Non-linear regression models for time to flowering in wild chickpea combine genetic and climatic factors

Background: Accurate prediction of crop flowering time is required for reaching maximal farm efficiency. Several models developed to accomplish this goal are based on deep knowledge of plant phenology, requiring large investment for every individual crop or new variety. Mathematical modeling can be used to make better use of more shallow data and to extract information from it with higher efficiency. Cultivars of chickpea, Cicer arietanum, are currently being improved by introgressing wild C. reticulatum biodiversity with very different flowering time requirements. More understanding is required for how flowering time will depend on environmental conditions in these cultivars developed by introgression of wild alleles. Results: We built a novel model for flowering time of wild chickpeas collected at 21 different sites in Turkey and grown in 4 distinct environmental conditions over several different years and seasons. We propose a general approach, in which the analytic forms of dependence of flowering time on climatic parameters, their regression coefficients, and a set of predictors are inferred automatically by stochastic minimization of the deviation of the model output from data. By using a combination of Grammatical Evolution and Differential Evolution Entirely Parallel method, we have identified a model that reflects the influence of effects of day length, temperature, humidity and precipitation and has a coefficient of determination of R 2=0.97. Conclusions: We used our model to test two important hypotheses. We propose that chickpea phenology may be strongly predicted by accession geographic origin, as well as local environmental conditions at the site of growth. Indeed, the site of origin-by-growth environment interaction accounts for about 14.7% of variation in time period from sowing to flowering. Secondly, as the adaptation to specific environments is blueprinted in genomes, the effects of genes on flowering time may be conditioned on environmental factors. Genotype-by-environment interaction accounts for about 17.2% of overall variation in flowering time. We also identified several genomic markers associated with different reactions to climatic factor changes. Our methodology is general and can be further applied to extend existing crop models, especially when phenological information is limited

Major quantitative trait loci for flowering time in lentil

WOS: 000357748700008Quantitative trait loci (QTLs) for flowering time (FT) in lentil were located using a recombinant inbreed line population derived from an intraspecific cross of Precoz x WA8649041. Experiments for FT were conducted in 2 locations (Haymana, Turkey and Pullman, WA, USA) for 5 years (from 1998 to 2002 in both Pullman and Haymana). A linkage map was constructed using 149 markers (RAPD, AFLP, ISSR, SSR, and 2 morphological markers) located on 11 linkage groups (LGs). Analysis of variance of FT was found to be significant for all locations and years. One major QTL for FT was identified on LG6 across all environments, indicating the stability of the QTL. The LOD scores for FT varied between 3.25 and 13.64 among the environments. The markers UBC318_2, SSR212_1, UBC220, M09a, and M09 on the QTL region were statistically significant in all environments. The SSR212_1 marker itself explained 57% of total genotypic variation according to the average of all environments, indicating the potential use of these markers in marker-assisted selection studies

Simulation Model for Time to Flowering with Climatic and Genetic Inputs for Wild Chickpea

Accurate prediction of flowering time helps breeders to develop new varieties that can achieve maximal efficiency in a changing climate. A methodology was developed for the construction of a simulation model for flowering time in which a function for daily progression of the plant from one to the next phenological phase is obtained in analytic form by stochastic minimization. The resulting model demonstrated high accuracy on the recently assembled data set of wild chickpeas. The inclusion of genotype-by-climatic factors interactions accounted to 77% of accuracy in terms of root mean square error. It was found that the impact of minimal temperature is positively correlated with the longitude at primary collection sites, while the impact of day length is negatively correlated. It was interpreted as adaptation of accessions from highlands to lower temperatures and those from lower elevation river valleys to shorter days. We used bootstrap resampling to construct an ensemble of models, taking into account the influence of genotype-by-climatic factors interactions and applied it to forecast the time to flowering for the years 2021–2099, using generated daily weather in Turkey, and for different climate change scenarios. Although there are common trends in the forecasts, some genotypes and SNP groups have distinct trajectories

Magnetic Resonance Imaging Guided Vacuum Assisted and Core Needle Biopsies

Objective: The purpose of this study to present the results of Magnetic resonance imaging (MRI) guided cutting needle biopsy procedures of suspicious breast lesions that can be solely detected on Magnetic resonance (MR) examination

Data from: Ecology and genomics of an important crop wild relative as a prelude to agricultural innovation

Domesticated species are impacted in unintended ways during domestication and breeding. Changes in the nature and intensity of selection impart genetic drift, reduce diversity, and increase the frequency of deleterious alleles. Such outcomes constrain our ability to expand the cultivation of crops into environments that differ from those under which domestication occurred. We address this need in chickpea, an important pulse legume, by harnessing the diversity of wild crop relatives. We document an extreme domestication-related genetic bottleneck and decipher the genetic history of wild populations. We provide evidence of ancestral adaptations for seed coat color crypsis, estimate the impact of environment on genetic structure and trait values, and demonstrate variation between wild and cultivated accessions for agronomic properties. A resource of genotyped, association mapping progeny functionally links the wild and cultivated gene pools and is an essential resource chickpea for improvement, while our methods inform collection of other wild crop progenitor species

Data from: Ecology and genomics of an important crop wild relative as a prelude to agricultural innovation

Domesticated species are impacted in unintended ways during domestication and breeding. Changes in the nature and intensity of selection impart genetic drift, reduce diversity, and increase the frequency of deleterious alleles. Such outcomes constrain our ability to expand the cultivation of crops into environments that differ from those under which domestication occurred. We address this need in chickpea, an important pulse legume, by harnessing the diversity of wild crop relatives. We document an extreme domestication-related genetic bottleneck and decipher the genetic history of wild populations. We provide evidence of ancestral adaptations for seed coat color crypsis, estimate the impact of environment on genetic structure and trait values, and demonstrate variation between wild and cultivated accessions for agronomic properties. A resource of genotyped, association mapping progeny functionally links the wild and cultivated gene pools and is an essential resource chickpea for improvement, while our methods inform collection of other wild crop progenitor species