This study was conducted to test the efficiency of genomic selection for milk production traits in a Korean Holstein cattle population.A total of 506,481 milk production records from 293,855 animals  were used to estimate breeding value by single step best linear unbiased prediction.SB) and that for the production of cows (LSC) were 7.9 and 8.1 years, respectively, and the estimated generation intervals of dams for the production of bulls (LDB) and cows (LDC) were 4.9 and 4.2 years, respectively. In the overall data set, the reliability of genomic estimated breeding value (GEBV) increased by 9% on average over that of estimated breeding value (EBV), and increased by 7% in cows with test records, about 4% in bulls with progeny records, and 13% in heifers without test records. The difference in the reliability between GEBV and EBV was especially significant for the data from young bulls, i.e. 17% on average for milk (39% vs 22%), fat (39% vs 22%), and protein (37% vs 22%) yields, respectively. When selected for the milk yield using GEBV, the genetic gain increased about 7.1% over the gain with the EBV in the cows with test records, and by 2.9% in bulls with progeny records, while the genetic gain increased by about 24.2% in heifers without test records and by 35% in young bulls without progeny records.The heritability estimates for milk, fat, and protein yields in the first parity were 0.28, 0.26, and 0.23, respectively. As the parity increased, the heritability decreased for all milk production traits. The estimated generation intervals of sire for the production of bulls (LMore genetic gains can be expected through the use of GEBV than EBV, and genomic selection was more effective in the selection of young bulls and heifers without test records. With the development of DNA analysis technology and the reduced cost of single nucleotide polymorphism (SNP) chip analysis, a lot of research has been conducted on the genomic selection of dairy cattle –7. GenglIn the USA and Canada, young bulls have been evaluated using genomic information since 2009. For individuals evaluated in these countries without phenotypic information, it has been reported that the GEBV estimated using genomic information was more reliable than the estimated breeding value (EBV) estimated from the conventional best linear unbiased prediction (CBLUP) method . In JapaSchaeffer  proposed−6). Animals with SNP missing rates greater than 10% were also excluded from analyses. After the quality control tests, 2,007 individuals and 41,837 SNPs were used in the following analysis  and cows , were genotyped using a Bovine SNP50k chip , through which 50,908 SNPs were identified. To ensure the quality of the genotypic data obtained, SNPs were excluded from analyses if they were found on the sex chromosomes, lacked chromosomal information, had missing rates with higher than 10%, lacked polymorphism , had a minor allele frequency less than 1%, or were found with a chi-squared value of the Hard-Weinberg disequilibrium greater than 23.9 , or whose calving ages were outside the range of 17 to 31 months in the first parity, 31 to 45 months in the second parity, or 45 to 59 months in the third parity. These eliminations were due to the potential outliers or ambiguous parity. Therefore, a total of 506,481 milk production records from 293,855 animals were used for the final analyses .The HYS and parity×month of age at calving (PA) were included as fixed effects in a statistical analysis that used the following model:yi = n×1 vector of observation in the ith parity, bi = p×1 vector of the fixed effect, ai = q×1 vector of the additive random genetic effect, ei = n×1 vector of the residual effect, and Xi(n×p), Zi(n×q), and Wi(n×q) were known incidence matrices corresponding to bi, and ai, respectively. The total numbers of HYS, PA, and animals within pedigree values included in the analysis using this model, were 62,287, 75, and 384,406 head, respectively. Since there were no observed values comparable each parity by trait value, the value of the covariance matrix was set equal to zero in the matrix of the error variance and covariance shown below:Where The GEBV was estimated using Single-Step genomic best linear unbiased prediction which integrates the genomically derived relationships with pedigree relationships .The mixed model equation (MME) used in further analyses was as follows:g11, g22 = genetic variance, g12, g21 = genetic covariance, r11, r22 = error variance, r12, g21 = error covariance, A−1 = the inverse matrix of the numerator relationship matrix, G1− = the inverse matrix of the genomic relationship matrix, and r2) of breeding value was calculated using the prediction error variance (PEV) value by the following formula: where Variance components, and EBV and GEBV values were es timated using the BLUPF90 family program .SB) and that for the production of cows (LSC) were 7.9 and 8.1 years, respectively, and the estimated generation intervals of dams for the production of bulls (LDB) and cows (LDC) were 4.9 and 4.2 years, respectively  were about 7 years, and those for the LDB and LDC were about 4 years. After genomic selection had been applied for 5 years, the generation intervals for the LSB, LSC, LDB, and LDC were reported to decrease to about 3, 5, 3, and 3.6 years, respectively [DC it was 4.2 years and remained stable around this value [In the Korean dairy cattle population examined, the esti mated LWhen the regression coefficients between GEBV estimated from the single-step best linear unbiased prediction (ssBLUP) method and EBV estimated from the CBLUP method were compared, the coefficients estimated for young bulls without progeny records were the lowest, and in the range of 0.54 to 0.61. It can thus be concluded that genomic selection was more efficient in heifers and in young bulls without test records .The reliability of GEBV was higher than that of EBV, es pecially for animals without phenotypic data. These results agreed with those of Forni et al , who repThe reason for the increased accuracy resulting from using genomic information might be due to the fact that when doing this the pedigree coefficient matrix used in the CBLUP method was replaced by a genomic relationship matrix, which was derived from the genotype similarity calculated for all markers and considering Mendelian sampling ,24. In tIn other studies that compared the reliability of genomic and conventional selection methods for the estimation of breeding values, the reliability of GEBV was comparable to that of either the parent average or the pedigree index method –27. ThesFor the selection of proven bulls in Korea, first about 40 head of young bulls are selected and then 2 of them are further selected on the basis of the progeny test records from 20 of their daughter heifers. For the selection of young heifers, pedigree information is used. Therefore, the selection rate of young bulls is 5% and the selection intensity (i) is 2.06, while for young heifers the selection rate is 90% (9 out of 10) and the selection intensity (i) is 0.20 .When selected for the milk yield using GEBV, the genetic gain increased in this study by about 7.1% over the gain with the EBV method in cows with test records, and by 2.9% in bulls with progeny records, while it increased by about 24.2% in heifers without test records and by 35% in young bulls without progeny records. Therefore, the application of genomic selection to gene introgression can help to speed up the process of introgression of a gene while simultaneously increasing the genetic gain [Since the selection intensity actively used in the domestic population in Korea was applied in the present study, more genetic gains to this population can be expected through the use of genomic selection, since more young bulls and heifers can be selected to improve desirable traits.Wiggans et al  reportedTherefore, through the participation of Korea in interna tional genetic performance evaluation programs using genomic information, or by sharing data with overseas dairy cattle populations related to the genetic resources of domestic dairy cattle populations, the improvement of dairy cattle can be facilitated. Also, the efficiency of data utilization should be increased and the introduction of new technologies should be accelerated in Korea to facilitate dairy cattle improvement.


We investigated the effectiveness of mate allocation strategies accounting for non-additive genetic effects to improve crossbred performance in a two-way crossbreeding scheme. We did this by computer simulation of 10 generations of evaluation and selection. QTL effects were simulated as correlated across purebreds and crossbreds, and (positive) heterosis was simulated as directional dominance. The purebred-crossbred correlation was 0.30 or 0.68 depending on the genetic variance component used. Dominance and additive marker effects were estimated simultaneously for purebreds and crossbreds by multiple trait genomic BLUP. Four scenarios that differ in the sources of information  and mate allocation strategies  were evaluated under different cases of genetic variance components. Selecting purebred animals for purebred performance yielded a response of 0.2 genetic standard deviations of the trait “crossbred performance” per generation, whereas selecting purebred animals for crossbred performance doubled the genetic response. Mate allocation strategy to maximize the expected total genetic value of crossbred descendants resulted in a slight increase  of the crossbred performance. Purebred populations increased homozygosity, but the heterozygosity of the crossbreds remained constant. When purebred-crossbred genetic correlation is low, selecting purebred animals for crossbred performance using crossbred information is a more efficient strategy to exploit heterosis and increase performance at the crossbred commercial level, whereas mate allocation did not improve crossbred performance. The main goal of crossbreeding is to improve the performance of crossbred (CB) animals by exploiting heterosis and breed complementarity . Dominanulation) . In pigsulation) , the comulation) . Such stulation)  and on re.g., 0.63 on average in pigs, with 50% of the estimates between 0.45 and 0.87 . The effects of genetic causal variants depend on the environment where the animal is raised (GxE), and depend on the genetic backgrounds where the variants are expressed in (GxG). Both, GxE and GxG may result in a low et al.’s model also includes a regression on “genomic inbreeding” (observed homozygosity) in PB and CB animals to measure individual “inbreeding depression”. This can be seen as the opposite of heterosis and therefore it is possible to correct by, and to predict, individual heterosis . This lcendants . Then, tcendants . AlthougAlthough the methodology is available for simultaneously accounting for all these aspects in genomic evaluations there is a need of addressing the benefits of its implementation in practice in a crossbreeding scheme. This is crucial because its implementation would require large organizational changes in the way pig breeding schemes are organized today.The objective of this study was to investigate the effectiveness of mate allocation strategies and genomic evaluations that account for additive and dominance effects to improve CB performance. Breed-specific QTL and individual genomic heterosis effects were explicitly simulated in a two-way pig crossbreeding scheme. The effects of the sources of information used in the genetic evaluation (only PB data or PB and CB data), of several narrow and broad-sense heritability values, and of several options for mate allocation to produce the CB were examined.e.g., litter size) was simulated. The simulation was split into two steps. In the first step, the simulator QMSim  (undergoing drift and mutation), the simulator QMSim  was usede.g., morphological defects, disease resistance, etc). Hence, the best 12 males and 204 females were chosen within each breed at each generation to be parents of the next generation of PB animals. These animals were selected at birth (before having any own record) based on selection criteria that will be detailed later. In the second step, a two-way pig crossbreeding scheme with 10 generations of selection was simulated . The inii.e., with an additive genetic relationship of 0.125). For a selection candidate in P2, the most related CB animal with a performance record was an offspring of a paternal grand-grand-sire . This difference occurs because in P2, different sets of dams were selected to produce PB and CB offspring, while in P1, the same set of sires was used to create both PB and CB offspring . All PB and CB females were eventually phenotyped and genotyped, but at the time of selection, candidates to selection were not phenotyped. The information available for the estimation of breeding values at the time of selection differed between breeds. For a selection candidate in P1, the most related CB animal with a performance record was an offspring of the paternal grandsire  of 120 cM each. In the first generation of the historical population, biallelic markers  and QTL  were distributed at random along the chromosomes with 0.5 allele frequencies. The mutation rate (recurrent mutation process) for markers and QTL was assumed to be Marker quality control was done in each breed (generation 2030) independently. Markers with minor allele frequency (MAF) lower than 0.05 and out of Hardy-Weinberg equilibrium .Linkage disequilibrium patterns (LD) were also evaluated in the two breeds (P1 and P2) in generation 2030. LD was measured by calculating the squared correlation coefficient  controlled by additive and dominance QTL action was simulated. The assumed genetic model is the same as QTL positions were the same in P1, P2 and CB, but with different allele frequencies. The positions and effects of 2500 QTL were sampled anew in each replicate. A maternal trait  per 10% increase in genomic inbreeding) in P1, P2 and CB To simulate heterosis (superiority of heterozygotes over homozygotes), a possible model is directional dominance, which states that dominance effects tend  to be favorable for the trait, Then we simulated breed-specific QTL effects, but correlated across the three populations  to account for differences due to GxE and gen-by-gen (GxG) (epistasis) interactions. To do that, additive Residual variances  has two true breeding values, one on the PB scale e.g., P1 The The true total genetic value  of selection were simulated. They differed in the sources of information used for genomic evaluation, in selection criteria within-breed and in mate allocation strategies used to create the CB descendants. i.e., As explained before, the 4 scenarios were tested across three cases of genetic parameters: (1) Three extra scenarios were considered. Scenarios S1 (first) and S3 (second) were evaluated under case 3 considering a After selecting PB animals at each generation, two mate allocation strategies were tested in order to define the matings between males (from P1) and females (from P2) to create the CB animals. The first strategy (S2), aimed to minimize the average genomic inbreeding of the CB population. This strategy is commonly known as minimum coancestry mating. The second strategy (S4), was focused on maximizing the average total genetic value of the CB population by exploiting non-additive genetic effects. These two strategies were compared with the random mating used in S1 and S3.For each of 2448  possible matings, the expected future inbreeding and the expected total genetic value of CB progeny were calculated. The expected future inbreeding . Heterosis Scenarios were compared in terms of response to selection. The mean QTLjP2)2 . The reahttp://genoweb.toulouse.inra.fr/∼zvitezic/simuPB-CB_G3. A README file contains a description of the files, codes and programs; and general instructions to run the simulation. Supplemental material available at figshare: https://doi.org/10.25387/g3.12504638.Programs and simulated data are available at i.e., The As selection proceeds, the sults in  show thavs. S1. For i.e., case 2 vs. case 3) resulted in a higher response to selection in CB performance in all scenarios, whereas the increase of dominance variance  was more advantageous for S3 and S4 than for S1 and S2. In fact, in S3 and S4, the proportion of improvement in CB performance was roughly tripled in case 2 (271%), where the ratio between dominance and additive variances was equal to 1  improved with the ratio dominance/additive variance. In scenarios where only PB information was used and the selection criteria was based on i.e., S1 and S2), some proportion of the additive genetic gain obtained on the PB breeds is transmitted to the CB progeny depending on the purebred-crossbred genetic correlation i.e., case 1 vs. case 2). This is because, under case 2, the increase in the proportion of dominance variance lowers the i.e., case 2 vs. case 3), and the The mean ectively . An incrneration . This reA correlation (at the QTL level) between PB and CB populations These results show, on one hand, that the proportion of the genetic progress that is transmitted from the parental breeds to their CB descendants, by selecting PB on cenarios .In the CB population, the initial average genomic inbreeding was around 0.56, which reflects a reduction of 0.06 in genomic inbreeding compared to the parental breeds (0.62 in PB – 0.56 in CB). Regardless of the scenarios and cases under investigation, genomic inbreeding remained almost the same across generations in the CB population. In scenarios where mate allocation strategies were implemented, a very small reduction in the genomic inbreeding was observed .Estimates of inbreeding depression (or heterosis if the sign is changed) were close to the simulated value (-10 per 100% homozygosity) in the two parental breeds and lower in CB population. For instance, inbreeding depression was estimated in case 3 (standard deviation), as -13.3 (1.05) and -10.6 (1.0) for S1 and S3, respectively, in P1, and -5.94 (2.1) for CB in S3.i.e., case 2).Heterosis was also computed at the first and last generations from QTL frequencies and the dominance effect. Both scenarios (S1 and S3) had the same initial amount of heterosis, 0.71, 0.75 and 0.73 for cases 1 to 3, respectively. The heterosis in the last generation for S1 was 2.24, 1.88 and 1.99, and for S3 was 2.49, 5.77, 3.66, for cases 1 to 3, respectively. The absolute value of the QTL frequency differences e.g., S2 vs. S1) were contrasted in order to measure the effect of using a mate allocation strategy. The advantage of using mate allocation strategies (S2 and S4) to improve CB performance was negligible compared to random mating (S1 and S3). On one hand, S2 based on minimizing the average expected genomic inbreeding of the CB progeny produced a small improvement on the CB performance  compared to random mating (S1)  . This ising (S1) . On the ing (S1) . The mosvs. case 1) . This situation represents the maximum achievable gain in CB performance by implementing this mate allocation strategy. tion S4, . Even wh case 1) , the CB i.e., S3 and S4, the accumulated response to selection was clearly lower compared to when they were selected based on i.e., S1 and S2). Compared to P1, the accumulated response in P2 was lower in all scenarios. In S1 and S2 this difference between P1 and P2 was due to differences in genetic variances , whereas in S3 and S4, where CB information was included, this difference was also because the scheme was not symmetric . This absence of genetic response can be explained by a i.e., case 2 vs. case 3) resulted in a higher genetic response in P1 and P2 breeds in all scenarios.The genetic response  when Additionally, The purpose of this work was to investigate, in a crossbreeding scheme, genomic evaluation models and the potential benefit of applying mate allocation. Novelties of our work from previous works include: (1) we simulate QTL effects that are correlated, but not identical, across populations, (2) we optimize matings in the crossbreds and (3) we consider in the simulation genetic evaluation using a multiple-trait SNP-BLUP across the three populations (purebreds and crossbreds) including additive and dominance effects.To date, breed-specific QTL effects correlated across PB and CB have not been accounted for in simulations. Our results show, according to the literature, that a genomic evaluation model that explicitly includes dominance and phenotype and genotype data of PB and CB animals improves CB performance faster than a model considering PB data only, in particular for low values of All selection steps were within parental PB and mate allocation was only implemented to create CB population. Four scenarios that combined different sources of information (PB or PB and CB), selection criteria and mate allocation strategies were evaluated (over 10 generations) under different values of genetic variance components.e.g., Our results show that selecting PB animals based on the terature . Thus, cThe selection criteria . This model explicitly distinguishes between PB and CB data by modeling additive and dominance marker effects correlated among populations, therefore, the resulting estimated SNP effects are breed-specific. As dominance is involved in heterosis, its inclusion in the model was expected to be more efficient than pure additive models when PB animals are selected for CB performance, as it was demonstrated by In our simulation, genetic evaluation integrated PB and CB phenotypes and genotypes by using a trivariate SNP-BLUP model with additive and dominance effects  in genetic evaluation models, to correctly estimate dominance variance and heterosis / inbreeding depression, but also because it produces an improvement in the prediction of breeding values ,A substantial increase in genomic inbreeding in PB was observed in all scenarios. The main causes of this can be attributed to the small number of selected males per generation (12) in our study, and because in absence of information (The reduction in genomic inbreeding (or increase in heterozygosity) (0.06) in CB animals respect the parental populations (0.62 in PB – 0.56 in CB), shows the benefit of crossbreeding schemes for exploiting the phenomenon of heterosis, even if PB inbreeding increases over generations. However, the amount of heterosis across generations of selection on PB, can vary depending on the selection criterion. In the last generation, the amount of heterosis was higher when PB animals were selected on i.e., S2) does not seem a promising mate allocation strategy to boost CB performance. A slight reduction (0.5% for the three cases) in the CB genomic inbreeding was observed compared to random mating (S1) which resulted in an almost negligible improvement of CB performance across generations. During the optimization process of the mate allocation, it was observed that the distribution of the expected future inbreeding from all the potential matings between the selected parents, had a very small standard deviation . Due to this small variation in the inbreeding, the reduction in realized genomic inbreeding achieved by this mate allocation strategy was very small. On the other hand, maximizing the average expected total genetic value of the CB (S4) through mate allocation might promote SNP heterozygosity, especially in those regions where there are QTL with favorable dominance effects and, hence, increase CB performance. The CB performance in S4 was slightly better compared to random mating in S3, except in case 2 where the improvement was 4% in the last generation. These results show that the benefit of implement mate allocation is better when the ratio of dominance to additive variances is higher. That was also observed by The effectiveness of mate allocation strategies accounting for non-additive genetic effects to improve CB performance was evaluated. On the one hand, minimizing the expected future inbreeding of the progeny  standard deviations (SD), were 0.04, 0.21 and 0.13, for case 1, 2 and 3, respectively. Recently, we reported an increase in expected progeny performance of 0.09 SD, for the average piglet weight at birth within litter .In this study, first, PB animals were selected based on breeding values, and second, they were mated following different strategies to produce the CB animals. However, in this way, some matings with high progeny merit can be excluded . Anothere.g., semen logistic). The cost-benefit of implementing mate allocation is not clear.To conclude, our results show that a genomic evaluation model that simultaneously accounts for both PB and CB phenotype and genotype data and additive and dominance effects improves CB performance faster than a model considering PB data only, in particular for low values of


Zea mays L. and Eucalyptus globulus Labill. For this, the following methods of Machine Learning (ML) were implemented: (i) Deep Learning (DL) and (ii) Bayesian Regularized Neural Network (BRNN) both in combination with different hyperparameters. These ML methods were also compared with Genomic Best Linear Unbiased Prediction (GBLUP) and different Bayesian regression models . DL models, using Rectified Linear Units (as the activation function), had higher predictive ability values, which varied from 0.27  to 0.78 (flowering-related traits of maize). Moreover, the larger mini-batch size (100%) had a significantly higher predictive ability for wood-related traits than the smaller mini-batch size (10%). On the other hand, in the BRNN method, the architectures of one and two layers that used only the pureline function showed better results of prediction, with values ranging from 0.21 (pilodyn penetration) to 0.71 (flowering traits). A significant increase in the prediction ability was observed for DL in comparison with other methods of genomic prediction . Another important finding was the usefulness of DL models  as an SNP detection strategy for genome-wide association studies. The results of this study confirm the importance of DL for genome-wide analyses and crop/tree improvement strategies, which holds promise for accelerating breeding progress.Genomic selection models were investigated to predict several complex traits in breeding populations of  Artificial neural networks (ANNs) are computational methods of interest in the area of Machine Learning (ML) research, which has proved to be a powerful tool in several studies of genomic prediction , due to Over the last several years, complex ANN architectures have been implemented to predict complex traits in several plant species . For insEucalyptus globulus Labill. and Zea mays L. E. globulus is one of the most widely planted hardwood tree species in temperate regions of the world, mostly used as raw material for pulp and paper industry due to its high-quality cellulose pulp, low lignin, and lipid content , measured in each line as the number of days from sowing to anther extrusion from the tassel glumes and to visible silks, respectively, and the Anthesis-Silking Interval (ASI) calculated as the difference between MF and FF . The tarE. globulus located in the La Poza sector, Purranque, administrative region of Los Lagos, Chile. The experimental design was a randomized complete block, with 30 blocks, single-tree plots, and a spacing of 2.5 m between the trees within a block. The traits were assessed in this breeding population: pilodyn penetration (WD), stem straightness (ST), branch quality (BQ), diameter at breast height (DBH), and tree height (TH). WD was indirectly estimated based on pilodyn penetration (in millimeters) at breast height using a Pilodyn 6J Forest  according to ® hypsometer, while DBH was measured with a diameter tape at 1.3 m above ground level. Genomic DNA was isolated from leaf tissue of 646 individuals randomly selected from the breeding population  and genotyped using the EUChip60K SNP system   . The genE. globulus was estimated as follows:Narrow-sense heritability ; μ is an overall constant; Aij and Dij are genotype indicator variables for individuals i at locus j; ja and jd for j = 1, 2, … p are the additive (ja) and dominance (jd) genetic effect of the jth SNP; and εi is the residual associated to the observation on individual i, with distribution I is an identity matrix, and A matrix were coded as 0 for “aa,” 1 for “Aa,” and 2 for “AA” to capture additive effects, while that for modeling dominance effects in D matrix, the genotypes “aa,” “Aa,” and “AA” were coded as 0, 1, and 0, respectively.where ja) is distributed as follows: dfa and aS, respectively. The BL method assumes that the conditional prior distribution of each marker effect follows a double exponential (DE), dfε and scale parameter Sε  is assumed for j, which is equal to 1 if the marker j is fitted in the model and is 0 otherwise.where π is the proportion of markers with null genetic effects. A scaled inverse Chi-square prior distribution χ markers . Bayes CThe Bayesian models for maize data were implemented in the library BGLR  of R 3.6sommer (A.mat (D.mat (The GBLUP was performed using the R package sommer . The addr (A.mat  and D.mat (D.mat  functionK), which enables non-linear regression in a higher-dimensional feature space. This model considers that h is a bandwidth parameter that controls the rate of decay between pairs of markers, and i and i′ normalized to range from 0 to 1. The RKHS model was implemented in the library BGLR  as random variables with a given prior distribution (defined below) according to w is the unknown weights vector; M denotes the architecture of the neural network used; α and γ are the regularization parameters that control the compensation between the smoothing of the network and goodness of fit  is the posterior distribution of the connection strengths, P is the likelihood function, P is the prior distribution for the weights vector, and P is the marginal likelihood of the data; Y represents the observed data ; s of fit .Ten BRNN-based architectures were tested, in which the following hyperparameters were considered to find the optimal architecture that increases the PA of genomic prediction: activation functions  and layers number (1–3 layers). The architectures tested with one layer (and one neuron) were brnn1, brnn2, and brnn3, in which each one considered the activation function purelin, logsig, and tansig, respectively. The architectures brnn4, brnn5, brnn6, brnn7, and brnn8 considered two layers, with two neurons and one neuron in each layer, respectively. The architectures brnn4, brnn5, and brnn6 consider in both layers a repetition of the activation function purelin, logsig, and tansig, respectively. Brnn7 and brnn8 used a combination of activation functions, in which brnn7 considered the activation functions tansig and purelin, in the first and second layer, respectively, while for brnn8, the activation functions logsig (layer 1) and purelin (layer 2) were considered. The architectures brnn9 and brnn10 considered three layers, a combination of the three activation functions and two, two, and one neuron in layers 1, 2, and 3, respectively. Brnn9 used tansig, logsig, and purelin activation functions in layers 1, 2, and 3, respectively, while brnn10 used logsig (layer 1), tansig (layer 2), and purelin (layer 3) activation functions  designed to learn long-term dependencies . These nht–1) at time t − 1, and the current input (tX) at time t, for calculating the components that control the cell state and hidden state of the layer:The first step for constructing a LSTM network is to determine what kind of information is not required and will be removed from the memory cell state. This process is implemented by a sigmoid function , which is determined by a vector with values ranging from 0 to 1, corresponding to each number in the cell state . This fuCL represents the components of the LSTM layer , σ is the sigmoid function, while W and b are the weight matrices and bias (respectively). The learnable weights and bias of the LSTM layer are the input weights W (Input Weights), the recurrent weights R , and the bias b (Bias). These matrices are concatenations of their weights and bias in each component, respectively , and (2) deciding the level of importance (−1 to 1) of values that passed through a hyperbolic tangent (tanh) layer (tg). Next, these two parts are combined to trigger an update to the memory cell state, wherein this new memory is then added to old memory Ct–1 (at time t − 1) resulting in tC (at time t).The next step is divided into two parts: (1) deciding whether the new information should be updated or ignored (0 or 1) through a sigmoid layer (tC) (ranging between −1 and 1).The last step is to decide the output values, which is performed by multiplication between the value obtained from the output of the sigmoid layer and the new values created by the hyperbolic tangent (Tanh) layer from the cell state , the diagonal matrix of weights for variances of SNPs (D), and a matrix relating genotypes of each locus (Z), which represent substitution effects for each marker locus . It is assumed that the vector of genotype effects  is a function of SNP effects  as training dataset and the remainder 65 trees as validation set. The Tukey–Kramer test was performed to compare the PA values for each trait among the evaluated models.In each model , the PA was measured as the average of Pearson correlation coefficient between observed and predicted phenotypes in the validation set. The GP methods evaluated in this study were assessed by 50 cycles of cross-validation, in which the dataset was divided randomly into two independent training (90%) and validation (10%) groups. In the dataset of In BRNN architectures, the brnn1 and brnn4 networks showed the best PA. In the flowering traits, the PA of the brnn4 network was the highest within Bayesian models, while that in wood-related traits, the highest PA was in brnn1. Although the PA values of the brnn1 model were slightly lower than brnn4 in flowering traits of maize, the predictions of both networks were very competitive, and these evidenced no significant differences . NotablyThe above results indicated that lstm5 and brnn1 were the most efficient architectures in the prediction of the study traits, due to their high PA values and low computational time required. Therefore, these architectures were selected to compare PA values with Bayes A, Bayes B, Bayes Cπ, BL, GBLUP, RKHS, and BRR in the prediction of the study traits.The predictive abilities of complex traits for each of the nine methods: BL, BRR, Bayes A, Bayes B, Bayes Cπ, GBLUP, RKHS, BRNN (brnn1), and DL (lstm5) in maize and eucalypt populations are shown in ML methods (BRNN and DL) required less time in comparison to the other genomic prediction methods assessed . This may be due to the combination of hyperparameters used in both architectures, which increase the PA of genomic prediction and may increase computational efficiency. Moreover, the Bayes A, Bayes B, Bayes Cπ, BRR, BL, and RKHS algorithms are implemented via Markov chain Monte Carlo (MCMC) for sampling from the posterior distribution of SNP effects, which is computationally expensive . In this–8 to 4.0 × 10–3, with an average of 3.8 × 10–4, considering the first iteration of the algorithm, whereas for MF, the marker effects varied from 3.4 × 10–7 to 3.4 × 10–2, with a mean of 5.9 × 10–3.The SNP marker effects of the DL (lstm5) model, estimated using the iterative algorithm of 1, 147 candidate genes were identified nearby (1 Mb) the SNPs with greatest effect. For E. globulus, considering the physical position of the reference genome of E. grandis2, a total of 277 candidate genes were identified nearby (1 Mb) the five SNPs with major effect  and dominance effects of 0 or 0.15. In the present study, the DL approach outperformed GBLUP despite the low dominance effect (Several studies have reported that the combination of hyperparameters critically influences the predictive performance of the DL model, emphasizing the need to carefully optimize hyperparameters in the ML architectures . ParticuThe hyperparameters assessed in DL showed that the ReLU activation function was more efficient in terms of PA than the Tanh function when the mini-batch was larger. ReLU can represent a linear function and thus has the advantage of preserving the properties of linear and non-linear models, i.e., easy to optimize and backpropagate the error . FurtherThe Bayesian linear models did not show differences with BRNN in terms of PA. This was also noted by Eucalyptus trees, demonstrated that both genotyping methods are equivalent in terms of PA in the GP models RR-BLUP and Bayes B. In this regard, the missing marker data and MAF are two major quality control factors in genome-wide studies (From the genomic point of view, the molecular marker techniques used in this study present clear differences, which have been broadly studied and discussed . For exa studies . These f studies . In the Arabidopsis thaliana, which encodes phosphatidylinositol 3-kinase, a protein involved in the development and germination of pollen (The present study leveraged the iterative algorithm of f pollen . Finallyf pollen , 2014.Hyperparameter optimization is a fundamental step for successfully implementing a DL model. In this sense, the results of this study suggested that architectures with the activation function ReLU and a mini-batch of large size were the most optimal for the genomic prediction of complex traits in maize and eucalypt. Furthermore, our results showed that DL had a superior performance than GBLUP, Bayes A, Bayes B, Bayes Cπ, BRR, BL, RKHS, and BRNN. On the other hand, the iterative algorithm proposed by https://figshare.com/s/d5d83e58e4634532e706; https://figshare.com/s/5ecdc88adecedb 61e940.Publicly available datasets were analyzed in this study. These data can be found here: FM-P, CM, CAS, and ATDAJ conceived the research plans. SA and J-TC performed the data curation. CM and FM-P analyzed the genomic data and wrote the first draft of the manuscript. FM-P, RIC-S, and CAS supervised the field experiments. FM-P, CM, SA, J-TC, and ATDAJ reviewed and edited the final version of the manuscript. All authors reviewed and approved the manuscript for publication.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.


R—the coefficient of residual variance) by testing for differences in expression of CVR in animals from the same breed reared in different environments. Traits that have been under strong selection did not follow a null expectation of an increase in CVR in heterogenous environments , a pattern that may reflect evolution of increased uniformity in heterogeneous environments. When comparing CVR across environments of different levels of optimality, here measured by trait mean, we found a reduction in CVR in the more optimal environments for both life history and growth traits. Selection aimed at increasing trait means in livestock breeds typically occurs in the more optimal environments, and we therefore suspect that the decreased CVR is a consequence of evolution of the expression of micro‐environmental variance in this environment. Our results highlight the heterogeneity in micro‐environmental variance across environments and point to possible connections to the intensity of selection on trait means.The variance in phenotypic trait values is a product of environmental and genetic variation. The sensitivity of traits to environmental variation has a genetic component and is likely to be under selection. However, there are few studies investigating the evolution of this sensitivity, in part due to the challenges of estimating the environmental variance. The livestock literature provides a wealth of studies that accurately partition components of phenotypic variance, including the environmental variance, in well‐defined environments. These studies involve breeds that have been under strong selection on mean phenotype in optimal environments for many generations, and therefore represent an opportunity to study the potential evolution of trait sensitivity to environmental conditions. Here, we use literature on domestic cattle to examine the evolution of micro‐environmental variance (CV The expected magnitude of this reduction will depend on the environments compared, with a bigger reduction in CVe when there is a large increase in optimality, and therefore a large increase in selection, from one environment to the other. However, strong directional selection might also lead to an increase in Ve, because individuals with large Ve can produce the extreme phenotypes favoured by selection  estimates provided in the literature, but as the variance scales with the mean in most biological traits we here use the coefficient of residual variance . Based on these data, we ask the following questions: (a) Does environmental heterogeneity increase trait CVR or decrease it on the assumption that strong stabilizing selection on trait means buffer phenotypic heterogeneity in the environment? (b) Is there a consistent difference in CVR in more optimal environments compared to less optimal environments for traits that are expected to have been under past stabilizing versus directional selection? and (c) Is there an association between CVR and CVE  across traits, as might be expected if environmental variance components for different traits are influenced by the same processes  from each of these environments. Genotype‐by‐environment interactions are often assessed using the genetic correlation of a trait between two environments . To identify useful papers with high‐quality data, we set up the following strict criteria for a study to be added to our database: (a) the study comprises at least two different environments enabling direct comparison within study system; (b) the environments are clearly defined by geography, abiotic conditions or rearing conditions, allowing an assessment of the level of environmental quality (see below); (c) the study provides VR or, if VR is not provided, parameters necessary for estimation of VR ; and (d) the paper provides mean trait values for each environment, allowing scaling of Ve. Studies where environments were solely defined by the grouping of herds according to differences in mean trait values of another trait than that of interest  were not considered. The reference lists of the identified papers were used to identify additional papers complying with the above requirements. When multiple papers presented similar analyses on the same dataset, we only included data from the first paper identified. Only papers written in English were included. These criteria reduced the number of papers from 377 to 33 papers containing 305 data points, and the large reduction in number of papers was mostly due to missing data (criteria 3 and 4) or because comparisons were between traits or within traits between different time‐points  instead of between environments. This literature search is based on strict criteria to ensure high‐quality data and is not meant to be exhaustive. The literature search and several elements of data extraction as well as trait definitions described in the current study (and the description thereof) are shared with another study where we investigate the dependence of genetic correlations on environmental similarity . We also extracted repeatability when available or estimated repeatability based on estimates of Ve‐perm  if this parameter was available. When estimates were only graphically available, we extracted numerical data using WebPlotDigitizer (://arohatgi.info/WebPlotDigitizer/app/). Some of the studies that passed the above criteria used random regression analysis to estimate genetic correlations across a defined environmental dimension  and from an intermediate environment, according to the trait mean.From each study, we extracted trait mean and V2.2Environments in studies on domestic animals are typically defined by traits related to production. Environments are assumed more optimal when high milk yield or weight gain in cattle is observed. High trait values might also reflect genetic improvement although such effects are minimized by focussing on one breed or else controlling for breed effects. We therefore use trait mean as a measure of proximity to the optimal environment ; that is, the most optimal environment for the trait of interest is the one giving the highest mean, and therefore, the environment targeted in the selection process.V = 1,092, p < .001) and higher in environments of high compared to intermediate quality .As an alternative to describing an environment using trait mean, we also classified each environment into one of three levels of environmental quality (similarity to the environment targeted in the selection process) using country of rearing and environmental description provided in the study. We grouped countries according to latitude and level of development Table . We ackn2.3R across traits having some shared evolutionary history, we grouped traits into five categories based on evolutionary principles: disease indicator, growth, life history, morphology and physiology , which is therefore used as a proxy of Ve throughout the analyses. For each trait in a study, we estimated the difference in CVR between two environments by the coefficient of variation ratio (lnCVR) . The first term in the formula is the natural logarithm of CVR in the two environments, while the two additional terms are used to control for differences in sample size . When the number of individuals was not provided in the reference, we used the number of records. The sampling variance of lnCVR was estimated using equation 12 in Nakagawa et al.  in statistical analyses. Comparing CVR among traits of different dimensionalities can yield spurious differences as higher dimensionality causes higher CVR  and the other more homogeneous . We compared CVR between these environments by estimating lnCVR homogeneity.Eight studies reported estimates of CVR homogeneity reflects a decrease in CVR in the homogeneous environment, while a positive lnCVR homogeneity reflects an increase in CVR in the homogeneous environment. For this particular analysis, a limited number of estimates of lnCVR were available , preventing usage of generalized linear mixed models (GLMMs) accounting for study, trait and breed, and we therefore undertook one‐sample t tests to assess whether the lnCVR of each trait category differed from zero.Here, a negative lnCV2.5R with increasing proximity to a trait optimum, we tested if the difference in trait mean between two environments correlated with their difference in CVR . The difference in trait mean (x¯) was estimated as the response ratio (lnRR), with the highest trait value being defined as closer to the optimum and therefore more optimal.To investigate the change in CVAs the numerator is by definition bigger than the denominator, lnRR will always be positive, and a higher lnRR will reflect a higher increase in optimality from one environment to the other.R optimality in each of the trait categories using a linear mixed model (Gaussian distribution) in a Bayesian framework in the R‐package MCMCglmm (v.2.25)  inverse gamma priors  and performed 1,000,000 iterations of which the initial 20,000 were discarded and only one in 500 runs was used for estimating posterior probabilities. Convergence of the estimates was checked by running the model four times and inspecting trace plots and their overlap of the MCMC chain and the level of autocorrelation among posterior samples.We tested the effect of lnRR on lnCVR optimality, but with a highly skewed distribution across a high number of traits (n = 52). 43 traits had less than seven estimates and only four traits had more than 15 estimates. As a consequence, we focused on the four traits with highest replication: yearly milk yield (305‐d milk yield), birthweight, bodyweight at weaning (200–210‐d body weight) and dry matter intake. To analyse the effect of lnRR on lnCVR optimality in each of these traits, we followed the same approach as for the trait category analysis . We specified lnRR as the sole fixed effect and study as a random effect. As three of the traits predominantly consisted of estimates from one breed  and the last trait from two breeds , we restricted the analysis to these breeds.Within each trait category, some traits may show trait‐specific responses to lnRR. Our literature survey resulted in 368 estimates of lnCV2.6R in each of the trait categories. We used the meta‐analytic framework from above and compared CVR of a trait measured at two different environmental qualities within a study using lnCVR quality.As an alternative to using trait mean as a measure of how optimal an environment is in terms of proximity to trait optimum, we investigated whether our classification into environmental quality was related to estimates of CVR quality reflects a decrease in CVR in the higher quality environment, while a positive lnCVR quality reflects an increase in CVR in the higher quality environment. To test whether the lnCVR quality of the environmental contrasts was predominantly below or above zero, we used a linear mixed model with the same modelling approach as for lnRR . We specified environmental contrast  and trait category as well as their interaction as fixed effects. Study, trait and breed were specified as random effects.Here, a negative lnCV2.7E is predicted to also show high Ve and vice versa, resulting in a linear association between Ve and VE across traits. To estimate VE, we need measurements at multiple well‐defined environments within a study, but a comparison across traits and studies is only possible if the same environments are tested for all traits, excluding the majority of the surveyed literature. The largest subset of our surveyed literature fulfilling these requirements was three studies encompassing a total of 17 traits, all measured in two different feedlot environments and one pasture‐based environment . Hence, for this analysis the data structure is one estimate of CVE and multiple estimates of CVR (one for each of the three environments) for each trait in each study, preventing usage of lnCVR as a measure of difference in variability. We therefore investigated the relationship between CVR and CVE by fitting a linear mixed model with CVR as the response variable and CVE as the sole fixed effect. Study and trait were specified as random effects, whereas breed was not included as all data originated from one breed. The model was fitted using MCMCglmm (v.2.25)  and homogeneous environments   = 2.93, p = .027; Figure t(8) = −0.55, p = .592). However, morphological traits (a negative lnCVR homogeneity: t(14) = −2.76, p = .015) and physiological traits (a negative lnCVR homogeneity: t(19) = −2.85, p = .010) both had a lower CVR in the homogenous than in the heterogeneous environment. It should be noted here that low levels of replication made us prefer simple one‐sample t tests and not the more robust meta‐analytic approach used for the other analyses, and the results should be interpreted with this mind.When comparing CV3.2R from suboptimal to more optimal environments , one prediction is that no or a very small increase in optimality (lnRR ~ 0) should result in no or little change in CVR . This prediction was confirmed across trait categories and traits .When testing the change in CVs Figure  with ints Figure . We hypos Figure , Table 1d Figure , Table 1e Figure , Table 1R, as an alternative to the use of mean trait value as a measure of environmental optimality. Our assessment of quality was based on both geographical and environmental information provided in the studies, and resulted in 117 estimates of lnCVR quality comparing different qualities. When we considered life history traits , we found a significantly lower CVR at high compared to low environmental quality, represented by a negative lnCVR quality  to also have significantly higher among environmental variance (CVE)  Figure .3.4R (Table WI) estimated from repeatability and VP. Traits in the growth category had particularly low CVR with the exception of two traits measuring growth in narrow time intervals (weight gain and daily weight gain). The same pattern was observed for production traits related to life history, where traits measured across long time intervals (305 days) showed relatively low CVR in comparison with the limited time intervals of daily yield. The life history traits related to reproduction spanned the entire range of CVR, with age at first calving and gestation period being as low as the production traits, while traits quantifying the time interval between two reproduction events had higher CVR. Survival is dependent on farmer culling, and this trait also had a very high estimate of CVR. Morphological traits were distributed in two groups, one consisting of classical morphological traits such of size of a given muscle, which had low CVR, and a second describing the amount of fat in the muscles which had high CVR. The physiological traits comprised both the per cent fat and protein of milk, physiological characteristics of muscles as well as rectal temperature, which all generally had low CVR.From the literature survey, we obtained 306 estimates of CVR Table , which aR Table , along w44.1e from the domestic cattle literature (estimated as CVR) to identify patterns of Ve across environments with different histories of selection and different micro‐environmental variation. As expected, we found CVR to increase when a cattle breed is reared in heterogeneous environments and spends more time in pastures, compared to when the cattle breed is reared in more controlled environments predominantly involving confinement . Here, environmental heterogeneity decreased CVR (growth) or had no effect on CVR (life history). Production traits are expected to be under the strongest selection towards a constant trait optimum across environments. Such stabilizing selection can potentially reduce or remove any association between heterogeneity and Ve , high Ve becomes disadvantageous and a reduction in Ve may be expected under stabilizing and directional selection . This approach puts the environment on one single scale across traits, allowing us to test for overall differences in each trait category. The grouping of environments was based on the grounds that the vast majority of estimates of CVR used in this study originate from Holstein, Angus and Hereford cattle (or varieties hereof), all originally Northern European breeds  increasing the lower limit of CVR. This group also included reproduction traits quantifying the time interval between two reproduction events that will vary between seasons and years. The other group of traits with lower CVRs included yearly milk production and measures of gestation period and thus milk production, both traits expected to have been directionally selected under and after domestication. While these traits showed low micro‐environmental sensitivity compared to the other life history traits, CVR of milk production traits was surprisingly high given their link to production. Perhaps CVR is constrained by a low repeatability, but care should be taken here, as repeatability is measured across lactations, which may represent partly distinct traits  also found higher CVR in life history traits than for morphological traits (leg length and jaw length) and birthweight  include the common eland, bison species, the African buffalo, the rare saola, the addax as well as wildebeest and gazelle species Bibi, . Few gen4.4E) is typically presented as reflecting large changes in a few controlled environmental parameters, while Ve reflects variation in an infinite number of environmental parameters. However, there may still be overlap in genetic factors contributing to Ve and VE, which leads to the prediction that traits with low sensitivity to Ve will have a relatively low sensitivity to VE. We found support for this relationship  comparable across environments and studies, we followed typical practice and standardized Ve by the mean  have increased across the last century through strong directional selection aided by progeny testing . However, while the weight given to different production traits in a merit index will differ strongly between dairy and beef cattle, the general breeding goals are in the same direction in both groups  occur across countries and in different cattle breeds. Beef and dairy cattle are obviously selected for different traits, and within breeds, the emphasis of breeding programs can differ, such as for increased milk yield in one instance and increased milk fat percentage in another instance, leading to country‐specific breeding goals and production levels (5R in the environments where we expected strongest historical selection, a pattern that may reflect selection. Our findings send a clear signal to animal breeders and evolutionary biologists that micro‐environmental variance likely evolves in response to selection on a trait's mean. Evidence for genetic variation underlying micro‐environmental variance has been reported from several studies (Hill & Mulder, We quantified the environmental dependence of micro‐environmental variance, with the aim of relating these patterns to past selection on trait means in optimal environments. Using livestock data, we found patterns of a decrease in CVNone declared. Click here for additional data file. Click here for additional data file. Click here for additional data file. Click here for additional data file. Click here for additional data file.


Os01g0923600 and Os01g0923800 were identified as promising candidate genes in qCTB1t, a major associated locus. Through population genetic analyses, 22 and 29 divergent regions controlling cold adaptive differentiation inter‐subspecies (Xian/Indica and Geng/Japonica) and intra‐Geng, respectively, were identified. Joint analyses of four cloned cold‐tolerance genes showed that they had different origins and utilizations under various climatic conditions. bZIP73 and OsAPX1 differentiating inter‐subspecies evolved directly from wild rice, whereas the novel mutations CTB4a and Ctb1 arose in Geng during adaptation to colder climates. The cold‐tolerant Geng accessions have undergone stronger selection under colder climate conditions than other accessions during the domestication and breeding processes. Additive effects of dominant allelic variants of four identified genes have been important in adaptation to cold in modern rice varieties. Therefore, this study provides valuable information for further gene discovery and pyramiding breeding to improve cold tolerance at the reproductive stage in rice.Genetic studies on cold tolerance at the reproductive stage in rice could lead to significant reductions in yield losses. However, knowledge about the genetic basis and adaptive differentiation, as well as the evolution and utilization of the underlying natural alleles, remains limited. Here, 580 rice accessions in two association panels were used to perform genome‐wide association study, and 156 loci associated with cold tolerance at the reproductive stage were identified.  Oryza sativa L.), a staple cereal crop feeding half of the world population, evolved in tropical and subtropical areas and is sensitive to cold stress  were Geng, whereas 82.2% of cold‐sensitive accessions  were Xian  was set based on the permutation tests . The subspecies were highly divergent in 191 genomic regions . In addition, 68% of the DRCT had lower genetic diversity in Geng  was significantly lower than that in Xian  was significantly lower than that in cold‐sensitive Geng  and improved varieties  and improved varieties (20% and 31%) of Geng  but also intra‐Geng. Twenty‐two and 29 DRCT controlling cold adaptive differentiation inter‐subspecies and intra‐Geng were identified respectively. There were few overlaps of DRCT between inter‐subspecies and intra‐Geng, indicating distinctive genetic bases of cold adaptive differentiation between them. Analyses of four identified genes indicated that cold‐tolerance genes with different origins were retained and utilized during the adaptation of rice to colder environments. Our findings confirmed that gene pyramiding will be an effective method to improve cold tolerance at the reproductive stage in rice.We detected 156 associated loci regulating natural variation in cold tolerance at the reproductive stage through GWAS and identified two candidate genes in the associated locus et al., et al., et al., et al., A total of 580 cultivated rice accessions from 38 countries, including 156 accessions from the mini‐core collection  30 days later. Five plants of each accession at the reproductive stage were transferred to a pool irrigated with cold water (16–18°C) for one week and then transplanted back to the field. After harvest, the relative seed‐setting rates of the treated plants were assessed and recorded. In the summer of 2015, 522 accessions in Panel 1 were sown at Yuxi  in Yunnan province, at 10‐day intervals. Heading dates were recorded, and average seed‐setting rates of five plants for each accession flowering at the same time were investigated after harvest. Detailed descriptions of two cold treatment methods were described in Zhang et al .According to the seed‐setting rates, five levels of cold tolerance at the reproductive stage were set, including level 1 (108 accessions with seed‐setting rates ≥ 80%), level 3 (138 accessions with seed‐setting rates 60%–80%), level 5 (96 accessions with seed‐setting rates 40%–60%), level 7 (124 accessions with seed‐setting rates 10%–40%) and level 9 (56 accessions with seed‐setting rates < 10%).r2 < 0.3; Purcell et al., et al., et al., There were 6 997 165 and 4 625 142 SNPs in total in Panels 1 and 2, respectively. By using PLINK version 1.9  was considered as the local LD interval  from Panels 1 and 2, respectively, were used to perform GWAS using both the general linear model and compressed mixed linear model in the GAPIT package operated in an R environment , nucleotide diversity (π) and Tajima’s D, were calculated using VCFtools software  at the reproductive stage. For the identification of DRCT inter‐subspecies, only the loci detected in the full population, but neither in Xian nor in Geng subpopulations, were identified to contribute to the cold adaptive differentiation inter‐subspecies.To clarify the differentiation and selection of cold adaptability at the reproductive stage, several population genetic parameters, including the population differentiation statistics  at the reproductive stage, and young panicles were sampled at different time points after cold treatment. Total RNA was extracted using RNAiso Plus , and cDNA was generated using an M‐MLV reverse transcriptase . qRT‐PCR was performed on an ABI 7500 Real‐time PCR system (Applied Bio‐Systems). OsActin1 was used as the internal reference. Each experiment was performed with three biological samples and each sample assayed with three technical replications. Data for tissue expression analysis were collected from the Rice Genome Annotation Project website (http://rice.plantbiology.msu.edu/). Primers are listed in Table For evolutionary analysis, haplotypes of 494 cultivated rice and 75 wild rice accessions were divided using DnaSP 5.10 software  and compressed mixed linear model (CMLM) in different association populations.Figure S6 Genome‐wide threshold for GWAS based on the permutation tests.Figure S7 LD decay of the association populations.Figure S8 Loci associated with cold tolerance at the reproductive stage identified in subgroups of Panel 1.Figure S9 Loci associated with cold tolerance at the reproductive stage identified in Panel 1 based on a seven‐day grouping interval.Figure S10 Loci associated with cold tolerance at the reproductive stage identified in Panel 1 based on a five‐day grouping interval.Figure S11 Distribution of 156 loci on rice chromosomes.Figure S12 Tissue expression of important predicted genes in qCTB1t based on the data from the RGAP website.Figure S13 Association analysis of CTB4a in HAA‐full population.Figure S14 Characterization of 140 Geng and 169 Xian accessions from Panel 1.Figure S15 Nucleotide diversity of DRCT and genomic average in different populations.Figure S16 Characterization of 35 cold‐tolerant and 32 cold‐sensitive temperate Geng accessions from Panel 1.Figure S17 Association and haplotype analyses for cloned genes conferring cold tolerance at the reproductive stage in 132 accessions from Panel 2.Figure S18 Allelic distributions of CTB4a and Ctb1 in the world (left) and China (right).Figure S19 Allelic distributions of bZIP73 and OsAPX1 in the world (left) and China (right).Click here for additional data file.Table S1 The 522 accessions in Panel 1 evaluated under natural cold stress in a high‐altitude area.Table S2 The 155 accessions in Panel 2 evaluated under artificial cold stress in deep water.Table S3 Statistics of the seed‐setting rates of accessions used in this study.Table S4 Selected rice germplasms from level 1 with strong cold tolerance at the reproductive stage.Table S5 LD decay of different association populations.Table S6 Information of the loci associated with cold tolerance at the reproductive stage identified by GWAS in different association populations.Table S7 Information of the loci overlapping with QTLs identified by linkage analysis.Table S8 Genomic regions differentiated between Geng and Xian.Table S9 Population genetic parameters of DRCT between Geng and Xian.Table S10 Genomic regions differentiated within the Geng subspecies.Table S11 Population genetic parameters of DRCT within the Geng subspecies.Table S12 Accessions used in the analyses of evolution and breeding potential.Table S13 Pyramiding of loci overlapping with reported cold‐tolerance QTLs at the reproductive stage in temperate Geng accessions from Panel 1 which contain CTB4a, Ctb1, bZIP73 and OsAPX1.Table S14 Primers used in the expression pattern analysis.Click here for additional data file.


Quantitative red blood cell (RBC) traits are highly polygenic clinically relevant traits, with approximately 500 reported GWAS loci. The majority of RBC trait GWAS have been performed in European- or East Asian-ancestry populations, despite evidence that rare or ancestry-specific variation contributes substantially to RBC trait heritability. Recently developed combined-phenotype methods which leverage genetic trait correlation to improve statistical power have not yet been applied to these traits. Here we leveraged correlation of seven quantitative RBC traits in performing a combined-phenotype analysis in a multi-ethnic study population.n = 67,885 participants; 24% African American, 30% Hispanic/Latino, and 43% European American; 76% female). Thirty-nine loci in our multi-ethnic population contained at least one significant association signal (p < 5E-9), with lead SNPs at nine loci significantly associated with three or more RBC traits. A majority of the lead SNPs were common (MAF > 5%) across all ancestral populations. Nineteen additional independent association signals were identified at seven known loci . For example, the HBA1/2 locus contained 14 conditionally independent association signals, 11 of which were previously unreported and are specific to African and Amerindian ancestries. One variant in this region was common in all ancestries, but exhibited a narrower LD block in African Americans than European Americans or Hispanics/Latinos. GTEx eQTL analysis of all independent lead SNPs yielded 31 significant associations in relevant tissues, over half of which were not at the gene immediately proximal to the lead SNP.We used the adaptive sum of powered scores (aSPU) test to assess combined-phenotype associations between ~ 21 million SNPs and seven RBC traits in a multi-ethnic population (maximum HBA1/2 locus was only revealed by the inclusion of African Americans and Hispanics/Latinos, underscoring the continued importance of expanding large GWAS to include ancestrally diverse populations.This work identified seven loci containing multiple independent association signals for RBC traits using a combined-phenotype approach, which may improve discovery in genetically correlated traits. Highly complex genetic architecture at the  BCL11A-silencing gene therapy clinical trial for individuals with sickle cell disease (SCD) ), is calculated by comparing the minimum p-value across the sequence of powered scores to the reference distribution of minimum p-values across the sequence of powered scores computed using the simulated null data. The adaptive aspect of the test lies in the potential for different γ values to yield the maximal SPU across SNPs, maintaining power compared to a test with only a single possible alternative hypothesis.aSPU incorporated univariate summary z-scores, calculated for each SNP across all 7 traits, to yield a single p-value evaluating whether one or more of the traits were associated with a given SNP. Briefly, the procedure estimates Σ, the 7 × 7 correlation of null z-scores across univariate results and draws 10p < 5E-09).Sensitivity analyses were performed for combined-trait results by self-reported race/ethnicity among analytic subgroups with greater than 1000 participants . Given the number of known ancestry-specific variants driving blood trait values, it was necessary to ensure that all self-reported race/ethnic groups be evaluated individually for associations that may be undetectable in the larger population. Meta-analyses of univariate summary statistics followed by combined-phenotype analysis were performed within each self-reported race/ethnicity using the same methods described above for the overall study population to identify genome-wide association signals (p < 5E-09). Although RBC traits are expected to share genetic underpinnings, particularly within pairs of correlated traits, association signals which were trait-specific in the well-powered UK BioBank blood trait GWAS suggest that each trait has its own unique suite of associations [We also examined whether there was evidence of significant trait-specific loci that were not identified in combined-phenotype analyses. Meta-analyses of each univariate RBC trait across all analytic subpopulations, as described above, were evaluated for association signals exceeding genome-wide significance  in the MEGA-genotyped subgroup [p-values were therefore compared to p-values after conditioning on esv3637548.Finally, in an attempt to examine the influence of the previously identified 3.7 kb structural variant esv3637548 in the subgroup . This stn = 466, representing 1308 index SNPs previously reported for one or more of the seven RBC traits we evaluated). We first reported trait-specific associations—i.e., index variants that have been reported by trait. We did not report loci containing a SNP that exceeded genome-wide-significance for the first time in one RBC trait but were previously reported for another trait as discovery associations; therefore, we also used the aforementioned significance threshold to evaluate generalization of association signals in each trait across all known loci.All SNPs located within 500 kb of a variant previously reported for any RBC trait were evaluated for evidence of association in the combined-phenotype analysis as well as each individual trait analysis. A generalization significance threshold of 1.07E-4 was calculated using Bonferroni correction for the previous number of one-megabase genomic regions for which one or more genome-wide-significant variants were reported for one or more RBC traits  for evidence of association with gene expression using the Genotype Tissue Expression (GTEx) portal .Additional file 1: Figure S1. Manhattan and Quantile-Quantile plots for individual RBC traits in the total study population. In Manhattan plots, previously reported loci (published index SNP reported p < 5E-08 within 500 kb of PAGE combined-phenotype lead SNP) are shown in purple; previously unreported loci with a PAGE lead SNP p < 5E-09 are shown in green. In Q-Q plots, all (black) p-values and p-values for variants > 500 kb from a previously reported significant variant for any RBC trait (blue) are both shown. Figure S2. Evidence of genetic associations shared across correlated RBC traits. X-axis: chromosome and position (top) and rsid (bottom) for each combined-phenotype lead SNP. Y-axis: trait-specific –log10, with increased intensity representing higher significance, for each combined-phenotype lead SNP. P-values scaled to a maximum –log10 value of 25 for improved interpretation. Figure S3. Locus-Zoom plots of the association between rs6573766 and RBCC in PAGE African Americans on an African American LD background (A), Hispanics/Latinos on a Hispanic/Latino LD background (B), and European Americans on a European LD background (C). Each point represents one SNP; x-axis: increasing position on chromosome 14 left to right; y-axis: -log10 of the association with MCH SNP correlation with the lead SNP (r2) is colored according to the legend in Figure S3A. Annotated Refseq genes proximal to the lead SNP are shown by position above the X axis. Figure S4. Locus-Zoom plot of the association between MCH (A) and MCV (B) and rs145548796 in the total MEGA study population. Each point represents one SNP; x-axis: increasing position on chromosome 6 left to right; y-axis: -log10 of the association with MCH SNP correlation with the lead SNP (r2) is colored according to the legend in Figure S4A. Annotated Refseq genes proximal to the lead SNP are shown by position above the X axis.Additional file 2. Twelve supplemental tables supporting findings reported in the main text. Tables cover trait, genotyping, and QC description; ancestry- and trait-specific findings for combined-phenotype lead SNPs; sensitivity analysis of a deletion at the HBA1/2 locus; generalization of previously reported findings to PAGE study populations; and eQTL findings for PAGE lead SNPs in relevant tissue types.


This study aims to evaluate genetic and non-genetic factors influencing semen production potential of Gir bulls.Data on semen quantity (n=6911) and quality (n=466) available from January 2011 to December 2018 at BAIF’s frozen semen station, Jind, Haryana, India, were utilized for the study. Factors, namely, season of collection, age at collection, and bull (random effect) were studied for their effect on quantitative and qualitative semen traits. Least square means for the traits were obtained using a general linear model. The effect of age within bull for repeatable traits was analyzed using a longitudinal model with age as the control variable. Multivariate analysis using mixed repeatability model equation was utilized to estimate bull effect correlation , phenotypic correlations, and repeatability.9/ml, 8.09±0.05 109/ejaculate, 75.78±0.001%, 55.92±0.0001%, 55.13±0.005%, and 71.08±0.001%, respectively. The season of the collection showed a significant effect on volume, concentration, total sperm, and initial motility. The performance of bulls was superior in summer season, followed by winter and monsoon. Increase in semen attributes during summer season was due to the effect of lower temperature on sensitive stages of spermatogenesis. Age at collection had a significant effect on all semen traits. Volume and total sperm count showed increasing trend while concentration showed a decreasing trend with an increase in age. Motility and quality traits did not show any particular pattern. Individual bulls showed differences in all the semen performance traits with age. The repeatability of the traits ranged from 0.04 (HOST) to 0.58 (acrosome integrity). Bull effect correlation ranged from −0.73  to 0.93 (HOST and acrosome integrity).The overall least square means of ejaculate volume, sperm concentration, total sperms, initial and post-thaw motility, hypo-osmotic swelling test, and acrosome integrity of frozen semen were 6.62±0.03 ml, 1.22±0.01 10Individual bulls showed variation in traits measured over age. The result of the study could be utilized in suggesting suitable management plans to achieve the desired profit by improving semen quality in Gir bulls. Artificial insemination (AI) technique is considered a boon to the livestock industry. AI is a matter of great significance due to large-scale use of very few genetically superior sires to cover a large female population. As per the breeding policy of Government of India, selective breeding and upgrading of non-descript bovine population using frozen semen from indigenous cattle is recommended. Gir breed is a recognized milk purpose breed of India which has been extensively used for selective breeding and upgrading of non-descript cattle population. Gir cattle breed is also known for their heat tolerance resistance to disease and ability to survive on low-grade feeding resource.th livestock census, the population of indigenous cattle has declined by around 6% [As per the 20round 6%  and therround 6% , Red Sinround 6% , and Amrround 6%  are avaiThe present study was undertaken to determine the effect of age and season on semen traits and to estimate their genetic parameters in Gir cattle breed.Ethical approval was not necessary for this study as no experimental animals were used.Data on semen quantity (n=6911) and quality (n=466) of 38 Gir bulls, available from January 2011 to December 2018 at BAIF’s frozen semen station, Jind, Haryana, India, were utilized for the study.The BAIF frozen semen station is located in Haryana state, India, on the outskirts of Jind city  at an altitude of 227 m above sea level. The region experienced three different seasons, namely, summer (March-May), monsoon (June-October), and winter (November to February). The mean minimum temperature ranged from 6.4°C in January to 17.3°C in October. The mean maximum temperature varies from 29.1°C in November to 40.9°C in May. The mean humidity varied from 23% in April to 78% in August. The region received an annual rainfall of 536 mm on average, mostly during the months of July-September. The average monthly rainfall ranged from 1 (April) to 178 (August) mm.9/ml), initial and post-thaw motility (% of motile sperms after dilution and before/after thawing), total sperms , hypo-osmotic swelling test (HOST), and acrosome integrity test frozen semen (%). The observations beyond mean±4 standard deviations were considered as outliers and removed from the study. The various factors influencing semen characteristics considered in the study were season  and age  at collection.The semen traits included in the study were ejaculate volume (ml), sperm concentration . Semen straws were cooled to 4°C in 3 h and frozen step by step to reach −140°C in 7-8 min in a programmable freezer (IMV Technologies) followed by submersion and storage in liquid nitrogen at −196°C. Post-thaw motility assessment was then carried out 24 h post-freezing. Standardized initial and post-thaw motility assessments were separately carried out by different technicians. The assessment of mass activity, initial, and post-thaw motility was carried out using a phase contrast microscope . After cryopreservation of semen, random samples from a batch of collection from each bull were subjected to quality control testing. Quality control tests were carried out once in 3 months, allowing every bull to be tested 4 times a year. HOST was carried out using a quantity of 0.1 ml of frozen-thawed semen mixed with 1 ml of the hypo-osmotic solution and incubated at 37°C for at least 30 min. The stained smear later was observed using a phase contrast microscope to assess the “tail curling” appearance of the sperm. Intactness of acrosome was tested using acrosome integrity test with frozen semen which was carried out using Giemsa staining. The cutoffs for the mentioned tests were >40 and >65%, respectively.All the bulls were washed and cleaned early morning before semen collection. Handling and semen collection of a particular bull were performed by the same person responsible for that group of bulls. Dummy bulls were used for sexual stimulation and every bull was allowed 2-3 false mounts before actual semen collection. The time between false mounts varied among the bulls. One or two semen ejaculates were collected using an individual artificial vagina according to a standard procedure. The collm” function in R statistical software [lm” function, “car,” “emmeans,” and “agricolae” packages were used for ANOVA (Type III sum of square) and Duncan’s multiple range test. The model used for the analysis can be represented as follows:Before the analysis, percent data were transformed using the arcsine transformation. The semen production records were subjected to statistical analysis using least square analysis using “software . Along wijk = μ + Si + Aj + eijkYWhere,ijk = Semen traitYμ = Overall meani = Effect of the ith season of collection (i = 1-3)Sj = Effect of the jth age at collection of a bull (j = 1-9)Aijk = Random error associated with the Yijk which is assumed to be normally and independently distributed with mean zero and constant varianceelmerTest” package in R. A linear mixed model with age and season of collection as fixed effects and age within bull as random effect was used. The significance of within bull variation was analyzed using a likelihood-ratio test by comparing the likelihoods of models with and without the within bull random effect. To study the bull effect correlation  on the semen parameters, a repeatability animal model with bull as a random effect was used in WOMBAT software [The longitudinal data analysis was carried out using “software .et al. [et al. [et al. [et al. [et al. [The results of least squares analyses are presented in et al.   and Chikhaliya et al. [9) are in agreement with the results observed in the present study (1.22 × 109). Das et al. [et al. [9, respectively, in Gir bulls. The mean of total sperm observed in the present study was lower than total sperm output (9.01 × 109) observed by Pathak et al. [Sperm concentration of Gir cattle reported by Shelke and Dhami   on all semen attributes in the present study. The gradual increment in the volume was observed up to 120 months and later declined in older bulls. Similar trend of volume was followed by total sperm count. The trend of initial motility was inconsistent but showed a slight decreasing tendency with age. Fuerst-Waltl et al.  and Math [et al.  found siThe genetic parameters for all semen attributes are presented in et al. [et al. [Phenotypic correlation ranged from −0.31  to 0.61 . The estimates of repeatability and bull effect correlation could be regarded as a higher boundary of heritability and genetic correlation of the respective traits. The estimates reported in the present study show that the future performance of a bull can be explained based on initial values with some accuracy for ejaculate volume and acrosome integrity of frozen semen. A thorough search of literature indicated lack of studies on genetic parameters of semen traits in Gir bulls. However, few researchers presented phenotypic correlation among the semen traits in Gir bulls , 15 and et al.  reportedIt is concluded that seasonal variations in semen parameters were due to the influence of climatic condition during the sensitive stages of spermatogenesis. The age-related trend in semen attributes observed in the present study indicated that individual bulls showed variation in traits measured over age at collection. The estimates of repeatability and bull effects may be helpful in explaining the future performances of bulls with some accuracy. In the end, knowledge of the genetic and environmental factors influencing semen production can help an AI stud to achieve a high semen production of good quality with desired profit.KGB analyzed the data, prepared, and edited the manuscript. KTPJ and RV helped in designing the methodology and models, and conceptualizing the research work. RV, PK, TS, and CJ reviewed and edited the manuscript. PD processed the semen samples and helped in data collection. TSh helped in data retrieval and computer programing. All the authors read and approved the final copy of the manuscript.The authors declare that they have no competing interests.Veterinary World remains neutral with regard to jurisdictional claims in published institutional affiliation.


Avena sativa L.) is a nutritious food crop and also a valuable forage crop. However, little is known about the genetics of seed vigor in oats. To investigate seed vigor-related traits and their genetic architecture in oats, we developed an easy-to-implement image-based phenotyping pipeline and applied it to 650 elite oat lines from the Collaborative Oat Research Enterprise (CORE). Root number, root surface area, and shoot length were measured in two replicates. Variables such as growth rate were derived. Using a genome-wide association (GWA) approach, we identified 34 and 16 unique loci associated with root traits and shoot traits, respectively, which corresponded to 41 and 16 unique SNPs at a false discovery rate < 0.1. Nine root-associated loci were organized into four sets of homeologous regions, while nine shoot-associated loci were organized into three sets of homeologous regions. The context sequences of five trait-associated markers matched to the sequences of rice, Brachypodium and maize , including three markers matched to known gene models with potential involvement in seed vigor. These were a glucuronosyltransferase, a mitochondrial carrier protein domain containing protein, and an iron-sulfur cluster protein. This study presents the first GWA study on oat seed vigor and data of this study can provide guidelines and foundation for further investigations.Seed vigor is crucial for crop early establishment in the field and is particularly important for forage crop production. Oat ( Avena sativa L.) is the seventh most important cereal crop in the world in terms of production and cropping area  is a method to investigate the genetic architecture of traits of interest using available germplasm. It takes advantage of ancestral recombination events and can provide higher resolution compared to traditional quantitative trait locus (QTL) mapping using bi-parental populations . CampbelAvena sativa – OT3098 v1, PepsiCo, https://wheat.pw.usda.gov/GG3/graingenes_downloads/oat-ot3098-pepsico; June 23, 2020). Indeed, the robust consensus map  decay, where pairwise 2r = 0.10 corresponded to an average pairwise distance of 0.44 cM within the whole panel or 0.71 and 2.64 cM within spring and southern oat, respectively  with a large genome (12.5 Mbp)  to develop an image-based phenotyping pipeline for seed vigor; (ii) to characterize the phenotypic variation of seed vigor-related traits within elite oat lines under a temperature regime close to sub-tropic and tropic setup; and (iii) to characterize the genetic architecture of oat seed vigor-related traits using a GWA approach.The 650 lines of the Collaborative Oat Research Enterprise (CORE)  and the other in the USDA-ARS Small Grain and Potato Germplasm Research (Aberdeen). We used the augmented design  to accomth second, and aperture of F/5.6 using manual focus.A digital camera  was set at a height of 42 cm using a copy stand . Pictures were taken at day 3, 4, and 5 after setting the seeds into the pouch (day 0). At each photo-taking, germination paper was carefully moved out from the pouch with the help of the plastic board. The camera was set at ISO 400, 22 mm focal length, shutter speed at 1/13Image analysis was conducted using Fiji : i) the Images were first cropped to keep the area occupied by roots. The cropped images were then split into green, red, and blue channels using “Split channels” in Fiji. The blue channel was retained for its better separation of roots from the background. To extract the root surface area (white to light gray) from the background (dark gray), a threshold value was set empirically at approximate 1.7 times of median gray value of the image and was slightly adjusted within each batch to fully capture the root surface area. Background noise was removed with the help of the “Analyze Particles” function in Fiji: particles with size between 0 and 300 pixels were defined as noise removed; noise with particle size larger than 300 pixels were manually removed after visual inspection. “Create selection” and “Measure” were further used to select and measure root surface area. For each image, the total number of roots and the number of germinated seeds were counted through visual inspection. Other derived traits are listed in The shoot part of the image was cropped from the original image and converted to gray-scale using the “8-bit” function. To distinguish shoot from background, a threshold value was set empirically as 1.4 times of median gray value and was slightly adjusted within each batch. Images were visually inspected and noises were manually removed. Shoot was identified using “Analyze Particles” where particle size was set from 300 to infinity pixels. Because most shoots grew upright, we used “Feret diameter”, an option in “Set measurement” to measure the shoot length. All traits investigated in this study are listed in 2 day-1 in Taipei and Aberdeen, respectively. For shoot traits, a line was discarded if its SG_5 was less than 12 mm per day and 4 mm day-1 for lines assayed in Taipei and Aberdeen, respectively. The second set of parameters was related to the image quality. For root traits, all images were visually inspected and classified into four categories: category 1 indicated the best quality where all roots were captured using the image analysis method and category 4 indicated the worst quality where less than half of the roots were captured. Only images of categories 1 and 2 were retained for subsequent root-related analyses. For shoot traits, values of average shoot length may be biased by variable emergence of shoots. Therefore, a line was discarded from further analysis if more than one shoot or three shoots were emerged at day 5 for Taipei and Aberdeen, respectively.Lines showing poor growth were removed prior to GWA. “Good” or “poor” growth of each line was defined empirically using two sets of parameters. One set of parameters was root or shoot growth rate between day 4 and day 5 . For root traits, lines were discarded if its RG_5 was smaller than 7 and 4 mmde novo GBS-SNP calling pipeline which does not require a reference genome sequence, with the PstI‐MspI restriction enzyme combination, as described in (https://oat.triticeaetoolbox.org/) and we filtered the data at per marker level with a missing rate < 0.2, heterozygosity < 0.1 and minor allele frequency (MAF) ≧ 0.05.Genotypic data consisted of 22,767 single nucleotide polymorphism (SNP) markers, among which 4,561 were acquired using the Illumina iSelect 6K chip array and 18,206 were genotyping-by-sequencing (GBS) SNP markers (2H) was estimated based on the sum of squares of the ANOVA table. Batch effect was estimated as the marginal mean of the six check varieties and the adjusted trait value for each test oat line was calculated by subtracting batch effect from initial values. Further pairwise correlation and GWA were conducted based on the adjusted values. Pairwise correlation was conducted using cor function in R while GWA was conducted separately for each replicate, using the method of Fixed and random model Circulating Probability Unification (FarmCPU) implemented in R  of 10%. A linkage group homeologous circle diagram was created using R/circlize , maize , and rice . A word size of 7 was set to optimize sequence comparison between species. The E-value threshold was set at 10−10 due to relative short sequence of GBS markers (64 bp).BLASTN 2.9.0+  was estimated based on the sum of squared of the ANOVA table (Supplementary S2) where ever it was possible. Root number (RN) showed significant genotypic effect (P < 0.001) for both replicates and its 2H was high (0.86) in both replicates (P = 0.23) and was slightly significant in Taipei . For all the other traits, batch had significant effect (P < 0.001) for data collected in both replicates, while genotype showed no statistically significant effect. Therefore, it was necessary to calculate adjusted value for each genotype based on the batch effect evaluated on replicated check varieties. After removing poorly grown lines and images of bad quality, the final number of individuals for root-related traits was 564 and 452 for Taipei and Aberdeen, respectively. For shoot-related traits, the final number of individuals was 567 and 592 for Taipei and Aberdeen, respectively . A test of genotype × location interaction using mixed model on original traits showed significant genotype × location interaction . The sigplicates . The batectively . All tra2H followed similar trend which was generally higher in the data collected in Taipei than in Aberdeen, except for RN and RSA_4 : RN varied between 2.7 and 4.9 in Taipei and 2.6 and 5.1 in Aberdeen while the average (3.4) and the median (3.3) were identical for both replicates , while the correlation was significant, but to a lesser extent, for the joint consideration of both replicates . A similar trend was observed for the derived traits.In general, higher values were observed for data collected in Taipei than in Aberdeen, except for shoot relative growth rate for day 5 SSGR5, . H2 follnd RSA_4 . Root trplicates . Other op-value below the Bonferroni threshold. SNPs avgbs_29471.1.50 and avgbs_cluster_33692.1.63 were associated with more than one trait, both with highly correlated RSA_5 and AVRSA.We have identified the most appropriate GWA model for each trait based on quantile-quantile plots . For rooThe 16 SNPs associated with shoot related-trait were distributed on 10 LG, with one unmapped. Seven of these SNP were statistically significant at the Bonferroni threshold. For shoot trait-related SNPs, avgbs_cluster_19949.1.14 was associated with both SL_5 and SRGR_5 and the two SNPs associated with SG_5, avgbs_cluster_38112.1.47 and avgbs_cluster_1872.1.10, were also significantly associated with SRGR_5.i.e., spring vs.. Southern oats, we can notice a more pronounced allelic contrast in one genetic background than the other. For example, the allelic contrast of the marker avgbs_cluster_19949.1.14, associated with SL_5 collected in Taipei, was more pronounced in Southern germplasm than in the spring germplasm . The median of phenotypic value between the two alleles did not differ much, which was concordant with the weak substitution effect of the trait-associated markers . Meanwhiermplasm . When weWe further investigated the homeologous relationship between associated SNPs based on the homeologous regions inferred by Most of the significant associations were identified on measurements collected in Taipei except for five significant SNPs associations with shoot traits collected in Aberdeen  were retained. Context sequence around avgbs_cluster_41284.1.50, associated to RG_5, was matched to Os01g0675500, Bradi2g46410, and Zm00001d043879 which were predicted to be similar to a glucuronosyltransferase, the SNP (A/T) would change phenylalanine to isoleucine. Context sequence around GMI_ES17_c1687_437, associated with SRGR_5, was matched to Os03g0191100, Bradi1g71800, Zm00001d048218, and Zm00001d028008, which was predicted to be similar to a mitochondrial carrier protein domain containing protein in rice. The SNP (A/G) was located in the coding region and was a synonymous mutation. Context sequence around avgbs2_183559.1.25, also associated with SRGR_5, was matched to Os06g0146400d, Bradi1g51010.1d, and Zm00001d036145, which was predicted to code for an iron-sulfur cluster protein in rice and brachypodium. The SNP (T/C) was located in the coding region and would cause a change from leucine to proline. Context sequence around avgbs_29471.1.50, associated with AVRSA, matched to genome sequence of rice and maize, although no known gene model was predicted for the matched sequences.To identify candidate genes related to the seed vigor-related SNPs identified in this study, we blasted the context sequences around the SNPs to the reference genome of three model monocots, brachypodium, maize and rice . Four SNMeasuring root traits has long been difficult and laborious, especially if many accessions are to be assayed. In this study, we used a simple approach with germination pouches which allowed us to measure seed vigor-related traits on a large population of 650 individuals. Previous studies on seed or seedling vigor regarding root traits have used other methods, such as growing plants in paper rolls or hydroponic systems, with images acquired through scanning  instead of the more commonly used root length to characterize seedling root growth. Measurement of root surface area, potentially more reflective of root vigor, was made possible by the imaging system. This side-stepped difficulty with tangled or intertwined roots that can complicate root length measurement, even with image analysis tools. In addition, previous studies have shown high correlation between RSA and total root length , mitochondrial carrier protein (GMI_ES17_c1687_437), and iron-sulfur protein (avgbs2_183559.1.25). We have provided a first large GWA study in oat seed vigor within elite germplasm under mild temperature.The image-based phenotyping pipeline enabled us to measure seed vigor traits across a large oat panel in an efficient way. We observed variation within the traits that we have investigated. The moderate