Genome-wide analysis of polymorphisms identified domestication-associated long low-diversity region carrying important rice grain size/weight quantitative trait loci

Whole genome resequencing unveils low-temperature stress tolerance specific genomic variations in jute (Corchorus sp.)

Jute (Corchorus sp.), a commercially important and eco-friendly crop, is widely cultivated in Bangladesh, India, and China. Some varieties of this tropical plant such as the Corchorus olitorius. Variety accession no. 2015 (acc. 2015) has been found to be low-temperature tolerant. The current study was designed to explore the genome-wide variations present in the tolerant plant acc. 2015 in comparison to the sensitive farmer popular variety Corchorus olitorius var. O9897 using the whole genome resequencing technique. Among different variations, intergenic Single Nucleotide Polymorphism (SNPs) and Insertion-Deletion (InDels) were found in the highest percentage whereas approximately 3% SNPs and 2% InDels were found in exonic regions in both plants. Gene enrichment analysis indicated the presence of acc. 2015 specific SNPs in the genes encoding peroxidase, ER lumen protein retaining receptor, and hexosyltransferase involved in stress response (GO:0006950) which were not present in sensitive variety O9897. Besides, distinctive copy number variation regions (CNVRs) comprising 120 gene loci were found in acc. 2015 with a gain of function from multiple copy numbers but absent in O9897. Gene ontology analysis revealed these gene loci to possess different receptors like kinases, helicases, phosphatases, transcription factors especially Myb transcription factors, regulatory proteins containing different binding domains, annexin, laccase, acyl carrier protein, potassium transporter, and vesicular transporter proteins that are responsible for low temperature induced adaptation pathways in plants. This work of identifying genomic variations linked to cold stress tolerance traits will help to develop successful markers that will pave the way to develop genetically modified cold-resistant jute lines for year-round cultivation to meet the demand for a sustainable fiber crop economy.

Genetic Diversity and Breeding Signatures for Regional Indica Rice Improvement in Guangdong of Southern China

As the pioneer of the Green Revolution in China, Guangdong province witnessed the improvement and spread of semi-dwarf Xian/Indica rice cultivars and possessed diverse rice germplasm of landrace and cultivars. A total of 517 accessions containing a core germplasm of 479 newly sequenced landraces and modern cultivars were used to reveal breeding signatures and key variations for regional genetic improvement of indica rice from Guangdong. Four subpopulations were identified in the collection, which including Ind IV as a novel subpopulation that not covered by previously released accessions. Modern cultivars of subpopulation Ind II were inferred to have less deleterious variations, especially in yield related genes. About 15 Mb genomic segments were identified as potential breeding signatures by cross-population likelihood method (XP-CLR) of modern cultivars and landraces. The selected regions spanning multiple yield related QTLs (quantitative trait locus) which identified by GWAS (genome-wide association studies) of the same population, and specific variations that fixed in modern cultivars of Ind II were characterized. This study highlights genetic differences between traditional landraces and modern cultivars, which revealed the potential molecular basis of regional genetic improvement for Guangdong indica rice from southern China.

Cytological, transcriptome and miRNome temporal landscapes decode enhancement of rice grain size

BACKGROUND

Rice grain size (GS) is an essential agronomic trait. Though several genes and miRNA modules influencing GS are known and seed development transcriptomes analyzed, a comprehensive compendium connecting all possible players is lacking. This study utilizes two contrasting GS indica rice genotypes (small-grained SN and large-grained LGR). Rice seed development involves five stages (S1-S5). Comparative transcriptome and miRNome atlases, substantiated with morphological and cytological studies, from S1-S5 stages and flag leaf have been analyzed to identify GS proponents.

RESULTS

Histology shows prolonged endosperm development and cell enlargement in LGR. Stand-alone and comparative RNAseq analyses manifest S3 (5-10 days after pollination) stage as crucial for GS enhancement, coherently with cell cycle, endoreduplication, and programmed cell death participating genes. Seed storage protein and carbohydrate accumulation, cytologically and by RNAseq, is shown to be delayed in LGR. Fourteen transcription factor families influence GS. Pathway genes for four phytohormones display opposite patterns of higher expression. A total of 186 genes generated from the transcriptome analyses are located within GS trait-related QTLs deciphered by a cross between SN and LGR. Fourteen miRNA families express specifically in SN or LGR seeds. Eight miRNA-target modules display contrasting expressions amongst SN and LGR, while 26 (SN) and 43 (LGR) modules are differentially expressed in all stages.

CONCLUSIONS

Integration of all analyses concludes in a "Domino effect" model for GS regulation highlighting chronology and fruition of each event. This study delineates the essence of GS regulation, providing scope for future exploits. The rice grain development database (RGDD) ( www.nipgr.ac.in/RGDD/index.php ; https://doi.org/10.5281/zenodo.7762870 ) has been developed for easy access of data generated in this paper.

Rice Pangenome Genotyping Array: an efficient genotyping solution for pangenome-based accelerated genetic improvement in rice

The advent of the pangenome era has unraveled previously unknown genetic variation existing within diverse crop plants, including rice. This untapped genetic variation is believed to account for a major portion of phenotypic variation existing in crop plants. However, the use of conventional single reference-guided genotyping often fails to capture a large portion of this genetic variation leading to a reference bias. This makes it difficult to identify and utilize novel population/cultivar-specific genes for crop improvement. Thus, we developed a Rice Pangenome Genotyping Array (RPGA) harboring probes assaying 80K single-nucleotide polymorphisms (SNPs) and presence-absence variants spanning the entire 3K rice pangenome. This array provides a simple, user-friendly and cost-effective (60-80 USD per sample) solution for rapid pangenome-based genotyping in rice. The genome-wide association study (GWAS) conducted using RPGA-SNP genotyping data of a rice diversity panel detected a total of 42 loci, including previously known as well as novel genomic loci regulating grain size/weight traits in rice. Eight of these identified trait-associated loci (dispensable loci) could not be detected with conventional single reference genome-based GWAS. A WD repeat-containing PROTEIN 12 gene underlying one of such dispensable locus on chromosome 7 (qLWR7) along with other non-dispensable loci were subsequently detected using high-resolution quantitative trait loci mapping confirming authenticity of RPGA-led GWAS. This demonstrates the potential of RPGA-based genotyping to overcome reference bias. The application of RPGA-based genotyping for population structure analysis, hybridity testing, ultra-high-density genetic map construction and chromosome-level genome assembly, and marker-assisted selection was also demonstrated. A web application (http://www.rpgaweb.com) was further developed to provide an easy to use platform for the imputation of RPGA-based genotyping data using 3K rice reference panel and subsequent GWAS.

QTL mapping and candidate gene analysis of low temperature germination in rice (Oryza sativa L.) using a genome wide association study

Low temperature germination (LTG) is a key agronomic trait in rice (Oryza sativa L.). However, the genetic basis of natural variation for LTG is largely unknown. Here, a genome-wide association study (GWAS) was performed using 276 accessions from the 3,000 Rice Genomes (3K-RG) project with 497 k single nucleotide polymorphisms (SNPs) to uncover potential genes for LTG in rice. In total, 37 quantitative trait loci (QTLs) from the 6th day (D6) to the 10th day (D10) were detected in the full population, overlapping with 12 previously reported QTLs for LTG. One novel QTL, namely qLTG1-2, was found stably on D7 in both 2019 and 2020. Based on two germination-specific transcriptome datasets, 13 seed-expressed genes were isolated within a 200 kb interval of qLTG1-2. Combining with haplotype analysis, a functional uncharacterized gene, LOC_Os01g23580, and a seed germination-associated gene, LOC_Os01g23620 (OsSar1a), as promising candidate genes, both of which were significantly differentially expressed between high and low LTG accessions. Collectively, the candidate genes with favorable alleles may be useful for the future characterization of the LTG mechanism and the improvement of the LTG trait in rice breeding.

Genomics Analyses Reveal Unique Classification, Population Structure and Novel Allele of Neo-Tetraploid Rice

BACKGROUND

Neo-tetraploid rice (NTR) is a useful new germplasm that developed from the descendants of the autotetraploid rice (ATR) hybrids. NTR showed improved fertility and yield potential, and produced high yield heterosis when crossed with indica ATR for commercial utilization. However, their classification, population structure and genomic feature remain elusive.

RESULTS

Here, high-depth genome resequencing data of 15 NTRs and 18 ATRs, together with 38 publicly available data of diploid rice accessions, were analyzed to conduct classification, population structure and haplotype analyses. Five subpopulations were detected and NTRs were clustered into one independent group that was adjacent to japonica subspecies, which maybe the reason for high heterosis when NTRs crossed with indica ATRs. Haplotype patterns of 717 key genes that associated with yield and other agronomic traits were revealed in these NTRs. Moreover, a novel specific SNP variation was detected in the first exon of HSP101, a known heat-inducible gene, which was conserved in all NTRs but absent in ATRs, 3KRG and RiceVarMap2 databases. The novel allele was named as HSP101-1, which was confirmed to be a heat response factor by qRT-PCR, and knockout of HSP101-1 significantly decreased the thermotolerance capacity of NTR. Interestingly, HSP101-1 was also specifically expressed in the anthers of NTR at pre-meiotic and meiosis stages under optimal environment without heat stress, and its loss-of-function mutant showed significant decrease in fertility of NTR.

CONCLUSION

The construction of first genomic variation repository and the revelation of population structure provide invaluable information for optimizing the designs of tetraploid rice breeding. The detection of specific genomic variations offered useful genomic markers and new directions to resolve high fertility mechanism of NTR.

Explore the genetics of weedy traits using rice 3K database

BACKGROUND

Weedy rice, a conspecific weedy counterpart of the cultivated rice (Oryza sativa L.), has been problematic in rice-production area worldwide. Although we started to know about the origin of some weedy traits for some rice-growing regions, an overall assessment of weedy trait-related loci was not yet available. On the other hand, the advances in sequencing technologies, together with community efforts, have made publicly available a large amount of genomic data. Given the availability of public data and the need of "weedy" allele mining for a better management of weedy rice, the objective of the present study was to explore the genetic architecture of weedy traits based on publicly available data, mainly from the 3000 Rice Genome Project (3K-RGP).

RESULTS

Based on the results of population structure analysis, we have selected 1378 individuals from four sub-populations (aus, indica, temperate japonica, tropical japonica) without admixed genomic composition for genome-wide association analysis (GWAS). Five traits were investigated: awn color, seed shattering, seed threshability, seed coat color, and seedling height. GWAS was conducted for each sub-population × trait combination and we have identified 66 population-specific trait-associated SNPs. Eleven significant SNPs fell into an annotated gene and four other SNPs were close to a putative candidate gene (± 25 kb). SNPs located in or close to Rc were particularly predictive of the occurrence of seed coat color and our results showed that different sub-populations required different SNPs for a better seed coat color prediction. We compared the data of 3K-RGP to a publicly available weedy rice dataset. The profile of allele frequency, phenotype-genotype segregation of target SNP, as well as GWAS results for the presence and absence of awns diverged between the two sets of data.

CONCLUSIONS

The genotype of trait-associated SNPs identified in this study, especially those located in or close to Rc, can be developed to diagnostic SNPs to trace the origin of weedy trait occurred in the field. The difference of results from the two publicly available datasets used in this study emphasized the importance of laboratory experiments to confirm the allele mining results based on publicly available data.