Identifying environmental variables explaining genotype-by-environment interaction for body weight of rainbow trout (Onchorynchus mykiss): reaction norm and factor analytic models

GIS-FA: an approach to integrating thematic maps, factor-analytic, and envirotyping for cultivar targeting

KEY MESSAGE

We propose an "enviromics" prediction model for recommending cultivars based on thematic maps aimed at decision-makers. Parsimonious methods that capture genotype-by-environment interaction (GEI) in multi-environment trials (MET) are important in breeding programs. Understanding the causes and factors of GEI allows the utilization of genotype adaptations in the target population of environments through environmental features and factor-analytic (FA) models. Here, we present a novel predictive breeding approach called GIS-FA, which integrates geographic information systems (GIS) techniques, FA models, partial least squares (PLS) regression, and enviromics to predict phenotypic performance in untested environments. The GIS-FA approach enables: (i) the prediction of the phenotypic performance of tested genotypes in untested environments, (ii) the selection of the best-ranking genotypes based on their overall performance and stability using the FA selection tools, and (iii) the creation of thematic maps showing overall or pairwise performance and stability for decision-making. We exemplify the usage of the GIS-FA approach using two datasets of rice [Oryza sativa (L.)] and soybean [Glycine max (L.) Merr.] in MET spread over tropical areas. In summary, our novel predictive method allows the identification of new breeding scenarios by pinpointing groups of environments where genotypes demonstrate superior predicted performance. It also facilitates and optimizes cultivar recommendations by utilizing thematic maps.

Parsimonious genotype by environment interaction covariance models for cassava (Manihot esculenta)

The assessment of cassava clones across multiple environments is often carried out at the uniform yield trial, a late evaluation stage, before variety release. This is to assess the differential response of the varieties across the testing environments, a phenomenon referred to as genotype-by-environment interaction (GEI). This phenomenon is considered a critical challenge confronted by plant breeders in developing crop varieties. This study used the data from variety trials established as randomized complete block design (RCBD) in three replicates across 11 locations in different agro-ecological zones in Nigeria over four cropping seasons (2016-2017, 2017-2018, 2018-2019, and 2019-2020). We evaluated a total of 96 varieties, including five checks, across 48 trials. We exploited the intricate pattern of GEI by fitting variance-covariance structure models on fresh root yield. The goodness-of-fit statistics revealed that the factor analytic model of order 3 (FA3) is the most parsimonious model based on Akaike Information Criterion (AIC). The three-factor loadings from the FA3 model explained, on average across the 27 environments, 53.5% [FA (1)], 14.0% [FA (2)], and 11.5% [FA (3)] of the genetic effect, and altogether accounted for 79.0% of total genetic variability. The association of factor loadings with weather covariates using partial least squares regression (PLSR) revealed that minimum temperature, precipitation and relative humidity are weather conditions influencing the genotypic response across the testing environments in the southern region and maximum temperature, wind speed, and temperature range for those in the northern region of Nigeria. We conclude that the FA3 model identified the common latent factors to dissect and account for complex interaction in multi-environment field trials, and the PLSR is an effective approach for describing GEI variability in the context of multi-environment trials where external environmental covariables are included in modeling.

Wheat photosystem II heat tolerance: evidence for genotype-by-environment interactions

High temperature stress inhibits photosynthesis and threatens wheat production. One measure of photosynthetic heat tolerance is T_crit - the critical temperature at which incipient damage to photosystem II (PSII) occurs. This trait could be improved in wheat by exploiting genetic variation and genotype-by-environment interactions (GEI). Flag leaf T_crit of 54 wheat genotypes was evaluated in 12 thermal environments over 3 years in Australia, and analysed using linear mixed models to assess GEI effects. Nine of the 12 environments had significant genetic effects and highly variable broad-sense heritability (H² ranged from 0.15 to 0.75). T_crit GEI was variable, with 55.6% of the genetic variance across environments accounted for by the factor analytic model. Mean daily growth temperature in the month preceding anthesis was the most influential environmental driver of T_crit GEI, suggesting biochemical, physiological and structural adjustments to temperature requiring different durations to manifest. These changes help protect or repair PSII upon exposure to heat stress, and may improve carbon assimilation under high temperature. To support breeding efforts to improve wheat performance under high temperature, we identified genotypes superior to commercial cultivars commonly grown by farmers, and demonstrated potential for developing genotypes with greater photosynthetic heat tolerance.

Optimizing design to estimate genetic correlations between environments with common environmental effects

Breeding programs for different species aim to improve performance by testing members of full-sib (FS) and half-sib (HS) families in different environments. When genotypes respond differently to changes in the environment, this is defined as genotype by environment (G × E) interaction. The presence of common environmental effects within families generates covariance between siblings, and these effects should be taken into account when estimating a genetic correlation. Therefore, an optimal design should be established to accurately estimate the genetic correlation between environments in the presence of common environmental effects. We used stochastic simulation to find the optimal population structure using a combination of FS and HS groups with different levels of common environmental effects. Results show that in a population with a constant population size of 2,000 individuals per environment, ignoring common environmental effects when they are present in the population will lead to an upward bias in the estimated genetic correlation of on average 0.3 when the true genetic correlation is 0.5. When no common environmental effects are present in the population, the lowest standard error (SE) of the estimated genetic correlation was observed with a mating ratio of one dam per sire, and 10 offspring per sire per environment. When common environmental effects are present in the population and are included in the model, the lowest SE is obtained with mating ratios of at least 5 dams per sire and with a minimum number of 10 offspring per sire per environment. We recommend that studies that aim to estimate the magnitude of G × E in pigs, chicken, and fish should acknowledge the potential presence of common environmental effects and adjust the mating ratio accordingly.

Plasticity effect of rider-horse interaction on genetic evaluations for Show Jumping discipline in sport horses

To obtain a sport horse that excels in the highest levels of competition, breeders must take into account certain genetic and environmental factors that could influence the sport horse's performance, such as the rider-horse interaction (RHI). The main aim of this study was to describe this interaction in a genetic model by modelling it in relation to the horse's age. A total of 31,129 sport results from Spanish Sport Horses were used from a total of 1,101 animals evaluated, and these were grouped in three age levels and had been ridden by 606 different riders. Only riders who had ridden more than one horse (and vice-versa) were considered for the analyses. Five linear models with different random effects were analysed according to the covariates, the homogeneity/heterogeneity of the RHI and the relevant residual random effects. The model of best fit was then selected for the genetic evaluation of the animal. In general, models including the RHI effect (M2, M4 and M5) fitted better than the other models, and the best fit was obtained for M4 (with heterogeneous residual variance). The genetic variance increased constantly with age, whereas heritability showed a response on three intervals. This study revealed the varied evolution of the RHI with age, showing the different "plastic abilities" of this relationship.

BREEDING AND GENETICS SYMPOSIUM: Climate change and selective breeding in aquaculture

Aquaculture is the fastest growing food production sector and it contributes significantly to global food security. Based on Food and Agriculture Organization (FAO) of the United Nations, aquaculture production must increase significantly to meet the future global demand for aquatic foods in 2050. According to Intergovernmental Panel on Climate Change (IPCC) and FAO, climate change may result in global warming, sea level rise, changes of ocean productivity, freshwater shortage, and more frequent extreme climate events. Consequently, climate change may affect aquaculture to various extents depending on climatic zones, geographical areas, rearing systems, and species farmed. There are 2 major challenges for aquaculture caused by climate change. First, the current fish, adapted to the prevailing environmental conditions, may be suboptimal under future conditions. Fish species are often poikilothermic and, therefore, may be particularly vulnerable to temperature changes. This will make low sensitivity to temperature more important for fish than for livestock and other terrestrial species. Second, climate change may facilitate outbreaks of existing and new pathogens or parasites. To cope with the challenges above, 3 major adaptive strategies are identified. First, general 'robustness' will become a key trait in aquaculture, whereby fish will be less vulnerable to current and new diseases while at the same time thriving in a wider range of temperatures. Second, aquaculture activities, such as input power, transport, and feed production contribute to greenhouse gas emissions. Selection for feed efficiency as well as defining a breeding goal that minimizes greenhouse gas emissions will reduce impacts of aquaculture on climate change. Finally, the limited adoption of breeding programs in aquaculture is a major concern. This implies inefficient use of resources for feed, water, and land. Consequently, the carbon footprint per kg fish produced is greater than when fish from breeding programs would be more heavily used. Aquaculture should use genetically improved and robust organisms not suffering from inbreeding depression. This will require using fish from well-managed selective breeding programs with proper inbreeding control and breeding goals. Policymakers and breeding organizations should provide incentives to boost selective breeding programs in aquaculture for more robust fish tolerating climatic change.

Genetics of Growth Reaction Norms in Farmed Rainbow Trout

Rainbow trout is farmed globally under diverse uncontrollable environments. Fish with low macroenvironmental sensitivity (ES) of growth is important to thrive and grow under these uncontrollable environments. The ES may evolve as a correlated response to selection for growth in one environment when the genetic correlation between ES and growth is nonzero. The aims of this study were to quantify additive genetic variance for ES of body weight (BW), defined as the slope of reaction norm across breeding environment (BE) and production environment (PE), and to estimate the genetic correlation (r_{g(int, sl)}) between BW and ES. To estimate heritable variance of ES, the coheritability of ES was derived using selection index theory. The BW records from 43,040 rainbow trout performing either in freshwater or seawater were analysed using a reaction norm model. High additive genetic variance for ES (9584) was observed, inferring that genetic changes in ES can be expected. The coheritability for ES was either -0.06 (intercept at PE) or -0.08 (intercept at BE), suggesting that BW observation in either PE or BE results in low accuracy of selection for ES. Yet, the r_{g(int, sl)} was negative (-0.41 to -0.33) indicating that selection for BW in one environment is expected to result in more sensitive fish. To avoid an increase of ES while selecting for BW, it is possible to have equal genetic gain in BW in both environments so that ES is maintained stable.