Trait physiology and crop modelling as a framework to link phenotypic complexity to underlying genetic systems

2005 ◽  
Vol 56 (9) ◽  
pp. 947 ◽  
Author(s):  
Graeme L. Hammer ◽  
Scott Chapman ◽  
Erik van Oosterom ◽  
Dean W. Podlich
Keyword(s):  
Complex Traits ◽  
Crop Improvement ◽  
Target Population ◽  
Breeding Strategies ◽  
Stay Green ◽  
Gene Effects ◽  
Component Trait ◽  
Phenotypic Complexity ◽  
Eastern Australia ◽  
Genetic Systems

New tools derived from advances in molecular biology have not been widely adopted in plant breeding for complex traits because of the inability to connect information at gene level to the phenotype in a manner that is useful for selection. In this study, we explored whether physiological dissection and integrative modelling of complex traits could link phenotype complexity to underlying genetic systems in a way that enhanced the power of molecular breeding strategies. A crop and breeding system simulation study on sorghum, which involved variation in 4 key adaptive traits—phenology, osmotic adjustment, transpiration efficiency, stay-green—and a broad range of production environments in north-eastern Australia, was used. The full matrix of simulated phenotypes, which consisted of 547 location–season combinations and 4235 genotypic expression states, was analysed for genetic and environmental effects. The analysis was conducted in stages assuming gradually increased understanding of gene-to-phenotype relationships, which would arise from physiological dissection and modelling. It was found that environmental characterisation and physiological knowledge helped to explain and unravel gene and environment context dependencies in the data. Based on the analyses of gene effects, a range of marker-assisted selection breeding strategies was simulated. It was shown that the inclusion of knowledge resulting from trait physiology and modelling generated an enhanced rate of yield advance over cycles of selection. This occurred because the knowledge associated with component trait physiology and extrapolation to the target population of environments by modelling removed confounding effects associated with environment and gene context dependencies for the markers used. Developing and implementing this gene-to-phenotype capability in crop improvement requires enhanced attention to phenotyping, ecophysiological modelling, and validation studies to test the stability of candidate genetic regions.

Genotype by environment interactions affecting grain sorghum. I. Characteristics that confound interpretation of hybrid yield

2000 ◽  
Vol 51 (2) ◽  
pp. 197 ◽  
Author(s):  
S. C. Chapman ◽  
M. Cooper ◽  
D. G. Butler ◽  
R. G. Henzell
Keyword(s):  
Variance Components ◽  
Genetic Correlations ◽  
Target Population ◽  
Breeding Strategies ◽  
Random Set ◽  
Crop Water ◽  
Genotype By Environment ◽  
Eastern Australia ◽  
North Eastern ◽  
Seasonal Weather

Past sorghum hybrid trials in north-eastern Australia have detected substantial genotype by environment (G×E) interactions for yield in sampling a variable target population of environments (TPE) that is affected by spatial and seasonal differences in crop water supply. Three datasets, comprising yields of commercial and final stage experimental hybrids and covering 9–17 years (Y) and up to 30 locations (L), were analysed to quantify variance components for trial error, genotypic (σ2g), and G×E (σ2gl, σ;2gy, and σ2gly) interaction effects. Whereas trial means varied 2–3-fold across seasons, a greater range was estimated for variance components of trial error (range of 0.05–0.5), G (0–>0.3), and G×L interaction (0.05–>1.0). There was substantial seasonal variation in the ratio of σ2g to (σ2g +σ2gl), and in two datasets, 73% of the seasonal σ2gl was due to poor genetic correlations among locations. This implies that any given set of hybrids in a random set of locations would be ranked differently from season to season. Analysis of locations over years detected 90% of the total G×E interaction as G×L×Y, rather than G×L or G×Y, although this was reduced by accounting for genotype maturity. To achieve repeatabilities of >80%, trials would need to be conducted over at least 5 years and 20 locations per year. The variable and unpredictable nature of much of the G×E interaction in the region implies that broad adaptation to different water regimes is required, unless prior knowledge of the seasonal weather can be used to choose ‘narrowly adapted’ cultivars. With current approaches, a large sample of environments is needed to identify such hybrids, and testing across locations and years is equally important. Alternative breeding strategies based on classifying environment types are discussed.

Gene-to-phenotype models and complex trait genetics

2005 ◽  
Vol 56 (9) ◽  
pp. 895 ◽  
Author(s):  
Mark Cooper ◽  
Dean W. Podlich ◽  
Oscar S. Smith
Keyword(s):  
Quantitative Genetics ◽  
Complex Traits ◽  
Molecular Breeding ◽  
Complex Trait ◽  
Theoretical Framework ◽  
Breeding Strategies ◽  
Gene Effects ◽  
Gene Environment ◽  
Starting Point ◽  
Context Dependent

The premise that is explored in this paper is that in some cases, in order to make progress in the design of molecular breeding strategies for complex traits, we will need a theoretical framework for quantitative genetics that is grounded in the concept of gene-networks. We seek to develop a gene-to-phenotype (G→P) modelling framework for quantitative genetics that explicitly deals with the context-dependent gene effects that are attributed to genes functioning within networks, i.e. epistasis, gene × environment interactions, and pleiotropy. The E(NK) model is discussed as a starting point for building such a theoretical framework for complex trait genetics. Applying this framework to a combination of theoretical and empirical G→P models, we find that although many of the context-dependent effects of genetic variation on phenotypic variation can reduce the rate of genetic progress from breeding, it is possible to design molecular breeding strategies for complex traits that on average will outperform phenotypic selection. However, to realise these potential advantages, empirical G→P models of the traits will need to take into consideration the context-dependent effects that are a consequence of epistasis, gene × environment interactions, and pleiotropy. Some promising G→P modelling directions are discussed.

scholarly journals Tackling G × E × M interactions to close on-farm yield-gaps: creating novel pathways for crop improvement by predicting contributions of genetics and management to crop productivity

2021 ◽  
Author(s):  
Mark Cooper ◽  
Kai P. Voss-Fels ◽  
Carlos D. Messina ◽  
Tom Tang ◽  
Graeme L. Hammer
Keyword(s):  
Climate Change ◽  
Crop Improvement ◽  
Target Population ◽  
Crop Productivity ◽  
Climate Change Scenarios ◽  
Global Food Security ◽  
Genotype By Environment ◽  
Improvement Strategies ◽  
Yield Gaps ◽  
On Farm

Abstract Key message Climate change and Genotype-by-Environment-by-Management interactions together challenge our strategies for crop improvement. Research to advance prediction methods for breeding and agronomy is opening new opportunities to tackle these challenges and overcome on-farm crop productivity yield-gaps through design of responsive crop improvement strategies. Abstract Genotype-by-Environment-by-Management (G × E × M) interactions underpin many aspects of crop productivity. An important question for crop improvement is “How can breeders and agronomists effectively explore the diverse opportunities within the high dimensionality of the complex G × E × M factorial to achieve sustainable improvements in crop productivity?” Whenever G × E × M interactions make important contributions to attainment of crop productivity, we should consider how to design crop improvement strategies that can explore the potential space of G × E × M possibilities, reveal the interesting Genotype–Management (G–M) technology opportunities for the Target Population of Environments (TPE), and enable the practical exploitation of the associated improved levels of crop productivity under on-farm conditions. Climate change adds additional layers of complexity and uncertainty to this challenge, by introducing directional changes in the environmental dimension of the G × E × M factorial. These directional changes have the potential to create further conditional changes in the contributions of the genetic and management dimensions to future crop productivity. Therefore, in the presence of G × E × M interactions and climate change, the challenge for both breeders and agronomists is to co-design new G–M technologies for a non-stationary TPE. Understanding these conditional changes in crop productivity through the relevant sciences for each dimension, Genotype, Environment, and Management, creates opportunities to predict novel G–M technology combinations suitable to achieve sustainable crop productivity and global food security targets for the likely climate change scenarios. Here we consider critical foundations required for any prediction framework that aims to move us from the current unprepared state of describing G × E × M outcomes to a future responsive state equipped to predict the crop productivity consequences of G–M technology combinations for the range of environmental conditions expected for a complex, non-stationary TPE under the influences of climate change.

Using crop simulation to generate genotype by environment interaction effects for sorghum in water-limited environments

2002 ◽  
Vol 53 (4) ◽  
pp. 379 ◽  
Author(s):  
Scott C. Chapman ◽  
Mark Cooper ◽  
Graeme L. Hammer
Keyword(s):  
Cropping Systems ◽  
Target Population ◽  
Genotype By Environment Interaction ◽  
Expected Improvement ◽  
Stress Index ◽  
Environment Interaction ◽  
Genotype By Environment ◽  
Systems Model ◽  
Water Stress Index ◽  
Eastern Australia

Multi-environment trials (METs) used to evaluate breeding lines vary in the number of years that they sample. We used a cropping systems model to simulate the target population of environments (TPE) for 6 locations over 108 years for 54 ‘near-isolines’ of sorghum in north-eastern Australia. For a single reference genotype, each of 547 trials was clustered into 1 of 3 ‘drought environment types’ (DETs) based on a seasonal water stress index. Within sequential METs of 2 years duration, the frequencies of these drought patterns often differed substantially from those derived for the entire TPE. This was reflected in variation in the mean yield of the reference genotype. For the TPE and for 2-year METs, restricted maximum likelihood methods were used to estimate components of genotypic and genotype by environment variance. These also varied substantially, although not in direct correlation with frequency of occurrence of different DETs over a 2-year period. Combined analysis over different numbers of seasons demonstrated the expected improvement in the correlation between MET estimates of genotype performance and the overall genotype averages as the number of seasons in the MET was increased.

Estimating genetic gains from alternative breeding strategies for clonal forestry

1992 ◽  
Vol 22 (1) ◽  
pp. 14-23 ◽  
Author(s):  
T.J. Mullin ◽  
Y.S. Park
Keyword(s):  
Linear Model ◽  
Genetic Parameters ◽  
Genetic Variance ◽  
Genetic Model ◽  
Large Fraction ◽  
Breeding Strategies ◽  
Clonal Forestry ◽  
Components Of Variance ◽  
Gene Effects ◽  
Dominance Variance

Concepts and procedures are presented for the analysis of progeny trials that incorporate clonal replication as a means to resolve variance arising from nonadditive gene effects. Components of variance from the linear model may be expressed in terms of expected covariances among relatives, and these, in turn, may be used to derive approximations of additive, dominance, and epistatic components of genetic variance. In addition to the usual assumptions applied to conventional progeny trials, the use of this expanded genetic model in the analysis of tests with clonal replicates assumes that the greatest portion of the total epistasis is due to interactions involving groups of more than two or three loci. If this assumption is not satisfied, estimates of additive and dominance variance, including those from trials without clonal replicates, will be contaminated by a large fraction of epistasis, and total epistasis will be underestimated by a corresponding amount. Heritability and gain formulae for alternative selection and deployment schemes are developed and illustrate the use of genetic parameters in the comparison of seedling and clonal reforestation strategies.

scholarly journals How Do Crops Balance Water Supply and Demand when Water Is Limiting?

Proceedings ◽  
2020 ◽  
Vol 36 (1) ◽  
pp. 208
Author(s):  
Andrew Borrell ◽  
Barbara George-Jaeggli ◽  
Erik van Oosterom ◽  
Graeme Hammer ◽  
Emma Mace ◽  
...  
Keyword(s):  
Water Supply ◽  
Leaf Area ◽  
Supply And Demand ◽  
Crop Improvement ◽  
Limiting Factor ◽  
Crop Species ◽  
Stay Green ◽  
Canopy Development ◽  
Improvement Programs ◽  
Water Supply And Demand

Plants are sessile organisms requiring mechanisms that enable them to balance water supply and demand in dry environments. Demand (D) is largely driven by canopy size (transpirational leaf area), although differences in transpiration per unit leaf area also occur. Supply (S) is primarily driven by water capture via the root system. Drought stress can be defined as the situation where supply of water cannot meet demand of the crop, such that water availability is the limiting factor for biomass accumulation. Under such conditions, plants will need to reduce D in order to meet the limited S, access more water to increase S, or increase the efficiency with which water is utilised. We used sorghum, a model C4 crop species, to demonstrate how the stay-green trait can modulate canopy development and root architecture to enhance adaptation. We show how stay-green positively impacts the balance between S and D under post-flowering drought, including insights at the molecular level. We provide examples of how canopy and root traits impact the S/D balance in other cereals under water limitation. For example, on the supply side, the extent of genetic variation for root angle (RA) has been evaluated in sorghum, wheat and barley, and genomic regions associated with RA have been mapped. Furthermore, the relationship between RA and grain yield has been explored in barley and sorghum field trials. The capacity to manipulate components of S and D to optimise the S/D balance should assist crop improvement programs to develop enhanced ideotypes for dry environments.

scholarly journals Biological reality and parsimony in crop models—why we need both in crop improvement!

2019 ◽  
Vol 1 (1) ◽  
Author(s):  
Graeme Hammer ◽  
Charlie Messina ◽  
Alex Wu ◽  
Mark Cooper
Keyword(s):  
Complex Traits ◽  
High Speed ◽  
Crop Improvement ◽  
Model Complexity ◽  
Crop Models ◽  
Genome Prediction ◽  
Biological Reality ◽  
New Frontier ◽  
Parsimonious Models ◽  
Modern Breeding

Abstract The potential to add significant value to the rapid advances in plant breeding technologies associated with statistical whole-genome prediction methods is a new frontier for crop physiology and modelling. Yield advance by genetic improvement continues to require prediction of phenotype based on genotype, and this remains challenging for complex traits despite recent advances in genotyping and phenotyping. Crop models that capture physiological knowledge and can robustly predict phenotypic consequences of genotype-by-environment-by-management (G×E×M) interactions have demonstrated potential as an integrating tool. But does this biological reality come with a degree of complexity that restricts applicability in crop improvement? Simple, high-speed, parsimonious models are required for dealing with the thousands of genotypes and environment combinations in modern breeding programs utilizing genomic prediction technologies. In contrast, it is often considered that greater model complexity is needed to evaluate potential of putative variation in specific traits in target environments as knowledge on their underpinning biology advances. Is this a contradiction leading to divergent futures? Here it is argued that biological reality and parsimony do not need to be independent and perhaps should not be. Models structured to readily allow variation in the biological level of process algorithms, while using coding and computational advances to facilitate high-speed simulation, could well provide the structure needed for the next generation of crop models needed to support and enhance advances in crop improvement technologies. Beyond that, the trans-scale and transdisciplinary dialogue among scientists that will be required to construct such models effectively is considered to be at least as important as the models.

Combining Ability and Gene Action Analysis for Seed Yield and Component Traits in F2 Generation of Vigna mungo L. Hepper by using Griffing Approach

2020 ◽  
Author(s):  
Amandeep Singh ◽  
R. K. Mittal ◽  
V. K. Sood ◽  
Kulveer Singh Dhillon ◽  
Shailja Sharma
Keyword(s):  
Seed Yield ◽  
Combining Ability ◽  
Gene Action ◽  
Block Design ◽  
Crop Improvement ◽  
Gene Effects ◽  
Early Maturity ◽  
Plant Seed ◽  
Combining Abilities ◽  
Half Diallel

Background: Urdbean (2n=2x=22), also known as blackgram is an important short duration legume crop belonging to family Fabaceae, widely cultivated in Asia. Urdbean occupies an important position among pulses owing to its high seed protein (25-26%), carbohydrates (60%), fat (15%), minerals, amino acids and vitamins. Owing to its low water requirement, it is also suitable for rainfed conditions. It adapts well to various cropping systems owing to its ability to fix atmospheric nitrogen in symbiosis with soil bacteria, rapid growth and early maturity. Half diallel analysis was adopted in present study to gather information on gca (general combining abilities) and sca (specific combining abilities) of 6 diverse parents and simultaneously estimating various types of gene effects involved in the expression of seed yield and related attributes in urdbean. Methods: The F1’s were developed during 2017 in 9 x 9 half diallel fashion by Sharma et al. 2019 and these F1’s were evaluated. Significant crosses on the basis of gene action and combining ability were selected for further advancement. The experimental material comprised of the six parents and 15 F2’s of a 6 x 6 half diallel cross among six cultivars of urdbean (four are purelines viz., Palampur-93, HPBU-111, DU-1 and KU-553 and rest of the two are advanced and indigenous lines viz., Him Mash-1 and IC-281994). The F2’s along with the parental genotypes were grown in a Randomized block Design (RBD) with three replications at Experimental Farm of the Department of Crop Improvement, COA, CSK HPKV Palampur (H.P.) during Kharif, 2018. Results: The cross combination, Him Mash-1× HPBU-111, was identified the best for high seed yield on the basis of sca. The specific crosses, Palampur-93 × IC-281994, Palampur-93 × KU-553 were good specific combiners for most of the traits viz., plant height, branches per plant, pods per plant, biological yield per plant, seed yield per plant, harvest index, 100-seed weight and crude protein content.

scholarly journals Nitrogen Use Efficiency Phenotype and Associated Genes: Roles of Germination, Flowering, Root/Shoot Length and Biomass

2021 ◽  
Vol 11 ◽  
Author(s):  
Narendra Sharma ◽  
Vimlendu Bhushan Sinha ◽  
N. Arun Prem Kumar ◽  
Desiraju Subrahmanyam ◽  
C. N. Neeraja ◽  
...  
Keyword(s):  
Nitrogen Use Efficiency ◽  
Complex Traits ◽  
Indica Rice ◽  
Crop Improvement ◽  
Principal Component ◽  
Partial Least Square ◽  
Least Square ◽  
Nitrogen Use ◽  
Low Level ◽  
Use Efficiency

Crop improvement for Nitrogen Use Efficiency (NUE) requires a well-defined phenotype and genotype, especially for different N-forms. As N-supply enhances growth, we comprehensively evaluated 25 commonly measured phenotypic parameters for N response using 4 N treatments in six indica rice genotypes. For this, 32 replicate potted plants were grown in the green-house on nutrient-depleted sand. They were fertilized to saturation with media containing either nitrate or urea as the sole N source at normal (15 mM N) or low level (1.5 mM N). The variation in N-response among genotypes differed by N form/dose and increased developmentally from vegetative to reproductive parameters. This indicates survival adaptation by reinforcing variation in every generation. Principal component analysis segregated vegetative parameters from reproduction and germination. Analysis of variance revealed that relative to low level, normal N facilitated germination, flowering and vegetative growth but limited yield and NUE. Network analysis for the most connected parameters, their correlation with yield and NUE, ranking by Feature selection and validation by Partial least square discriminant analysis enabled shortlisting of eight parameters for NUE phenotype. It constitutes germination and flowering, shoot/root length and biomass parameters, six of which were common to nitrate and urea. Field-validation confirmed the NUE differences between two genotypes chosen phenotypically. The correspondence between multiple approaches in shortlisting parameters for NUE makes it a novel and robust phenotyping methodology of relevance to other plants, nutrients or other complex traits. Thirty-Four N-responsive genes associated with the phenotype have also been identified for genotypic characterization of NUE.

scholarly journals Causal Haplotype Block Identification in Plant Genome-Wide Association Studies

2021 ◽  
Author(s):  
Xing Wu ◽  
Wei Jiang ◽  
Christopher Fragoso ◽  
Jing Huang ◽  
Geyu Zhou ◽  
...  
Keyword(s):  
Fine Mapping ◽  
Complex Traits ◽  
Haplotype Block ◽  
Association Studies ◽  
Crop Improvement ◽  
Plant Genome ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Haplotype Blocks ◽  
Genome Wide

Genome wide association studies (GWAS) can play an essential role in understanding genetic basis of complex traits in plants and animals. Conventional SNP-based linear mixed models (LMM) used in many GWAS that marginally test single nucleotide polymorphisms (SNPs) have successfully identified many loci with major and minor effects. In plants, the relatively small population size in GWAS and the high genetic diversity found many plant species can impede mapping efforts on complex traits. Here we present a novel haplotype-based trait fine-mapping framework, HapFM, to supplement current GWAS methods. HapFM uses genotype data to partition the genome into haplotype blocks, identifies haplotype clusters within each block, and then performs genome-wide haplotype fine-mapping to infer the causal haplotype blocks of trait. We benchmarked HapFM, GEMMA, BSLMM, and GMMAT in both simulation and real plant GWAS datasets. HapFM consistently resulted in higher mapping power than the other GWAS methods in simulations with high polygenicity. Moreover, it resulted in higher mapping resolution, especially in regions of high LD, by identifying small causal blocks in the larger haplotype block. In the Arabidopsis flowering time (FT10) datasets, HapFM identified four novel loci compared to GEMMA results, and its average mapping interval of HapFM was 9.6 times smaller than that of GEMMA. In conclusion, HapFM is tailored for plant GWAS to result in high mapping power on complex traits and improved mapping resolution to facilitate crop improvement.

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