Models for navigating biological complexity in breeding improved crop plants

Hammer, Graeme; Cooper, Mark; Tardieu, François; Welch, Stephen M.; Walsh, Bruce; Eeuwijk, Fred van; Chapman, Scott C.; Podlich, Dean

doi:10.1016/j.tplants.2006.10.006

Cited by 354 publications

(286 citation statements)

References 43 publications

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“…The genetic basis of these traits is important for plant breeding and improvement strategies (Cooper & Hammer 1996;Hammer et al 2006) as well as for predicting responses to changing climates (Davis et al 2005;Aitken et al 2008). Extensive quantitative genetic studies have demonstrated that most of these phenological traits have a heritable genetic basis (reviewed in Cooper & Hammer 1996;Howe et al 2003;Savolainen et al 2007).…”

Section: Seasonal Cues Regulating Plant Phenologymentioning

confidence: 99%

Genetic and physiological bases for phenological responses to current and predicted climates

Wilczek

Burghardt

Cobb

et al. 2010

Phil. Trans. R. Soc. B

191

178

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We are now reaching the stage at which specific genetic factors with known physiological effects can be tied directly and quantitatively to variation in phenology. With such a mechanistic understanding, scientists can better predict phenological responses to novel seasonal climates. Using the widespread model species Arabidopsis thaliana, we explore how variation in different genetic pathways can be linked to phenology and life-history variation across geographical regions and seasons. We show that the expression of phenological traits including flowering depends critically on the growth season, and we outline an integrated life-history approach to phenology in which the timing of later life-history events can be contingent on the environmental cues regulating earlier life stages. As flowering time in many plants is determined by the integration of multiple environmentally sensitive gene pathways, the novel combinations of important seasonal cues in projected future climates will alter how phenology responds to variation in the flowering time gene network with important consequences for plant life history. We discuss how phenology models in other systems-both natural and agricultural-could employ a similar framework to explore the potential contribution of genetic variation to the physiological integration of cues determining phenology.

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Section: Seasonal Cues Regulating Plant Phenologymentioning

confidence: 99%

Genetic and physiological bases for phenological responses to current and predicted climates

Wilczek

Burghardt

Cobb

et al. 2010

Phil. Trans. R. Soc. B

191

178

View full text Add to dashboard Cite

show abstract

“…Complementary to evaluating methods in terms of their ability to recover genuine genetic associations, the ability to predict phenotypic variation from genotype has gained considerable attention, in particular in the field of animal and plant breeding, for example, [42][43][44][45] and also more recently in the genetic analysis of model systems 11,46 and human genetics 47,48 . Existing approaches to predict phenotype from genotype include dynamical systems approaches to model organism/environment interactions 43 , or employ (regularized) linear regression methods using genome-wide SNP markers 42,44,48 or random effect models using a kinship matrix 11,45,46,48 . The task of phenotype prediction is also linked to 'missing heritability', and several studies have noted that single-marker association methods are not sufficient to fully explain the heritable component of phenotype variability 11,12,42,46 .…”

Section: Resultsmentioning

confidence: 99%

A random forest approach to capture genetic effects in the presence of population structure

2015

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The accurate mapping of causal variants in genome-wide association studies requires the consideration of both, confounding factors (for example, population structure) and nonlinear interactions between individual genetic variants. Here, we propose a method termed 'mixed random forest' that simultaneously accounts for population structure and captures nonlinear genetic effects. We test the model in simulation experiments and show that the mixed random forest approach improves detection power compared with established approaches. In an application to data from an outbred mouse population, we find that mixed random forest identifies associations that are more consistent with prior knowledge than competing methods. Further, our approach allows predicting phenotypes from genotypes with greater accuracy than any of the other methods that we tested. Our results show that approaches that simultaneously account for both, confounding due to population structure and epistatic interactions, are important to fully explain the heritable component of complex quantitative traits.

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“…Thus, the timing of temperature increase is very important for the growth and development of the crop. The effect on pollen viability, seed set, and hence yield depends on the magnitude of the temperature event and its duration during the critical developmental window around flag leaf full expansion [17]. According to these authors, the effect of temperatures below 36°C, was very little but became severe if maximum temperature was set to be greater than 38°C.…”

Section: Resultsmentioning

confidence: 99%

Modeling Impact of Climate Change and Variability on Sorghum Production in Southern Zone of Tigray, Ethiopia

Gebrekiros¹,

Araya²

2015

J Earth Sci Clim Change

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Even though, there is a general understanding on the impact of climate change and variability on agricultural crops, the spatial and temporal variability of these impacts remains yet uncertain. Thus, modeling the likely impact of climate change on cereal crops at local level is crucial for developing possible future options of adaptation and mitigation strategies. This research was conducted with the objective of modeling the impact of climate change and variability on sorghum production. Hence, calibration and evaluation of agricultural production system simulator (APSIM) model was conducted using four years research data (2006 to 2009) and its performance was assessed using coefficient of determination (r 2 ), root mean square error (RMSE) and index of agreement (d). According to the HadGEM 2 -ES model, an increase of maximum (5.9°C) and minimum (6.4°C) temperature was revealed in the end term (2080s) over the base year. Similarly precipitation was predicted to increase by 27% in the end term while almost constant for the other periods. The monthly accumulated heat unit was increased in all periods and this has shortened the maturity date of sorghum by about a week compared to the historical. Keeping the current sowing window (April) and other management activities the same, future Sorghum yield has simulated to decrease between 5% and 24%. Using both the historical and predicted climate data, sowing on March followed by April has shown a reasonable yield advantage in both RCPs. The response of the different sorghum cultivars to the future climate changes should further be studied for deciding which cultivar could give a better yield for the future under different management options.

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Models for navigating biological complexity in breeding improved crop plants

Cited by 354 publications

References 43 publications

Genetic and physiological bases for phenological responses to current and predicted climates

Genetic and physiological bases for phenological responses to current and predicted climates

A random forest approach to capture genetic effects in the presence of population structure

Modeling Impact of Climate Change and Variability on Sorghum Production in Southern Zone of Tigray, Ethiopia

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