Molecular dissection of connected rice populations revealed important genomic regions for agronomic and biofortification traits

Breeding staple crops with increased micronutrient concentration is a sustainable approach to address micronutrient malnutrition. We carried out Multi-Cross QTL analysis and Inclusive Composite Interval Mapping for 11 agronomic, yield and biofortification traits using four connected RILs populations of rice. Overall, MC-156 QTLs were detected for agronomic (115) and biofortification (41) traits, which were higher in number but smaller in effects compared to single population analysis. The MC-QTL analysis was able to detect important QTLs viz: qZn5.2, qFe7.1, qGY10.1, qDF7.1, qPH1.1, qNT4.1, qPT4.1, qPL1.2, qTGW5.1, qGL3.1, and qGW6.1, which can be used in rice genomics assisted breeding. A major QTL (qZn5.2) for grain Zn concentration has been detected on chromosome 5 that accounted for 13% of R2. In all, 26 QTL clusters were identified on different chromosomes. qPH6.1 epistatically interacted with qZn5.1 and qGY6.2. Most of QTLs were co-located with functionally related candidate genes indicating the accuracy of QTL mapping. The genomic region of qZn5.2 was co-located with putative genes such as OsZIP5, OsZIP9, and LOC_OS05G40490 that are involved in Zn uptake. These genes included polymorphic functional SNPs, and their promoter regions were enriched with cis-regulatory elements involved in plant growth and development, and biotic and abiotic stress tolerance. Major effect QTL identified for biofortification and agronomic traits can be utilized in breeding for Zn biofortified rice varieties

Genome-Wide Association Mapping in a Rice MAGIC Plus Population Detects QTLs and Genes Useful for Biofortification

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, qDF6.2qDF9.1qPH5.1qGL3.1, 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

Identification of genomic regions associated with agronomic and biofortification traits in DH populations of rice.

Rice provides energy and nutrition to more than half of the world's population. Breeding rice varieties with the increased levels of bioavailable micronutrients is one of the most sustainable approaches to tackle micronutrient malnutrition. So, high zinc and iron content in the grain are primary targets in rice biofortification breeding. In this study, we conducted QTL mapping using doubled haploid (DH) populations, PSBRc82 x Joryeongbyeo and PSBRc82 x IR69428, phenotyped for agronomic traits and micronutrients during two growing seasons and using genotypic information from analysis with the 6K SNP chip. A number of DH lines were identified as having high grain Zn and Fe content in polished rice. Importantly, we identified 20 QTLs for agronomic traits and 59 QTLs for a number of biofortification traits. Of the 79 QTLs, 12 were large-effect QTLs (>25% PVE), nine QTLs were consistent across seasons in either population, and one QTL was identified in both populations. Moreover, at least two QTLs were clustered in defined regions of chromosomes 1, 2, 3, 4, 5, 7 and 9. Eight epistatic interactions were detected for Cu, Mg, Na, and Zn in population 1. Furthermore, we identified several candidate genes near QTLs for grain Zn (OsNRAMP, OsNAS, OsZIP, OsYSL, OsFER, and OsZIFL family) and grain yield (OsSPL14 and OsSPL16). These new QTLs and candidate genes help to further elucidate the genetic basis for grain micronutrient concentration, and may prove useful for marker assisted breeding for this important trait

Genetic Dissection of Grain Nutritional Traits and Leaf Blight Resistance in Rice

Colored rice is rich in nutrition and also a good source of valuable genes/quantitative trait loci (QTL) for nutrition, grain quality, and pest and disease resistance traits for use in rice breeding. Genome-wide association analysis using high-density single nucleotide polymorphism (SNP) is useful in precisely detecting QTLs and genes. We carried out genome-wide association analysis in 152 colored rice accessions, using 22,112 SNPs to map QTLs for nutritional, agronomic, and bacterial leaf blight (BLB) resistance traits. Wide variations and normal frequency distributions were observed for most of the traits except anthocyanin content and BLB resistance. The structural and principal component analysis revealed two subgroups. The linkage disequilibrium (LD) analysis showed 74.3% of the marker pairs in complete LD, with an average LD distance of 1000 kb and, interestingly, 36% of the LD pairs were less than 5 Kb, indicating high recombination in the panel. In total, 57 QTLs were identified for ten traits at p < 0.0001, and the phenotypic variance explained (PVE) by these QTLs varied from 9% to 18%. Interestingly, 30 (53%) QTLs were co-located with known or functionally-related genes. Some of the important candidate genes for grain Zinc (Zn) and BLB resistance were OsHMA9, OsMAPK6, OsNRAMP7, OsMADS13, and OsZFP252, and Xa1, Xa3, xa5, xa13 and xa26, respectively. Red rice genotype, Sayllebon, which is high in both Zn and anthocyanin content, could be a valuable material for a breeding program for nutritious rice. Overall, the QTLs identified in our study can be used for QTL pyramiding as well as genomic selection. Some of the novel QTLs can be further validated by fine mapping and functional characterization. The results show that pigmented rice is a valuable resource for mineral elements and antioxidant compounds; it can also provide novel alleles for disease resistance as well as for yield component traits. Therefore, large opportunities exist to further explore and exploit more colored rice accessions for use in breeding