The fundamental equation in evolutionary quantitative genetics, the Lande equation, describes the response to directional selection as a product of the additive genetic variance and the selection gradient of trait value on relative fitness. Comparisons of both genetic variances and selection gradients across traits or populations require standardization, as both are scale dependent. The Lande equation can be standardized in two ways. Standardizing by the variance of the selected trait yields the response in units of standard deviation as the product of the heritability and the variance-standardized selection gradient. This standardization conflates selection and variation because the phenotypic variance is a function of the genetic variance. Alternatively, one can standardize the Lande equation using the trait mean, yielding the proportional response to selection as the product of the squared coefficient of additive genetic variance and the mean-standardized selection gradient. Mean-standardized selection gradients are particularly useful for summarizing the strength of selection because the mean-standardized gradient for fitness itself is one, a convenient benchmark for strong selection. We review published estimates of directional selection in natural populations using mean-standardized selection gradients. Only 38 published studies provided all the necessary information for calculation of mean-standardized gradients. The median absolute value of multivariate mean-standardized gradients shows that selection is on average 54% as strong as selection on fitness. Correcting for the upward bias introduced by taking absolute values lowers the median to 31%, still very strong selection. Such large estimates clearly cannot be representative of selection on all traits. Some possible sources of overestimation of the strength of selection include confounding environmental and genotypic effects on fitness, the use of fitness components as proxies for fitness, and biases in publication or choice of traits to study.

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Comparing strengths of directional selection: how strong is strong?

We selected on phenotypic plasticity of thorax size in response to temperature in Drosophila melanogaster using a family selection scheme. The results were compared to those of lines selected directly on thorax size. We found that the plasticity of a character does respond to selection and this response is partially independent of the response to selection on the mean of the character. One puzzling result was that a selection limit of zero plasticity was reached in the lines selected for decreased plasticity yet additive genetic variation for plasticity still existed in the lines. We tested the predictions of three models of the genetic basis of phenotypic plasticity: overdominance, pleiotropy, and epistasis. The results mostly support the epistasis model, that the plasticity of a character is determined by separate loci from those determining the mean of the character.

https://onlinelibrary.wiley.com/doi/pdf/10.1046/j.1420-9101.1991.4010023.x

The genetics of phenotypic plasticity. II. Response to selection

Global climate change imposes increasing impacts on our environments and crop production. To decipher environmental impacts on crop plants, the concept "envirotyping" is proposed, as a third "typing" technology, complementing with genotyping and phenotyping. Environmental factors can be collected through multiple environmental trials, geographic and soil information systems, measurement of soil and canopy properties, and evaluation of companion organisms. Envirotyping contributes to crop modeling and phenotype prediction through its functional components, including genotype-by-environment interaction (GEI), genes responsive to environmental signals, biotic and abiotic stresses, and integrative phenotyping. Envirotyping, driven by information and support systems, has a wide range of applications, including environmental characterization, GEI analysis, phenotype prediction, near-iso-environment construction, agronomic genomics, precision agriculture and breeding, and development of a four-dimensional profile of crop science involving genotype (G), phenotype (P), envirotype (E) and time (T) (developmental stage). In the future, envirotyping needs to zoom into specific experimental plots and individual plants, along with the development of high-throughput and precision envirotyping platforms, to integrate genotypic, phenotypic and envirotypic information for establishing a high-efficient precision breeding and sustainable crop production system based on deciphered environmental impacts.

/pdf/envirotyping-for-deciphering-environmental-impacts-on-crop-3z8zd0740z.pdf

Envirotyping for deciphering environmental impacts on crop plants

IT in India has seen amazing growth, thanks to the liberalization of economic policies, conducive government policies, growth in higher education, and the uptake in entrepreneurship. However, unless Indian education and research (on the supply side), IT companies (on the demand side), and the government and media (enablers) continue to innovate, India might not be able to sustain its current leadership position. This article is part of a special issue on IT in Emerging Markets.

IT in India

Genomic selection (GS) unlike marker-assisted backcrossing (MABC) predicts breeding values of lines using genome-wide marker profiling and allows selection of lines prior to field-phenotyping, thereby shortening the breeding cycle. A collection of 320 elite breeding lines was selected and phenotyped extensively for yield and yield related traits at two different locations (Delhi and Patancheru, India) during the crop seasons 2011–12 and 2012–13 under rainfed and irrigated conditions. In parallel, these lines were also genotyped using DArTseq platform to generate genotyping data for 3000 polymorphic markers. Phenotyping and genotyping data were used with six statistical GS models to estimate the prediction accuracies. GS models were tested for four yield related traits viz. seed yield, 100 seed weight, days to 50% flowering and days to maturity. Prediction accuracy for the models tested varied from 0.138 (seed yield) to 0.912 (100 seed weight), whereas performance of models did not show any significant difference for estimating prediction accuracy within traits. Kinship matrix calculated using genotyping data reaffirmed existence of two different groups within selected lines. There was not much effect of population structure on prediction accuracy. In brief, present study establishes the necessary resources for deployment of GS in chickpea breeding.

https://digital.library.adelaide.edu.au/dspace/bitstream/2440/116255/2/hdl_116255.pdf

A A El Bayoumi

Papers

Estimates of genetic and environmental variability in soybeans (Glycine max. L.) [Egypt].