteins of the superior haplotypes are very informative, highlighting that natural selection favors abiotic stress tolerant variants in resource poor areas. Thus, justifying our choice of Aus landraces for association mapping of Fe deficiency tolerant genes in rice.

Rice consumption is a major exposure route of humans to cadmium, and cadmium also causes toxicity to rice plants. In this study the Bengal and Assam Aus Panel (BAAP) of rice was screened in hydroponics for cadmium tolerance and cadmium accumulation. Across the population, there were significant cultivar, treatment, and cultivar by treatment interactions for root and shoot length, and root, shoot and total dry weights. For treatment effects, the mean reductions caused by the cadmium treatment compared to control were 15%, 21%, 32%, 23% and 24% for root length, shoot length, root dry weight, shoot dry weight, and total dry weight respectively. Cultivars in the BAAP population displayed shoot cadmium concentrations in the range of 22–187 mg kg⁻¹, with most of the rice cultivars having a cadmium concentration between 40 and 85 mg kg⁻¹. GWA mapping identified 49 quantitative trait loci (QTL) across all traits, with 13 detected for more than one trait. Thirty-nine QTLs detected in this study were found to co-localise with previously identified QTLs for cadmium traits. A number of candidate genes for cadmium tolerance and accumulation were identified in these QTL regions. The QTL on chromosome 7 between 8.59 and 9.04 Mb contained both the candidate genes OsNRAMP1 and OsNRAMP5. The QTL on chromosome 6 at 29.20–29.73 Mb contained the candidate gene OsHMA2, while the QTL on chromosome 4 at 19.03 – 19.53 Mb contained the candidate gene OsHIPP29. The haplotype variation of these candidate genes is further explored in this study.