Genome-wide association mapping in a rice MAGIC Plus population detects QTLs and genes useful for biofortification - Descalsota, Gwen Iris L.; Swamy, B.P. Mallikarjuna; Zaw, Hein; Inabangan-Asilo, Mary Ann; Amparado, Amery; Mauleon, Ramil; Chadha-Mohanty, Prabhjit; Arocena, Emily C.; Raghavan, Chitra; Leung, Hei; Hernandez, Jose E.; Lalusin, Antonio B.; et al. (Frontiers Media, 2018)
The development of rice genotypes with micronutrient-dense grains and disease resistance is one of the major priorities in rice improvement programs. We conducted Genome-wide association studies (GWAS) using a Multi-parent Advanced Generation Inter-Cross (MAGIC) Plus population to identify QTLs and SNP markers that could potentially be integrated in biofortification and disease resistance breeding. We evaluated 144 MAGIC Plus lines for agronomic and biofortification traits over two locations for two seasons, while disease resistance was screened for one season in the screen house. X-ray fluorescence technology was used to measure grain Fe and Zn concentrations. Genotyping was carried out by genotype by sequencing and a total of 14,242 SNP markers were used in the association analysis. We used Mixed linear model (MLM) with kinship and detected 57 significant genomic regions with a -log10 (P- value) ≥3.0. The PH1.1 and Zn7.1 were consistently identified in all the four environments, ten QTLs qDF3.1, qDF6.2 qDF9.1 qPH5.1 qGL3.1, qGW3.1, qGW11.1 and qZn6.2 were detected in two environments, while two major loci qBLB11.1 and qBLB5.1 were identified for Bacterial Leaf Blight (BLB) resistance. The associated SNP markers were found to co-locate with known major genes and QTLs such as OsMADS50 for days to flowering, osGA20ox2 for plant height, and GS3 for grain length. Similarly, Xa4 and Xa5 genes were identified for BLB resistance and Pi5(t), Pi28(t) and Pi30(t) genes were identified for Blast resistance. A number of metal homeostasis genes OsMTP6, OsNAS3, OsMT2D, OsVIT1 and OsNRAMP7 were co-located with QTLs for Fe and Zn. The marker-trait relationships from Bayesian network analysis showed consistency with the results of GWAS. A number of promising candidate genes reported in our study can be further validated. We identified several QTLs/genes pyramided lines with high grain Zn and acceptable yield potential, which are a good resource for further evaluation to release as varieties as well as for use in breeding programs