Guoyou Ye's Lab

About the lab

Objectives of the CAAS-IRRI Joint Laboratory for Genomics-Assisted Germplasm Enhancement

Establish an efficient platform for genomics-assisted germplasm enhancement to develop germplasm for South-East Asia and East Africa.

Establish rice breeding information platform for data collection, management and analysis to improve phenotyping efficiency.

Establish rice information platform for managing –omics data to promote the application of –omics techniques in rice genetics and breeding.

Featured projects (1)

Tolerance to mineral element deficiency and toxicity ; Grain contents of mineral elements ;

Featured research (13)

Multi-trait (MT) genomic prediction models enable breeders to save phenotyping resources and increase the prediction accuracy of unobserved target traits by exploiting available information from non-target or auxiliary traits. Our study evaluated different MT models using 250 rice accessions from Asian countries genotyped and phenotyped for grain content of zinc (Zn), iron (Fe), copper (Cu), manganese (Mn), and cadmium (Cd). The predictive performance of MT models compared to a traditional single trait (ST) model was assessed by 1) applying different cross-validation strategies (CV1, CV2, and CV3) inferring varied phenotyping patterns and budgets; 2) accounting for local epistatic effects along with the main additive effect in MT models; and 3) using a selective marker panel composed of trait-associated SNPs in MT models. MT models were not statistically significantly (p < 0.05) superior to ST model under CV1, where no phenotypic information was available for the accessions in the test set. After including phenotypes from auxiliary traits in both training and test sets (MT-CV2) or simply in the test set (MT-CV3), MT models significantly (p < 0.05) outperformed ST model for all the traits. The highest increases in the predictive ability of MT models relative to ST models were 11.1% (Mn), 11.5 (Cd), 33.3% (Fe), 95.2% (Cu) and 126% (Zn). Accounting for the local epistatic effects using a haplotype-based model further improved the predictive ability of MT models by 4.6% (Cu), 3.8% (Zn), and 3.5% (Cd) relative to MT models with only additive effects. The predictive ability of the haplotype-based model was not improved after optimizing the marker panel by only considering the markers associated with the traits. This study first assessed the local epistatic effects and marker optimization strategies in the MT genomic prediction framework and then illustrated the power of the MT model in predicting trace element traits in rice for the effective use of genetic resources to improve the nutritional quality of rice grain.
Mesocotyl is a crucial organ for pushing buds out of soil, which plays a vital role in seedling emergence and establishment in direct-seeded rice. Thus, the identification of quantitative trait loci (QTL) associated with mesocotyl length (ML) could accelerate genetic improvement of rice for direct seeding cultivation. In this study, QTL sequencing (QTL-seq) applied to 12 F 2 populations identified 14 QTL for ML, which were distributed on chromosomes 1, 3, 4, 5, 6, 7, and 9 based on the Δ(SNP-index) or G -value statistics. Besides, a genome-wide association study (GWAS) using two diverse panels identified five unique QTL on chromosomes 1, 8, 9, and 12 (2), respectively, explaining 5.3–14.6% of the phenotypic variations. Among these QTL, seven were in the regions harboring known genes or QTLs, whereas the other 10 were potentially novel. Six of the QTL were stable across two or more populations. Eight high-confidence candidate genes related to ML were identified for the stable loci based on annotation and expression analyses. Association analysis revealed that two PCR gel-based markers for the loci co-located by QTL-seq and GWAS, Indel-Chr1:18932318 and Indel-Chr7:15404166 for loci qML1.3 and qML7.2 respectively, were significantly associated with ML in a collection of 140 accessions and could be used as breeder-friendly markers in further breeding.
Salinity is one of the most important abiotic stresses, which seriously affects rice production. In this study, a multiparent advanced generation intercross population, DC1, derived from intercrossing four elite indica varieties, was used to identify QTLs conferring salt tolerance. The whole population and four parents were genotyped with a 55K rice SNP array. A total of 7 QTLs delineated from 186 significant marker-trait associations were detected on chromosomes 1, 2, 5 and 9, which accounted for 7.42–9.38% of the total phenotypic variations. Among these QTLs, one novel QTL (qRRL2) on chromosome 2 for relative root length and one multi-trait QTL (qSLST1/qRDSW1/qRB1) on chromosome 1 affecting shoot length, root dry weight and root biomass under salt treatment were detected. Gene expression analysis revealed that a transcription factor gene (LOC_Os01g66280) within the multi-trait QTL is the potential candidate gene for salt tolerance. Interestingly, we identified one significant nonsynonymous SNP in the coding region of this candidate gene. These results will facilitate fine mapping of the candidate gene and QTL pyramiding to genetically improve salt tolerance in rice.
Aluminum (Al) toxicity in acid soils is a significant limitation to crop production worldwide, as 13% of the world's rice is produced in acid soil with high Al content. Rice is likely the most Al-resistant cereal and also the cereal, where Al resistance is the most genetically complex with external detoxification and internal tolerance. Many Al-resistance genes in rice have been cloned, including Al resistance transcription factor 1 (ART1) and other transcription factors, organic acid transporter genes, and metal ion transporter gene. This review summarized the recent characterized genes affecting Al tolerance in rice and the interrelationships between Al and other plant nutrients.
Background: The construction of genetic maps based on molecular markers is a crucial step in rice genetic and genomic studies. Pure lines derived from multiple parents provide more abundant genetic variation than those from bi-parent populations. Two four-parent pure-line populations (4PL1 and 4PL2) and one eight-parent pure-line population (8PL) were developed from eight homozygous indica varieties of rice by the International Rice Research Institute (IRRI). To the best of our knowledge, there have been no reports on linkage map construction and their integration in multi-parent populations of rice. Results: We constructed linkage maps for the three multi-parent populations and conducted quantitative trait locus (QTL) mapping for heading date (HD) and plant height (PH) based on the three maps by inclusive composite interval mapping (ICIM). An integrated map was built from the three individual maps and used for QTL projection and meta-analysis. QTL mapping of the three populations was also conducted based on the integrated map, and the mapping results were compared with those from meta-analysis. The three linkage maps developed for 8PL, 4PL1 and 4PL2 had 5905, 4354 and 5464 bins and were 1290.16, 1720.01 and 1560.30 cM in length, respectively. The integrated map was 3022.08 cM in length and contained 10,033 bins. Based on the three linkage maps, 3, 7 and 9 QTLs were detected for HD while 6, 9 and 10 QTLs were detected for PH in 8PL, 4PL1 and 4PL2, respectively. In contrast, 19 and 25 QTLs were identified for HD and PH by meta-analysis using the integrated map, respectively. Based on the integrated map, 5, 9, and 10 QTLs were detected for HD while 3, 10, and 12 QTLs were detected for PH in 8PL, 4PL1 and 4PL2, respectively. Eleven of these 49 QTLs coincided with those from the meta-analysis. Conclusions: In this study, we reported the first rice linkage map constructed from one eight-parent recombinant inbred line (RIL) population and the first integrated map from three multi-parent populations, which provide essential information for QTL linkage mapping, meta-analysis, and map-based cloning in rice genetics and breeding.

Lab head

Guoyou Ye
About Guoyou Ye
  • A plant geneticist and breeder with experiences in designing conventional and molecular breeding strategy, quantitative genetic analysis and developing analytical tools for breeders.

Members (5)

Long-Biao Guo
  • Chinese Academy of Agricultural Sciences
Sang He
  • Agriculture Victoria
Li-jun Meng
  • Chinese Academy of Agricultural Sciences
Chen Jingguang
  • Nanjing Agricultural University
Jindong Liu
  • Chinese Academy of Agricultural Sciences
wang yamei
wang yamei
  • Not confirmed yet