1
|
Zhou L, Li K, Hunt AG. Natural variation in the plant polyadenylation complex. FRONTIERS IN PLANT SCIENCE 2024; 14:1303398. [PMID: 38317838 PMCID: PMC10839035 DOI: 10.3389/fpls.2023.1303398] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/27/2023] [Accepted: 12/22/2023] [Indexed: 02/07/2024]
Abstract
Messenger RNA polyadenylation, the process wherein the primary RNA polymerase II transcript is cleaved and a poly(A) tract added, is a key step in the expression of genes in plants. Moreover, it is a point at which gene expression may be regulated by determining the functionality of the mature mRNA. Polyadenylation is mediated by a complex (the polyadenylation complex, or PAC) that consists of between 15 and 20 subunits. While the general functioning of these subunits may be inferred by extending paradigms established in well-developed eukaryotic models, much remains to be learned about the roles of individual subunits in the regulation of polyadenylation in plants. To gain further insight into this, we conducted a survey of variability in the plant PAC. For this, we drew upon a database of naturally-occurring variation in numerous geographic isolates of Arabidopsis thaliana. For a subset of genes encoding PAC subunits, the patterns of variability included the occurrence of premature stop codons in some Arabidopsis accessions. These and other observations lead us to conclude that some genes purported to encode PAC subunits in Arabidopsis are actually pseudogenes, and that others may encode proteins with dispensable functions in the plant. Many subunits of the PAC showed patterns of variability that were consistent with their roles as essential proteins in the cell. Several other PAC subunits exhibit patterns of variability consistent with selection for new or altered function. We propose that these latter subunits participate in regulatory interactions important for differential usage of poly(A) sites.
Collapse
Affiliation(s)
| | | | - Arthur G. Hunt
- Department of Plant and Soil Sciences, University of Kentucky, Lexington, KY, United States
| |
Collapse
|
2
|
Lu K, Gong H, Yang D, Ye M, Fang Q, Zhang XY, Wu R. Genome-Wide Network Analysis of Above- and Below-Ground Co-growth in Populus euphratica. PLANT PHENOMICS (WASHINGTON, D.C.) 2024; 6:0131. [PMID: 38188223 PMCID: PMC10769449 DOI: 10.34133/plantphenomics.0131] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/13/2023] [Accepted: 12/12/2023] [Indexed: 01/09/2024]
Abstract
Tree growth is the consequence of developmental interactions between above- and below-ground compartments. However, a comprehensive view of the genetic architecture of growth as a cohesive whole is poorly understood. We propose a systems biology approach for mapping growth trajectories in genome-wide association studies viewing growth as a complex (phenotypic) system in which above- and below-ground components (or traits) interact with each other to mediate systems behavior. We further assume that trait-trait interactions are controlled by a genetic system composed of many different interactive genes and integrate the Lotka-Volterra predator-prey model to dissect phenotypic and genetic systems into pleiotropic and epistatic interaction components by which the detailed genetic mechanism of above- and below-ground co-growth can be charted. We apply the approach to analyze linkage mapping data of Populus euphratica, which is the only tree species that can grow in the desert, and characterize several loci that govern how above- and below-ground growth is cooperated or competed over development. We reconstruct multilayer and multiplex genetic interactome networks for the developmental trajectories of each trait and their developmental covariation. Many significant loci and epistatic effects detected can be annotated to candidate genes for growth and developmental processes. The results from our model may potentially be useful for marker-assisted selection and genetic editing in applied tree breeding programs. The model provides a general tool to characterize a complete picture of pleiotropic and epistatic genetic architecture in growth traits in forest trees and any other organisms.
Collapse
Affiliation(s)
- Kaiyan Lu
- College of Science,
Beijing Forestry University, Beijing 100083, P. R. China
| | - Huiying Gong
- Center for Computational Biology, College of Biological Sciences and Technology,
Beijing Forestry University, Beijing 100083, P. R. China
| | - Dengcheng Yang
- Center for Computational Biology, College of Biological Sciences and Technology,
Beijing Forestry University, Beijing 100083, P. R. China
| | - Meixia Ye
- Center for Computational Biology, College of Biological Sciences and Technology,
Beijing Forestry University, Beijing 100083, P. R. China
| | - Qing Fang
- Faculty of Science,
Yamagata University, Yamagata 990, Japan
| | - Xiao-Yu Zhang
- College of Science,
Beijing Forestry University, Beijing 100083, P. R. China
| | - Rongling Wu
- Yanqi Lake BeijingInstitute of Mathematical Sciences and Applications, Beijing 101408, China
- Center for Computational Biology, College of Biological Sciences and Technology,
Beijing Forestry University, Beijing 100083, P. R. China
| |
Collapse
|
3
|
Lu K, Wang X, Gong H, Yang D, Ye M, Fang Q, Zhang XY, Wu R. The genetic architecture of trait covariation in Populus euphratica, a desert tree. FRONTIERS IN PLANT SCIENCE 2023; 14:1149879. [PMID: 37089657 PMCID: PMC10113509 DOI: 10.3389/fpls.2023.1149879] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/23/2023] [Accepted: 03/20/2023] [Indexed: 05/03/2023]
Abstract
Introduction The cooperative strategy of phenotypic traits during the growth of plants reflects how plants allocate photosynthesis products, which is the most favorable decision for them to optimize growth, survival, and reproduction response to changing environment. Up to now, we still know little about why plants make such decision from the perspective of biological genetic mechanisms. Methods In this study, we construct an analytical mapping framework to explore the genetic mechanism regulating the interaction of two complex traits. The framework describes the dynamic growth of two traits and their interaction as Differential Interaction Regulatory Equations (DIRE), then DIRE is embedded into QTL mapping model to identify the key quantitative trait loci (QTLs) that regulate this interaction and clarify the genetic effect, genetic contribution and genetic network structure of these key QTLs. Computer simulation experiment proves the reliability and practicability of our framework. Results In order to verify that our framework is universal and flexible, we applied it to two sets of data from Populus euphratica, namely, aboveground stem length - underground taproot length, underground root number - underground root length, which represent relationships of phenotypic traits in two spatial dimensions of plant architecture. The analytical result shows that our model is well applicable to datasets of two dimensions. Discussion Our model helps to better illustrate the cooperation-competition patterns between phenotypic traits, and understand the decisions that plants make in a specific environment that are most conducive to their growth from the genetic perspective.
Collapse
Affiliation(s)
- Kaiyan Lu
- College of Science, Beijing Forestry University, Beijing, China
| | - Xueshun Wang
- Department of Artificial Intelligence and Data Science, Guangzhou Xinhua University, Guangzhou, China
| | - Huiying Gong
- College of Biological Sciences and Technology, Center for Computational Biology, Beijing Forestry University, Beijing, China
| | - Dengcheng Yang
- College of Biological Sciences and Technology, Center for Computational Biology, Beijing Forestry University, Beijing, China
| | - Meixia Ye
- College of Biological Sciences and Technology, Center for Computational Biology, Beijing Forestry University, Beijing, China
| | - Qing Fang
- Faculty of Science, Yamagata University, Yamagata, Japan
| | - Xiao-Yu Zhang
- College of Science, Beijing Forestry University, Beijing, China
- *Correspondence: Xiao-Yu Zhang, ; Rongling Wu,
| | - Rongling Wu
- College of Biological Sciences and Technology, Center for Computational Biology, Beijing Forestry University, Beijing, China
- Yau Mathematical Sciences Center, Tsinghua University, Beijing, China
- *Correspondence: Xiao-Yu Zhang, ; Rongling Wu,
| |
Collapse
|
4
|
Zhang M, Lu N, Jiang L, Liu B, Fei Y, Ma W, Shi C, Wang J. Multiple dynamic models reveal the genetic architecture for growth in height of Catalpa bungei in the field. TREE PHYSIOLOGY 2022; 42:1239-1255. [PMID: 34940852 DOI: 10.1093/treephys/tpab171] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/28/2021] [Accepted: 12/19/2021] [Indexed: 06/14/2023]
Abstract
Growth in height (GH) is a critical determinant for tree survival and development in forests and can be depicted using logistic growth curves. Our understanding of the genetic mechanism underlying dynamic GH, however, is limited, particularly under field conditions. We applied two mapping models (Funmap and FVTmap) to find quantitative trait loci responsible for dynamic GH and two epistatic models (2HiGWAS and 1HiGWAS) to detect epistasis in Catalpa bungei grown in the field. We identified 13 co-located quantitative trait loci influencing the growth curve by Funmap and three heterochronic parameters (the timing of the inflection point, maximum acceleration and maximum deceleration) by FVTmap. The combined use of FVTmap and Funmap reduced the number of candidate genes by >70%. We detected 76 significant epistatic interactions, amongst which a key gene, COMT14, co-located by three models (but not 1HiGWAS) interacted with three other genes, implying that a novel network of protein interaction centered on COMT14 may control the dynamic GH of C. bungei. These findings provide new insights into the genetic mechanisms underlying the dynamic growth in tree height in natural environments and emphasize the necessity of incorporating multiple dynamic models for screening more reliable candidate genes.
Collapse
Affiliation(s)
- Miaomiao Zhang
- State Key Laboratory of Tree Genetics and Breeding, Key Laboratory of Tree Breeding and Cultivation of State Forestry Administration, Research Institute of Forestry, Chinese Academy of Forestry, Beijing 100091, China
| | - Nan Lu
- State Key Laboratory of Tree Genetics and Breeding, Key Laboratory of Tree Breeding and Cultivation of State Forestry Administration, Research Institute of Forestry, Chinese Academy of Forestry, Beijing 100091, China
| | - Libo Jiang
- School of Life Sciences and Medicine, Shandong University of Technology, Zibo 255049, China
| | - Bingyang Liu
- State Key Laboratory of Tree Genetics and Breeding, Key Laboratory of Tree Breeding and Cultivation of State Forestry Administration, Research Institute of Forestry, Chinese Academy of Forestry, Beijing 100091, China
| | - Yue Fei
- State Key Laboratory of Tree Genetics and Breeding, Key Laboratory of Tree Breeding and Cultivation of State Forestry Administration, Research Institute of Forestry, Chinese Academy of Forestry, Beijing 100091, China
| | - Wenjun Ma
- State Key Laboratory of Tree Genetics and Breeding, Key Laboratory of Tree Breeding and Cultivation of State Forestry Administration, Research Institute of Forestry, Chinese Academy of Forestry, Beijing 100091, China
| | - Chaozhong Shi
- State Key Laboratory of Tree Genetics and Breeding, Key Laboratory of Tree Breeding and Cultivation of State Forestry Administration, Research Institute of Forestry, Chinese Academy of Forestry, Beijing 100091, China
| | - Junhui Wang
- State Key Laboratory of Tree Genetics and Breeding, Key Laboratory of Tree Breeding and Cultivation of State Forestry Administration, Research Institute of Forestry, Chinese Academy of Forestry, Beijing 100091, China
| |
Collapse
|
5
|
Feng L, Dong T, Jiang P, Yang Z, Dong A, Xie SQ, Griffin CH, Wu R. An eco-evo-devo genetic network model of stress response. HORTICULTURE RESEARCH 2022; 9:uhac135. [PMID: 36061617 PMCID: PMC9433980 DOI: 10.1093/hr/uhac135] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/01/2022] [Accepted: 06/04/2022] [Indexed: 05/23/2023]
Abstract
The capacity of plants to resist abiotic stresses is of great importance to agricultural, ecological and environmental sustainability, but little is known about its genetic underpinnings. Existing genetic tools can identify individual genetic variants mediating biochemical, physiological, and cellular defenses, but fail to chart an overall genetic atlas behind stress resistance. We view stress response as an eco-evo-devo process by which plants adaptively respond to stress through complex interactions of developmental canalization, phenotypic plasticity, and phenotypic integration. As such, we define and quantify stress response as the developmental change of adaptive traits from stress-free to stress-exposed environments. We integrate composite functional mapping and evolutionary game theory to reconstruct omnigenic, information-flow interaction networks for stress response. Using desert-adapted Euphrates poplar as an example, we infer salt resistance-related genome-wide interactome networks and trace the roadmap of how each SNP acts and interacts with any other possible SNPs to mediate salt resistance. We characterize the previously unknown regulatory mechanisms driving trait variation; i.e. the significance of a SNP may be due to the promotion of positive regulators, whereas the insignificance of a SNP may result from the inhibition of negative regulators. The regulator-regulatee interactions detected are not only experimentally validated by two complementary experiments, but also biologically interpreted by their encoded protein-protein interactions. Our eco-evo-devo model of genetic interactome networks provides an approach to interrogate the genetic architecture of stress response and informs precise gene editing for improving plants' capacity to live in stress environments.
Collapse
Affiliation(s)
| | | | | | - Zhenyu Yang
- Center for Computational Biology, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083, China
| | - Ang Dong
- Center for Computational Biology, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083, China
| | - Shang-Qian Xie
- Key Laboratory of Ministry of Education for Genetics and Germplasm Innovation of Tropical Special Trees and Ornamental Plants, College of Forestry, Hainan University, Haikou 570228, China
| | - Christopher H Griffin
- Applied Research Laboratory, The Pennsylvania State University, University Park, PA 16802, USA
| | | |
Collapse
|
6
|
Du C, Sun P, Cheng X, Zhang L, Wang L, Hu J. QTL mapping of drought-related traits in the hybrids of Populus deltoides 'Danhong'×Populus simonii 'Tongliao1'. BMC PLANT BIOLOGY 2022; 22:238. [PMID: 35545765 PMCID: PMC9092850 DOI: 10.1186/s12870-022-03613-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/28/2021] [Accepted: 04/21/2022] [Indexed: 06/15/2023]
Abstract
BACKGROUND Poplar trees provide a large amount of wood material, but many parts of the world are arid or semi-arid areas because of insufficient annual precipitation, which seriously affects the growth of poplar trees. Populus simonii 'Tongliao1' shows strong tolerance to stress environments, and Populus deltoides 'Danhong' shows a stronger growth rate in a suitable environment. To identify drought tolerance-related QTLs and genes, an F1 population derived from the cross between the 'Danhong' and 'Tongliao 1' Populus was assessed under drought stress. RESULTS We measured drought-related traits such as the relative height growth, relative diameter growth, leaf senescence number, specific leaf area, and leaf relative water content in the population under control and drought environments. The results showed that drought stress reduced the plant height relative growth, ground diameter relative growth, specific leaf area and leaf relative water content and increased the number of leaf drops. A total of 208 QTLs were identified by QTL mapping analysis, and they consisted of 92, 63 and 53 QTLs under control, drought stress treatment and drought index conditions, respectively. A molecular identification marker for drought tolerance, np2841, which was associated with a QTL (qDLRWC-LG10-1) for relative leaf water content, was initially developed. We mined 187 candidate genes for QTL regions of five traits under a drought environment. The reference genome annotation for Populus trichocarpa and a homologous gene analysis of Arabidopsis thaliana identified two candidate genes, Potri.003G171300 and Potri.012G123900, with significant functions in response to drought stress. We identified five key regulatory genes (Potri.006G273500, Potri.007G111500, Potri.007G111600, Potri.007G111700, and Potri.007G111800) related to drought tolerance through the poplar coexpression network. CONCLUSION In this study, our results indicate that the QTLs can effectively enhance the drought tolerance of poplar. It is a step closer towards unravelling the genetic basis of poplar drought tolerance-related traits, and to providing validated candidate genes and molecular markers for future genetic improvement.
Collapse
Affiliation(s)
- Changjian Du
- State Key Laboratory of Tree Genetics and Breeding, Key Laboratory of Tree Breeding and Cultivation of National Forestry and Grassland Administration, Research Institute of Forestry, Chinese Academy of Forestry, Beijing, 100091, China
- Co-Innovation Center for Sustainable Forestry in Southern China, Nanjing Forestry University, Nanjing, 210037, Jiangsu, China
| | - Pei Sun
- State Key Laboratory of Tree Genetics and Breeding, Key Laboratory of Tree Breeding and Cultivation of National Forestry and Grassland Administration, Research Institute of Forestry, Chinese Academy of Forestry, Beijing, 100091, China
- Institute of Forestry and Pomology, Beijing Academy of Agriculture and Forestry Sciences, Beijing, 100093, China
| | - Xingqi Cheng
- State Key Laboratory of Tree Genetics and Breeding, Key Laboratory of Tree Breeding and Cultivation of National Forestry and Grassland Administration, Research Institute of Forestry, Chinese Academy of Forestry, Beijing, 100091, China
- Co-Innovation Center for Sustainable Forestry in Southern China, Nanjing Forestry University, Nanjing, 210037, Jiangsu, China
| | - Lei Zhang
- State Key Laboratory of Tree Genetics and Breeding, Key Laboratory of Tree Breeding and Cultivation of National Forestry and Grassland Administration, Research Institute of Forestry, Chinese Academy of Forestry, Beijing, 100091, China
- Co-Innovation Center for Sustainable Forestry in Southern China, Nanjing Forestry University, Nanjing, 210037, Jiangsu, China
| | - Lijuan Wang
- State Key Laboratory of Tree Genetics and Breeding, Key Laboratory of Tree Breeding and Cultivation of National Forestry and Grassland Administration, Research Institute of Forestry, Chinese Academy of Forestry, Beijing, 100091, China
- Co-Innovation Center for Sustainable Forestry in Southern China, Nanjing Forestry University, Nanjing, 210037, Jiangsu, China
| | - Jianjun Hu
- State Key Laboratory of Tree Genetics and Breeding, Key Laboratory of Tree Breeding and Cultivation of National Forestry and Grassland Administration, Research Institute of Forestry, Chinese Academy of Forestry, Beijing, 100091, China.
- Co-Innovation Center for Sustainable Forestry in Southern China, Nanjing Forestry University, Nanjing, 210037, Jiangsu, China.
| |
Collapse
|
7
|
Zhang M, Lu N, Zhu T, Yang G, Qu G, Shi C, Fei Y, Liu B, Ma W, Wang J. A Bivariate Mapping Model Identifies Major Covariation QTLs for Biomass Allocation Between Leaf and Stem Growth of Catalpa bungei. Front Genet 2021; 12:758209. [PMID: 34868235 PMCID: PMC8637733 DOI: 10.3389/fgene.2021.758209] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2021] [Accepted: 10/21/2021] [Indexed: 11/13/2022] Open
Abstract
Biomass allocation plays a critical role in plant morphological formation and phenotypic plasticity, which greatly impact plant adaptability and competitiveness. While empirical studies on plant biomass allocation have focused on molecular biology and ecology approaches, detailed insight into the genetic basis of biomass allocation between leaf and stem growth is still lacking. Herein, we constructed a bivariate mapping model to identify covariation QTLs governing carbon (C) allocation between the leaves and stem as well as the covariation of traits within and between organs in a full-sib mapping population of C. bungei. A total of 123 covQTLs were detected for 23 trait pairs, including six leaf traits (leaf length, width, area, perimeter, length/width ratio and petiole length) and five stem traits (height, diameter at breast height, wood density, stemwood volume and stemwood biomass). The candidate genes were further identified in tissue-specific gene expression data, which provided insights into the genetic architecture underlying C allocation for traits or organs. The key QTLs related to growth and biomass allocation, which included UVH1, CLPT2, GAD/SPL, COG1 and MTERF4, were characterised and verified via gene function annotation and expression profiling. The integration of a bivariate Quantitative trait locus mapping model and gene expression profiling will enable the elucidation of genetic architecture underlying biomass allocation and covariation growth, in turn providing a theoretical basis for forest molecular marker-assisted breeding with specific C allocation strategies for adaptation to heterogeneous environments.
Collapse
Affiliation(s)
- Miaomiao Zhang
- State Key Laboratory of Tree Genetics and Breeding, Key Laboratory of Tree Breeding and Cultivation of State Forestry Administration, Research Institute of Forestry, Chinese Academy of Forestry, Beijing, China
| | - Nan Lu
- State Key Laboratory of Tree Genetics and Breeding, Key Laboratory of Tree Breeding and Cultivation of State Forestry Administration, Research Institute of Forestry, Chinese Academy of Forestry, Beijing, China
| | - Tianqing Zhu
- State Key Laboratory of Tree Genetics and Breeding, Key Laboratory of Tree Breeding and Cultivation of State Forestry Administration, Research Institute of Forestry, Chinese Academy of Forestry, Beijing, China
| | - Guijuan Yang
- State Key Laboratory of Tree Genetics and Breeding, Key Laboratory of Tree Breeding and Cultivation of State Forestry Administration, Research Institute of Forestry, Chinese Academy of Forestry, Beijing, China
| | - Guanzheng Qu
- State Key Laboratory of Tree Genetics and Breeding, Northeast Forestry University, Harbin, China
| | - Chaozhong Shi
- State Key Laboratory of Tree Genetics and Breeding, Key Laboratory of Tree Breeding and Cultivation of State Forestry Administration, Research Institute of Forestry, Chinese Academy of Forestry, Beijing, China
| | - Yue Fei
- State Key Laboratory of Tree Genetics and Breeding, Key Laboratory of Tree Breeding and Cultivation of State Forestry Administration, Research Institute of Forestry, Chinese Academy of Forestry, Beijing, China
| | - Bingyang Liu
- State Key Laboratory of Tree Genetics and Breeding, Key Laboratory of Tree Breeding and Cultivation of State Forestry Administration, Research Institute of Forestry, Chinese Academy of Forestry, Beijing, China
| | - Wenjun Ma
- State Key Laboratory of Tree Genetics and Breeding, Key Laboratory of Tree Breeding and Cultivation of State Forestry Administration, Research Institute of Forestry, Chinese Academy of Forestry, Beijing, China
| | - Junhui Wang
- State Key Laboratory of Tree Genetics and Breeding, Key Laboratory of Tree Breeding and Cultivation of State Forestry Administration, Research Institute of Forestry, Chinese Academy of Forestry, Beijing, China
| |
Collapse
|
8
|
The genetic basis of the root economics spectrum in a perennial grass. Proc Natl Acad Sci U S A 2021; 118:2107541118. [PMID: 34799444 DOI: 10.1073/pnas.2107541118] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/09/2021] [Indexed: 11/18/2022] Open
Abstract
Construction economics of plant roots exhibit predictable relationships with root growth, death, and nutrient uptake strategies. Plant taxa with inexpensively constructed roots tend to more precisely explore nutrient hotspots than do those with costly constructed roots but at the price of more frequent tissue turnover. This trade-off underlies an acquisitive to conservative continuum in resource investment, described as the "root economics spectrum (RES)." Yet the adaptive role and genetic basis of RES remain largely unclear. Different ecotypes of switchgrass (Panicum virgatum) display root features exemplifying the RES, with costly constructed roots in southern lowland and inexpensively constructed roots in northern upland ecotypes. We used an outbred genetic mapping population derived from lowland and upland switchgrass ecotypes to examine the genetic architecture of the RES. We found that absorptive roots (distal first and second orders) were often "deciduous" in winter. The percentage of overwintering absorptive roots was decreased by northern upland alleles compared with southern lowland alleles, suggesting a locally-adapted conservative strategy in warmer and acquisitive strategy in colder regions. Relative turnover of absorptive roots was genetically negatively correlated with their biomass investment per unit root length, suggesting that the key trade-off in framing RES is genetically facilitated. We also detected strong genetic correlations among root morphology, root productivity, and shoot size. Overall, our results reveal the genetic architecture of multiple traits that likely impacts the evolution of RES and plant aboveground-belowground organization. In practice, we provide genetic evidence that increasing switchgrass yield for bioenergy does not directly conflict with enhancing its root-derived carbon sequestration.
Collapse
|
9
|
Wang H, Ye M, Fu Y, Dong A, Zhang M, Feng L, Zhu X, Bo W, Jiang L, Griffin CH, Liang D, Wu R. Modeling genome-wide by environment interactions through omnigenic interactome networks. Cell Rep 2021; 35:109114. [PMID: 33979624 DOI: 10.1016/j.celrep.2021.109114] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2020] [Revised: 03/11/2021] [Accepted: 04/21/2021] [Indexed: 10/21/2022] Open
Abstract
How genes interact with the environment to shape phenotypic variation and evolution is a fundamental question intriguing to biologists from various fields. Existing linear models built on single genes are inadequate to reveal the complexity of genotype-environment (G-E) interactions. Here, we develop a conceptual model for mechanistically dissecting G-E interplay by integrating previously disconnected theories and methods. Under this integration, evolutionary game theory, developmental modularity theory, and a variable selection method allow us to reconstruct environment-induced, maximally informative, sparse, and casual multilayer genetic networks. We design and conduct two mapping experiments by using a desert-adapted tree species to validate the biological application of the model proposed. The model identifies previously uncharacterized molecular mechanisms that mediate trees' response to saline stress. Our model provides a tool to comprehend the genetic architecture of trait variation and evolution and trace the information flow of each gene toward phenotypes within omnigenic networks.
Collapse
Affiliation(s)
- Haojie Wang
- Beijing Advanced Innovation Center for Tree Breeding by Molecular Design, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083, China
| | - Meixia Ye
- Beijing Advanced Innovation Center for Tree Breeding by Molecular Design, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083, China
| | - Yaru Fu
- Beijing Advanced Innovation Center for Tree Breeding by Molecular Design, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083, China
| | - Ang Dong
- Beijing Advanced Innovation Center for Tree Breeding by Molecular Design, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083, China
| | - Miaomiao Zhang
- Beijing Advanced Innovation Center for Tree Breeding by Molecular Design, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083, China
| | - Li Feng
- Beijing Advanced Innovation Center for Tree Breeding by Molecular Design, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083, China
| | - Xuli Zhu
- Beijing Advanced Innovation Center for Tree Breeding by Molecular Design, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083, China
| | - Wenhao Bo
- Beijing Advanced Innovation Center for Tree Breeding by Molecular Design, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083, China
| | - Libo Jiang
- Beijing Advanced Innovation Center for Tree Breeding by Molecular Design, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083, China
| | - Christopher H Griffin
- Applied Research Laboratory, The Pennsylvania State University, University Park, PA 16802, USA
| | - Dan Liang
- Beijing Advanced Innovation Center for Tree Breeding by Molecular Design, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083, China
| | - Rongling Wu
- Beijing Advanced Innovation Center for Tree Breeding by Molecular Design, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083, China; Center for Statistical Genetics, Departments of Public Health Sciences and Statistics, The Pennsylvania State University, Hershey, PA 17033, USA.
| |
Collapse
|
10
|
Zhang Q, Su Z, Guo Y, Zhang S, Jiang L, Wu R. Genome-wide association studies of callus differentiation for the desert tree, Populus euphratica. TREE PHYSIOLOGY 2020; 40:1762-1777. [PMID: 32761189 DOI: 10.1093/treephys/tpaa098] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/16/2020] [Accepted: 07/31/2020] [Indexed: 05/22/2023]
Abstract
Callus differentiation is a key developmental process in plant regeneration from cells. A better understanding of the genetic architecture of callus differentiation timing can help improve tissue transformation and the efficiency of artificial propagation. In this study, we investigated genotypic variation in callus differentiation capacity among 297 diverse P. euphratica trees sampled from a natural population. We employed a genome-wide association study (GWAS) of binary and growth-based parameters to identify loci and characterize the genetic architecture and genetic network underlying regulation of callus differentiation in P. euphratica. The results of this GWAS experiment suggested potential associations controlling whether the callus could differentiate and the process of callus differentiation. We identified multiple significant quantitative trait loci (QTLs), including the genes LOG1 and LOG7 and a locus containing WOX1. We reconstructed a genetic network that visualizes how each QTL interacts uniquely with other variants, and several core QTLs were detected that are involved in the degree of callus differentiation, providing potential targets for selection. This study represents one of the first to identify genetic variants affecting callus differentiation in a forest tree. Our results suggest that callus differentiation may be a typical qualitative-quantitative trait controlled by a major gene as well as polygenes across the genome of P. euphratica. This GWAS will help to design more complex and specific molecular tools for systematically manipulating organ regeneration.
Collapse
Affiliation(s)
- Qianru Zhang
- Beijing Advanced Innovation Center for Tree Breeding by Molecular Design, Beijing Forestry University, Beijing 100083, China
- Center for Computational Biology, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083, China
| | - Zhifang Su
- Center for Computational Biology, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083, China
| | - Yunqian Guo
- Center for Computational Biology, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083, China
| | - Shilong Zhang
- Beijing Advanced Innovation Center for Tree Breeding by Molecular Design, Beijing Forestry University, Beijing 100083, China
- Center for Computational Biology, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083, China
| | - Libo Jiang
- Beijing Advanced Innovation Center for Tree Breeding by Molecular Design, Beijing Forestry University, Beijing 100083, China
- Center for Computational Biology, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083, China
| | - Rongling Wu
- Beijing Advanced Innovation Center for Tree Breeding by Molecular Design, Beijing Forestry University, Beijing 100083, China
- Center for Computational Biology, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083, China
- Center for Statistical Genetics, The Pennsylvania State University, Hershey, PA 17033, USA
| |
Collapse
|
11
|
Lu N, Zhang M, Xiao Y, Han D, Liu Y, Zhang Y, Yi F, Zhu T, Ma W, Fan E, Qu G, Wang J. Construction of a high-density genetic map and QTL mapping of leaf traits and plant growth in an interspecific F 1 population of Catalpa bungei × Catalpa duclouxii Dode. BMC PLANT BIOLOGY 2019; 19:596. [PMID: 31888555 PMCID: PMC6937828 DOI: 10.1186/s12870-019-2207-y] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/10/2019] [Accepted: 12/17/2019] [Indexed: 05/08/2023]
Abstract
BACKGROUND Catalpa bungei is an important tree species used for timber in China and widely cultivated for economic and ornamental purposes. A high-density linkage map of C. bungei would be an efficient tool not only for identifying key quantitative trait loci (QTLs) that affect important traits, such as plant growth and leaf traits, but also for other genetic studies. RESULTS Restriction site-associated DNA sequencing (RAD-seq) was used to identify molecular markers and construct a genetic map. Approximately 280.77 Gb of clean data were obtained after sequencing, and in total, 25,614,295 single nucleotide polymorphisms (SNPs) and 2,871,647 insertions-deletions (InDels) were initially identified in the genomes of 200 individuals of a C. bungei (7080) × Catalpa duclouxii (16-PJ-3) F1 population and their parents. Finally, 9072 SNP and 521 InDel markers that satisfied the requirements for constructing a genetic map were obtained. The integrated genetic map contained 9593 pleomorphic markers in 20 linkage groups and spanned 3151.63 cM, with an average distance between adjacent markers of 0.32 cM. Twenty QTLs for seven leaf traits and 13 QTLs for plant height at five successive time points were identified using our genetic map by inclusive composite interval mapping (ICIM). Q16-60 was identified as a QTL for five leaf traits, and three significant QTLs (Q9-1, Q18-66 and Q18-73) associated with plant growth were detected at least twice. Genome annotation suggested that a cyclin gene participates in leaf trait development, while the growth of C. bungei may be influenced by CDC48C and genes associated with phytohormone synthesis. CONCLUSIONS This is the first genetic map constructed in C. bungei and will be a useful tool for further genetic study, molecular marker-assisted breeding and genome assembly.
Collapse
Affiliation(s)
- Nan Lu
- State Key Laboratory of Tree Genetics and Breeding, Key Laboratory of Tree Breeding and Cultivation of State Forestry Administration, Research Institute of Forestry, Chinese Academy of Forestry, Beijing, 100091 People’s Republic of China
| | - Miaomiao Zhang
- State Key Laboratory of Tree Genetics and Breeding, Key Laboratory of Tree Breeding and Cultivation of State Forestry Administration, Research Institute of Forestry, Chinese Academy of Forestry, Beijing, 100091 People’s Republic of China
| | - Yao Xiao
- State Key Laboratory of Tree Genetics and Breeding, Key Laboratory of Tree Breeding and Cultivation of State Forestry Administration, Research Institute of Forestry, Chinese Academy of Forestry, Beijing, 100091 People’s Republic of China
| | - Donghua Han
- College of Landscape Architecture, Nanjing Forestry University, Nanjing, 210037 Jiangsu People’s Republic of China
| | - Ying Liu
- College of Forestry, Northwest A&F University, Yangling, 712100 Shaanxi People’s Republic of China
| | - Yu Zhang
- State Key Laboratory of Tree Genetics and Breeding, Key Laboratory of Tree Breeding and Cultivation of State Forestry Administration, Research Institute of Forestry, Chinese Academy of Forestry, Beijing, 100091 People’s Republic of China
| | - Fei Yi
- State Key Laboratory of Tree Genetics and Breeding, Key Laboratory of Tree Breeding and Cultivation of State Forestry Administration, Research Institute of Forestry, Chinese Academy of Forestry, Beijing, 100091 People’s Republic of China
| | - Tianqing Zhu
- State Key Laboratory of Tree Genetics and Breeding, Key Laboratory of Tree Breeding and Cultivation of State Forestry Administration, Research Institute of Forestry, Chinese Academy of Forestry, Beijing, 100091 People’s Republic of China
| | - Wenjun Ma
- State Key Laboratory of Tree Genetics and Breeding, Key Laboratory of Tree Breeding and Cultivation of State Forestry Administration, Research Institute of Forestry, Chinese Academy of Forestry, Beijing, 100091 People’s Republic of China
| | - Erqin Fan
- State Key Laboratory of Tree Genetics and Breeding, Key Laboratory of Tree Breeding and Cultivation of State Forestry Administration, Research Institute of Forestry, Chinese Academy of Forestry, Beijing, 100091 People’s Republic of China
- State Key Laboratory of Tree Genetics and Breeding, Northeast Forestry University, Harbin, People’s Republic of China
| | - Guanzheng Qu
- State Key Laboratory of Tree Genetics and Breeding, Northeast Forestry University, Harbin, People’s Republic of China
| | - Junhui Wang
- State Key Laboratory of Tree Genetics and Breeding, Key Laboratory of Tree Breeding and Cultivation of State Forestry Administration, Research Institute of Forestry, Chinese Academy of Forestry, Beijing, 100091 People’s Republic of China
| |
Collapse
|
12
|
Sun P, Jia H, Zhang Y, Li J, Lu M, Hu J. Deciphering Genetic Architecture of Adventitious Root and Related Shoot Traits in Populus Using QTL Mapping and RNA-Seq Data. Int J Mol Sci 2019; 20:ijms20246114. [PMID: 31817197 PMCID: PMC6941115 DOI: 10.3390/ijms20246114] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2019] [Revised: 11/25/2019] [Accepted: 11/29/2019] [Indexed: 11/16/2022] Open
Abstract
Understanding the genetic architecture of adventitious root and related shoot traits will facilitate the cultivation of superior genotypes. In this study, we measured 12 adventitious root and related shoot traits of 434 F1 genotypes originating from Populus deltoides ‘Danhong’ × Populus simonii ‘Tongliao1’ and conducted an integrative analysis of quantitative trait locus (QTL) mapping and RNA-Seq data to dissect their genetic architecture and regulatory genes. Extensive segregation, high repeatability, and significant correlation relationship were detected for the investigated traits. A total of 150 QTLs were associated with adventitious root traits, explaining 3.1–6.1% of phenotypic variation (PVE); while 83 QTLs were associated with shoot traits, explaining 3.1–19.8% of PVE. Twenty-five QTL clusters and 40 QTL hotspots were identified for the investigated traits. Ten QTL clusters were overlapped in both adventitious root traits and related shoot traits. Transcriptome analysis identified 10,172 differentially expressed genes (DEGs) among two parents, three fine rooting and three poor-rooting genotypes, 143 of which were physically located within the QTL intervals. K-means cluster and weighted gene co-expression network analysis showed that PtAAAP19 (Potri.004G111400) encoding amino acid transport protein was tightly associated with adventitious roots and highly expressed in fine-rooting genotypes. Compare with ‘Danhong’, 153 bp deletion in the coding sequence of PtAAAP19 in ‘Tongliao1’ gave rise to lack one transmembrane domain, which might cause the variation of adventitious roots. Taken together, this study deciphered the genetic basis of adventitious root and related shoot traits and provided potential function genes for genetic improvement of poplar breeding.
Collapse
Affiliation(s)
- Pei Sun
- State Key Laboratory of Tree Genetics and Breeding, Key Laboratory of Tree Breeding and Cultivation of State Forestry and Grassland Administration, Research Institute of Forestry, Chinese Academy of Forestry, Beijing 100091, China; (P.S.); (H.J.); (M.L.)
| | - Huixia Jia
- State Key Laboratory of Tree Genetics and Breeding, Key Laboratory of Tree Breeding and Cultivation of State Forestry and Grassland Administration, Research Institute of Forestry, Chinese Academy of Forestry, Beijing 100091, China; (P.S.); (H.J.); (M.L.)
| | - Yahong Zhang
- State Key Laboratory of Tree Genetics and Breeding, Key Laboratory of Tree Breeding and Cultivation of State Forestry and Grassland Administration, Research Institute of Forestry, Chinese Academy of Forestry, Beijing 100091, China; (P.S.); (H.J.); (M.L.)
| | - Jianbo Li
- Experimental Center of Forestry in North China, Chinese Academy of Forestry, Beijing 102300, China
| | - Mengzhu Lu
- State Key Laboratory of Tree Genetics and Breeding, Key Laboratory of Tree Breeding and Cultivation of State Forestry and Grassland Administration, Research Institute of Forestry, Chinese Academy of Forestry, Beijing 100091, China; (P.S.); (H.J.); (M.L.)
- Collaborative Innovation Center of Sustainable Forestry in Southern China, Nanjing Forestry University, Nanjing 210037, China
| | - Jianjun Hu
- State Key Laboratory of Tree Genetics and Breeding, Key Laboratory of Tree Breeding and Cultivation of State Forestry and Grassland Administration, Research Institute of Forestry, Chinese Academy of Forestry, Beijing 100091, China; (P.S.); (H.J.); (M.L.)
- Collaborative Innovation Center of Sustainable Forestry in Southern China, Nanjing Forestry University, Nanjing 210037, China
- Correspondence: ; Tel: +86-10-62888862
| |
Collapse
|
13
|
Wang P, Jiang L, Ye M, Zhu X, Wu R. The Genomic Landscape of Crossover Interference in the Desert Tree Populus euphratica. Front Genet 2019; 10:440. [PMID: 31156703 PMCID: PMC6530421 DOI: 10.3389/fgene.2019.00440] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2018] [Accepted: 04/29/2019] [Indexed: 11/13/2022] Open
Abstract
Crossover (CO) interference is a universal phenomenon by which the occurrence of one CO event inhibits the simultaneous occurrence of other COs along a chromosome. Because of its critical role in the evolution of genome structure and organization, the cytological and molecular mechanisms underlying CO interference have been extensively investigated. However, the genome-wide distribution of CO interference and its interplay with sex-, stress-, and age-induced differentiation remain poorly understood. Multi-point linkage analysis has proven to be a powerful tool for landscaping CO interference, especially within species for which CO mutants are rarely available. We implemented four-point linkage analysis to landscape a detailed picture of how CO interference is distributed through the entire genome of Populus euphratica, the only forest tree that can survive and grow in saline desert. We identified an extensive occurrence of CO interference, and found that its strength depends on the length of chromosomes and the genomic locations within the chromosome. We detected high-order CO interference, possibly suggesting a highly complex mechanism crucial for P. euphratica to grow, reproduce, and evolve in its harsh environment.
Collapse
Affiliation(s)
- Ping Wang
- Center for Computational Biology, College of Biological Sciences and Biotechnology, Beijing Forestry University, Beijing, China
| | - Libo Jiang
- Center for Computational Biology, College of Biological Sciences and Biotechnology, Beijing Forestry University, Beijing, China
| | - Meixia Ye
- Center for Computational Biology, College of Biological Sciences and Biotechnology, Beijing Forestry University, Beijing, China
| | - Xuli Zhu
- Center for Computational Biology, College of Biological Sciences and Biotechnology, Beijing Forestry University, Beijing, China
| | - Rongling Wu
- Center for Computational Biology, College of Biological Sciences and Biotechnology, Beijing Forestry University, Beijing, China.,Center for Statistical Genetics, The Pennsylvania State University, Hershey, PA, United States
| |
Collapse
|
14
|
Sang M, Shi H, Wei K, Ye M, Jiang L, Sun L, Wu R. A dissection model for mapping complex traits. THE PLANT JOURNAL : FOR CELL AND MOLECULAR BIOLOGY 2019; 97:1168-1182. [PMID: 30536697 DOI: 10.1111/tpj.14185] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/20/2018] [Revised: 10/26/2018] [Accepted: 11/26/2018] [Indexed: 06/09/2023]
Abstract
Many quantitative traits are composites of other traits that contribute differentially to genetic variation. Quantitative trait locus (QTL) mapping of these composite traits can benefit by incorporating the mechanistic process of how their formation is mediated by the underlying components. We propose a dissection model by which to map these interconnected components traits under a joint likelihood setting. The model can test how a composite trait is determined by pleiotropic QTLs for its component traits or jointly by different sets of QTLs each responsible for a different component. The model can visualize the pattern of time-varying genetic effects for individual components and their impacts on composite traits. The dissection model was used to map two composite traits, stemwood volume growth decomposed into its stem height, stem diameter and stem form components for Euramerican poplar adult trees, and total lateral root length constituted by its average lateral root length and lateral root number components for Euphrates poplar seedlings. We found the pattern of how QTLs for different components contribute to phenotypic variation in composite traits. The detailed understanding of the genetic machineries of composite traits will not only help in the design of molecular breeding in plants and animals, but also shed light on the evolutionary processes of quantitative traits under natural selection.
Collapse
Affiliation(s)
- Mengmeng Sang
- Beijing Advanced Innovation Center for Tree Breeding by Molecular Design, Beijing Forestry University, Beijing, 100083, China
- Center for Computational Biology, College of Biological Sciences and Technology, Beijing Forestry University, Beijing, 100083, China
| | - Hexin Shi
- Beijing Advanced Innovation Center for Tree Breeding by Molecular Design, Beijing Forestry University, Beijing, 100083, China
- Center for Computational Biology, College of Biological Sciences and Technology, Beijing Forestry University, Beijing, 100083, China
| | - Kun Wei
- Beijing Advanced Innovation Center for Tree Breeding by Molecular Design, Beijing Forestry University, Beijing, 100083, China
- Center for Computational Biology, College of Biological Sciences and Technology, Beijing Forestry University, Beijing, 100083, China
| | - Meixia Ye
- Beijing Advanced Innovation Center for Tree Breeding by Molecular Design, Beijing Forestry University, Beijing, 100083, China
- Center for Computational Biology, College of Biological Sciences and Technology, Beijing Forestry University, Beijing, 100083, China
| | - Libo Jiang
- Beijing Advanced Innovation Center for Tree Breeding by Molecular Design, Beijing Forestry University, Beijing, 100083, China
- Center for Computational Biology, College of Biological Sciences and Technology, Beijing Forestry University, Beijing, 100083, China
| | - Lidan Sun
- Beijing Key Laboratory of Ornamental Plants Germplasm Innovation and Molecular Breeding, National Engineering Research Center for Floriculture, College of Landscape Architecture, Beijing Forestry University, Beijing, 100083, China
| | - Rongling Wu
- Beijing Advanced Innovation Center for Tree Breeding by Molecular Design, Beijing Forestry University, Beijing, 100083, China
- Center for Computational Biology, College of Biological Sciences and Technology, Beijing Forestry University, Beijing, 100083, China
- State Key Laboratory of Tree Genetics and Breeding, Chinese Academy of Forestry, Beijing, 100091, China
- Center for Statistical Genetics, Departments of Public Health Sciences and Statistics, Pennsylvania State University, Hershey, PA, 17033, USA
| |
Collapse
|
15
|
Wei K, Wang J, Sang M, Zhang S, Zhou H, Jiang L, Clavijo Michelangeli JA, Vallejos CE, Wu R. An ecophysiologically based mapping model identifies a major pleiotropic QTL for leaf growth trajectories of Phaseolus vulgaris. THE PLANT JOURNAL : FOR CELL AND MOLECULAR BIOLOGY 2018; 95:775-784. [PMID: 29882297 DOI: 10.1111/tpj.13986] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/05/2018] [Revised: 05/22/2018] [Accepted: 05/30/2018] [Indexed: 06/08/2023]
Abstract
Crop modeling, a widely used tool to predict plant growth and development in heterogeneous environments, has been increasingly integrated with genetic information to improve its predictability. This integration can also shed light on the mechanistic path that connects the genotype to a particular phenotype under specific environments. We implemented a bivariate statistical procedure to map and identify quantitative trait loci (QTLs) that can predict the form of plant growth by estimating cultivar-specific growth parameters and incorporating these parameters into a mapping framework. The procedure enables the characterization of how QTLs act differently in response to developmental and environmental cues. We used this procedure to map growth parameters of leaf area and mass in a mapping population of the common bean (Phaseolus vulgaris L.). Different sets of QTLs are responsible for various aspects of growth, including the initiation time of growth, growth rate, inflection point and asymptotic growth. A major QTL of a large effect was identified to pleiotropically affect trait expression in distinct environments and different traits expressed on the same organism. The integration of crop models and QTL mapping through our statistical procedure provides a powerful means of building a more precise predictive model of genotype-phenotype relationships for crops.
Collapse
Affiliation(s)
- Kun Wei
- Center for Computational Biology, College of Biological Sciences and Technology, Beijing Forestry University, Beijing, 100083, China
| | - Jing Wang
- Center for Computational Biology, College of Biological Sciences and Technology, Beijing Forestry University, Beijing, 100083, China
| | - Mengmeng Sang
- Center for Computational Biology, College of Biological Sciences and Technology, Beijing Forestry University, Beijing, 100083, China
| | - Shilong Zhang
- Center for Computational Biology, College of Biological Sciences and Technology, Beijing Forestry University, Beijing, 100083, China
| | - Houchao Zhou
- Center for Computational Biology, College of Biological Sciences and Technology, Beijing Forestry University, Beijing, 100083, China
| | - Libo Jiang
- Center for Computational Biology, College of Biological Sciences and Technology, Beijing Forestry University, Beijing, 100083, China
| | | | - C Eduardo Vallejos
- Department of Horticultural Sciences, University of Florida, Gainesville, FL, 32611, USA
| | - Rongling Wu
- Center for Computational Biology, College of Biological Sciences and Technology, Beijing Forestry University, Beijing, 100083, China
- Center for Statistical Genetics, The Pennsylvania State University, Hershey, PA, 17033, USA
| |
Collapse
|
16
|
Baker RL, Leong WF, Welch S, Weinig C. Mapping and Predicting Non-Linear Brassica rapa Growth Phenotypes Based on Bayesian and Frequentist Complex Trait Estimation. G3 (BETHESDA, MD.) 2018; 8:1247-1258. [PMID: 29467188 PMCID: PMC5873914 DOI: 10.1534/g3.117.300350] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/11/2017] [Accepted: 02/08/2018] [Indexed: 12/23/2022]
Abstract
Predicting phenotypes based on genotypes and understanding the effects of complex multi-locus traits on plant performance requires a description of the underlying developmental processes, growth trajectories, and their genomic architecture. Using data from Brassica rapa genotypes grown in multiple density settings and seasons, we applied a hierarchical Bayesian Function-Valued Trait (FVT) approach to fit logistic growth curves to leaf phenotypic data (length and width) and characterize leaf development. We found evidence of genetic variation in phenotypic plasticity of rate and duration of leaf growth to growing season. In contrast, the magnitude of the plastic response for maximum leaf size was relatively small, suggesting that growth dynamics vs. final leaf sizes have distinct patterns of environmental sensitivity. Consistent with patterns of phenotypic plasticity, several QTL-by-year interactions were significant for parameters describing leaf growth rates and durations but not leaf size. In comparison to frequentist approaches for estimating leaf FVT, Bayesian trait estimation resulted in more mapped QTL that tended to have greater average LOD scores and to explain a greater proportion of trait variance. We then constructed QTL-based predictive models for leaf growth rate and final size using data from one treatment (uncrowded plants in one growing season). Models successfully predicted non-linear developmental phenotypes for genotypes not used in model construction and, due to a lack of QTL-by-treatment interactions, predicted phenotypes across sites differing in plant density.
Collapse
Affiliation(s)
- R L Baker
- Department of Biology, Miami University, Oxford, OH 45056,
| | - W F Leong
- Department of Agronomy, Kansas State University, Manhattan, KS 66506
| | - S Welch
- Department of Agronomy, Kansas State University, Manhattan, KS 66506
| | - C Weinig
- Department of Molecular Biology and
- Department of Botany, University of Wyoming, Laramie, WY 82071
| |
Collapse
|