Home » Predictive modelling for yield at Bioenergy Genomics 2017

Predictive modelling for yield at Bioenergy Genomics 2017

It’s often over simplistic to say there’s a gene for this or one for that, so bringing genotype and phenotype together is challenge. Jack Bailey-Bale looks ahead how how some people plan to tackle that.

The serious threat of climate change is illustrated by the ambitious nature of particular aims set by the 2015 Paris Agreement. A stand out example is the pledge to limit temperature rise to 2°C, with an ideal of 1.5°C. The formulation of strategies to maximise the likelihood of meeting outlined goals is critical. Bioenergy Genomics 2017 presents an amalgamation of research contributing to future sustainable delivery of biomass and the mitigation of climate change. Tuesday afternoon we will immerse ourselves in the work exploring future bioenergy scenarios with predictive modelling – linking genotype and phenotype.

Genome wide association (GWA) mapping is now a commonly used tool for identifying natural polymorphisms. Differences in the DNA sequence are used to elucidate variation in a populations observable traits, in turn associating genotype and phenotype. The expansive employment of GWA mapping has revealed that natural genetic diversity at specific regions on a chromosome can have pleiotropic effects on a number of characteristics – meaning one gene can influence multiple unrelated phenotypic traits. This information aids genomic prediction, as genome-wide markers highlighting variation can be used to estimate observable traits. In combination with models for crop growth, genomic prediction is being used to determine the interactions between the genotype, environment and management type (G × E × M).

Understanding complex traits such as biomass accumulation is further complicated by epistatic interactions. The impact of a gene can therefore depend of the expression of one or more other genes, termed ‘modifier genes’. Multi-trait models have in turn been designed to link genotype-to-phenotype for complex characteristics.

Techniques discussed in the session have experimentally investigated the variation in water relations responses to drought conditions in a Populus nigra genome wide association population, originating from western Europe. Bioenergy crop deployment has also been simulated, exploring G × E × M effects of short rotation willow, determining viable areas of Britain for production. Such large-scale modelling has also been reported for perennial grasses in mid-western United States. Research scaling up from plots to ecosystems is absolutely essential for calculating the viability of bioenergy crop cultivation. The information generated is extremely valuable for understanding the feasibility of emerging energy scenarios. An example includes the “negative emissions” technology, bioenergy with carbon capture and storage (BECCS), which would require considerable land-use change to bioenergy crops in the UK.

The successful establishment of renewable bioenergy crops could potentially lead to a multitude of perks. In addition to meeting energy demand without food production competition, biomass usage could offset carbon emissions – combating climate change. Moreover, utilising bioenergy crops with robust policy in place could have environmental and societal benefits too, enriching natural capital with the preservation and generation of ecosystem services. As modelling systems continue to progress, session five outlines how the technology will play a pivotal role in deciphering future energy pathways, both in the UK and globally.

Jack Bailey-Bale

Jack Bailey-Bale is a PhD researcher based at the University of Southampton researching the use of genome editing to investigate the impact of candidate genes for yield enhancement and drought tolerance in Populus nigra as a bioenergy crop. Currently a challenging area of this project is reliably growing transformed P. nigra lines out of culture in order to run proof-of-concept studies for the mutated genes.

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