Genetic Basis of Climate Specificity and Sensitivity in Plants
(Future Fellowship 2013)
A5. Summary of Proposal (750char)
Whether populations can or will respond to climate change and variability affects breeding strategies and management practices in agricultural crops and foundation species of threatened ecosystems. Knowing the genetic architecture underlying yield and fitness will allow us to facilitate climate adaptation. Genome wide association studies will be used to identify allelic variation controlling photosynthesis, development, and reproduction across simulated seasonal climates, testing specific effects in Australian conditions and general climate sensitivity as variation in phenotypic plasticity. A complementary approach of landscape genomics will investigate patterns of allelic variation across real climatic gradients to test adaptation in situ.
A6. Summary of Project for Public Release (350char)
Climate, water, and land use change, is altering growing conditions in many regions of the world forcing agriculture and native plant populations to adapt. Research will identify natural variation controlling growth and reproduction across climate regions to predict how populations can or will adapt genetically to a variable future.
Part D RESEARCH PROPOSAL
Title: Genetic Basis of Climate Specificity and Sensitivity in Plants
Climate change is extending winter growing seasons and altering geographic range limits. This is forcing natural populations to migrate, adapt, or face extinction (Hawkins et al 2008) and requiring structural changes in Australian (Stokes and Howden, 2010) and global agriculture. The research program aims to uncover the genetic mechanisms underlying climate adaptation using plant model organisms and will have fundamental implications for crop and foundation species of agroecosystems. I will address the central question of whether natural selection has acted on standing genetic variation or new localized mutations. This determines pre-breeding approaches and the way we value diverse germplasm collections. Genome wide association mapping will uncover causal genetic variation for plastic and adaptive traits under simulated Australian seasonal conditions. Tests, accounting for polygenic background effects, will identify genetic specificity between, and sensitivity among, regional climate regimes. In parallel, a landscape genomic approach will geographically map the actual climate range of adaptive alleles, apart from neutral background markers, to determine if, and how broadly natural selection has acted on these loci. Taken together, these two approaches represent a powerful way of ‘closing the loop’ between genotypes, phenotypes, and the environment (Fig. 1), by identifying the alleles underlying adaptive traits, and climates that shape them.
A central trait underlying yield and fitness, which is exquisitely sensitive to seasonal climate conditions, is the timing of flowering. Appropriate reproductive transition allows plants to survive and thrive in their local environment by building biomass during the growing season and avoiding terminal drought. When conditions change through environmental disturbance, migration, and/or climate change, new selective pressures are placed on standing genetic variation pushing populations to adapt or perish. The proposed research will capitalize on transformational advances in genomics and phenomics to identify the causative alleles, relative climate effects, and geographic distributions of loci controlling flowering time and associated traits filtered by natural selection under contrasting growing seasons. This knowledge will allow us to predict how genetic variation will respond to environmental change. It can then be used to determine best management and breeding strategies to facilitate genetic adaptation in agricultural crops and foundation species of threatened ecosystems.
Figure 1. Conceptual model showing how knowledge of genotypic variation can reveal the loci underlying phenotypic variation and those under selection by the environment. Alleles at these loci can then be directly selected for optimal phenotypes in appropriate environments jumping limitations of phenotypic selection.
Light, temperature, and moisture are climate variables that shape growing seasons. Plants complete their life history, from germination or re-sprout, through to seed set and senescence, during these extended periods of rather warm and moist conditions. Growing seasons and life histories can be annual, overwintering, spring annual, or over summer. They are quantified by the number of growing days, or more precisely as the accumulation of photo-thermal hours (Wilczek et al, 2009). In many regions, moisture is a key signal defining growing seasons where regular rainfall events can trigger germination or flowering. When moisture is limited, growth can be arrested, and/or seeds can lie dormant to survive the dry times. Plants are exquisitely tuned to these environmental signals and their development integrates variable weather into discrete phenological stages that are changing dramatically with climate change (Rosenzweig et al, 2008). Different plant species and varieties vary in their ability to perform in different environments. Seedlings compete yearly as conditions change with genotypes being redistributed on the landscape first as pollen and then as seed. Certain alleles, in certain environments, may confer a greater fitness leading to enhanced survivorship in a new location. The association of allelic variation along an environmental gradient determines the adaptive advantage and the spatial scale. In agriculture, adapting varieties to specific local environments can increase sustainable yield. It is important to understand the genetic architecture underlying climate specificity in order to identify suitable varieties for key growing regions under current and future conditions.
Climates are not only changing, they are also becoming more variable. The next hurdle for adaptation, is coping with the extreme weather that comes along with climate change and expansion into less favourable lands. Phenotypic plasticity is one mechanism to deal with intra and inter-annual climate variability. Plasticity allows the same genotype to have different phenotypes in different environments and this can be adaptive when it results in increased seed yield or biomass in those environments (Nicotra et al, 2010). Some genotypes may be more plastic than others. In addition, certain traits like branching architecture are more plastic than others, such as seeds per fruit. Accurate quantification using the phenomic approaches described below will allow the dissection of the causal genetic basis of phenotypic plasticity as has been elegantly done for gene expression (Jimenez-Gomez et al, 2011). This will allow breeders to manipulate the extent of plasticity to the desired level. The alleles that underlie low climate sensitivity may provide general wide adaptability and yield stability, while alternative alleles provide specialization and maximal yield in a particular location and year. Careful genomic selection can balance the benefits and costs of plasticity. Thus, by understanding the genetic basis of both climate specificity and sensitivity, breeders could directly select varieties for suitable regional climates while ensuring resilience to annual variability.
Individuals are filtered by natural selection; however, it is often unclear what phenotypes have been selected in a given environment. Heritable phenotypes, traits, have an underlying genetic basis and are responsive to selection. Powerful new landscape genomic approaches allow us to look directly at DNA sequence variation in situ for the signs of selection (Fig. 1). This addresses the central question of whether selection is acting on new local variants, or standing genetic variation across the range. Here, allele frequency changes along environmental gradients indicate the range of selection, once overall genomic differentiation due to neutral demographic processes is controlled for. In contrast, genome wide association studies (GWAS) directly identify alleles underlying phenotypic variation and can be performed in common environments. In GWAS the phenotypes are accurately measured, however the experimental conditions may not be the same as those in the field. This research strategy unites these two approaches to survey genetic variation on the landscape and then experimentally evaluates adaptive phenotypes on a balanced genetic diversity set under recreated environmental conditions.
The proposed research program aims to determine the genetic basis of seasonal climate response. It will utilize wild lines of Arabidopsis and Brachypodium grown under simulated Australian climates to determine what genes and alleles control growth, development, and reproduction. This will test the genetic variation necessary for naturalization of these weedy species into, and among, non native climates. The work expands upon my previous research that looked at how population genetic variation correlates with spatial patterns on the landscape (Platt et al, 2010). Recently, genome scans for selection, in a very large set, directly identified loci with divergence among European subpopulations. We performed the first genome wide association studies in plants to determine what genes control traits (Atwell et al, 2010) and their environmental sensitivities (Li et al, 2010 & 2013). GWAS on appropriately selected regional collections has explained a majority of the heritability and revealed major loci involved in local adaptation (Li et al, 2010, Brachi et al, 2011). Together these experiments allowed us to identify the phenotypic effects of loci showing geographic differentiation, the molecular patterns of selection, and how environmental variation shapes phenotypic variation at the genetic level.
The key to success in the dissection of complex traits is the use of balanced mapping population that we have recently created in Arabidopsis (Li et al, 2010). This outstanding resource will be used to make inferences about the architecture of genetic control of adaptive phenotypes (including GxE effects) among novel Australian climates suggesting optimal study designs for incorporating new genetic material (eg new crops) into new environments. Work is underway to develop a similarly powerful haplotype mapping population in Brachypodium. Additional model-organism studies are needed to generalize which classes of traits, and in which populations, major effect loci can be found. This will also address whether selection is acting on standing genetic variation or new mutations. If general trends can be inferred, through work in model organisms as proposed here, then predictions about the scale and rate of local adaptation can be made. These will be tested in foundation species through collaboration (Eucalyptus melliodora, and E. marginata LP130100455) and in ecological indicator species (Pelargonium australe, NSF subaward in progress). The results will strongly influence the approach to breeding agricultural crops for changing environments and in management and conservation of foundation species in natural ecosystems.
The Geographic Scale of Local Adaptation
It has long been understood that locally adapted genotypes perform better in their home environment when compared with non-native genotypes. But, how local is local, and how stable are local conditions and populations? Rapid advances in population genomics now allow the geographic scale of local adaptation to be accurately tested. This requires phenotypic studies in controlled conditions and surveys of genetic variation on the landscape. Prerequisite work in the Borevitz Lab geographically mapped population genetic diversity in Arabidopsis thaliana (Platt et al, 2010) to develop a global resource for fine mapping alleles linked to adaptive traits. We have taken a parallel approach to field based common garden experiments by studying local adaptation in the laboratory (Li et al 2006, 2010, 2013). Here, simulated climates are be recreated in growth chambers with diurnal and seasonal changes in light, temperature, and moisture. The genetic basis and environmental sensitivity of flowering time and yield in these conditions identified novel and known genes under selection (Li et al, 2010). Subsequently, landscape genomic studies of local adaptation made use of our Arabidopsis line set planted in field conditions. Hancock et al, scanned the genome for SNPs that are correlated with local climate variables while accounting for geographic and genomic correlations. The beneficial alleles that were identified, could explain fitness in a local common garden (Hancock et al, 2011). In parallel, Fournier-Level et al performed four common garden experiments across the European range. Alleles that confer a fitness disadvantage in a particular location were found to be more sparse in that region suggesting that they were excluded by natural selection (Founier-Level et al, 2011). Further, our work on the genetic basis of sodium accumulation shows a clear signal of adaptation to coastal and inland salty soils (Baxter et al, 2010). These studies cover a broad continental range and provide preliminary evidence for selection acting on standing variation, a promising mechanism of rapid adaptation.
Climate change effects on overwintering
Recent results (Li et al, 2013) addressed the temporal scale of local adaptation in the laboratory by comparing current and future climates. The global Arabidopsis set was grown under overwintering conditions simulating current and future warmer seasonal climates (+1,2,3C increase in diurnal average temp). Fall germination time and genotype had major effects. Four of nine major quantitative trait loci (QTL) controlling rapid cycling or overwintering were sensitive to future temperate. Background and major QTL genotype could accurately predict flowering time (R2 > 0.9) of new strains in future controlled climates.
A new infrastructure award to Borevitz et al in 2013 will implement ‘Spectral climate chamber facilities for phenomic studies of plant light response adaptation’ (LE130100081 $500k ARC + $340k internal). This will enable the fine control of light intensity and spectrum, temperature, and moisture, to simulate climates at any location and time around the globe (Fig 2A). Climate conditions simulating sun, shade, and transients between them allow studies of test photoprotection and shade avoidance. Cyclic drought, and chronic thermal stress are also possible. This major equipment facility will also be equipped with high spatial and temporal resolution (NIR-RGB) image based phenotyping of hundreds of plants per chamber and are being set up at ANU and participating universities (UWA, Monash). My early work validated the platform for assaying real-time growth under simulated growing seasons. Here, images were taken every 10 minutes and were processed to calculate the green pixels of rosette area throughout the growing season on hundreds of plants (Fig 2BC). GWAS was then performed at each time point to identify four and five loci controlling juvenile and adult growth (Zhang et al 2012).
Figure 2. Quantitative analysis of growth in a simulated seasonal climate. A) SpecralPhenoClimatron chamber, B) Time lapse of Arabidopsis growth, C) quantification of plant area (green pixels) over time.
AIMS AND APPROACH
The overarching aim of my research is to dissect the genetic basis of natural variation in a suite of physiological and developmental fitness traits. My research program combines both experimental plantings and field sampling. The experiments proposed here focus on model plants in climate chambers to identify the genetic basis for variation in growth rate (photosynthesis), development (photomorphogenesis), and reproduction (flowering time) across contrasting climates and their growing seasons. We will expand our use of modern quantitative genetic methods of Genome Wide Association Studies (GWAS) to dissect the genetic basis of natural variation in a suite of physiological and developmental fitness traits. This approach allows one to determine the loci controlling adaptive trait variation that will be necessary for agriculture and for managing foundation species in critical ecosystems. These loci have been shown to be important in local adaptation and function in unique geographic and environmental contexts. They have patterns on the landscape distinct from neutral loci in the genome. This implies that they would be identified through landscape genomic approaches as has been successful in Arabidopsis across Europe (Hancock et al, 2011 and Founier-Level et al, 2011).
Research will continue to use Arabidopsis thaliana and will further the development of genetic and genomic tools in the emerging model grass Brachypodium distachyon (Brachy). Arabidopsis was the first plant genome to be sequenced in 2000 by postdoc advisor (Ecker and colleagues) and is being studied by ~16,000 labs across the world. I am a co-organizer and will speak at the International Arabidopsis meeting in Sydney in June 2013, however naturalized populations of Arabidopsis do not exist in Australia. In contrast, the Borevitz Lab has recently completed a transect from Canberra to Adelaide and collected 1000s of Brachy accessions from >50 new Australian populations. They are being amplified as self pollinating inbred lines, sequenced, and shared with key collaborators. Brachy also has a short stature appropriate for climate chambers, is closely related to wheat, but has a small genome (270Mb, International Brachypodium Initiative. Nature. 2010). We have amplified and validated hundreds of commonly used Mediterranean lines and have access to >50 genomes currently being sequenced by the Joint Genome Institute. So far variation in agronomic and ecological traits are highly heritable. My PhD student, comes from my collaborator, Todd Mockler’s lab, whom is one of the groups leading the effort to characterize phenotypic and genetic variation and maintains www.brachypodium.org. Together, studying Arabidopsis and Brachypodium allows for deep evolutionary (>100MYA) genomic comparisons separating monocots and dicots. Yet they are both agricultural weeds and similar ecologically, with life history strategies of overwintering and rapid cycling. Identifying the genetic basis of climate adaptation allows comparisons of the types of mutations and genes recruited by largely independent evolutionary pathways.
Below I outline proposed specific research to map the genetic architecture of adaptive traits in climate chambers across growing seasons.
Genetics Basis of Growing Season Adaptation
Major environmental components controlling growing season are light, temperature, and moisture. Four climate contrasts will be created simulating North, South, East, and West portions of the Australian Brachypodium range. The light regime (intensity, spectral quality, day length), temperature, and humidity and soil moisture (set by automatic watering) will be adjusted at 10min intervals simulating parallel growing seasons. Experiments will assay genetic variation for climate specificity along the N/S and E/W gradients and for overall climate sensitivity. This is being lead by a postdoc Pip Wilson who studied climate and weather effects on plasticity in wheat at CSIRO. GWAS will fine map common QTL for adaptive traits and plasticity under these conditions. Growth and development will be finely dissected across multiple planting times in autumn and spring. This experimental design seeks to partition the environmental and temporal sensitivities of alleles that are likely to play a role in local vs wide adaptation. Arabidopsis and Brachypodium mapping populations will be used and growth phenotypes assayed in real time with time-lapse cameras (Fig 2).
The simulated climate conditions do not necessarily expose greater phenotypic variation. When the duration of winter cold varies, and the temperature, humidity, light quality, and day length, cycle diurnally and seasonally, the multiple climate signals may canalize phenotypes to coordinate spring flowering. Alternatively, in the autumn when day-length and temperatures decrease, genotypes can either quickly flower or overwinter. These studies will test a combination of multiple seasonal climate signals and may identify novel loci or novel sensitivities at known loci. By phenotyping under simulated climates, the results will link QTL to adaptive variation showing allele frequency variation across the landscape where the samples were collected. Furthermore, novel loci, not identified in standard conditions, may reveal new genes involved in season and climate sensing.
A photothermal model of flowering time will be used to quantify the number of degree hours until flowering across growing seasons and can account for variation in winter vernalisation, day-length, and temperature pathways (Wilczek et al 2009). Photothermal units quantify climate contrasts by down-weighting shorter cooler days and up-weighting warmer longer days so the growth potential across various growing seasons can be more directly compared (Brachi et al 2010). This allows climate plasticity to be calculated in comparable units of degree hours for individual accessions. In a GWAS framework, a GxE scan will evaluate each SNP for allelic variation in the number of degree hours until flowering across simulated climates. Climate will be modelled as a fixed effect to test genetic specificity and as a random effect to test for genetic sensitivity.
Phenomics: High Throughput Phenology in Climate Chambers
In addition to flowering time, real time quantification of growth and development will be performed in high throughput (Fig 2). Plant growth and development can be viewed as behaviour when annual life history is reduced to minutes in time-lapse such that various vegetative traits (leaf number, shape, rhythm, pigmentation) can be assayed continuously in under controlled conditions. The setup and techniques are being lead by senior postdoc Tim Brown (formerly time-science.com) adding to current strengths that ANU and CSIRO have in Plant Phenomics. He is implementing TrayScan for high throughput analysis of plant growth, photosynthesis, photoprotection, and transpiration. This will be enhanced with new protocols and analysis software in a pending Linkage grant that I have authored (LP130100081). TrayScan has RGB, fluorescence, and thermographic imaging to quantify photosynthetic efficiency via Non-Photochemical Quenching (NPQ) and leaf temperature change. Photosynthetic efficiency has been dissected through traditional QTL mapping (Jung and Niyogi, 2009, and new unpublished data from my lab) but not yet in the context of GWAS or under contrasting climates as proposed here. Using the state of the art phenomics capacity, new spectral climate chambers, and high power mapping populations, detailed genetic networks will be resolved dissecting acclimation via physiology and developmental plasticity, to identify adaptive QTL filtered by the environment.
GWAS method: Genotype, Phenotype, and Environment
This proposal addresses the genetic architecture of standing variation underlying growth, development, and reproduction in contrasting climates and associated growing seasons. Detailed phenotypic data from these dynamic environments will be used for multi-trait, multi-environment GWAS, utilizing essentially complete genome sequences (1001genomes.org). This will identify the number of loci, their effects sizes, and regional allele frequencies with unprecedented power to detect climate specific and sensitive QTL through development. These loci will be tested for the signature of adaptation across the native climate range (Hancock et al, 2011). I have pioneered the statistical genetic methods in this proposal for the plant community. They are technically straightforward and now in routine use, though not yet a common in Australia. I have employed a senior statistician, Dr Riyan Cheng, whom has authored his own GWAS mixed-model analysis methods that control for cryptic family, and population structure (Cheng et al, 2011). Shared ancestry is modelled as kinship and is estimated from dense SNP data. This random effect in the model measures the polygenic control of the complex trait and accounts for background variation providing more power to identify causal QTL and QTL x Climate interactions. Finally, multiple major QTL will be included as covariates as we have recently done when mapping seasonal flowering time (Li et al, 2010 and 2013).
Genotyping By Sequencing
The high density genotype data generated using our custom genotyping array was essential for the GWAS success in Arabidopsis. However, next generation sequencing now allows an unbiased look at new populations and new species (Baird et al, 2008, Lu et al, 2013). It is no longer cost prohibitive and will be the tool of choice going forward (see our recent work in switchgrass, Morris et al 2011). Genotyping By Sequencing allows ancestral recombination events to be identified with low coverage sequencing and imputation based on linkage disequilibrium (LD) (Huang et al 2010). Hundreds of samples are barcoded and sequenced in a single Illumina lane. The necessary coverage is determined by LD, which is more extensive in closely related samples. For example, in rice 1X sequencing was used to type 517 lines and identify 3.6M SNPs. This resulted in the 27% average per sample coverage and 67% total missing data. However by using imputation based on linkage disequilibrium (that spans 100-200kb), near complete coverage was obtained (Huang et al 2010). Similarly, in Arabidopsis, complete haplotypes from 19 founder lines were imputed for 527 Multiparent Advanced Generation InterCross lines, originally typed at only 1206 SNPs (Gan et al, 2011, Kover et al, 2009). Thus marker density should be appropriate to capture the recombination events present in the population and is essentially not limiting nor cost prohibitive with current technology. Collaborative efforts in the Research School of Biology are implementing very high throughput automated next generation sequencing library construction as part of our recently funded LIEF grant (LE130100073). This includes automated DNA/RNA/metabolite extraction from difficult plant samples. Further, I am coordinating bioinformatics training sessions with the Genome Discovery Unit on campus to bring these methods to the school. To date, my lab and 6 others at ANU are using new sequencing methods and analysis with great success. To extend this, I have hired a new postdoc, Norman Warthmann, with extensive experience in high throughput library construction and bioinformatics, whom will contribute to this research.
Population Genomics in Brachypodium (Brachy)
The Brachypodium HapMap is being done in collaboration with Todd Mockler at the Danforth Plant Sciences Center utilizing sequencing work underway at the Joint Genome Initiative. Because Brachy is also small, the environmental conditions and phenotyping approaches used in Arabidopsis will also be appropriate in this model grass. The first step will be to develop association mapping lines by characterizing the deep genomic diversity across the geographic range. This has just been done with sequencing methods just discussed. Second, a balanced core haplotype mapping set (~500 lines) will be selected. These lines will be sequenced to the depth required by linkage disequilibrium (LD) to allow complete genomic imputation of haplotype variation. Third, the core set will be phenotyped in climate chambers recording parameters of photosynthesis and development and flowering time as described for Arabidopsis. Genome wide association mapping will be performed to identify QTL for climate sensitivity and specificity. Because we now have local Australian Brachypodium populations we will be able to test for landscape level association of these QTL with climate variables (light, temperature, and moisture). Both climate range and genetic variation can now guide future collections to validate phenotype and climate associations. I expect such a complete toolkit for adaptation genomics in a crop relative will be widely used by Australian researchers in the fields spanning plant breeding, molecular biology, and ecology and evolution.
In both Arabidopsis and Brachy, QTL replication and confirmation will be performed by evaluating select hybrids and F2s that differ at functional loci. This will be performed using 5-10 F2 families and 24-48 plants each. They will be phenotyped and genotyped with QTL validation determined by nested association mapping (McMullen et al 2009). Once QTL are confirmed, often at the genomic resolution of 5-50kb, candidate genes will be selected. Knock out lines will be ordered and used for quantitative complementation and as a null transgenic background. These are available for nearly all Arabidopsis genes and BrachyTAG has 60,000 lines. Finally, the function of causal quantitative trait nucleotides will be verified in transgenic plants.
Patterns of allelic variation on the landscape highlight past selection at target loci. These are separated from historical processes, such as migration, which affects the entire genome. This method complements GWAS by leveraging the spatial patterns of locally adaptive alleles. In Landscape Genomics natural populations spanning a range of geographies, habitats, and environmental clines, are genotyped at high density. Then SNP alleles are tested for association with location, and associated climate factors from where they were isolated, while controlling for population structure that affects all SNPs in the genome (Hancock et al, 2011).
Figure 3. Integration of GWAS and Landscape Genomics approaches for non models to be applied in pre-breeding crops and facilitating adaptation in foundation species.
Model Organisms to Foundation Species
When landscape genomic studies identify the same loci as GWAS, as we saw in Arabidopsis (Li et al, 2010), then it can be directly applied to other species not amenable to rapid and/or detailed phenotypic analysis in controlled experiments. Knowledge of adaptive loci will give breeders tools to adapt crops into marginal and changing growing conditions and enable land managers to conserve and restore foundation species in sensitive ecosystems. Figure 3 provides an overview of the integration of both approaches to identify the adaptive alleles in non model systems. Sequencing identifies segregating alleles in the population sample, which are then associated with phenotypes measured in simulated climates, and in situ climate variables. My lab has used these methods in switchgrass (Morris et al, 2011) and Pelargonium (current NSF award, Ong, Borevitz, Nicotra, in prep) and will now apply these validated genomic methods in Eucalypts species (E. marginata used in reforestation, E. melliodora target of conservation (LP130100455), and E. pauciflora an extreme alpine specialist).
GWAS in seasonal climate conditions
QTL gene cloning
QTL gene cloning
Select/ Sequence Regional HapMap
GWAS in seasonal climate conditions
QTL gene cloning
GWAS in seasonal climate conditions
SIGNIFICANCE AND INNOVATION
The proposed research asks, ‘What is the scale and rate of local adaptation?’ Is it similar among species responding to the same environmental pressures? With the acceleration of the genomic revolution we can now answer these questions across multiple species. As the cost of sequencing drops we are no longer limited by databases and marker sets previously available only in select model species. Today, this research is largely limited by sampling (biodiversity) and phenotyping (Phenomics), both areas where Australia has recognized strengths. To track directional changes in allele frequencies due to selection, spatial and longitudinal data sets are being sampled across environmental gradients. These data sets can answer how often new mutations and soon epi-mutations (Eichen and Borevitz, 2013 Nature News & Views), as opposed to new combinations of existing alleles, are underlying adaptation. This would allow predictions to be made about the local potential for adaptation and the need for assisted migration (Schwartz ,.. Borevitz.. 2013…), which has implications for conservation and restoration genetics.
Selection acts on phenotypic traits related to fitness, to change the allele frequencies at genetic loci controlling these traits. In isolation, populations can diverge such that neutral genome wide markers and markers at Quantitative Trait Loci (QTL) are correlated reducing power to distinguish the causative loci. In well mixed samples, genome wide makers are used to account for residual population structure, allowing direct mapping of causative loci via GWAS. In practice, efficient study designs for identification of QTL in wild populations, can be done through selecting informative individuals for repeated phenotyping from an initial screen of a large population sample (Platt et al 2010). Once loci controlling adaptive traits are identified, the effect of selection can be identified when allelic variation maps along environmental conditions. In considering the adaptive potential in a population we ask, ‘To what extent are advantageous alleles present in new populations where they would provide an advantage?’ Answers will help establish the effective scale and rate of local adaptation. If adaptation is limited by gene flow, this can be overcome with assisted migration of target populations.
The long term goal of the Borevitz Lab is to understand the amount and patterns of genetic diversity underlying local adaptation. Large scale studies can address whether increased genetic diversity and new allelic combinations are responding to shifting micro-climates from water, land use, and climate change. Alternatively low diversity, isolated, populations may be limited in their ability to adapt to shifting conditions and assisted relocation may be required. Quantifying and archiving divergent lineages within species is the core of conservation genetics and is becoming increasingly important for preserving endangered species and threatened habitats. The extent to which this is generally important and addressable in foundation species is now coming into play (Whitham et al, 2008). The experimental methods discussed in the proposal can be applied to foundation species such as Eucalyptus in Australia. This applied form of restoration genetics, facilitates adaptation using standing variation, by capturing ecotype pools and amplifying them as cultivars. It is especially important in foundation plant species.
Australia and specifically ANU has a strong research program in the areas of Ecophysiology, Photosynthesis, and Phenomics. My work using high power genome wide association studies brings unique and complementary tools to attack the highly integrated problems underlying ecological climate adaptation. Thus the proposed research is highly collaborative. High throughput Phenomics work will take place among multiple labs within the ANU (Murray Badger, and Barry Pogson) and CSIRO (Bob Furbank). We have worked together to streamline and standardize optimal phenotyping conditions including the recently awarded LIEF (SpectralPhenoClimatron, PI Borevitz $500k) and pending Linkage (LP1300081 TraitCapture: Genotype to Phenotype analysis pipeline for plant phenomics data). I will also be a CI on the Plant Energy Biology Centre renewal bid in 2013 and have begun collaborations with other CIs. Regarding international collaboration, I have specifically budgeted yearly travel to the Danforth Plant Sciences Center, (St. Louis, USA) and Wageningen University (NL) to coordinate new Phenomics efforts on powerful mapping populations. Joost Keurentjes from Wageningen has already spent several months on sabbatical in my ANU lab developing the collaboration.
NATIONAL RESEARCH PRIORITIES AND TARGETED PRIORITY AREAS
The proposed future fellowship will increase national research capacity by accelerating international research. My lab employs a German, Chinese, American, and two Australian postdocs, and two American and a Thai graduate students. I have many contacts in N American and Europe, two of which are proposed collaborators on this fellowship. I am currently recruiting students, postdocs, and visiting scientists from international locations that will further increase research capacity. Regarding Targeted Research areas for funding in 2013 include, Bioinformatics is a primary part of my proposed research. Next generation sequencing, a core technology of this proposal, generates billions of sequence reads that requires advanced Bioinformatic processing tools. This will come mainly in the development of custom processing pipelines for filtering, assembly, alignment, SNP calling, structural rearrangement identification, homology/duplication determination, and more. This proposal will push further with model organisms (Arabidopsis) and bring new tools and datasets for the next grass model Brachypodium. Skills will be transmitted throughout the department, ANU, and Canberra wide and we will host summer courses to facilitate broader distribution of tools in bioinformatics. National Research Priorities of ‘An Environmentally Sustainable Australia’ with goals of ‘responding to climate change and variability’ is directly addressed in the proposal as growing seasons are becoming more variable through the coming decades. Genetic adaptation is a necessary response. ‘Water – a critical resource’ is also addressed as growing seasons are effected by seasonal water availabilities that will be included in our simulated climates. ‘Sustainable use of Australia’s biodiversity’ is indirectly addressed through investigations on the distribution of genetic diversity across the landscape and the validation of Landscape Genomic approaches. Methods developed in model organisms will be directly transferred to foundation species. ‘Overcoming soil loss, salinity, and acidity’ is a secondary goal of the proposed work. Soil conditions will be tested for genetic adaptation in collaboration with the Centre for Plant Energy Biology. These goals can be met at the Australian National University where world-class strengths exist photosynthesis, Phenomics, and eco-physiology. A further targeted research area where this proposal overlaps is ‘Managing Innovation, Renewable Energy, and Green Technology’. The approaches to genomic breeding outlined above detail ways to innovate ‘climate ready plants’ to be deployed in marginal conditions that can be used for biofuel, bioenergy, and carbon sequestration, as well as and water and soil conservation, thus promoting ecological agriculture.
COMMUNICATION OF RESULTS
My group has and will continue to take a multifaceted approach. However the primary form of communication remains well timed publishing in peer reviewed research journals. I will continue to publish the best results in high impact journals such as Science and Nature but also the Public Library of Science (PLoS) Biology, PLoS Genetics, Nature Genetics, and PNAS. Second tier, especially open access journals are appropriate for specialist and methodological results. In addition we will continue and expand our web communications outlining proposals, projects in progress, resources, data, and methods to be shared in near real time. Finally, research communication will be speaking at national and international meetings. I expect to make ~2 North American and/or European trips to speak at meetings and seminars during the summer and winter breaks. Local presentations to universities, industry and citizen groups are also a priority for myself and lab members. Select lectures and seminars will be posted online.
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