G×E: Bringing genotype by environment interactions to the fore to tackle the formidable challenges ahead

Just like people, plants are the products of both nature and nurture—of the complex interaction between their genes and their environments. Breeders have long known that certain crop genotypes perform better than others in certain environments and have tried to strike a balance between broad adaptation—finding genotypes that will do the best over as large an area as possible—and specific adaptation—fitting the right genotype to the right location where it will do better than all others. However, Natalia de Leon, an Associate Professor in the Department of Agronomy at the University of Wisconsin, acknowledges that, at times, that balance may have shifted more towards minimizing genotype by environment (G×E) interactions, which she says “may not be the most efficient strategy.” “Maybe we should try to understand these interactions, and if we understand them, we can use different sources of information to better predict how different genotypes will fare across a larger number of environments, which we will continue to see because of the tendency of climate to become more erratic,” says de Leon, associate editor of a special section on G×E coming out in the September–October issue of Crop Science. Climate change is rendering environmental components like temperature and soil available water a lot more capricious than they used to be. And the locavore movement, whose proponents value (and will pay handsomely for) heirloom and hyper-local crop varieties, can also induce breeders to more seriously consider G×E interactions. But the biggest push towards honoring, rather than minimizing, G×E interactions in plant breeding comes from recent technological advances. Cheap marker data are making it possible to genotype new breeding lines at higher densities for less than ever before; new automated phenotyping platforms are making repeated measures of crop morphology and physiology feasible; we can now generate better characterization of different environments using similar sensors; and cheaper computation now enables the crunching of all of this data: summarizing images into measures of plant architecture, running millions of iterations of crop growth models, and analyzing data with larger and more complex linear models than ever before. Scientists publishing in this special section of Crop Science are leading the way in studies to tackle the formidable challenges ahead. Most plant genomes have only been sequenced within the last 15 years or so. Funding to complete the corn genome was raised throughout the 1990s, but the sequence was not completed until 2009—which is somewhat ironic, given that maize was the model organism that Barbara McClintock used to make her Nobel Prize winning discoveries about some very fundamental aspects of genetics and inheritance. With the genome sequence in hand, corn growers across the country had a collective desire to link that genomic knowledge to phenotype in order to predict and improve crop performance. David Ertl, the Technology Commercialization Manager at the Iowa Corn Promotion Board, recalls that the realization hit at the Corn Breeding Research Group's Maize Genetics Conference in 2013. “The next step was to collect phenotype data to figure out what all these genes do and how they interact with the environment,” he says. Shawn Kaeppler, editor of Crop Science and Professor in the Department of Agronomy at the University of Wisconsin, concurs. “Now the growers are interested in seeing gene sequences translated into products in the field.” And thus, The Genomes to Fields (G2F) Initiative was conceived. It is a public–private consortium with the stated objective to “leverage genomic information with phenotypic and environmental data to enable working knowledge and prediction of plant performance under variable growing conditions.” In 2014, its first G×E trials were started, growing corn hybrids in 13 states. Since we now have the ability to measure genotypes, phenotypes, and environmental parameters in different ways, these types of wide-scale studies will allow for the recognition of patterns that might not otherwise be viable. While the initiative is currently limited to corn, it intends to eventually broaden out to include other commodity groups. “One of the main sorts of limitations for translating genomic information into phenotypes is the fact that we know that when you put a plant in the field, the environment influences the plant in different ways,” de Leon says. “There is not necessarily a linear relationship between the genotype of a plant and the phenotype. Certain genotypes will perform one way in certain environments and then completely change the ranking in terms of productivity in another environment. So understanding what it is that is causing that is a very complex scientific question.” Natalia de Leon. Photo by Margaret Broeren, GLBRC Communications. “Advancements in next-generation sequencing have made genomic data very inexpensive,” says Jesse Poland, Assistant Professor at Kansas State University who serves as the Associate Director of the Wheat Genetics Resource Center and Director of the Feed the Future Innovation Lab for Applied Wheat Genomics. “But measuring phenotypes is still difficult. It is the critical bottleneck in plant breeding programs that have to look through thousands of candidate varieties in field plots to find the right one.” To meet this need, a team at Kansas State University led by Poland built the Phenocart, described in his paper “Development and Deployment of a Portable Field Phenotyping Platform” in the May–June 2016 issue of Crop Science.1 It contains an infrared thermometer to measure canopy temperature, a GreenSeeker sensor to measure vegetation, a web camera for imaging, and a global navigation satellite system receiver for geo-referenced data collection, all grafted onto a bicycle frame. It is thus inexpensive and highly maneuverable, and the sensor height, orientation, and handlebars are adjustable to accommodate the needs of different crops and users. Poland and colleagues used the cart to analyze 10 wheat-breeding lines during the 2013–2014 growing system in Ciudad Obregon in Mexico. The measurements it took agreed with those taken concurrently with a handheld infrared thermometer, and the fact that it could collect spectral reflectance —“a measure of greenness, which is a fast way to measure the overall health of plants by telling that they are capturing carbon and doing everything plants are supposed to do,” according to Poland—at the same time makes it much more efficient than capturing this data by hand and entering it in a laboratory later. “We need to realize that despite the power of DNA markers, the baseline that feeds all of our analyses are the phenotypes that we can collect in the field,” de Leon says. “Innovations like the Phenocart provide the data needed to advance G×E science.” Phenocart to carry instruments in the field. (A) Completed Phenocart with arrows showing adjustable areas of the cart. (B) Attaching a handle to the cart. (C) Bracket used to hold the GreenSeeker sensor in combination with hose clamps. (D) Bracket and GreenSeeker attached to the cart. “Historically speaking, year-to-year variation has been a big deal,” says Jode Edwards, a research geneticist in the Corn Insects and Crop Genetics Research Group at USDA-ARS and also an associate editor of the G×E special section. “Yearly variation in climate contributes the most to the environmental part of G×E, which is a big problem because it is unpredictable and can't be modeled. We can only speculate on what the future climate can be.” Elizabeth Lee, Associate Professor of maize breeding and genetics in the Department of Plant Agriculture at the University of Guelph, couldn't agree more. In her paper in this special issue, “Involvement of Year-to-Year Variation in Thermal Time, Solar Radiation, and Soil Available Moisture in Genotype-by-Environment Effects In Maize,” she demonstrates that annual meteorological fluctuations have a greater bearing on G×E interactions, and therefore on phenotypes like growth and grain yield, than location does. This was true for all 128 different genotypes of maize that she examined, each of which she planted at three different population densities. Although she reports that soil texture and climate definitely varied over the three locations in Ontario and six years (2004–2009) of the study, she notes that these factors did not have too much of an effect on the plants. Precipitation, temperature, and especially light were much more impactful. “Temperature has always been used in order to predict the flowering time of a given genotype in a new environment based on its performance in another environment,” Lee says. Flowering time is a complex and highly variable trait; it is a key factor in plant adaptation, and it is linked to important developmental characteristics like the total number of leaves, the ultimate height of the cornstalk, and the complete maturation of the kernels. But Lee found that temperature was not really that predictive of flowering time; it turned out to be much more useful to keep track of the photosynthetically active range of light. “Measuring the intensity of light, and the length of time that the plants are exposed to light, had not been done before,” Kaeppler adds. “Before, just the high and low temperatures were recorded. But the amount of sunlight a plant gets—which is less on a warm but cloudy day than on an equally warm but sunny day—is important in predicting when plants will flower and how they'll perform.” Since agriculture is essentially the process of harnessing sunshine and trying to convert it into biomass—and thereby, sustenance—perhaps this should not be all that surprising. Lee is currently analyzing data from the 2014 season of the Maize G2F G×E project, quantifying the differences in different environments. Kaeppler underscored that the G2F Initiative, which includes corn farmers in more than 20 states as well as in Canada, has enabled the collation of large data sets and allowed for these new types of insights. Shawn Kaeppler. Photo by University of Wisconsin–Madison CALS/Sevie Kenyon. These and other promising studies led Kaeppler to propose a special section in Crop Science devoted to renewed understanding of the history of analyses of G×E as well as highlighting new methods under development and cutting-edge empirical research. Jean-Luc Jannink, a USDA-ARS research geneticist at the R.W. Holley Center for Agriculture and Health in Ithaca, NY, is the technical editor of the special section. He studies the quantitative genetics of improvement of wheat, barley, oat, and cassava. Jannink sums up the need to feature G×E research right now this way: “The issue is timely because of a coming together of new technologies: cheap marker data making high-density genotyping affordable early in breeding program pipelines, automated phenotyping platforms cutting the cost and labor of repeated measures of morphology and physiology, better characterization of environments using similar sensors, and cheaper computation that enables collecting and crunching all of this data. We hope this initial salvo of articles in Crop Science will help focus plant breeders on the exciting times ahead for G×E research.” The GXE special section of papers in Crop Science is scheduled for the September–October issue, which should be available online here http://bit.ly/1Yppgya by late August. View some of the articles before then on the Just Published page at http://bit.ly/1Ubbz6v.

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