Genotype-by-environment interactions are a significant challenge for crop breeding as well as being important for understanding the genetic basis of environmental adaptation. In this study, we analyzed genotype-by-environment interactions in a maize multiparent advanced generation intercross population grown across 5 environments. We found that genotype-by-environment interactions contributed as much as genotypic effects to the variation in some agronomically important traits. To understand how genetic correlations between traits change across environments, we estimated the genetic variance–covariance matrix in each environment. Changes in genetic covariances between traits across environments were common, even among traits that show low genotype-by-environment variance. We also performed a genome-wide association study to identify markers associated with genotype-by-environment interactions but found only a small number of significantly associated markers, possibly due to the highly polygenic nature of genotype-by-environment interactions in this population. Both the effect of a given genotype on a trait, and the impact of that effect on fitness, often vary across environments. Such genotype-by-environment interactions (G×E) are widespread, and have been commonly observed in plants . G×E intWhile alleles affecting a trait will demonstrate G×E for fitness across environments when there is selection for different trait optima, it is also often observed that the effect of individual alleles on traits will vary as well. This indicates that these alleles affect plasticity and they may be present in a population due to selection for or against plasticity . AlternaEDA locus in threespine stickleback fish, which is associated with adaptation to the freshwater environment, and Sub1A in rice, which is associated with tolerance to submergence , which describes how the genetic value at 1 trait covaries with genetic values at other traits . GeneticBut the G-matrix itself is not constant, as G×E at underlying loci may impact trait variation and covariation among traits . If in aMaize is a crop species adapted to a wide diversity of environments, from temperate to tropical and from low to high altitude . G×E hasWe find that G×E contributes as much as genotypic main effects to variance for some traits. While G×E interactions are significant, genome-wide association only finds a small number of markers significantly associated with G×E interactions, perhaps reflecting the highly polygenic nature of most traits. Nonetheless, estimation of the G-matrix in each environment reveals that changes in genetic covariance are common and may be contributing to observed G×E. For example, we find that while only a small proportion of variance in flowering time depends on G×E, the genetic covariance between flowering time and grain yield is strongly affected by the environment.We developed a maize MAGIC population by repeatedly crossing the offspring of 16 maize inbred lines to generate recombinant individuals . Inbred The MAGIC F1 plants were phenotyped in 4 different field locations in 4 different years, resulting in 5 distinct environment-years . The envWe measured the following traits: male flowering date, female flowering date, anthesis-silking interval (ASI), plant height, % harvest grain moisture (HGM), grain yield, and thousand kernel weight (TKW), where values were averaged over plots. Both flowering time phenotypes were measured as the sum of degree days since sowing with a base temperature of 6°C (48°F). Male flowering date was considered as the growing degree days (GDD) until 50% of plants in a plot were shedding pollen on approximately 1 quarter of the central tassel spike. Female flowering date was considered as the GDD until 50% of plants in a plot were flowering with 2 cm of silk outside of husk leaves. Plant height was measured as the distance from the base of the plant to the top of the tassel. Grain was collected using a combine harvest. Grain yield and TKW were both adjusted to 15% humidity. TKW was estimated from a 100 kernel sample. Data was also collected from an additional environment, Szeged, Hungary in 2017. We did not use this data in the analyses presented here as flowering date was not collected on the same schedule as in the other environments and this caused issues with the G×E analyses. Data from Szeged are available in the data repository associated with this paper. Between 292 and 309 of the MAGIC F1 lines were grown in each environment. There were a total of 325 lines that had both genotype data and phenotype data from at least 1 environment.We genotyped each of the DH lines using the Affymetrix Axiom Maize Genotyping Array, which successfully genotyped 551,460 SNPs. The probability of each founder contributing to each segment in the genome was imputed from the genotyped SNPs .sommer , α is a scalar representing ½ the deviation of trait means between the 2 environments, kth column is the probability that each of the n individuals inherited from the kth founder at marker m, n × 16 matrix formed by multiplying w with each column of n × r design matrix of additive genotypic effects, n × r design matrix of genotype deviations formed by multiplying each column of w, n × r design matrix of nonadditive genotypic effects, e is a vector of error terms. K, where K is the additive genetic relatedness matrix, and e both have covariance proportional to the identity matrix. The statistical test to identify markers influencing G×E was against H0: where w is a length n vector with each element taking the mean value of the phenotype within the environment of the observation, and n vector of the mean value of the phenotype within the environment of the observation.We tested whether a locus had an effect on the slope of the observations of a genotype across the mean phenotypic value of all genotypes in an environment. This model has the benefit of including the maximum amount of data. Compared to the main effect and deviation model (1), this model might be more likely to pick up G×E effects that have smaller effects within those 2 environments but a larger effect on the overall slope across environments. The model is the same as in (1) except for the following: we now include all 5 environments, P-values indicated that the test was poorly calibrated. Instead of asking whether allowing a marker to have a slope across environments improved prediction of a trait in each environment as in (2), we thus asked whether the marker significantly predicted the slope of each genotype.Finally, we tested whether a locus had an effect on the slope of the observations of a genotype across the mean grain yield of all genotypes in an environment. Mean grain yield here serves as a proxy for stress or environment quality and as such this GWA is testing whether a locus affects the response to stress. This is known as a Finlay–Wilkinson analysis . For this is a length r vector of slopes for each genotype of trait values on mean grain yield in each environment, K. Other model terms are as in (1).where To determine significance thresholds for the first 2 models, we permuted phenotypic values among lines within each environment and ran the GWA 100 times. For the third model, we permuted the slopes among the genotypes and ran the GWA 100 times.brms (brms implements Bayesian multilevel models using Markov chain Monte Carlo (MCMC) algorithms. This is important as the samples from the MCMC chains allow us to estimate uncertainty and significance in our downstream analyses. We used the model:We estimated the G-matrix in each environment using the R package brms . brms imn observations for the ith trait, Z is a n × r design matrix of genotypes, U and E are random effects drawn from multivariate normal distributions: r × r identity matrix where r is the number of lines grown in an environment, n × n identity matrix where n is the number of observations, and G and R are 5 × 5 genetic variance–covariance and residual variance–covariance matrices estimated from the data. G and R are parameterized as the products of standard deviations and correlation matrices with a half Student-T distribution and LKJ-correlation prior. T distributions as priors.where brms documentation states that most models will converge with only a few thousand iterations. We assessed convergence by checking that all statistics output by brms—such as All traits were scaled by the mean value across all environments and centered before analysis in order to make them unitless and improve model convergence. We performed this same analysis with nonscaled traits so that our results can be compared with those of previous studies with nonscaled phenotypic data. The G-matrices we estimated were broad sense G-matrices as they included both additive and nonadditive sources of genetic variance. We ran 4 chains with 1,500 iterations of burn-in followed by 3,500 iterations. We chose these numbers as the To quantitatively assess differences among the G-matrices estimated in the 5 environments, we performed eigenanalysis of a covariance tensor as described in We evaluated 7 phenotypes for each of 344 hybrids of DH lines crossed with a tester in replicated trials across 5 environments that varied in temperature, daylength, and watering or drought conditions . Each DHGenotypic main effects and G×E interactions contributed a significant amount of the variance of all measured traits . Across Our test of the deviation effect of a marker within environments did not recover any markers significant at the 5% permutation threshold for any trait. In contrast, our plasticity GWAS identified 2 peaks which were significant at the 5% significance level, which were for ASI and female flowering . NeitherTo understand how the environment affected pleiotropy, we estimated the genetic variance/covariance matrix (G-matrix) of 5 traits in each environment . We dropTo quantitatively assess how individual elements of the G-matrix contributed to variation among environments, we performed an eigentensor analysis. The eigentensors of a set of G-matrices describe independent dimensions of variation among the G-matrices and can be used to identify which elements are contributing the most variation among the set. All of the 4 nonzero eigentensors explained significantly more variance than expected by chance . The eleResults of the analysis with nonscaled phenotypes are presented in the Supplementary figures.Genotype × environment interactions are known to be important for many agronomically important traits in maize, and our results on the relative importance of G×E across traits confirm these earlier findings. For example, male and female flowering date have been shown to be influenced predominantly by additive genetic effects and are not strongly influenced by G×E interactions , while gIf genotypes are adapted to different environments, we would expect to see G×E for fitness-related traits. The high variance contributed by G×E to grain yield seen in this study thus indicates that the founder maize lines, despite all having been bred in temperate environments, still carry many alleles that are differentially adapted to this set of environments. For traits that are further removed from fitness it is less clear how to interpret the contribution of G×E. It may be that the G×E we observe for a trait like HGM, which has a high proportion of G×E variance and a low genetic covariance with grain yield, is an example of neutral plasticity and is not under strong selection .Despite the presence of substantial G×E variance for several traits, we found relatively few markers which were significantly associated with G×E. One possible explanation is that the G×E variance we observed is largely polygenic and caused by many loci of small effect which we did not have power to detect with our GWAS. Previous studies investigating loci with main effects on traits such as grain yield and flowering time in maize have found that they are highly polygenic . It may The G-matrix has previously been shown to differ as much between environments as between populations (evidence reviewed in The magnitude of the genetic covariances between traits can be reduced solely as a function of reduced genetic variance for one or both of these traits without a change in the correlation between them. However, by looking at genetic correlations, we show that the correlations between traits varied across environments beyond effects of the differences in the variances . In addiWhile differences between environments presumably shape these changes in the G-matrix, previous work has found that neither measures of environmental novelty nor differences in phenotypic means predicted differences in the G-matrix when looking across all the studies in a meta-analysis . In our In addition, both the severity and timing of drought seem to be important in determining the effects of water deficit on covariances between traits. In this study, we find that in Nerac, the most drought-stressed environment, the genetic covariance between flowering time and yield is negative and that this genetic covariance contributes to differentiating it from the other environments. The fact that the genetic covariance between flowering date and grain yield in the other water deficit environment, St. Paul, was not significantly negative may be because that population was given water during flowering while in Nerac water deficit extended through flowering. It appears that how the G-matrix is affected by environmental stress is highly dependent on the species and population studied and the exact stress applied.Using a MAGIC population of maize grown in 5 environments × year combinations we were able to analyze the genetic basis of G×E in a set of diverse maize lines. We observed G×E variance for all traits and for some traits we observed comparable amounts of genotypic and G×E variance. Estimating the G-matrix within each environment revealed that changes in genetic variances and covariances across environments were common. Notably, the genetic covariance between yield and female flowering time was positive in most environments but negative in 1 of the environments. GWAS identified 1 locus significantly associated with G×E for ASI. Given the substantial G×E variance, the low number of significant loci suggests that G×E for the traits we analyzed may have a polygenic basis.Phenotypic and environmental data are located on Figshare with doi 10.6084/m9.figshare.14963034. Genotypic data are available through a data repository associated with companion paper .G3 online.jkac013_Supplementary_DataClick here for additional data file.


Properly quantifying environmental heat stress (HS) is still a major challenge in livestock breeding programs, especially as adverse climatic events become more common. The definition of critical periods and climatic variables to be used as the environmental gradient is a key step for genetically evaluating heat tolerance (HTol). Therefore, the main objectives of this study were to define the best critical periods and environmental variables (ENV) to evaluate HT and estimate variance components for HT in Large White pigs. The traits included in this study were ultrasound backfat thickness (BFT), ultrasound muscle depth (MDP), piglet weaning weight (WW), off-test weight (OTW), interval between farrowing (IBF), total number of piglets born (TNB), number of piglets born alive (NBA), number of piglets born dead (NBD), number of piglets weaned (WN), and weaning to estrus interval (IWE). Seven climatic variables based on public weather station data were compared based on three criteria, including the following: (1) strongest G×E estimate as measured by the slope term, (2) ENV yielding the highest theoretical accuracy of the genomic estimated breeding values (GEBV), and (3) variable yielding the highest distribution of GEBV per ENV. Relative humidity  and maximum temperature  are the recommended ENV based on the analyzed criteria. The acute HS (average of 30 days before the measurement date) is the critical period recommended for OTW, BFT, and MDP in the studied population. For WN, WW, IBF, and IWE, a period ranging from 34 days prior to farrowing up to weaning is recommended. For TNB, NBA, and NBD, the critical period from 20 days prior to breeding up to 30 days into gestation is recommended. The genetic correlation values indicate that the traits were largely , moderately , or weakly  affected by G×E interactions. This study provides relevant recommendations of critical periods and climatic gradients for several traits in order to evaluate HS in Large White pigs. These observations demonstrate that HT in Large White pigs is heritable, and genetic progress can be achieved through genetic and genomic selection. The incidence and severity of adverse climatic conditions is increasing as a result of climate change, and this has the potential to negatively impact livestock production . Specifii.e., reproductive, growth, and body composition traits).Improved climatic resilience can be achieved by crossbreeding thermally sensitive animals with more heat-tolerant (HT) breeds. Despite the favorable adaptation to HS, a result of these crosses is relatively low production (in comparison to the more productive breeds), which is not profitable under intensive production systems , temperature, relative humidity (RH), and temperature–humidity index (THI)].The genetic variability for HTol in livestock species has been previously reported e.g., . In geneA genetic or genomic evaluation that accounts for HS requires each record to be associated with some easy-to-measure environmental variables (ENV). Several studies have focused on alternative environmental gradients, such as estimated average performance of CGe  and climAnimal welfare and ethics committee approval was not needed for this study, as all the datasets used were provided by commercial breeding operations.e.g., contemporary group–CG) are assumed to account for these effects. The traits analyzed were related to body composition [ultrasound backfat thickness  and ultrasound muscle depth ], growth [weaning weight of the piglets  and off-test weight ], and reproduction . Contemporary groups (CG) were defined by the concatenation of farrowing year, farrowing season, and farrowing farm for the reproductive traits and of birth year, birth season, and birth farm for the growth and body composition traits, respectively. The phenotypic datasets were edited independently for each trait by removing records deviating ±3.5 SD from the mean. In addition, CG with less than 10 records were removed from further analyses. The descriptive statistics and the number of animals for each studied trait are presented in All datasets were provided by the Smithfield Premium Genetics company . Phenotypic nucleus-herd records were obtained from January 2004 to December 2019 for Large White animals distributed among 33 farms located across North America. The farm latitudes range from 34° to 42° north, and the longitudes range from -77° to -113°. Different management strategies have been used across farms, such as equipment to alleviate heat stress. However, these practices changed over time, and this information was not recorded. Moreover, the systematic effects included in the statistical models , PorcineSNP50K , PorcineSNP60K , and PorcineSNP80K  Bead Chips . In each SNP panel, animals with genotype call rate smaller than 0.90 were removed. Genotype imputation was performed using the FImpute v3 software . The miswww.ncdc.noaa.gov/cdo-web/datatools/lcd?prior%2520=%2520N). Based on the location of the farms, climatic information was collected from the nearest airports. The average distance between an airport and the farm was 30 km (ranging from 7 to 64 km). Seven ENV were evaluated, including the average of mean temperature (MeanT), average of maximum temperature (MaxT), average of minimum temperature (MinT), dew point (DewP), average relative humidity (RH), average discomfort index  and acute  HS might have great impact in carcass-related traits, such as OTW, MDP, and BFT and, therefore, were evaluated for the mentioned traits. In addition, pigs exposed to in utero HS have been reported to develop a variety of postnatal phenotypes that prevent profitable production and compromised health and welfare in commercial production systems  effect]. Model comparisons were made, and the significant random effects were defined based on the Akaike Information Criterion  the systematic (fixed) effects were defined based on the backwards elimination procedure using the ftware R  and (2) ftware R  by compariterion . Specifik, i.e., a, pe, and ce), e is the residual variance, A is the pedigree-based relationship matrix, and I is an identity matrix. A chain containing a total of 600,000 iterations, with thin and burn-in of 60 and 300,000, respectively, was used for all traits except IBF. For IBF, a chain containing 900,000 iterations with thin and burn-in of 60 and 600,000 was used. These parameters allowed the model convergence for all traits analyzed in this study. The convergence was verified based on both graphical analyses and Raftery and Lewis criterion  for each animal considering each ENV individually. Legendre orthogonal polynomials . The parameters used for the comparison of the results are described in The same single-trait RNM previously mentioned in this study was used to perform ssGBLUP analyses by replacing the H matrix . As the software  were usei.e., allowing to more easily differentiate tolerant and susceptible individuals). Weights of 0.5, 0.3, and 0.2 were given to each criterion  to facilitate the ENV selection. The climatic variable resulting in the highest final value was selected as the best indicator to evaluate HTol in Large White pigs.Three criteria were used to select the optimal ENV to analyze HTol based on the studied traits: (1) the ENV yielding the strongest G×E estimate as measured by the parameter n random effect (described above) as in n effect and k of ENV (h2k) was calculated as follows:k and n effects  for the ENV k.After obtaining the RN components, a genetic (co)variance matrix The theoretical accuracy of GEBV predicted for the slope and intercept of the trait in consideration of the animal or slope .Genotype-by-environment interaction can be confirmed, among other factors, by a genetic correlation lower than 0.70 across the range of ENV . The folThe approximated weighted genetic correlation between the studied traits was assessed based on the correlation between the GEBV. Animals with a GEBV theoretical accuracy for the intercept and slope terms lower than 0.30 were removed from the calculations. The following approach was used :r^g(xy)The descriptive statistics after the QC are shown in 2a1) using RNM. For completeness, 2a1 for RH and DewP were 94.28 and 89.05% lower than the higher σ2a (THI), respectively. For OTW_30, MaxT and THI resulted in a similar σ2a1 (52.351 and 53.123 kg2). Meanwhile, when considering a critical period of, on average, 120 days for OTW (OTW_120) and despite the fact that MaxT also gave the highest σ2a1 (32.890 kg2), it was lower than in OTW_30, suggesting that a period of, on average, 30 days would be better to evaluate HTol for OTW. The same trend from OTW was observed for MDP and BFT, where the average of 30 days  resulted in higher σ2a1 estimates. However, as opposed to OTW, higher σ2a1 was observed when considering RH as the climatic gradient for both MDP_30 and BFT_30 . A similar trend was observed between IBF and IWE when considering MaxT, resulting in the largest estimate of σ2a1 , and RH, resulting in the lowest σ2a1 .2a1 estimates for all three of them. For TNB, MaxT had the highest σ2a1 estimate (0.2095) and RH the lowest (0.1042). MaxT was the highest for NBA (0.2727) and DI the lowest (0.0364). The trait NBD had the lowest σ2a1 estimates compared to all the other studied traits, being RH the ENV with the highest σ2a1 (0.0122) and MaxT the lowest (0.0022), showing an opposite trend than TNB. Two critical time periods were evaluated for WN and WW considering 34 days prior to the farrow date  and 34 days prior to farrowing until weaning date , resulting in the largest estimates of σ2a1 considering the critical period until weaning date. For WN_fd and WN_wd, RH had the largest σ2a1 estimate , while the lowest was DI (0.719) for WN_fd and MaxT (0.4102) for WN_wd. The same pattern of WN was observed for WW, where for WW_fd and WW_wd the largest estimate of σ2a1 came from RH  and the lowest from DI (0.077 kg2) for WW_fd and MaxT (0.046 kg2) for WW_wd. In summary, RH  and MaxT  were the ENV yielding the highest estimates of the slope term. In addition, for OTW_30, both MaxT and THI could be recommended based on similar results.Considering the traits TNB, NBA, NBD, WN, and WW, RH had the highest σOverall, the accuracy of GEBV was greater for the intercept than for the slope term. The largest accuracy for the intercept was observed for BFT_30 considering MaxT (0.7224) as the ENV, and the lowest was for WN_fd considering RH (0.3121) as the ENV. Considering the slope term, the highest accuracy was observed for MaxT (0.6375) in OTW_30 and the lowest was MinT (0.2547) in MDP_120. Within each trait, it was not possible to observe a clear pattern regarding the ranking of ENV, as it was variable depending on the trait analyzed. The accuracy estimates for each trait considering all ENV are shown in The critical period of 30 days, on average, presented higher accuracies than the critical period of 120 days, on average, for BFT, OTW, and MDP. For BFT_30, the estimated accuracy for the intercept term ranged from 0.6960 (THI) to 0.7161 (DewP), while for the slope it ranged from 0.4118 (DI) to 0.5772 (RH). A different pattern, with lower estimates was observed for BFT_120, ranging from 0.6855 (MaxT) to 0.7151 (MinT) for the intercept and from 0.4153 (MeanT) to 0.4677 (MinT) for the slope. For OTW_30, the accuracy ranged from 0.5985 (RH) to 0.7132 (MaxT) for the intercept and from 0.5562 (DewP) to 0.6375 (MaxT) for the slope term, while OTW_120 ranged from 0.6156 (DewP) to 0.7738 (MaxT) and from 0.4279 (MinT) to 0.0.4909 (THI) for the intercept and slope, respectively. For MDP_30, the accuracy estimates for the intercept were similar between ENV, with 0.6405 (THI) and 0.6629 (MaxT), for the lowest and the highest values, respectively. For the slope term of MDP_30, the accuracy of GEBV ranged from 0.3103 (DI) to 0.3554 (RH). Considering MDP_120, the estimates ranged from 0.6330 (THI) to 0.0.6759 (MaxT) for the intercept and from 0.2547 (MinT) to 0.3209 (RH).For TNB, NBA, and NBD, the accuracy estimates of GEBV for the intercept and slope terms had similar values between traits and ENV. The accuracy of GEBV for the intercept term ranged from 0.6473 (THI) to 0.6562 (MaxT), from 0.6287 (MeanT) to 0.6463 (MinT), and from 0.6128 (THI) to 0.6239 (MeanT) for TNB, NBA, and NBD, respectively. For the slope, the GEBV accuracies ranged from 0.4201 (MeanT) to 0.4702 (MaxT), from 0.3809 (RH) to 0.4694 (MaxT), and from 0.4090 (MaxT) to 0.4388 (RH) for TNB, NBA, and NBD, respectively. Regarding IBF, GEBV accuracy ranged from 0.4776 (DewP) to 0.5776 (THI) and from 0.3208 (MeanT) to 0.4587 (MaxT) for the intercept and slope terms, respectively. For IWE, the GEBV accuracies ranged from 0.5084 (DI) to 0.4049 (MaxT) and from 0.3372 (MinT) to 0.4587 (MaxT), respectively, for the intercept and slope terms.Finally, considering the critical period of 34 days prior to farrowing date and 34 days prior to farrowing date until weaning date for WN and WW, respectively, more accurate GEBVs were observed for the intercept term as for the other discussed traits. For WN_fd, GEBV accuracies ranged from 0.3121 (RH) to 0.4710 (DI) and from 0.2735 (MinT) to 0.3475 (RH) for the intercept and slope terms, respectively. For WN_wd, the estimates ranged from 0.3253 (MaxT) to 0.4680 (RH) and from 0.2792 (MinT) to 0.3693 (RH) for the intercept and slope terms, respectively. The only trait with higher GEBV accuracy for the slope than intercept was for WN_fd, where an accuracy of 0.3121 for the intercept and 0.3475 for the slope was observed for RH. Considering WW_fd, the estimated accuracy of GEBV ranged from 0.4144 (THI) to 0.5172 (RH) and from 0.3373 (MaxT) to 0.4126 (RH) for the intercept and slope terms, respectively. For WW_wd, the estimates for the intercept and slope ranged from 0.4215 (MinT) to 0.5895 (THI) and from 0.3703 (MinT) to 0.4726 (RH), respectively. As observed for the first criterion, RH  and MaxT (IBF and IWE) were the ENV yielding the highest theoretical accuracy of GEBV. For the other traits not previously mentioned, more than one climatic variable could be recommended .2a and accuracy results. For OTW_30, OTW_120, TNB, NBA, IBF, and IWE, MaxT yielded the greatest deviation in most of the five categories . For the MDP_30, MDP_120, BFT_30, NBD, WN_wd, WN_fd, and WW_wd, the ENV based on RH yielded the higher deviation in most of the ENV values. Lastly, for the remaining traits, there was no absolute ENV that yielded higher deviations. In the case of BFT_120, MaxT, MinT, and DI yielded the highest values, and for WW_fd, the highest values were for RH, MeanT, and MinT. A complete estimation of the deviation in each “ENV × critical period × trait” combination is shown in High GEBV standard deviations in a certain environmental value means that the identification of animals with the highest and lowest genetic values will be more evident. In this regard, the deviation estimates were similar within each value of ENV within trait. The deviation estimate followed a pattern similar to that found for the RN slope and accuracy of GEBV estimates, being the ENV with the largest deviations the same, in most cases, as those that yielded higher σ2a and accuracy and deviation of GEBV per ENV. Additionally, the critical period ranging from 34 days prior to farrowing date until weaning date yielded the highest estimation of the three criteria. Moreover, the recommended ENVs for OTW_30 were THI and MaxT, for BFT_30, MDP_30, NBD, WN_wd, and WW_wd was RH, and for TNB, NBA, IBF, and IWE was MaxT. Subsequent results will be presented only for the recommended ENV and critical periods.ptA summary of the results of the three criteria presented above is shown in h2) estimates across the range of the selected ENV for each trait are shown in h2 ranged from 0.04 (IBF) to 0.42 (BFT_30). A similar pattern was observed for TNB (from 0.09 to 0.12), NBA (from 0.08 to 0.12), and NBD (from 0.04 to 0.09), where a slight increase in h2 is observed as the ENV values increase. The traits OTW_30 (from 0.21 to 0.47), WN_wd (from 0.07 to 0.31), and WW_wd (from 0.05 to 0.26) also had a similar pattern, with higher h2 in extreme ENV values and lower estimates closer to the mean. An opposite pattern of average h2 was observed for MDP_30 (from 0.27 to 0.31) and BFT_30 (from 0.38 to 0.47). As the values of ENV increased, the h2 for MDP_30 also increases, and for BFT_30 it decreases. The heritability estimates within each trait  had similar patterns and are shown in The heritability . The reaction norms for the slope term of the additive genetic effect of the five most HT and the five most heat-susceptible (HSusc) sires are reported in Sires were selected to have at least 30 offspring so that the phenotypes were distributed under a large range in ENV  correlations are shown in The genetic correlations of additive genetic effects across environmental gradients are shown in Individuals with GEBV theoretical accuracies higher than 0.30 were used to calculate the weighted Pearson correlation among the RN intercepts and slopes between all studied traits considering the recommended ENV for each trait. The minimum number of selected animals and the lowest average accuracies were 5,718 and 0.4224 (95% CI of 0.4210–0.4236), respectively . The appThe greatest genetic correlations were observed between the intercept terms. The greatest positive correlations were observed between TNB and NBA, with 0.928, and between BFT_30 and OTW_30 (0.581) for the RN intercept. A moderate genetic correlation was also observed between TNB and NBD (0.328), TNB and WN_wd (0.305), and NBA and WN_wd (0.305). Moderate genetic correlations between intercepts were also observed between IBF and IWE (0.375), MDP_30 and OTW_30 (0.332), and BFT_30 and MDP_30 (0.248). For WW_wd, moderate to low genetic correlations were observed with IBF (0.121), OTW_30 (0.216), MDP_30 (0.234), and BFT_30 (0.188). The combinations for the remaining traits for the intercept term had low genetic correlations and are shown in Approximate genetic correlations between the intercept and slope terms indicate what happens to the performance of an animal for a specific trait when another trait is expressed under challenging heat conditions. In this matter, the greatest positive correlation was between TNB slope and NBA intercept (0.149) and between NBD intercept and WN slope (0.135). A similar positive correlation was also observed between WW intercept and OTW slope (0.123) and between the pairs of BFT slope with NBD intercept and IBF intercept with WN slope . The WN slope and TNB intercept and the NBD slope and WW intercept had the same correlation value of 0.105. The greatest negative correlation was observed between the NBA slope and the TNB slope with WW intercept . All other combinations between intercept and slope had values lower than 0.100 .The smallest genetic correlations were observed between the slope terms. This correlation indicates the relationship between the traits under a stressful environmental condition. The greatest positive slope × slope genetic correlation was observed for WW_wd and IBF (0.146). A negative correlation of -0.131 was observed for NBD and WN_wd and of -0.111 between WN_wd and TNB. All the other trait combinations had correlations under the absolute value of 0.100 and are shown in A set of comprehensive analyses to reveal the genetic background of HTol based on routinely measured phenotypic records and publicly recorded weather variables was performed in this study.2a1 term, was also used by other studies evaluating HS effects in reproductive and carcass traits in pure breeds and crossbred animals  between HT and HSusc animals. A large variability in GEBV  allows better discrimination of the animals, which facilitates the selection of the desired animals. Another metric that could also be evaluated was the deviance information criterion (DIC). However, in this study, the DIC was not included as another criteria for ENV selection due to criticisms found in the literature, such as the lack of consistency, weak theoretical justification, and overfitting . The first criterion, based on the ENV yielding the higher G×E interaction measured by the σ animals . The secrfitting .e.g., breeds) and geographical regions to confirm our results.Climate records from the National Climatic Data Center weather stations were used to describe the environmental conditions experienced by the sows at different stages of ovulation, pregnancy, and post-pregnancy. Weather stations used in the current study were on average within 30 km from the farm. In the current study, acute HS had greater impact on OTW, BFT, and MDP than chronic HS, which suggests that, during the period of chronic HS, the animal might be able to recover from the thermal stress suffered. For these traits , the variance components for the slope term were higher considering a period of 30 days before measurement than 120 days. The GEBV accuracy estimates were also greater for the acute HS. For WW and WN, the period ranging from the last stage of gestation (around day 80) until the weaning date resulted in higher G×E interaction and GEBV accuracies. Therefore, the time period ranging from the last stage of gestation until weaning date is the recommended critical period to evaluate HT for WW and WN .e.g., i.e., rumen, ability to sweat, body mass, etc.) are found between cattle and swine that directly influence thermotolerance. For OTW, a high (0.79) rank correlation between GEBV estimates using THI and MaxT was observed. Therefore, due to the caution in interpreting the THI values for swine and the high rank correlation between THI with MaxT, MaxT was recommended as the ENV to evaluate HTol in maternal-line pigs for OTW. For five out of the 10 traits evaluated, the ENV selected  as well as the additive genetic variance for the tolerance to a given environmental stressor . The additive genetic variance estimates obtained agree with the estimates by h2 for TNB, NBA, WW, and MDP. A higher average h2 for NW was estimated using RH as the environmental gradient when compared to CGe  was also a trend observed by h2 for TNB in contemporary groups with better performance, and a similar trend was observed by e.g., using a quadratic function could mean that, when temperature increases, the impact of heat stress would be higher, but after a certain point, this impact would be favorable even though the temperature continues to increase).As described by The most common thresholds recommended for considering the presence of G×E interaction are based on genetic correlations across the range of ENV lower than 0.70  or 0.80 i.e., comfortable and controlled climatic conditions). Except for TNB, NBD, MDP, and BFT, the best-performing animals under challenging environmental conditions  are under-performing in comfortable conditions. The identification of these animals (or their descendants) means that the use of their genetic resources will be better applied on farms experiencing high RH or temperature values. For TNB and NBD, the most HT animals performed better under both comfortable and challenging conditions, and the opposite trend occurred with MDP and BFT, where the more HSusc animals outperformed the most HT across the entire ENV range. These results agree with the G×E interaction , the fact that the breeding values for HTol based on alternative traits are different or that re-ranking occurred cannot be used for choosing the best environmental variable for each trait. In addition, the fact that these correlations between slopes are low to moderate indicates the need to evaluate traits in different environments. These findings contribute to improving selection strategies under HT and suggest that different animals would be selected depending on the trait evaluated. Selection can be achieved by selecting for all traits, especially when including the traits in a selection sub-index, according to the desired breeding goal.The highest positive genetic correlations observed between TNB and NBA intercept terms were also observed by i.e., indicator of G×E interaction), while the accuracy based on the recommended ENV was 0.350 for the slope term. A Spearman (rank) correlation between the GEBV of individuals regressed on the recommended ENV and CGe shows a negative moderate rank correlation for BFT (−0.57), WW (−0.47), and MDP (−0.31) and a low positive rank correlation for the other traits . Moreover, the use of different critical periods and climatic variables defined based on public weather station records was evaluated. Our results indicate that evaluating HS in the period of 30 days before the measurement date provided the best estimates of G×E for OTW, MDP, and BFT. Additionally, the critical period ranging from the last trimester of gestation until weaning provided better estimates for WN and WW. From the climatic variables evaluated, the average RH  and MaxT  are the recommended variables to evaluate HS based on the mentioned traits. Large  and moderate  G×E interactions were observed for most of the studied traits, while other traits  are less impacted by heat stress, showing a weaker evidence of G×E. The low to moderate genetic correlations between HTol breeding values based on different traits indicate that a selection sub-index combining the breeding values for the slopes of multiple traits might be needed for identifying the most HT animals throughout their entire productive life. The current study also demonstrates that HTol is heritable, and genetic progress can be achieved through direct genetic and genomic selection. Therefore, selecting for improved HTol based on reproductive, growth, and body composition performance and public weather station data is feasible in swine. Future research using within-barn environmental records will be performed next.


There is an increasing need to account for genotype-by-environment (G × E) interactions in livestock breeding programs to improve productivity and animal welfare across environmental and management conditions. This is even more relevant for pigs because selection occurs in high-health nucleus farms, while commercial pigs are raised in more challenging environments. In this study, we used single-step homoscedastic and heteroscedastic genomic reaction norm models (RNM) to evaluate G × E interactions in Large White pigs, including 8686 genotyped animals, for reproduction , growth , and body composition  traits. Genetic parameter estimation and single-step genome-wide association studies (ssGWAS) were performed for each trait.The average performance of contemporary groups (CG) was estimated and used as environmental gradient in the reaction norm analyses. We found that the need to consider heterogeneous residual variance in RNM models was trait dependent. Based on estimates of variance components of the RNM slope and of genetic correlations across environmental gradients, G × E interactions clearly existed for TNB and NBA, existed for WW but were of smaller magnitude, and were not detected for NW, OW, MD, and BF. Based on estimates of the genetic variance explained by the markers in sliding genomic windows in ssGWAS, several genomic regions were associated with the RNM slope for TNB, NBA, and WW, indicating specific biological mechanisms underlying environmental sensitivity, and dozens of novel candidate genes were identified. Our results also provided strong evidence that the X chromosome contributed to the intercept and slope of RNM for litter size traits in pigs.We provide a comprehensive description of G × E interactions in Large White pigs for economically-relevant traits and identified important genomic regions and candidate genes associated with GxE interactions on several autosomes and the X chromosome. Implementation of these findings will contribute to more accurate genomic estimates of breeding values by considering G × E interactions, in order to genetically improve the environmental robustness of maternal-line pigs.The online version contains supplementary material available at 10.1186/s12711-021-00645-y. In recent years, increased attention has been directed towards the genetic evaluation of genotype-by-environment (G × E) interactions for economically important traits in livestock –3. In piFrom a technological point of view, the evaluation of G × E interactions has been facilitated by recent advances in genomic technologies and analytical methods , 10, becAnalysis of G × E interactions can be accomplished by two main approaches . The firIn the swine industry, environmental variation can be described in terms of quantitative differences in environmental THI, nutrition, management practices, health status, and other unknown factors, noting that accurately quantifying environmental variation regarding nutrition and management practices is not easy, because these cannot be summarized in an index such as THI . An alteIn early studies of G × E interactions in livestock, individuals recorded in different environments were usually connected based on breed origin, sire progeny groups, or pedigree records . In the Due to its specific gene content and dosage regulation, the X chromosome can have substantial effects on the reproductive performance of both female and male mammals . HoweverAll the datasets analyzed in this study were provided by Smithfield Premium Genetics . Phenotypic records were collected on Large White pigs born from January 2004 to December 2019 on 33 farms that were geographically distributed across North America. Seven traits were analyzed [see Additional file After phenotypic quality control, the raw pedigree file included 265,943 animals across more than 10 generations. Ten generations were traced back when calculating the genetic and genomic relationships. In total, 8992 animals were initially genotyped using the PorcineSNP10K , PorcineSNP50K , PorcineSNP60K , or PorcineSNP80K  Bead Chips . Animals with genotyping call rates lower than 90% were discarded . For animals that were genotyped with more than one SNP panel (N = 82), the data from the higher-density SNP panel were kept for further analysis. Finally, 8686 animals  with genotype information remained in the dataset.We imputed the genotypes from low- to high-density SNP panels using the FImpute software with default parameters  based onP < 0.05) of the lm function in the R software [All recorded categorical fixed effects  and covariates  were selected for inclusion in the model based on the backward elimination procedure  was similar to RNM1, except that heterogeneous residual variances replaced the homogenous residual variance. Similarly to a previous report , the resvariance . All varvariance , 36. TheThe heritability for CG  follows :\documenmentclass2pt{minimThe genetic correlation for a trait between CG The accuracy of genomic estimated breeding values (GEBV) of animal ollowing  as:\docuribed in :\documen of 0.35 , 45.For each trait, the optimal RNM was re-run using the estimated variance components and estimates of the SNP effects for the RNM intercept  using the biomaRt R package [P values and a threshold of 0.05 was set. We further illustrated the related biological processes of the candidate genes identified for each trait using the ClueGO software [We searched all candidate genomic regions in the Pig QTL Database   in order package . Functio package , includi package , Kyoto E package , and the package . The defsoftware .Descriptive statistics for the reproduction, growth, and body composition traits after quality control are in Table Estimates of variance components Table  and theiP < 0.05 based on one-tailed t-test). Based on estimates of the variance components of the additive genetic effects, the estimate of the genetic correlation between the RNM intercept and slope were moderate for TNB (0.602 ± 0.024), NBA (0.425 ± 0.028), WW (− 0.368 ± 0.021), and BF (0.573 ± 0.196), but large for NW, OW, and MD (> 0.94).Significant additive genetic variances for RNM slope were observed for TNB, NBA, and OW , estimates of heritability across environments are shown in Fig. Among the animals that had more than 30 offspring , Fig. For completeness, we also calculated the estimates of heritabilities and the GEBV across environments and performed GWAS for the four traits that did not show significant G × E interactions . These results are described in Additional file Before estimating the correlations of GEBV between traits, we investigated the accuracies of GEBV for the RNM intercept and slope for each trait [see Additional file Among the seven traits analyzed, the highest positive correlations of GEBV were between TNB and NBA, with values of 0.91 for the RNM intercept and 0.85 for the RNM slope. Both TNB and NBA had negative correlations with NW for the GEBV of the RNM slope (0.11–0.35) and with WW for the GEBV of the RNM intercept (0.13–0.28). Also, TNB and NBA had negative correlations with growth and body composition traits for both the RNM intercept and slope . The RNM intercept and slope of WW had positive (~ 0.28) and negative (~ 0.27) correlations with OW, respectively. The GEBV correlations ranged from 0.15 to 0.25 between MD and BF for the RNM intercept and slope. However, we observed moderate and positive GEBV correlations of OW with MD 0.36–0.40) and BF (0.57–0.60). Between the intercept-by-intercept and slope-by-slope comparisons, the GEBV correlations were in opposite directions for 12 of the comparisons, such as for TNB with NW, WW with OW, and WW with MD  chromosome X (SSCX) and one window on SSC12 overlapped between TNB and NBA. For TNB and NBA, no genomic windows were significant for both the RNM intercept and slope, and the numbers of relevant genomic windows were larger for the slope than for the intercept . In contrast, for WW more relevant genomic windows were found for the RNM intercept (4/5) than for the RNM slope (1/5). More than half of the relevant genomic windows were located on SSCX for TNB (5/8) and NBA (12/14), while only one genomic window (1/5) was located on SSCX for WW. Based on the estimated SNP effects  were located within previously reported QTL for the same  trait Table . Among t/16 were MAMLD1 (mastermind like domain containing 1), MTM1 (myotubularin 1), and MID1 (midline 1) genes that were associated with NBA [see Additional file No significantly enriched GO biological process or KEGG pathway was revealed for any of the traits in the functional enrichment analyses. Thus, we investigated the involved biological processes for each candidate gene [see Additional file . [Although the existence of G × E interactions for quantitative traits in livestock has been widely recognized for many decades , signifi.  reared m. . These s. , Wallenb. , Brandt . , Li et a. , Rosé et. , and God.  were conUsing the pedigree-based relationship matrix  using a comprehensive dataset and suggested that RNM with heterogeneous residual variances (termed heteroscedastic RNM) provided a better fit to the data than a homoscedastic RNM. However, our findings show that, actually, the choice of homogenous or heterogeneous residual variances is trait-dependent because the best fit was obtained with the homoscedastic RNM for three of the seven traits . The two RNM studies performed on pigs, by Song et al. [Another important issue regarding the evaluation of G × E interactions involves the modelling of homogenous or heterogeneous residual variances between different environmental conditions in RNM. Carvalheiro et al.  analyzedg et al.  and Tiezg et al. , only usEstimates of the variance components for the RNM slope and of the genetic correlations between different environmental conditions can be used to evaluate whether G × E interactions are present or not. In general, estimates of genetic variance of RNM slopes that significantly differed from zero and/or of genetic correlations between different environments that are lower than 0.8 have been proposed as evidence of G × E interactions in livestock , 23. In Heritability estimates vary considerably between environments when taking G × E interactions into consideration. Silva et al.  used theTo date, few studies have investigated the genetic relationships between traits when accounting for G × E interactions. According to the GEBV correlations calculated among all the studied traits, we found the highest positive correlations between TNB and NBA for both the RNM intercept and slope, which are similar to the high estimates previously reported , 70. ThiP-value of SNPs, as used in previous studies [Although several candidate genomic windows were found to be associated with the traits investigated in this study, we believe that this number is probably conservative, because of the stringent setting applied, i.e. the relevant genomic windows were required to explain 0.50% or more of the total additive genetic variance. Alternatively, a less stringent method would be to select the top-N candidate genomic windows according to the magnitude of the explained variance or approximate  studies , 14. How studies , 23).MID1 and TRPC5 , have been reported to be significantly associated with NBA and other reproduction traits in Landrace and Large White pigs [MAMLD1 and MTM1 genes. These genes, together with MID1, are known to have a role in reproduction-related phenotypes in humans [In this study, we observed that most of the candidate genomic windows (> 70%) for TNB and NBA are involved in environmental sensitivity, as they are associated with the RNM slope. In this context, it is important to highlight that these relevant genomic windows were mainly located on SSCX. However, no SNP in the candidate genomic regions for TNB and NBA overlapped with the hundreds of candidate SNPs for the two traits published in a recent similar study in pigs by Tiezzi et al. . A possiite pigs . The othn humans , 74. BotTRPC5) for TNB and NBA. In addition, the relevant genomic regions for WW overlap with multiple previously reported QTL for health-related traits in pigs, including melanoma susceptibility and immune response [UBE3A (ubiquitin protein ligase E3A), has also been reported to be associated with TNB in pigs [Among the five relevant genomic windows found for WW, four and one genomic windows were independently associated with the RNM intercept and slope, respectively. Although little biological evidence is available in the literature on candidate genes for WW, the genomic region on SSCX is closely located to one candidate gene  and suggested for WW. No G × E interaction was observed for the other four traits . For the RNM, the choice of homogeneous or heterogeneous residual variances depended on the trait studied. By dissecting the SNPs with different effects across environmental gradient levels, we detected several candidate SNPs, genes, and genomic regions, which contribute to better understand the biological basis of G × E interactions for these traits. Furthermore, our results emphasize the biological contribution of the X chromosome to reproduction traits in pigs, especially regarding their G × E interactions.Additional file 1: Table S1. Summary of the phenotypic records used in this study before data editing and quality control. Table S2. Selection of the fixed effects, covariates, and random effects included in the final mixed animal models. Table S3. Standard errors of variance components estimates based on the homogenous (RNM1) and heterogeneous (RNM2) residuals. Table S4. Spearman’s rank correlations of genomic estimated breeding values across three representative environmental gradients. Table S5. Genomic windows with the significantly explained variances and associated candidate genes for the four traits without clear G × E interaction. Table S6. Related quantitative trait loci (QTL) within relevant genomic regions for the four traits without clear G × E interaction. Table S7. Accuracies and reliabilities of genomic estimated breeding values for all animals. Table S8. Sample sizes and accuracies used for calculating the approximate genetic correlations (weighted Pearson coefficients). Table S9. Detailed information for the candidate genes found in this study.Additional file 2: Fig. S1. Density distribution of the estimated effects of contemporary groups. Fig. S2. Genetic correlations across environmental gradients using the optimal reaction norm models for the four traits without G × E interaction. Fig. S3. Heritability estimates using reaction norm models with homogenous (RNM1) and heterogeneous (RNM2) residual variances for the four traits without G × E interaction. Fig. S4. Genomic estimated breeding values using the optimal reaction norm models for 20 sires with the highest and lowest reaction norm slopes, respectively (for the four traits without G × E interaction). Fig. S5. Miami plots for the proportion of explained variance by 5-SNP sliding genomic windows for the four traits without clear G × E interaction. Fig. S6. Biological processes involved for all candidate genes identified in this study. Fig. S7. Five enriched terms of Human Phenotype Ontology for the number of piglets born alive (NBA). Fig. S8. Heritability estimates for the total number of piglets born (TNB) and number of piglets born alive (NBA) across environmental gradients using three scenarios regarding the X-chromosome markers.Additional file 3: Detailed results for the four traits that did not show significant G × E interactions.


Breeding maize lines with the improved level of desired agronomic traits under optimum and drought conditions as well as increased levels of resistance to several diseases such as maize lethal necrosis (MLN) is one of the most sustainable approaches for the sub-Saharan African region. In this study, 879 doubled haploid (DH) lines derived from 26 biparental populations were evaluated under artificial inoculation of MLN, as well as under well-watered (WW) and water-stressed (WS) conditions for grain yield and other agronomic traits. All DH lines were used for analyses of genotypic variability, association studies, and genomic predictions for the grain yield and other yield-related traits. Genome-wide association study (GWAS) using a mixed linear FarmCPU model identified SNPs associated with the studied traits i.e., about seven and eight SNPs for the grain yield; 16 and 12 for anthesis date; seven and eight for anthesis silking interval; 14 and 5 for both ear and plant height; and 15 and 5 for moisture under both WW and WS environments, respectively. Similarly, about 13 and 11 SNPs associated with gray leaf spot and turcicum leaf blight were identified. Eleven SNPs associated with senescence under WS management that had depicted drought-stress-tolerant QTLs were identified. Under MLN artificial inoculation, a total of 12 and 10 SNPs associated with MLN disease severity and AUDPC traits, respectively, were identified. Genomic prediction under WW, WS, and MLN disease artificial inoculation revealed moderate-to-high prediction accuracy. The findings of this study provide useful information on understanding the genetic basis for the MLN resistance, grain yield, and other agronomic traits under MLN artificial inoculation, WW, and WS conditions. Therefore, the obtained information can be used for further validation and developing functional molecular markers for marker-assisted selection and for implementing genomic prediction to develop superior elite lines. Both biotic and abiotic stresses are the major threats to crop production, particularly maize in SSA. Drought stress, high costs of improved seeds and fertilizers [Maize is an important staple food crop in sub-Saharan Africa (SSA) where a large area is under maize production . In eastMaize chlorotic mottle virus (MCMV) and sugarcane mosaic virus (SCMV) viruses were the confirmed pathogens that have jointly incited the MLN disease [MLN was first reported in Kenya in 2011 and later reported in Tanzania, Uganda, Rwanda, D.R. Congo, and Ethiopia ,4,5. Mai disease ,7. Both  disease . MCMV ha disease ,9,10,11. disease . Thus, t disease .Doubled haploid (DH) lines allow complete homozygosity over lines developed through pedigree breeding; this allows precision in phenotyping over multiple locations and years . Further2 =0.1 was used [Advancement in next-generation sequencing tools promoted genome-wide association studies (GWAS) in many crops including maize . Associawas used . In maizwas used .GWAS is useful in allele mining by dissecting the quantitative traits . QTL or Genomic selection (GS) uses genome-wide markers to predict the breeding values of individuals by trapping the effects of both major and minor genes . In GS, To understand how WS affects grain yield and other key traits, this study was performed using a tropical maize population under drought and optimum conditions across multi-location field trials and the MLN effect under artificial inoculation in Kenya. The objectives of the study were to (i) evaluate the large set of 879 tropical and subtropical maize DH lines for their responses to MLN disease severity under artificial inoculation, grain yield (GY), and other yield-related traits under WW and WS conditions; (ii) identify genomic regions and putative candidate genes associated with these traits across the three management conditions; and (iii) assess the potential of GS within management conditions. This study will provide valuable information for uncovering the genetic basis of GY under WW and WS conditions.In this study, 1462 DH lines from 40 populations were phenotyped in multiple locations under WW, WS, and MLN artificial inoculation conditions. Whereas, among these DH lines, 879 DH lines derived from 26 DH populations were genotyped, for the final analyses, we used only 879 DH lines. There are 26 parental lines were used to develop these DH populations . Among tThe detailed protocol on the preparation of inoculum is explained in earlier studies ,13,37. IThe DH lines were evaluated at Kakamega under WW management, Kiboko under both WW and WS at different sites, and Naivasha under artificial inoculation of MLN for two seasons. Grain yield , anthesis date , silking date , anthesis silking interval , plant height  measured from the ground level to the base of the tassel after milk stage, ear height  measured from the ground level to the node bearing the uppermost ear after milking stage, moisture , senescence , grey leaf spot , turcicum leaf blight , common rust , and MLN-DS  were recorded and analyzed. All the traits were phenotyped in all the trials but not in all the management conditions. For example, AD, ASI, EH, PH, MOI, and GY traits were phenotyped under both WW and WS; TLB, GLS, and CR under WW; SEN and ER under WS; and MLN-DS and AUDPC under MLN management conditions.ith genotype, jth environment in kth replication of the lth incomplete block, μ is overall means, iG is the genetic effect of the ith genotype, jL is the effect of the jth environment, kth replication at the jth environment, lth incomplete block in the kth replication at the jth environment, and 2) was calculated as follows:http://www.R-project.org, accessed on 17 November 2021).Statistical model fitting for different traits was checked by plotting the histogram with standardized residuals. A plot of residuals against fitted values has shown that the residuals were symmetrically distributed with constant variance for all traits; thus, the data were not transformed. The phenotypic traits were analyzed with the restricted maximum likelihood (REML) method designed in the multi-environment trial analysis (META) R software developed in CIMMYT . The fol2 = 0.2 and r2 = 0.1 using average pairwise distance, where the nonlinear model r2 was used [All 879 DH lines were genotyped with a high-density genotype by sequencing (GBS) platform using the pre-developed protocol at the Institute for Genomic Diversity, Cornell University, Ithaca, USA ,31,40. Dwas used ,46. Scatwas used .p = 0.001. The period of length of burn-in was set to 10,000, and Markov Chain Monte Carlo (MCMC) values were set to 10,000 cycles [The genetic relationship tree construction for 879 DH lines was performed using Darwin 6.0.21 software. At the first step, the genetic distance matrix was calculated based on the mean Euclidean method where homozygote 100 and heterozygote 50% similarity were considered. Secondly, the unweighted neighbor-joining clustering method was employed to construct the diversity tree of the genotypes. The population structure of 879 DH lines, which had both phenotypic and genotypic data, were analyzed and sub-grouped using structure software 2.3.4 version with 6745 SNPs ,49 based0 cycles .p threshold of 0.01, a quantitative trait nucleotide (QTN) threshold of 0.01, and a MAF threshold of 0.05. The maxLoop refers to the total number of iterations used. The p values selected into the model for the first iteration, the p-value selected into the model from the second iteration, and the minimum MAF of SNPs used in the analysis refers to the p threshold, QTN threshold, and MAF threshold, respectively. To determine the significance threshold, multiple testing correction was conducted where the total number of tests was estimated based on the average extent of LD at r2 = 0.1. Concerning the above, the significant associations were declared when p values in independent tests were less than 9 × 10−6 [http://blast.maizegdb.org/home.php, accessed on 23 November 2021; http://www.maizegdb.org, accessed on 23 November 2021). CurlyWhurly Version 1.19 was used to plot and visualize the first three analyzed PCA components .GWAS analysis was performed with the R package “FarmCPU—Fixed and random model Circulating Probability Unification” . GBS mar9 × 10−6 ,52. The The phenotypic traits BLUEs were used for the GS analysis. Ridge-regression BLUP (RR-BLUP) with five-fold cross-validation was applied. From the GBS data, a subset of 6745 SNPs distributed uniformly across the genome, with no missing values, and minor allele frequency >0.10 were used for GS in GWAS panel under different management conditions. Details of the implementation of the RR-BLUP model are described in Zhao et al. . We appl2) under both WW and WS conditions, while MLN-DS and TLB had relatively high H2 with 0.67 and 0.80, respectively.The normal distribution was observed for each trait under WW and WS conditions . The anaUnder WS management, the genotypes CKDHL140940, CKDHL142056, CKDHL142091, CKDHL141377, and CKDHL142061 had produced the highest GY values of 5.67, 4.91, 4.85, 4.81, and 4.77 t/ha, respectively. The genotype CKDHL140037 had a lesser AD (62.11 days) than the grand mean (67.55 days) and the best check Duma43 (62.68 days). Genotype CKDHL140091 has shown 0.38 days less than the best check (0.74 days) and grand mean (2.28 days). Comparatively, plant height (185.55 cm), which is not too tall or short, was obtained in the genotype CKDHL140125, even though it was slightly higher than the best check KD8031 (183.88 cm) and grand mean (176.40 cm). Under WW management, the genotype CKDHL141097 was performed better than the best check CML444 and overall mean, which had the GY values of 9.17, 7.20, and 8.28 t/ha, respectively. An AD (65.93 days) was recorded in the CKDHL140933 genotype, which was earlier than the overall mean (67.78 days) and comparable to the best check Duma43 (64.47 days) under WW management. Similarly, the genotype CKDHL140876 had a good ASI of 0.37 days lesser than the grand mean (0.42 days) but not better than the check CZL04003 (0.35 days). About 52 DH lines were rated from two to four and have depicted the resistance reactions against the MLN-DS, while 735 DH lines rated from four to seven were grouped as moderately resistant, and the remaining 92 DH lines had seven to nine values and were grouped as susceptible genotypes .The phenotypic traits correlation analysis was performed independently for genotypes evaluated under both WW and WS management conditions. Significant strong positive correlations were observed between PH and EH both under WW and WS management, which was 0.71 and 0.80, respectively . EH and The selected markers distribution was graphically presented in 2 and plotted against their distance. LD sliding window type with 11,225 comparisons were obtained from adjacent markers, while each dot represented a pair of distances between two markers on the window and their squared correlation coefficient. The LD decay cut-off point (r2) at 0.2 and 0.1 had 3.69 and 10.49 Kbs average physical distance, respectively  values . An Evanectively .Based on principal component analyses all the DH lines were broadly categorized into three groups: Category One (215), Category Two (223), and Category Three (441) based on the displayed plot . The popp-value at the significance threshold cut-off (p = 9 × 10−6), the marker positions, putative candidate gene, and its biological function were annotated for each trait. The GWAS results for all traits are summarized using Manhattan plots  essential for the development of gametophyte and sporophyte [GRMZM2G365374 encoded as heat shock 70 kDa protein (HSPA1A) under WS was known to respond to heat-shock stress [The two important SNPs linked to PH athogens . Lysine athogens . SNP, S2k stress .S1_188031152 and S8_11662494 associated with the MOI were detected with well-described putative candidate genes, GRMZM2G419436 and GRMZM2G700386, respectively. The gene GRMZM2G419436 is characterized as well-associated receptor kinase 5 (WAK5), which significantly controls cell expansion, morphogenesis, and development [GRMZM2G700386 gene characterized as β-1,2-xylosyltransferase XYXT1 is involved in the xylosylation of xylan, the primary and secondary walls or major hemicellulose of angiosperms [S1_204865984, linked to SEN under WS environment was the annotated putative candidate gene, GRMZM2G328309, explained as ribonuclease E/G-like protein, chloroplastic, which is a family of proteins that plays a pivotal function to metabolize RNA [The SNPs elopment , while tiosperms . The SNPlize RNA .GRMZM2G009591 and GRMZM2G101117, annotated from the SNPs S1_246469847 and S7_82649117 linked to GLS disease resistance had been characterized as pyrophosphate fructose 6-phosphate 1-phosphotransferase and GDSL esterase/lipase, respectively. The first gene, GRMZM2G009591, is known to catalyze D-fructose 6-phosphate phosphorylation [GRMZM2G101117, is known for the molecular function hydrolytic activities of GDSL esterases and lipases enzymes [GRMZM2G039173, GRMZM2G071023, and GRMZM2G106119 were identified based on the associated SNPs S6_157820129 and S4_212595942 with the TLB resistance. Rédei [GRMZM2G039173 gene as the major facilitator superfamily protein that aided in transporting small solutes based on the chemiosmotic ion gradients, while the second putative gene characterized by Chai et al. [The putative genes, rylation ; the sec enzymes . Under We. Rédei  has descS3_184235364 and S6_38115747 are annotated with GRMZM2G429982 and GRMZM5G818106 candidate genes that have osmotin-like protein and phospholipase A1-II 7 functions, respectively [GRMZM2G429982 as being involved in biotic and abiotic stresses tolerance in plants, whereas the candidate gene GRMZM5G818106 has been described as protective of high temperature, cold, salt, and drought [GRMZM2G003752, which was characterized as fasciclin-like arabinogalactan protein 10, was to respond to abiotic stress and mediate the growth and development of the plant. This candidate gene was annotated from the S2_16652265 marker associated with AUDPC values. Similarly, the S10_125845596 marker was linked to the AUDPC value and then the putative candidate gene, GRMZM2G003917, was identified. This gene has been described by Wu et al. [MLN-DS trait-associated SNPs ectively ,80. Kuma drought . Wu et aIn the present study, a total of 98, 54, and 22 SNPs associated with various agronomic traits under WW, WS, and MLN conditions, respectively, were identified. Among these SNPs, some existed within different gene models whose genetic role is associated with either biotic or abiotic stress mechanisms. The favorable alleles can be identified by resequencing the detected candidate genes from contrasting, and these SNPs could be potentially converted to simple PCR-based markers to follow MAS in molecular breeding . SimilarHigh genetic gain can be achieved for complex traits by integrating modern tools into maize breeding ,86. WithPhenotypic evaluation of 879 DH lines under artificially inoculated MLN has identified about 52 genotypes resistant/tolerant to MLN-DS, while seven of the selected genotypes  can be used as sources of resistance to MLN. GWAS identified SNPs associated with the studied traits i.e., about seven and eight SNPs for the GY; 17 and 31 for anthesis date; 10 and 22 for anthesis silking interval; 14 and 6 for ear height; and 15 and 5 for moisture content under WW and WS environments, respectively. Similarly, about 13 and 11 SNPs associated with GLS and TLB, respectively, were detected. Eleven SNPs were significantly associated with senescence were identified under WS management. Under MLN artificial inoculation, a total of 12 and 10 SNPs were associated with MLN-DS and AUDPC traits, respectively; these SNPs and the identified candidate genes for each trait can be used in the trait improvement program in maize breeding. GS under WW, WS, and MLN disease artificial inoculation environments revealed moderate-to-high prediction accuracies. All the detected SNPs in this study need further validation before introducing to breeding pipelines, and it will be a great help for the understanding of complex genetic architecture traits under WW and WS. Overall, the present study identified several significant SNPs associated with GY and other agronomic traits that help in the selection of donor lines with favorable alleles for multiple traits. These results provide insights into the genetics of MLN resistance and other agronomic traits under optimum and drought stress conditions.


Arachis hypogaea L.) is an important oilseed crop worldwide, and peanut height has been shown to be closely related to yield, therefore a better understanding of the genetic base of plant height-related traits may allow us to have better control of crop yield. Plant height-related traits are quantitative traits that are genetically controlled by many genes, and distinct quantitive trait loci (QTLs) may be identified for different peanut accessions/genotypes. In the present study, in order to gain a more complete picture of the genetic base for peanut height-related traits, we first make use of the high quality NGS sequence data for 159 peanut accessions that are available within our research groups, to carry out a GWAS study for searching plant height-related regions. We then perform a literature survey and collect QTLs for two plant height-related traits  from earlier related QTL/GWAS studies in peanut. In total, we find 74 and 21 genomic regions that are, associated with traits Ph and Fbl, respectively. Annotation of these regions found a total of 692 and 229 genes for, respectively, Ph and Fbl, and among those genes, 158 genes are shared. KEGG and GO enrichment analyses of those candidate genes reveal that Ph- and Fbl-associated genes are both enriched in the biosynthesis of secondary metabolites, some basic processes, pathways, or complexes that are supposed to be crucial for plant development and growth.Peanut ( Arachis hypogaea L.) is widely cultivated across warm temperate, subtropical, and tropical zones [As one important source of edible oil, the groundnut  ratio for the analyzed peanut genome data is 1.81.A total of 15,556 SNPs and 1561 InDels have been identified from the collected peanut genomes , leadingH2: 0.44–0.51) for all three traits, which are positively correlated with each other (r: 0.55–0.64)  .p-values that deviate dramatically less from expectation than the p-values using the GLM and GLM (Q) models . For Ph, two of the identified associated peak SNPs are located on chromosome Arahy.02, three on Arahy.16, one on each of Arahy.19, and Arahy.20 (H2 = 0.51) comparing to Ph, only four associated peak SNPs have been identified, three from Arahy.16 and one on Arahy.18. It is worth noting that traits Ph and Fb1 share one associated peak SNPs on Arahy.16, Arahy.16:32726692, and even more interestingly, from the candidate genome region that is centered on this shared peak SNP, two genes  are annotated to be LRR receptor-like kinase. LRR receptor-like kinases are mostly involved in signal transduction and play an essential role in plant development and stress resistance [Genome-wide association analysis of the phenotypically characterized peanut accessions has been conducted to see if any of the acquired SNPs are associated with the studied plant height-related traits. For traits Ph and Fbl, tests with the MLM (K) and MLM (QK) models, especially MLM (QK), produce matrices . NeverthArahy.20 . For Fblsistance ,22. No sWe have performed a thorough literature survey and found twelve QTL/GWAS studies that also looked at the same peanut phenotypic traits as we do though with different sets of peanut accessions . These sKEGG enrichment analysis found that the Ph-associated genes are enriched in four metabolic pathways  as well as one genetic information processing complex (Proteasome) . While GFor Fbl, its associated genes are over-represented in one KEGG genetic information processing complex (Spliceosome) and several KEGG metabolic pathways: Biosynthesis of secondary metabolites , Amino acid metabolism , and beta-Alanine metabolism . In the The cultivated peanut is an important oilseed crop and it is widely cultivated across warm temperate, subtropical, and tropical areas ,3. PeanuThe plant height-related traits are quantitative traits that depend on the cumulative actions of multiple genes and environments. Here, in the present study, we have focused on three plant-height related traits  and had good control of the environmental factors during both experiments and data analyses. We find significantly associated SNPs for two of the studied traits (Ph and Fbl), KEGG and GO analyses of the genes from these regions together with those from twelve earlier QTL/GWAS studies for the same traits show that both Ph- and Fbl-associated genes are enriched in the biosynthesis of secondary metabolites. Secondary metabolites are usually specific to a narrow arrangement of species, defining the diverse “personalities” of different organisms, caffeine, alkaloids, flavonoids, and tannins represents good examples of secondary metabolites in plants . SecondaCandidate genes for Ph and Fbl are also both highly represented in beta-Alanine metabolism. As the precursor of CoA, beta-alanine is important for the metabolisms of phospholipids and fatty acids, as well as for the tricarboxylic acid cycle. It has also been shown that beta-alanine can protect plants from a variety of abiotic and biotic stresses  ,32,33, pIn addition to the above-mentioned common pathways/activities for Ph and Fbl, there are also trait-specific enrichments that are mostly in basic processes, pathways, or complexes, which are supposed to be crucial for plant development and growth. For example, Ph-associated genes are also enriched in brassinosteroids biosynthesis, ascorbate and aldarate metabolism, and regulation of epidermis development ,34. For However, the interpretation of both GO and KEGG results need to be cautious, considering the fact that not all predicted peanuts proteins can be mapped to a known GO term or KO ID and it is common for one enzyme to be involved in several different pathways or processes . TherefoA. hypogaea ssp. hypogaea (66 var. hypogea and 23 var. hirsuta), 61 A. hypogaea ssp. fastigiata (51 var. vulgaris and 10 var. fastigiata), and nine irregular types ). Within this model, all included explanatory variables had random effects: the nine combinations between growing years and locations were considered as environmental blocks, the five repeats for each studied plant accession were nested within a block, the peanut accessions, and the interaction between accession and environmental blocks. From the constructed model, the BLUP (Best Linear Unbiased Prediction) estimates of the random genetic effect of peanut accessions on the trait that was on focus were extracted, and it was these BLUP values that would be used (as response variable) later on in the GWAS analyses to study their associations with the identified high-quality SNPs (see below) [https://cran.r-project.org/bin/windows/base/, (accessed on 5 July 2019)), and the broad-sense heritability (2H) for each trait was estimated using the R package “lem4”.All peanut accessions were planted at three different locations  in China during 2013, 2014, and 2016. Each accession was represented by 34–40 plants that were grown in a two-row plot (5.00 m long and 0.80 m wide). Three plant height-related traits were measured for each accession: main stem height (Ph), the number of nodes on the main stem (Nsk), and the first branch length (Fbl). Each trait for each harvested accession per location and per year was repeatedly measured five times. To minimize environmental effects, a mixed linear model was built for each trait (as response variable) using the R version 3.6.1 function “lem4” (e below) . The corhttps://www.peanutbase.org/data/public/Arachis_hypogaea/Tifrunner.gnm1.KYV3/ (accessed on 5 July 2019)) using BWA v0.6.2 (-t 4 –M –k 32 –r 1 –c 1) [https://software.broadinstitute.org/gatk (accessed on 5 July 2019)). The identified variants were filtered to reduce the false-positive errors using GATK Variant Filtration. To im-prove variation quality, the following criteria were adopted: (i) quality score >20; (ii) coverage depth >3 fold; (iii) missing ratio within each population <80%; (iv) a global minor allele frequency (MAF) >0.05. The genic and intergenic locations of all high-quality variants (including SNPs) were determined using ANNOVAR [High-quality NGS sequence reads acquired via a genotyping by sequencing (GBS) approach for the 159 studied peanut accessions were downloaded from the Sequence Read Archive database (SRA accession: PRJNA525244) . The acq 1 –c 1) . Variant ANNOVAR .Y = Xa + e; GLM(G), Y = Xa + Qb + e, and two Mixed Linear Model: MLM(K), Y = Xa + Ku + e; MLM(GK), Y = Xa + Qb + Ku + e, where Y denoted the phenotype  and X the genotype at each SNP locus, Q represented population structure estimated by Admixture and K was the relationship between samples (i.e. kinship coefficients). For each trait, the best model out of the tested four based on the Q-Q plot was accepted as the final model. We used a p-value of 10−5 or less to establish the significance. The candidate genome regions that might contain genes responsible for the studied plant height-related traits were the most strongly associated SNPs (hereafter referred to as peak SNPs) plus a 100kb-long genomic region that were centered on the peak SNPs [Genome-wide association study (GWAS) of the three plant height-related traits based on the acquired high-quality SNPs were conducted using TASSEL v5.0 ,38 Four eak SNPs ,40,41,42eak SNPs .www.omicshare.com/tools, (accessed on 5 July 2020)). To accommodate the multiple testing problem in the enrichment analysis, Benjamini–Hochberg adjusted p-values were calculated [To look for more candidate genes underlying peanut height-related traits, we carried out a thorough literature survey of related QTL/GWAS studies. All genes within the candidate genomic regions that were identified by both the present GWAS analyses and earlier studies were considered as candidate genes for the related traits. Both Gene Ontology (GO) enrichment analysis, and KEGG (Kyoto Encyclopedia of Genes and Genomes pathway database) pathway enrichment analysis was carried out on the candidate genes using the OmicShare tools, a free online platform for data analysis (lculated .