Genomic selection is a promising tool to select for spot blotch resistance and index-based selection can simultaneously select for spot blotch resistance, heading and plant height.Bipolaris sorokiniana. Since genomic selection (GS) is a promising selection tool, we evaluated its potential for spot blotch in seven breeding panels comprising 6736 advanced lines from the International Maize and Wheat Improvement Center. Our results indicated moderately high mean genomic prediction accuracies of 0.53 and 0.40 within and across breeding panels, respectively which were on average 177.6% and 60.4% higher than the mean accuracies from fixed effects models using selected spot blotch loci. Genomic prediction was also evaluated in full-sibs and half-sibs panels and sibs were predicted with the highest mean accuracy (0.63) from a composite training population with random full-sibs and half-sibs. The mean accuracies when full-sibs were predicted from other full-sibs within families and when full-sibs panels were predicted from other half-sibs panels were 0.47 and 0.44, respectively. Comparison of GS with phenotypic selection (PS) of the top 10% of resistant lines suggested that GS could be an ideal tool to discard susceptible lines, as greater than 90% of the susceptible lines discarded by PS were also discarded by GS. We have also reported the evaluation of selection indices to simultaneously select non-late and non-tall genotypes with low spot blotch phenotypic values and genomic-estimated breeding values. Overall, this study demonstrates the potential of integrating GS and index-based selection for improving spot blotch resistance in bread wheat.A major biotic stress challenging bread wheat production in regions characterized by humid and warm weather is spot blotch caused by the fungus The online version contains supplementary material available at 10.1007/s00122-022-04087-y. Triticum aestivum) production in regions characterized by humid and warm weather conditions is spot blotch , caused by the fungus Bipolaris sorokiniana (Sacc.) Shoemaker (teleomorph: Cochliobolus sativus (Ito and Kuribayashi) Drechsler ex Dastur). The disease is widely prevalent in the eastern Gangetic plains of India, the Terai region of Nepal, Bangladesh, parts of Southeast Asia, Africa and Latin America   model  in subsequent crop cycles from 2013–2014 to 2019–2020 and are referred to by the harvest year and site of evaluation (for example: panel 2014 Agua Fria).The breeding population used in this study comprised seven panels, each with 1092 advanced breeding lines from the CIMMYT bread wheat program’s stage 2 yield trial nurseries. These breeding lines were developed using the selected bulk breeding approach , Cascabel × Ciano T79 (CC), Kath × Ciano T79 (KC) and Wuya × Ciano T79 (WC)  BC 2013 Agua Fria (2) BC 2014 Agua Fria (3) BC 2015 Agua Fria (4) CC 2013 Agua Fria (5) CC 2014 Agua Fria (6) CC 2015 Agua Fria (7) KC 2013 Agua Fria (8) KC 2014 Agua Fria (9) KC 2015 Agua Fria (10) WC 2013 Agua Fria (11) WC 2014 Agua Fria (12) WC 2015 Agua Fria. In addition, some full-sibs populations were also evaluated in two South Asian spot blotch hot spots including Varanasi, India (25° 16′ N and 82° 59′ E) and Dinajpur, Bangladesh (25° 38′ N and 88° 38′ E). The CC population was evaluated for spot blotch in Varanasi in the 2012–2013 cycle (CC 2013 Varanasi), the KC population was evaluated in Dinajpur in the 2012–2013 cycle (KC 2013 Dinajpur) and the WC population was evaluated in Dinajpur in the 2012–2013 and 2013–2014 cycles (WC 2013 Dinajpur and WC 2014 Dinajpur).Field response to spot blotch in Agua Fria in all the seven breeding panels and the four full-sibs panels was evaluated on lines that were planted in November and harvested in March each season. The lines were sown in plots using a randomized complete block design. Each plot comprised two rows that were 1 m in length, where the lines were grown on raised beds. While the breeding populations were unreplicated, the full-sibs populations were evaluated in two replications. A mixture of virulent races collected from leaf samples that were naturally infected in Agua Fria was used for inoculation as described in Singh et al.  and He eEvaluations of spot blotch in the KC and WC populations in Dinajpur and the CC population in Varanasi are described in Gahtyari et al.  and Roy K = 4) spreads from the center were considered as missing. Statistical analysis of the spot blotch rAUDPC values was done and the mean and standard deviation in the different panels were obtained. The Pearson’s correlation between the spot blotch rAUDPC values, days to heading and plant height, along with the p-values for the test of significance of the correlations were also obtained and visualized using the ‘R’ package ‘ggplot2’  markers  was used to predict the spot blotch breeding values of the remaining one-fifth of the lines . These random folds were sampled independently 20 times and the mean and standard deviation of the prediction accuracies  across the 20 repetitions were obtained for all the datasets in the breeding and full-sibs panels.K was assumed to be 1) (Huber and Ronchetti Genomic-best linear unbiased prediction (GBLUP)y was the vector of spot blotch rAUDPC values, Z was the design matrix for the random effects and ε was the error term. We assumed that the joint distribution of the vector of additive genetic effects (ε) was multivariate normal i.e., G indicates the genomic relationship matrix calculated from markers .The fixed effects model with selected markers significantly associated with spot blotch as fixed effects was fitted in the ‘R’ statistical software. First, the significant markers were identified using genome-wide association mapping in TASSEL version 5  were then used for the estimation of marker effects that were used for obtaining the estimated breeding values for spot blotch in the validation population.For spot blotch predictions using the fixed effects model in the full-sibs panels, we used a stepwise least-squares approach In the GBLUP + fixed effects model, the markers that were used as fixed effects in the fixed effects models in the breeding and full-sibs panels were used in combination with markers fitted as random effects in the GBLUP model, and the model can be represented as:mentclass2pt{minimt tests. The mean differences in the prediction accuracies between the different model pairs and the p-values for the test of significance of the mean differences were obtained. A p-value threshold of 0.005 was used to declare significance of the mean differences for both one and two-tailed t-tests.We compared the prediction accuracies obtained from the different models and tested if they were significantly different from each other using paired-Given the significant correlations between days to heading and spot blotch rAUDPC values in all the breeding panels, we generated subsets of the breeding panels after excluding early and late lines by treating them as outliers using the Huber’s robust fit outliers method  to predict a given full-sibs panel with 224–231 full-sibs. For this, we used the full-sibs evaluations in Agua Fria in the 2012–2013, 2013–2014 and 2014–2015 cycles  and 9268 filtered DArTseq markers.We also performed fivefold cross validations using random folds in the 6736 lines from all breeding panels, 4216 lines from all breeding panels subsets and 912 half-sibs, to understand if random sets of breeding lines and sibs can be predicted from the remaining individuals. For this, (1) in the breeding panels, 1347 random lines were predicted from a training population of the remaining 5389 lines, (2) in the breeding panels subsets, 843 random lines were predicted from a training population of the remaining 3373 lines and (3) in the sibs panels, 182 random full-sibs and half-sibs were predicted from the remaining 730 full-sibs and half-sibs. These random folds were sampled independently five times and the mean prediction accuracies were obtained.Genomic prediction across the breeding and half-sibs panels was done using the GBLUP model (1). For predictions across breeding panels using the fixed effects (2) and the GBLUP + fixed effects models (3), we used 26 and 36 significant markers from Supplementary Tables 1 and 2, that were significant in more than one panel in the breeding panels and the subsets of breeding panels, respectively. Similarly, for predictions across half-sibs panels with the fixed effects and the GBLUP + fixed effects models, we used the marker selection approach and the stepwise regression model described before and one to eight markers were used as fixed effects for predicting each of the panels.To compare PS with GS for spot blotch, we used two breeding panels (panel 2014 and panel 2018) where the correlations between the spot blotch rAUDPC values and days to heading were low, along with the subsets of these panels. We selected 10% of lines that had the lowest spot blotch rAUDPC values in these panels and also the lowest GEBVs obtained from the GBLUP model, for both within and across panel predictions. From the 10% lines that were selected by PS, we obtained the percentage of lines that were also selected by GS and the percentage of lines that were selected by PS only. From the remaining 90% of lines that were discarded by PS, we obtained the percentage of lines that were discarded by both PS and GS and the percentage of lines that were selected by GS only, but discarded by PS. Similarly for comparing PS with GS in the full-sibs panels, we used only the environments that had the highest within-panel genomic prediction accuracies for each full-sibs panel. This included the BC 2015 Agua Fria, CC 2016 Agua Fria, KC 2014 Agua Fria and WC 2015 Agua Fria datasets, where PS was compared to GS using both within and across-panel GEBVs.To identify lines with low spot blotch severities and GEBVs that are also not late and tall, we used the 912 half-sibs from the four bi-parental populations that were all phenotyped for spot blotch in Agua Fria during three seasons . We evaluated two selection index approaches: (1) the Eigen selection index method (ESIM) proposed by Cerón-Rojas et al.  and (2) I , where t’ being the number of traits  for spot blotch rAUDPC values, GEBVs, days to heading and plant height evaluated using the LPSI index were w′ = [− 1 − 1 − 0.8 − 0.8], w′ = [− 1 − 1 − 0.6 − 0.6], w′ = [− 1 − 1 − 0.4 − 0.4] and w′ = [− 1 − 1 − 0.2 − 0.2].The ESIM and the LPSI were used for selecting the top 10% of spot blotch resistant sibs that were not late and tall. We then obtained the expected genetic gain per trait for 10% selection using both the indices with the ‘RIndSel’ package in ‘R’ as described in Alvarado et al. . We alsop-value threshold of 0.001) moderate to high negative correlations between the spot blotch rAUDPC values and days to heading, that ranged between − 0.18 and − 0.66 in different breeding panels  and lowest in panel 2019 (37.5 ± 13.1). We observed significant  and 78.1 ± 12.8 (KC 2013 Dinajpur). The correlations between spot blotch rAUDPC values and days to heading were significant at a p-value = 6.5E-07) (2) the GBLUP + fixed effects model gave significantly higher prediction accuracies compared to the fixed effects model  and (3) the GBLUP and the GBLUP + fixed effects model accuracies were not significantly different . We also observed that for within-panel predictions in both the breeding panels with all the lines and the subsets, the GBLUP gave a mean 177.6% increase in accuracy over the fixed effects model. To understand if prediction accuracies in the breeding panels with all the lines were different from those in the subsets of breeding panels, we tested the significance of the mean differences in prediction accuracies between them and observed that they were not significant using all the models.In the breeding panels with all the lines, the mean spot blotch prediction accuracy was the highest using the GBLUP model (0.53 ± 0.07), followed by the GBLUP + fixed effects model 0.52 ± 0.06) and the fixed effects model (0.28 ± 0.14)  and (ii) the GBLUP + fixed effects and GBLUP models . However, the mean difference in prediction accuracies (0.11) between the GBLUP + fixed effects and the fixed effects models was significant and the GBLUP + fixed effects model gave significantly higher accuracies compared to the fixed effects model in the full-sibs panels . We also observed that for within full-sibs panels predictions, the GBLUP model gave a mean 22.1% increase in accuracy over the fixed effects model. In the different full-sibs panels, we observed that the genomic prediction accuracies using the GBLUP model were the highest in the BC panel (0.52 ± 0.03) and CC panel (0.52 ± 0.16), followed by the WC panel (0.47 ± 0.06) and KC panel (0.39 ± 0.06).In within full-sibs panels predictions, the highest mean prediction accuracy was obtained using the GBLUP + fixed effects model (0.52 ± 0.11), followed by the GBLUP model (0.47 ± 0.10) and fixed effects model (0.42 ± 0.12) Fig. . The meaPopulation structure analysis indicated no clear clustering of the different breeding panels based on the first two principal components and substantial overlap between the lines from the different panels Fig. a. Considp-value for the one-sided t-test = 1.7E − 07) and the GBLUP + fixed effects  model and (2) the GBLUP + fixed effects and the GBLUP models were not significantly different . In across-panel predictions for the breeding panels with all the lines and the subsets, the GBLUP gave a mean 60.4% increase in accuracy over the fixed effects model. To understand if the across-panel spot blotch prediction accuracies of specific breeding panels were different from the prediction accuracies of random sets of lines across the breeding panels, we obtained the mean difference in prediction accuracies (0.04) between them for all the models and observed that they were not significant .In the breeding panels with all the lines and the subsets, we observed that the across-panel prediction accuracies from: (1) the fixed effects model was significantly lower than the GBLUP model . We also observed that for across half-sibs panels predictions (excluding BC 2014 Agua Fria and CC 2015 Agua Fria where the fixed effects model had accuracies close to 0), (1) the GBLUP gave a mean 26.7% increase in accuracy over the fixed effects model and (2) the GBLUP + fixed effects model gave a mean 0.9% increase in accuracy over the GBLUP model (ranged between 6.4% decrease and 15.8% increase). We also observed that using all the prediction models, the mean differences in prediction accuracies (ranged between 0 and 0.03) when the four full-sibs panels were predicted from other half-sibs panels were not significantly different .In predictions across half-sibs panels, we observed that the mean differences in prediction accuracies (ranged between 0 and 0.12) were not significant for all the model pairs  and − 19.2 (LPSI w′ = [− 1 − 1 − 0.2 − 0.2]), while the expected genetic gain for 10% selection ranged between − 18 (LPSI w′ = [− 1 − 1 − 0.8 − 0.8]) and − 21 (LPSI w′ = [− 1 − 1 − 0.2 − 0.2]). This indicated that a higher expected genetic gain for spot blotch rAUDPC values could be obtained with the lowest weights on days to heading and plant height (− 0.2). However, we also observed that an increase in emphasis on days to heading and plant height was inversely related to the expected genetic gain for spot blotch rAUDPC values.The mean of all half-sibs, mean of selected 10% of half-sibs, selection differential and the expected genetic gain for 10% selected half-sibs are shown in Table w′ = [− 1 − 1 − 0.8 − 0.8]) and − 18.4 (LPSI w′ = [− 1 − 1 − 0.2 − 0.2]), while the expected genetic gain for 10% selected half-sibs ranged between − 15.4 (ESIM and LPSI w′ = [− 1 − 1 − 0.8 − 0.8]) and − 15.9 (LPSI w′ = [− 1 − 1 − 0.2 − 0.2] and LPSI w′ = [− 1 − 1 − 0.4 − 0.4]). For days to heading, the selection differential ranged between 2.1 (ESIM) and 5.2 (LPSI w′ = [− 1 − 1 − 0.2 − 0.2]), while the expected genetic gain for 10% selection ranged between 3 (ESIM) and 4.5 (LPSI w′ = [− 1 − 1 − 0.2 − 0.2]). We also observed that both the selection differential and the expected genetic gain for days to heading were positive, despite specifying negative weights. Considering plant height, we observed that the selection differential ranged between 0.2 (LPSI w′ = [− 1 − 1 − 0.2 − 0.2]) and − 5.8 (LPSI w′ = [− 1 − 1 − 0.8 − 0.8]), while the expected genetic gain for 10% selection ranged between 3.6 (LPSI w′ = [− 1 − 1 − 0.2 − 0.2]) and − 2.7 (LPSI w′ = [− 1 − 1 − 0.8 − 0.8]).For the spot blotch GEBVs, we observed that the selection differential ranged between − 17.4  category were selected, which is undesirable. Similarly, with the LPSI w′ = [− 1 − 1 − 0.2 − 0.2] values that laid a low emphasis on days to heading and plant height, some half-sibs that had low disease, but were in the tall and late (76–80 days to heading and 131–135 cm plant height) category were also selected, which is also undesirable. The selections made by the LPSI w′ = [− 1 − 1 − 0.4 − 0.4] and LPSI w′ = [− 1 − 1 − 0.6 − 0.6] were quite similar to each other and to the ESIM, but the ESIM also resulted in the selection of lines with moderately high spot blotch, low days to heading and plant height (66–75 days and 47–60 cm) and late to normal plant height (76–80 days and 61–120 cm).The box plots of trait distributions for the selected and unselected half-sibs using the ESIM and LPSI indices Fig.  indicateIn this study, we first evaluated the scenario of using genomic prediction for spot blotch in bread wheat within advanced breeding panels, where we aimed to understand whether phenotyped random sets of four-fifths of the lines can be used to predict the spot blotch response in the remaining one-fifth of the lines in a panel/year. Our results indicated moderately high fivefold cross-validation mean prediction accuracies of 0.53 and 0.47 in the breeding panels and subsets of breeding panels, respectively, implying that genomic prediction for spot blotch within breeding panels is promising and can be implemented by breeding programs. In the second scenario, we evaluated genomic prediction across breeding panels to understand if a breeding panel can be predicted using historic training populations comprising other breeding panels. Our results indicated moderately high mean across-panel genomic prediction accuracies of 0.40 and 0.38 in the breeding panels and their subsets, respectively, which were only slightly lower than the corresponding within-panel cross-validation genomic prediction accuracies of 0.53 and 0.47. These results are in agreement with previous studies that have indicated inflated prediction accuracies using cross-validations within populations and the challenges in predicting across populations/years using historic training populations due to the lower proportion of shared marker effects or genomic relationships and a high genotype x environment interaction  from a composite training population with random full-sibs and half-sibs. When full-sibs were predicted from other full-sibs within families and when full-sibs panels were predicted from other half-sibs panels, our results indicated mean prediction accuracies of 0.47 and 0.44, respectively. These are in agreement with previous studies suggesting that composite training sets with combined half-sibs families and a combination of half-sibs and full-sibs provide higher prediction accuracies compared to single families  values. Table S2: Markers used as fixed effects in the five folds of the fixed effects model in the seven breeding panels for within breeding panels predictions. Table S3: Markers used as fixed effects in the fixed effects model in the seven breeding panels for across breeding panels predictions. Table S4: Markers used as fixed effects in the five folds of the fixed effects model in the full-sibs for within full-sibs panels predictions. BC refers to the Bartai x Ciano T79 population, CC refers to the Cascabel × Ciano T79 population, KC refers to the Kath × Ciano T79 population and WC refers to the Wuya x Ciano T79 population. (XLSX 198 KB)Below is the link to the electronic supplementary material.


The integration of genomic data into genetic evaluations can facilitate the rapid selection of superior genotypes and accelerate the breeding cycle in trees. In this study, 390 trees from 74 control-pollinated families were genotyped using a 36K Axiom SNP array. A total of 15,624 high-quality SNPs were used to develop genomic prediction models for mammalian bark stripping, tree height, and selected primary and secondary chemical compounds in the bark. Genetic parameters from different genomic prediction methods—single-trait best linear unbiased prediction based on a marker-based relationship matrix (genomic best linear unbiased prediction), multitrait single-step genomic best linear unbiased prediction, which integrated the marker-based and pedigree-based relationship matrices (single-step genomic best linear unbiased prediction) and the single-trait generalized ridge regression—were compared to equivalent single- or multitrait pedigree-based approaches (ABLUP). The influence of the statistical distribution of data on the genetic parameters was assessed. Results indicated that the heritability estimates were increased nearly 2-fold with genomic models compared to the equivalent pedigree-based models. Predictive accuracy of the single-step genomic best linear unbiased prediction was higher than the ABLUP for most traits. Allowing for heterogeneity in marker effects through the use of generalized ridge regression did not markedly improve predictive ability over genomic best linear unbiased prediction, arguing that most of the chemical traits are modulated by many genes with small effects. Overall, the traits with low pedigree-based heritability benefited more from genomic models compared to the traits with high pedigree-based heritability. There was no evidence that data skewness or the presence of outliers affected the genomic or pedigree-based genetic estimates. The implementation of genomic prediction in plants has offered new possibilities for maximizing genetic gains for economically important traits (H (Pinus radiata D. Don) to marsupial bark stripping and (2) the bark chemical traits, some of which may impact susceptibility to herbivory. These methods were selected to represent alternative approaches and assumptions related to marker effects  that occurs between the ages of 1 and 6 years, has become the most important pest problem in Tasmania , especially by the native marsupial Bennett’s wallaby  models for selected primary and secondary chemical compounds in the bark, including those associated with bark stripping in radiata pine. A key focus was examination of the potential improvement in heritability estimates of the bark chemical compounds and their additive genetic correlation with bark stripping and tree height with GBLUP;assess the potential improvement in predictive ability/accuracy of the GBLUP through use of nonlinear GRR model; andassess the contribution of additional genotyped data to the improvement in heritability, predictive ability/accuracy, and genetic correlations in integrated multitrait analyses (ssBLUP).The genetic field trial at Wilmot in Tasmania, Australia  described in ξ) and uncorrelated (η) residuals. The spatially correlated error (ξ) was modeled using a first-order separable autoregressive model in the row and column directions (ψI150) was also added as a random term. Therefore, the residual structure R follows:p) is a first-order autoregressive correlation matrix with autocorrelation p for columns (col) and rows (row) . Half of the plants were subject to artificial bark stripping  at time zero (T0) and half were untreated (n = 195) and used as controls , trans-farnesol[18], agathadiol[20], and inositol[56] increased between 2.1- and 2.7-fold with the GBLUP. Apart from the heritability of linoleic acid[59] that slightly reduced, the compounds experienced between 1.10- and 2.09-fold increases in heritability with GBLUP compared to ABLUP. Significant additive genetic variation was also detected for agathadiol[20], dehydroabietic acid[30], and inositol[56] that were not significant with the ABLUP models. Regardless of the set of samples or the type of analysis, the heritability estimates for bark stripping and tree height were low. For example, the height heritability of the ABLUP (h2 = 0.05 ± 0.02) based on the 20 unprotected replicates as well as the GBLUP heritability (h2 = 0.09 ± 0.07) based on 6 protected replicates were low. Based on unprotected trees, significant additive genetic variation but low heritability was exhibited for the spatially adjusted marsupial bark stripping (h2 = 0.09 ± 0.03)  .t14 = 0.17, P = 0.43) and the GRR . Accounting for marker heterogeneity has little influence on Pab/Pac, although marker effects changed up to 10-fold in GRR compared to GBLUP . Similarly, the predictive accuracy did not differ between the ABLUP (average Pac = 0.47) and GBLUP (average Pac = 0.45) , ABLUP and GRR (average Pac =0.44) , nor between GBLUP and GRR .The predictive ability (Pab) and predictive accuracy (Pac) for the height and the bark chemical compounds varied between −0.02 to 0.40 and −0.06 to 0.77, respectively, depending on the trait and statistical model . Comparito GBLUP . For exaH matrix in the multitrait ssGBLUP analysis compared with estimates from the equivalent ABLUP analysis which incorporates genotyped (6 replicates) and un-genotyped trees (20 replicates) , and on average 1.5-fold, greater than the heritabilities estimated from multitrait ABLUP (average = 0.27 ± 0.10). In the multitrait analysis of height involving all individuals in the trial, the improvement in heritability was increased 1.4 times . However, for bark stripping the multitrait ssGBLUP heritability estimate (h2 = 0.12 ± 0.04) was 3 times higher than the ABLUP estimate (h2 = 0.04 ± 0.02). The multitrait ABLUP heritability estimates for marsupial bark stripping and height were relatively constant irrespective of the chemical compound fitted in the model (data not shown).The heritability estimates for the 15 bark chemical compounds were similarly improved with the incorporation of genomic-derived pedigree information and additional phenotyped individuals through the use of the licates) . The mult14 = 5.72, P < 0.001). The predictive accuracy similarly increased from an average of 0.41 to 0.69 . The linear relationship between heritability and predictive ability for the 15 chemical compounds was high, indicating that there was a tendency for compounds with higher heritability estimates to reach higher predictive ability for all models  compared with ABLUP . Similarly, genetic correlations between chemical compounds with height increased with ssGBLUP (gr = 0.19) compared with ABLUP . Further, the SE of the genetic correlation estimates obtained with ssGBLUP were on average significantly lower than those obtained with ABLUP for both marsupial bark stripping  and height , suggesting slightly more accurate estimates with the ssGBLUP model. The ABLUP correlation estimates that were associated with the highest SEs more strongly increased in accuracy than those that had low SEs of estimation (gr ± av. SE = 0.41 ± 0.27) compared to ABLUP (av. gr ± av. SE = 0.41 ± 0.29) although the correlation varied depending on the chemical compound in the model . Instead, the SE of the genetic correlation estimates showed a negative linear relationship with predictive ability. This suggests that the precision rather than the magnitude of the correlation (precision interpreted based on SE) has a greater impact on the predictive ability/accuracy. The genetic correlations that were associated with low ABLUP error for estimating the genetic correlation were better predicted than those that had higher genetic correlation SEs . The effThe heritability estimates were moderate for pedigree-based models. However, significant improvement in the estimates were detected for both single-trait and multitrait genomic models. Various studies have provided evidence that genomic models, using realized relationships based on marker information, lead to a substantial increase in the prediction accuracies for various traits in trees compared to those using pedigree-based relationships  method. In addition, several chemical traits that did not exhibit statistically significant additive genetic variance with the single-trait ABLUP model did with the GBLUP model. The GBLUP performed better than the ABLUP for most chemical traits except linoleic acidSimilar to the chemical traits, heritability for height slightly improved with the multitrait ssGBLUP compared with the multitrait ABLUP, consistent with the observations in Norway spruce populations  as well as α and β  have a significant impact on the predictive ability. In this study, a low w was selected, which signified that most of the additive genetic variance was captured by the markers, which may not have been the case especially given that the genotyped population was small. The effect of the scaling factor, w, has been assessed in various studies and a range of optimum w for different traits, up to 0.95 have been established  were higher with the multitrait ssGBLUP than the multitrait ABLUP. This finding is consistent with some studies in conifers that demonstrated potential improvement of prediction of breeding values with genomic models . In our The GRR modeled increased effects of specific markers, although allowing for such heterogeneity did not improve the Pab/Pac when compared to GBLUP. This contrasts with some studies that have indicated that Pab/Pac could be improved by utilizing the subset of markers with the largest magnitude rather than all markers , indicatIn summary, the results here provide the expected response to genomic selection of chemical defense traits in the context of breeding. The results suggest that genomic selection can be useful for improving the accuracy of selecting for chemical phenotypes. This is in addition to other benefits such as reducing the length of the selection cycle, which has been demonstrated in other studies . Genomicjkac245_Supplemental_Figure_S1Click here for additional data file.jkac245_Supplemental_Figure_S2Click here for additional data file.jkac245_Supplemental_Figure_S3Click here for additional data file.jkac245_Supplemental_Figure_S4Click here for additional data file.jkac245_Supplemental_MaterialClick here for additional data file.jkac245_Supplemental_Table_S1Click here for additional data file.jkac245_Supplemental_Table_S2Click here for additional data file.


Rht-B1 and Ppd-A1 genes. Population structure analyses revealed the presence of three main groups, composed by old, modern and genotypes with European or CIMMYT ancestry. The photoperiod sensitivity Ppd-A1b allele showed higher frequency (75%) than the insensitivity one Ppd-A1a (GS105). The semi-dwarfism Rht-B1b and the Ppd-A1a (GS105) alleles were associated with increases in harvest index and decreases in plant height, grain protein content and earlier heading date, although only the varieties carrying the Rht-B1 variants showed differences in grain yield. Out of the two main yield components, grain number per plant was affected by allelic variants at Rht-B1 and Ppd-A1 loci, while no differences were observed in thousand kernel weight. The increases in grain number per spike associated with Rht-B1b were attributed to a higher grain number per spikelet, whereas Ppd-A1a (GS105) was associated with higher grain number per spikelet, but also with lower spikelets per spike.Exploring the genetic variability in yield and yield-related traits is essential to continue improving genetic gains. Fifty-nine Argentinian durum wheat cultivars were analyzed for important agronomic traits in three field experiments. The collection was genotyped with 3565 genome-wide SNPs and functional markers in order to determine the allelic variation at  Triticum turgidum L. var. durum) is the raw material for producing semolina for the pasta industry. Increasing the production of durum wheat contributes to supplying the future demand for food caused by steady growth of the world population1. As the arable land is a limited resource and reaching the crop yield potential is still a challenge, so exploring the genetic variation of yield-related traits in current elite germplasm is an important approach to understand whether enough variability is available to drive genetic gains.Durum wheat  (http://datosestimaciones.magyp.gob.ar/), being the southeast of Buenos Aires province the main production area. In this region, durum wheat has a great regional importance playing a central role for the national pasta industry that has been increased in recent years reaching 400,025 tons in 20186. In the international ranking of pasta consuming countries, Argentina is among the top ten, with 8.8 kg per capita per year7.In Argentina, durum wheat is a traditional crop that has been cultivated for more than 80 years, showing the highest level of production in the 1970sRht) during the 1960s and 1970s produced the greatest increases in wheat yield during the Green Revolution8. Semi-dwarfism was introduced into Argentinian durum wheat genotypes during the 1970s through crosses with CIMMYT germplasm2 and it is found in all of the genotypes currently grown in Argentina and most of the durum wheat cultivated worldwide. The Rht-B1 gene, located on chromosome 4B, is the main determinant of plant height in most durum wheat cultivars9, being the Rht-B1b semi-dwarfing allele the most common mutation. The Rht-B1b allele has been associated with high yield caused by an increased partitioning of assimilates to developing spikes11. Moreover, McClung et al.12 reported a pleiotropic effect of a semi-dwarfism gene that resulted in a greater number of tillers and kernels per spike, and lower grain protein content in durum wheat. Lower grain protein associated with Rht-B1b was also found in the early stages of seed development in bread wheat13.The introduction of semi-dwarfing alleles of plant height genes  and photoperiod sensitivity (Ppd) genes. Vernalization is the requirement of low temperatures to induce flowering, Vrn-1 being the major gene involved in this process for both bread and durum wheat17. The dominant spring alleles of the homologous Vrn-1 genes (Vrn-A1 and Vrn-B1) are the most common variants in durum wheat19. Similar to major elite durum wheat gene pools worldwide20 and bread wheat cultivars in Argentina21, most of the Argentinian durum wheat cultivars have a spring growth habit, thus they do not have major vernalization requirements.Flowering time plays an important role in determining the environmental conditions during the grain filling period, having a significant impact on yieldPpd-1 gene22. The allelic variants of Ppd-A1, located on chromosome 2A, have the strongest effect on photoperiod sensitivity in durum wheat23. The two most important types of deletions within the wild-type sequence of the Ppd-A1 gene in durum wheat were reported by Wilhelm et al.24 and named GS100 and GS105 . Both of these deletions on the Ppd-A1 gene are related to photoperiod insensitivity, thus accelerating flowering20. Photoperiod insensitivity increased yield up to 35% in bread wheat cultivars in the south of Europe15. Furthermore, early flowering is associated with a reduction in plant height, lower spike and spikelet numbers and a higher number of grains in bread wheat15. In durum wheat, Royo et al.20 reported increases in grain yield associated with photoperiod insensitivity by allowing more favorable environmental conditions during grain filling in Spain and Mexico.Wheats that are photoperiod sensitive need to be exposed to long days in order to flower. The major photoperiod sensitivity genes in wheat are the homologs of the 27. The study of the relationships between genetic groups and phenotypes for agronomically relevant traits is an approach that has been used in different wheat populations, allowing the characterization of the existing genetic variability31. Moreover, taking into account the effect of the main genes that affect key traits in wheat, such as plant height and heading date, may help to elucidate part of the differences between population groups.A large number of SNP markers has been developed, distributed throughout the wheat genome. These markers have been shown to be efficient tools to assess the genetic diversity of wheat germplasm collections worldwideRht-B1) and photoperiod sensitivity (Ppd-A1) genes in our collection and (3) to elucidate the effect of the clusters defined by the genetic structure and the allelic variants of major genes on agronomic traits.An in-depth analysis of the genetic variation in yield and yield related traits and the study of genetic diversity are yet lacking for Argentinian durum wheat germplasm. In this study, a wide range of spring durum wheat genotypes grown in Argentina over the last 70 years was included to analyze how yield components contribute to yield formation in local environments and its relationship with the effect of major genes. This information could help breeders to define an appropriate strategy to increase yield potential. To study the population structure, a subset of genome-wide SNP markers selected from the Axiom 35 K array was used. The aims of this study were (1) to evaluate the phenotypic variation in 12 agronomic traits (2) to assess the population structure and genetic variation in semi-dwarfing . Out of the 18,122 polymorphic markers, the SNPs with more than 10% of missing data and more than 10% of heterozygosity were removed, leaving 10,776 markers. Then, of these markers, the ones with a frequency of minor alleles (MAF) lower than 5% were removed, leaving 6462 markers. Finally, 3565 SNPs met the quality cutoff according to the scores for the probes produced by the Affymetrix software (‘Poly High Resolution’ and ‘Off-Target Variant’ categories were considered). These SNPs were widely distributed across the whole genome of durum wheat Table  and so tRht-B1 and Ppd-A1 genes was analyzed and two allelic variants were found on both loci , while 54 genotypes carried the Rht-B1b semi-dwarfing allele. The allelic characterization at the Ppd-A1 gene revealed that 15 genotypes (25%) carried the Ppd-A1a (GS105) allele, conferring photoperiod insensitivity, whereas the remaining genotypes had the sensitive Ppd-A1b one. Our results showed that the Rht-B1b allele, associated with semi-dwarfism in wheat, was incorporated into Argentinian genotypes during the 1970s, and all the genotypes bred after 1980 carry this allele. However, the photoperiod insensitive Ppd-A1a (GS105) allele started to appear in local genotypes in 1987.The allelic diversity in the Population stratification was analyzed through the STRUCTURE software Fig. A. The esNa) being 1.95, the number of effective alleles (Ne) 1.52, Shannon’s information index (I) 0.47, unbiased expected heterozygosity (uHe) 0.31 and the percentage of polymorphic loci (%P) 94.5% (Table Fst = 0.293)  showed that the genetic variation between the STRUCTURE groups  only represented 19.7% of the total variance, whereas most of the genetic variation (80.3%) was observed within the groups Table . ConsideRht-B1a semi-dwarfing allele   included most of the oldest genotypes. Eight out of nine genotypes belonging to this group were released in 1980 or earlier  were detected for all traits (Table P < 0.04) was significant for all traits, except for aerial biomass. A significant effect of the environment (P < 0.05) was observed for most of the traits, except for plant height, grain number per spike and grain number per spikelet. The minimum, mean and maximum values for all the traits in the three environments are summarized in Table −1 in the three environments, whereas the mean values for each environment were 3120, 4310 and 3313 kg ha−1 for CA14, BW17 and PS14, respectively.The Argentinian durum wheat genotypes showed a wide range of variation in all the studied traits. Significant differences between genotypes  (P < 0.001), grain protein content (r = − 0.58) (P < 0.001) and heading date (r = − 0.36) (P < 0.01). Harvest index showed the highest positive correlation with grain yield (r = 0.72) (P < 0.001), followed by grain number related-traits (r = 0.5) (P < 0.001). Grain number per plant showed a similar correlation with spikes per plant (r = 0.75) (P < 0.001) and grain number per spike (r = 0.7) (P < 0.001). An in-depth analysis of the components of grain number in the spike showed that this trait was more strongly associated with the grain number per spikelet (r = 0.91) (P < 0.001) than with the spikelets per spike (r = 0.12) (P > 0.05).Pearson’s correlation coefficients exhibited a wide range of values for the traits Table . Grain yRht-B1 alleles  was used to illustrate the relationships between phenotypic variables and the different genetic groups. The first two components explained 64.3% of the total variation. PC1 explained 41.3% of the variation and it was mainly associated with positive values for harvest index, grain number-related traits and grain yield, and negative values for plant height and grain protein content  relative to the genotypes carrying the Rht-B1a, whereas the genotypes with the Ppd-A1a allele (conferring photoperiod insensitivity) showed increases of 314 kg ha−1 relative to the ones carrying the Ppd-A1b, but this was not significant (P = 0.063)  than those carrying the other allele   Table . MoreovePpd-A1b variant, the genotypes carrying Rht-B1b or Ppd-A1a were grouped in order to study the effect of the two major genes separately. When the effect of Ppd-A1 was only analyzed in the semi-dwarf genotypes (n = 54), significant differences persisted in seven out of nine traits (the spikes per plant and grain number per spike showed no significant differences). The effect of Rht-B1 on photoperiod sensitivity genotypes (n = 44) was also tested and the significant differences persisted in eight out of nine traits (spikes per plant showed no significant differences). These results are summarized in Table As all the tall genotypes carried the Rht-B1 and Ppd-A1 genes and its effects on agronomic traits were analyzed in a representative collection of Argentinian spring durum wheats.Genetic diversity and allelic variants of major plant height and phenology genes had been studied in bread and durum wheat collections from various geographic regions, however not in the Argentinian durum wheat. Moreover, the relationships between genetic variability and agronomic traits had been poorly tested in durum wheat. In the current study, the population structure and the genetic variation at Rht-B1 and Ppd-A1 loci were found in our collection. The Rht-B1b semi-dwarfing allele had the highest frequency (92%) due to the rapid incorporation of this allele in Argentinian breeding programs since the 1970s, as reported by Rajaram et al.2. Regarding the Ppd-A1 gene, two (Ppd-A1b and GS105) out of three main allelic variants reported in durum wheat24 were present in our collection, the photoperiod insensitivity GS100 allele being absent. A fourth variant of this gene conferring photoperiod sensitivity was recently identified in durum wheat, named Deletion Capelle-Desprez (DelCD), which was found with a frequency of 20% in Mediterranean landraces and was not found in any of the 20 modern cultivars analyzed by Royo et al.32. This last variant was not analyzed here.Different allelic variants at the Ppd-A1b) was more common in the Argentinian germplasm (75%) than the insensitivity one (GS105). On the contrary, higher frequency of the photoperiod insensitivity alleles at Ppd-A1 than at Ppd-A1b had been found in other studies, with frequencies of 54% of GS105 type allele and 10% of GS100 in a collection of 474 durum wheat genotypes33. Royo et al.32 also observed a high frequency of the GS105 allele (65%) followed by GS100 (20%) in 20 Mediterranean adapted modern cultivars. Photoperiod insensitivity is considered one of the main tools to reach a crop wide adaptation34, which has been selected by breeders to improve the adaptation of the new varieties to more favorable conditions during grain filling23. The shuttle-breeding strategy takes advantage of using the insensitive alleles, which speeds up the rate of progress in wheat breeding34. Of the photoperiod insensitivity variants at Ppd-A1, the GS100 allele showed a stronger effect on the reduction in flowering time in durum wheat than the GS10535. However, this allele was absent in Argentinian germplasm and it has only been found in a few durum wheat cultivars from Spain, Italy, North America and North Africa33. Therefore, the incorporation of the GS100 allele is an interesting option to exploit novel sources of earliness in Argentinian durum wheat breeding programs.Our results showed that the photoperiod sensitive allele . The moderate genetic differentiation estimated between the two modern germplasm groups, Q2 and Q3 (Fst = 0.19), could be attributed to their pedigrees. However, only 19.7% of the variability was explained for the differences between the groups. The highest variability was found within populations (80.3%), which was similar to the results shown in previous population analyses of durum wheat collections37.The analysis of the genetic structure in our collection revealed a first main division between old, most carrying the GS105). This allele was mainly present among genotypes belonging to Q3, the group with a high influence from CIMMYT in its pedigree. This result is in line with prior knowledge that the photoperiod insensitivity was derived from CIMMYT germplasm and then used in many breeding programs worldwide20.This study demonstrated that ‘Bonaerense Quilaco’, registered in 1987, was the first cultivar released in Argentina carrying the photoperiod insensitivity allele  showed similar or higher diversity values than those reported in other studies involving durum wheat collections40. Similar values of genetic diversity (0.30 on average) were found in a collection of 91 durum wheat landraces from Central Fertile Crescent by Baloch et al.41, whereas lower genetic diversity (0.19 and 0.12 on average) using SNP markers was detected in two different worldwide durum wheat collections of 150 and 370 accessions27.The genetic diversity using 3565 SNP in our collection  can be partially attributed to the consistent reduction in plant height as a result of the introduction of the Rht-B1 and Ppd-A1, while no differences were found in the thousand kernel weight. The genotypes carrying the Rht-B1b or Ppd-A1a alleles showed 30 and 16% more grains per plant, respectively, than genotypes carrying the other allele. These increases were associated with increases in both the grain number per spike and the number of spikes per plant. The increases in grain number in the photoperiod insensitive genotypes could be attributed to the lower temperatures during the spike growth phase, as reported by Ratjen et al.49. Increases in grain number per spike and spikes per plant in semi-dwarf genotypes have previously been reported in durum wheat12. Detailed analysis of grain number per spike indicated that the differences between the Rht-B1 and Ppd-A1 allelic variants were explained more by the grain number per spikelet than by the spikelets per spike, as was shown by the correlation analyses between these last three traits. The presence of the Rht-B1b allele was associated with 15.6% more grains per spikelet than Rht-B1a, whereas no effect was observed between the alleles in the spikelets per spike. Álvaro et al.50 found similar increases in grains per spikelet in Italian and Spanish durum wheat varieties  related to Rht-B1b and neither of them observed an effect of spikelets per spike associated with Rht-B1 in these varieties. On the other hand, increases in grain number per spike caused by Ppd-A1a were mainly associated with increases in grain number per spikelet (13%), and also with low spikelets per spike (5%). On the contrary, Royo et al.51 and Arjona et al.35 reported that the number of spikelets per spike explained most of the differences in grain number per spike but it was associated with Ppd-B1 variants, suggesting that Ppd-A1 and Ppd-B1 could be affecting the number of grains per spike through different mechanisms.Of the two main yield components, the number of grains per plant was affected by different allelic variants for both Ppd-A1a allele was present in 43% and 17% of genotypes , but it was absent in the Q1 and Q2 groups. The Ppd-A1a allele has shown a positive effect on grain number related-traits. Thus, the increases in these traits in the Q3 and admixture groups could be associated with the effect of the Ppd-A1 locus. However, the increase in grain number was not translated into higher yield within these groups, probably due to the influence of the negative correlation with the thousand kernel weight. Although the thousand kernel weight did not show any significant differences between the STRUCTURE groups or allelic variants, the Q3 and admixture showed lower values (6% on average) than Q1 and Q2.The Q3 and admixture groups included genotypes with a high grain number per spike, mostly due to increases in grain number per spikelet. In these groups, the Rht-B1 and Ppd-A1. Genotypes carrying the Rht-B1b or GS105 (Ppd-A1a) allelic variants showed heading 4.8 and 5.9 days earlier, respectively. The effect of Ppd-A1 variants on heading date showed similar patterns considering all genotypes or only the semi-dwarf ones. By considering only the Rht-B1 alleles in the photoperiod sensitive genotypes, the differences in heading date were reduced to 3.3 days. A similar or lower effect in flowering time caused by Rht-B1b has previously been demonstrated in bread wheat53. Reductions in flowering time due to Ppd-A1a (GS105) compared with the Ppd-A1b allele were also reported previously, decreasing by 3–12 days in spring durum wheats in Spain and Mexico environments23. The early-flowering alleles tend to be associated with higher yields than the later-flowering ones, although in our study no-significant effect on grain yield associated with Ppd-A1 was found, similar to the results of Arjona et al.35. However, several traits strongly correlated with yield showed differences between the Ppd-A1 allelic variants. The Ppd-A1a (GS105) allele was associated with reductions of 7% in plant height and 5% in grain protein content, and increases in the harvest index with values of 0.40 related to the GS105 and 0.36 to the Ppd-A1b alleles. Reductions of 11% in plant height and similar values of harvest index associated with these Ppd-A1 variants were observed in 151 durum wheat landraces51.Differences in the heading date between allelic variants were observed for both Rht-B1 and Ppd-A1 loci on agronomic traits.Further studies in durum wheat collections like recombinant inbred lines (RILs), near isogenic lines (NILs) or mutants could provide more evidence on the effect of the alleles of Rht-B1 semi-dwarfism allele in 1980 and introduced the Ppd-A1 photoperiod insensitivity allele in modern cultivars after 1987. Our study showed that phenotypic variability in yield-related traits could be partially explained through the allelic variants of major genes and population groups. This highlights the potential to exploit this variability for a more targeted yield improvement considering the key role of using functional molecular markers in this approach.In conclusion, our results showed high genotypic and phenotypic variability available in the Argentinian germplasm of durum wheat, which can be exploited through breeding. Breeding efforts in Argentinian durum wheat fixed the Triticum turgidum L. var. durum) genotypes were evaluated in the current study , Barrow [BW]  and Pieres [PS] , and they were conducted during 2014 (CA and PS), and 2017 (BW). A randomized complete block design with two replications was used. The plots consisted of seven rows, 4.2 to 6.4-m long and 18 or 20 cm apart, but smaller areas of 5.5, 5.0 and 4.2 m54. Our collection was genotyped using the 35 K Axiom Wheat Breeder’s Genotyping Array from Affymetrix55, with a call rate cutoff threshold ≥ 90%, performed at TraitGenetics . In addition, the allelic variation in the Rht-B1 and Ppd-A1 genes in the collection was assessed at CIMMYT using Kompetitive Allele‐Specific PCR (KASP) markers for polymorphisms described by Ellis et al.56 and Beales et al.57, respectively. The photoperiod sensitivity Ppd-A1 gene was also evaluated using a sequence-tagged-sites (STS) marker and two KASP specific assays for GS100 and GS105 deletions24. The primers are summarized in Table DNA extraction was carried out from fresh leaves of 10-days-old seedlings of each genotype using a modified cetyltrimethylammonium bromide (CTAB) method as described in Dreisigacker et al.Ppd-A1 was assayed by PCR using two forward primers and a common reverse primer in a reaction mixture of 10 μl comprising final concentrations of 1X Buffer with Green Dye , 0.2 mM Deoxynucleoside 5’-triphosphates (dNTPs), 1.2 mM magnesium chloride, 0.5 μM of each primer, 1U of DNA polymerase  and 50 ng of DNA. The cycling conditions were performed at 95 °C for 2 min, followed by 40 cycles of 94 °C for 1 min, 55 °C for 2 min, and finally 72 °C for 2 min. The amplified products were separated by electrophoresis in 12% polyacrylamide gel and visualized by a silver staining protocol.The STS marker for A touchdown PCR protocol was used for KASP markers starting with 94° for 15 min, followed by 11 cycles of 94° for 30 s, 65°-55 °C for 60 s (−0.8 °C/cycle), 72 °C for 30 s and continued with 26 cycles of 94 °C for 30 s, followed by 57 °C for 60 s and a final step at 72 °C for 30 s. PCR was carried out arrayed in a 384 PCR plate, using 5 μl of PCR volume. DNA samples (150 ng/well) were briefly centrifuged and oven dried at 60 °C for 1 h. SNP-specific KASP reagents (5 μl), such as the assay mix and the 2X KASP Master mix, including the fluorescent dyes FAM and VIC, were added to dried DNA samples.58 using a model-based Bayesian approach assuming 1 to 10 groups (K) and 5000 burn-in iterations followed by 10,000 Markov Chain Monte Carlo (MCMC) iterations with five independent replicates for each K value. The Structure Harvester software59 implementing the Evanno method was applied to detect the true subpopulation (K) number60. Population structure was plotted using the Pophelper R library. Principal coordinate analysis (PCoA) was also performed in order to visualize the genetic relationships between the individuals and to compare this analysis with the groups defined by the STRUCTURE. The analysis of molecular variance (AMOVA) was used to estimate the percentage of variance explained between and within the STRUCTURE groups. The PCoA and the AMOVA were performed using the GenAlEx v6.5 software61.The population structure was analyzed with the software STRUCTURE 2.3.4Na), number of effective alleles (Ne), Shannon information index (I) and the unbiased expected heterozygosity (uHe = (2N/(2N − 1)) * He; where N is the number of genotypes and He is the expected heterozygosity), and pairwise fixation index (Fst) using the GenAlEx v6.5 software.Genetic diversity was assessed within each STRUCTURE group by calculating the percentage of polymorphic loci (%P), number of alleles  of three 100 grain samples; and the grain protein content (%) measured at INTA Barrow in a clean sample of 30 g of grains from each plot using Near-infrared spectroscopy , as an average of seven measurements, at 13.5% base humidity.Twelve traits were evaluated in all the 59 genotypes in the three environments. Grain yield was obtained as the weight of clean whole grains from the entire harvest plot, expressed as kg haTen plants were collected at random from the middle row of each plot at maturity and the plant height, aerial biomass, harvest index and the spike number from each plant were evaluated. Plan height (cm) was measured from the base to the top of the plant, including the awns. Aerial biomass (g) was recorded as dry weight of the aerial part of the plant. Harvest index was calculated as the ratio between the grain weight and aerial biomass from each plant. The spike number per plant was the number of fertile tillers per plant. Moreover, the number of spikelets and grains were counted for all the spikes in the 10 plants. The number of grains per plant was considered as the sum of the number of grains on all the spikes of the plant, and the grain number per spikelet as the ratio between grain number per spike and spikelet number per spike. Regarding the traits measured per spike, all spikes from each plant were considered and a mean value per plant was obtained. The mean values of the 10 plants for each trait were calculated and considered by plot.2) was estimated according to Nyquist63: H2 = σ2G/[σ2G + σ2GE/E) + σ2e/rE)], where σ2G is the genetic variance, σ2GE is the genetic x environment interaction variance, σ2e is the residual variance, E is the number of environments and r is the number of replications.A linear mixed model with restricted maximum likelihood (REML) method  was used to evaluate the effect of Genotype (G), Environment (E), and Genotype × Environment (G × E) interaction, considering these three factors as fixed and the effect of the block nested within environment as random. The broad sense heritability (Hr) and principal components analyses (PCA).Best linear unbiased predictor (BLUP) was carried out using the REML method in SAS for estimating the phenotypic values for each trait and genotype in the three environments and these values were used for Pearson correlation coefficients  were calculated and, when the group effect was significant, the Tukey test at The plant material was provided by durum wheat Argentinian breeding programs, and sown in order to multiply and purify seed under the supervision of PFR. Research experiments follow the statements signed by Argentina under the International Convention for the Protection of new varieties of plants  and the Argentinian national law N° 20.247. In this study, permissions were not required to carry out field experiments in durum wheat as part of research activities.Supplementary Information.


Malacosoma disstria, a widespread and irruptive pest of numerous deciduous tree species in North America. We collected larvae from multiple populations across Eastern Canada, where the species experiences a diversity of environmental gradients and feeds on a number of different host tree species, including trembling aspen (Populus tremuloides), sugar maple (Acer saccharum), red oak (Quercus rubra), and white birch (Betula papyrifera). Using a combination of reciprocal causal modelling (RCM) and distance‐based redundancy analyses (dbRDA), we show that differentiation of thousands of genome‐wide single nucleotide polymorphisms (SNPs) among individuals is best explained by a combination of isolation by distance, isolation by environment , and differences in host association. Configuration of suitable habitat inferred from ecological niche models was not significantly related to genomic differentiation, suggesting that M. disstria dispersal is agnostic with respect to habitat quality. Although population structure was not discretely related to host association, our modelling framework provides the first molecular evidence of host‐associated differentiation in M. disstria, congruent with previous documentation of reduced growth and survival of larvae moved between natal host species. We conclude that ecologically mediated selection is contributing to variation within M. disstria, and that divergent adaptation related to both environmental conditions and host association should be considered in ongoing research and management of this important forest pest.Diverse geographic, environmental, and ecological factors affect gene flow and adaptive genomic variation within species. With recent advances in landscape ecological modelling and high‐throughput DNA sequencing, it is now possible to effectively quantify and partition their relative contributions. Here, we use landscape genomics to identify determinants of genomic differentiation in the forest tent caterpillar,  The emeGenomic differentiation can arise from a number of different mechanisms, often categorized as forms of geographic, environmental, or ecological isolation , variation in environmental conditions (IBE), and host association  on population structure and genomic differentiation. Malacosoma disstria larvae are important and irruptive forest defoliators in North America, known to feed on at least 15 plant species  generated from reduced‐representation sequencing of 159 larvae collected from four different host tree species in Eastern Canada. Our landscape analyses implemented a series of ecological niche models to map suitable habitat within the study area and generate a series of resistance distance measures between sequenced individuals. These measures were then contrasted with environmental and ecological distance measures, including differences in local environmental conditions and host association, to partition genomic variation into geographic (IBD + IBR) and environmental/ecological (IBE + HAD) components. Finally, we employed a combination of genotype‐environment association and FST‐based outlier analyses to assess whether our reduced‐representation SNP dataset included genomic regions that may be under divergent selection related to variation in ecological/environmental conditions or host association.To evaluate the relative contributions of IBD, IBR, and IBE/HAD in 22.1M. disstria populations across Eastern Canada , sugar maple (Acer saccharum), red oak (Quercus rubra), and white birch (Betula papyrifera). Malacosoma disstria females lay a single egg band and emerging larvae form a cohesive family group . Thoracic tissue was dissected from adult individuals (n = 45). We extracted genomic DNA from these tissues using the Qiagen DNeasy Blood and Tissue Extraction kit (QIAGEN), following the manufacturer's protocol with the addition of bovine pancreatic ribonuclease A treatment . Following extraction, genomic DNA was ethanol precipitated and stored in 50 μl Millipore water at −20°C. We prepared double‐digest restriction site‐associated DNA sequencing (ddRADseq) libraries from 200 ng genomic DNA using MspI and PstI restriction enzymes, following the protocol of MacDonald et al.  comprised of 1090 scaffolds (each over 10 kb) using Burrows–Wheeler Aligner 0.7.17 (BWA‐MEM)  Li, . ResultiMEM) Li, . We thenMEM) Li, , removinn = 37). We then reverted to original BAM files, recalled SNPs, and repeated filtering according to the parameters specified above.In a number of cases, we extracted and sequenced genomic DNA from multiple larvae reared from single egg bands, meaning full‐sibling relationships were expected and could bias population genomic analyses  repetitions were set to 100,000 and 1,000,000, respectively. Location prior values (locprior parameter) were set to collection localities (n = 21) to inform the MCMC algorithm without biasing the model. The alpha prior (relative admixture levels between populations) was set to 0.25; equal to one divided by the number of host tree species (n = 4), we collected individuals from  on genomic data using the R package adegenet v. 2.1.1 Jombart, . Next, tom Wang, .2.5M. disstria within the study area using ecological niche models generated with MaxEnt software . We generated a 50‐km minimum convex polygon around the collection locations of our sequenced individuals to define the study area . All duplicate localities were removed, resulting in 595 unique occurrence records . Geographic predictor variables included terrain ruggedness, heat load (based on terrain slope and aspect), and land cover (12 categories). Environmental predictor variables included mean temperature of the warmest month, mean temperature of the coldest month, the difference between mean temperatures of the warmest and coldest months , degree days below 0°C (chilling degree days), degree days above 5°C (growing degree days), extreme minimum temperature, mean summer (June to August) precipitation, and length of the frost‐free period. Terrain ruggedness and heat load indices were calculated using the R packages raster  and generated using 2015 Landsat satellite imagery. Environmental and elevation GIS data were compiled using ClimateNA v5.10 software , compares relative support between pairs of geographic, environmental, or ecological distances, allowing us to infer which variables best explain variation in genomic differentiation after controlling for the effects of all others. The second, distance‐based redundancy analysis (dbRDA), partitions variation in genomic differentiation among multiple predictor variables, allowing us to simultaneously evaluate contributions of geographic isolation, variation in environmental conditions, and host association. For both analyses, genomic differentiation among sequenced individuals was estimated as pairwise Euclidean genetic distance using the “dist” function within the R package adegenet (sum of squared Euclidean distances between ith and the jth genotype). This simple distance measure has been shown to effectively quantify within‐species genomic variation in both simulations , variation in environmental conditions (IBE), and host association  on genomic differentiation within 2.7.1R coefficient “RPM‐A" between genetic distance and one of two geographic, environmental, or ecological distances  conditioned on the other distance . For partial Mantel test B, the focal and alternative variables were reversed, producing partial Mantel's R coefficient “RPM‐B". If RPM‐A > RPM‐B, the focal variable from partial Mantel test A is better supported. Conversely, if RPM‐A < RPM‐B, the alternative variable from partial Mantel test A is better supported. A simple index, estimated as RPM‐A–RPM‐B, quantifies relative support among the two variables involved in one reciprocal model. To visualize these results, we summarized index values using a heatmap, wherein variables with more warm colors in their rows are better supported.Our first analysis implemented RCM with partial Mantel tests  were then extracted from PCoA as individual‐based geographic distance measures.To simultaneously investigate and partition the effects of geographic isolation, variation in environmental conditions, and host association on genomic differentiation, we used dbRDA using genetic distance as the response matrix  criterion  procedure  and putative full siblings (n = 38), and filtering loci based on read depth, minor allele frequency, missing data, and physical proximity, resulted in a final genomic dataset of 3114 SNPs with a mean read depth of 26.95 for 104 individuals.A total of 162,409,234 reads were sequenced and passed Illumina quality filters across all 149 sequenced individuals. After running “process_radtags” and associated filters, 158,059,066 reads were aligned to the 3.2M. disstria is not discretely structured by host association . For each reciprocal model, the focal variable from partial Mantel test A is reported on the Y‐axis and the alternative variable on the X‐axis. This heatmap is best interpreted by focusing on rows; focal variables with more warm colors  in their rows are more supported. Overall, the strongest correlate of genetic distance after partialling out alternative variables was host association, followed by Euclidean distance, difference in the mean temperature of the warmest month, and difference in growing degree days. This suggests that both geographic and environmental/ecological factors, including host association, have significant effects on genomic differentiation within M. disstria.We summarized support for the effects of IBD, IBR, IBE, and HAD on genomic differentiation in RCM analysis using a heatmap Figure , with re3.4.2M. disstria. First, we completed a preliminary spatial dbRDA using dbMEM positive eigenvectors as predictor variables (n = 11). Permutational ANOVA applied to dbRDA identified four dbMEM variables that were significantly related to genetic distance at α = 0.05 , variation in environmental conditions (IBE), and host association (HAD) on genomic differentiation in M. disstria. Permutational ANOVA applied to dbRDA resolved that IBD, IBE, and HAD each had significant effects (Table p = 0.032), all three composite environmental variables , and host association (p < 0.001). Overall, geographic isolation (IBD) accounted for 3.93% of genomic differentiation in M. disstria, variation in environmental conditions (IBE) accounted for 3.74%, and host association (HAD) accounted for 3.20%.Our final dbRDA model explained 10.87% of genomic differentiation in 3.5FST outliers were detected using the FDR criterion . Minimum and maximum FST values for individual loci for each pairwise comparison, as well as overall FST  after controlling for multiple tests with Bonferroni corrections applied to adjusted p‐values. Based on median |z|‐score, nine of these loci were most strongly associated with mean temperature of the warmest month, one with mean temperature of the coldest month, one with continentality, six with chilling degree days, five with growing degree days, two with extreme minimum temperature, 26 with mean summer precipitation, and 13 with length of the frost‐free period. For LFMM, visual inspection of adjusted p‐value histograms indicated that two latent factors (K = 2) adequately controlled for background population structure , variation in environmental conditions (IBE), and host association  on population structure and genomic differentiation. Our analyses resolved that geographic isolation, spatial variation in summer temperatures, and host association are important factors contributing to genomic differentiation, demonstrating that multiple mechanisms that have not previously been considered are acting concurrently to structure intraspecific variation in this major forest pest. Evidence of these mechanisms was not apparent in genetic clustering analyses such as PCA and structure, highlighting the utility of landscape genomic methods in forest pest research.The aim of this study was to identify specific biotic and abiotic factors that underlie genomic differentiation in the forest tent caterpillar, 4.1M. disstria, with genomic differentiation among individuals increasing with their geographic separation  reduced tendency of individuals to disperse across environmental/ecological gradients  Discovery Grant to F.A.H.S. (RGPIN‐2018‐04920), an NSERC Alexander Graham Bell Canada Graduate Scholarship – Doctoral (CGS‐D) and UCLA La Kretz Center for California Conservation Science Postdoctoral Fellowship to Z.G.M., and funding from Natural Resources Canada to A.D.R.The authors have no conflicts of interest to declare.Appendix S1Click here for additional data file.


This study aims to estimate genetic parameters and genetic trends for early growth and reproductive traits of Doyogena sheep. Data used in the study were collected over 6 years (2013–2018). Studied traits were birth weight (BWT), weaning weight (WWT), 6-month weight (SMWT), average daily gains from birth to weaning (ADG0-3), average daily gains from weaning to 6-month age (ADG3-6), average daily gain from birth to 6-month age (ADG0-6), litter size (LS), lambing interval (LI), age at first lambing (AFL), and annual reproductive rate (ARR). (Co) variance components and genetic parameters were estimated using restricted maximum likelihood (REML). The analyses were carried out using WOMBAT program. Univariate analysis was applied to estimate genetic parameters. Six different animal models were fitted by including or excluding maternal effects. The direct heritability estimates for BWT, WWT, SMWT, ADG0-3, ADG3-6 and ADG0-6 were 0.33 ± 0.06, 0.31 ± 0.06, 0.14 ± 0.06, 0.13 ± 0.04, 0.11 ± 0.07, and 0.02 ± 0.05 respectively. Direct heritability for LS, LI, and AFL were 0.28 ± 0.12, 0.20 ± 0.5, and 0.001 ± 0.3, respectively. The maternal heritability estimates for BWT, WWT, and LS were 0.24 ± 0.12, 0.60 ± 0.07, and 0.24 ± 0.08, respectively. The genetic correlation between BWT with WWT and BWT with SMWT were 0.21 ± 0.07 and 0.21 ± 0.09, respectively. Genetic progress for most of the studied traits has shown promising improvements. Thus, continuation of selection, therefore, suggested for more improvements in the performance of Doyogena sheep. Direct heritability estimates decrease as lamb age increases and selection based on earlier body weight will be more efficient. Doyogena sheep; Community based breeding; Heritability; Genetic trend. Thus, cCommunity-based breeding programs (CBBPs) have recently attracted global interest as genetic improvement strategies in low input systems . CBBP haThe potential genetic improvement of traits of interest was largely dependent on its heritability and genetic relationship among the traits of economic importance upon which selection may be applied. Information on heritability is essential for planning efficient breeding programs, and for prediction of response to selection . AccordiThe present study has been planned to estimate the genetic parameters and trends for growth and reproductive traits and generate information for the optimization of the ongoing CBBP.22.1The study area (Doyogena sheep CBBP) is found in Kembata Tembaro Zone of southern Ethiopia at a distance of 258 km to the Southwest of Addis Ababa . The district is located between 7°20′ N latitude and 37°50' E longitude. Altitude ranges from 1900 to 2800 m. a.s.l. The average annual rainfall of the district is 1221 mm.2.2Animals were identified by plastic ear tags. Enumerators were employed for routine animal identification, data recording, and follow-up. Enumerators use herd books for data recording. Selection of breeding rams takes place on a programmed date, twice a year. Researchers identify the candidate breeding rams by estimating best linear unbiased prediction (BLUP) breeding values for selection criteria traits using the performance and pedigree data recorded by the enumerators. Lambs wean at the age of 90 days (three-months age). Then the candidate rams pre-selection and ranking take place based on weaning weight. In the second stage-breeding ram ranking was carried out based on SMWT estimated breeding values (EBVs). Top (10% of the candidates) breeding rams were retained for breeding to be used in the community flock while the next best (positive EBVs) were sold for breeding purposes to other communities.Culled males (negative EBVs) were either castrated or marketed to prevent unwanted mating . SelecteEnset or false banana (Ensete ventricosum (Welw. Chessman) products of Amicho , corm , crop residue, improved forage/grass, kitchen leftover, and purchased concentrates. Flocks graze with tethering on the small private land. Free veterinary service was provided for CBBP participant farmers by ICARDA and SARI.The main feed sources for animals included 2.3BWT = Birth weight,WWT = Three month weight/weaning weight at 90 days,SMWT = 6-month weight at 180 daysThe empirical data for the study were obtained from five ongoing breeder cooperatives. The performance data along with pedigree information were maintained in the data-recording book of individual breeder cooperatives. The data routinely collected by the enumerators were recorded at the time of the event. BWT was recorded within 24 h of lambing; WWT was taken at 90 days of age, and 6-month weight was taken at the age of 180 days. The average daily body weight gains from birth to weaning age; weaning to six months of age, and from birth to six months of age have been estimated as under:ALS = Average litter size;LI = Lambing interval;365 = Days of one year;The annual reproductive rate, which is the number of lambs born per breeding ewes per year has been estimated according to 2.4y = vector of observed traits;b, a, m, c = Vectors of fixed effects, direct additive genetic effects,Maternal additive genetic effects, maternal permanent environmental effects andVector respectively. e = residual effectsThe variance components and resulting genetic parameters were estimated on a model-fitting effect of parity, year of birth/year of lambing, season of birth/season of lambing, type of birth, sex, and sites (cooperatives) as fixed factors. Based on the first performed analysis of variance using SAS , signifi1, Z2, and Z3 = Incidence matrices, respectively relating fixed effects, direct additive genetic effects, maternal additive genetic effects, and maternal permanent effects to y; All components with the phenotypic variance (σ2p) being the sum of σ2a, σ2m, σam, σ2c, and σ2e, were derived at convergence. Depending on the model, we computed X, Z,am = σam/σ2p), with a corresponding estimate of the direct-maternal correlation [ram = cam/(σ2a ∗σ2m)]. Similarly maternal environmental variance ratio was estimated by the maternal permanent environmental variance as a proportion of σ2p (p2 = σ2c/σ2p). The genetic correlation between direct and maternal genetic effects (ram) is estimated as the ratio of the estimates of the σam to the product of the square roots of the estimates of σ2a and σ2m  was calculated according to the following equation h2t=. Model 5 and Model 6 included additive maternal and maternal permanent environmental effects, ignoring and fitting, respectively, direct-maternal covariance.To determine the most appropriate model, likelihood ratio tests (logL) were used for each trait. An effect was considered to have significant influence, when its inclusion caused significant increase in log-likelihood, compared to the model in which it was ignored. When log-likelihoods did not differ significantly (P > 0.05), the model that has fewer parameters was selected as the most appropriate model. All models included direct additive genetic effect and this was the only random factor in Model 1. Model 2 included the maternal permanent environmental effect, fitted as an additional random effect. Model 3 included an additive maternal effect fitted as second random effect. Model 4 was the same as Model 3 but allowed for a direct maternal covariance CovThe genetic trends were estimated by the weighted regression of the average breeding value of the animals on the year of birth or year of lambing. These procedures were carried out with statistical program R core software  and MS-e33.13.1.1In 4The coefficient of inbreeding (F) showed an increasing trend within the 6-year selection period . The coe52a) for BWT, WWT, and SMWT were 0.33 ± 0.06, 0.31 ± 0.06, and 0.14 ± 0.06, respectively  was found in the range reported by 2a estimated for Afar sheep (0.1–0.38) and blackhead somalin (BHS) sheep (0.2–0.58) using univariate analysis. 2a of 0.46 from a multi-trait animal model for Menz sheep, while 2a of 0.20 ± 0.05 for Horro sheep using the same model and reported lower estimates than the currently estimated h2a for Doyogena sheep. The current result was higher than the estimate of Based on the appropriate models the estimates of direct heritability (hectively . Except 2a for WWT (0.31 ± 0.06) was also found in the estimated range reported by 2a for WWT by The estimate of h2a for SMWT 0.14 ± 0.06 was found in the range of h2a estimated for Afar sheep (0.11–0.37) and BHS (0–0.29), while, the report of 2a for Horro sheep, which is lower than the current estimate. From the genetic point of view, h2a estimated for BWT and WWT indicated that high variation within the breed would be a greater opportunity for selection response during genetic improvement through selection for these traits. Moreover, WWT will be the best criterion for selection to increase the pre-weaning growth rate because, selection based on BWT, which has the highest heritability could cause dystocia. However, the confounding effect of direct genetic and maternal genetic effects needs care.The estimate of h2) for BWT was moderate in this study (0.20 ± 0.09). This indicates the importance of maternal environment and care at the birth of lambs. The current estimate is similar to the findings of 2m) is an important ratio for BWT, WWT, and the estimates were 0.24 ± 0.12 and 0.60 ± 0.07 respectively.The permanent maternal environmental effect (c2m estimate was in the range estimated (0.06–0.46) for blackhead somilan sheep (BHS) by 2m of 0.12 ± 0.2, 0.24, and 0.02–0.21 for Horro sheep, Sabi sheep, and Afar sheep respectively and these all value are lower than the present estimate. However, 2m for Moroccoian Timahdit sheep (0.59). The present estimated maternal heritability of 0.6 ± 0.07 for WWT was higher than the above-mentioned maternal heritability estimates. High and negative additive maternal genetic correlation estimates were observed (am) for both the traits become negative means improvement in one will result in a reduction of another. The result might be due to the structure of the data set used in the analysis i.e. the number of generations the animals were measured both directly and as dams were limited causing lack of large pedigree. The estimates of total heritability (h2t) for BWT, WWT, and SMWT were 0.21, 0.12, and 0.14, respectively. The h2t estimated by In the current study, for BWT, hobserved  for BWT 2a for ADG0-3, ADG3-6 and ADG0-6 were 0.12 ± 0.04, 0.11 ± 0.07, and 0.02 ± 0.05 respectively  for ADG0-3 (0.21 ± 0.03) and ADG0-6 (0.26 ± 0.04) have been found significantly higher than later age daily weight gain traits of ADG3-6 (0.09 ± 0.04). It decreases with increasing lamb age. This could be due to the influences of feeding levels at the later age of the lambs and the maternal behavior of the dam, especially for pre-weaning growth traits in the lambs. The value of maternal permanent environmental variance in model (2) for this trait is not significantly different from other models' values. The result is also found in the range reported by 2a for ADG3-6 was comparable with the report of The estimate indicates variance due to permanent maternal environmental effects  was 0.31 ± 0.01. The results indicated that the inclusion of maternal permanent environmental effects in the analyses could improve the models for litter size traits. The reflection of variance due to maternal permanent environmental effect indicates improving the environment could improve the reproductive performance of ewes.The h2a and h2t for LI were 0.20 ± 0.5 and 0.20 respectively. 2a of 0.06 and 0.02 for LI for Brazilian multi-breed sheep and Iranian Lori-Bakhtiari sheep and the values reported were lower than the current estimate. The h2a and h2t estimate for AFL were 0.001 ± 0.32 and 0.007, respectively. Since AFL is strongly influenced by environmental effects, low heritability estimate was obtained. Selection based on AFL performance may result in slow genetic improvement. Therefore, selection for ewes for AFL trait of ewes should be based on female relatives of ewes or on correlated traits, which have high and positive genetic correlation with ewes AFL. 2a for Brazilian multibreed meat sheep and Lori-Bakhtiari sheep breeds respectively. The h2 values reported were higher than the current finding.Under the best-fitted model, the estimated h7The bivariate analysis of genetic correlation between reproductive traits is given in 8The present study indicated that BWT had weak genetic correlation with the studied body weights and daily weight gain traits . The neg99.1. The estimated direct and maternal genetic changes were 0.00085 kg and -0.004 kg respectively. When compared to other studies, Both direct genetic and maternal genetic trends had fluctuating trends . The ann9.2th generation of Menz sheep which is higher than the current finding.The maternal genetic trend had a decreasing trend by -0.051 kg/year and -0.23 kg per 6-years selection. The direct genetic trend of 0.048 kg/year was higher than the study of th generation.In 10Direct genetic trend for litter size was fluctuations  and non-LI shows  a decrea11The objective of this study was to evaluate community-based breeding program breed improvement strategy to further improve genetic gains in Doyogena sheep. Promising results of selection were observed from the ongoing Doyogena sheep CBBP. The different estimates of heritability obtained from the different models suggest that model choice is an important aspect of obtaining reliable parameter estimates to be used in genetic parameter estimation. The moderate to high estimated heritability for the growth traits suggested the scope for further improvement of these traits. Coefficient of inbreeding showed an increasing trend across selection years. There was a negative genetic trend observed for BWT trait. However, WWT and SMWT traits were genetically improved across the years. The estimate of genetic gain for SMWT trait was the greatest among the body weight traits. The result suggested that selection for litter size trait would be more efficient, due to its higher genetic variance; however, it may hurt growth performance.Kebede Habtegiorgis: Conceived and designed the experiments; Analyzed and interpreted the data; Wrote the paper.Aynalem Haile; Tesfaye Getachew: Performed the experiments; Contributed reagents, materials, analysis tools or data; Wrote the paper.Manzoor Ahmed Kirmani: Performed the experiments; Contributed reagents, materials, analysis tools or data.Deribe Gemiyo: Contributed reagents, materials, analysis tools or data.International Centre for Agricultural Research in the Dry Areas (ICARDA) and Southern Agricultural Research Institute (SARI).This work was supported by the Data will be made available on request.The authors declare no conflict of interest.No additional information is available for this paper.


The narrow base of genetic diversity of modern rice varieties is mainly attributed to the overuse of the common backbone parents that leads to the lack of varied favorable alleles in the process of breeding new varieties. Introgression lines (ILs) developed by a backcross strategy combined with marker-assisted selection (MAS) are powerful prebreeding tools for broadening the genetic base of existing cultivars. They have high power for mapping quantitative trait loci (QTLs) either with major or minor effects, and are used for precisely evaluating the genetic effects of QTLs and detecting the gene-by-gene or gene-by-environment interactions due to their low genetic background noise. ILs developed from multiple donors in a fixed background can be used as an IL platform to identify the best alleles or allele combinations for breeding by design. In the present paper, we reviewed the recent achievements from ILs in rice functional genomics research and breeding, including the genetic dissection of complex traits, identification of elite alleles and background-independent and epistatic QTLs, analysis of genetic interaction, and genetic improvement of single and multiple target traits. We also discussed how to develop ILs for further identification of new elite alleles, and how to utilize IL platforms for rice genetic improvement. Introgression lines (ILs) are a set of lines constructed by using a combination of continuous backcrossing and selfing to replace chromosome fragments of the recipient parent (RP) with chromosome fragments of the donor parent (DP), which can directly transfer the desired traits of interest from exotic varieties to adapted varieties. IL sets containing overlapping introgression segments covering the entire genome of DP in the RP background are referred to as chromosome segment substitution lines (CSSLs).The number of backcrosses, number of backcrossed lines in each generation and number of selfings vary in different rice ILs construction programs, depending on the purpose of ILs construction, crossing methodology, genetic distance between parents, compatibility of genotypes, and number or size of target chromosome segment introgressions . The bacILs are not only powerful prebreeding tools for broadening the genetic base of existing cultivars, but also valuable resources for quantitative trait loci (QTLs) mapping, gene effects evaluation, favorable alleles identification and genetic interactions analysis. The genetic background noise of ILs with a few chromosome-segment substitutions is low, so ILs with significant differences from the recurrent parents can be rapidly identified by evaluating the phenotype of target trait. Although the construction of ILs is time-consuming and labor-intensive, ILs have been widely reported in cereals, oil crops, vegetables, and commercial fiber crops due to their advantages . Harlan indica and japonica  and 22 wild species  with an independent origin of domestication are also a valuable exotic gene pool for Asian cultivated rice and have been used as DPs for IL development (O. glaberrima (CG 14) into three japonica rice varieties. Blending traits from the African traditional and Asian varieties, NERICA varieties have superior agronomic characteristics such as better weed resistance, resilience against major African biotic and abiotic stresses, high fertilizer returns, and high yields  in the seedling stage were detected, and O. rufipogon-derived alleles at one locus reduced sensitivity to heat  segments in the 93–11 background  in the background of the elite japonica rice variety Koshihikari, a total of 15 major QTLs for eight yield-related traits were detected, and a novel QTL controlling the number of grains per panicle were identified on chromosome 10 . A total of 48 QTLs for these traits were identified, and most wild alleles of these detected QTLs had negative effects on the traits  were identified form these two ILs. Among these detected QTLs, a stable QTL GNP1 was further finely mapped to a 33.7-kb region and a GA biosynthesis gene GA20-oxidase 1 was identified as the candidate gene for GNP1  and japonica rice variety Asominori to investigate heterotic loci (HLs) associated with six yield-related traits . Compared with the HJX74 allele, the qHD-3 allele from donor variety Lemont delayed rice heading, while qHD-3 from another donor parent, IR64, promoted rice heading. Further sequencing analysis revealed that different variants of DTH3 were identified between Lemont and IR64 (Ghd8 on yield mid-parent heterosis (MPH), Ghd8 from a particular donor in the same background of ZS97, and identified the combination (Ghd8ACC10/Ghd8MH63) with the highest MPH for both spikelet number and grain yield per plant using a half-diallel mating design.ILs derived from advanced backcrosses generally carry only one or a few chromosome fragments from the donor, which greatly minimizes the genetic background noise and facilitates the evaluations of the genetic effects of QTLs/genes on corresponding traits. The target introgressed segment of an IL is homozygous, conferring its genomic stability, so ILs can be repeatedly planted in multiple sites to evaluate the genetic effects of QTLs across different environments . A set oand IR64 . To inveReciprocal ILs developed in both parental backgrounds have the advantage of enabling evaluation of differences in allelic effects of QTLs in both genetic backgrounds . ReciproGhd7, Ghd8, OsPRR37, and Hd1, was performed in a 4-gene segregating population in the near-isogenic background under both natural long-day (NLD) and natural short-day (NSD) conditions  interactions is of fundamental importance in plant breeding . As in rnditions . Tetrage OsPRR37 . Genetic OsPRR37 , grain s OsPRR37 , tilleri OsPRR37 , cold to OsPRR37 , and dro OsPRR37 , was alsa) and additive × environment interaction effect (ae). A total of 18 QTLs for PN were identified, and three types of QTLs were suggested according to their effects expressed. Two QTLs expressed stably across environments due to the association with only a, nine QTLs with only ae were unstable, and the remaining seven QTLs were identified with both a and ae, which were also unstable across environments , soft gel consistency (GC), and high gelatinization temperature (GT), was introgressed into Zhenshan 97B and then transferred into Zhenshan 97A  and three photo-thermo genetic male sterility (P-TGMS) lines. The blast resistance of these lines was significantly improved, while other traits were not changed , 2019. Ud Jin23B , and bacand Xa21 . In the and Xa21 .GRF4 into the elite variety 93–11 carrying sd1 can significantly improve its NUE, thereby increasing yield (NAL1 and the gain-of-function mutation dep1-1 have been reported to play important roles in N-responsive tillering regulation, and pyramiding of the dep1-1 and NAL1NJ6 alleles can achieve sustainable improvements in NUE and grain yield in japonica rice breeding (OsTCP19 and OsSBM1 also showed great potential for genetic improvement of high NUE in rice (The excess application of fertilizers in rice production not only increases cost, but also causes severe environmental problems . Breedinng yield . NAL1 anbreeding . The int in rice .MADS50, gs3, fgr, Wxgl, and ALK. Xa27, Pi9, Sub1A, and badh2.1. Through genotyping-based identification and backcrossing with MAS, 2 ILs were developed to introgress Xa7 for bacterial blight and Pi-2 for blast resistance. Then, the ILs were crossed and pyramided with these two genes. A new two-line thermosensitive genic male-sterile line named Hua1228S that was resistant against both rice blast and bacterial blight was developed (To meet people’s diverse demands for rice, modern rice varieties should be improved mainly by multiple locus introgression to improve the overall features of rice in terms of grain yield and quality, resistance to biotic and abiotic stresses and nitrogen use efficiency, etc. For example, eveloped .IL platform developed by multiple donors in an elite genetic background facilitates the discovery of new QTLs and identification of multiple alleles of a gene. IL platform can be used to identify the optimal allele or allele combination by evaluating their genetic effects in the fixed background and then improve varieties by QTL pyramiding . In addiILs simplify the genetic dissection of complex traits and accelerate whole-genome large-scale gene discovery in rice. To date, a large number of the natural variations associated with agronomic traits in rice have been identified from ILs and then cloned using IL-derived mapping populations ; Table 2Oryza species as donors due to the strong incompatibility barriers (Oryza officinalis and the high biomass production in CCDD-genome wild species (A large number of genetic dissections of traditional agronomic traits have been performed in rice , so it ibarriers . These w species . Thus, u species . Thus, t species .As important prebreeding materials, ILs facilitate the genetic improvement of rice via gene transfer and pyramiding. However, QTL pyramiding for rice variety improvement requires multiple crosses and the avoidance of genetic drag, which usually requires several generations and large populations . ApplyinYX provided the idea for the review. BZ and XQ collected the data and wrote the manuscript. LM assisted in literature collection. YX, XQ, BZ, LM, and BW revised the manuscript. All authors contributed to the article and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.