1
|
Tanaka R, Wu D, Li X, Tibbs-Cortes LE, Wood JC, Magallanes-Lundback M, Bornowski N, Hamilton JP, Vaillancourt B, Li X, Deason NT, Schoenbaum GR, Buell CR, DellaPenna D, Yu J, Gore MA. Leveraging prior biological knowledge improves prediction of tocochromanols in maize grain. THE PLANT GENOME 2023; 16:e20276. [PMID: 36321716 DOI: 10.1002/tpg2.20276] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/29/2022] [Accepted: 09/21/2022] [Indexed: 06/16/2023]
Abstract
With an essential role in human health, tocochromanols are mostly obtained by consuming seed oils; however, the vitamin E content of the most abundant tocochromanols in maize (Zea mays L.) grain is low. Several large-effect genes with cis-acting variants affecting messenger RNA (mRNA) expression are mostly responsible for tocochromanol variation in maize grain, with other relevant associated quantitative trait loci (QTL) yet to be fully resolved. Leveraging existing genomic and transcriptomic information for maize inbreds could improve prediction when selecting for higher vitamin E content. Here, we first evaluated a multikernel genomic best linear unbiased prediction (MK-GBLUP) approach for modeling known QTL in the prediction of nine tocochromanol grain phenotypes (12-21 QTL per trait) within and between two panels of 1,462 and 242 maize inbred lines. On average, MK-GBLUP models improved predictive abilities by 7.0-13.6% when compared with GBLUP. In a second approach with a subset of 545 lines from the larger panel, the highest average improvement in predictive ability relative to GBLUP was achieved with a multi-trait GBLUP model (15.4%) that had a tocochromanol phenotype and transcript abundances in developing grain for a few large-effect candidate causal genes (1-3 genes per trait) as multiple response variables. Taken together, our study illustrates the enhancement of prediction models when informed by existing biological knowledge pertaining to QTL and candidate causal genes.
Collapse
Affiliation(s)
- Ryokei Tanaka
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell Univ., Ithaca, NY, 14853, USA
| | - Di Wu
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell Univ., Ithaca, NY, 14853, USA
| | - Xiaowei Li
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell Univ., Ithaca, NY, 14853, USA
| | | | - Joshua C Wood
- Institute for Plant Breeding, Genetics & Genomics, Center for Applied Genetic Technologies, Dep. of Crop & Soil Sciences, Univ. of Georgia, Athens, GA, 30602, USA
| | | | - Nolan Bornowski
- Dep. of Plant Biology, Michigan State Univ., East Lansing, MI, 48824, USA
| | - John P Hamilton
- Institute for Plant Breeding, Genetics & Genomics, Center for Applied Genetic Technologies, Dep. of Crop & Soil Sciences, Univ. of Georgia, Athens, GA, 30602, USA
| | - Brieanne Vaillancourt
- Institute for Plant Breeding, Genetics & Genomics, Center for Applied Genetic Technologies, Dep. of Crop & Soil Sciences, Univ. of Georgia, Athens, GA, 30602, USA
| | - Xianran Li
- USDA ARS, Wheat Health, Genetics, and Quality Research Unit, Pullman, WA, 99164, USA
| | - Nicholas T Deason
- Dep. of Biochemistry and Molecular Biology, Michigan State Univ., East Lansing, MI, 48824, USA
| | | | - C Robin Buell
- Institute for Plant Breeding, Genetics & Genomics, Center for Applied Genetic Technologies, Dep. of Crop & Soil Sciences, Univ. of Georgia, Athens, GA, 30602, USA
| | - Dean DellaPenna
- Dep. of Biochemistry and Molecular Biology, Michigan State Univ., East Lansing, MI, 48824, USA
| | - Jianming Yu
- Dep. of Agronomy, Iowa State Univ., Ames, IA, 50011, USA
| | - Michael A Gore
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell Univ., Ithaca, NY, 14853, USA
| |
Collapse
|
2
|
Liu R, Cui Y, Kong L, Zheng F, Zhao W, Meng Q, Yuan J, Zhang M, Chen Y. Evaluating the Genetic Background Effect on Dissecting the Genetic Basis of Kernel Traits in Reciprocal Maize Introgression Lines. Genes (Basel) 2023; 14:genes14051044. [PMID: 37239404 DOI: 10.3390/genes14051044] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2023] [Revised: 05/03/2023] [Accepted: 05/04/2023] [Indexed: 05/28/2023] Open
Abstract
Maize yield is mostly determined by its grain size. Although numerous quantitative trait loci (QTL) have been identified for kernel-related traits, the application of these QTL in breeding programs has been strongly hindered because the populations used for QTL mapping are often different from breeding populations. However, the effect of genetic background on the efficiency of QTL and the accuracy of trait genomic prediction has not been fully studied. Here, we used a set of reciprocal introgression lines (ILs) derived from 417F × 517F to evaluate how genetic background affects the detection of QTLassociated with kernel shape traits. A total of 51 QTL for kernel size were identified by chromosome segment lines (CSL) and genome-wide association studies (GWAS) methods. These were subsequently clustered into 13 common QTL based on their physical position, including 7 genetic-background-independent and 6 genetic-background-dependent QTL, respectively. Additionally, different digenic epistatic marker pairs were identified in the 417F and 517F ILs. Therefore, our results demonstrated that genetic background strongly affected not only the kernel size QTL mapping via CSL and GWAS but also the genomic prediction accuracy and epistatic detection, thereby enhancing our understanding of how genetic background affects the genetic dissection of grain size-related traits.
Collapse
Affiliation(s)
- Ruixiang Liu
- Provincial Key Laboratory of Agrobiology, Institute of Food Crops, Jiangsu Academy of Agricultural Sciences, Nanjing 210014, China
| | - Yakun Cui
- Provincial Key Laboratory of Agrobiology, Institute of Food Crops, Jiangsu Academy of Agricultural Sciences, Nanjing 210014, China
| | - Lingjie Kong
- Provincial Key Laboratory of Agrobiology, Institute of Food Crops, Jiangsu Academy of Agricultural Sciences, Nanjing 210014, China
| | - Fei Zheng
- Provincial Key Laboratory of Agrobiology, Institute of Food Crops, Jiangsu Academy of Agricultural Sciences, Nanjing 210014, China
| | - Wenming Zhao
- Provincial Key Laboratory of Agrobiology, Institute of Food Crops, Jiangsu Academy of Agricultural Sciences, Nanjing 210014, China
| | - Qingchang Meng
- Provincial Key Laboratory of Agrobiology, Institute of Food Crops, Jiangsu Academy of Agricultural Sciences, Nanjing 210014, China
| | - Jianhua Yuan
- Provincial Key Laboratory of Agrobiology, Institute of Food Crops, Jiangsu Academy of Agricultural Sciences, Nanjing 210014, China
| | - Meijing Zhang
- Provincial Key Laboratory of Agrobiology, Institute of Food Crops, Jiangsu Academy of Agricultural Sciences, Nanjing 210014, China
| | - Yanping Chen
- Provincial Key Laboratory of Agrobiology, Institute of Food Crops, Jiangsu Academy of Agricultural Sciences, Nanjing 210014, China
| |
Collapse
|
3
|
Gupta PK, Vasistha NK, Singh S, Joshi AK. Genetics and breeding for resistance against four leaf spot diseases in wheat ( Triticum aestivum L.). FRONTIERS IN PLANT SCIENCE 2023; 14:1023824. [PMID: 37063191 PMCID: PMC10096043 DOI: 10.3389/fpls.2023.1023824] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/20/2022] [Accepted: 02/28/2023] [Indexed: 06/19/2023]
Abstract
In wheat, major yield losses are caused by a variety of diseases including rusts, spike diseases, leaf spot and root diseases. The genetics of resistance against all these diseases have been studied in great detail and utilized for breeding resistant cultivars. The resistance against leaf spot diseases caused by each individual necrotroph/hemi-biotroph involves a complex system involving resistance (R) genes, sensitivity (S) genes, small secreted protein (SSP) genes and quantitative resistance loci (QRLs). This review deals with resistance for the following four-leaf spot diseases: (i) Septoria nodorum blotch (SNB) caused by Parastagonospora nodorum; (ii) Tan spot (TS) caused by Pyrenophora tritici-repentis; (iii) Spot blotch (SB) caused by Bipolaris sorokiniana and (iv) Septoria tritici blotch (STB) caused by Zymoseptoria tritici.
Collapse
Affiliation(s)
- Pushpendra Kumar Gupta
- Department of Genetics and Plant Breeding, Chaudhary Charan Singh University, Meerut, India
- Murdoch’s Centre for Crop and Food Innovation, Murdoch University, Murdoch, WA, Australia
- Borlaug Institute for South Asia (BISA), National Agricultural Science Complex (NASC), Dev Prakash Shastri (DPS) Marg, New Delhi, India
| | - Neeraj Kumar Vasistha
- Department of Genetics and Plant Breeding, Chaudhary Charan Singh University, Meerut, India
- Department of Genetics-Plant Breeding and Biotechnology, Dr Khem Singh Gill, Akal College of Agriculture, Eternal University, Baru Sahib, Sirmour, India
| | - Sahadev Singh
- Department of Genetics and Plant Breeding, Chaudhary Charan Singh University, Meerut, India
| | - Arun Kumar Joshi
- Borlaug Institute for South Asia (BISA), National Agricultural Science Complex (NASC), Dev Prakash Shastri (DPS) Marg, New Delhi, India
- The International Maize and Wheat Improvement Center (CIMMYT), National Agricultural Science Complex (NASC), Dev Prakash Shastri (DPS) Marg, New Delhi, India
| |
Collapse
|
4
|
Takanashi H. Genetic control of morphological traits useful for improving sorghum. BREEDING SCIENCE 2023; 73:57-69. [PMID: 37168813 PMCID: PMC10165342 DOI: 10.1270/jsbbs.22069] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/17/2022] [Accepted: 11/14/2022] [Indexed: 05/13/2023]
Abstract
Global climate change and global warming, coupled with the growing population, have raised concerns about sustainable food supply and bioenergy demand. Sorghum [Sorghum bicolor (L.) Moench] ranks fifth among cereals produced worldwide; it is a C4 crop with a higher stress tolerance than other major cereals and has a wide range of uses, such as grains, forage, and biomass. Therefore, sorghum has attracted attention as a promising crop for achieving sustainable development goals (SDGs). In addition, sorghum is a suitable genetic model for C4 grasses because of its high morphological diversity and relatively small genome size compared to other C4 grasses. Although sorghum breeding and genetic studies have lagged compared to other crops such as rice and maize, recent advances in research have identified several genes and many quantitative trait loci (QTLs) that control important agronomic traits in sorghum. This review outlines traits and genetic information with a focus on morphogenetic aspects that may be useful in sorghum breeding for grain and biomass utilization.
Collapse
Affiliation(s)
- Hideki Takanashi
- Graduate School of Agricultural and Life Sciences, The University of Tokyo, 1-1-1 Yayoi, Bunkyo-ku, Tokyo 113-8657, Japan
- Corresponding author (e-mail: )
| |
Collapse
|
5
|
Budhlakoti N, Mishra DC, Majumdar SG, Kumar A, Srivastava S, Rai SN, Rai A. Integrated model for genomic prediction under additive and non-additive genetic architecture. FRONTIERS IN PLANT SCIENCE 2022; 13:1027558. [PMID: 36531414 PMCID: PMC9749549 DOI: 10.3389/fpls.2022.1027558] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/25/2022] [Accepted: 10/11/2022] [Indexed: 06/17/2023]
Abstract
Using data from genome-wide molecular markers, genomic selection procedures have proved useful for estimating breeding values and phenotypic prediction. The link between an individual genotype and phenotype has been modelled using a number of parametric methods to estimate individual breeding value. It has been observed that parametric methods perform satisfactorily only when the system under study has additive genetic architecture. To capture non-additive (dominance and epistasis) effects, nonparametric approaches have also been developed; however, they typically fall short of capturing additive effects. The idea behind this study is to select the most appropriate model from each parametric and nonparametric category and build an integrated model that can incorporate the best features of both models. It was observed from the results of the current study that GBLUP performed admirably under additive architecture, while SVM's performance in non-additive architecture was found to be encouraging. A robust model for genomic prediction has been developed in light of these findings, which can handle both additive and epistatic effects simultaneously by minimizing their error variance. The developed integrated model has been assessed using standard evaluation measures like predictive ability and error variance.
Collapse
Affiliation(s)
- Neeraj Budhlakoti
- Division of Agricultural Bioinformatics, ICAR-Indian Agricultural Statistics Research Institute, New Delhi, India
| | - Dwijesh Chandra Mishra
- Division of Agricultural Bioinformatics, ICAR-Indian Agricultural Statistics Research Institute, New Delhi, India
| | - Sayanti Guha Majumdar
- Division of Agricultural Bioinformatics, ICAR-Indian Agricultural Statistics Research Institute, New Delhi, India
| | - Anuj Kumar
- Department of Microbiology and Immunology, Dalhousie University, Halifax, NS, Canada
| | - Sudhir Srivastava
- Division of Agricultural Bioinformatics, ICAR-Indian Agricultural Statistics Research Institute, New Delhi, India
| | - S. N. Rai
- Bioinformatics and Biostatistics Department, University of Louisville, Louisville, KY, United States
| | - Anil Rai
- Division of Agricultural Bioinformatics, ICAR-Indian Agricultural Statistics Research Institute, New Delhi, India
| |
Collapse
|
6
|
Genomic prediction through machine learning and neural networks for traits with epistasis. Comput Struct Biotechnol J 2022; 20:5490-5499. [PMID: 36249559 PMCID: PMC9547190 DOI: 10.1016/j.csbj.2022.09.029] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2022] [Revised: 09/20/2022] [Accepted: 09/20/2022] [Indexed: 11/22/2022] Open
Abstract
Performance of machine learning and neural netowrks in Genomic analysis. Heritability and QTL number impacts on performance machine learning methods. Machine learning models in genomic analyses. Neural networks can present better performance for complex quantitative traits.
Genomic wide selection (GWS) is one contributions of molecular genetics to breeding. Machine learning (ML) and artificial neural networks (ANN) methods are non-parameterized and can develop more accurate and parsimonious models for GWS analysis. Multivariate Adaptive Regression Splines (MARS) is considered one of the most flexible ML methods, automatically modeling nonlinearities and interactions of the predictor variables. This study aimed to evaluate and compare methods based on ANN, ML, including MARS, and G-BLUP through GWS. An F2 population formed by 1000 individuals and genotyped for 4010 SNP markers and twelve traits from a model considering epistatic effect, with QTL numbers ranging from eight to 480 and heritability (h2) of 0.3, 0.5 or 0.8 were simulated. Variation in heritability and number of QTL impacts the performance of methods. About quantitative traits (40, 80, 120, 240, and 480 QTLs) was observed highest R2 to Radial Base Network (RBF) and G-BLUP, followed by Random Forest (RF), Bagging (BA), and Boosting (BO). RF and BA also showed better results for traits to h2 of 0.3 with R2 values 16.51% and 16.30%, respectively, while MARS methods showed better results for oligogenic traits with R2 values ranging from 39,12 % to 43,20 % in h2 of 0.5 and from 59.92% to 78,56% in h2 of 0.8. Non-additive MARS methods also showed high R2 for traits with high heritability and 240 QTLs or more. ANN and ML methods are powerful tools to predict genetic values in traits with epistatic effect, for different degrees of heritability and QTL numbers.
Collapse
|
7
|
Zia B, Shi A, Olaoye D, Xiong H, Ravelombola W, Gepts P, Schwartz HF, Brick MA, Otto K, Ogg B, Chen S. Genome-Wide Association Study and Genomic Prediction for Bacterial Wilt Resistance in Common Bean ( Phaseolus vulgaris) Core Collection. Front Genet 2022; 13:853114. [PMID: 35711938 PMCID: PMC9197503 DOI: 10.3389/fgene.2022.853114] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2022] [Accepted: 04/14/2022] [Indexed: 11/30/2022] Open
Abstract
Common bean (Phaseolus vulgaris) is one of the major legume crops cultivated worldwide. Bacterial wilt (BW) of common bean (Curtobacterium flaccumfaciens pv. flaccumfaciens), being a seed-borne disease, has been a challenge in common bean producing regions. A genome-wide association study (GWAS) was conducted to identify SNP markers associated with BW resistance in the USDA common bean core collection. A total of 168 accessions were evaluated for resistance against three different isolates of BW. Our study identified a total of 14 single nucleotide polymorphism (SNP) markers associated with the resistance to BW isolates 528, 557, and 597 using mixed linear models (MLMs) in BLINK, FarmCPU, GAPIT, and TASSEL 5. These SNPs were located on chromosomes Phaseolus vulgaris [Pv]02, Pv04, Pv08, and Pv09 for isolate 528; Pv07, Pv10, and Pv11 for isolate 557; and Pv04, Pv08, and Pv10 for isolate 597. The genomic prediction accuracy was assessed by utilizing seven GP models with 1) all the 4,568 SNPs and 2) the 14 SNP markers. The overall prediction accuracy (PA) ranged from 0.30 to 0.56 for resistance against the three BW isolates. A total of 14 candidate genes were discovered for BW resistance located on chromosomes Pv02, Pv04, Pv07, Pv08, and Pv09. This study revealed vital information for developing genetic resistance against the BW pathogen in common bean. Accordingly, the identified SNP markers and candidate genes can be utilized in common bean molecular breeding programs to develop novel resistant cultivars.
Collapse
Affiliation(s)
- Bazgha Zia
- Department of Horticulture, University of Arkansas, Fayetteville, AR, United States
| | - Ainong Shi
- Department of Horticulture, University of Arkansas, Fayetteville, AR, United States
| | - Dotun Olaoye
- Department of Horticulture, University of Arkansas, Fayetteville, AR, United States
| | - Haizheng Xiong
- Department of Horticulture, University of Arkansas, Fayetteville, AR, United States
| | - Waltram Ravelombola
- Organic & Specialty Crop Breeding, Texas A&M AgriLife Research, Vernon, TX, United States
| | - Paul Gepts
- Department of Plant Sciences/MS1, University of California, Davis, Davis, CA, United States
| | - Howard F Schwartz
- Department of Agricultural Biology, Colorado State University, Fort Collins, CO, United States
| | - Mark A Brick
- Department of Soil and Crop Sciences, Colorado State University, Fort Collins, CO, United States
| | - Kristen Otto
- Department of Agricultural Biology, Colorado State University, Fort Collins, CO, United States
| | - Barry Ogg
- Department of Soil and Crop Sciences, Colorado State University, Fort Collins, CO, United States
| | - Senyu Chen
- Department of Plant Pathology, University of Minnesota, Minneapolis, MN, United States
| |
Collapse
|
8
|
Sun R, Sun B, Tian Y, Su S, Zhang Y, Zhang W, Wang J, Yu P, Guo B, Li H, Li Y, Gao H, Gu Y, Yu L, Ma Y, Su E, Li Q, Hu X, Zhang Q, Guo R, Chai S, Feng L, Wang J, Hong H, Xu J, Yao X, Wen J, Liu J, Li Y, Qiu L. Dissection of the practical soybean breeding pipeline by developing ZDX1, a high-throughput functional array. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2022; 135:1413-1427. [PMID: 35187586 PMCID: PMC9033737 DOI: 10.1007/s00122-022-04043-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/22/2021] [Accepted: 01/22/2022] [Indexed: 05/13/2023]
Abstract
KEY MESSAGE We developed the ZDX1 high-throughput functional soybean array for high accuracy evaluation and selection of both parents and progeny, which can greatly accelerate soybean breeding. Microarray technology facilitates rapid, accurate, and economical genotyping. Here, using resequencing data from 2214 representative soybean accessions, we developed the high-throughput functional array ZDX1, containing 158,959 SNPs, covering 90.92% of soybean genes and sites related to important traits. By application of the array, a total of 817 accessions were genotyped, including three subpopulations of candidate parental lines, parental lines and their progeny from practical breeding. The fixed SNPs were identified in progeny, indicating artificial selection during the breeding process. By identifying functional sites of target traits, novel soybean cyst nematode-resistant progeny and maturity-related novel sources were identified by allele combinations, demonstrating that functional sites provide an efficient method for the rapid screening of desirable traits or gene sources. Notably, we found that the breeding index (BI) was a good indicator for progeny selection. Superior progeny were derived from the combination of distantly related parents, with at least one parent having a higher BI. Furthermore, new combinations based on good performance were proposed for further breeding after excluding redundant and closely related parents. Genomic best linear unbiased prediction (GBLUP) analysis was the best analysis method and achieved the highest accuracy in predicting four traits when comparing SNPs in genic regions rather than whole genomic or intergenic SNPs. The prediction accuracy was improved by 32.1% by using progeny to expand the training population. Collectively, a versatile assay demonstrated that the functional ZDX1 array provided efficient information for the design and optimization of a breeding pipeline for accelerated soybean breeding.
Collapse
Affiliation(s)
- Rujian Sun
- College of Agriculture, Northeast Agricultural University, Harbin, 150030, People's Republic of China
- National Key Facility for Crop Gene Resources and Genetic Improvement, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, No.12 Zhongguancun South Street, Haidian District, Beijing, 100081, People's Republic of China
- Hulunbuir Institute of Agriculture and Animal Husbandry, Hulunbuir, 021000, People's Republic of China
| | - Bincheng Sun
- Hulunbuir Institute of Agriculture and Animal Husbandry, Hulunbuir, 021000, People's Republic of China
| | - Yu Tian
- National Key Facility for Crop Gene Resources and Genetic Improvement, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, No.12 Zhongguancun South Street, Haidian District, Beijing, 100081, People's Republic of China
| | - Shanshan Su
- Beijing Compass Biotechnology Co, Ltd, Beijing, 102200, People's Republic of China
| | - Yong Zhang
- Keshan Branch of Heilongjiang Academy of Agricultural Sciences, Qiqihar, 161600, People's Republic of China
| | - Wanhai Zhang
- Hulunbuir Institute of Agriculture and Animal Husbandry, Hulunbuir, 021000, People's Republic of China
| | - Jingshun Wang
- Hulunbuir Institute of Agriculture and Animal Husbandry, Hulunbuir, 021000, People's Republic of China
| | - Ping Yu
- Hulunbuir Institute of Agriculture and Animal Husbandry, Hulunbuir, 021000, People's Republic of China
| | - Bingfu Guo
- National Key Facility for Crop Gene Resources and Genetic Improvement, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, No.12 Zhongguancun South Street, Haidian District, Beijing, 100081, People's Republic of China
| | - Huihui Li
- National Key Facility for Crop Gene Resources and Genetic Improvement, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, No.12 Zhongguancun South Street, Haidian District, Beijing, 100081, People's Republic of China
| | - Yanfei Li
- National Key Facility for Crop Gene Resources and Genetic Improvement, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, No.12 Zhongguancun South Street, Haidian District, Beijing, 100081, People's Republic of China
| | - Huawei Gao
- National Key Facility for Crop Gene Resources and Genetic Improvement, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, No.12 Zhongguancun South Street, Haidian District, Beijing, 100081, People's Republic of China
| | - Yongzhe Gu
- National Key Facility for Crop Gene Resources and Genetic Improvement, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, No.12 Zhongguancun South Street, Haidian District, Beijing, 100081, People's Republic of China
| | - Lili Yu
- National Key Facility for Crop Gene Resources and Genetic Improvement, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, No.12 Zhongguancun South Street, Haidian District, Beijing, 100081, People's Republic of China
| | - Yansong Ma
- National Key Facility for Crop Gene Resources and Genetic Improvement, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, No.12 Zhongguancun South Street, Haidian District, Beijing, 100081, People's Republic of China
| | - Erhu Su
- Inner Mongolia Academy of Agricultural and Animal Husbandry Sciences, Hohhot, 010000, People's Republic of China
| | - Qiang Li
- Inner Mongolia Academy of Agricultural and Animal Husbandry Sciences, Hohhot, 010000, People's Republic of China
| | - Xingguo Hu
- Hulunbuir Institute of Agriculture and Animal Husbandry, Hulunbuir, 021000, People's Republic of China
| | - Qi Zhang
- Hulunbuir Institute of Agriculture and Animal Husbandry, Hulunbuir, 021000, People's Republic of China
| | - Rongqi Guo
- Hulunbuir Institute of Agriculture and Animal Husbandry, Hulunbuir, 021000, People's Republic of China
| | - Shen Chai
- Hulunbuir Institute of Agriculture and Animal Husbandry, Hulunbuir, 021000, People's Republic of China
| | - Lei Feng
- Hulunbuir Institute of Agriculture and Animal Husbandry, Hulunbuir, 021000, People's Republic of China
| | - Jun Wang
- National Key Facility for Crop Gene Resources and Genetic Improvement, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, No.12 Zhongguancun South Street, Haidian District, Beijing, 100081, People's Republic of China
| | - Huilong Hong
- National Key Facility for Crop Gene Resources and Genetic Improvement, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, No.12 Zhongguancun South Street, Haidian District, Beijing, 100081, People's Republic of China
| | - Jiangyuan Xu
- National Key Facility for Crop Gene Resources and Genetic Improvement, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, No.12 Zhongguancun South Street, Haidian District, Beijing, 100081, People's Republic of China
| | - Xindong Yao
- Department of Crop Sciences, University of Natural Resources and Life Sciences Vienna (BOKU), 3430, Tulln, Austria
| | - Jing Wen
- National Key Facility for Crop Gene Resources and Genetic Improvement, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, No.12 Zhongguancun South Street, Haidian District, Beijing, 100081, People's Republic of China
| | - Jiqiang Liu
- Beijing Compass Biotechnology Co, Ltd, Beijing, 102200, People's Republic of China
| | - Yinghui Li
- College of Agriculture, Northeast Agricultural University, Harbin, 150030, People's Republic of China.
- National Key Facility for Crop Gene Resources and Genetic Improvement, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, No.12 Zhongguancun South Street, Haidian District, Beijing, 100081, People's Republic of China.
| | - Lijuan Qiu
- College of Agriculture, Northeast Agricultural University, Harbin, 150030, People's Republic of China.
- National Key Facility for Crop Gene Resources and Genetic Improvement, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, No.12 Zhongguancun South Street, Haidian District, Beijing, 100081, People's Republic of China.
| |
Collapse
|
9
|
He Z, Li S, Li W, Ding J, Zheng M, Li Q, Fahey AG, Wen J, Liu R, Zhao G. Comparison of genomic prediction methods for residual feed intake in broilers. Anim Genet 2022; 53:466-469. [PMID: 35292985 DOI: 10.1111/age.13186] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2022] [Revised: 02/24/2022] [Accepted: 02/28/2022] [Indexed: 11/30/2022]
Abstract
Residual feed intake (RFI) is a measure of the feed efficiency of animals. Previous studies have identified SNPs associated with RFI. The objective of this study was to compare the GBLUP model with the GA-BLUP model including previously identified associated SNPs. The nine associated SNPs were obtained from the genome-wide association study on a discovery population as preselection information. These models were analysed using ASREML software using a 5-fold cross-validation method on a validation population. With the genetic architecture (GA) matrix used, which was conducted with the nine RFI-associated SNPs, the prediction accuracy of RFI was improved compared with the original GBLUP model. The calculated optimal ω was 0.981 for RFI, which is in line with the optimal range from 0.9 to 1.0 in the gradient test. The prediction accuracy increased by 2% in the GA-BLUP model with ω being 0.981 compared with the GBLUP model. In conclusion, the GA-BLUP with the nine RFI-associated SNPs and an optimal ω can improve the prediction accuracy for a specific trait compared with GBLUP.
Collapse
Affiliation(s)
- Zhengxiao He
- State Key Laboratory of Animal Nutrition, Key Laboratory of Animal (Poultry) Genetics Breeding and Reproduction, Ministry of Agriculture, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100193, China.,School of Agriculture and Food Science, University College Dublin, Dublin, Ireland
| | - Sen Li
- State Key Laboratory of Animal Nutrition, Key Laboratory of Animal (Poultry) Genetics Breeding and Reproduction, Ministry of Agriculture, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100193, China
| | - Wei Li
- State Key Laboratory of Animal Nutrition, Key Laboratory of Animal (Poultry) Genetics Breeding and Reproduction, Ministry of Agriculture, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100193, China
| | - Jiqiang Ding
- State Key Laboratory of Animal Nutrition, Key Laboratory of Animal (Poultry) Genetics Breeding and Reproduction, Ministry of Agriculture, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100193, China
| | - Maiqing Zheng
- State Key Laboratory of Animal Nutrition, Key Laboratory of Animal (Poultry) Genetics Breeding and Reproduction, Ministry of Agriculture, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100193, China
| | - Qinghe Li
- State Key Laboratory of Animal Nutrition, Key Laboratory of Animal (Poultry) Genetics Breeding and Reproduction, Ministry of Agriculture, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100193, China
| | - Alan G Fahey
- School of Agriculture and Food Science, University College Dublin, Dublin, Ireland
| | - Jie Wen
- State Key Laboratory of Animal Nutrition, Key Laboratory of Animal (Poultry) Genetics Breeding and Reproduction, Ministry of Agriculture, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100193, China
| | - Ranran Liu
- State Key Laboratory of Animal Nutrition, Key Laboratory of Animal (Poultry) Genetics Breeding and Reproduction, Ministry of Agriculture, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100193, China
| | - Guiping Zhao
- State Key Laboratory of Animal Nutrition, Key Laboratory of Animal (Poultry) Genetics Breeding and Reproduction, Ministry of Agriculture, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100193, China
| |
Collapse
|
10
|
Zhao S, Li X, Song J, Li H, Zhao X, Zhang P, Li Z, Tian Z, Lv M, Deng C, Ai T, Chen G, Zhang H, Hu J, Xu Z, Chen J, Ding J, Song W, Chang Y. Genetic dissection of maize plant architecture using a novel nested association mapping population. THE PLANT GENOME 2022; 15:e20179. [PMID: 34859966 DOI: 10.1002/tpg2.20179] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/14/2021] [Accepted: 10/25/2021] [Indexed: 06/13/2023]
Abstract
The leaf angle (LA), plant height (PH), and ear height (EH) are key plant architectural traits influencing maize (Zea mays L.) yield. However, their genetic determinants have not yet been well-characterized. Here, we developed a maize advanced backcross-nested association mapping population in Henan Agricultural University (HNAU-NAM1) comprised of 1,625 BC1 F4 /BC2 F4 lines. These were obtained by crossing a diverse set of 12 representative inbred lines with the common GEMS41 line, which were then genotyped using the MaizeSNP9.4K array. Genetic diversity and phenotypic distribution analyses showed considerable levels of genetic variation. We obtained 18-88 quantitative trait loci (QTLs) associated with LA, PH, and EH by using three complementary mapping methods, named as separate linkage mapping, joint linkage mapping, and genome-wide association studies. Our analyses enabled the identification of ten QTL hot-spot regions associated with the three traits, which were distributed on nine different chromosomes. We further selected 13 major QTLs that were simultaneously detected by three methods and deduced the candidate genes, of which eight were not reported before. The newly constructed HNAU-NAM1 population in this study will further broaden our insights into understanding of genetic regulation of plant architecture, thus will help to improve maize yield and provide an invaluable resource for maize functional genomics and breeding research.
Collapse
Affiliation(s)
- Sheng Zhao
- National Key Laboratory of Wheat and Maize Crop Science, Henan Agricultural Univ., Zhengzhou, 450002, China
- Shenzhen Branch, Guangdong Laboratory for 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, 518120, China
| | - Xueying Li
- National Key Laboratory of Wheat and Maize Crop Science, Henan Agricultural Univ., Zhengzhou, 450002, China
| | - Junfeng Song
- National Key Laboratory of Wheat and Maize Crop Science, Henan Agricultural Univ., Zhengzhou, 450002, China
- State Key Laboratory of Plant Physiology and Biochemistry, National Maize Improvement Center, College of Agronomy and Biotechnology, China Agricultural Univ., Beijing, 100193, China
| | - Huimin Li
- National Key Laboratory of Wheat and Maize Crop Science, Henan Agricultural Univ., Zhengzhou, 450002, China
| | - Xiaodi Zhao
- National Key Laboratory of Wheat and Maize Crop Science, Henan Agricultural Univ., Zhengzhou, 450002, China
| | - Peng Zhang
- Shenzhen Branch, Guangdong Laboratory for 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, 518120, China
- College of Life Science and Technology, Guangxi Univ., Nanning, 530004, China
| | - Zhimin Li
- National Key Laboratory of Wheat and Maize Crop Science, Henan Agricultural Univ., Zhengzhou, 450002, China
| | - Zhiqiang Tian
- National Key Laboratory of Wheat and Maize Crop Science, Henan Agricultural Univ., Zhengzhou, 450002, China
| | - Meng Lv
- National Key Laboratory of Wheat and Maize Crop Science, Henan Agricultural Univ., Zhengzhou, 450002, China
| | - Ce Deng
- National Key Laboratory of Wheat and Maize Crop Science, Henan Agricultural Univ., Zhengzhou, 450002, China
| | - Tangshun Ai
- National Key Laboratory of Wheat and Maize Crop Science, Henan Agricultural Univ., Zhengzhou, 450002, China
| | - Gengshen Chen
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural Univ., Wuhan, 430070, China
| | - Hui Zhang
- Shenzhen Branch, Guangdong Laboratory for 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, 518120, China
| | - Jianlin Hu
- Food Crops Institute, Hubei Academy of Agricultural Sciences, Wuhan, 430064, China
| | - Zhijun Xu
- Zhanjiang Experiment Station, Chinese Academy of Tropical Agricultural Sciences, Zhanjiang, 524013, China
| | - Jiafa Chen
- National Key Laboratory of Wheat and Maize Crop Science, Henan Agricultural Univ., Zhengzhou, 450002, China
| | - Junqiang Ding
- National Key Laboratory of Wheat and Maize Crop Science, Henan Agricultural Univ., Zhengzhou, 450002, China
| | - Weibin Song
- State Key Laboratory of Plant Physiology and Biochemistry, National Maize Improvement Center, College of Agronomy and Biotechnology, China Agricultural Univ., Beijing, 100193, China
| | - Yuxiao Chang
- Shenzhen Branch, Guangdong Laboratory for 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, 518120, China
| |
Collapse
|
11
|
Roth M, Beugnot A, Mary-Huard T, Moreau L, Charcosset A, Fiévet JB. Improving genomic predictions with inbreeding and nonadditive effects in two admixed maize hybrid populations in single and multienvironment contexts. Genetics 2022; 220:6527635. [PMID: 35150258 PMCID: PMC8982028 DOI: 10.1093/genetics/iyac018] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2021] [Accepted: 01/28/2022] [Indexed: 11/12/2022] Open
Abstract
Genetic admixture, resulting from the recombination between structural groups, is frequently encountered in breeding populations. In hybrid breeding, crossing admixed lines can generate substantial nonadditive genetic variance and contrasted levels of inbreeding which can impact trait variation. This study aimed at testing recent methodological developments for the modeling of inbreeding and nonadditive effects in order to increase prediction accuracy in admixed populations. Using two maize (Zea mays L.) populations of hybrids admixed between dent and flint heterotic groups, we compared a suite of five genomic prediction models incorporating (or not) parameters accounting for inbreeding and nonadditive effects with the natural and orthogonal interaction approach in single and multienvironment contexts. In both populations, variance decompositions showed the strong impact of inbreeding on plant yield, height, and flowering time which was supported by the superiority of prediction models incorporating this effect (+0.038 in predictive ability for mean yield). In most cases dominance variance was reduced when inbreeding was accounted for. The model including additivity, dominance, epistasis, and inbreeding effects appeared to be the most robust for prediction across traits and populations (+0.054 in predictive ability for mean yield). In a multienvironment context, we found that the inclusion of nonadditive and inbreeding effects was advantageous when predicting hybrids not yet observed in any environment. Overall, comparing variance decompositions was helpful to guide model selection for genomic prediction. Finally, we recommend the use of models including inbreeding and nonadditive parameters following the natural and orthogonal interaction approach to increase prediction accuracy in admixed populations.
Collapse
Affiliation(s)
- Morgane Roth
- Plant Breeding Research Division, Agroscope, Wädenswil, 8820 Zurich, Switzerland,Corresponding author: INRAE GAFL, 67 Allée des Chênes 84140 Montfavet, France.
| | - Aurélien Beugnot
- Université Paris-Saclay, INRAE, CNRS, AgroParisTech, UMR GQE-Le Moulon, 91190 Gif-sur-Yvette, France
| | - Tristan Mary-Huard
- Université Paris-Saclay, INRAE, CNRS, AgroParisTech, UMR GQE-Le Moulon, 91190 Gif-sur-Yvette, France,Université Paris-Saclay, INRAE, AgroParisTech, UMR MIA-Paris Paris, 75005 Paris, France
| | - Laurence Moreau
- Université Paris-Saclay, INRAE, CNRS, AgroParisTech, UMR GQE-Le Moulon, 91190 Gif-sur-Yvette, France
| | - Alain Charcosset
- Université Paris-Saclay, INRAE, CNRS, AgroParisTech, UMR GQE-Le Moulon, 91190 Gif-sur-Yvette, France
| | - Julie B Fiévet
- Université Paris-Saclay, INRAE, CNRS, AgroParisTech, UMR GQE-Le Moulon, 91190 Gif-sur-Yvette, France
| |
Collapse
|
12
|
Thumma BR, Joyce KR, Jacobs A. Genomic studies with preselected markers reveal dominance effects influencing growth traits in Eucalyptus nitens. G3 GENES|GENOMES|GENETICS 2022; 12:6423988. [PMID: 34791210 PMCID: PMC8728041 DOI: 10.1093/g3journal/jkab363] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/18/2021] [Accepted: 10/13/2021] [Indexed: 11/17/2022]
Abstract
Genomic selection (GS) is being increasingly adopted by the tree breeding community. Most of the GS studies in trees are focused on estimating additive genetic effects. Exploiting the dominance effects offers additional opportunities to improve genetic gain. To detect dominance effects, trait-relevant markers may be important compared to nonselected markers. Here, we used preselected markers to study the dominance effects in a Eucalyptus nitens (E. nitens) breeding population consisting of open-pollinated (OP) and controlled-pollinated (CP) families. We used 8221 trees from six progeny trials in this study. Of these, 868 progeny and 255 parents were genotyped with the E. nitens marker panel. Three traits; diameter at breast height (DBH), wood basic density (DEN), and kraft pulp yield (KPY) were analyzed. Two types of genomic relationship matrices based on identity-by-state (IBS) and identity-by-descent (IBD) were tested. Performance of the genomic best linear unbiased prediction (GBLUP) models with IBS and IBD matrices were compared with pedigree-based additive best linear unbiased prediction (ABLUP) models with and without the pedigree reconstruction. Similarly, the performance of the single-step GBLUP (ssGBLUP) with IBS and IBD matrices were compared with ABLUP models using all 8221 trees. Significant dominance effects were observed with the GBLUP-AD model for DBH. The predictive ability of DBH is higher with the GBLUP-AD model compared to other models. Similarly, the prediction accuracy of genotypic values is higher with GBLUP-AD compared to the GBLUP-A model. Among the two GBLUP models (IBS and IBD), no differences were observed in predictive abilities and prediction accuracies. While the estimates of predictive ability with additive effects were similar among all four models, prediction accuracies of ABLUP were lower than the GBLUP models. The prediction accuracy of ssGBLUP-IBD is higher than the other three models while the theoretical accuracy of ssGBLUP-IBS is consistently higher than the other three models across all three groups tested (parents, genotyped, and nongenotyped). Significant inbreeding depression was observed for DBH and KPY. While there is a linear relationship between inbreeding and DBH, the relationship between inbreeding and KPY is nonlinear and quadratic. These results indicate that the inbreeding depression of DBH is mainly due to directional dominance while in KPY it may be due to epistasis. Inbreeding depression may be the main source of the observed dominance effects in DBH. The significant dominance effect observed for DBH may be used to select complementary parents to improve the genetic merit of the progeny in E. nitens.
Collapse
Affiliation(s)
- Bala R Thumma
- Gondwana Genomics Pty Ltd , Canberra, ACT 2600, Australia
| | | | | |
Collapse
|
13
|
Martins Oliveira IC, Bernardeli A, Soler Guilhen JH, Pastina MM. Genomic Prediction of Complex Traits in an Allogamous Annual Crop: The Case of Maize Single-Cross Hybrids. Methods Mol Biol 2022; 2467:543-567. [PMID: 35451790 DOI: 10.1007/978-1-0716-2205-6_20] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
For many plant and animal species, commercial products are hybrids between individuals from different genetic groups. For allogamous plant species such as maize, the breeding objective is to produce single-cross hybrid varieties from two inbred lines each selected in complementary groups. Efficient hybrid breeding requires methods that (1) quickly generate homozygous and homogeneous parental lines with high combining abilities, (2) efficiently choose among the large number of available parental lines the most promising ones, and (3) predict the performances of sets of non-phenotyped single-cross hybrids, or hybrids phenotyped in a limited number of environments, based on their relationship with another set of hybrids with known performances. The maize breeding community has been developing model-based prediction of hybrid performances well before the genomic era. This chapter (1) provides a reminder of the maize breeding scheme before the genomic era; (2) describes how genomic data were incorporated in the prediction models involved in different steps of genomic-based single-cross maize hybrid breeding; and (3) reviews factors affecting the accuracy of genomic prediction, approaches for optimizing GP-based single-cross maize hybrid breeding schemes, and ensuring the long-term sustainability of genomic selection.
Collapse
Affiliation(s)
| | - Arthur Bernardeli
- Department of Agronomy, Universidade Federal de Viçosa, Viçosa-MG, Brazil
| | | | | |
Collapse
|
14
|
Siekmann D, Jansen G, Zaar A, Kilian A, Fromme FJ, Hackauf B. A Genome-Wide Association Study Pinpoints Quantitative Trait Genes for Plant Height, Heading Date, Grain Quality, and Yield in Rye ( Secale cereale L.). FRONTIERS IN PLANT SCIENCE 2021; 12:718081. [PMID: 34777409 PMCID: PMC8586073 DOI: 10.3389/fpls.2021.718081] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/31/2021] [Accepted: 09/22/2021] [Indexed: 06/03/2023]
Abstract
Rye is the only cross-pollinating Triticeae crop species. Knowledge of rye genes controlling complex-inherited traits is scarce, which, currently, largely disables the genomics assisted introgression of untapped genetic variation from self-incompatible germplasm collections in elite inbred lines for hybrid breeding. We report on the first genome-wide association study (GWAS) in rye based on the phenotypic evaluation of 526 experimental hybrids for plant height, heading date, grain quality, and yield in 2 years and up to 19 environments. We established a cross-validated NIRS calibration model as a fast, effective, and robust analytical method to determine grain quality parameters. We observed phenotypic plasticity in plant height and tiller number as a resource use strategy of rye under drought and identified increased grain arabinoxylan content as a striking phenotype in osmotically stressed rye. We used DArTseq™ as a genotyping-by-sequencing technology to reduce the complexity of the rye genome. We established a novel high-density genetic linkage map that describes the position of almost 19k markers and that allowed us to estimate a low genome-wide LD based on the assessed genetic diversity in elite germplasm. We analyzed the relationship between plant height, heading date, agronomic, as well as grain quality traits, and genotype based on 20k novel single-nucleotide polymorphism markers. In addition, we integrated the DArTseq™ markers in the recently established 'Lo7' reference genome assembly. We identified cross-validated SNPs in 'Lo7' protein-coding genes associated with all traits studied. These include associations of the WUSCHEL-related homeobox transcription factor DWT1 and grain yield, the DELLA protein gene SLR1 and heading date, the Ethylene overproducer 1-like protein gene ETOL1 and thousand-grain weight, protein and starch content, as well as the Lectin receptor kinase SIT2 and plant height. A Leucine-rich repeat receptor protein kinase and a Xyloglucan alpha-1,6-xylosyltransferase count among the cross-validated genes associated with water-extractable arabinoxylan content. This study demonstrates the power of GWAS, hybrid breeding, and the reference genome sequence in rye genetics research to dissect and identify the function of genes shaping genetic diversity in agronomic and grain quality traits of rye. The described links between genetic causes and phenotypic variation will accelerate genomics-enabled rye improvement.
Collapse
Affiliation(s)
- Dörthe Siekmann
- Julius Kühn Institute, Federal Research Centre for Cultivated Plants, Institute for Breeding Research on Agricultural Crops, Sanitz, Germany
- HYBRO Saatzucht GmbH & Co. KG, Schenkenberg, Germany
| | - Gisela Jansen
- Julius Kühn Institute, Federal Research Centre for Cultivated Plants, Institute for Resistance Research and Stress Tolerance, Sanitz, Germany
| | - Anne Zaar
- Julius Kühn Institute, Federal Research Centre for Cultivated Plants, Institute for Resistance Research and Stress Tolerance, Sanitz, Germany
| | | | | | - Bernd Hackauf
- Julius Kühn Institute, Federal Research Centre for Cultivated Plants, Institute for Breeding Research on Agricultural Crops, Sanitz, Germany
| |
Collapse
|
15
|
Derbyshire MC, Khentry Y, Severn-Ellis A, Mwape V, Saad NSM, Newman TE, Taiwo A, Regmi R, Buchwaldt L, Denton-Giles M, Batley J, Kamphuis LG. Modeling first order additive × additive epistasis improves accuracy of genomic prediction for sclerotinia stem rot resistance in canola. THE PLANT GENOME 2021; 14:e20088. [PMID: 33629543 DOI: 10.1002/tpg2.20088] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/30/2020] [Accepted: 01/13/2021] [Indexed: 06/12/2023]
Abstract
The fungus Sclerotinia sclerotiorum infects hundreds of plant species including many crops. Resistance to this pathogen in canola (Brassica napus L. subsp. napus) is controlled by numerous quantitative trait loci (QTL). For such polygenic traits, genomic prediction may be useful for breeding as it can capture many QTL at once while also considering nonadditive genetic effects. Here, we test application of common regression models to genomic prediction of S. sclerotiorum resistance in canola in a diverse panel of 218 plants genotyped at 24,634 loci. Disease resistance was scored by infection with an aggressive isolate and monitoring over 3 wk. We found that including first-order additive × additive epistasis in linear mixed models (LMMs) improved accuracy of breeding value estimation between 3 and 40%, depending on method of assessment, and correlation between phenotypes and predicted total genetic values by 14%. Bayesian models performed similarly to or worse than genomic relationship matrix-based models for estimating breeding values or overall phenotypes from genetic values. Bayesian ridge regression, which is most similar to the genomic relationship matrix-based approach in the amount of shrinkage it applies to marker effects, was the most accurate of this family of models. This confirms several studies indicating the highly polygenic nature of sclerotinia stem rot resistance. Overall, our results highlight the use of simple epistasis terms for prediction of breeding values and total genetic values for a complex disease resistance phenotype in canola.
Collapse
Affiliation(s)
- Mark C Derbyshire
- Centre for Crop and Disease Management, School of Molecular and Life Sciences, Curtin University, Perth, Western Australia, Australia
| | - Yuphin Khentry
- Centre for Crop and Disease Management, School of Molecular and Life Sciences, Curtin University, Perth, Western Australia, Australia
| | - Anita Severn-Ellis
- School of Biological Sciences, University of Western Australia, Perth, Western Australia, Australia
| | - Virginia Mwape
- Centre for Crop and Disease Management, School of Molecular and Life Sciences, Curtin University, Perth, Western Australia, Australia
| | - Nur Shuhadah Mohd Saad
- School of Biological Sciences, University of Western Australia, Perth, Western Australia, Australia
| | - Toby E Newman
- Centre for Crop and Disease Management, School of Molecular and Life Sciences, Curtin University, Perth, Western Australia, Australia
| | - Akeem Taiwo
- Centre for Crop and Disease Management, School of Molecular and Life Sciences, Curtin University, Perth, Western Australia, Australia
| | - Roshan Regmi
- Centre for Crop and Disease Management, School of Molecular and Life Sciences, Curtin University, Perth, Western Australia, Australia
| | - Lone Buchwaldt
- Agriculture and Agri-Food, Saskatoon, Saskatchewan, Canada
| | | | - Jacqueline Batley
- School of Biological Sciences, University of Western Australia, Perth, Western Australia, Australia
| | - Lars G Kamphuis
- Centre for Crop and Disease Management, School of Molecular and Life Sciences, Curtin University, Perth, Western Australia, Australia
| |
Collapse
|
16
|
Zenda T, Liu S, Dong A, Duan H. Advances in Cereal Crop Genomics for Resilience under Climate Change. Life (Basel) 2021; 11:502. [PMID: 34072447 PMCID: PMC8228855 DOI: 10.3390/life11060502] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2021] [Revised: 05/21/2021] [Accepted: 05/25/2021] [Indexed: 12/12/2022] Open
Abstract
Adapting to climate change, providing sufficient human food and nutritional needs, and securing sufficient energy supplies will call for a radical transformation from the current conventional adaptation approaches to more broad-based and transformative alternatives. This entails diversifying the agricultural system and boosting productivity of major cereal crops through development of climate-resilient cultivars that can sustainably maintain higher yields under climate change conditions, expanding our focus to crop wild relatives, and better exploitation of underutilized crop species. This is facilitated by the recent developments in plant genomics, such as advances in genome sequencing, assembly, and annotation, as well as gene editing technologies, which have increased the availability of high-quality reference genomes for various model and non-model plant species. This has necessitated genomics-assisted breeding of crops, including underutilized species, consequently broadening genetic variation of the available germplasm; improving the discovery of novel alleles controlling important agronomic traits; and enhancing creation of new crop cultivars with improved tolerance to biotic and abiotic stresses and superior nutritive quality. Here, therefore, we summarize these recent developments in plant genomics and their application, with particular reference to cereal crops (including underutilized species). Particularly, we discuss genome sequencing approaches, quantitative trait loci (QTL) mapping and genome-wide association (GWAS) studies, directed mutagenesis, plant non-coding RNAs, precise gene editing technologies such as CRISPR-Cas9, and complementation of crop genotyping by crop phenotyping. We then conclude by providing an outlook that, as we step into the future, high-throughput phenotyping, pan-genomics, transposable elements analysis, and machine learning hold much promise for crop improvements related to climate resilience and nutritional superiority.
Collapse
Affiliation(s)
- Tinashe Zenda
- State Key Laboratory of North China Crop Improvement and Regulation, Hebei Agricultural University, Baoding 071001, China; (S.L.); (A.D.)
- North China Key Laboratory for Crop Germplasm Resources of the Education Ministry, Hebei Agricultural University, Baoding 071001, China
- Department of Crop Genetics and Breeding, College of Agronomy, Hebei Agricultural University, Baoding 071001, China
- Department of Crop Science, Faculty of Agriculture and Environmental Science, Bindura University of Science Education, Bindura P. Bag 1020, Zimbabwe
| | - Songtao Liu
- State Key Laboratory of North China Crop Improvement and Regulation, Hebei Agricultural University, Baoding 071001, China; (S.L.); (A.D.)
- North China Key Laboratory for Crop Germplasm Resources of the Education Ministry, Hebei Agricultural University, Baoding 071001, China
- Department of Crop Genetics and Breeding, College of Agronomy, Hebei Agricultural University, Baoding 071001, China
| | - Anyi Dong
- State Key Laboratory of North China Crop Improvement and Regulation, Hebei Agricultural University, Baoding 071001, China; (S.L.); (A.D.)
- North China Key Laboratory for Crop Germplasm Resources of the Education Ministry, Hebei Agricultural University, Baoding 071001, China
- Department of Crop Genetics and Breeding, College of Agronomy, Hebei Agricultural University, Baoding 071001, China
| | - Huijun Duan
- State Key Laboratory of North China Crop Improvement and Regulation, Hebei Agricultural University, Baoding 071001, China; (S.L.); (A.D.)
- North China Key Laboratory for Crop Germplasm Resources of the Education Ministry, Hebei Agricultural University, Baoding 071001, China
- Department of Crop Genetics and Breeding, College of Agronomy, Hebei Agricultural University, Baoding 071001, China
| |
Collapse
|
17
|
Liu X, Hu X, Li K, Liu Z, Wu Y, Feng G, Huang C, Wang H. Identifying quantitative trait loci for the general combining ability of yield-relevant traits in maize. BREEDING SCIENCE 2021; 71:217-228. [PMID: 34377070 PMCID: PMC8329886 DOI: 10.1270/jsbbs.20008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/20/2020] [Accepted: 12/14/2020] [Indexed: 06/13/2023]
Abstract
Maize is the most important staple crop worldwide. Many of its agronomic traits present with a high level of heterosis. Combining ability was proposed to exploit the rule of heterosis, and general combining ability (GCA) is a crucial measure of parental performance. In this study, a recombinant inbred line population was used to construct testcross populations by crossing with four testers based on North Carolina design II. Six yield-relevant traits were investigated as phenotypic data. GCA effects were estimated for three scenarios based on the heterotic group and the number of tester lines. These estimates were then used to identify quantitative trait loci (QTL) and dissect genetic basis of GCA. A higher heritability of GCA was obtained for each trait. Thus, testing in early generation of breeding may effectively select candidate lines with relatively superior GCA performance. The GCA QTL detected in each scenario was slightly different according to the linkage mapping. Most of the GCA-relevant loci were simultaneously detected in all three datasets. Therefore, the genetic basis of GCA was nearly constant although discrepant inbred lines were appointed as testers. In addition, favorable alleles corresponding to GCA could be pyramided via marker-assisted selection and made available for maize hybrid breeding.
Collapse
Affiliation(s)
- Xiaogang Liu
- Institute of Crop Science, National Key Facility of Crop Gene Resources and Genetic Improvement, Chinese Academy of Agricultural Sciences, Beijing 100081, China
| | - Xiaojiao Hu
- Institute of Crop Science, National Key Facility of Crop Gene Resources and Genetic Improvement, Chinese Academy of Agricultural Sciences, Beijing 100081, China
| | - Kun Li
- Institute of Crop Science, National Key Facility of Crop Gene Resources and Genetic Improvement, Chinese Academy of Agricultural Sciences, Beijing 100081, China
| | - Zhifang Liu
- Institute of Crop Science, National Key Facility of Crop Gene Resources and Genetic Improvement, Chinese Academy of Agricultural Sciences, Beijing 100081, China
| | - Yujin Wu
- Institute of Crop Science, National Key Facility of Crop Gene Resources and Genetic Improvement, Chinese Academy of Agricultural Sciences, Beijing 100081, China
| | - Guang Feng
- Liaoning Dandong Academy of Agricultural Sciences, Dandong 118109, China
| | - Changling Huang
- Institute of Crop Science, National Key Facility of Crop Gene Resources and Genetic Improvement, Chinese Academy of Agricultural Sciences, Beijing 100081, China
| | - Hongwu Wang
- Institute of Crop Science, National Key Facility of Crop Gene Resources and Genetic Improvement, Chinese Academy of Agricultural Sciences, Beijing 100081, China
| |
Collapse
|
18
|
Ma J, Cao Y. Genetic Dissection of Grain Yield of Maize and Yield-Related Traits Through Association Mapping and Genomic Prediction. FRONTIERS IN PLANT SCIENCE 2021; 12:690059. [PMID: 34335658 PMCID: PMC8319912 DOI: 10.3389/fpls.2021.690059] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/02/2021] [Accepted: 06/14/2021] [Indexed: 05/21/2023]
Abstract
High yield is the primary objective of maize breeding. Genomic dissection of grain yield and yield-related traits contribute to understanding the yield formation and improving the yield of maize. In this study, two genome-wide association study (GWAS) methods and genomic prediction were made on an association panel of 309 inbred lines. GWAS analyses revealed 22 significant trait-marker associations for grain yield per plant (GYP) and yield-related traits. Genomic prediction analyses showed that reproducing kernel Hilbert space (RKHS) outperformed the other four models based on GWAS-derived markers for GYP, ear weight, kernel number per ear and row, ear length, and ear diameter, whereas genomic best linear unbiased prediction (GBLUP) showed a slight superiority over other modes in most subsets of the trait-associated marker (TAM) for thousand kernel weight and kernel row number. The prediction accuracy could be improved when significant single-nucleotide polymorphisms were fitted as the fixed effects. Integrating information on population structure into the fixed model did not improve the prediction performance. For GYP, the prediction accuracy of TAMs derived from fixed and random model Circulating Probability Unification (FarmCPU) was comparable to that of the compressed mixed linear model (CMLM). For yield-related traits, CMLM-derived markers provided better accuracies than FarmCPU-derived markers in most scenarios. Compared with all markers, TAMs could effectively improve the prediction accuracies for GYP and yield-related traits. For eight traits, moderate- and high-prediction accuracies were achieved using TAMs. Taken together, genomic prediction incorporating prior information detected by GWAS could be a promising strategy to improve the grain yield of maize.
Collapse
|
19
|
Adoption and Optimization of Genomic Selection To Sustain Breeding for Apricot Fruit Quality. G3-GENES GENOMES GENETICS 2020; 10:4513-4529. [PMID: 33067307 PMCID: PMC7718743 DOI: 10.1534/g3.120.401452] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Genomic selection (GS) is a breeding approach which exploits genome-wide information and whose unprecedented success has shaped several animal and plant breeding schemes through delivering their genetic progress. This is the first study assessing the potential of GS in apricot (Prunus armeniaca) to enhance postharvest fruit quality attributes. Genomic predictions were based on a F1 pseudo-testcross population, comprising 153 individuals with contrasting fruit quality traits. They were phenotyped for physical and biochemical fruit metrics in contrasting climatic conditions over two years. Prediction accuracy (PA) varied from 0.31 for glucose content with the Bayesian LASSO (BL) to 0.78 for ethylene production with RR-BLUP, which yielded the most accurate predictions in comparison to Bayesian models and only 10% out of 61,030 SNPs were sufficient to reach accurate predictions. Useful insights were provided on the genetic architecture of apricot fruit quality whose integration in prediction models improved their performance, notably for traits governed by major QTL. Furthermore, multivariate modeling yielded promising outcomes in terms of PA within training partitions partially phenotyped for target traits. This provides a useful framework for the implementation of indirect selection based on easy-to-measure traits. Thus, we highlighted the main levers to take into account for the implementation of GS for fruit quality in apricot, but also to improve the genetic gain in perennial species.
Collapse
|
20
|
Kajiya-Kanegae H, Takanashi H, Fujimoto M, Ishimori M, Ohnishi N, Wacera W F, Omollo EA, Kobayashi M, Yano K, Nakano M, Kozuka T, Kusaba M, Iwata H, Tsutsumi N, Sakamoto W. RAD-seq-Based High-Density Linkage Map Construction and QTL Mapping of Biomass-Related Traits in Sorghum using the Japanese Landrace Takakibi NOG. PLANT & CELL PHYSIOLOGY 2020; 61:1262-1272. [PMID: 32353144 DOI: 10.1093/pcp/pcaa056] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/30/2019] [Accepted: 04/21/2020] [Indexed: 06/11/2023]
Abstract
Sorghum [Sorghum bicolor (L.) Moench] grown locally by Japanese farmers is generically termed Takakibi, although its genetic diversity compared with geographically distant varieties or even within Takakibi lines remains unclear. To explore the genomic diversity and genetic traits controlling biomass and other physiological traits in Takakibi, we focused on a landrace, NOG, in this study. Admixture analysis of 460 sorghum accessions revealed that NOG belonged to the subgroup that represented Asian sorghums, and it was only distantly related to American/African accessions including BTx623. In an attempt to dissect major traits related to biomass, we generated a recombinant inbred line (RIL) from a cross between BTx623 and NOG, and we constructed a high-density linkage map based on 3,710 single-nucleotide polymorphisms obtained by restriction-site-associated DNA sequencing of 213 RIL individuals. Consequently, 13 fine quantitative trait loci (QTLs) were detected on chromosomes 2, 3, 6, 7, 8 and 9, which included five QTLs for days to heading, three for plant height (PH) and total shoot fresh weight and two for Brix. Furthermore, we identified two dominant loci for PH as being identical to the previously reported dw1 and dw3. Together, these results corroborate the diversified genome of Japanese Takakibi, while the RIL population and high-density linkage map generated in this study will be useful for dissecting other important traits in sorghum.
Collapse
Affiliation(s)
- Hiromi Kajiya-Kanegae
- Graduate School of Agricultural and Life Sciences, The University of Tokyo, 1-1-1 Yayoi, Bunkyo-ku, Tokyo, 113-8657 Japan
- Research Center for Agricultural Information Technology, National Agriculture and Food Research Organization, Tsukuba, Ibaraki 305-8517, Japan
| | - Hideki Takanashi
- Graduate School of Agricultural and Life Sciences, The University of Tokyo, 1-1-1 Yayoi, Bunkyo-ku, Tokyo, 113-8657 Japan
| | - Masaru Fujimoto
- Graduate School of Agricultural and Life Sciences, The University of Tokyo, 1-1-1 Yayoi, Bunkyo-ku, Tokyo, 113-8657 Japan
| | - Motoyuki Ishimori
- Graduate School of Agricultural and Life Sciences, The University of Tokyo, 1-1-1 Yayoi, Bunkyo-ku, Tokyo, 113-8657 Japan
| | - Norikazu Ohnishi
- Institute of Plant Science and Resources, Okayama University, Kurashiki, Okayama, 710-0046 Japan
| | - Fiona Wacera W
- Institute of Plant Science and Resources, Okayama University, Kurashiki, Okayama, 710-0046 Japan
| | - Everlyne A Omollo
- Institute of Plant Science and Resources, Okayama University, Kurashiki, Okayama, 710-0046 Japan
| | - Masaaki Kobayashi
- Department of Life Sciences Faculty of Agriculture, Meiji University, Kawasaki, Kanagawa, 214-8571 Japan
| | - Kentaro Yano
- Department of Life Sciences Faculty of Agriculture, Meiji University, Kawasaki, Kanagawa, 214-8571 Japan
| | - Michiharu Nakano
- Graduate School of Integral Science for Life, Hiroshima University, Kagamiyama, Higashi-Hiroshima, Hiroshima, 739-8526 Japan
| | - Toshiaki Kozuka
- Graduate School of Integral Science for Life, Hiroshima University, Kagamiyama, Higashi-Hiroshima, Hiroshima, 739-8526 Japan
| | - Makoto Kusaba
- Graduate School of Integral Science for Life, Hiroshima University, Kagamiyama, Higashi-Hiroshima, Hiroshima, 739-8526 Japan
| | - Hiroyoshi Iwata
- Graduate School of Agricultural and Life Sciences, The University of Tokyo, 1-1-1 Yayoi, Bunkyo-ku, Tokyo, 113-8657 Japan
| | - Nobuhiro Tsutsumi
- Graduate School of Agricultural and Life Sciences, The University of Tokyo, 1-1-1 Yayoi, Bunkyo-ku, Tokyo, 113-8657 Japan
| | - Wataru Sakamoto
- Institute of Plant Science and Resources, Okayama University, Kurashiki, Okayama, 710-0046 Japan
| |
Collapse
|
21
|
Moreira FF, Oliveira HR, Volenec JJ, Rainey KM, Brito LF. Integrating High-Throughput Phenotyping and Statistical Genomic Methods to Genetically Improve Longitudinal Traits in Crops. FRONTIERS IN PLANT SCIENCE 2020; 11:681. [PMID: 32528513 PMCID: PMC7264266 DOI: 10.3389/fpls.2020.00681] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/26/2020] [Accepted: 04/30/2020] [Indexed: 05/28/2023]
Abstract
The rapid development of remote sensing in agronomic research allows the dynamic nature of longitudinal traits to be adequately described, which may enhance the genetic improvement of crop efficiency. For traits such as light interception, biomass accumulation, and responses to stressors, the data generated by the various high-throughput phenotyping (HTP) methods requires adequate statistical techniques to evaluate phenotypic records throughout time. As a consequence, information about plant functioning and activation of genes, as well as the interaction of gene networks at different stages of plant development and in response to environmental stimulus can be exploited. In this review, we outline the current analytical approaches in quantitative genetics that are applied to longitudinal traits in crops throughout development, describe the advantages and pitfalls of each approach, and indicate future research directions and opportunities.
Collapse
Affiliation(s)
- Fabiana F. Moreira
- Department of Agronomy, Purdue University, West Lafayette, IN, United States
| | - Hinayah R. Oliveira
- Department of Animal Sciences, Purdue University, West Lafayette, IN, United States
| | - Jeffrey J. Volenec
- Department of Agronomy, Purdue University, West Lafayette, IN, United States
| | - Katy M. Rainey
- Department of Agronomy, Purdue University, West Lafayette, IN, United States
| | - Luiz F. Brito
- Department of Animal Sciences, Purdue University, West Lafayette, IN, United States
| |
Collapse
|
22
|
Liu X, Hu X, Li K, Liu Z, Wu Y, Wang H, Huang C. Genetic mapping and genomic selection for maize stalk strength. BMC PLANT BIOLOGY 2020; 20:196. [PMID: 32380944 PMCID: PMC7204062 DOI: 10.1186/s12870-020-2270-4] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/24/2019] [Accepted: 01/29/2020] [Indexed: 05/31/2023]
Abstract
BACKGROUND Maize is one of the most important staple crops and is widely grown throughout the world. Stalk lodging can cause enormous yield losses in maize production. However, rind penetrometer resistance (RPR), which is recognized as a reliable measurement to evaluate stalk strength, has been shown to be efficient and useful for improving stalk lodging-resistance. Linkage mapping is an acknowledged approach for exploring the genetic architecture of target traits. In addition, genomic selection (GS) using whole genome markers enhances selection efficiency for genetically complex traits. In the present study, two recombinant inbred line (RIL) populations were utilized to dissect the genetic basis of RPR, which was evaluated in seven growth stages. RESULTS The optimal stages to measure stalk strength are the silking phase and stages after silking. A total of 66 and 45 quantitative trait loci (QTL) were identified in each RIL population. Several potential candidate genes were predicted according to the maize gene annotation database and were closely associated with the biosynthesis of cell wall components. Moreover, analysis of gene ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway further indicated that genes related to cell wall formation were involved in the determination of RPR. In addition, a multivariate model of genomic selection efficiently improved the prediction accuracy relative to a univariate model and a model considering RPR-relevant loci as fixed effects. CONCLUSIONS The genetic architecture of RPR is highly genetically complex. Multiple minor effect QTL are jointly involved in controlling phenotypic variation in RPR. Several pleiotropic QTL identified in multiple stages may contain reliable genes and can be used to develop functional markers for improving the selection efficiency of stalk strength. The application of genomic selection to RPR may be a promising approach to accelerate breeding process for improving stalk strength and enhancing lodging-resistance.
Collapse
Affiliation(s)
- Xiaogang Liu
- Institute of Crop Sciences, National Key Facility of Crop Gene Resources and Genetic Improvement, Chinese Academy of Agricultural Sciences, Beijing, 100081, China
| | - Xiaojiao Hu
- Institute of Crop Sciences, National Key Facility of Crop Gene Resources and Genetic Improvement, Chinese Academy of Agricultural Sciences, Beijing, 100081, China
| | - Kun Li
- Institute of Crop Sciences, National Key Facility of Crop Gene Resources and Genetic Improvement, Chinese Academy of Agricultural Sciences, Beijing, 100081, China
| | - Zhifang Liu
- Institute of Crop Sciences, National Key Facility of Crop Gene Resources and Genetic Improvement, Chinese Academy of Agricultural Sciences, Beijing, 100081, China
| | - Yujin Wu
- Institute of Crop Sciences, National Key Facility of Crop Gene Resources and Genetic Improvement, Chinese Academy of Agricultural Sciences, Beijing, 100081, China
| | - Hongwu Wang
- Institute of Crop Sciences, National Key Facility of Crop Gene Resources and Genetic Improvement, Chinese Academy of Agricultural Sciences, Beijing, 100081, China.
| | - Changling Huang
- Institute of Crop Sciences, National Key Facility of Crop Gene Resources and Genetic Improvement, Chinese Academy of Agricultural Sciences, Beijing, 100081, China.
| |
Collapse
|
23
|
Melandri G, Sikirou M, Arbelaez JD, Shittu A, Semwal VK, Konaté KA, Maji AT, Ngaujah SA, Akintayo I, Govindaraj V, Shi Y, Agosto-Peréz FJ, Greenberg AJ, Atlin G, Ramaiah V, McCouch SR. Multiple Small-Effect Alleles of Indica Origin Enhance High Iron-Associated Stress Tolerance in Rice Under Field Conditions in West Africa. FRONTIERS IN PLANT SCIENCE 2020; 11:604938. [PMID: 33584748 PMCID: PMC7874229 DOI: 10.3389/fpls.2020.604938] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/10/2020] [Accepted: 12/15/2020] [Indexed: 05/03/2023]
Abstract
Understanding the genetics of field-based tolerance to high iron-associated (HIA) stress in rice can accelerate the development of new varieties with enhanced yield performance in West African lowland ecosystems. To date, few field-based studies have been undertaken to rigorously evaluate rice yield performance under HIA stress conditions. In this study, two NERICA × O. sativa bi-parental rice populations and one O.sativa diversity panel consisting of 296 rice accessions were evaluated for grain yield and leaf bronzing symptoms over multiple years in four West African HIA stress and control sites. Mapping of these traits identified a large number of QTLs and single nucleotide polymorphisms (SNPs) associated with stress tolerance in the field. Favorable alleles associated with tolerance to high levels of iron in anaerobic rice soils were rare and almost exclusively derived from the indica subpopulation, including the most favorable alleles identified in NERICA varieties. These findings highlight the complex genetic architecture underlying rice response to HIA stress and suggest that a recurrent selection program focusing on an expanded indica genepool could be productively used in combination with genomic selection to increase the efficiency of selection in breeding programs designed to enhance tolerance to this prevalent abiotic stress in West Africa.
Collapse
Affiliation(s)
- Giovanni Melandri
- Plant Breeding and Genetics, Cornell University, Ithaca, NY, United States
| | - Mouritala Sikirou
- Africa Rice Center, Ibadan, Nigeria
- School of Horticulture and Green Landscaping, Kétou, Bénin
| | - Juan D. Arbelaez
- Plant Breeding and Genetics, Cornell University, Ithaca, NY, United States
| | | | | | | | | | | | - Inoussa Akintayo
- Central Agricultural Research Institute, Suakoko, Liberia
- Africa Rice Center, Suakoko, Liberia
| | - Vishnu Govindaraj
- Plant Breeding and Genetics, Cornell University, Ithaca, NY, United States
| | - Yuxin Shi
- Plant Breeding and Genetics, Cornell University, Ithaca, NY, United States
| | | | | | - Gary Atlin
- Bill & Melinda Gates Foundation, Seattle, WA, United States
| | | | - Susan R. McCouch
- Plant Breeding and Genetics, Cornell University, Ithaca, NY, United States
- Venuprasad Ramaiah,
| |
Collapse
|