Agroecological genetics of biomass allocation in wheat uncovers genotype interactions with canopy shade and plant size

How plants distribute biomass among organs influences resource acquisition, reproduction and plant-plant interactions, and is essential in understanding plant ecology, evolution, and yield production in agriculture. However, the genetic mechanisms regulating allocation responses to the environment are largely unknown. We studied recombinant lines of wheat (Triticum spp.) grown as single plants under sunlight and simulated canopy shade to investigate genotype-by-environment interactions in biomass allocation to the leaves, stems, spikes, and grains. Size-corrected mass fractions and allometric slopes were employed to dissect allocation responses to light limitation and plant size. Size adjustments revealed light-responsive alleles associated with adaptation to the crop environment. Combined with an allometric approach, we demonstrated that polymorphism in the DELLA protein is associated with the response to shade and size. While a gibberellin-sensitive allelic effect on stem allocation was amplified when plants were shaded, size-dependent effects of this allele drive allocation to reproduction, suggesting that the ontogenetic trajectory of the plant affects the consequences of shade responses for allocation. Our approach provides a basis for exploring the genetic determinants underlying investment strategies in the face of different resource constraints and will be useful in predicting social behaviours of individuals in a crop community.

Multi-environment genomic prediction for soluble solids content in peach (Prunus persica)

Genotype-by-environment interaction (G × E) is a common phenomenon influencing genetic improvement in plants, and a good understanding of this phenomenon is important for breeding and cultivar deployment strategies. However, there is little information on G × E in horticultural tree crops, mostly due to evaluation costs, leading to a focus on the development and deployment of locally adapted germplasm. Using sweetness (measured as soluble solids content, SSC) in peach/nectarine assessed at four trials from three US peach-breeding programs as a case study, we evaluated the hypotheses that (i) complex data from multiple breeding programs can be connected using GBLUP models to improve the knowledge of G × E for breeding and deployment and (ii) accounting for a known large-effect quantitative trait locus (QTL) improves the prediction accuracy. Following a structured strategy using univariate and multivariate models containing additive and dominance genomic effects on SSC, a model that included a previously detected QTL and background genomic effects was a significantly better fit than a genome-wide model with completely anonymous markers. Estimates of an individual's narrow-sense and broad-sense heritability for SSC were high (0.57-0.73 and 0.66-0.80, respectively), with 19-32% of total genomic variance explained by the QTL. Genome-wide dominance effects and QTL effects were stable across environments. Significant G × E was detected for background genome effects, mostly due to the low correlation of these effects across seasons within a particular trial. The expected prediction accuracy, estimated from the linear model, was higher than the realised prediction accuracy estimated by cross-validation, suggesting that these two parameters measure different qualities of the prediction models. While prediction accuracy was improved in some cases by combining data across trials, particularly when phenotypic data for untested individuals were available from other trials, this improvement was not consistent. This study confirms that complex data can be combined into a single analysis using GBLUP methods to improve understanding of G × E and also incorporate known QTL effects. In addition, the study generated baseline information to account for population structure in genomic prediction models in horticultural crop improvement.

Non-alcoholic Fatty Liver Disease and Depression: Evidence for Genotype × Environment Interaction in Mexican Americans

This study examines the impact of G × E interaction effects on non-alcoholic fatty liver disease (NAFLD) among Mexican Americans in the Rio Grande Valley (RGV) of South Texas. We examined potential G × E interaction using variance components models and likelihood-based statistical inference in the phenotypic expression of NAFLD, including hepatic steatosis and hepatic fibrosis (identified using vibration controlled transient elastography and controlled attenuation parameter measured by the FibroScan Device). We screened for depression using the Beck Depression Inventory-II (BDI-II). We identified significant G × E interactions for hepatic fibrosis × BDI-II. These findings provide evidence that genetic factors interact with depression to influence the expression of hepatic fibrosis.

Gene × Environment Interactions in the Development of Preschool Effortful Control, and Its Implications for Childhood Externalizing Behavior

This study examined the role of gene × environment interaction (G × E) in the development of effortful control (EC) and externalizing symptoms (EXT). Participants included 361 adopted children, and their Adoptive Parents (APs) and Birth Mothers (BMs), drawn from the Early Growth and Development Study. The primary adoptive caregivers' (AP1) laxness and overreactivity were assessed when children were 27-months-old, and used as indices of environmental influences on EC. Heritable influences on child EC were assessed by the BMs' personality characteristics (emotion dysregulation, agreeableness). Secondary adoptive caregivers (AP2) reported on children's EC at 54 months, and EXT at 7 years. Interactions between BM characteristics and AP1 laxness were related to EC and indirectly predicted EXT via EC. Parental laxness and EC were positively associated if children had high heritable risk for poor EC (BM high emotion dysregulation or low agreeableness), but negatively associated if children had low heritable risk for poor EC (BM low emotion dysregulation or high agreeableness). BM agreeableness also moderated associations between AP1 overreactivity and effortful control, and yielded a similar pattern of results. Our findings suggest that G × E is an important first step in the development of EXT via its effect on EC. Consistent with "goodness of fit" models, heritable tendencies can affect which parenting practices best support EC development.

Multi-Trait Multi-Environment Genomic Prediction of Agronomic Traits in Advanced Breeding Lines of Winter Wheat

Genomic prediction is a promising approach for accelerating the genetic gain of complex traits in wheat breeding. However, increasing the prediction accuracy (PA) of genomic prediction (GP) models remains a challenge in the successful implementation of this approach. Multivariate models have shown promise when evaluated using diverse panels of unrelated accessions; however, limited information is available on their performance in advanced breeding trials. Here, we used multivariate GP models to predict multiple agronomic traits using 314 advanced and elite breeding lines of winter wheat evaluated in 10 site-year environments. We evaluated a multi-trait (MT) model with two cross-validation schemes representing different breeding scenarios (CV1, prediction of completely unphenotyped lines; and CV2, prediction of partially phenotyped lines for correlated traits). Moreover, extensive data from multi-environment trials (METs) were used to cross-validate a Bayesian multi-trait multi-environment (MTME) model that integrates the analysis of multiple-traits, such as G × E interaction. The MT-CV2 model outperformed all the other models for predicting grain yield with significant improvement in PA over the single-trait (ST-CV1) model. The MTME model performed better for all traits, with average improvement over the ST-CV1 reaching up to 19, 71, 17, 48, and 51% for grain yield, grain protein content, test weight, plant height, and days to heading, respectively. Overall, the empirical analyses elucidate the potential of both the MT-CV2 and MTME models when advanced breeding lines are used as a training population to predict related preliminary breeding lines. Further, we evaluated the practical application of the MTME model in the breeding program to reduce phenotyping cost using a sparse testing design. This showed that complementing METs with GP can substantially enhance resource efficiency. Our results demonstrate that multivariate GS models have a great potential in implementing GS in breeding programs.

Performance and Stability of Pearl Millet Varieties for Grain Yield and Micronutrients in Arid and Semi-Arid Regions of India

Pearl millet [Pennisetum glaucum (L.) R. Br.] is grown under both arid and semi-arid conditions in India, where other cereals are hard to grow. Pearl millet cultivars, hybrids, and OPVs (open pollinated varieties) are tested and released by the All India Coordinated Research Project on Pearl Millet (AICRP-PM) across three zones (A₁, A, and B) that are classified based on rainfall pattern. Except in locations with extreme weather conditions, hybrids dominate pearl millet growing areas, which can be attributed to hybrid vigor and the active role of the private sector. The importance of OPVs cannot be ruled out, owing to wider adaptation, lower input cost, and timely seed availability to subsidiary farmers cultivating this crop. This study was conducted to scrutinize the presently used test locations for evaluation of pearl millet OPVs across India, identify the best OPVs across locations, and determine the variation in grain Fe and Zn contents across locations in these regions. Six varieties were evaluated across 20 locations in A₁ and A (pooled as A) and B zones along with three common checks and additional three zonal adapted checks in the respective zones during the 2019 rainy season. Recorded data on yield and quality traits were analyzed using genotype main effects and genotype × environment interaction biplot method. The genotype × environment (G × E) interaction was found to be highly significant for all the grain yield and agronomic traits and for both micronutrients (iron and zinc). However, genotypic effect (G) was four (productive tillers) to 49 (grain Fe content) times that of G × E interaction effect for various traits across zones that show the flexibility of OPVs. Ananthapuramu is the ideal test site for selecting pearl millet cultivars effectively for adaptation across India, while Ananthapuramu, Perumallapalle, and Gurugram can also be used as initial testing locations. OPVs MP 599 and MP 600 are identified as ideal genotypes, because they showed higher grain and fodder yields and stability compared with other cultivars. Iron and zinc concentration showed highly significant positive correlation (across environment = 0.83; p < 0.01), indicating possibility of simultaneous effective selection for both traits. Three common checks were found to be significantly low yielders than the test entries or zonal checks in individual zones and across India, indicating the potential of genetic improvement through OPVs.

Adaptation to climate change through genetic accommodation and assimilation of plastic phenotypes

Theory suggests that evolutionary changes in phenotypic plasticity could either hinder or facilitate evolutionary rescue in a changing climate. Nevertheless, the actual role of evolving plasticity in the responses of natural populations to climate change remains unresolved. Direct observations of evolutionary change in nature are rare, making it difficult to assess the relative contributions of changes in trait means versus changes in plasticity to climate change responses. To address this gap, this review explores several proxies that can be used to understand evolving plasticity in the context of climate change, including space for time substitutions, experimental evolution and tests for genomic divergence at environmentally responsive loci. Comparisons among populations indicate a prominent role for divergence in environmentally responsive traits in local adaptation to climatic gradients. Moreover, genomic comparisons among such populations have identified pervasive divergence in the regulatory regions of environmentally responsive loci. Taken together, these lines of evidence suggest that divergence in plasticity plays a prominent role in adaptation to climatic gradients over space, indicating that evolving plasticity is also likely to play a key role in adaptive responses to climate change through time. This suggests that genetic variation in plastic responses to the environment (G × E) might be an important predictor of species' vulnerabilities to climate-driven decline or extinction. This article is part of the theme issue 'The role of plasticity in phenotypic adaptation to rapid environmental change'.

Genetic and Environmental Predictors of Adolescent PTSD Symptom Trajectories Following a Natural Disaster

: Genes, environmental factors, and their interplay affect posttrauma symptoms. Although environmental predictors of the longitudinal course of posttraumatic stress disorder (PTSD) symptoms are documented, there remains a need to incorporate genetic risk into these models, especially in youth who are underrepresented in genetic studies. In an epidemiologic sample tornado-exposed adolescents (n = 707, 51% female, Mage = 14.54 years), trajectories of PTSD symptoms were examined at baseline and at 4-months and 12-months following baseline. This study aimed to determine if rare genetic variation in genes previously found in the sample to be related to PTSD diagnosis at baseline (MPHOSPH9, LGALS13, SLC2A2), environmental factors (disaster severity, social support), or their interplay were associated with symptom trajectories. A series of mixed effects models were conducted. Symptoms decreased over the three time points. Elevated tornado severity was associated with elevated baseline symptoms. Elevated recreational support was associated with lower baseline symptoms and attenuated improvement over time. Greater LGLAS13 variants attenuated symptom improvement over time. An interaction between MPHOSPH9 variants and tornado severity was associated with elevated baseline symptoms, but not change over time. Findings suggest the importance of rare genetic variation and environmental factors on the longitudinal course of PTSD symptoms following natural disaster trauma exposure.

QTL × environment interactions underlie adaptive divergence in switchgrass across a large latitudinal gradient

Understanding how individual genetic loci contribute to trait variation across geographic space is of fundamental importance for understanding evolutionary adaptations. Our study demonstrates that most loci underlying locally adaptive trait variation have beneficial effects in some geographic regions while conferring little or no detectable cost in other parts of the geographic range of switchgrass over two field seasons of study. Thus, loci that contribute to local adaptation vary in the degree to which they are costly in alternative environments but typically confer greater benefits than costs. Further, our study suggests that breeding locally adapted varieties of switchgrass will be a boon to the biofuel industry, as locally adaptive loci could be combined to increase local yields in switchgrass.

Local adaptation is the process by which natural selection drives adaptive phenotypic divergence across environmental gradients. Theory suggests that local adaptation results from genetic trade-offs at individual genetic loci, where adaptation to one set of environmental conditions results in a cost to fitness in alternative environments. However, the degree to which there are costs associated with local adaptation is poorly understood because most of these experiments rely on two-site reciprocal transplant experiments. Here, we quantify the benefits and costs of locally adaptive loci across 17° of latitude in a four-grandparent outbred mapping population in outcrossing switchgrass (Panicum virgatum L.), an emerging biofuel crop and dominant tallgrass species. We conducted quantitative trait locus (QTL) mapping across 10 sites, ranging from Texas to South Dakota. This analysis revealed that beneficial biomass (fitness) QTL generally incur minimal costs when transplanted to other field sites distributed over a large climatic gradient over the 2 y of our study. Therefore, locally advantageous alleles could potentially be combined across multiple loci through breeding to create high-yielding regionally adapted cultivars.

A comprehensive gene-environment interaction analysis in Ovarian Cancer using genome-wide significant common variants

As a follow-up to genome-wide association analysis of common variants associated with ovarian carcinoma (cancer), our study considers seven well-known ovarian cancer risk factors and their interactions with 28 genome-wide significant common genetic variants. The interaction analyses were based on data from 9971 ovarian cancer cases and 15,566 controls from 17 case-control studies. Likelihood ratio and Wald tests for multiplicative interaction and for relative excess risk due to additive interaction were used. The top multiplicative interaction was noted between oral contraceptive pill (OCP) use (ever vs. never) and rs13255292 (p value = 3.48 × 10-4 ). Among women with the TT genotype for this variant, the odds ratio for OCP use was 0.53 (95% CI = 0.46-0.60) compared to 0.71 (95%CI = 0.66-0.77) for women with the CC genotype. When stratified by duration of OCP use, women with 1-5 years of OCP use exhibited differential protective benefit across genotypes. However, no interaction on either the multiplicative or additive scale was found to be statistically significant after multiple testing correction. The results suggest that OCP use may offer increased benefit for women who are carriers of the T allele in rs13255292. On the other hand, for women carrying the C allele in this variant, longer (5+ years) use of OCP may reduce the impact of carrying the risk allele of this SNP. Replication of this finding is needed. The study presents a comprehensive analytic framework for conducting gene-environment analysis in ovarian cancer.

Limitations at the Limit? Diminishing of Genetic Effects in Norway Spruce Provenance Trials

Provenance trials are used to study the effects of tree origin on climate-growth relationships. Thereby, they potentially identify provenances which appear more resilient to anticipated climate change. However, when studying between provenance variability in growth behavior it becomes important to address potential effects related to site marginality in the context of provenance trials. In our study we focus on provenance-specific climate sensitivity manifested under marginal growth conditions. We hypothesized that the provenance effects are masked if trials are located at marginal environmental conditions of the natural species distribution. Under this framework, we investigate 10 Norway spruce provenances growing at two contrasting locations, i.e., a relatively drought-prone site in western Poland (at the climatic margin of Norway spruce's natural distribution) and a mild and moist site in north-eastern Poland (within its natural range). Combining principal component analysis with climate-growth relationships, we found distinguishable growth patterns and climate correlations among provenances. That is, at the mild and moist north-eastern site, we observed provenance-specific growth patterns and thus a varying drought susceptibility. In contrast, at the dryer western site, provenance-specific growth patterns were less pronounced and all provenances expressed a common and strong sensitivity to drought. Our results indicate that the genetic specificity of growth reactions diminishes toward the distributional margins of a given species. We conclude that the climate conditions at the margins of a species' distribution are constraining tree growth independently of tree origin. Because of this, the marginality of a site has to be considered when evaluating climate sensitivity of provenances within trials. As a consequence, the yet different responses of provenances to adverse growing conditions may synchronize under more extreme conditions in course of the anticipated climate change.

Does the Genotype Have a Significant Effect on the Formation of Intra-Annual Density Fluctuations? A Case Study Using Larix decidua from Northern Poland

Intra-annual density fluctuations (IADFs) can imprint environmental conditions within the growing season and most of the research on IADFs has been focused on their climatic signal. However, to our knowledge, the genetic influence on the frequency and type of IADFs has not been evaluated. To understand if the genotype can affect the formation of IADFs we have used a common garden experiment using eight families of Larix decidua established in two neighboring forest stands in northern Poland. Four types of IADFs were identified using X-ray density profiles: latewood-like cells within earlywood (IADF-type E), latewood-like cells in the transition from early- to latewood (IADF type E+), earlywood-like cells within latewood (IADF-type L), and earlywood-like cells in the border zone between the previous and present annual ring (IADF-type L+). The influence of explanatory variables i.e., families, sites, and years on identified density fluctuations was analyzed using generalized estimating equations (GEE). We hypothesized that trees from different families will differ in terms of frequency and type of IADFs because each family will react to precipitation and temperature in a different way, depending on the origin of those trees. The most frequent fluctuation was E+ and L types on both sites. The most important factors in the formation of IADFs were the site and year, the last one reflecting the variable climatic conditions, with no significant effect of the family. However, the relation between the formation of IADFs and selected climate parameters was different between families. Although, our results did not give a significant effect of the genotype on the formation of IADFs, the different sensitivity to climatic parameters among different families indicate that there is a genetic influence.

Influence of cultivar and harvest year on the volatile profiles of leaves and roots of carrots (Daucus carota spp. sativus Hoffm.)

The focus of the present work centers on the diversity of volatile patterns of carrots. In total 15 main volatiles were semiquantified in leaves and roots using isolation by headspace solid phase microextraction followed by gas chromatography with FID and MS detection. Significant differences in the main number of compounds were detected between the cultivars as well as the years. Genotype-environment interactions (G × E) are discussed. The most abundant metabolites, β-myrcene (leaves) and terpinolene (roots), differ in the sum of all interactions (cultivar × harvest year) by a factor of 22 and 62, respectively. A statistical test indicates significant metabolic differences between cultivars for nine volatiles in leaves and 10 in roots. In contrast to others the volatiles α-pinene, γ-terpinene, limonene, and myristicine in leaves as well as β-pinene, humulene, and bornyl acetate in roots are relatively stable over years. A correlation analysis shows no strict clustering regarding root color. While the biosynthesis in leaves and roots is independent between these two organs for nine of the 15 volatiles, a significant correlation of the myristicine content between leaves and roots was determined, which suggests the use of this compound as a bitter marker in carrot breeding.

Deciphering Genome Environment Wide Interactions Using Exposed Subjects Only

The recent successes of genome-wide association studies (GWAS) have renewed interest in genome environment wide interaction studies (GEWIS) to discover genetic factors that modulate penetrance of environmental exposures to human diseases. Indeed, gene-environment interactions (G × E), which have not been emphasized in the GWAS era, could be a source contributing to the missing heritability, a major bottleneck limiting continuing GWAS successes. In this manuscript, we describe a design and analytic strategy to focus on G × E using only exposed subjects, dubbed as e-GEWIS. Operationally, an e-GEWIS analysis is equivalent to a GWAS analysis on exposed subjects only, and it has actually been used in some earlier GWAS without being explicitly identified as such. Through both analytics and simulations, e-GEWIS has been shown better efficiency than the usual cross-product-based analysis of G × E interaction with both cases and controls (cc-GEWIS), and they have comparable efficiency to case-only analysis of G × E (c-GEWIS), with potentially smaller sample sizes. The formalization of e-GEWIS here provides a theoretical basis to legitimize this framework for routine investigation of G × E, for more efficient G × E study designs, and for improvement of reproducibility in replicating GEWIS findings. As an illustration, we apply e-GEWIS to a lung cancer GWAS data set to perform a GEWIS, focusing on gene and smoking interaction. The e-GEWIS analysis successfully uncovered positive genetic associations on chromosome 15 among current smokers, suggesting a gene-smoking interaction. Although this signal was detected earlier, the current finding here serves as a positive control in support of this e-GEWIS strategy.

Asymmetrical local adaptation of maize landraces along an altitudinal gradient

Crop landraces are managed populations that evolve in response to gene flow and selection. Cross-pollination among fields, seed sharing by farmers, and selection by management and environmental conditions play roles in shaping crop characteristics. We used common gardens to explore the local adaptation of maize (Zea mays ssp. mays) landrace populations from Chiapas, Mexico to altitude. We sowed seeds of 21 populations from three altitudinal ranges in two common gardens and measured two characteristics that estimate fitness: likelihood of producing good quality seed and the total mass of good quality seed per plant. The probability of lowland plants producing good quality seed was invariably high regardless of garden, while highland landraces were especially sensitive to altitude. Their likelihood of producing good seed quadrupled in the highland site. The mass of good quality seed showed a different pattern, with lowland landraces producing 25% less seed mass than the other types at high elevations. Combining these two measures of fitness revealed that the highland landraces were clearly adapted to highland sites, while lowland and midland landraces appear more adapted to the midland site. We discuss this asymmetry in local adaptation in light of climate change and in situ conservation of crop genetic resources.

Runaway coevolution: adaptation to heritable and nonheritable environments

Populations evolve in response to the external environment, whether abiotic (e.g., climate) or biotic (e.g., other conspecifics). We investigated how adaptation to biotic, heritable environments differs from adaptation to abiotic, nonheritable environments. We found that, for the same selection coefficients, the coadaptive process between genes and heritable environments is much faster than genetic adaptation to an abiotic nonheritable environment. The increased rate of adaptation results from the positive association generated by reciprocal selection between the heritable environment and the genes responding to it. These associations result in a runaway process of adaptive coevolution, even when the genes creating the heritable environment and genes responding to the heritable environment are unlinked. Although tightening the degree of linkage accelerates the coadaptive process, the acceleration caused by a comparable amount of inbreeding is greater, because inbreeding has a cumulative effect on reducing functional recombination over generations. Our results suggest that that adaptation to local abiotic environmental variation may result in the rapid diversification of populations and subsequent reproductive isolation not directly but rather via its effects on heritable environments and the genes responding to them.

Beyond the single gene: How epistasis and gene-by-environment effects influence crop domestication

Domestication is a multifaceted evolutionary process, involving changes in individual genes, genetic interactions, and emergent phenotypes. There has been extensive discussion of the phenotypic characteristics of plant domestication, and recent research has started to identify the specific genes and mutational mechanisms that control domestication traits. However, there is an apparent disconnect between the simple genetic architecture described for many crop domestication traits, which should facilitate rapid phenotypic change under selection, and the slow rate of change reported from the archeobotanical record. A possible explanation involves the middle ground between individual genetic changes and their expression during development, where gene-by-gene (epistatic) and gene-by-environment interactions can modify the expression of phenotypes and opportunities for selection. These aspects of genetic architecture have the potential to significantly slow the speed of phenotypic evolution during crop domestication and improvement. Here we examine whether epistatic and gene-by-environment interactions have shaped how domestication traits have evolved. We review available evidence from the literature, and we analyze two domestication-related traits, shattering and flowering time, in a mapping population derived from a cross between domesticated foxtail millet and its wild progenitor. We find that compared with wild progenitor alleles, those favored during domestication often have large phenotypic effects and are relatively insensitive to genetic background and environmental effects. Consistent selection should thus be able to rapidly change traits during domestication. We conclude that if phenotypic evolution was slow during crop domestication, this is more likely due to cultural or historical factors than epistatic or environmental constraints.

The role of D4 receptor gene exon III polymorphisms in shaping human altruism and prosocial behavior

Human beings are an extraordinarily altruistic species often willing to help strangers at a considerable cost (sometimes life itself) to themselves. But as Darwin noted "… he who was ready to sacrifice his life, as many a savage has been, rather than betray his comrades, would often leave no offspring to inherit his noble nature." Hence, this is the paradox of altruism. Twin studies have shown that altruism and other prosocial behavior show considerable heritability and more recently a number of candidate genes have been identified with this phenotype. Among these first provisional findings are genes encoding elements of dopaminergic transmission. In this article we will review the evidence for the involvement of one of these, the dopamine D4 receptor (DRD4) gene, in shaping human prosocial behavior and consider the methodologies employed in measuring this trait, specific molecular genetic findings and finally, evidence from several Gene × Environment (G × E) studies that imply differential susceptibility of this gene to environmental influences.

Local adaptation and evolutionary potential along a temperature gradient in the fungal pathogen Rhynchosporium commune

To predict the response of plant pathogens to climate warming, data are needed on current thermal adaptation, the pathogen's evolutionary potential, and the link between them. We conducted a common garden experiment using isolates of the fungal pathogen Rhynchosporium commune from nine barley populations representing climatically diverse locations. Clonal replicates of 126 genetically distinct isolates were assessed for their growth rate at 12°C, 18°C, and 22°C. Populations originating from climates with higher monthly temperature variation had higher growth rate at all three temperatures compared with populations from climates with less temperature fluctuation. Population differentiation in growth rate (Q_ST) was significantly higher at 22°C than population differentiation for neutral microsatellite loci (G_ST), consistent with local adaptation for growth at higher temperatures. At 18°C, we found evidence for stabilizing selection for growth rate as Q_ST was significantly lower than G_ST. Heritability of growth rate under the three temperatures was substantial in all populations (0.58–0.76). Genetic variation was lower in populations with higher growth rate at the three temperatures and evolvability increased under heat stress in seven of nine populations. Our findings imply that the distribution of this pathogen is unlikely to be genetically limited under climate warming, due to its high genetic variation and plasticity for thermal tolerance.

Effect of Partial Resistance on Phytophthora Stem Rot Incidence and Yield of Soybean in Ohio

Phytophthora root and stem rot of soybean commonly causes losses in both stand and yield in Ohio. Environmental conditions which favor the pathogen typically occur in many areas of the state during late spring and summer. This study examined the performance of 12 soybean cultivars with partial resistance, with or without Rps genes, to different populations of Phytophthora sojae and various levels of disease pressure. The soybean cultivars were evaluated in seven field environments with and without metalaxyl over 4 years. There was a highly significant genotype-environment interaction which was due in part to variable disease pressure. The incidence of Phytophthora stem rot in subplots ranged from 0 to 10 plants in the most susceptible cultivar, Sloan, while significantly less stem rot developed in cultivars with high levels of partial resistance or partial resistance combined with an Rps gene in three of the seven environments. Metalaxyl applied in-furrow had a significant effect on early and final plant populations as well as yield (P < 0.001) in two of the seven environments, and for yield (P = 0.05) in one environment. This indicates that at these two environments, 2001 Lakeview and VanBuren, early season Phytophthora disease was controlled with the in-furrow fungicide treatment. When diverse populations of P. sojae were present, yields from soybean cultivars with high levels of partial resistance were significantly higher than those with low levels of partial resistance. Soybean cultivars with specific resistance genes Rps1k, Rps1k + Rps6, or Rps1k +Rps3a had higher yields than plants with only partial resistance in environments where race determination indicated that the populations of P. sojae present were not capable of causing disease on plants with the Rps1k gene. However, in an environment with very low disease pressure, yields of soybean cultivars with partial resistance were not significantly different from those with single Rps genes or Rps gene combinations. These results demonstrate that genetic traits associated with high levels of partial resistance do not have a negative effect on yield. Soybean cultivars that had the most consistent ranking across environments were those with moderate levels of partial resistance in combination with either Rps1k or Rps3a.