Genome-Wide Association Study for Milled Grain Appearance Traits Using Multi-Parent Advanced Generation Intercross Population in Rice

The identification of loci and markers associated with milled grain appearance traits is essential for breeding high-yielding and good-quality rice variety.To detect stable loci for these characteristics,grain length(GL),grain width(GW),grain length/width(GLW),chalkiness degree(CD),chalky-grain rate(CR)and translucency degree(TD)of 378 rice lines were evaluated in three seasons.These lines were derived from a multi-parent advanced generation intercross(MAGIC)population.

Genomic prediction using composite training sets is an effective method for exploiting germplasm conserved in rice gene banks

Germplasm conserved in gene banks is underutilized,owing mainly to the cost of characterization.Genomic prediction can be applied to predict the genetic merit of germplasm.Germplasm utilization could be greatly accelerated if prediction accuracy were sufficiently high with a training population of practical size.Large-scale resequencing projects in rice have generated high quality genome-wide variation information for many diverse accessions,making it possible to investigate the potential of genomic prediction in rice germplasm management and exploitation.We phenotyped six traits in nearly 2000 indica(XI)and japonica(GJ)accessions from the Rice 3K project and investigated different scenarios for forming training populations.A composite core training set was considered in two levels which targets used for prediction of subpopulations within subspecies or prediction across subspecies.Composite training sets incorporating 400 or 200 accessions from either subpopulation of XI or GJ showed satisfactory prediction accuracy.A composite training set of 600 XI and GJ accessions showed sufficiently high prediction accuracy for both XI and GJ subspecies.Comparable or even higher prediction accuracy was observed for the composite training set than for the corresponding homogeneous training sets comprising accessions only of specific subpopulations of XI or GJ(within-subspecies level)or pure XI or GJ accessions(across-subspecies level)that were included in the composite training set.Validation using an independent population of 281 rice cultivars supported the predictive ability of the composite training set.Reliability,which reflects the robustness of a training set,was markedly higher for the composite training set than for the corresponding homogeneous training sets.A core training set formed from diverse accessions could accurately predict the genetic merit of rice germplasm.

Identification of Potential Zinc Deficiency Responsive Genes and Regulatory Pathways in Rice by Weighted Gene Co-expression Network Analysis

Zinc(Zn)malnutrition is a major public health issue.Genetic biofortification of Zn in rice grain can alleviate global Zn malnutrition.Therefore,elucidating the genetic mechanisms regulating Zn deprivation response in rice is essential to identify elite genes useful for breeding high grain Zn rice varieties.Here,a meta-analysis of previous RNA-Seq studies involving Zn deficient conditions was conducted using the weighted gene co-expression network analysis(WGCNA)and other in silico prediction tools to identify modules(denoting cluster of genes with related expression pattern)of co-expressed genes,modular genes which are conserved differentially expressed genes(DEGs)across independent RNA-Seq studies,and the molecular pathways of the conserved modular DEGs.WGCNA identified 16 modules of co-expressed genes.Twenty-eight and five modular DEGs were conserved in leaf and crown,and root tissues across two independent RNA-Seq studies.Functional enrichment analysis showed that 24 of the 28 conserved modular DEGs from leaf and crown tissues significantly up-regulated 2 Kyoto Encyclopedia of Genes and Genomes(KEGG)pathways and 15 Gene Ontology(GO)terms,including the substrate-specific transmembrane transporter and the small molecule metabolic process.Further,the well-studied transcription factors(OsWOX11 and OsbHLH120),protein kinase(OsCDPK20 and OsMPK17),and miRNAs(OSA-MIR397A and OSA-MIR397B)were predicted to target some of the identified conserved modular DEGs.Out of the 24 conserved and up-regulated modular DEGs,19 were yet to be experimentally validated as Zn deficiency responsive genes.Findings from this study provide a comprehensive insight on the molecular mechanisms of Zn deficiency response and may facilitate gene and pathway prioritization for improving Zn use efficiency and Zn biofortification in rice.

Post-genomics revolution in the design of premiumquality rice in a high-yieldingbackground to meet consumer demands in the 21st century

The eating and cooking quality(ECQ)of rice is critical for determining its economic value in the marketplace and promoting consumer acceptance.It has therefore been of paramount importance in rice breeding programs.Here,we highlight advances in genetic studies of ECQ and discuss prospects for further enhancement of ECQ in rice.Innovations in gene-and genome-editing techniques have enabled improvements in rice ECQ.Significant genes and quantitative trait loci(QTLs)have been shown to regulate starch composition,thereby affecting amylose content and thermal and pasting properties.A limited number of genes/QTLs have been identified for other ECQ properties such as protein content and aroma.Marker-assisted breeding has identified rare alleles in diverse genetic resources that are associated with superior ECQ properties.The post-genomics-driven information summarized in this review is relevant for augmenting current breeding strategies to meet consumer preferences and growing population demands.