Intriguing effects of selection intensity on the evolution of prosocial behaviors

In many models of evolving populations, genetic drift has an outsized role relative to natural selection, or vice versa. While there are many scenarios in which one of these two assumptions is reasonable, intermediate balances between these forces are also biologically relevant. In this study, we consider some natural axioms for modeling intermediate selection intensities, and we explore how to quantify the long-term evolutionary dynamics of such a process. To illustrate the sensitivity of evolutionary dynamics to drift and selection, we show that there can be a “sweet spot” for the balance of these two forces, with sufficient noise for rare mutants to become established and sufficient selection to spread. This balance allows prosocial traits to evolve in evolutionary models that were previously thought to be unconducive to the emergence and spread of altruistic behaviors. Furthermore, the effects of selection intensity on long-run evolutionary outcomes in these settings, such as when there is global competition for reproduction, can be highly non-monotonic. Although intermediate selection intensities (neither weak nor strong) are notoriously difficult to study analytically, they are often biologically relevant; and the results we report suggest that they can elicit novel and rich dynamics in the evolution of prosocial behaviors.

Impact of Genomic Prediction Model, Selection Intensity, and Breeding Strategy on the Long-Term Genetic Gain and Genetic Erosion in Soybean Breeding

Genomic-assisted breeding has become an important tool in soybean breeding. However, the impact of different genomic selection (GS) approaches on short- and long-term gains is not well understood. Such gains are conditional on the breeding design and may vary with a combination of the prediction model, family size, selection strategies, and selection intensity. To address these open questions, we evaluated various scenarios through a simulated closed soybean breeding program over 200 breeding cycles. Genomic prediction was performed using genomic best linear unbiased prediction (GBLUP), Bayesian methods, and random forest, benchmarked against selection on phenotypic values, true breeding values (TBV), and random selection. Breeding strategies included selections within family (WF), across family (AF), and within pre-selected families (WPSF), with selection intensities of 2.5, 5.0, 7.5, and 10.0%. Selections were performed at the F4 generation, where individuals were phenotyped and genotyped with a 6K single nucleotide polymorphism (SNP) array. Initial genetic parameters for the simulation were estimated from the SoyNAM population. WF selections provided the most significant long-term genetic gains. GBLUP and Bayesian methods outperformed random forest and provided most of the genetic gains within the first 100 generations, being outperformed by phenotypic selection after generation 100. All methods provided similar performances under WPSF selections. A faster decay in genetic variance was observed when individuals were selected AF and WPSF, as 80% of the genetic variance was depleted within 28–58 cycles, whereas WF selections preserved the variance up to cycle 184. Surprisingly, the selection intensity had less impact on long-term gains than did the breeding strategies. The study supports that genetic gains can be optimized in the long term with specific combinations of prediction models, family size, selection strategies, and selection intensity. A combination of strategies may be necessary for balancing the short-, medium-, and long-term genetic gains in breeding programs while preserving the genetic variance.

Automated Machine Learning: a case study of genomic “image-based” prediction in maize hybrids

Machine learning methods such as Multilayer perceptrons (MLP) and Convolutional Neural Networks (CNN) have emerged as promising methods for genomic prediction (GP). In this sense, we assess the performance of MLP and CNN on regression and classification tasks in a case study with maize hybrids. The genomic information was provided to the MLP as a relationship matrix and to the CNN as “genomic images”. In the regression task, the machine learning models were compared along with GBLUP. Under the classification task, MLP and CNN were compared. In this case, the traits (plant height and grain yield) were discretized in such a way to create balanced (moderate selection intensity) and unbalanced (extreme selection intensity) datasets for further evaluations. An automatic hyperparameter search for MLP and CNN was performed, and the best models were reported. For both task types, several metrics were calculated under a validation scheme to assess the effect of the prediction method and other variables. Overall, MLP and CNN presented competitive results to GBLUP but improved a little using only the additive genomic layer. It is expected that the average effect of allele substitution is mostly linear. Nevertheless, the methodology’s potential for GP is unprecedented because we can create “multispectral genome images,” including other effects and layers of data, such as dominance, epistasis, g × e, transcriptome, and so on, capturing linear and non-linear effects and boosting prediction accuracies. Hence, we bring new insights on automated machine learning for genomic prediction and its implications to plant breeding.

Automated Machine Learning: a case study of genomic “image-based” prediction in maize hybrids

Machine learning methods such as Multilayer perceptrons (MLP) and Convolutional Neural Networks (CNN) have emerged as promising methods for genomic prediction (GP). In this sense, we assess the performance of MLP and CNN on regression and classification tasks in a case study with maize hybrids. The genomic information was provided to the MLP as a relationship matrix and to the CNN as “genomic images”. In the regression task, the machine learning models were compared along with GBLUP. Under the classification task, MLP and CNN were compared. In this case, the traits (plant height and grain yield) were discretized in such a way to create balanced (moderate selection intensity) and unbalanced (extreme selection intensity) datasets for further evaluations. An automatic hyperparameter search for MLP and CNN was performed, and the best models were reported. For both task types, several metrics were calculated under a validation scheme to assess the effect of the prediction method and other variables. Overall, MLP and CNN presented competitive results to GBLUP but improved a little using only the additive genomic layer. It is expected that the average effect of allele substitution is mostly linear. Nevertheless, the methodology’s potential for GP is unprecedented because we can create “multispectral genome images,” including other effects and layers of data, such as dominance, epistasis, g × e, transcriptome, and so on, capturing linear and non-linear effects and boosting prediction accuracies. Hence, we bring new insights on automated machine learning for genomic prediction and its implications to plant breeding.

The characteristics of average abundance function with mutation of multi-player threshold public goods evolutionary game model under redistribution mechanism

Background In recent years, the average abundance function has attracted much attention as it reflects the degree of cooperation in the population. Then it is significant to analyse how average abundance functions can be increased to promote the proliferation of cooperative behaviour. However, further theoretical analysis for average abundance function with mutation under redistribution mechanism is still lacking. Furthermore, the theoretical basis for the corresponding numerical simulation is not sufficiently understood. Results We have deduced the approximate expressions of average abundance function with mutation under redistribution mechanism on the basis of different levels of selection intensity $$\omega$$ ω (sufficiently small and large enough). In addition, we have analysed the influence of the size of group d, multiplication factor r, cost c, aspiration level $$\alpha$$ α on average abundance function from both quantitative and qualitative aspects. Conclusions (1) The approximate expression will become the linear equation related to selection intensity when $$\omega$$ ω is sufficiently small. (2) On one hand, approximation expression when $$\omega$$ ω is large enough is not available when r is small and m is large. On the other hand, this approximation expression will become more reliable when $$\omega$$ ω is larger. (3) On the basis of the expected payoff function $$\pi \left( \centerdot \right)$$ π ⋅ and function $$h(i,\omega )$$ h ( i , ω ) , the corresponding results for the effects of parameters (d,r,c,$$\alpha$$ α ) on average abundance function $$X_{A}(\omega )$$ X A ( ω ) have been explained.

Comparison of the favorable gain values of genetic improvement among Indonesian grade cow breeds selected for agrotechnopark intensification

This research was aimed to compare the small and big truncation point proportions intended to evaluate gain values of genetic improvement for Indonesian Local cow breed groups selected for Agrotechnopark (integrated bio-cycle farming system) intensification. Animal live weights were collected from 674 Indonesian grade breed cows kept by local household farmers in North Sulawesi province. Data of cows were corrected by adjusting to six years old ages. All cows were divided into three breed groups with different genetic compositions of Bali breed cow generation (BG) of 207 cows, Ongole grade cow generation (OG) of 189 cows, and Local grade cow generation (LG) of 178 cows. The genetic improvements of BG, OG and LG were analyzed involving selection intensity (i), accuracy of selection (r), and standard deviation (SD) of breed group traits under selection. Results of this study showed that the critical components was genetic development of local grade breeds by choosing small proportion of 10% truncation point for Agrotechnopark intensification of selected elite cows groups among BG, OG and LG populations with the positive live weight gains of 58.6 kg, 23.15 kg, and 28.62 kg per generation, respectively compared with larger percentages of 20% and 30% proportions of truncation points.

Long-term comparison between index selection and optimal independent culling in plant breeding programs with genomic prediction

In the context of genomic selection, we evaluated and compared breeding programs using either index selection or independent culling for recurrent selection of parents. We simulated a clonally propagated crop breeding program for 20 cycles using either independent culling or an economic index with two unfavourably correlated traits under selection. Cycle time from crossing to selection of parents was kept the same for both strategies. Both methods led to increasingly unfavourable genetic correlations between traits and, compared to independent culling, index selection led to larger changes in the genetic correlation between the two traits. When linkage disequilibrium was not considered, the two methods had similar losses of genetic diversity. Two independent culling approaches were evaluated, one using optimal culling levels and one using the same selection intensity for both traits. Optimal culling levels outperformed the same selection intensity even when traits had the same economic importance. Therefore, accurately estimating optimal culling levels is essential for maximizing gains when independent culling is performed. Once optimal culling levels are achieved, independent culling and index selection lead to comparable genetic gains.

The Effect of Consanguinity on Reproductive Fitness and Selection Intensity in the Muslim Population of Western Uttar Pradesh, India

ABSTRACT The study sought to ascertain the adverse effects of consanguinity among Muslim parents of Aligarh city located in western Uttar Pradesh in India. More than 478 families were visited and data were collected from only 100 of them being inbred to varying degrees of consanguinity. It was found that increasing degree of consanguinity decreases fertility (R2 = 0.2671, r = -0.1568), but increases mortality (R2 = 0.3161, r = 0.5622). Selection intensity (R2 = 0.1734, r = 0.4164) and secondary sex ratio (R2 = 0.3757, r = 0.6129) also go up as the degree of consanguinity increases. However, the genomic basis of Runs of Homozygosity (ROH) is a more accurate method of calculating homozygosity. Next Generation Sequencing may help better understand ROH and their utility as a tool for inbreeding detection. To avoid the inbreeding load, there is a need to raise public awareness of reproductive health and the potential negative effects of consanguinity.

Development of UAV-based high-throughput phenotyping technologies towards improving soybean breeding efficiency

Development of new crop varieties with improved traits through crop breeding programs is one of the promising solutions for the estimated food crisis in 2050 when agricultural production needs to double its current growth rate to feed 10 billion people in the world. Conventional crop breeding strategies largely rely on 'trial-and-error' and human input, limiting the trial capacity of breeding materials, the accuracy of measuring plant traits, and selection intensity of elite genotypes. These limitations have become a bottleneck to boost breeding efficiency as it is expected to meet the food demands. The goal of this research was to develop an integrated and automated high-throughput phenotyping (HTP) framework leveraging advanced technologies in remote sensing and artificial intelligence for estimating key traits and selecting elite genotypes towards improving the selection intensity and accuracy of conventional soybean breeding. To achieve this goal, this research features three objectives: (1) develop an integrated and automated UAV HTP framework for measuring crop traits accurately and efficiently, (2) estimate soybean yield and maturity date of breeding materials using UAV image features and machine learning models., and (3) select elite soybean lines using UAV image features towards improving the selection intensity and accuracy. A UAV-based HTP platform was developed to carry multispectral and high-resolution digital cameras and geo-referencing units. The platform was able to cover a 9-acre field within 2 hours. An automated pipeline was developed to process the collected time-series images and generate labeled image features. It was shown that the developed methods can deliver accurate measurements on plant height (coefficient of determination R[superscript 2] up to 0.90 with average errors within 5 cm) and consistent spectral reflectance. The UAV platform and image processing pipeline were applied to estimate two key agronomic traits for soybean breeding, i.e., the maturity date and yield. A group of image features was collected on 326 soybean progeny lines near their maturity stages (R7-8). Their maturity dates were estimated using a partial least square regression (PLSR) model with the image features as inputs and the visual maturity dates taken by breeders as outputs. The results showed that the image-based maturity dates highly agreed (R[superscript 2] [equals] 0.81) with the visual ones with the root mean square error (RMSE) of 1.4 days. For estimating the soybean yield, 972 soybean breeding plots in three maturity groups were planted under rainfed conditions. A mixed convolutional neural network (CNN) model was built to estimate soybean yield by taking seven image features (associated with plant height, canopy color, and canopy texture) and two categorical factors, i.e. maturity group and drought tolerance, as predictors. The prediction model could explain 78 [percent] of the measured yield with an RMSE of 391.0 kg/ha[superscript -1] (33.8 [percent] to average yield). To model the breeder's selection criteria and select elite soybean genotypes, a soybean breeding program was traced for three years. The progeny trial (PT) had 11,473 rows, and 1,773 among them were selected for a preliminary yield trial (PYT) and 238 were further selected for an advance yield trial (AYT). Seven agronomic traits, including yield, plant height, maturity data, flower and pubescence color, moisture and lodging were manually measured for soybeans in the two yield trials. The UAV imagery was collected every two weeks over the growing seasons, and a group of image features was extracted for each trial. Results show the progeny lines had the most variation among the three trials and the images collected at earlier stages (before R5) explained more variation than those at later stages. A Lasso model for selecting soybean lines with image features correctly identified 71 [percent] and 76 [percent] of the breeder's selection for the PT and PYT. The model selections in PT and PYT had respectively 4 [percent] and 5 [percent] higher yield, comparing the breeder [percent]s selection. In summary, the developed UAV HTP platform is capable of collecting image features of soybean breeding materials efficiently and delivering estimations of agronomic traits accurately. The accurate and subjective estimations of plant traits decrease the phenotypic variations in breeding trials. By liberating human labor from the onerous field evaluation, the population size of soybean breeding lines could be increased a lot, leading to increased selection intensity. Moreover, the proposed variety selection model was able to narrow down the breeder selections, further increasing the selection accuracy and intensity. Therefore, it is be concluded that the developed UAV HTP platform has great potential in improving soybean breeding efficiency by decreasing the phenotypic variations, increasing the selection accuracy and intensity. This research could be scaled up to other crop breeding programs and offered a paradigm of improving the breeding efficiency using HTP technologies.