Phaseolus vulgaris L.) has two major origins of domestication, Andean and Mesoamerican, which contribute to the high diversity of growth type, pod and seed characteristics. The climbing growth habit is associated with increased days to flowering (DF), seed iron concentration (SdFe), nitrogen fixation, and yield. However, breeding efforts in climbing beans have been limited and independent from bush type beans. To advance climbing bean breeding, we carried out genome-wide association studies and genomic predictions using 1,869 common bean lines belonging to five breeding panels representing both gene pools and all growth types. The phenotypic data were collected from 17 field trials and were complemented with 16 previously published trials. Overall, 38 significant marker-trait associations were identified for growth habit, 14 for DF, 13 for 100 seed weight, three for SdFe, and one for yield. Except for DF, the results suggest a common genetic basis for traits across all panels and growth types. Seven QTL associated with growth habits were confirmed from earlier studies and four plausible candidate genes for SdFe and 100 seed weight were newly identified. Furthermore, the genomic prediction accuracy for SdFe and yield in climbing beans improved up to 8.8% when bush-type bean lines were included in the training population. In conclusion, a large population from different gene pools and growth types across multiple breeding panels increased the power of genomic analyses and provides a solid and diverse germplasm base for genetic improvement of common bean.Common bean ( Phaseolus vulgaris L.) was domesticated about 8,000 years ago in two geographic regions, resulting in the Andean and the Mesoamerican gene pools , and 19% for the rate of plant production, including traits such as yield and seed weight  and genomic prediction models  climbing and bush type beans show, despite specific growth habit loci, overall a high genetic similarity, that (ii) combined analysis will increase the power of GWAS as well as the genomic predictions across all growth types and that (iii) predictions for climbing beans with an optimized TP including bush beans will outperform predictions which were based on the climbing bean growth types only. Hence, we evaluated whether breeding programs can supplement their trial data with already existing data to improve GWAS and genomics predictions. The overall objective was to provide molecular markers and genomic prediction models which can be used to speed up the selection of new bean lines across all growth types and gene pools.In this study, comprehensive genetic analyses across all bean growth types and the two gene pools were carried out using 1,869 lines belonging to five breeding panels. We hypothesized that , the Andean diversity panel (ADP), the panel of progeny from two-way crosses between five Andean and five Mesoamerican parents (AxM), the Mesoamerican introgression panel (MIP), and the elite Andean breeding panel (VEF), whereas the latter four are bush type bean panels known from previous studies.The VEC comprised climbing bean lines of growth types III and IV. The lines were selected for grain quality, commercial seed type, disease resistance, SdFe, seed zinc concentration (SdZn), and agronomic performance. They represent the genetic variation used in climbing bean breeding at the International Center for Tropical Agriculture (CIAT). The VEC was composed mainly of lines from the Andean gene pool, but it also included a line of the Mesoamerican gene pool (G2333) and a few lines of admixed origin. In total, the VEC was comprised of 290 lines including twelve breeding groups, four genebank accessions, and six cultivars .Field trials including all VEC lines were conducted in Colombia, Uganda, and Tanzania to collect phenotypic data for this study.i) the ADP consisting of 352 Andean bush cultivars and breeding lines from public and private breeding programs as described by Cichy et al. (via the ARS Feed-the-Future Grain Legumes Project (arsftfbean.uprm.edu/bean/?p=472); (ii) the AxM as described by Mayor Duran et al. (iii) the MIP consisting of Mesoamerican breeding lines with interspecific introgressions from P. acutifolious, P. dumosus, and P. coccineus in their pedigrees and their parental lines as described by Diaz et al. (iv) the VEF as described by Keller et al.  tz et al. ; (iv) thr et al. .For the VEF, MIP, and AxM, phenotypic and genotypic data of 605, 217 and 200 lines were available, respectively. For the ADP, field trials were conducted in Mozambique, Tanzania, and in the United States to collect phenotypic data for this study.Agronomic traits were evaluated in the VEC and ADP as previously described by Keller et al. . Briefly′′N 76°31′00′′W, altitude of 1,491 m a.s.l.), Palmira , and Popayán  in Colombia; Kawanda  in Uganda; and Kagera  in Tanzania  with about 70 g/kg and 140 g/kg of soil organic matter, respectively, and a pH of around 5  fertilizer was manually applied at 125 kg/ha. Trials were manually weeded two times. Climbing bean plants were staked with 2 to 3 m long wooden poles. Five poles were used per plot.In Uganda and Tanzania, the plots were planted in single 3.0 m rows with about 30 seeds planted per row and at 0.6 m between the rows . Granula′′S 36°37′34′′E, altitude of 1,387 m a.s.l.), Morogoro , and Mbeya  in Tanzania; Chokwe  in Mozambique; and Wilcox  in the United States  were calculated across all trials using the following model:Best linear unbiased estimators (BLUEs) were extracted from phenotypic data in two stages. In the first stage, the field data were corrected for spatial effects using the SpATS R package, setting row and column as random effects  of the mean was used as a weight, i.e., ε~N; SEij) is the standard error of a mean of the ith line in the jth trial  were calculated for each population in sliding windows of 100 markers. The LD measures were corrected for kinship in the population  algorithm implemented in GAPIT was used . The Gibbs sampler ran with 20,000 iterations of which the first 10,000 were burned-in and the remaining were thinned by factor 5. Three different model approaches were tested.The GEBV were estimated using Bayesian generalized linear regression (BGLR) implemented in the BGLR R package  were modeled as the sum of the GEBV for each line (gi) estimated based on SNP marker information, i.e., the additive relationship matrix (K), a fixed effect for the trial (Ej), and an error term For each trait, the phenotypes adjusted per trial , for all trials separately (using first-stage BLUEs), or across all trials (using second-stage BLUEs). In the case of the genotype model using all trials separately, j represents always the same environment. The same applied to the genotype model when using second-stage BLUEs across all trials.with jth environment and ith GEBV (gEij) to the model (2). This resulted in the following model:In the GxE model, the GEBV for each trait were estimated for each environment  by adding an effect for the interaction between the with gij) were estimated for each trait using SNP marker information and the covariance of the phenotypes (yij) between the trials. The following equation was used:In the factor analysis (FA) model, the GEBV for each line in each environment  and Ψ is a diagonal matrix whose non-null entries give the variances of factors that are trait-specific. The model residuals were assumed to follow a multivariate normal distribution ε~Nj, where Rε is a covariance matrix of model residuals and In represents an n-dimensional identity matrix, where n is the number of phenotypes per environment. Three common factors were selected.with For each VEC line in the validation set, the 50 closest related lines from all five panels were added to the TP (TP optimized). Genetic relationships were calculated as the cophenetic distances of the hierarchically clustered lines based on the SNP matrix, i.e., as the height of the dendrogram where the two branches including the considered lines join each other. This means that the TP could consist of between 50 lines  and 2,500 lines (if they would not have the same closest relatives). The number of related lines (50) was chosen arbitrarily but seemed reasonable for a panel of 1,500 lines with some population structure. The optimized TP was compared to a TP containing only VEC lines (TP VEC) and a TP containing all lines from all five panels (TP extended).r) between predicted and observed values was calculated at each cross-validation step to assess the PA. The prediction accuracy (PAcc) is defined as the quotient of PA and the square root of heritability.The cross-validation was done 100 times, randomly splitting the dataset into training and validation set, i.e., for each cross-validation step, 70% of the lines were selected for the training and 30% for the validation set. The lines in the training and validation sets were referred to as TP and new lines, respectively. The Pearson correlation coefficient  for all traits, especially for yield, where the variance component for GxE was higher than for the lines  which differentiated the two gene pools  and Andean origin, whereas the AxM and part of the VEC formed an admixture branch . These gne pools . The secp-value distribution showed a clear deviation of the identified significant SNPs from the expected uniform distribution if no genetic linkage was present  were calculated either with parts of the VEC or with parts of all five panels as TP.r calculated using all available lines as TP. PAs for yield reached about r≈0.5 in the high-altitude locations Darién and Popayán, where climbing beans are better adapted. The PAs for yield in other locations were lower. The trials Dar14B and Pop15B showed lower PAs with higher variability because these trials comprised only 100 lines.The PAs for new VEC lines within each trial and across all trials differed between the traits as well as the used model approaches . In geneOn average, the FA model showed the best performance for DF. The genotype model for single trials performed best for 100SdW, SdFe, and yield. The FA and GxE model reached slightly lower average PAs for SdFe and yield, respectively. The strong GxE for yield was reflected in the different marker effects among the locations . The PAcTo increase PAs for new VEC lines, three different approaches were tested: when only VEC lines were in the TP (TP VEC), when all lines of the five panels were in the TP (TP extended), or when distantly related lines were excluded   conferring photoperiod sensitivity  was located on Chr 7 at 35.31 Mbp  on Chr 9 was tagged by molecular markers at 13.39 Mbp , a QTL associated with climbing ability and plant height was previously reported (linked to Pvctt001 marker at 0.51 Mbp) , on Chr 6 (between 28.2 and 29.5 Mbp), on Chr 9 (between 11.7 and 13.5 Mbp), and Chr 11 (between 2.3 and 5.3 Mbp) , the Phvul.006G113100 gene was annotated as a homologous to a ferric-chelate reductase, reported to be involved in iron uptake from the soil  , and by the AVISA-Accelerated varietal improvement and seed delivery of legumes and cereals in Africa (OPP1198373) projects funded by the Bill & Melinda Gates Foundation. The Norman Borlaug Cooperative Research Initiative (NBCRI) Grain Legumes Project of the US Agency for International Development, Project No. 0210-22310-004-96R supported the Tanzanian trials in Arusha and Mbeya. We would like to thank USAID for their contributions through the CGIAR Research Program on Grain Legumes and Dryland Cereals. Open access publication is kindly covered by ETH Zurich.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


It is nicely summarised in Paige Harden’s book “The Genetic Lottery: Why DNA Matters for Social Equality”2. Genetic variation underlying human traits is the result of two genetic lotteries. Firstly, it is the lottery of who your biological parents are and secondly it is the lottery of which genetic variants you have inherited from your parents. The first explains a proportion of the differences between families whereas the latter explains a proportion of the differences between brothers and sisters. Nature is not fair, hence the title of Harden’s book.Genetic variation for ability and learning is ubiquitous in animals, including in humans for physical, cognitive and social abilities. Empirical evidence supporting the thesis that individual differences in humans for cognitive ability and socio-economic outcomes are in part due to genetic factors is overwhelming3. A GWAS is an experimental design where millions of DNA variants in the genome are tested for association with traits measured on a (large) sample of individuals in the population. The latest GWAS on educational attainment is from a sample of more than 3 million (!) individuals. The authors (disclosure: I am one of them) report about 3000 DNA variants that reach stringent statistical significance for association. More importantly, a “polygenic score” (PGS) derived from the estimated effects of the DNA variants in the study explains about 15% of individual differences in out-of-sample prediction, among people who are genetically of European descent. This means that the correlation between outcome (the number of years of schooling or attaining college completion) and the PGS is approximately 0.4, using only a DNA sample as information. Another way of looking at these results is to consider the effect size: if the standard deviation (SD) of the number of years of education is 4 years, then the PGS has a SD of 0.4*4 = 1.6 years, which is much larger than the effects on educational attainment estimated from policy changes or randomised intervention studies2. Indeed, the difference in prevalence of college completion between individuals in the top decile versus bottom decile of the distribution of the PGS was 50% (60% versus 10%)3, which is huge.An example of empirical data on genetics and social outcomes comes from the latest genome-wide association study (GWAS) of educational attainment1. Two recent papers in this journal fall into this space.Therefore, genes matter when it comes to educational performance and social outcomes . There are multiple ways of dealing with this “inconvenient truth”, from ignorance or denial to embracing the knowledge and use it in research with the ultimate aim to inform better policy4. In 1974, when I was in my last year of primary school (aged 12) in the Netherlands, I and other children took a national exam which was used to track students into secondary education. This test is still used today and Knigge et al. use data from this test on thousands of twins, some of whom were tracked at a later age, and their attained secondary school level, to quantify the effect of delayed tracking. The “genetics” in this study comes from the twin design: identical twins share all their genetic variants they inherited from their parents whereas non-identical twins only share 50% (on average), and researchers use this difference to partition observed trait variation into genetic, shared environmental and person-specific unique environmental components. Their main result, using state-of-the-art statistical modelling, was that the effect of shared family environment was substantially reduced among twins whose tracking was delayed until a later age. The results are consistent with the hypothesis that delayed streaming improves equality of opportunity by reducing the effect of prior family environmental influence. However, this conclusion is indirect and the design did not lend itself to a direct estimate of the causal effect of delayed tracking on outcomes later in life. The study would have been more powerful if it had used PGS for ability (which the authors measured using the age 12 exam) and PGS for educational attainment. More research is needed to estimate a direct causal effect of the time of tracking on educational outcomes, but this study provides a useful proof-of-principle that links genetics with education policy.Many countries track (stream) children into secondary education based upon their performance at primary school. For policy makers, relevant questions are whether tracking is beneficial and fair and at what age kids should be tracked. Knigge and colleagues use a genetically informed experimental design to investigate whether delayed tracking (streaming) might reduce inequality of opportunity, using a very large number of identical and non-identical twin pairs from the Netherlands5 comes from using PGS in a novel experimental setting. Here, the authors (the aforementioned Paige Harden is one of them) attempt to quantify and test the effect of a PGS for educational attainment on learning trajectory. The idea is to separate the effect of genetics on cognitive performance  from its effect of learning. The authors used data from the COGITO study, in which adults aged 20-80 were measured for memory and perceptual speed at baseline, and repeatedly thereafter using individualised difficulty levels across 100 days, concluding with a post-test assessment6. In the paper from Youn et al.5 the authors used 9 cognitive tests in a sample of 131 adults from whom they also had a DNA sample and therefore could measure their PGS for educational attainment. The main novelty of this study is in the design because it was too small to draw any meaningful conclusions from the empirical data presented, as recognised by the authors. A sample size of the order of thousands might be required to quantify genetic effects on baseline performance and subsequent learning. Nevertheless, with the power of PGS increasing over time , and larger experimental sample sizes, this kind of longitudinal experiment could be used to dissect the genetics of learning and perhaps identify biologically distinct patterns of learning.The “genetics” in the second study by Youn et al.These two new studies are just a snapshot of how researchers can use genetics to address important questions about cognitive performance, learning and social outcomes . In my opinion, human genomics, in particular the use of polygenic scores, should become fully embedded into social sciences research, including economics, sociology and psychology. If a substantial proportion of the differences between people in important outcomes in life is due to their DNA, then why not utilise this information to design better experiments and to inform policy? In research settings, the use of human genomics data would allow for more powerful experimental designs by accounting for a proportion of variation among samples in the experiment, to detect biases and confounders  and to move from associations to causation. The same principles also apply to studies in neuroscience that use human subjects. Variation in any trait we could measure, including brain imaging data, is partly genetic, and exploiting this fact will lead to a better understanding of the causes and consequences of human behaviour and its underlying biology.2. An extreme example of such policy would be to track individuals into secondary or higher education using, among other sources of information, their genetic predictor for cognitive ability and other traits. Such a “personalised education”7 approach is clearly not endorsed widely2. However, existing tracking methods that were used when I grew up in the Netherlands  and are employed in many other countries around the world are highly selective on cognitive performance (ability) and therefore indirectly already selective on genetic factors. Measuring these factors directly through a PGS would allow a fairer comparison across individuals that is not influenced by their socio-economic background.Could genetics inform policy that enhances learning and educational outcomes? Harden describes a number of examples in her book


Time-dependent traits are often subject to censorship, where instead of precise phenotypes, only a lower and/or upper bound can be established for some of the individuals. Censorship reduces the precision of phenotypes but can represent compromise between measurement cost and animal ethics considerations. This compromise is particularly relevant for genetic evaluation because phenotyping initiatives often involve thousands of individuals. This research aimed to: 1) demonstrate a data augmentation approach for analysing censored phenotypes, and 2) quantify the implications of phenotype censorship on estimation of heritabilities and predictions of breeding values. First, we simulated uncensored phenotypes, representing fine-scale “age at puberty” for each individual in a population of some 5,000 animals across 50 herds. Analysis of these uncensored phenotypes provided a gold-standard control. We then produced seven “test” phenotypes by superimposing varying degrees of left, interval, and/or right censorship, as if herds were measured on only one, two or three occasions, with a binary measure categorized for animals at each visit . We demonstrated that our estimates of heritabilities and predictions of breeding values obtained using a data augmentation approach were remarkably robust to phenotype censorship. Our results have important practical implications for measuring time-dependent traits for genetic evaluation. More specifically, we suggest that data collection can be designed with relatively infrequent repeated measures, thereby reducing costs and increasing feasibility across large numbers of animals. Maximizing the number of individuals contributing phenotypes to an analysis is particularly important in genetic evaluation and selection. Accuracy of evaluation and selection intensity are two key drivers of genetic improvement in a population . For anyThere are several situations where animal breeders deliberately censor phenotypes. First, when a continuous trait, such as shoulder height, is measured using ordinal categories (for example a score of 1–9) instead of the underlying continuous variable. This type of censoring of continuous phenotypes makes them easier and faster to measure. Where time-dependent traits, such as age of puberty, are subject to left and interval censoring, individuals are often assigned a phenotype based on their age when they were first observed to have reached the threshold criterion  demonstrate a data augmentation approach for analysing left, interval and right-censored data, and 2) quantify the implications of varying phenotype censorship on estimates of heritabilities and predictions of EBVs, using a categorical, time-dependent trait. Our hypothesis was that the heritability and EBV rankings would be robust to phenotype censorship.We used the software XSim , implemeWe superimposed varying degrees of censorship to simulate these animals being observed at only 1, 2 or 3 herd visits for a seasonal window during which they would have been expected to attain puberty. In the first censored scenario, three herd visits  were simulated for each herd. The mid observation was on the day where 50% of the herd had attained puberty, and the early and late observations were 20 days either side of that day. This timing resulted in an even number of animals with left, interval and right censoring. In the second to fourth censored scenarios, herd visits were restricted to just the early and mid (EM), the mid and late (ML), or the early and late (EL) visits. In the fifth to seventh censored scenarios, there was only one visit to each herd, with an early only (E), a mid only (M) or a late only (L) visit. Under censorship, the continuous variable GOLD was unobserved. Instead, the phenotype for each animal was only known to fall within a lower and/or upper bound .We used a Markov-chain Monte Carlo (MCMC) technique that included data augmentation  to obtaivia single-site Gibbs sampling to construct a Markov chain of plausible values of unknowns. The model included random marker effects using BayesC priors , b is a vector of herd effects, a is a vector of additive marker effects. The vector e represents residuals corresponding to each of the phenotypes. The residuals are assumed to be independently normally distributed, with homogeneous residual variance. The incidence matrix X relates each phenotype record to relevant fixed herd effects. The covariate matrix M relates each phenotype record to the number of one of the alleles at each SNP marker. The matrix M has a column for each SNP marker, and a row for each phenotype.The resultant MCMC samples of effects represented fixed herd effects, marker effects, and variance parameters. Matrix representation of the linear marker effects model equation is: b and a and the scalars representing genetic and residual variances. Where phenotypes are censored with known lower or upper bounds, the vector y is also unknown, except for the bounds on each observation.In all analyses, the unknowns include the vectors We used command line bash scripts and Julia  packagesn = 4,935).We used Pearson’s correlation coefficient to quantify the extent of re-ranking between EBVs obtained from each of our censorship scenarios. Correlations of EBVs included all animals with simulated phenotypes  phenotype were strong and positive across all censorship scenarios . Strong Correlations between the posterior means of sampled phenotypes from the EML censorship scenario and other censored scenarios ranged from 0.86 to 0.98 . LikewisThe posterior mean for the heritability of AGEP estimated using GOLD phenotypes was 0.29 . The posWe determined that a data augmentation approach to analysing left-, interval- and right-censored data resulted in precisely estimated phenotypes for a time-dependent categorical trait, using simulated AGEP phenotypes as a case study. The extent of animal re-ranking, indicated by comparing correlations between censored phenotypes and their precise phenotypes (control), was relatively low even under extreme censorship scenarios, where animals only had a single observation. Furthermore, EBVs were robust to phenotype censorship and animal rankings were largely consistent with our gold standard control scenario. In particular, the correlations between control EBVs and EBVs from any censored scenario where there were at least two visits per herd were greater than 0.90. We also determined that heritability estimates were relatively unaffected by phenotype censoring; across both control and censored scenarios, heritabilities tended to be around 0.29. Previous studies focusing on the implications of right censoring also indicated concordance of EBVs across varying degrees of censorship  and thatIntentional phenotype censorship is useful for reducing cost and/or animal welfare concerns associated with phenotype collection. This is especially true for time-dependent traits, where repeated measurements are required to produce precise phenotypes. The trait AGEP provides a good case study as all animals begin pre-pubertal and, over time, reach sexual maturity and become post-pubertal. The timing of puberty varies between individuals and is influenced by a range of genetic and environmental factors. Puberty status can be determined through behavior monitoring, ovarian ultrasonography and/or blood testing for plasma progesterone concentrations ; howeverThe current analysis has not exhaustively considered the implications of timing of observations. We timed our simulated herd visits to obtain about 25% of animals with left, interval E to M, interval M to L and right censoring using knowledge of the median AGEP for each herd. In reality, that information is not available in advance to plan the timing of observations. As fewer herd visits are undertaken, timing may become more important. For example, if there is only one visit per herd, and it occurs earlier or later than the day of median AGEP there will be less variation among observations. In the worst case, all animals may be yet to reach puberty, or all animals may be post pubertal. Their phenotypes would not add value to genetic analysis, as any animal effects would be entirely confounded by the herd effect. We have investigated the implications of visit timing within the bounds of our visit schedule. For example, our E scenario represents a single early visit, while our L scenario represents a single late visit. Our results indicate strong correlations between EBVs produced by all three scenarios with two herd visits. Therefore, when there are at least two visits, the timing is less important. Conversely, the correlations between EBVs produced by our three single visit scenarios are slightly weaker, indicating that when there is only one herd visit, animal selection decisions may be materially altered depending on visit timing. Further investigation is required to quantify the accuracy of EBVs produced using phenotypes from different visit timing before a recommendation on optimal timing can be made for a specific trait. If a visit was earlier than desirable, and most if not all animals were pre pubertal, there is still the option of visiting again to capture more information. A similar option is not available if a visit was later than desirable.Based on the case study presented here, we conclude that heritability and EBV estimations for categorical, time-dependent traits are likely to be robust to left- interval- and right-censorship of phenotypes. In regard to the design of phenotyping strategies for specific traits, further simulation may be warranted. We assumed that once an animal attained puberty, she would be observed as pubertal thereafter with little measurement error. However, in reality, a trait like age at puberty could incur a relatively high incidence of false negative measures . The extent of errors in allocating a phenotype to an animal would have implications on optimal measurement design. Further, we investigated only a single trait heritability, but the simulation could be applied to phenotypes with varied heritabilities, such that the implications of censorship on very low or very high heritability traits might also be quantified.


Trifolium repens) is integral to mixed pastures in New Zealand and temperate agriculture globally. It provides quality feed and a sustainable source of plant-available nitrogen (N) via N-fixation through symbiosis with soil-dwelling Rhizobium bacteria. Improvement of N-fixation in white clover is a route to enhancing sustainability of temperate pasture production. Focussing on seedling growth critical for crop establishment and performance, a population of 120 half-sibling white clover families was assessed with either N-supplementation or N-fixation via inoculation with a commercial Rhizobium strain (TA1). Quantitative genetic analysis identified significant (p < 0.05) family additive genetic variance for Shoot and Root Dry Matter (DM) and Symbiotic Potential (SP), and Root to Shoot ratio. Estimated narrow-sense heritabilities for above-ground symbiotic traits were moderate (0.24–0.33), and the strong (r ≥ 0.97) genetic correlation between Shoot and Root DM indicated strong pleiotropy or close linkage. The moderate (r = 0.47) phenotypic correlation between Shoot DM under symbiosis vs. under N-supplementation suggested plant growth with mineral-N was not a strong predictor of symbiotic performance. At 5% among-family selection pressure, predicted genetic gains per selection cycle of 19 and 17% for symbiotic traits Shoot DM and Shoot SP, respectively, highlighted opportunities for improved early seedling establishment and growth under symbiosis. Single and multi-trait selection methods, including a Smith-Hazel index focussing on an ideotype of high Shoot DM and Shoot SP, showed commonality of top-ranked families among traits. This study provides a platform for proof-of-concept crosses to breed for enhanced seedling growth under Rhizobium symbiosis and is informative for other legume crops.White clover ( Trifolium repens L.). In addition to its contribution to the sward as a high-quality and palatable forage, white clover forms root nodules where atmospheric dinitrogen (N2) is fixed to provide bio-available nitrogen (N) through symbiosis with soil-borne Rhizobium bacteria    rather tRhizobium interaction at the seedling stage, a genetically structured breeding population comprising 120 half-sibling  families was assessed for symbiotic traits. This population structure is commonly used for breeding in outcrossing species, such as white clover, and facilitates integration of the quantitative genetic estimates derived from this study into a breeding programme. The objectives of this study were to: a) estimate the magnitude of additive genetic variation among these half-sib families for key traits associated with N-fixation using the commercial Rhizobium inoculant TA1; b) identify high N-fixing families as parents for crossing to improve symbiotic traits; and c) determine the predicted rate of genetic gain based on deterministic modelling using among half-sib family selection methodology. This will give insight into the potential of the clover-Rhizobium symbiosis as a target trait for improved seedling establishment for reduced-input agriculture.To investigate and determine the quantitative genetic parameters of the white clover-1 synthetic population of 141 half-sib families, as described  in a single bee-proof tunnel house, and the plants were repositioned randomly at regular time intervals to mitigate against pollen bias. The population of 120 half-sib families used in this study was a random sample taken from the 274 F3 generation half-sib families.The half-sib family populations were developed in 2012 from 31 clonal cuttings from top performing breeding lines that were polycrossed to form an Fescribed . BrieflyRhizobium leguminosarum bv. trifolii strain TA1 or 2) supplied with mineral N in the form of ammonium nitrate (NH4NO3) (positive control), as described below. Each container was divided into quadrants, and each quadrant was planted with one of four half-sib families that were assigned randomly to treatment container and quadrant within each replicate using a row and column experimental design. Seeds from the half-sib families were lightly scarified using sandpaper and surface-sterilised following a method described by Rhizobium nor provided with mineral N (negative control), were included per environmental replicate. This was to allow seed N reserves to be considered in the calculation of symbiotic potential post-harvest and indicated the absence of Rhizobium contamination among experimental treatments. This negative control container was planted with a balanced bulked mixed sample of all half-sib families. Each quadrant had 16 individuals, hence 64 balanced bulk seedlings per negative control container, 256 per replicate and 512 in the experiment to represent the mean genetics of the half-sib families. The trial plan is detailed in White clover half-sib families were assessed in two controlled environment growth rooms using a two environmental replicate randomised split plot experimental design generated with DeltaGen softwareRhizobium leguminosarum bv. trifolii strain TA1 was used to screen the symbiotic performances of half-sib families based on its known effective N-fixation capacity and use as a commercial inoculant strain in New Zealand and Australia. Cultures of the TA1 strain were sourced directly from glycerol stocks stored at −80°C and plated initially onto yeast mannitol agar (YMA) slopes. The TA1 strain was sub-cultured onto new YMA slopes and grown in an incubator for four days at 25°C in continuous darkness. For white clover seedling inoculation, the TA1 cultures were suspended in 500 ml of sterile deionised water to provide a concentration of 1 × 107 cells mL−1. Each white clover seedling was inoculated one day after sowing by applying a 0.5 ml aliquot of Rhizobium suspension. The number of cells applied was confirmed by spreading a 1 × 10−7 dilution of the inoculant suspension on YMA plates and counting colony forming units after four days incubation at 25°C in continuous darkness.The −1 NH4NO3 (1.2 g L−1) with the first dose applied nine days after sowing and weekly thereafter (200 ml container−1) for three weeks. The initial aliquot was timed such that the positive control seedlings received N when N-fixation most likely began in Rhizobium-inoculated seedlings, based on preliminary vermiculite assays when white clover individuals inoculated with TA1 had one or more pink/effective nodules , was provided by four Apollo 600 W light emitting diode (LED) (seegree®) light banks with two variants (6 and 12 bulbs) and two 4 × 100 W Cob integrated LED (Easy Grow Ltd) light banks per growth room. Radiation corresponding to far-red (660 to 730 nm), and infrared  was provided by six 100 W/240 V R95 Spotone incandescent lamps (Philips) per growth room. Each treatment container was watered to approximately 80% of its initial moistened weight at sowing (~2000 g) at intervals of 1 to 3 days to maintain plant water status.Shoot dry matter (DM) and Root DM were determined after 35 days of post-inoculation growth. Shoots and roots of each plant were separated at the hypocotyl, dried for 72 h in paper bags at 60°C and weighed using a four-decimal place precision balance. The Root to Shoot ratio (RSR) of each white clover individual was calculated post-harvest by dividing the amount of Root DM by the amount of Shoot DM. Shoot and Root DM values were used to calculate symbiotic potential (SP). The SP allowed the impact of biological N-fixation of white clover half-sib families to be compared. The SP describes the proportion of biomass generated through symbiosis relative to supplementation with mineral N (positive control) achieved by an individual of a white clover half-sib family. This calculation also adjusts for the influence on DM production caused by seed weight and seed N reserves by accounting for DM production from uninoculated plants. Symbiotic potential of each half-sib plant was calculated using −1); inoculated plant, an individual from a specific half-sib family inoculated with Rhizobium; uninoculated plants, the mean Shoot DM value (mg plant−1) of all individuals from a random subsample of a balanced bulk of all half-sib families grown without Rhizobium or supplemental N; positive N control plants, the mean Shoot DM value (mg plant−1) of individuals from the specific half-sib family grown without Rhizobium but with supplemental N.Where: SP, symbiotic potential; DM, dry matter  procedure  to estimDeltaGen  based oni and modelled as coming from a normal distribution with the given mean and variance, i and environmental replicate k in environmental replicate j, l in environmental replicate j,i in environmental replicate j in row k and column l,where: For each trait, half-sib family means were adjusted for spatial variation effects within the experimental design by generating best linear unbiased predictor (BLUP) values. Initial data analyses using the split-plot design in the statistical model found that the variance components associated with the sub-plots  were not significant with variances of zero. Therefore, a model was used from which the sub-plot structure was excluded. The BLUPs were generated from REML analysis of all traits as described by p ≤ 0.05) were generated using the R package ‘car’  and Root SP. The Pearson correlation coefficient for each trait was generated from the BLUP values of white clover half-sib families which had been separated into treatments using the ‘cor’ and ‘corr.test’ functions in the R package ‘psych’ . The corge ‘car’ . The cororrplot’ .Narrow-sense heritability  was performed to determine the genetic correlation between the traits within treatments. This correlation is the family additive genetic covariance between traits normalized by the geometric mean of individual trait variances and provides an estimate of genetic factors shared between traits Pattern analysis was conducted using a combination of cluster analysis and principal component analysis (PCA) to assess the grouping of half-sib families within each treatment based on expression of all traits. The aim was to identify and group half-sib families with relatively similar trait performance . Patternp ≤ 0.05) family additive genetic variance among the 120 half-sib families. To model predicted response to selection, based on among half-sib family selection, the p), equating to selection intensities (Genetic gain (Where: The Smith-Hazel (SH) index described by Where: This generates an index based on quantitative genetic parameters to enable co-selection of multiple traits with a single value which permits the identification of half-sib families with, in the case of this study, high Shoot DM and Shoot SP. The economic weightings were substituted by desired genetic gain . Equal wThe correlated selection of traits enables selection for a secondary (target) trait Where: Rhizobium strain TA1 was the provider of nitrogen via symbiosis, mean Shoot dry matter (DM) was 11.4 mg plant−1 with a five-fold difference between the highest and lowest half-sib family , Rhizobium treatment and when supplemented with mineral N, respectively  of the symbiotic traits showed variation among the 120 half-sib families. When b family . Root DMineral N . By contShoot SP . These fectively . The negp ≤ 0.05) family additive genetic variation  for these traits, these were equal to the family additive genetic variation component , as was Root DM , the range differential was similar (p ≤ 0.05) family additive genetic variation among the 120 half-sib families grown with mineral nitrogen for Shoot and Root DM but not RSR  for the measured traits  genetic correlation coefficients for five traits expressed by the 120 half-sib families when inoculated with the ectively . While tectively .Rhizobium strain TA1 showed that components PC1 and PC2 explained 83.4 and 10% of the variation, respectively  or inoculated with the ectively .There was a positive association  between Shoot DM and Root DM as well as the symbiotic traits Shoot SP and Root SP  which reRhizobium-inoculated half-sib families had reduced expression for Shoot and Root DM and the corresponding SPs but had increased RSR. Within these trends, four Cluster groups were identified , but also contained two half-sib families (lines 78 and 187) that had been inoculated with Rhizobium strain TA1 (mean Shoot DM = 20.3 mg plant−1). These two TA1-inoculated half-sib families had performed similarly to mineral N treated families and displayed above average expression for Shoot and Root DM and SP, and RSR. The mean Root SP was 121%, indicating these two half-sib families generated more symbiotic root biomass relative to the N supplemented root biomass. Clusters 3 and 4 consisted mostly of half-sib families inoculated with the Rhizobium strain TA1, highlighting the performances of these families when N is derived from symbiosis. Cluster 3 had one half-sib family (line 83) from the mineral N treatment (Shoot DM = 9.2 mg plant−1) indicating this family performed similarly to, or worse than, half-sib families inoculated with Rhizobium (mean Shoot DM = 15.4 mg plant−1). Features of Clusters 3 and 4 were below-average expression, relative to the mineral N treatment, of all traits except RSR. This indicated increased partitioning into root biomass above shoot biomass when half-sib families were reliant on the symbiosis for N supply.There was a trend separating clusters based on treatment as the half-sib families grown under mineral N showed above average expression of Shoot and Root DM . By compentified ; Table 3The calculated variance components enabled deterministic prediction of genetic gain (%ressures . Selecti(11–16%) . RSR hadvia symbiosis relative to that produced with mineral N. These traits can be selected for individually or concurrently in a multi-trait selection index. A desirable target would be to have high SP packaged with a high Shoot DM plant, therefore aligning high biomass with efficient use of symbiotically-derived N. Selecting for these traits independently has drawbacks as SP alone does not reflect total plant biomass since small plants may have a high SP. To this effect, a Smith-Hazel multi-trait selection index for Shoot DM and SP was calculated by integrating phenotypic correlation and family additive genetic variation components for Shoot DM and Shoot SP. The mean values of the top ranked half-sib lines identified at 5% among-family selection pressure were 20.6 mg plant−1, 75% and 12.0 for Shoot DM BLUPs, Shoot SP and a Smith-Hazel index (SH), respectively , there was commonality among the top performing lines at the different selection pressures . When seTo gain further insight into the relationship between key white clover agronomic symbiotic traits and how these may be deployed in a breeding programme, the correlated response to selection for Shoot DM and Shoot SP was investigated. The correlated response to selection  between Shoot DMs with either supplemental mineral N or under symbiosis indicates that growth under mineral N was not an accurate predictor of symbiotic performance. This highlights that N-supplementation may confound analysis of symbiosis-based traits.By contrast, the moderate genetic correlation (0.62) between Shoot DM and Shoot SP indicates fewer genetic mechanisms in common than Shoot and Root DM as there were instances when families had high Shoot DM and low Shoot SP, and vice versa. This is understandable as Shoot SP is somewhat independent of total biomass. For example, a plant or family with low biomass under symbiosis may have a high SP if the biomass of the N-supplemented control is also low. Conversely, a plant or family with a large symbiotic biomass may have a low SP if the N-control is markedly greater. Even so, the moderate genetic correlation between Shoot DM and Shoot SP suggests potential for co-selecting these two traits to develop highly productive plants with high SP. There were, however, moderate negative genetic and phenotypic correlations between Shoot DM and root to shoot ratio (RSR). This suggests antagonistic genetic mechanisms and increased partitioning into root biomass relative to shoot biomass when half-sib families were grown in a N-reduced environment. This phenomenon was particularly evident in the no N, no Rhizobium strain.Overall, the genetic correlation data from this half-sib family study, in tandem with the phenotypic correlations, show potential to breed for an ideotype combining both high biomass and high SP under symbiosis using a commercial 15N analysis across three trial sites, the genetic gain for N-fixation of a range of New Zealand white clover cultivars was 1.2% per year. They concluded that the increase in N-fixation was possibly the result of indirect selection for forage yield traits. In the current study, 15N analysis was not performed on the Rhizobium treatments since the only source of N, other than an initial trace amount, was obtained from the atmosphere through the legume-TA1 Rhizobium symbiosis. It maybe that selection for yield traits in the field could lead to indirect selection for increased N-fixation, but this is difficult to assess, especially if the selection is undertaken on soils high in mineral N. This is supported by the current study where, as described previously, N-supplementation may confound analysis of symbiotic traits.Genetic gain calculations across the selection pressures showed notable per cycle estimates with up to 19, 17 and 23% for symbiotic Shoot DM, Shoot SP and Root SP, respectively. There is, therefore, opportunity for marked improvement in DM production of seedlings reliant on symbiotic N-fixation in this population. The predicted genetic gain also reflected the The predicted genetic gains per cycle for the symbiotic traits at 5% selection pressure were similar to values for white clover agronomic traits such as stolon number (~11%) and stolon branching (15–17%) reported in a recent field trial study using deterministic modelling based on 200 half-sib families . PredictRhizobium, the commonality of some top ranked lines at higher selection pressures indicates performance at the seedling stage under symbiosis may give some insight into long-term field performance.Interestingly, there was some overlap between the top performing 10% (n = 20) families for field trial multi-site DM yield across years identified by Rhizobium strain TA1, a white clover commercial inoculant. Combined with the predicted genetic gains based on among-family selection, these insights indicate potential to breed for enhanced performance under symbiosis and tailor clover genetics to major commercial Rhizobium strains for enhanced seedling growth and establishment. This can be improved further, as a key feature of the half-sib family structure is only ¼ of the total additive variation is evaluated  index. Although based on different methodologies, there was a high degree of commonality in half-sib families selected using the multi-trait procedures, but detailed comparison of the two methods is difficult. This is because pattern analysis is a diagnostic tool based on a graphical representation of association among all the traits assessed, hence family selection is influenced by the breeder’s interpretation of the biplot. The SH index, by contrast, is a predictive assessment tool based on estimates of additive and genetic correlations for the specific traits of interest , and allHordeum vulgare) (Zea mays) (Panicum virgatum)  , wheat (vulgare)  and maizea mays)  and the irgatum) . There airgatum) .The likely success of a multi-trait selection, such as the SH index, for Shoot DM and Shoot SP is also supported by the 11–13% correlated response to selection for these two traits. Even so, while selecting for Shoot DM will also increase Shoot SP, not all the highest Shoot DM plants will have a high SP. Implementation of a SH index will assist in facilitating combined improvement of these two traits. Correlated responses to selection have been investigated for a range of white clover seed, stolon and yield traits . There iRhizobium symbiotic agronomic traits.There are advantages and disadvantages for single or multi-trait selection schemes. Single-trait selection can achieve higher rates of genetic gain per cycle per trait compared to a multi-trait approach. The results of this study demonstrate application of single and multi-trait selection indices, such as the SH index, for facilitating the ranking of half-sib families in future symbiotic work. The half-sib families selected across these indices could provide the basis of proof-of-concept crosses for among-family breeding, that may also be enhanced by genomic selection, to improve white clover-Rhizobium symbiosis in a half-sib family population. The significant family additive genetic variation  between Shoot DMs with either supplemental mineral N or under symbiosis indicated N-supplementation was not an accurate predictor of symbiotic performance and may confound analysis of symbiosis-based traits. This suggests that symbiotic traits themselves should be the focus for improving interactions with Rhizobium.To our knowledge, this is the first study to detail a quantitative genetic analysis of white clover-A breeding target based on an ideotype aligning high biomass with efficient use of symbiotically-derived N requires integration of high Shoot DM and high Shoot SP. The SH index would be an efficient method to facilitate this process, particularly given the moderate genetic correlation between the traits and the commonality of families in the top-ranked selections of the two individual traits and the SH index. Genetic gain will likely be enhanced through application of genomic selection in this half-sib family population. Additionally, the strong genetic correlations between shoot and root symbiotic traits indicate strong pleiotropy or linkage among genetic mechanisms for these features. This suggests that if above ground traits are the target, then there is reduced value in measuring root traits, freeing resources for use elsewhere.Rhizobium strain was assessed in a controlled environment to reduce the GPlant × GRhizobium × E interactions, thus increasing ability to estimate variance components. Implementation of these insights for enhanced symbiosis in the field could be through aligning clover genetics to a commercial Rhizobium strain applied as an inoculum coating prior to sowing. Using this approach as a vehicle for improved white clover establishment without requirement of N-fertiliser application will reduce farm-system inputs and has implications for other forage and annual grain legume crops. Moving beyond seedling establishment to investigating symbiotic potential of perennial mature plants in the field will require further investigation. An avenue to explore includes developing methods to provide the commercial Rhizobium strain as a regular pasture application throughout the year to the aligned white clover genetics as the plants mature and transition to the perennial creeping stoloniferous form. The current study can also be extended to assess clover germplasm with soil-derived Rhizobium populations as a basis for identifying clover germplasm with a wider effective symbiotic compatibility. In the near future, the half-sib population used in this study and the data generated provide a platform to breed white clover with improved symbiotic traits, and may be regarded as a training population to explore genomic selection for improvement of these complex plant-microbial interactions.In this study, a single The original contributions presented in the study are included in the article/SW: data curation, formal analysis, investigation, methodology, visualisation, writing – original draft, and writing – review and editing. MJ: formal analysis, visualisation, writing – original draft, and writing – review and editing. RH: supervision and writing – review and editing. CA: data curation, investigation, and methodology. DL: formal analysis. OE: investigation and resources. GC: resources. EJ: supervision and writing – review and editing. RB: supervision, writing – original draft, and writing – review and editing. AG: conceptualization, project administration, investigation, supervision, visualisation, writing – original draft, and writing – review and editing. All authors contributed to the article and approved the submitted version.This work was conducted within the New Zealand Ministry of Business, Innovation and Employment (MBIE) grant C10X1308 ‘Improving Legume-Rhizobia Performance’. Additional funding was provided by the Lincoln University, Agriculture and Life-Science Faculty post-graduate fund.OE and GC were employed by PGG Wrightson Seeds Ltd.The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


The temperature experienced during organismal development can affect adult phenotypes. This phenomenon is called thermal developmental plasticity and is common for insect pigmentation. Plasticity can help organisms cope with environmental heterogeneity, such as that across yearly seasons, and might also impact how populations deal with environmental perturbation, such as climate change. Because plasticity can be adaptive and heritable, it is liable to evolve. The evolution of plasticity will depend on the availability of genetic variation contributing to variation in plasticity, and on the integration across plastic traits, which might limit opportunities for their independent evolutionary change. Here we address these two topics by focusing on thermal plasticity in butterfly wing pattern elements called eyespots, which are composed of concentric rings of different colors. We found differences in plasticity between genotypes and concluded that mutants of strong effect on pigmentation can also affect thermal plasticity therein. We also found differences in plasticity between distinct pigmentation features, suggesting plasticity can evolve somewhat independently for those features.Bicyclus anynana genetic backgrounds, corresponding to allelic variants affecting eyespot size and color composition. Our results reveal genetic variation for the slope and curvature of reaction norms, with differences between eyespots and between eyespot color rings, as well as between sexes. Our report of prevalent temperature-dependent and compartment-specific allelic effects underscores the complexity of genotype-by-environment interactions and their consequence for the evolution of developmental plasticity.Developmental plasticity refers to the property by which a genotype corresponds to distinct phenotypes depending on the environmental conditions experienced during development. This dependence of phenotype expression on environment is graphically represented by reaction norms, which can differ between traits and between genotypes. Even though genetic variation for reaction norms provides the basis for the evolution of plasticity, we know little about the genes that contribute to that variation. This includes understanding to what extent those are the same genes that contribute to inter-individual variation in a fixed environment. Here, we quantified thermal plasticity in butterfly lines that differ in pigmentation phenotype to test the hypothesis that alleles affecting pigmentation also affect plasticity therein. We characterized thermal reaction norms for eyespot color rings of distinct  Schistocerca nymphs produce gregarious or solitary locusts, which also differ in body pigmentation . Thiewed in ); and teiewed in ,6,7). Iniewed in ,6,7,8.Studies on a variety of insect species have provided valuable insight into the ecological significance and the mechanistic underpinnings of plasticity e.g., ,9,10,11),10,119,1Despite great progress in the dissection of the genetic basis of variation and evolutionary change in many adaptive traits (compiled in ), not muBicyclus anynana wing pattern ornaments called eyespots .B. anynana eyespots combine analyses of the mechanisms regulating eyespot plasticity with knowledge about the ecological significance and evolution of that plasticity  also contribute to variation in levels of pigmentation plasticity. We do this by characterizing thermal reaction norms for the size of eyespot color rings for Bicyclus anynana butterflies with different pigmentation phenotypes  differing in ambient temperature (±0.5 °C). We chose temperatures typically used to induce experimentally the natural dry (19 °C) and wet (27 °C) seasonal morphs, and an intermediate temperature (23 °C). Larvae were kept in large cages and fed ad libitum with young maize plants sprayed with anti-fungal solution. Adults were frozen 24 h after eclosion, and their wings were cut and stored in the freezer until analysis. Due to a microsporidial infection in all laboratory stocks, we obtained fewer than 120 adults per stock per temperature. Final sample sizes are provided in B. anynana, e5  under a binocular microscope (Leica MZ12) at 10× magnification. This was performed under controlled light conditions, using the support of a ruler for conversion from pixels to millimeters and a color reference card (QPcard 201) for color calibration and background correction. The resulting images were analyzed with a custom image processing system (cf. ) using t5  and genotype  as fixed factors in general linear models assuming a Gaussian distribution of errors. Prior to that, parametric assumptions were considered by checking for normality  and homoscedasticity  of residuals, and transforming data where appropriate. When a significant difference  was found for the effects of G and/or T, we performed post hoc pairwise comparisons using Tukey’s honest significant difference (HSD) tests .All data analyses were performed in R , separatring_area~wing_area + temperature*genotype, with temperature  and genotype  as fixed factors and wing_area as a covariate. Second, we used principal component analysis (PCA) [Dim~temperature*genotype, with temperature and genotype as fixed factors.First, for each of our eight target eyespot traits , we testis (PCA)  to reducis (PCA)  and missis (PCA) . PCA wasring_area~wing_area + temperature*phenotype using a general linear model with a Gaussian distribution of error. This was performed for the BE and Fr stocks separately, with temperature  and phenotype  as fixed factors and wing_area as a covariate.Finally, we compared wild-type-looking and mutant-looking individuals that segregate within each of the BE and Fr stocks. Note that we did not include the wild-type-looking individuals from the BE and Fr stocks in the previous analyses. We tested the model B. anynana pigmentation variants, we collected phenotypic data from individuals of four different genetic backgrounds reared at three developmental temperatures  and for the two eyespots on equivalent positions on the hindwing (e2 and e5) in four genetic lines . We obseIn The PCA describing the patterns of variation for the eight eyespot traits (4 eyespots × 2 color rings) in butterflies from our four target genetic stocks reared at three temperatures reduced variation to four main Dims, together accounting for about 94% of the variation in our female and male datasets . The loap < 2.2 × 10−16 for both females, F = 313.0, and males, F = 333.2), by genotype , and by the interaction between these two factors . WT was the least plastic genotype for Dim 1 , and BE (eyespot size mutant) stood out once more for the intercept of Dim 1 reaction norm relative to all other lines .For both females and males, all eyespot traits seemed to contribute equivalently to defining Dim 1, which explained more than 70% of the variation in each respective dataset  and by genotype x temperature  for both sexes. Differences between temperatures were marginally significant in the female dataset  but not in the male dataset . Dim 2 for Fr stood out from that of other genotypes, in both intercept and slope  a. Two trnd slope b.Evaluating how the individuals we measured are distributed in the space defined by Dim 1 and Dim 2 , it becoB. anynana. We compared eyespots eP and e5 between siblings within each of the genetic stocks  between the four “genotypes” . HoweverB. anynana genetic lines differing in pigmentation phenotype , as welterflies ).B. anynana eyespots (deliberately maintained in laboratory populations), contrasts with results from previous studies concerning alleles of more subtle phenotypic effect . In our study, we found that eyespot variants can differ in slope and curvature of thermal reaction norms. However, previous artificial selection experiments targeting quantitative variation, albeit able to alter eyespot size [Drosophila melanogaster have shown that mutants of large phenotypic effect can affect thermal plasticity [By comparing thermal reaction norms between genetic lines, we found abundant examples of inter-genotype differences in plasticity. We documented significant GxT interactions, corresponding to inter-genotype differences in slope and/or curvature of thermal reaction norms, for the size of eyespot rings , as wellpot size ,39,40 anpot size ,52. Thispot size , as has asticity , includiasticity ,67,68,69asticity .B. anynana butterflies display serially repeated eyespots along the wing margins, each composed of white, black, and golden color rings produced in response to a concentration gradient of a signaling molecule diffusing from the presumptive eyespot center in developing pupal wings  and integration across components thereof . iewed in ). Eyespoiewed in ,47,49,70iewed in ,43) inpuiewed in , of indiiewed in ,40, and  anynana ,51,72. Tterflies , betweennogaster , and betla flies . Moreovela flies . More gela flies ), there  anynana , for whi anynana . Differe anynana ,77. It is; e.g., ), as wele; e.g., ,79). Fure; e.g., ,80), as e; e.g., ,82), wilDevelopmental plasticity is not only a mechanistically interesting and ecologically relevant property, but also one with tight connections to adaptive evolution (discussed in ). On the


Tools that operate on real genotypes and integrate easily with other analysis software can guide users toward crossing decisions that best balance genetic gains and genetic diversity required to maintain gains in the future. Here, we present genomicSimulation, a fast and flexible tool for the stochastic simulation of crossing and selection based on real genotypes. It is fully written in C for high execution speeds, has minimal dependencies, and is available as an R package for the integration with R’s broad range of analysis and visualization tools. Comparisons of a simulated recreation of a breeding program to a real data set demonstrate the simulated offspring from the tool correctly show key population features, such as genomic relationships and approximate linkage disequilibrium patterns. Both versions of genomicSimulation are freely available on GitHub: The R package version at  When real breeding schemes and genetic improvement programs are high-effort, multiyear undertakings, simulation tools offer a valuable opportunity to judge feasibility or balance trade-offs in design so as to get the most out of the real programs .Here, we introduce genomicSimulation, a fast, flexible R package for the stochastic simulation of breeding programs beginning with real genotype data. The package is designed to provide high-speed crossing simulation capabilities that can be easily integrated with R’s wide range of visualization and genetic analysis tools . genomicFor even faster simulations, genomicSimulation’s underlying C library  is also available. It is distinguished from the R package only by the lack of default parameters and lack of R vector data output options.The package was originally developed for use on self-pollinated crop species, but its flexible set of crossing operations allows it to be used more broadly in outcrossing species. genomicSimulation has full documentation, user guides, and is in ongoing development.genomicSimulation is structured as a set of “building block” scripting functions. Users have full flexibility to intersperse requests to perform a cross and produce new offspring and restructure groups to perform selection or restructure the breeding pool.The first step in using genomicSimulation is a call to the setup function, load.data. Two data files are required to set up the tool: a matrix file contains the alleles of at least one founder genotype in diploid format (i.e. 2 positions per locus) at a set of discrete positions, and a linkage map to situate those positions in the model of the genome. If the phase at heterozygous positions in the founder genotypes is not known, it is randomized on load. Alleles can be any nonspace character, which allows for the package to be used on defined and known Mendelian genes as well as SNPs.The simulation stores every simulated genotype as a sequence of characters in memory.To create a cross, the simulation generates a gamete independently from each of the 2 parents. No distinction is made between male and female parent at present, although the direction of the cross can be specified by the user. The count-location method of simulating meiosis ignores crossover interference. The specific distributions chosen add the additional assumptions that the number of crossovers is proportional to the chromosome length and that crossovers are uniformly distributed along the chromosome. This choice of distributions and assumptions is shared with the existing simulation tool Plabsoft . If required, the breeding value can be masked to simulate phenotypes of a quantitative additive trait, e.g. by adding to the breeding value a random variable sampled from a normal distribution with a defined variance (see below). This function can be used to compare different breeding schemes with regard to rates of genetic gain for a defined trait, or to assess the genetic merit of specific crosses for the traits for which they have estimated marker effects, as a basis for crossing decisions.The example simulation, below, makes use of genomicSimulation’s mechanisms for scripting custom selection methods to mask the breeding values calculated by the simulation tool to make a simulated phenotype. The phenotype was simulated as The simulated phenotypes were produced by adding a draw from the distribution At any time after the initial setup command, more external genotypes can be loaded using the command load.more.genotypes, and the simulation’s optional stored set of marker effects can be substituted for another with load.different.effects. With these features, users can simulate the introduction of new genetic material into an ongoing breeding program, calculate genotypic values for multiple traits with separate sets of marker effects, and/or manually simulate environmental fluctuations across years and/or locations by using different sets of marker effects.Simulated data can be saved to tab-separated text files or pulled into the user’s R environment as vectors.A range of functions is available for simulating the production of offspring. These include cross.randomly, self.n.times, make.doubled.haploids, and cross.all.pairs. To carry out specific crosses or crossing plans, users can call cross.combinations with vectors laying out the parents to cross.Every genotype loaded or produced in genomicSimulation is allocated to a group. Mixing and separating groups allow for significant flexibility in regards to simulating multigenerational breeding pools, or having several interacting streams in the breeding program. Inbuilt (noncustom) options for group manipulation include combine.groups, break.group.into.families, break.group.into.halfsib.families, and a range of functions to randomly partition a group , alongside custom group manipulation using make.group.https://vllrs.github.io/genomicSimulationC/html/templates.html).The function select.by.gebv performs truncation selection on breeding values calculated by the internal breeding value calculator. Custom selection methods are also possible: this involves using data from see.group.data and some R scripting to identify the best individuals, followed by passing those individuals’ indexes to make.group to move them into a new “selected” group. Because of the limited list of noncustom options, most selection in genomicSimulation will involve this custom selection method interface and the relevant template script from the documentation get.top.by.phenotype <- function {  info <- data.table,                     GEBV=see.group.data)    # simulate phenotype = genotype + environmental variation  # using normally distributed Ve and heritability H^2 = Vg/(Vg + Ve)  Vg <- var(info$GEBV)  Ve <- Vg/heritability—Vg  info$Pheno <- info$GEBV + rnorm(length(info$GEBV), mean = 0, sd = sqrt(Ve))    # Select those with the top phenotype  n <- length(info$Index) * portion  return,]))}for (cyc in 1: ncycles) {  f1 <- cross.randomly    f2 <- self.n.times  f2s <- get.top.by.phenotype    f3 <- self.n.times  f3s <- get.top.by.phenotype    f4 <- self.n.times  f4.info <- see.group.data #result for plotting    delete.group)  g0 <- f4}The simulated breeding program was divided into cycles. The first stage of each cycle involved random crosses between founder inbred lines (using cross.randomly) to generate new recombinants. One selfed progeny from each recombinant was “grown,” and phenotyped at 10% broad-sense heritability for the yield trait  to mimic selection on heterozygous potentially unreplicated material early in the breeding cycle. The top 20% of the generation by phenotype (percentage arbitrarily chosen) was selected to pass on to the next generation. One selfed progeny of each of these was “grown” and phenotyped at 40% heritability for the yield trait (number arbitrarily chosen) to mimic more accurate selection later in the breeding cycle . The top 50% of that generation were selected and progressed by selfing to the last generation of the cycle. These final progeny then served as the founders of the next cycle. In this simulation, genomicSimulation’s groups were used to represent the new population of plants grown each generation, and also to pull the selected plants out of the generation’s wider generated population.Two conditions were tested in simulation:Selection across the whole set, that is, the top 20% of all the lines in that generation is to be selected.Selection within each family (the set of seeds sharing the same parent-crossed plants), that is, the top 20% of each family is to be selected.In both scenarios, the selection intensities were simulated as described above. Each simulation condition was replicated 10 times.The results of running the script were consistent with expectations. A structured durum wheat population from R packages SelectionTools v. 19.4 and ComplexHeatmap v. 2.9.3 were used to produce the heatmaps of genetic (Roger’s) distance shown in Linkage disequilibrium decay of the real NAM population is slightly lower than in the simulated population , probablThe tool was benchmarked against existing R packages MoBPS . genomicSimulation is a valuable tool to investigate the choices needed to carry out a breeding program. Various simulation tools in this area exist. Some, such as QU-GENE  and ADAMhttps://vllrs.github.io/genomicSimulationC/html/templates.html), for example, to simulate a phenotype (as in the example in this article), or recalculate and reload SNP effect values.R’s popularity in biological data analysis means it houses a range of cutting-edge genetic analysis and genomic selection packages with which users may already be familiar. Developing a breeding program simulation in base R, however, requires significant effort and planning, and may produce a slow-running tool. genomicSimulation provides an engine to carry out simulations according to breeding programs that can be scripted in R. At present, it offers an additive internal breeding value calculator, although this offering will be expanded to include nonadditive genetic effects. It is expected that users script more complex genetic evaluation methods themselves, taking advantage of the available templates/guides available in the documentation  make it a useful tool to simulate, test, and compare breeding strategies. genomicSimulation should be straightforward to install thanks to its lack of dependencies beyond C standard libraries. Documentation and use guides are available. Development is ongoing.jkac216_Supplemental_Figures_Supplementary_Material_1Click here for additional data file.jkac216_Supplementary_Material_2Click here for additional data file.jkac216_Supplementary_Material_3Click here for additional data file.jkac216_Supplementary_Material_4Click here for additional data file.jkac216_Supplementary_Material_5Click here for additional data file.jkac216_Supplementary_Material_6Click here for additional data file.jkac216_Supplementary_Material_7Click here for additional data file.jkac216_Supplementary_Material_8Click here for additional data file.