A high-performance computational workflow to accelerate GATK SNP detection across a 25-genome dataset.

BACKGROUND: Single-nucleotide polymorphisms (SNPs) are the most widely used form of molecular genetic variation studies. As reference genomes and resequencing data sets expand exponentially, tools must be in place to call SNPs at a similar pace. The genome analysis toolkit (GATK) is one of the most widely used SNP calling software tools publicly available, but unfortunately, high-performance computing versions of this tool have yet to become widely available and affordable. RESULTS: Here we report an open-source high-performance computing genome variant calling workflow (HPC-GVCW) for GATK that can run on multiple computing platforms from supercomputers to desktop machines. We benchmarked HPC-GVCW on multiple crop species for performance and accuracy with comparable results with previously published reports (using GATK alone). Finally, we used HPC-GVCW in production mode to call SNPs on a "subpopulation aware" 16-genome rice reference panel with ~ 3000 resequenced rice accessions. The entire process took ~ 16 weeks and resulted in the identification of an average of 27.3 M SNPs/genome and the discovery of ~ 2.3 million novel SNPs that were not present in the flagship reference genome for rice (i.e., IRGSP RefSeq). CONCLUSIONS: This study developed an open-source pipeline (HPC-GVCW) to run GATK on HPC platforms, which significantly improved the speed at which SNPs can be called. The workflow is widely applicable as demonstrated successfully for four major crop species with genomes ranging in size from 400 Mb to 2.4 Gb. Using HPC-GVCW in production mode to call SNPs on a 25 multi-crop-reference genome data set produced over 1.1 billion SNPs that were publicly released for functional and breeding studies. For rice, many novel SNPs were identified and were found to reside within genes and open chromatin regions that are predicted to have functional consequences. Combined, our results demonstrate the usefulness of combining a high-performance SNP calling architecture solution with a subpopulation-aware reference genome panel for rapid SNP discovery and public deployment.

HASCH - A high-throughput amplicon-based SNP-platform for medicinal cannabis and industrial hemp genotyping applications.

BACKGROUND: Cannabis sativa is seeing a global resurgence as a food, fiber and medicinal crop for industrial hemp and medicinal Cannabis industries respectively. However, a widespread moratorium on the use and research of C. sativa throughout most of the 20th century has seen the development of improved cultivars for specific end uses lag behind that of conventional crops. While C. sativa research and development has seen significant investments in the recent past, resulting in a suite of publicly available genomic resources and tools, a versatile and cost-effective mid-density genotyping platform for applied purposes in breeding and pre-breeding is lacking. Here we report on a first mid-density fixed-target SNP platform for C. sativa. RESULTS: The High-throughput Amplicon-based SNP-platform for medicinal Cannabis and industrial Hemp (HASCH) was designed using a combination of filtering and Integer Linear Programming on publicly available whole-genome sequencing and RNA sequencing data, supplemented with in-house generated genotyping-by-sequencing (GBS) data. HASCH contains 1,504 genome-wide targets of high call rate (97% mean) and even distribution across the genome, designed to be highly informative (> 0.3 minor allele frequency) across both medicinal cannabis and industrial hemp gene pools. Average numbers of mismatch SNP between any two accessions were 251 for medicinal cannabis (N = 116) and 272 for industrial hemp (N = 87). Comparing HASCH data with corresponding GBS data on a collection of diverse C. sativa accessions demonstrated high concordance and resulted in comparable phylogenies and genetic distance matrices. Using HASCH on a segregating F2 population derived from a cross between a tetrahydrocannabinol (THC)-dominant and a cannabidiol (CBD)-dominant accession resulted in a genetic map consisting of 310 markers, comprising 10 linkage groups and a total size of 582.7 cM. Quantitative Trait Locus (QTL) mapping identified a major QTL for CBD content on chromosome 7, consistent with previous findings. CONCLUSION: HASCH constitutes a versatile, easy to use and cost-effective genotyping solution for the rapidly growing Cannabis research community. It provides consistent genetic fingerprints of 1504 SNPs with wide applicability genetic resource management, quantitative genetics and breeding.

Genomic variation in 3,010 diverse accessions of Asian cultivated rice.

Here we analyse genetic variation, population structure and diversity among 3,010 diverse Asian cultivated rice (Oryza sativa L.) genomes from the 3,000 Rice Genomes Project. Our results are consistent with the five major groups previously recognized, but also suggest several unreported subpopulations that correlate with geographic location. We identified 29 million single nucleotide polymorphisms, 2.4 million small indels and over 90,000 structural variations that contribute to within- and between-population variation. Using pan-genome analyses, we identified more than 10,000 novel full-length protein-coding genes and a high number of presence-absence variations. The complex patterns of introgression observed in domestication genes are consistent with multiple independent rice domestication events. The public availability of data from the 3,000 Rice Genomes Project provides a resource for rice genomics research and breeding.

A terpene synthase supergene locus determines chemotype in Melaleuca alternifolia (tea tree).

Leaf oil terpenes vary categorically in many plant populations, leading to discrete phenotypes of adaptive and economic significance, but for most species, a genetic explanation for the concerted fluctuation in terpene chemistry remains unresolved. To uncover the genetic architecture underlying multi-component terpene chemotypes in Melaleuca alternifolia (tea tree), a genome-wide association study was undertaken for 148 individuals representing all six recognised chemotypes. A number of single nucleotide polymorphisms in a genomic region of c. 400 kb explained large proportions of the variation in key monoterpenes of tea tree oil. The region contained a cluster of 10 monoterpene synthase genes, including four genes predicted to encode synthases for 1,8-cineole, terpinolene, and the terpinen-4-ol precursor, sabinene hydrate. Chemotype-dependent null alleles at some sites suggested structural variants within this gene cluster, providing a possible basis for linkage disequilibrium in this region. Genotyping in a separate domesticated population revealed that all alleles surrounding this gene cluster were fixed after artificial selection for a single chemotype. These observations indicate that a supergene accounts for chemotypes in M. alternifolia. A genetic model with three haplotypes, encompassing the four characterised monoterpene synthase genes, explained the six terpene chemotypes, and was consistent with available biparental cross-segregation data.

Structural variants in 3000 rice genomes.

Investigation of large structural variants (SVs) is a challenging yet important task in understanding trait differences in highly repetitive genomes. Combining different bioinformatic approaches for SV detection, we analyzed whole-genome sequencing data from 3000 rice genomes and identified 63 million individual SV calls that grouped into 1.5 million allelic variants. We found enrichment of long SVs in promoters and an excess of shorter variants in 5' UTRs. Across the rice genomes, we identified regions of high SV frequency enriched in stress response genes. We demonstrated how SVs may help in finding causative variants in genome-wide association analysis. These new insights into rice genome biology are valuable for understanding the effects SVs have on gene function, with the prospect of identifying novel agronomically important alleles that can be utilized to improve cultivated rice.

Progress in single-access information systems for wheat and rice crop improvement.

Improving productivity of the staple crops wheat and rice is essential to feed the growing global population, particularly in the context of a changing climate. However, current rates of yield gain are insufficient to support the predicted population growth. New approaches are required to accelerate the breeding process, and many of these are driven by the application of large-scale crop data. To leverage the substantial volumes and types of data that can be applied for precision breeding, the wheat and rice research communities are working towards the development of integrated systems to access and standardize the dispersed, heterogeneous available data. Here, we outline the initiatives of the International Wheat Information System (WheatIS) and the International Rice Informatics Consortium (IRIC) to establish Web-based single-access systems and data mining tools to make the available resources more accessible, drive discovery and accelerate the production of new crop varieties. We discuss the progress of WheatIS and IRIC towards unifying specialized wheat and rice databases and building custom software platforms to manage and interrogate these data. Single-access crop information systems will strengthen scientific collaboration, optimize the use of public research funds and help achieve the required yield gains in the two most important global food crops.

Identification, characterization and epitope mapping of proteins encoded by putative allergenic napin genes from Brassica rapa.

BACKGROUND: Brassica rapeseed crops contain high concentrations of oil in the seed. The remaining meal, following oil extraction, has a high protein content, but is of low value due to the presence of high amounts of napin seed storage proteins. These 2S albumin-like proteins are difficult to digest and have been identified as major allergens in humans. OBJECTIVE: To comprehensively characterize the napin gene (NG) family in Brassica rapa and to gain an understanding of the structural basis of allergenicity of the expressed proteins. METHODS: To identify candidate napin genes in B rapa, 2S albumin-like napin genes of Arabidopsis thaliana were used as query sequences to search for similarity against the B rapa var. pekinensis Chiifu-401 v2 and the var. trilocularis R-o-18 v1.5 genomes. Multiple sequence alignment (MSA) and epitope modelling was carried out to determine structural and evolutionary relationships of NGs and their potential allergenicity. RESULTS: Four candidate napin genes in R-o-18 and ten in Chiifu-401 were identified with high sequence similarity to A thaliana napin genes. Multiple sequence alignment revealed strong conservation among the candidate genes. An epitope survey indicated high conservation of allergenic epitope motifs with known 2S albumin-like allergens. CONCLUSION: Napin is thought to be responsible for a  high prevalence of food allergies. Characterization of the napin gene family in B rapa will give important insight into the protein structure, and epitope modelling will help to advance studies into allergenicity including the development of precise diagnostic screenings and therapies for this potential food allergy as well as the possible manipulation of napin levels in the seed by gene editing technology.

Rice SNP-seek database update: new SNPs, indels, and queries.

We describe updates to the Rice SNP-Seek Database since its first release. We ran a new SNP-calling pipeline followed by filtering that resulted in complete, base, filtered and core SNP datasets. Besides the Nipponbare reference genome, the pipeline was run on genome assemblies of IR 64, 93-11, DJ 123 and Kasalath. New genotype query and display features are added for reference assemblies, SNP datasets and indels. JBrowse now displays BAM, VCF and other annotation tracks, the additional genome assemblies and an embedded VISTA genome comparison viewer. Middleware is redesigned for improved performance by using a hybrid of HDF5 and RDMS for genotype storage. Query modules for genotypes, varieties and genes are improved to handle various constraints. An integrated list manager allows the user to pass query parameters for further analysis. The SNP Annotator adds traits, ontology terms, effects and interactions to markers in a list. Web-service calls were implemented to access most data. These features enable seamless querying of SNP-Seek across various biological entities, a step toward semi-automated gene-trait association discovery. URL: http://snp-seek.irri.org.

Development of the Automated Primer Design Workflow Uniqprimer and Diagnostic Primers for the Broad-Host-Range Plant Pathogen Dickeya dianthicola.

Uniqprimer, a software pipeline developed in Python, was deployed as a user-friendly internet tool in Rice Galaxy for comparative genome analyses to design primer sets for PCRassays capable of detecting target bacterial taxa. The pipeline was trialed with Dickeya dianthicola, a destructive broad-host-range bacterial pathogen found in most potato-growing regions. Dickeya is a highly variable genus, and some primers available to detect this genus and species exhibit common diagnostic failures. Upon uploading a selection of target and nontarget genomes, six primer sets were rapidly identified with Uniqprimer, of which two were specific and sensitive when tested with D. dianthicola. The remaining four amplified a minority of the nontarget strains tested. The two promising candidate primer sets were trialed with DNA isolated from 116 field samples from across the United States that were previously submitted for testing. D. dianthicola was detected in 41 samples, demonstrating the applicability of our detection primers and suggesting widespread occurrence of D. dianthicola in North America.

SNP-Seek database of SNPs derived from 3000 rice genomes.

We have identified about 20 million rice SNPs by aligning reads from the 3000 rice genomes project with the Nipponbare genome. The SNPs and allele information are organized into a SNP-Seek system (http://www.oryzasnp.org/iric-portal/), which consists of Oracle database having a total number of rows with SNP genotypes close to 60 billion (20 M SNPs × 3 K rice lines) and web interface for convenient querying. The database allows quick retrieving of SNP alleles for all varieties in a given genome region, finding different alleles from predefined varieties and querying basic passport and morphological phenotypic information about sequenced rice lines. SNPs can be visualized together with the gene structures in JBrowse genome browser. Evolutionary relationships between rice varieties can be explored using phylogenetic trees or multidimensional scaling plots.

Root aquaporins contribute to whole plant water fluxes under drought stress in rice (Oryza sativa L.).

Aquaporin activity and root anatomy may affect root hydraulic properties under drought stress. To better understand the function of aquaporins in rice root water fluxes under drought, we studied the root hydraulic conductivity (Lpr) and root sap exudation rate (Sr) in the presence or absence of an aquaporin inhibitor (azide) under well-watered conditions and following drought stress in six diverse rice varieties. Varieties varied in Lpr and Sr under both conditions. The contribution of aquaporins to Lpr was generally high (up to 79% under well-watered conditions and 85% under drought stress) and differentially regulated under drought. Aquaporin contribution to Sr increased in most varieties after drought, suggesting a crucial role for aquaporins in osmotic water fluxes during drought and recovery. Furthermore, root plasma membrane aquaporin (PIP) expression and root anatomical properties were correlated with hydraulic traits. Three chromosome regions highly correlated with hydraulic traits of the OryzaSNP panel were identified, but did not co-locate with known aquaporins. These results therefore highlight the importance of aquaporins in the rice root radial water pathway, but emphasize the complex range of additional mechanisms related to root water fluxes and drought response.

Rice phenylalanine ammonia-lyase gene OsPAL4 is associated with broad spectrum disease resistance.

Most agronomically important traits, including resistance against pathogens, are governed by quantitative trait loci (QTL). QTL-mediated resistance shows promise of being effective and long-lasting against diverse pathogens. Identification of genes controlling QTL-based disease resistance contributes to breeding for cultivars that exhibit high and stable resistance. Several defense response genes have been successfully used as good predictors and contributors to QTL-based resistance against several devastating rice diseases. In this study, we identified and characterized a rice (Oryza sativa) mutant line containing a 750 bp deletion in the second exon of OsPAL4, a member of the phenylalanine ammonia-lyase gene family. OsPAL4 clusters with three additional OsPAL genes that co-localize with QTL for bacterial blight and sheath blight disease resistance on rice chromosome 2. Self-pollination of heterozygous ospal4 mutant lines produced no homozygous progeny, suggesting that homozygosity for the mutation is lethal. The heterozygous ospal4 mutant line exhibited increased susceptibility to three distinct rice diseases, bacterial blight, sheath blight, and rice blast. Mutation of OsPAL4 increased expression of the OsPAL2 gene and decreased the expression of the unlinked OsPAL6 gene. OsPAL2 function is not redundant because the changes in expression did not compensate for loss of disease resistance. OsPAL6 co-localizes with a QTL for rice blast resistance, and is down-regulated in the ospal4 mutant line; this may explain enhanced susceptibility to Magnoporthe oryzae. Overall, these results suggest that OsPAL4 and possibly OsPAL6 are key contributors to resistance governed by QTL and are potential breeding targets for improved broad-spectrum disease resistance in rice.

Metabolic and transcriptomic signatures of rice floral organs reveal sugar starvation as a factor in reproductive failure under heat and drought stress.

Heat and drought stress are projected to become major challenges to sustain rice (Oryza sativa L.) yields with global climate change. Both stresses lead to yield losses when they coincide with flowering. A significant knowledge gap exists in the mechanistic understanding of the responses of rice floral organs that determine reproductive success under stress. Our work connects the metabolomic and transcriptomic changes in anthers, pistils before pollination and pollinated pistils in a heat-tolerant (N22) and a heat-sensitive (Moroberekan) cultivar. Systematic analysis of the floral organs revealed contrasts in metabolic profiles across anthers and pistils. Constitutive metabolic markers were identified that can define reproductive success in rice under stress. Six out of nine candidate metabolites identified by intersection analysis of stressed anthers were differentially accumulated in N22 compared with Moroberekan under non-stress conditions. Sugar metabolism was identified to be the crucial metabolic and transcriptional component that differentiated floral organ tolerance or susceptibility to stress. While susceptible Moroberekan specifically showed high expression of the Carbon Starved Anthers (CSA) gene under combined heat and drought, tolerant N22 responded with high expression of genes encoding a sugar transporter (MST8) and a cell wall invertase (INV4) as markers of high sink strength.

CannSeek? Yes we Can! An open-source single nucleotide polymorphism database and analysis portal for Cannabis sativa.

A growing interest in Cannabis sativa uses for food, fiber, and medicine, and recent changes in regulations have spurred numerous genomic studies of this once-prohibited plant. Cannabis research uses Next Generation Sequencing technologies for genomics and transcriptomics. While other crops have genome portals enabling access and analysis of numerous genotyping data from diverse accessions, leading to the discovery of alleles for important traits, this is absent for cannabis. The CannSeek web portal aims to address this gap. Single nucleotide polymorphism datasets were generated by identifying genome variants from public resequencing data and genome assemblies. Results and accompanying trait data are hosted in the CannSeek web application, built using the Rice SNP-Seek infrastructure with improvements to allow multiple reference genomes and provide a web-service Application Programming Interface. The tools built into the portal allow phylogenetic analyses, varietal grouping and identifications, and favorable haplotype discovery for cannabis accessions using public sequencing data. Availability and implementation: The CannSeek portal is available at https://icgrc.info/cannseek, https://icgrc.info/genotype_viewer.

Soil microbial communities and degradation of pesticides in greenhouse effluent through a woodchip bioreactor.

Pesticides, including insecticides and fungicides, are major contaminants in the effluent from intensive agricultural systems, such as greenhouses. Because of their constant use and persistence, some pesticides can accumulate in soil and/or run off into adjacent waterways. Microbial communities in soil can degrade some pesticides, and bioreactors with enhanced microbial communities have the potential to facilitate decontamination before the effluent is released into the environment. In this study, we sampled the soil along a gradient from immediately below greenhouses, into, through and below a bioreactor. Multi-analyte pesticide screening was undertaken along with shotgun metagenomic sequencing, to assess microbial community taxonomic profiles and metabolic pathway responses for functional analysis. Two insecticides (imidacloprid and fipronil) and nine fungicides were identified in the soil samples, with a general decrease in most pesticides with increasing distance from the greenhouses. Diversity indexes of taxonomic profiles show changes in the microbial community along the gradient. In particular, microbial communities were significantly different in the bioreactor, with lower Shannon diversity compared to immediately below the greenhouses, in the channels leading into the bioreactor and further downstream. Metabolic pathway analysis revealed significant changes in a wide range of core housekeeping genes such as protein/amino acid synthesis and lipid/fatty acid biosynthesis among the sampling sites. The result demonstrates that the composition and potential functional pathways of the microbial community shifted towards an increased tendency for phytol and contaminant degradation in the bioreactor, facilitated by high organic matter content. This highlights the potential to use enhanced microbial communities within bioreactors to reduce contamination by some pesticides in sediment receiving run-off from greenhouses.

Building a community-driven bioinformatics platform to facilitate Cannabis sativa multi-omics research.

Global changes in cannabis legislation after decades of stringent regulation and heightened demand for its industrial and medicinal applications have spurred recent genetic and genomics research. An international research community emerged and identified the need for a web portal to host cannabis-specific datasets that seamlessly integrates multiple data sources and serves omics-type analyses, fostering information sharing. The Tripal platform was used to host public genome assemblies, gene annotations, quantitative trait loci and genetic maps, gene and protein expression data, metabolic profiles and their sample attributes. Single nucleotide polymorphisms were called using public resequencing datasets on three genomes. Additional applications, such as SNP-Seek and MapManJS, were embedded into Tripal. A multi-omics data integration web-service Application Programming Interface (API), developed on top of existing Tripal modules, returns generic tables of samples, properties and values. Use cases demonstrate the API's utility for various omics analyses, enabling researchers to perform multi-omics analyses efficiently. Availability and implementation: The web portal can be accessed at www.icgrc.info.

RicePilaf: a post-GWAS/QTL dashboard to integrate pangenomic, coexpression, regulatory, epigenomic, ontology, pathway, and text-mining information to provide functional insights into rice QTLs and GWAS loci.

BACKGROUND: As the number of genome-wide association study (GWAS) and quantitative trait locus (QTL) mappings in rice continues to grow, so does the already long list of genomic loci associated with important agronomic traits. Typically, loci implicated by GWAS/QTL analysis contain tens to hundreds to thousands of single-nucleotide polmorphisms (SNPs)/genes, not all of which are causal and many of which are in noncoding regions. Unraveling the biological mechanisms that tie the GWAS regions and QTLs to the trait of interest is challenging, especially since it requires collating functional genomics information about the loci from multiple, disparate data sources. RESULTS: We present RicePilaf, a web app for post-GWAS/QTL analysis, that performs a slew of novel bioinformatics analyses to cross-reference GWAS results and QTL mappings with a host of publicly available rice databases. In particular, it integrates (i) pangenomic information from high-quality genome builds of multiple rice varieties, (ii) coexpression information from genome-scale coexpression networks, (iii) ontology and pathway information, (iv) regulatory information from rice transcription factor databases, (v) epigenomic information from multiple high-throughput epigenetic experiments, and (vi) text-mining information extracted from scientific abstracts linking genes and traits. We demonstrate the utility of RicePilaf by applying it to analyze GWAS peaks of preharvest sprouting and genes underlying yield-under-drought QTLs. CONCLUSIONS: RicePilaf enables rice scientists and breeders to shed functional light on their GWAS regions and QTLs, and it provides them with a means to prioritize SNPs/genes for further experiments. The source code, a Docker image, and a demo version of RicePilaf are publicly available at https://github.com/bioinfodlsu/rice-pilaf.

Brassinosteroids antagonize gibberellin- and salicylate-mediated root immunity in rice.

Brassinosteroids (BRs) are a unique class of plant steroid hormones that orchestrate myriad growth and developmental processes. Although BRs have long been known to protect plants from a suite of biotic and abiotic stresses, our understanding of the underlying molecular mechanisms is still rudimentary. Aiming to further decipher the molecular logic of BR-modulated immunity, we have examined the dynamics and impact of BRs during infection of rice (Oryza sativa) with the root oomycete Pythium graminicola. Challenging the prevailing view that BRs positively regulate plant innate immunity, we show that P. graminicola exploits BRs as virulence factors and hijacks the rice BR machinery to inflict disease. Moreover, we demonstrate that this immune-suppressive effect of BRs is due, at least in part, to negative cross talk with salicylic acid (SA) and gibberellic acid (GA) pathways. BR-mediated suppression of SA defenses occurred downstream of SA biosynthesis, but upstream of the master defense regulators NONEXPRESSOR OF PATHOGENESIS-RELATED GENES1 and OsWRKY45. In contrast, BR alleviated GA-directed immune responses by interfering at multiple levels with GA metabolism, resulting in indirect stabilization of the DELLA protein and central GA repressor SLENDER RICE1 (SLR1). Collectively, these data favor a model whereby P. graminicola coopts the plant BR pathway as a decoy to antagonize effectual SA- and GA-mediated defenses. Our results highlight the importance of BRs in modulating plant immunity and uncover pathogen-mediated manipulation of plant steroid homeostasis as a core virulence strategy.

Critical disease burdens of Australian adults with cystic fibrosis: Results from an online survey.

BACKGROUND: The objective of this study was to conduct a web-based questionnaire to investigate self-reported phenotypes and disease burdens of individuals living in Australia and diagnosed with cystic fibrosis (CF) using a case-control study design. METHODS: An online questionnaire was distributed to individuals with CF and healthy control subjects. Overall health rating, medications, family history, education, clinical indicators of disease, and symptoms, including their severity and frequency, were evaluated. RESULTS: There was a total of 119 respondents consisting of 59 people living with CF and 60 controls. The CF cohort had significantly lower tertiary educational levels compared to controls. The analysis specific to the CF cohort depicted a significant correlation between the frequency of hospitalizations and the level of education in the CF cohort. Of the 26 self-reported symptoms of CF that were analyzed, 14 were significantly higher in the people living with CF. The CF cohort reporting symptoms of chronic pain (25%) described an increase in the burden of disease, depicting a 30% longer mean hospitalization, increased consumption of medications and significant relationships with four other symptoms, including muscle aches, digestive issues, pancreatic insufficiency, and abdominal swelling. CONCLUSIONS: The nationwide survey identified a diverse range of clinical manifestations experienced by the Australian CF population. Chronic pain, linked to aging and the changing landscape of disease, was a significant indicator of the burden of disease. A comprehensive understanding of the phenotypic profiles and symptom variability will contribute to future research and provide insights into the impacts of disease and the burden of therapy, particularly in children, at the start of their health journey.

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.