In soybean variety development and genetic improvement projects, iron deficiency chlorosis (IDC) is visually assessed as an ordinal response variable. Linear Mixed Models for Genomic Prediction (GP) have been developed, compared, and used to select continuous plant traits such as yield, height, and maturity, but can be inappropriate for ordinal traits. Generalized Linear Mixed Models have been developed for GP of ordinal response variables. However, neither approach addresses the most important questions for cultivar development and genetic improvement: How frequently are the ‘wrong’ genotypes retained, and how often are the ‘correct’ genotypes discarded? The research objective reported herein was to compare outcomes from four data modeling and six algorithmic modeling GP methods applied to IDC using decision metrics appropriate for variety development and genetic improvement projects. Appropriate metrics for decision making consist of specificity, sensitivity, precision, decision accuracy, and area under the receiver operating characteristic curve. Data modeling methods for GP included ridge regression, logistic regression, penalized logistic regression, and Bayesian generalized linear regression. Algorithmic modeling methods include Random Forest, Gradient Boosting Machine, Support Vector Machine, K-Nearest Neighbors, Naïve Bayes, and Artificial Neural Network. We found that a Support Vector Machine model provided the most specific decisions of correctly discarding IDC susceptible genotypes, while a Random Forest model resulted in the best decisions of retaining IDC tolerant genotypes, as well as the best outcomes when considering all decision metrics. Overall, the predictions from algorithmic modeling result in better decisions than from data modeling methods applied to soybean IDC. Iron deficiency chlorosis (IDC) in soybean is associated with yield losses of 340 million tons, worth an estimated $120 million per year [Initially, the effort to develop MAS methods for IDC was based on finding statistical associations between segregating molecular markers and IDC scores (QTL). After the first QTL were identified for IDC, commercial soybean breeders attempted to use marker alleles to stack desirable IDC QTL alleles but without successful outcomes. In contrast to many disease resistance QTL studies in soybean, there have been few consistently identified QTLs with large estimated genetic effects for tolerance to IDC. The exception was a single large-effect IDC QTL that explained more than 70% of the phenotypic variation in a sample of lines derived from Anoka x A7 , 6. HoweGenomic selection (GS) was developed as a high-density marker-based method to obtain genomic estimated breeding values (GEBV’s) for purposes of improving complex polygenic traits . The effThe data modeling approach uses linear or non-linear models composed of fixed and random effect parameters. Most proposed GP methods belong to the data modeling approach based on the Linear Mixed Model (LMM) framework, which assumes that the traits of interest are continuous, with normal, identically distributed random effects. A best linear unbiased prediction algorithm using ridge-regression (RR) is the most widely used LMM method for GP, largely because it has been available as the R package rrBLUP , 28. HowIn contrast to the data modeling methods, algorithmic modeling methods make no assumptions about the distribution of the response variables, linear or non-linear relationships among parameters in a model, nor about the distributions of random effect parameters. Examples of algorithmic models include Random Forest classification, Support Vector Machines, Artificial Neural Networks, and Deep Learning algorithms. Algorithmic models are typically applied to large complex data sets and have become known by plant breeders as machine learning analysis tools.gm) between "true" genotypic values g, and genotypic values predicted by marker effects, m. The true genotypic values are seldom known, except in simulation experiments or determined by validation using large numbers of replicated phenotypes. Alternatively, rgm is calculated as rpm/h, the correlation coefficient between predicted phenotypic values, p, and marker predicted genotypic values, m, divided by the square root of the estimated heritability. Prediction accuracies of quantitative traits evaluated in field trials range from -0.59 to 1.0 depending on the heritability of the trait and assumed underlying genetic architectures [gm do not address whether desired genotypes are correctly retained nor if undesirable genotypes are correctly discarded. Plant breeding is a decision-making discipline. In the context of variety development, decisions are binary: advance to the next stage or don’t advance to the next stage. Thus, in addition to estimates of rgm, decision metrics such as sensitivity, specificity, precision, decision accuracy, and the area under the receiver operating characteristic curve (defined in the Methods section below) are metrics that should be used when comparing results from analytic methods for cultivar development and genetic improvement.Most comparative studies of GP methods have been conducted on continuous traits , 34–39. tectures . HoweverHerein, we report a comparison of GP models using five metrics of relevance for plant breeding decisions in variety development and genetic improvement projects. Four methods consisting of ridge regression (RR), Bayesian Generalized Linear Regression(BGLR), Logistic regression(LR), and Penalized Logistic Regression(PLR) represent data modeling approaches. Six methods consisting of Naïve Bayes (NB), Random Forest (RF), K-nearest Neighbour (KNN), Support Vector Machine (SVM), Gradient Boost Machine (GBM), and Artificial Neural Network (ANN) represent algorithmic modeling approaches.We obtained observed IDC ordinal scores from large-scale field screening trials conducted by a commercial soybean breeding program. Depending on the prevalence of ephemeral IDC conditions, high quality IDC scores might be obtained for a few hundred to many thousands of lines at each IDC environment (combination of location and year). The observed IDC scores were obtained using visual evaluations  of 38,80i2 [Subsets of experimental genotypes and check varieties were randomly assigned to plots within each block. Since families of lines assigned to incomplete blocks were not of the same maturity, the check varieties assigned to blocks consisting of early maturing families were not the same as checks assigned to blocks consisting of late maturing families. However, since all maturity groups overlap, the check varieties between blocks of adjacent maturity groups also overlap. Thus the replicated check varieties are organized in a randomized connected block design. The IDC scores for 640 check varieties were distributed among ~18,000 blocks nested within 48 environments. Since the replicated check varieties provide information about consistency of IDC conditions among incomplete blocks within and among environments, the IDC values for the checks were used to obtain BLUP values of block effects . These Bi2 , of IDC i2 = σ2g/σ2p) on an entry mean basis and obtain an IDC BLUP value for each line (see below). Additional IDC field evaluations for these 1000 lines were used by a group of commercial soybean breeders to determine and validate a “true decision” about the tolerance of the line to IDC conditions. The 1000 IDC_BLUP values and true_decision outcomes were merged with genotypic data for the same set of lines  and since sets of check varieties assigned to each block overlapped but were not identical among all blocks, best linear unbiased predicted values for block effects were obtained using an LMM:C represents an incidence matrix for the check varieties, κ is the vector of unknown fixed effects represented by the check varieties, B is an incidence matrix indicating whether the IDC value was obtained from a block, π is the vector of unknown random effects represented by each block, I is the identity matrix, σ2blk is the variance among blocks, ε represents residual values not accounted for in the model, and σ2res is the variance among residual values. BLUP values for block effects were obtained using the lme4 package in R [IDC represents a vector of 20,606 IDC scores for 640 check varieties evaluated in 3499 blocks in 48 environments , C repreage in R , 43.IDC is a vector of 4,171 observed IDC scores for 1000 experimental lines evaluated in 40 of 48 environments  used in the training sets , Z is a 900x1200 matrix consisting of zij elements indicating whether the alleles at the jth SNP locus for the ith line is homozygous for a reference genotype (= 1), heterozygous (= 0) or homozygous for an allele that is not from the reference genotype (= -1). The vector of unknown random effects that need to be predicted from the 1200 SNP markers is designated as m, I is a 900 x 900 identity matrix, σ2m is the genotypic variance for the markers, ε represents the 900 x 1 vector of residual values, and σ2res is the residual variance. Note that with high densities of molecular markers, absence of major QTL effects, and QTL distributed uniformly across the genome, Habier et al [ing sets , 1 is a er et al  showed ter et al  was usedues from  used in ηc is the link for the cth category, γc is the threshold of the cth link for the combinations of fixed, β, and random, υ, effects while X and Z are the incidence matrices for fixed and random effects. For our purposes, rather than utilizing BLUP values of block effects to represent variability among environments as a continuous fixed effect, we modeled environments as categorical fixed effects [ηc, denoted ηcge, are the links for the cth category of the gth genotype (line) evaluated in the eth environment. The cumulative probit was used as the link function for 900 genotypes replicated k times in e environments of the training sets  to an underlying continuous normal variable . The res effects . The eleing sets . BecauseBased on prior work by Montesinos-López et al  we chosei = 1, i = 1,2,3…1000) is distributed as a binomial random variable. As a consequence, the logit link function ηi = logit (πi) = log(πi /(1-πi)) [η = Xβ + ε, is built using a stepwise regression algorithm with significant genetic markers trait associations serving the role of explanatory variables. Estimated genetic effects of significant marker trait associations were used to obtain GBLUP decision values that were compared with “true_decisions” using five decision metrics (described below). An LR model was built with the R base package "stats," where the maximal number of reweighted least squares iterations was set to 50. The R code can be found in "Because true decisions about IDC tolerance for the lines exist, this information, instead of the observed ordinal IDC scores, were used to develop a genomic prediction model. The binary response variable with two possible values can be viewed as a special case of the threshold model (4). The probability (π) of the true_IDC response variable = 1 can vary between 0 and 1 and the probability that line i = 1, (π/(1-πi))  is appliη = 1μ + Zm + ε. As with the RR-BLUP method, the ridge regression method of PLR shrinks the estimated marker effects [Since the number of markers is greater than the number of lines, an alternative to LR is penalized logistic regression for binary traits:  effects , which a effects . The R cK represents a vector of known values for the response variable, m represents the matrix of genetic marker genotypic values, A represents the algorithm that finds patterns in m that are associated with K to produce predicted values P based on the patterns of m. Herein we use K consisting of 1000 true_decision outcomes (A(m).Unlike data modeling methods, there are no assumptions about the distribution of response variables, nor are there any specified parameters with assumed distributions. Rather the predictor models are better represented as black box models:outcomes  and six In the context of GP for decision making, ANNs model the relationship between a set of marker scores and true_decision scores that is analogous to an understanding of how a biological brain responds to stimuli from sensory inputs. ANN can be applied to solve complex but unknown relationships between variables and non-linear relationship between predictive and response variables . ANN modRandom Forest (RF) is an ensemble learning method for classification, regression, and feature selection that operates by computationally constructing multiple decision trees based on the set of molecular marker scores that could result in the IDC true_decision outcomes using a training set. After constructing the trees, the RF method will determine the mode of the classes (classification) or mean prediction among all possible decision trees. Random forests can handle large datasets, where the so-called "curse of dimensionality" might cause other models to fail. RF can extract important features and their patterns associated with the trait of interest with high prediction accuracy [accuracy  and tendaccuracy . Random Gradient Boosting Machine (GBM) is a technique for classification based on decision trees. It allows iterative boosts to the performance of weak predictors to attain the performance of stronger predictors to improve the overall prediction accuracy [accuracy . GBM hasaccuracy , 60. GBMaccuracy  with maxSupport Vector Machines (SVM) are based on the concept of creating a computational boundary between multi-dimensional points of data and their feature values [e values . In the e values . The SVMe values . SVM anaNaive Bayes (NB) methods consist of a family of simple probabilistic classifiers based on applying Bayes’ theorem with strong independence assumptions among the features. NB Classifiers using naïve Bayes methods utilize training data to calculate an observed probability of each outcome based on the evidence provided by feature values [e values . NB is bK-nearest neighbors (KNNs) is known as an instance-based learning method, where the function of minimizing the mismatches is only approximated locally [ locally , 65. KNN locally  with thePrediction accuracies as well as Sensitivity, Specificity, Precision, Decision Accuracy, and AUC were estimated for each of the 10-fold cross-validation outcomes for all 10 models.pm/h, the correlation coefficient [GBLUP and E-GBLUP genotypic values produced by RR and BGLR respectively were compared with the BLUP IDC values derived from the observed_IDC scores using rfficient  between fficient .Sensitivity is the estimated frequency of lines that are truly tolerant to IDC [t to IDC . SensitiSpecificity is the estimated frequency of lines that are correctly identified as susceptible to IDC [e to IDC . SpecifiPrecision is used to evaluate the ability to identify truly tolerant IDC lines from among a group consisting of both truly tolerant lines and falsely identified tolerant lines. The higher precision (closer to 1), the lower risk of advancing lines susceptible to IDC.Decision accuracy is the proportion of true positives and true negatives among all lines.The receiver operating characteristic (ROC) curve is generated by plotting the true positive rate (TPR) on the y-axis against the false positive rate (FPR) on the x-axis at various decision threshold settings. The ROC curve is plotted for all FPR values between 0 to 1. The area under the ROC curve (AUC) is used to estimate the model’s stability for making correct decisions about retaining and discarding lines. AUC values closer to 1 indicate that the model provides stable decision-making outcomes [outcomes , 70. Theoutcomes . The R cBecause these metrics have unknown distributions, the methods were ranked according to the metric values for each of the 10-fold cross-validation data sets and the Kruskal Wallis test was applied to the rankings. The Kruskal–Wallis test is a non-parametric method for testing whether the metrics’ median values are derived from the same distribution of outcomes based on Fisher’s least significant difference. The Kruskal–Wallis test was computed with R package "agricolae" where the significance level parameter was set to 0.05 see ".In addition to comparing GP methods using each of the individual metrics, we combined rankings of decision accuracy and AUC for a combined assessment of the stability of correct decisions among all 10 methods using the Kruskal Wallis test. We also assessed patterns among the 10-fold cross validations for all five metrics using principal components analysis (PCA). PCA provided a method to identify correlated responses among the metrics as principal components for classification and visualization. The PCA was implemented with the "prcomp" function and parameter scale = True from R Package "stats". The resulting clusters based on the separation of PCAs were visualized with "pca3d". The R code can be found in, "The estimated reliability of IDC scores for the 1000 lines is 0.77. The prediction accuracy for the RR generated GBLUP genotypic values across 10 fold cross-validation sets is 0.73/0.88 = 0.83. The prediction accuracy for E-GBLUP genotypic values across 10 fold validation sets is 0.56/0.88 = 0.64.From the perspectives of variety development, the more critical question is whether decisions based on genomic prediction models will be correct. If the LR method is used to build a model consisting of significant marker associations to predict the true decisions, then all of the decision metrics are about 0.5, indicating that results from the decisions using the LR method are not much better than tossing a coin .The sensitivity of the LR, BGLR, and SVM methods is not very good, indicating that these methods have a tendency to incorrectly identify tolerant lines as susceptible to IDC. The most sensitive decisions were made using either the RF or GBM algorithmic modeling methods. While the sensitivity of the ANN method was not as good as for RF and GBM, it was better than any of the data modeling methods. The specificity of decisions had reasonably high values for all data modeling methods except LR. The SVM and RF methods provided the most specific decisions, indicating that they are best at identifying susceptible lines without incorrectly identifying susceptible lines as tolerant to IDC. The most precise methods included the SVM and RF methods. The SVM method will result in retaining IDC tolerant lines with almost no risk of incorrectly retaining false positive IDC lines. The RF and GBM methods had the best ability to correctly identify both susceptible and tolerant lines, i.e., they are the most accurate methods. According to the AUC values, RF, GBM and ANN are methods that produce the most stable decisions about classifying lines as either tolerant or susceptible. While the PLR and BGLR data modeling methods did not produce the most sensitive, specific, precise or accurate decisions, these two methods produced consistent decisions.Overall, the RF method resulted in among the best values for sensitivity, specificity, precision, accuracy, and AUC  and 3, iThe remaining three algorithmic models generally did better than the data modeling methods, but not uniformly. For example, the sensitivity of identifying truly tolerant lines among the lines using the SVM was 0.61, much less than RR and indicating that almost 40% of the truly tolerant lines would be mis-classified as susceptible using the SVM model. In contrast, the estimated specificity of the SVM indicates that only three to four percent of the truly susceptible lines would be incorrectly identified as tolerant . The difThe boxplots associated with the Kruskal Wallace tests applied to Decision Accuracies  indicateThe boxplots associated with the Kruskal Wallace tests applied to AUC affirm that KNN and LR have small AUC values, indicating both models are unable to provide stable decisions about which lines are tolerant which are susceptible to IDC Figs  and 5. TResults from the comparisons between pairs of AUC values indicate that LR and KNN are similar  and, more importantly, validated decisions about IDC tolerance and susceptibility. We then evaluated four data modeling and six algorithmic GP methods using metrics that quantify correct and incorrect decisions relative to the validated decisions.Herein, we recognize that plant breeding is a decision-making discipline, so the metrics used to make decisions should reflect genetic improvement and variety development objectives. This led to a question about whether IDC ordinal scores are needed to predicting accurate binary decisions using established GP methods. To answer the question, we utilized high-quality experimental data  provided better predictions than Bayesian logistic ordinal regression (Bayes A and Bayesian LASSO) for pig scrotal hernia disease . NotablyAs a practical matter, it difficult to recommend a single algorithmic modeling method. The RF model provides the best trade-off among all decision metrics Tables  and 2, bWe only investigated one of several possible LMMs, one of many possible multinomial threshold models, and one of many possible PLR models . Thus, iAs Stroup  pointed S1 File(R)Click here for additional data file.S2 File(R)Click here for additional data file.S3 File(R)Click here for additional data file.S4 File(RMD)Click here for additional data file.S5 File(R)Click here for additional data file.S6 File(R)Click here for additional data file.S7 File(R)Click here for additional data file.S8 File(CSV)Click here for additional data file.S9 File(CSV)Click here for additional data file.S10 File(CSV)Click here for additional data file.


This study was carried out using 22 promising restorer lines of rice and their parental lines to study genetic variability and genetic advance for yield and yield-associated grain quality traits and floral traits. These genotypes are evaluated in a replicated trial using Randomized Complete Block Design (RCBD) with three replications at the Experimental Farm of Sakha Agricultural Research Station, Sakha, Kafr El-Sheikh, Egypt, during the seasons from 2012 to 2020. Analysis of variance revealed that highly significant variations were observed among the genotypes for all the studied characters. Both GCV% and PCV% were high for the number of spikelets per panicle, the number of filled grains per panicle, and panicle weight. The genetic advance in the percentage of mean was high for days to plant height, panicle length, number of spikelets per panicle, number of filled grains per panicle, panicle weight, grain yield per plant, anther length, anther breadth, duration of floret opening, and head rice percentage. Mean performance of the rice genotypes indicated that the genotypes NRL 59, NRL 55, NRL 62, NRL 63, NRL 66, and NRL 54-2 were promising for grain yield and associated desirable traits. Thus, some of these promising lines can be promoted as a new rice variety and could be used as a source for developing new hybrid combinations in hybrid rice breeding programs. The percentage of advantage over better parent and Giza 178 as the commercial variety was significant and there were highly significant desirable values among the genotypes for all the studied traits in the two years, indicating that the selection is effective in the genetic improvements for these traits. Rf) gene. This is found in lead rice-type CMS, discovered in the indica variety ‘Lead Rice;’ the fertility of the CMS plant is restored by the single nuclear-encoded gene Rf2 in a gametophytic manner. Researchers have performed the map-based cloning of Rf2, and proved that it encodes a protein consisting of 152 amino acids with a glycine-rich domain. The expression of Rf2 mRNA was detected in developing and mature anthers. An RF2–GFP fusion was shown to be targeted to mitochondria. Replacement of isoleucine by threonine at amino acid 78 of the RF2 protein was considered to be the cause of functional loss in the rf2 allele. As Rf2 does not encode a pentatricopeptide repeat protein, unlike a majority of previously identified Rf genes, the data from this study provide new insights into the mechanism for restoring fertility in CMS  was calculated by the formula given by [The genotypic and phenotypic variances were calculated as per the formulae proposed by . The gengiven by  as suggegiven by . From thgiven by . Mean squares were used to estimate:2ph = σ2e + σ2g, where broad-sense heritability (h2bs) was estimated as follows:σ2bs = (σ2g/σ2ph) × 100 and the phenotypic (PCV) and genotypic (GCV) coefficients of variation were computed as follows:hExpected genetic advance (GA): Expected genetic advance from direct selection for all studied traits was calculated according to  as folloRef.  categoriModerate = 10–20% and high = >20%.2bs estimates were categorized as:As suggested by , h2bs esThe advantage over better parent was calculated as percentage of the newly developed restorer lines over its better parent (BP) .The adThe advantage over the high yielding commercial variety calculated as percentage of increased or decreased of the newly restorer lines over the commercial one (CK) .The adAppropriate LSD values were calculated to test the significance of the advantage over commercial variety, and the advantage over better-parent, according the method suggested by :LSD fort: Value at certain probability level and given degrees of freedom for error.MSe: Error mean squares from the analysis of variance. r: Number of replications.B.P: The better parent mean in the newly developed restorer lines.A set of 252 iso-cytoplasmic restorer lines of rice were developed from six promising rice hybrids in Egypt. Out of 252 is cytoplasmic restorer lines, 94 lines based on their phenotypic and yield acceptability were selected for crosses with two cytoplasmic male sterile lines . Out of Pollen fertility and spikelet fertility for hybrid combinations as shown in . The polIn contrast, the spikelet fertility percentage of tested hybrids varied from 87.08% (IR69625A × NRL 65) to 95.12% (G 46A × NRL 62).The newly developed selected restorer lines have better performance in all studied traits . For theThe genotypes NRL 52, NRL 72, NRL 67, and NRL 74 showed highly spikelet fertility . ConcernFor the mean performances of floral traits studied, i.e., anther length (mm), anther breadth (mm), filament length (mm), and duration of floret opening (min) of all the studied genotypes For hulling percentage, the genotype NRL 52 showed the highest mean values in the first and second seasons. Concerning milling percentage, the genotype NRL 55 showed the highest mean values in the two years. For head rice percentage, the rice genotypes NRL 46 and NRL 59 recorded the highest mean values in the second and first season .All developed lines were resistant to both blast disease and the rice-stem borer insect .Analysis of variance and CV% are shown in The genotypic and phenotypic coefficient of variation in different traits maintained correspondence for all the traits under study. In general, the phenotypic coefficient of variation was higher than the genotypic coefficient of variation. The results revealed a high estimate of phenotypic and genotypic coefficient of variation for spikelets per panicle, number of filled grains per panicle, and panicle weight (g) . A moderThe genetic advance in percentage (expected) of the mean was high for plant height, number of panicles per plant, number of spikelets per panicle, number of filled grains per panicle, panicle weight, grain yield per plant, anther length, anther breadth, duration of floret opening, and head rice percentage . MoreoveIn this study, the traits expressed moderate to high heritability estimates ranging from 66.19 to 99.41 and 48.84 to 99.40 percent in the first and second years, respectively. High heritability was observed for the traits days to 50% heading, plant height, panicle length, number of spikelets per panicle, number of filled grains per panicle, panicle weight, 1000-grain weight, grain yield per plant, anther length, anther breadth, filament length, duration of floret opening, and head rice percentage . ModeratThe data in Regarding number of panicles/plant, 13 newly developed restorer lines showed significant and highly significant estimates values in both years. Concerning panicle length, 13 new restore lines gave significant or highly estimated values in the first and second years. For the number of spikelets per panicle, 12 newly developed restorer lines showed significant and high estimate values in the first and second year over better parent. The results revealed that 12 lines showed significant and highly significant positive estimate advantages over Giza 178 as a commercial restorer line; see For panicle weight, the newly developed restorer lines showed significant or highly significant positive estimate advantages over Giza 178 as a commercial restorer line; see Regarding grain yield, the data showed that most of the lines (19 lines) showed significant and highly significant positive estimate advantages over better parent as well over Giza 178 as a commercial variety . Most ofOn the contrary, some new restorer lines showed significant and highly significant positive estimate advantages over better parent as well as over Giza 178 as a commercial variety, for hulling percentage, milling percentage, and head rice percentage  and FiguRf1 gene on chromosome 1. All studied materials including parental lines  have the allele of Rf1. Rice microsatellite RM3425 was used to detect the Rf3 gene in the tested lines. The results show that the RM3425 marker showed inconstant data with the known restorer lines. The rice microsatellite RM 171 marker is known to be linked with the Rf4 gene on chromosome 10 in WA CMS lines. The data revealed a band detected by RM 171 marker in studied genotypes, which suggests that these lines may have the allele of the Rf4 gene.Awad-Allah 2006 and 2011 ,18 repor2 started in 2013 and went from F2 up to F7 in 2019. Promising lines were selected and grown along with the parental lines and evaluated for phenotypic performance and yielding ability during 2019 and 2020 rice seasons. The newly developed restorer lines contain restorer genes from the parents. Grain yield is the most important trait for comparing the performance of restorer lines as it reflects the performance of all component traits. Most restorer lines (21 lines) were found to possess a grain yield higher than the check variety. As iso-cytoplasmic restorer lines are derived from rice hybrids by continued selfing and selection, they all possess the sterile cytoplasm from the female line (CMS). Wild abortive cytoplasm (WA) and Gambica cytoplasm have a significant effect on panicle excretion; it is observed that lines resulting from CMS as female parents show incomplete panicle excretion [Based on the results of molecular analysis and field analysis from Awad-Allah 2006 and 2011 ,18, the xcretion . Thus, vxcretion . SpikeleIso-cytoplasmic restorers, based on their performances, were identified. The identified lines can be screened for the presence of fertility genes and their fertility restoration behavior by crossing with CMS lines . The autThis method was efficient in developing a promising is cytoplasmic restorer lines from a highly diverse set of 390 iso-cytoplasmic restorers derived from diverse rice hybrids, ensuring coverage of phenotypic traits without the comparison of relative characteristics within the genotypes. The core set of iso-cytoplasmic restorers can be used for further improvement of restorers while the promising iso-cytoplasmic restorer lines can help in developing heterotic hybrids when crossed with diverse non-parental CMS lines .For effective restoration, restorers having synchronized flowering duration as male sterile lines are required to obtain a higher seed yield, because this depends on the amount of pollen supplied from the male parent during the flowering period . Thus, lConcerning grain yield and agronomic traits, the genotypes NRL 59, NRL 55, NRL 62, NRL 63, and NRL 66 gave high grain yields and desirable values of other traits . Thus, sThe anther filament starts to elongate rapidly after the opening of the spikelet, which increases the possibility of outcrossing in the fields of hybrid rice seed production . An incrWith regard to grain quality traits, the genotypes NRL 52, NRL 59, NRL 55, and NRL 46 were recorded with one or more desirable traits . TherefoGiza 178 is moderately tolerant to the stem borer insect at its ripening phase. While the newly developed restorer lines showed tolerance to the insect at the ripening phase, in general the striped stem borer causes significant losses for susceptible genotypes, along with blast .A test cross nursery to identify restorer and maintainer in hybrid rice breeding is the first step for developing new hybrids. Pollen fertility and spikelet fertility analysis is used to identify restorer and maintainer lines . Pollen Analysis of variance showed highly significant differences between all genotypes for all the studied traits , which iGenetic variability is a prerequisite for the selection of superior genotypes over the existing cultivars. Estimates of PCV%, GCV%, and genetic advances as revealed from the results indicate that there is a large amount of variability among the genotypes of all the traits studied. In general, the phenotypic coefficient of variation was higher than the genotypic coefficient of variation suggesting the influence of environment on the expression of these traits. A similar observation has also been noted by ,60. The results of this study revealed that high values, moderate values, and lower values were estimated for phenotypic and genotypic coefficient of variation for the studied traits, but with a narrow difference between phenotypic and genotypic coefficients of variation for all studied traits . This suThe genetic advance is a useful indicator of the progress that can be expected as a result of selection in the related population. Heritability in conjunction with genetic advances would give a more reliable index of selection value . High PCHigh heritability with high genetic advance as a percentage of the mean indicates that these characters are largely controlled by additive gene action, which indicates that improvement in these characters is possible through mass selection and progeny selection. High heritability with low genetic advance was observed for spikelet fertility percentage, hulling percentage, and milling percentage. This suggests non-additive gene action for the expressions of these traits. The high heritability was exhibited due to the favorable influence of environment rather than genotype and selection for such traits might not be rewarding.The estimates of heritability as a predictor are a tool in expressing the reliability of phenotypic value. Therefore, high heritability helps in effective selection for a particular trait. In this study, the traits expressed moderate to high heritability estimates ranging from 66.19 to 99.41% and 48.84 to 99.40% in the first and second years, respectively . High heModerate heritability was observed in the number of panicles per plant, spikelet fertility percentage, hulling percentage, and milling percentage in the second year , SimilarHigh heritability values indicate that the traits under study are less affected by the environment in their expression. Therefore, a plant breeder may safely select them based on the phenotypic expression of these traits in the individual plant by adopting simple selection methods.The data showed that the percentage of advantage over better parent and Giza 178 as the commercial variety was significant and highly significant among the genotypes for all the traits studied in the two years, indicating that the selection is effective in the genetic improvements for these traits . For dayMoreover, most of the newly developed restorer lines showed significant and highly significant positive estimates over Giza 178 as a commercial restorer line for anther length, anther breadth , filamenThe presence of genetic variability is a basic requirement in any crop improvement program. In conclusion, there are significant differences between the genotypes of all the traits under study. This indicates that there is broad scope for the selection of promising genotypes from the present set of genotypes for yield, components, floral traits, and some grain quality traits improvement. The new restorer lines NRL 59, NRL 55, NRL 62, NRL 63, NRL 66, and NRL 54-2 were promising for the grain yield and other traits. Thus, some of these genotypes can be used as a new rice variety plus a source for developing new hybrid combinations in rice breeding programs. The magnitude of the phenotypic coefficient of variation was higher than the genotypic coefficient of variation for all the traits under study. Both GCV% and PCV% were high for spikelets per panicle, the number of filled grains per panicle, and panicle weight. The genetic advance in the percentage of mean was high for plant height, number of panicle per plant, panicle length, number of spikelets per panicle, number of filled grains per panicle, panicle weight, grain yield per plant, anther length, anther breadth, duration of floret opening, and head rice percentage. The percentage of advantage over better parent and the Giza 178 commercial variety was significant and highly significant, and desirable values were found among the genotypes for all the traits studied in the two years, indicating that the selection is effective in the genetic improvements for these traits. The 22 promising restorer lines with good floral traits were identified as effective restorers for two CMS lines, IR69625A and Gang46A. The high fertility of these hybrids can be usefully used in developing promising hybrid combinations.


Vigna radiata (L.) Wilczek] is a drought-tolerant, short-duration crop, and a rich source of protein and other valuable minerals, vitamins, and antioxidants. The main objectives of this research were (1) to study the root traits related with the phenotypic and genetic diversity of 375 mung bean genotypes of the Iowa (IA) diversity panel and (2) to conduct genome-wide association studies of root-related traits using the Automated Root Image Analysis (ARIA) software. We collected over 9,000 digital images at three-time points . A broad sense heritability for days 15 (0.22–0.73) and 18 (0.23–0.87) was higher than that for day 12 (0.24–0.51). We also reported root ideotype classification, i.e., PI425425 (India), PI425045 (Philippines), PI425551 (Korea), PI264686 (Philippines), and PI425085 (Sri Lanka) that emerged as the top five in the topsoil foraging category, while PI425594 (unknown origin), PI425599 (Thailand), PI425610 (Afghanistan), PI425485 (India), and AVMU0201 (Taiwan) were top five in the drought-tolerant and nutrient uptake “steep, cheap, and deep” ideotype. We identified promising genotypes that can help diversify the gene pool of mung bean breeding stocks and will be useful for further field testing. Using association studies, we identified markers showing significant associations with the lateral root angle (LRA) on chromosomes 2, 6, 7, and 11, length distribution (LED) on chromosome 8, and total root length-growth rate (TRL_GR), volume (VOL), and total dry weight (TDW) on chromosomes 3 and 5. We discussed genes that are potential candidates from these regions. We reported beta-galactosidase 3 associated with the LRA, which has previously been implicated in the adventitious root development via transcriptomic studies in mung bean. Results from this work on the phenotypic characterization, root-based ideotype categories, and significant molecular markers associated with important traits will be useful for the marker-assisted selection and mung bean improvement through breeding.Mung bean [ Lens culinaris), horse beans (Dolichos spp.), lupins , common beans (Phaseolus vulgaris), chickpea (Cicer arietinum), field peas (Pisum sativum), cowpeas (Vigna unguiculata), fava beans (Vicia faba), mung bean [Vigna radiata (L.) Wilczek], urd beans (Vigna mungo), and food-grade soybeans (Glycine max) have consistently been used as protein sources in the global south , initially domesticated in India, is now cultivated in over 7 million hectares worldwide (Mung bean (orldwide . Mung beorldwide . Mung beorldwide . Mung beorldwide . Mung beorldwide . The comorldwide . The demorldwide . Howeverorldwide .Root system architecture (RSA) can be defined as the morphology of the root system at many scales, both global  and local , as well as the spatial variability of the morphology . The morUpper). The steep, cheap, and deep ideotype contained a deeper primary root length (PRL), faster TRL_GR, steep lateral root angles (LRA), and lower solidity traits (SOL2). These works have been possible due to the use of computer vision and machine learning in extraction of complex traits.The desired root phenotypes by plant breeders will be ones that enhances plant adaptation to the edaphic stress while maintaining or increasing yields, for example, deeper and proliferating roots are desired during water-deficient stresses in the changing climate . Lynch aAdvances in computer vision, machine learning, and high-throughput phenotyping (HTP) technologies, coupled with efficient statistical methods and collaborative research, have opened the way for more research to be carried out in plants as reviewed in Previous methods developed for extracting roots in the field include destructive methods such as “shovelomics” , the useGenome-wide association studies (GWAS) is a statistical tool that uses historical recombination events to uncover the significant genotypic variation associated with the phenotypic variation for the trait of interest . GWAS haThe current study was conducted with the objectives to (1) study the diversity of the RSA trait in the Iowa (IA) mung bean panel, (2) contextualize these RSA traits with root-based ideotypes, and (3) conduct GWAS on RSA traits and identify candidate genes for these associations.A total of 376 accessions were used in this study. A total of 372 Plant Introductions (PI) were filtered from the 482 IA mung bean panel  using th2 and constantly monitored using the LI-250A photometer . On the 5th day of germination, a representative sample for each genotype was picked and carefully placed onto the 12″ × 18″ blue germination paper . Labeled bar-coded tags are stapled onto the 1″ folded top of the blue paper. A 12″ × 16″ brown blotting paper  was carefully placed on top of the blue paper. Two full blue papers are clipped together using binder clips and placed in the rack on the plot number in the chamber. Chamber conditions were monitored daily.This study used a randomized incomplete block design, with each growth chamber serving as a replicate, for a total of eight replications for the experiment. Two growth chambers were used for an increased throughput. Each chamber had four blocks. Each block contained six complete and two incomplete sub-blocks. Each complete sub-block held twelve genotypes, while each incomplete sub-block held eleven genotypes. The genotypes were randomized within each block and sub-blocks. Randomization was generated using the R package blocksdesign . The pro2 and stored for weighing. Each root and shoot of the genotype were measured using the Ohaus portable weighing balance .A high-throughput imaging station was set up similar to the one reported by More than 9,000 (376 genotypes × 8 reps × 3 time points) images were collected from the whole experiment. The images were first rotated manually to portrait orientation to enable consistent preprocessing. Images with no germinated seed, herein referred to as blank, were excluded from processing. All the analyses were carried out using the R statistical software . A separYijkl is the phenotypic trait, Chamberi is the fixed effect of the ith growth chamber (1| Chamber:Block)ij is the random interaction effect between the ith chamber and the jth block (1| Chamber:Block:Sub-block)ijk is the three way random interaction effect between the ith chamber, jth block and kth sub-block (1| Genotype)l is the random effect of the lth genotype, and eijkl is the random error term following the N.Where μ is the overall population mean, Where c effect .Upper. The sum of the ranks was ranked, and this yielded to the final ranking of each genotype. A similar approach was used for the “steep, cheap, and deep” ideotype using the TRL_GR, steep LRA, and SOL2.The iRoots were formed by first ranking the genotypes under each trait, getting the sum of the ranks and then ranking the sums for each category. For topsoil foraging, the genotypes were ranked individually under the TRL_GR, WID, and TRLThe principal component analysis (PCA) and hierarchical clustering were used in both the phenotypic and genotypic clustering of the genotypes using the Euclidean distance matrix. The base R function hclust with methods “complete” and “prcomp” was used. The package factoextra  was usedIn total, 26,550 SNPs (marker data) were obtained using genotype-by-sequencing and preprocessed earlier by Upper, and CVA that had SD < 100, while RHZO had an SD > 500. TRL, TRLUpper, CVA, WID, NWA, RHZO, and TRL_GR had 10% < CV < 20%, while the rest of the traits had CV < 10% (We observed the significant phenotypic variability for root traits. The coefficient of variation (CV) ranged from 2 to 19% and standard deviation (SD) from 0.01 to 628.67 (different units of measurements for traits). Most of the traits had low SD, i.e., <10 except for TRL, VOL, TRLCV < 10% . Day 12 CV < 10% .Upper was highly correlated with WID, CVA, and TRL_GR. NWA was highly correlated with WID, CVA, TRL_GR, TRLUpper, RHIZO, and TRL. LRA had the lowest correlation with the other traits. There was no correlation between LRA and VOL, and DIA  showing higher levels at 0.84, 0.87, and 087, respectively , PI425045 (Philippines), PI425551 (Korea), PI264686 (Philippines), and PI425085 (Sri Lanka) emerged as top five in the topsoil foraging category. PI425594 (unknown origin), PI425599 (Thailand), PI425610 (Afghanistan), PI425485 (India), and AVMU0201 (Taiwan) were top five in the “steep, cheap, and deep” ideotype . For dayThree distinct phenotypic clusters were observed using the root trait data, while two clusters were observed from the SNP data of the genotypes . PhenotyUpper, RHZO, NWA, WID, and CVA, were in genotypic cluster 2, while cluster 1 was dominated by low values in the above traits. LRA, SOL2, LED, LRB, PRL, and DEP looked evenly distributed within genotypic clusters 1 and 2 . Marker 2_19972687 is located within an exon encoding a gene (-)-germacrene D synthase-like (LOC106753988). Marker 7_19972687 is located within an exon of the beta-galactosidase 3 gene (LOC106768494). Marker 11_7608353 associated with LRA also located within an exon for a gene described as protein FAR1-RELATED SEQUENCE 5 (LOC106776541). The same significant SNP marker 8_11481602 associated with LED from days 15 and 18 was found within the exon of a monodehydroascorbate reductase gene (LOC106772343). Day 18 SNP marker 8_10447903 was found within an intron for an uncharacterized gene but close to the LOC106772343 gene. Day 18 SNP marker 3_10004492 associated with TRL_GR is located within the exon for a gene coding for mannose-1-phosphate guanylyltransferase 1 (LOC106757974). Day 18 SNP maker 5_35265704 associated with TDW is located in the exon for a gene described as putative dehydration-responsive element-binding protein 2H (LOC106760865).Upper (2_22583526) was found within an exon in the gene encoding coilin (LOC106756657), while marker for DEP (5_23119832) was found in an exon within the gene encoding expansin-A11 (LOC106761944).Selection of variables with embedded screening resulted in several significant markers for most of the traits across the 3 days . Two marin situ environments  plant. They have also been identified as major enzymes in cell wall lignification and found in large quantities in the xylem tissue  mutants of fhy3 were less sensitive to both osmotic and salinity stress while also reducing the ABA-dependent inhibition of seedling root elongation, seedling greening, and germination - Gem tissue . (-)- Geung bean . In riceung bean . Beta-gamination .Festuca arundinaceaSchreb.cv. “K-31”) when exposed to water stress (Arabidopsis CYT1 gene encoding mannose-1-phosphate guanylyltransferase 1 associated with LOC106757974 (day 18 TRL_GR) showed deficiency in the cell wall after depletion of GDP mannose. The mutants exhibited radial swelling and accumulation of callose at the root tip. The functional analysis revealed mannose-1-phosphate guanylyltransferase 1 is involved in N-glycosylation during the cellulose synthesis , an antioxidant enzyme, were significantly reduced in the root elongation zone when roots for tall fescue  and LOC106761944 (day 18 DEP) were associated with the adventitious root development in mung bean like the TASSEL results . Coilin The high H among the dry weight measurements can be used in the selection of parents with the root to shoot ratio (RSR) previously used as a measure of the photosynthetic materials allocations . During Our study has elucidated the phenotypic and genotypic variability for the root traits in the 375 genotypes in the IA mung bean panel. We identified candidate genotypes that can now be advanced to the greenhouse or field for further testing, especially for the root ideotypes. If their trait response and expression can be confirmed, these can be utilized as parents in the breeding program. Using GWAS, we identified significant markers associated with several RSA traits. Taken together, the ideotypes after field evaluation and significant markers can be utilized as tools for marker-assisted selection and crop improvement in mung bean breeding programs.https://github.com/yalek/mungbeanpaper.git. Raw data is provided in the 10.5061/dryad.wdbrv15mb. Images can be provided upon request.A subset of the dataset and the R scripts used in the analysis can be found in the github account mungbeanpaper, KC and AS conceptualized and designed the experiments. KC performed data collection with the assistance of student workers, did the image preprocessing, data analysis, and wrote the first draft manuscript with feedback from AS and SC on GWAS, and SD and AS on phenotypic analysis. TJ helped with image preprocessing. BG provided feedback on root imaging software. AS provided overall leadership on the project. All authors revised and approved the submitted manuscript.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.


Pleiotropy  leads to genetic correlations between traits and contributes to the development of many syndromes. Identifying variants with pleiotropic effects on multiple health-related traits can improve the biological understanding of gene action and disease etiology, and can help to advance disease-risk prediction. Sequential testing is a powerful approach for mapping genes with pleiotropic effects. However, the existing methods and the available software do not scale to analyses involving millions of SNPs and large datasets. This has limited the adoption of sequential testing for pleiotropy mapping at large scale. In this study, we present a sequential test and software that can be used to test pleiotropy in large systems of traits with biobank-sized data. Using simulations, we show that the methods implemented in the software are powerful and have adequate type-I error rate control. To demonstrate the use of the methods and software, we present a whole-genome scan in search of loci with pleiotropic effects on seven traits related to metabolic syndrome (MetS) using UK-Biobank data (n~300 K distantly related white European participants). We found abundant pleiotropy and report 170, 44, and 18 genomic regions harboring SNPs with pleiotropic effects in at least two, three, and four of the seven traits, respectively. We validate our results using previous studies documented in the GWAS-catalog and using data from GTEx. Our results confirm previously reported loci and lead to several novel discoveries that link MetS-related traits through plausible biological pathways. Many human diseases  cluster into syndromes. Evidence from quantitative genetic studies , 2 and fTo confront the challenges posed by the analysis of systems of many traits, some authors considered using phenotype-derived principal components (PCs) as traits in GWA analyses. For example, this approach has been used to identify variants associated with patterns shared across multiple MetS-related traits ; howeverp traits in the system. This sequential test controls the type-I error rate while offering the power of multi-trait models. The methodology developed by Schaid et al. has several attractive features. Firstly, the test uses all the information available, without relying on dimension reduction techniques such as PCs. Secondly, the approach groups the possible pleiotropic configurations into a few meaningful states where the locus affects at least q trait(s) in the system , thus facilitating interpretability. Finally, the p values for each of these states are derived using a well-established intersection-union test that guarantees adequate type I error control, regardless of the complexity of the system [Schaid et al.  proposede system . Howevere system  that impTherefore, in this study, we developed an approximation to the sLRT that achieves the same power and error-control performance but is orders of magnitude faster to compute. Our approach (pleiotest) succeeds at evaluating the sequential test faster by using three main strategies: (i) instead of using a likelihood ratio test with decorrelated data, we use Wald’s test  with a sn > 300 K; K = 1000). Finally, to demonstrate the use of pleiotest to map pleiotropic loci with big data, we applied the sequential testing to seven traits related to metabolic syndrome (MetS), using a dataset of distantly related white-European individuals from the UK-Biobank (n = ~316 K and ~624 K variants).In this study we describe the methodology implemented in pleiotest, present extensive simulations that show that the proposed approximation has the same power and error control than the original test, and provide a benchmark that shows that our method is orders of magnitude faster than the sLRT and scales well to analyses involving many traits  and very large sample sizes  is regressed on an SNP genotype (x) using a linear model of the formjth trait , βj is the SNP effect on the jth trait, and jth trait which we will assume to be normally distributed (more below).Consider a single-marker regression (SMR) in which a (centered and covariate-adjusted) phenotype  with zero mean and (co)variance matrix R⊗In. Here, ⊗ represents the Kronecker product operator, In stands for an n-dimensional identity matrix, and R is a (p×p) within-subject (co)variance matrix of the error terms.For the balanced case  a multi-trait SMR can be expressed using:R is unknown; therefore, estimates are often obtained using a two-stage procedure where in the first step, each trait is regressed on the SNP separately and the residuals from these single-trait regressions are used to estimate R using a method of moments estimatorR. This procedure requires fitting models twice for each SNP, which is computationally demanding.For the balanced case, the Maximum Likelihood Estimator of the SNP effects  instead of single-marker regression residuals . Therefore, the proposed approximation is expected to preserve error control and power – our simulation results confirm this expectation.For most complex traits, individual SNPs typically explain a small fraction of the variance of phenotypes . Therefore, following Turley et al. , we propSchaid et al.  proposedle sizes   test [p values for all possible nulls are computed and the final p value is the one corresponding to the minimum Wald’s statistic:wk is Ck) can be obtained by removing the kth row  of a p-dimensional identity matrix (see Section II of the Appendix for an example). If this test is rejected, the following steps in the sequence test whether the SNP has effects on at least 3 traits, then 4 traits, and so on.Implementing a sequential test for pleiotropy requires evaluating multiple configurations in which SNPs may have effects on 2, 3, …, IU) test : the p vp values for the sequential test for the case of unbalanced data. We identify all missing-value patterns present in the data, then we compute the relevant summary statistics for each group, and finally we combine them to obtain the GLS estimates–further details are presented in Section III of the Appendix. Our approach assumes that the missing values are non-informative. Under these conditions, it can be shown that the resulting GLS estimator is unbiased because it is a weighted average of group-specific estimators which are themselves unbiased [Above, we described the implementation of SUR and Wald sequential tests for the case of balanced data. The same tests can be extended to cases involving incomplete phenotype data. We designed an efficient approach for obtaining GLS estimates, SEs and unbiased . Howeverpleiotest. The computationally demanding steps of the algorithm are implemented in C++ language, embedded into R-code using the RcppArmadillo package. Our package offers functions to perform multi-trait GWA analyses, a sequential test for pleiotropy, and graphical display of pleiotropy analysis. The package and documentation, including standard pipelines and examples can be found in the GitHub repository https://github.com/FerAguate/pleiotest and in The Comprehensive R Archive Network https://cran.r-project.org/web/packages/pleiotest.We implemented the methods described in the preceding sections into the R-package p = 3; j = 1,..., p; xi) were simulated from a Binomial distribution with allele frequencies sampled the empirical distribution of minor-allele frequencies ranging from 0.01 to 0.5. In a first setting, there were no genetic effects on any trait variance matrix (β1 twice as big as β2 and β3 (effect-ratio = 0.5). We also considered scenarios with homogeneous error correlations of 0.2, 0.5, or 0.8 and sample sizes of 3000, 5000, or 10,000. Combining these scenarios led to 360 (2 × 20 × 3 × 3) different analysis settings. For power analysis (since the Monte Carlo Error is expected to be much lower than for Type-I error rate estimations) we conducted 100,000 MC replicates per setting.To quantify power, we performed an MC simulation of the three traits simultaneously affected by the same locus. We considered different settings regarding absolute and relative effect sizes. The proportion of variance explained by the locus on trait one varied from 0 (no effect) to 1%. For each setting we considered: (i) equal effect size across traits (We measured the computation time that takes for pleiotest to prepare the data (pre-processing), fit the model, and perform the sequential test for pleiotropy (processing). To this end, we simulated 1000 variants, in systems involving three, five, or ten traits, with a sample size of 10,000, 50,000, or 300,000, and a proportion of missing of either 0.0 or 0.3 . The computation time was measured 1000 times on a Dual-core Intel Core i5 processor with 2.3 GHz and 16 GB of RAM LPDDR3.Finally, we used pleiotest to perform a multi-trait GWA analysis to map loci with pleiotropic effects on seven continuous traits related to MetS: body mass index, systolic blood pressure, serum urate, glucose level, low-density lipoproteins, triglycerides, and creatinine. The traits serum urate and triglycerides were log-transformed to make their distribution symmetric. These traits were selected based on the articles used for comparison and data availability. Genotypes and phenotypes derived from distantly related white Europeans from the UK Biobank . All phekth SNP) among white Europeans and retained only individuals that had genomic relationship smaller than 0.05. Genomic relationships were computed using the BGData R-package. For further details about the sample selection process refer to Kim et al [SNP-genotypes were from the UKBiobank Axiom Array   into non-overlapping genomic regions, each harboring at least one significant association.We report our results in terms of independent regions of significance. We obtained these regions using the Finally, we used data on tissue-specific eQTL from GTEx  to test S instead of The simulation results Table , which wThe results of the power analysis showed that sLRT and pleiotest had indistinguishable power across scenarios Fig. . MC erroFor balanced data, the benchmark Fig.  showed tn = 300,000), it took on average ~85 seconds for pleiotest to analyze 1000 SNP for 5 traits. For systems involving 10 traits, the computational times required to process 1000 SNPs were about 50–60% higher than for analysis with the same sample size and 5 traits. This represents a remarkable computational improvement relative to the sLRT of the pleio R-package [n = 300,000, yielded a median computation time of 20.1, 25.8, and 44.6 s per variant for 3, 5, and 10 traits, respectively.For balanced data and a biobank-sized sample  is characterized by a group of conditions that are often co-morbid and are considered risk factors for cardiovascular disease (CVD) and type-2 diabetes . MetS scOur analyses of seven MetS-related traits found widespread pleiotropy, including 170 non-overlapping genomic regions 2982 SNPs) with pleiotropic effects in at least two traits  2, positioned between 26.8 and 28.6 Mbp, contains the SNPs with the lowest p values and harbors three important MetS-related genes: GCKR, C2orf16, and ZNF512. Many of the genes in Table https://www.ebi.ac.uk/gwas/), do not contain SNPs reported in association with MetS. Two of these regions, which harbor genes ARL15 (CHR 5) and TM6SF2 (CHR 19), were reported in MetS studies [p values) in these regions are MAP4, SEMA3F-AS1, ADH1B, BLK, and SEMA7A.We also tested for higher-order pleiotropy, including three- and four-traits pleiotropy. We found 18 well-defined regions harboring 246) SNPs with significant associations with at least four traits  could explain the partial overlap of findings.To further determine which of the associations found in our study were previously reported, we compared our findings against the GWAS catalog and two specific studies, Avery et al.  and KrajFinally, using data from the gene expression data (GTEx) portal,  we foundp values from single-trait association analysis. However, defining significance with this approach is challenging because it is not clear how to determine the number of independent tests being performed. This happens because the tests statistics are often correlated between traits; as a result, accurate error control is challenging. To address this, Schaid et al. [q traits for q = 2, …, p); this facilitates the communication and interpretation of results. However, the computation of the required test statistics as proposed by Schaid et al., does not scale well to large sample size and hundreds of thousands (possibly millions) of SNPs. Therefore, to overcome those limitations we developed an approximation to the test and software package (pleiotest) that scales to big data. Our approach uses a Wald’s test instead of a likelihood ratio test and, more importantly, an approximation to the residual (co)variance matrix, which reduces the computational burden of the test and provides some computational improvements.Quantitative genetic studies  and GWA d et al.  proposedn < 3000) our approach could be slightly conservative; however, this does not deteriorate power because the test is only conservative in situations in which power is very high .The simulations presented in this study show that the proposed approximation (pleiotest) has adequate error control and achieves the same power of the original sequential test. In systems involving many traits and with a small sample size  which ip values. This approach is computationally efficient when the number of missing-value groups is small . However, the approach can involve a substantial computational burden when there are many missing-value groups, each involving a small number of observations. To avoid substantial increases in computation time due to highly fragmented data, pleiotest offers the option of dropping groups involving a small number of observations .Unlike existing software  for Seemp values are higher than a user-specified threshold , which also saves computation time. Finally, the algorithm offers the possibility of processing variants in parallel at multiple cores within a single R session. However, for a large-scale analysis conducted in high-performance computing clusters it may be more effective to parallelize the analysis into multiple jobs, each processing a chunk of DNA  in a single core – our software also offers this possibility.The package also offers the possibility to set an early stop to the sequential tests if The test implemented in this work assumes that traits follow a multivariate normal distribution. For analysis of quantitative traits this is not a major limitation because traits with strong departure from normality can be transformed and because with a large sample size, the Central Limit Theorem guarantees the normality of estimates obtained from linear models . Schaid Deng and Pan  presenteWe used pleiotest to map variants with pleiotropic effects on seven MetS-related traits. We found abundant evidence of pleiotropy: 170, 44, and 18 independent genomic regions that contained significant associations with at least two, three, and four of the seven traits studied, respectively. These regions cover many previously reported findings as well as many novel ones. As expected, the proportion of shared risk loci between any pair of traits was directly related to the phenotypic correlation between traits  4, which plays a role in hypertension and has been found to be associated to type-2 diabetes [ARL15 (CHR 5) and TM6SF2 (CHR 19) were not included in the GWAS catalog as MetS-associated genes; however, these genes were reported elsewhere [The SNPs discovered to have at least 4-trait pleiotropy , FTO (CHR 16), APOC1, and TOMM40 (CHR 19) [Chromosome 6 harbors the longest genomic region (~6-Mbp) containing SNPs with pleiotropic effects on MetS-traits. This region involves many BMI-related genes, such as and MetS . Other g(CHR 19) .five novel regions associated with at least four MetS-related traits. These regions harbor multiple genes potentially associated with MetS including genes MAP4 and SEMA3F-AS1 (both in CHR 3), ADH1B (CHR 4), BLK (CHR 8), and SEMA7A (CHR 15). While these genes have not been directly related to MetS, some have been implicated with other MetS-related traits and behaviors such as SBP (gene MAP4 [ADH1B [BLK), and neuronal processes and the risk of acute atherothrombotic stroke , alcoho4 [ADH1B ), rheumae SEMA7A ).Gianola et al.  noted thSupplemental material


Plasmodium falciparum, an obligately sexual, hermaphroditic protozoan, using mice carrying human hepatocytes (the human liver-chimeric FRG NOD huHep mouse) as the vertebrate host. We report on two genetic crosses—(1) an allopatric cross between a laboratory-adapted parasite (NF54) of African origin and a recently patient-derived Asian parasite, and (2) a sympatric cross between two recently patient-derived Asian parasites. We generated 144 unique recombinant clones from the two crosses, doubling the number of unique recombinant progeny generated in the previous 30 years. The allopatric African/Asian cross has minimal levels of inbreeding and extreme segregation distortion, while in the sympatric Asian cross, inbred progeny predominate and parental alleles segregate evenly. Using simulations, we demonstrate that these progeny provide the power to map small-effect mutations and epistatic interactions. The segregation distortion in the allopatric cross slightly erodes power to detect linkage in several genome regions. We greatly increase the power and the precision to map biomedically important traits with these new large progeny panels.Genetic crosses are most powerful for linkage analysis when progeny numbers are high, parental alleles segregate evenly and numbers of inbred progeny are minimized. We previously developed a novel genetic crossing platform for the human malaria parasite  Plasmodium falciparum derived from Asian and African origin parasites, and generate large numbers of recombinant progeny. These crosses are characterized and have the potential to map biologically important traits across parasite genomes.Button-Simons et al. report on two different genetic crosses of  Previous studies in Plasmodium, Trypanosome, and Leishmania parasites revealed that genetic architecture of drug resistance is often complex6. For example, emergent artemisinin resistance in the human malaria parasite, Plasmodium falciparum, has been causally associated with multiple independent mutations in one gene, kelch13 (pfk13), which explain nearly all the variation in this phenotype9. However, mutations in the ferredoxin (pffd), apicoplast ribosomal protein 10 (pfarps10), multi-drug resistance protein 2 (pfmdr2), and chloroquine resistance transporter (pfcrt) genes are significantly associated with artemisinin resistance, and have been proposed to constitute a genetic background highly predisposed to the development of resistance7. Several techniques have been used to identify the genetic determinants of complex phenotypes in eukaryotic pathogens including genome-wide association studies (GWAS)10, in vitro selections8, quantitative trait loci (QTL) analysis in controlled genetic crosses14 and bulk segregant analysis/linkage group selection/extreme QTL (BSA/LGS/XQTL)15. Controlled genetic crosses offer a powerful way to dissect the genetic architecture of complex traits. For example, the progeny of an experimental cross revealed that P. falciparum sensitivity to quinine was associated with loci on chromosomes 5, 7, and 13, with the chromosome 5 and 7 loci containing known drug resistance transporters pfcrt and pfmdr13.Eukaryotic parasites inflict a high burden of morbidity and mortality particularly in the developing world. Control of these pathogens is threatened by drug resistanceP. falciparum has the potential to be a particularly powerful genetic mapping system due to its unusually high recombination rate (11–13.3 kb/cM17), haploid state for most of the life cycle, and the ability to clone blood stage progeny in vitro, creating effectively immortal F1 mapping populations in a single generation. In addition, P. falciparum has a small genome (23 Mb) and a high quality reference assembly18 with frequent annotation updates;20 consequently, re-sequencing and comprehensive analysis of the genome of progeny is simple and cost effective21. Generating controlled genetic crosses in P. falciparum, however, has historically been a difficult and time-consuming process requiring splenectomized chimpanzees in place of a human host. This resulted in only four genetic crosses being performed over a 30-year period. F1 mapping populations from all four previous P. falciparum genetic crosses have been small, containing 33, 35, 15, and 27 individual recombinant progeny21. When compared to the thousands of progeny possible in many plants and fruit flies22, these numbers are small. To use genetic mapping to elucidate the genetic architecture of emerging drug resistance in P. falciparum we need to be able to rapidly create genetic crosses with large numbers of progeny from recent field isolated parasites which exemplify relevant clinical traits such as drug resistance.P. falciparum genetic crosses23, until now they have failed to produce more progeny than historic crosses. Here we report on two genetic crosses that were carried out using recent clinically derived P. falciparum isolates with emerging drug resistance phenotypes. This effort was aided by a progeny characterization bioinformatics framework that filters single nucleotide polymorphisms (SNP) and identifies clonal unique recombinant progeny. We generated genetic maps for each cross (84 and 60 unique recombinant progeny) and provide detailed investigation of inbreeding, plastid inheritance, and cross-over rates. Through simulation and mapping we show that we are better powered to detect genetic associations than previous crosses, even for small effect sizes. We show that while segregation distortion (SD) can locally reduce power we are still able to detect major effect loci in our expanded progeny panels.Here we report on the production of large numbers of unique recombinant progeny utilizing human liver-chimeric FRG huHep mice infused with human red blood cells. Although these mice were previously reported as an option for During a single year we conducted two independent genetic crosses using multiple FRG NOD huHep mice. The first, an allopatric cross between a laboratory-adapted African line (NF54) and a newly cloned clinical isolate (NHP4026) from the Thai–Myanmar border; the second, a sympatric cross between two newly cloned clinical isolates (MKK2835 and NHP1337) from the Thai–Myanmar border. These crosses yielded 84 and 60 clonal unique recombinant progeny lines respectively. The pipeline to generate recombinant progeny is technically challenging and takes ~6 months . NF54 and NF54HT-GFP–luc25 are largely drug sensitive parasite lines that readily produce gametocytes; MKK2835 is an isolate from Thailand that was collected in 2003 before the emergence of artemisinin resistance26. NHP402626, and NHP133726 are recent isolates from the Thai/Myanmar border with reduced in vivo sensitivity to artemisinin (Table 25 integration into the genome was the cause of a segregation distortion peak (described below). The progeny from the first crosses were combined (subsequently referred to as NF54 × NHP4026) to form one genetic map (described below). A bulk analysis of the uncloned progeny from the MKK2835 × NHP1337 cross was previously published24.In total, we initiated three independent crosses  injection or mosquito bite (MB) (one cage per mouse). One mouse was infected by IV injection of one million sporozoites dissected from 250 mosquitos with infection prevalence of 73% and median 2 oocysts/infected mosquito. Two mice were infected by MB using cages with 250 mosquitos with prevalence of 73% and 58% and median 3 oocyst/infected mosquito. Assuming no attrition in parasite genotypes, equal initial gametocyte ratios and random mating, this translates to 730 unique recombinant progeny for the IV infection  was used to infect a single mouse via IV injection with 2.7 million sporozoites, containing up to 979 unique recombinants. Following exsanguination, the parasitemia of the NF54WT × NHP4026 cross mice were 0.017% (MB), 0.02% (MB) and 0.013% (IV) .The F28 and unique recombinant progeny were recently sequenced to create a community resource21. We initially performed MS genotyping on the progeny from the first two cloning attempts for the NF54 × NHP4026 cross, filtering out most non-clonal, selfed and non-unique recombinant progeny and then performed genome sequencing of select progeny. For subsequent cloning attempts for the NF54 × NHP4026 cross and the MKK2835 × NHP1337 cross, we performed genome sequencing of all cloned parasites. For each progeny, sequencing reads were mapped to the P. falciparum genome (version 3)29, SNPs were called jointly across parents and progeny, and filtered to contain SNPs in the 20.8 Mb core genome21.Genetic characterization of previous crosses was initially carried out using restriction fragment length polymorphism (RFLP) or microsatellite (MS) markersFor NF54 × NHP4026, 10,472 high quality bi-allelic SNPs (1 SNP per 2.0 kb) differentiate the two parents. We cloned 175 parasites and sequenced 161 parasites following MS filtering. In total, filtering removed seven progeny with low genome coverage, 25 non-clonal and three selfed progeny . Patterns of allele frequency variation in both replicates of the NF54 × NHP4026 were highly repeatable , mutations in which lead to chloroquine resistance and a fitness disadvantage in some genetic backgrounds34.We examined each distorted locus in the NF54 × NHP4026 cross for plausible driver genes . SNPs and microindels in pfmrp2 have significant associations with in vitro response to chloroquine, mefloquine, and piperaquine and in vivo parasite clearance35.On chromosome 12, a 295 kb region with 71 genes showed SD with an overabundance of NHP4026 alleles when cloning was initiated at least 5 days after exsanguination. The segregation peak contains five genes  and pf48/45 (PF3D7_1346700). In the NF54HT-GFP–luc × NHP4026 replicate of this cross the GFP–luc transgene replaces the functional pf47 locus23 but the same distortion was also seen when wildtype NF54 was used for the cross, suggesting that GFP–luc transgene is not the driver of the skewed inheritance.On chromosome 13, a 230 kb region predominantly inherited from NHP4026 with 56 genes shows SD in all cloning rounds. The SD peak contains 37 genes including pfarps10 (PF3D7_1460900) which has been associated with slow clearance of parasites from patient blood after artemisinin combination therapy (ACT) treatment in GWAS studies7 and is hypothesized to contribute to a permissive background for evolution of pfk13 mutations which have been shown to confer artemisinin resistance in vitro9.On chromosome 14, a 205 kb region containing 62 genes predominantly inherited from NF54 showed SD when cloning was initiated at least 5 days after exsanguination. The SD peak contains 15 genes including n = 30–40) only very large ES > 0.5) can be mapped with high power (>80%)  that contribute equally to the phenotype, 60 progeny, with replicated phenotypes can detect a significant statistical association at ES = 0.3, whereas 84 progeny are needed to detect a significant statistical association at ES = 0.2  were underpowered. In progeny sets of this size, reliable detection of significant statistical associations is possible only for phenotypes with large ES (≥0.5)  have higher power and are capable of reliably detecting significant associations for phenotypes with ES as low as 0.2.Phenotypes are frequently complex in nature0.2 Fig. . When a 0.2 Fig. . Replicaoci Fig. . This an37. Analysis of our progeny genotypes and simulated phenotype data . Consistent with our simulations, we are able to map the chloroquine drug response to the locus containing pfcrt  to identify loci linked to phenotypes of interest as demonstrated in rodent malariaUsing our progeny arrays and simulated phenotypes, we have shown that traditional QTL mapping with 84 unique recombinant progeny can detect associations for phenotypes with effects sizes (ES) as low as 0.2 and strong epistatic interactions. We can also map phenotypes with small to modest effect sizes more precisely. For instance, at an ES of 0.5 increasing our mapping population from 30 to 84 progeny, we can eliminate 40 candidate genes  individual clones were due to selfing28. However, our sympatric MKK2835 × NHP1337 cross produced many selfed progeny. Interestingly, NHP1337 dominated the selfed progeny almost entirely, consistent with bulk allele frequencies in samples taken at similar times24. While we aimed to infect mosquitos with equal number of MKK2835 and NHP1337 gametocytes, the unequal selfing rates may reflect an imbalance in the initial gametocyte ratio or in gametocyte viability between MKK2835 and NHP1337. It is also possible that there are inherent difference in selfing rates between MKK2835 and NHP1337, although both lines successfully selfed in mosquitos infected with only one parent  it is clear that inheriting the pfcrt NHP4026 allele contributes a large fitness cost that is evident upon transition to in vitro culture.In the NF54 × NHP4026 progeny, the SD peaks contain candidate genes that are related to drug resistance or immune evasion. The SD peak on chromosome 7 includes pfarps2 which has been associated with artemisinin resistance in GWAS studies and is thought to contribute to a permissive background for development of artemisinin resistance7. While NHP4026 is pfk13 WT it does have a slow clearance phenotype  and the potential to detect false positives (type I error)P. falciparum quantitative genetics. It is now feasible to generate crosses on demand to study the genetic architecture of emerging phenotypes. We can also use complex cross designs to further improve power to detect associations for phenotypes where a genetic variant only controls a small amount of variation.In conclusion, we believe that the use of the human liver-chimeric FRG NOD huHep/huRBC mouse to generate genetic crosses will revolutionize 23. The pipeline begins with the identification of isolates with interesting phenotypes that have been culture adapted and cloned. Cloned isolates are then evaluated for their ability to produce gametocytes. Cloned isolates were maintained under standard culture conditions in human erythrocytes (O+ at 5% hematocrit) suspended in complete media  at 37 °C under an atmosphere of 5% CO2, 5% O2 and 90 N2. Gametocyte cultures were initiated at 0.8–1% parasitemia. Cages of 200–250 adult female Anopheles stephensi mosquitos were infected using gametocytes of either parental strain or both parental strains as described in Supplemental Table + human RBCs on days 6 and 7 after infection. The mice were exsanguinated 4 h after the final huRBC injection to recover P. falciparum infected huRBCs. The recovered blood was washed 3 times with 10 mL of complete media, pelleted at 200 g and resuspended in an equal volume of O+ huRBCs and complete media at 2% hematocrit. Thin smears were made to estimate parasitemia and cloning via limiting dilution was initiated as soon as possible . Specific methodological information for each replicate of each cross is provided in Supplemental Table The study was performed in strict accordance with the recommendations in the Guide for the Care and Use of Laboratory Animals of the National Institutes of Health (NIH), USA. All of the work carried out in this study was specifically reviewed and approved by the Seattle Children’s Research Institute IACUC under ACU00598 Harnessing the power of genetic crosses.56. Wells with a CT score of 25 or lower were moved into 24 well plates and assigned a unique alphanumeric identifier. These cultures were then expanded to 1 mL for further expansions and cryopreservation of stocks and material for genotyping using the assigned unique identifier. A protocol for this screening method is available in the S1 File.Beginning at week 2 post cloning and continuing on weeks 3, 4, and 6, the Phusion Blood Direct PCR Kit (Thermo Scientific) was utilized to identify positive clones. This kit is very sensitive, detecting positive parasitemia using only 1 μL of infected culture streamlining our detection of positive clones. We are able to detect parasites at parasteimias as low as 0.001%All progeny of NF54 × NHP4026 were initially genotyped via microsatellite markers to identify unique recombinants. The progeny isolated in cloning rounds 1 and 2 or replicate 1 of the NF54 × NHP4026 were genotyped at 17 MS markers. The progeny isolated in cloning round 3 of NF54 × NHP4026 were genotyped at 8 MS markers. Primers for each MS marker used are listed in Supplemental Data DNA was extracted from 35 to 50 μL of packed red blood cells using Quick DNA Kit (Zymo). Libraries were prepared with ¼ reaction volumes of the KAPA HyperPlus DNA Library Kit and 20–50 ng of extracted DNA according to manufacturer directions with slight modifications. Fragmentation time was 26 min; adapter ligation was increased to 1 h; PCR was performed for 7 cycles; and size selection was performed post PCR using full volume methods. We used KAPA Dual-Indexed Adapter Kit, adding 7.5 μM adapter to the appropriate well. Samples were measured for DNA quantity using the QBit BR DNA Kit. Samples were then pooled for sequencing based on their QBit measurements to normalize input. The pooled sample was quantified using the KAPA Library Quantification Kit, and adjusted to 2–4 nM with 10 mM Tris-HCl, pH 7.5–8.0 (Qiagen) for sequencing on Illumina platforms. The pool was also run on the Agilent Tape Station using the D1000 BR Kit to assess sample size and lack of primer dimers. Pools were run on the Illumina HiSeq 2500 or Illumina NextSeq for 2×100 bp run.http://www.plasmodb.org) using BWA MEM v0.7.5a57. After removing PCR duplicates and reads mapping to the ends of chromosomes (Picard v1.56) we recalibrated base quality scores, realigned around indels, and called genotypes using GATK v3.558 in the GenotypeGVCFs mode using QualByDepth, FisherStrand, StrandOddsRatio VariantType, GC Content and max_alterate_alleles set to 6. We recalibrated quality scores and calculated VQSLOD scores using SNP calls conforming to Mendelian inheritance in previous genetic crosses, and excluding sites in highly error-prone genomic regions .We aligned raw sequencing reads to v3 of the 3D7 genome reference  and high quality scores (GQ ≥99) were retained. Next, low quality SNPs across parents and progeny were filtered with a VQSLOD < 2.5. This final SNP set was defined as our high quality SNP set for further analysis.P. falciparum crosses to produce the F1 mapping population, it is necessary to filter out potential progeny that are non-clonal and repeated sampling of the same genotype. Initially, potential progeny with more than 80% missing data were removed from further analysis.In P. falciparum parasites are haploid throughout the entirety of the human portion of their life-cycle including the intraerythrocytic stage during which they are cloned we expect that clonal infections should have predominantly homozygous SNP calls except for rare instances of sequencing error. In contrast, non-clonal infections where the mixture contains full siblings or full siblings and parent genotypes would have contiguous regions with high numbers of heterozygous SNP calls at above the rate expected from sequencing error along.Since The sequencing error rate was estimated for each cross as the mean from a distribution of percent heterozygous SNP calls across all potential progeny  were coded as 0 while the drug resistant parent (NHP4026 and NHP1337) were coded as 1. Progeny SNPs that matched the drug sensitive parent’s SNPs were coded as 0 while SNPs that matched the drug resistant parent’s SNPs were coded as 1. Heterozygous SNPs were coded as missing.Our high quality phased dataset for clonal progeny was formatted for the qtl package in R and loaded as a genetic map. Genotype similarity scores were computed using the comparegeno function. A similarity score cut-off of 0.9 was used to define clusters of genetically distinct recombinant progeny. Individual progeny were selected from each cluster of genetically similar progeny using igraph in R. Only unique recombinant progeny and parents were retained to create a final dataset of all SNPs. Unique recombinant progeny cryopreserved stocks were thawed and expanded within two weeks to created generation 1 stocks for later phenotyping and long term storage.5 kb windows were defined across the core genome to construct a heatmap depicting a recombination map for each cross. For each progeny, in each 5 kb window the most common parental genotype was determined, if a window contained only missing data then it was filled if the next window with data had a matching genotype to the previous window with data, otherwise it was left missing.https://github.com/kbuttons/P01_ProgenyCharacterization) with documentation.All phased genotype data for clonal, unique recombinant progeny and parents were loaded into R qtl as a genetic map. The findDupMarkers function was used to identify clusters of markers with identical genotype data and the most central marker with the least missing data from each cluster was retained in a set of informative markers. Sets of markers were excluded if they spanned less than 100 bp. Non-duplicated markers were included in the set of informative markers if they had a stronger correlation to flanking marker sets than the flanking marker sets did to each other. This set of informative genotype markers for all clonal, unique recombinant progeny was used for all subsequent analysis and figures. The entire filtering pipeline is available on github  at 5% hematocrit. Cultures were grown separately in sealed flasks containing 5% CO2, 5% O2, and 90% N2 at 37 °C. Drug tests were performed when cultures were at least 80% ring with at least 96 h between tests. IC50 curves were generated for parent lines for chroloquine, piperaquine, and dihydroartemisinin and for chloroquine for 56 NF54 × NHP4026 progeny by standard [3H]-hypoxanthine incorporation assays3. eRRSA, an in vitro artemisinin resistance surrogate, were measured following our published methodology56.Parasites were cultured under standard culture conditions in human red blood cells suspended in complete medium (CM) containing RPMI 1640 with L-glutamine (Invitrogen Corp.), 50 mg/L hypoxanthine (Sigma-Aldrich), 25 mM HEPES , 0.5% Albumax II (Invitrogen Corp.), 10 mg/L gentamicin (Invitrogen Corp.) and 0.225% NaHCO1 progeny set with N = 84 and for subsamples with N = 30, 40, 50, 60, and 70. Under the one locus model, a phenotype was simulated as either a single replicate value or the average of 5 replicates at effect sizes ranging from 0.1 to 0.8. Under the two additive loci model, a phenotype was simulated as either a single replicate value or the average of 5 replicates for effect sizes for each locus ranging from 0.1 to 0.4. Under the two epistatic loci model, the first locus controlled whether a trait was present in an on/off fashion and the second locus controlled the level of the phenotype  and the main effects of both loci ranged between 0.1 and 0.4. A set of markers with 1:1 mendelian inheritance patterns were used as the 1 or 2 loci in the models. All QTL mapping was performed with r qtl. For each simulation, significance thresholds were defined based on 1000 permutations. True positives were defined as a LOD peak that meant the α = 0.05 significance threshold and whose 1.5 LOD interval contained the actual marker used in the model.Progeny from NF54 × NHP4026 were used to estimate power under three different scenarios, one genetic locus contributing to phenotypic variation, 2 loci with additive contributions to phenotypic variation and 2 loci with epistatic interaction controlling phenotypic variation. All models were simulated for the full FThis analysis was similar to the 1 locus model in the previous section. In these simulations effect sizes were calculated based on balanced inheritance and levels included 0.2, 0.3, 0.4, 0.6, and 0.8. All markers were categorized by their allele frequency and sorted into bins for each level of allele frequency skew . QTL mapping, significance levels and definition of true positives were the same as in the power analysis above.50 and eRRSA) are reported as the mean and standard deviation of 5 individual biological replicates (drug tests were loaded from cultures grown for at least 2 cycles independently).Drug response profile data , USA. To this end, the Seattle Children’s Research Institute (SCRI) has an Assurance from the Public Health Service (PHS) through the Office of Laboratory Animal Welfare (OLAW) for work approved by its Institutional Animal Care and Use Committee (IACUC). All of the work carried out in this study was specifically reviewed and approved by the SCRI IACUC under ACU00598 Harnessing the power of genetic crosses.All unique recombinant progeny generated in this study can be provided upon request subject to a reasonable fee to cover shipping, reagents, and cost of creating progeny stocks. We also encourage collaborative projects using this research.Further information on research design is available in the Peer Review FileSupplemental InformationDescription of Additional Supplementary FilesSupplemental Data 1Supplemental Data 2Supplemental Data 3Supplemental Data 4Reporting Summary