1
|
He S, Liang S, Meng L, Cao L, Ye G. Sparse Phenotyping and Haplotype-Based Models for Genomic Prediction in Rice. RICE (NEW YORK, N.Y.) 2023; 16:27. [PMID: 37284992 DOI: 10.1186/s12284-023-00643-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Subscribe] [Scholar Register] [Received: 08/31/2022] [Accepted: 05/20/2023] [Indexed: 06/08/2023]
Abstract
The multi-environment genomic selection enables plant breeders to select varieties resilient to diverse environments or particularly adapted to specific environments, which holds a great potential to be used in rice breeding. To realize the multi-environment genomic selection, a robust training set with multi-environment phenotypic data is of necessity. Considering the huge potential of genomic prediction enhanced sparse phenotyping on the cost saving of multi-environment trials (MET), the establishment of a multi-environment training set could also benefit from it. Optimizing the genomic prediction methods is also crucial to enhance the multi-environment genomic selection. Using haplotype-based genomic prediction models is able to capture local epistatic effects which could be conserved and accumulated across generations much like additive effects thereby benefitting breeding. However, previous studies often used fixed length haplotypes composed by a few adjacent molecular markers disregarding the linkage disequilibrium (LD) which is of essential role in determining the haplotype length. In our study, based on three rice populations with different sizes and compositions, we investigated the usefulness and effectiveness of multi-environment training sets with varying phenotyping intensities and different haplotype-based genomic prediction models based on LD-derived haplotype blocks for two agronomic traits, i.e., days to heading (DTH) and plant height (PH). Results showed that phenotyping merely 30% records in multi-environment training set is able to provide a comparable prediction accuracy to high phenotyping intensities; the local epistatic effects are much likely existent in DTH; dividing the LD-derived haplotype blocks into small segments with two or three single nucleotide polymorphisms (SNPs) helps to maintain the predictive ability of haplotype-based models in large populations; modelling the covariances between environments improves genomic prediction accuracy. Our study provides means to improve the efficiency of multi-environment genomic selection in rice.
Collapse
Affiliation(s)
- Sang He
- Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, 518124, China
- CAAS-IRRI Joint Laboratory for Genomics-Assisted Germplasm Enhancement, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, 518124, China
| | - Shanshan Liang
- Tianjin Key Laboratory of Animal and Plant Resistance, College of Life Sciences, Tianjin Normal University, Tianjin, 300387, China
| | - Lijun Meng
- Kunpeng Institute of Modern Agriculture at Foshan, Foshan, 528200, China
| | - Liyong Cao
- Key Laboratory for Zhejiang Super Rice Research, China National Rice Research Institute, Hangzhou, 310006, China.
| | - Guoyou Ye
- CAAS-IRRI Joint Laboratory for Genomics-Assisted Germplasm Enhancement, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, 518124, China.
- Rice Breeding Innovations Platform, International Rice Research Institute, Metro Manila, Philippines.
| |
Collapse
|
2
|
Atanda SA, Steffes J, Lan Y, Al Bari MA, Kim JH, Morales M, Johnson JP, Saludares R, Worral H, Piche L, Ross A, Grusak M, Coyne C, McGee R, Rao J, Bandillo N. Multi-trait genomic prediction improves selection accuracy for enhancing seed mineral concentrations in pea. THE PLANT GENOME 2022; 15:e20260. [PMID: 36193571 DOI: 10.1002/tpg2.20260] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/27/2022] [Accepted: 08/10/2022] [Indexed: 06/16/2023]
Abstract
Multi-trait genomic selection (MT-GS) has the potential to improve predictive ability by maximizing the use of information across related genotypes and genetically correlated traits. In this study, we extended the use of sparse phenotyping method into the MT-GS framework by split testing of entries to maximize borrowing of information across genotypes and predict missing phenotypes for targeted traits without additional phenotyping expenditure. Using 300 advanced breeding lines from North Dakota State University (NDSU) pulse breeding program and ∼200 USDA accessions that were evaluated for 10 nutritional traits, our results show that the proposed sparse phenotyping aided MT-GS can further improve predictive ability by >12% across traits compared with univariate (UNI) genomic selection. The proposed strategy departed from the previous reports that weak genetic correlation is a limitation to the advantage of MT-GS over UNI genomic selection, which was evident in the partially balanced phenotyping-enabled MT-GS. Our results point to heritability and genetic correlation between traits as possible metrics to optimize and further improve the estimation of model parameters, and ultimately, prediction performance. Overall, our study offers a new approach to optimize the prediction performance using the MT-GS and further highlight strategy to maximize the efficiency of GS in a plant breeding program. The sparse-testing-aided MT-GS proposed in this study can be further extended to multi-environment, multi-trait GS to improve prediction performance and further reduce the cost of phenotyping and time-consuming data collection process.
Collapse
Affiliation(s)
| | - Jenna Steffes
- Dep. of Plant Sciences, North Dakota State Univ., Fargo, ND, 58108-6050, USA
| | - Yang Lan
- Dep. of Plant Sciences, North Dakota State Univ., Fargo, ND, 58108-6050, USA
| | - Md Abdullah Al Bari
- Dep. of Plant Sciences, North Dakota State Univ., Fargo, ND, 58108-6050, USA
| | - Jeong-Hwa Kim
- Dep. of Plant Sciences, North Dakota State Univ., Fargo, ND, 58108-6050, USA
| | - Mario Morales
- Dep. of Plant Sciences, North Dakota State Univ., Fargo, ND, 58108-6050, USA
| | - Josephine P Johnson
- Dep. of Plant Sciences, North Dakota State Univ., Fargo, ND, 58108-6050, USA
| | - Rica Saludares
- Dep. of Plant Sciences, North Dakota State Univ., Fargo, ND, 58108-6050, USA
| | - Hannah Worral
- North Central Research Extension Center, NDSU, 5400 Hwy. 83, South Minot, ND, 58701, USA
| | - Lisa Piche
- Dep. of Plant Sciences, North Dakota State Univ., Fargo, ND, 58108-6050, USA
| | - Andrew Ross
- Dep. of Plant Sciences, North Dakota State Univ., Fargo, ND, 58108-6050, USA
| | - Mike Grusak
- Edward T. Schafer Agricultural Research Center, USDA-ARS, 1616 Albrecht Blvd. N, Fargo, ND, 58102-2765, USA
| | - Clarice Coyne
- USDA-ARS Plant Germplasm Introduction and Testing, Washington State Univ., Pullman, WA, 99164, USA
| | - Rebecca McGee
- USDA-ARS, Grain Legume Genetics and Physiology Research, Pullman, WA, 99164, USA
- Dep. of Horticulture, Washington State Univ., Pullman, WA, 99164, USA
| | - Jiajia Rao
- Dep. of Plant Sciences, North Dakota State Univ., Fargo, ND, 58108-6050, USA
| | - Nonoy Bandillo
- Dep. of Plant Sciences, North Dakota State Univ., Fargo, ND, 58108-6050, USA
| |
Collapse
|