Pennisetum glaucum (L.) R. Br.] is a climate-resilient dryland cereal that has been identified as a potential staple food crop that can contribute to alleviating micronutrient malnutrition, particularly with respect to grain iron (Fe) and zinc (Zn) contents, in Sub-Saharan Africa and India. In this regard, an understanding of the inheritance pattern of genes involved in Fe and Zn contents is vital for devising appropriate breeding methods to genetically enhance their levels in grains. In this study, we aimed to determine the genetic effects underlying such inheritance and their interactions based on the generation mean analyses. Four experimental crosses and their six generations  were independently evaluated in a compact family block design in 2017 rainy and 2018 summer seasons. ANOVA revealed highly significant mean squares (p < 0.01) among different generations for grain Fe and Zn contents. Six-parameter generation mean analyses revealed a predominance of additive genetic effect and a significant (p < 0.05) additive × dominant interaction for the grain Fe content. The additive genetic effect for the grain Zn content was also highly significant (p < 0.01). However, interaction effects contributed minimally with respect to most of the crosses for the grain Zn content and hence we assume that a simple digenic inheritance pattern holds true for it. Furthermore, we established that narrow-sense heritability was high for the grain Fe content (>61.78%), whereas it was low to moderate for the grain Zn content (30.60–59.04%). The lack of superior parent heterosis coupled with non-significant inbreeding depression for Fe and Zn contents in grains further confirmed the predominance of an additive genetic effect. These findings will contribute to strategizing a comprehensive breeding method to exploit the available variability of grain Fe and Zn contents for the development of biofortified hybrids of pearl millet.Pearl millet [ Pennisetum glaucum (L.) R. Br.] is a climate-resilient dryland cereal crop predominantly grown for its grain and as fodder across the arid and semi-arid tropical regions of Africa (18 m ha) and Asia (10 m ha), particularly among poor farmers , supported by the Consortium of International Agricultural Research Centers (CGIAR) HarvestPlus, is continuously working toward the development of grain Fe- and Zn-biofortified pearl millet varieties and hybrids, and at present, the grains of commercially cultivated pearl millet varieties and hybrids have Fe and Zn contents of 42 and 32 mg kgectively . Geneticper se performances of parental lines have been found to be significant and highly correlated with their general combining ability. However, although studies have expanded genetic models to estimate different genetic effects , dominance deviations, and effects due to non-allelic genetic interactions, which can assist in quantifying the genotypic value of individuals, and in turn, would contribute to determining the average generation genotypic value. In this regard, it is noteworthy that the magnitude and type of epistasis can have major repercussions with respect to both the reliability of predictions and the design of breeding strategies. Hence, in the present study, we sought to dissect the information regarding the classical inheritance gene interaction models using six basic generations evaluated in two contrasting seasons (rainy and summer) for grain Fe and Zn contents in pearl millet.1) plants obtained from four crosses, along with their respective eight parents  were developed using selected parents (P1 and P2) with contrasting grain Fe and Zn characteristics. The crossing program was performed in the 2017 summer to develop segregating generations, i.e., backcross and second filial (F2) generations. In each cross combination, a BCP1 generation was developed by crossing F1 individuals with the respective parent P1 . Similarly, BCP2 generation progeny was obtained by crossing F1 individuals with parent P2 . Finally, the F2 populations for each cross were developed by selfing F1 plants.Four single-cross hybrids , pollen (bulk pollen) from the male parent (P1 and P2) was collected in a parchment paper bag and dusted on the female lines (F1) by gentle thorough tapping in the morning hours between 08:00 a.m. and 11:30 a.m. Soon after the pollination, the crossed panicles were covered with parchment paper bags to avoid foreign pollen contamination, appropriately labeled, harvested at maturity, sun-dried for more than 15 days, and threshed to collect hybrid seeds. Prior to threshing, we removed one-third of the top and bottom of the panicles to ensure the high quality of crossed seeds and to minimize the likelihood of collecting any selfed seeds.In the backcross block, the panicles of male and female parental line plants were bagged using 30 cm × 10 cm parchment paper bags at the boot leaf stage to avoid contamination by foreign pollen. When developing BCP1, P2, F1, BCP1, BCP2, and F2) for each cross were evaluated in a compact family block design (CFBD) with three replications at ICRISAT, Patancheru, in two contrasting seasons, namely the 2017 rainy (E1) and the 2018 summer (E2). The rainfall, temperature, and relative humidity during field evaluation in E1 and E2 are presented in 1, P2, and F1; six rows each of BCP1 and BCP2; and eight rows of F2, thereby ensuring a sufficient number of plant samples per generation. Sowing was carried out using a tractor-mounted four-cone planter  with seeds planted in 4-m-long rows spaced at 75 and 60 cm in E1 and E2, respectively. All recommended agronomic practices were followed for good and healthy crop growth. Observations were recorded only for grain Fe and Zn contents in 15 plants of the P1, P2, and F1 generations, whereas we selected 120 BCP1 and BCP2 plants. Among the F2s, 300 plants were selected together across three plots. Soil samples were collected from the experimental fields from depths of between 0 and 30 cm and bulked to prepare single composite samples. Soil micronutrient analysis was performed using the diethylenetriaminepentaacetic acid extractable method   .–1. At the time of harvesting, 5–10 representative main panicles from plants growing in each plot were harvested at physiological maturity (80–90 days after planting) for P1, P2, and F1 generation plants, whereas, for the BCP1, BCP2, and F2 generations, we separately harvested single plant panicles (tagged plants). The harvested panicles were placed in a separate cloth bag to avoid soil contamination and dried in the sun. The dried panicles were threshed manually, and samples of approximately 20 g of grain were collected for grain Fe and Zn content analyses. Grains were cleaned of the glumes, panicle chaff, and debris, and thereafter transferred to non-metal fold envelopes and stored at a cold temperature (12°C), with care being taken at each step to avoid contaminating the grains with dust, following Open-pollinated grains were sampled and used to obtain estimates of grain Fe and Zn contents, which are expressed as mg kgGrain Fe and Zn contents were analyzed using an X-Supreme 8000 energy-dispersive X-ray fluorescence spectrometry device , installed at the HarvestPlus Lab, ICRISAT, Patancheru. The XRF method used for pearl millet has been calibrated and validated in Flinders University, Adelaide, Australia  and ICRIThe data obtained were subjected to ANOVA using a compact family block design, as described by Where 1, P2, F1, F2, BCP1, and BCP2 generations, respectively. SEs for A, B, C, and D scales were calculated by estimating the square root of the respective variances. The test of deviation from the hypothetical value of zero was tested using the t-test. The calculated t-values were compared with “t” table values at 5 and 1% level of significance at their respective degrees of freedom. In each test, the degrees of freedom was taken as the sum of the degrees of freedom of various generations involved in that scaling test, and the degrees of freedom for any generation was calculated as a total number of observations minus the number of replications.Where, VA, VB, VC, and VD are the variances of respective scales A, B, C, and D; viz., mean (m), additive gene effects (d), dominance gene effects (h), and three types of non-allelic gene interactions viz., additive × additive (i), additive × dominance (j), and dominance × dominance (l).After computing scaling tests, if any one of them was found significant then the genetic effects were estimated by fitting the data into six-parameter models of the generation mean analysis, as suggested by Where 1, P2, F1, F2, BCP1, and BCP2 generations, respectively.where t-test. First, SE is worked out for each component separately by taking the square root of the variance of the respective component. The significance of the genetic effect is tested using the t-test in a similar manner as in the case of the scaling test. All these statistical analyses were conducted using the DOS-based Excel program, TNAUSTAT-Statistical package  was expressed as the percentage increase or reduction observed in the Fnt value . The resBroad-sense heritability 1, VP2, VF1, VBCP1, VBCP2, and VF2 are the variances of P1, P2, F1, BCP1, BCP2, and F2, respectively; K is the selection differential, the value of which is 2.06 at a 5% selection intensity; and VE is the environmental variance.Where VPp < 0.01) mean squares for grain Fe and Zn contents in E1 and E2, thereby indicating the presence of significant genetic variability for the two micronutrient traits  during Enificant , which ip < 0.01), and its magnitude was higher than that of the other genetic effects assessed, namely additive (d) and dominant (h), and the three interaction effects, additive × additive (i), additive × dominant (j), and dominant × dominant (l), among the four crosses studied. The cross-wise direct and inter-allelic interaction effects for the grain Fe content in cross-I showed the presence of a significant additive genetic effect (p < 0.01) and an additive × dominant interaction effect (p < 0.05) in E1  and dominant (p < 0.01) genetic effects were significant, wherein the magnitude of the additive genetic effect was 1% higher than that of the dominant genetic effect. Among the interaction effects, additive × additive and additive × dominant effects were significant (p < 0.01), wherein the magnitude of the additive × additive genetic effect was 70% higher than that of the additive × dominant effect. In cross-II, the additive (p < 0.01) genetic effect along with additive × additive (p < 0.05) and additive × dominant (p < 0.05) interaction effects were significant in E1. The magnitude of the additive × additive genetic effect was 61% higher than that of the additive × dominant effect. In E2, only additive genetic effect (p < 0.01) and the additive × dominant (p < 0.05) interaction effect were significant. In cross-III in E1, all the four scaling tests were non-significant that indicate the absence of epistasis. Nevertheless among direct genetic effects, both additive and dominant effects were significant (p < 0.01), wherein the magnitude of the dominant genetic effect was 24% higher than that of the additive. In E2, both additive and dominant genetic effects along with additive × additive and additive × dominant interaction effects were significant (p < 0.05), wherein the magnitude of the dominant genetic effect was 54% higher than that of the additive effect, and the magnitude of the additive × additive was 60% higher than that of the additive × dominant interaction effect. Cross-IV in E1 was characterized by significant additive (p < 0.01) and dominant (p < 0.05) genetic effects with a higher magnitude of an additive genetic effect. Among interaction effects, additive × dominant and dominant × dominant were significant (p < 0.01), wherein the magnitude of dominant × dominant was 65% higher than that of the additive × dominant. The contrasting directions of the dominant genetic effect and dominant × dominant interaction effects indicated the presence of duplicate epistasis. In E2, an additive genetic effect along with additive × dominant and dominant × dominant interaction effects were significant (p < 0.01), wherein the magnitude of the dominant × dominant interaction effect was 63% higher than that of the additive × dominant.With respect to the grain Fe content, partitioning of the generation mean into six different genetic components revealed that the effect of the mean was significant (5) in E1 . In E2, p < 0.01) predominant effect compared with all other assessed genetic effects, including the different interaction effects. The cross-wise direct genetic and interaction effects in E1 and E2 for the grain Zn content revealed that in cross-I, the additive genetic effect was significant (p < 0.01) in E1 and E2. Among interaction effects only in E2, the additive × additive (p < 0.05) and additive × dominant (p < 0.01) interaction effects were significant, wherein the magnitude of the additive × additive effect was 61% higher than that of the additive × dominant effect. In cross-II, only the additive genetic effect was significant (p < 0.01) in E1 and E2. All three interaction effects were non-significant, both in E1 and E2, indicating an absence of epistasis. In cross-III in E1, the additive genetic effect along with additive × dominant interaction effect (p < 0.01) was significant (p < 0.01). In E2, all four scaling tests were found to be non-significant, indicating the absence or lack of role of epistasis. Nevertheless among the direct genetic effects, both additive and dominant genetic effects were significant (p < 0.01), wherein the magnitude of the dominant genetic effect was 51% higher than that of the additive genetic effect in E2. Unlike in earlier crosses, non-significant additive and significant (p < 0.01) dominant genetic effect was observed in cross-IV in E1. Among the interaction effects, the additive × dominant (p < 0.01) and dominant × dominant (p < 0.01) were significant, wherein the magnitude of the dominant × dominant was 79% higher than that of the additive × dominant. The contrasting directions of dominant genetic and dominant × dominant interaction effects provided evidence for the presence of duplicate epistasis in E1. In contrast to E1, we detected a significant additive genetic effect, along with significant additive × dominant (p < 0.05) and dominant × dominant (p < 0.01) interaction effects in E2, wherein the magnitude of the dominant × dominant effect was 85% higher than that of additive × dominant effect.The mean effect for the Zn content in grains indicated a significant  negative MPH was recorded among all the four crosses for the grain Fe content with the highest being obtained for cross-III (−30.20%) and the lowest for cross-II (−8.90%) in E1 (p < 0.01) for the grain Fe content in a negative direction, wherein the magnitude was the highest for cross-III (−15.14%) and the lowest for cross-II (−7.66%) in E1. In E2, a significant (p < 0.01) MPH was recorded only in cross-III (−19.36%). Surprisingly, we established MPH estimates in a positive direction for crosses I and IV, but the effects were non-significant. RHM in E2 was significant only among crosses III and IV, wherein the magnitude of RHM was the highest for cross-IV (−9.70%) and the lowest for cross-II (−0.43%). Furthermore, it was observed that except cross-I, the rest of the three crosses recorded RHM in negative direction in E2. None of the F1 populations exceeded the respective better parent in either E1 or E2. Furthermore, we detected no significant ID among the crosses in E1 and E2. The degree of dominance among the four crosses varied from as low as 0.32 for cross-III to as high as 0.75 for cross-II in E1 and from 0.13 for cross-I to 0.74 for cross-IV in E2.Significant (%) in E1 . Similarp < 0.01) and IV (p < 0.05). Among four crosses, the highest magnitude of MPH was recorded in cross-III (−12.28%) and the lowest in cross-I (1.95%), although the difference was not significant. With the exception of cross-I, the recorded MPH for rest of the crosses was in a negative direction in E1. Furthermore, among the four crosses in E1, we recorded a significant (p < 0.01) RHM for crosses I, III, and IV, with the highest magnitude being recorded in cross-III (−9.91%) and the lowest in cross-II (0.93%) which was found to be non-significant. However, the RHM was observed to be in a positive direction for crosses I and II, whereas it was in a negative direction for crosses III and IV. In E2, a significant (p < 0.01) MPH was recorded only in cross-III. The magnitude of MPH was found to be the highest in cross-III (−13.53%) and the lowest in cross-II (−2.00%), the difference between which was non-significant. Furthermore, MPH recorded was in a positive direction for crosses I and IV, whereas, it was in a negative direction for crosses II and III. RHM was significant (p < 0.01) among crosses II, III, and IV, wherein the highest magnitude was recorded in cross-III (−7.86%) and the lowest in cross-II (2.59%). As in E1, the RHM recorded for crosses I and II was in a positive direction, and that for crosses III and IV was in a negative direction in E2. None of the F1 populations performed better than the better parent in E1 and E2, thereby indicating an absence of BPH. In both E1 and E2, we detected no significant ID among the assessed crosses. Furthermore, the degree of dominance estimates among the four crosses varied from as low as 0.91 for cross-I to as high as 1.25 for cross-IV in E1 and from 0.58 for cross-I to 1.66 for cross-III in E2.With respect to grain Zn content, we detected a significant MPH for crosses III  and E2 . A similar positive correlation was observed between grain Fe and Zn contents in backcross populations, with the exception of cross-III (r = − 0.089). Highly significant positive correlations (p < 0.01) between grain Fe and Zn contents were also detected for all three segregating generations  in E1 and E2, with the magnitude of correlation being the lowest in cross-IV in both E1  and E2 .With respect to correlations between Fe and Zn contents in pearl millet grain, we performed Pearson’s correlation analysis to assess the different generations among each cross in each of the two environments (seasons)  and acco1 and E2, thereby indicating a substantial degree of genetic variability for grain Fe and Zn contents among segregating populations obtained from crossing contrasting parents. The mean performances of parents and F1 progeny revealed a slight increase in the levels of Fe and Zn contents in the grains of pearl millet cultivated in the second (E2) season of the present study compared with the first (E1) season. This is in line with the expected observation, given that the accumulation of Fe and Zn contents in grains is predicted to be higher in the summer environment due to the higher absorption from roots in response to higher rates of transpiration. Moreover, in the case of the grain Fe content, we established that the levels of available Fe in soil were higher in E2 (5.0 mg kg–1 Fe). Interestingly though the available Zn in soil was relatively low, this was not reflected in the grain Zn content in E2; thus, the mean performance with respect to the grain Zn content would probably be stable in E1 and E2. Previous studies have likewise reported similar patterns of grain Fe and Zn contents in pearl millet during rainy and summer seasons , whereas we found that the mean F2 distribution was normally distributed for all crosses  indicates that the parent with lower grain Fe and Zn contents was selected as P1, and the parent with a higher grain Fe content was selected as P2. Hence, when comparing the magnitude of genetic effects, absolute values were taken into consideration. In this regard, the findings of a previous study have also indicated that the positive or negative direction of additive and dominant genetic effects signifies that it is the parent with the highest number of genes or positive alleles that contributes to the increment in the trait of interest  indicates that the genes underlying the grain Fe content in pearl millet are predominantly governed by additive genetic effects. Similarly, with the exception of cross-IV, highly significant additive genetic effects among the crosses were detected for the Zn content in grains in Einterest . Similarinterest , sorghuminterest , maize , indicating that the grain Zn content is governed by duplicate epistasis. However, the non-significant effects of the dominant × dominant interactions among crosses I, II, and III precluded a determination of the respective types of epistasis. The duplicate type of epistasis found for both Fe and Zn in cross-IV in E1 suggests that the selection for high grain Fe and Zn recombinants is possible through pedigree breeding by delaying the selection to later generations. Similar duplicate epistatic effects have previously been reported for Fe and Zn in sorghum  that govern the low levels of Fe and Zn contents in grains. Similar negative MPH and partial dominance have been detected previously in pearl millet  for agronomic traits and delayed selection (>F3−F4s) in Fe and Zn trait mainstreaming breeding pipelines.In summary, the generation mean analysis revealed that with additive genetic variance, additionally, there are gene interaction contributions indicating additive × additive and additive × dominant interaction effects for grain Fe and Zn contents in pearl millet. This study confirms the significant role of additive gene effects in grain Fe and Zn improvement reported earlier in pearl millet, whereas there is a chance of specific gene interactions exist merits further strategic genetic and genomic studies using more inbreds for a profound understanding of biofortified hybrid breeding. Among the direct genetic effects, additive genetic effects predominantly govern its evidence of grain Fe and Zn contents. Therefore, duplicate epistatic interactions would not constitute a substantial impediment to the improvement of grain Fe and Zn contents in pearl millet, which could be overcome by practicing early selection (FThe datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/MG conceptualized the research and contributed to the experimental materials. MG, SG, and MP designed the experiments. MP executed the field/laboratory experiments and data collection. MP, AK, and MG interpreted the analysis of data. MP, MG, AK, SG, TG, BD, and KS prepared 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.


Sclerotinia stem rot (SSR) is a fungal disease of rapeseed/canola that causes significant seed yield losses and reduces its oil content and quality. In the present study, the reaction of 187 diverse canola genotypes to SSR was characterized at full flowering stage using the agar plug to stem inoculation method in four environments. Genome-wide association study (GWAS) using three different algorithms identified 133 significant SNPs corresponding with 123 loci for disease traits like stem lesion length (LL), lesion width (LW), and plant mortality at 14 (PM_14D) and 21 (PM_21D) days. The explained phenotypic variation of these SNPs ranged from 3.6 to 12.1%. Nineteen significant SNPs were detected in two or more environments, disease traits with at least two GWAS algorithms. The strong correlations observed between LL and other three disease traits evaluated, suggest they could be used as proxies for SSR resistance phenotyping. Sixty-nine candidate genes associated with disease resistance mechanisms were identified. Genomic prediction (GP) analysis with all the four traits employing genome-wide markers resulted in 0.41–0.64 predictive ability depending on the model specifications. The highest predictive ability for PM_21D with three models was about 0.64. From our study, the identified resistant genotypes and stable significant SNP markers will serve as a valuable resource for future SSR resistance breeding. Our study also suggests that genomic selection holds promise for accelerating canola breeding progress by enabling breeders to select SSR resistance genotypes at the early stage by reducing the need to phenotype large numbers of genotypes. Sclerotinia sclerotiorum (Lib.) de Bary is a devastating non-host specific, necrotrophic and ubiquitous plant pathogenic fungus that infects at least 408 plant species including economically important dicotyledonous crops such as oilseed rape, edible dry bean, soybean, sunflower, pea, chickpea, lentils, and different types of vegetables and some monocotyledonous crops such as tulip and onion2. The disease caused by this pathogen in rapeseed/canola is commonly referred to as sclerotinia stem rot (SSR), and it significantly limits rapeseed yield/production worldwide. This disease imposes 10–20% yield loss per year in China, but the loss could be up to 80% in severely infected fields3. In the United States, where every percentage unit of incidence reduces on average 0.5% of canola potential yields4, the annual seed loss due to this pathogen attack has been estimated at about $24 million5. Besides seed yield loss, the disease also reduces the oil content and makes changes in the fatty acid profile of affected plants that reduces oil quality8.10. Under favorable conditions  the sclerotia germinate carpogenically. Airborne ascospores released during the day11 and start infection by colonizing on senescent petals. In the presence of free moisture, ascospores germinate and move from petals to leaves12 and main stems, where forming lesions may completely girdle the stem and cause death of plant13.The pathogen survives in the soil up to 8–10 years by producing long-lived melanized resting structures called sclerotia14. However, long-term persistent survivability of the sclerotia in the soil and wider host range of the fungus makes crop rotations less effective. Moreover, properly timing fungicide applications to manage the disease is a difficult task to achieve and adds additional input cost and has a negative impact on the environment. Therefore, breeding for disease resistant rapeseed/canola varieties would be an economically feasible, more efficient, and environmentally friendly option. Hereafter, it is crucial to study different germplasm from diverse regions in order to unravel the nature of durable genetic resistance and identify responsible genes for the resistance to S. sclerotiorum. Subsequently, such identified SSR resistance genes can be introduced into high performing elite canola cultivars which ultimately diminish the dependence of the canola growers on cultural practices and use of fungicides and making canola production more profitable.Currently, rapeseed/canola growers depend primarily on use of crop rotation with non-host crop species and prophylactic fungicide applications for SSR management due to the unavailability of SSR resistant varietiesBrassica napus and its close relatives in more than three decades of investigation16. Instead, few germplasms with partial resistance have been identified and utilized in the SSR resistance breeding. Several genetic studies have shown that the mode of SSR resistance is quantitatively inherited with additive effect and have medium to high heritability20. To date, genetic mapping studies has been carried out to identify the sclerotinia resistance loci in multiple bi-parental mapping populations developed from crosses between resistant and susceptible parents24. QTL mapping using bi-parental populations have detected several QTLs for SSR resistance, and the majority of them located on chromosomes A09, C02, and C06. However, in very few instances common markers have been detected in different mapping populations24, which makes it difficult to identify the overlapping QTLs. The availability of the B. napus reference genome sequence25 offers an opportunity to determine the physical location of the previously identified QTLs by aligning the QTL primers with the B. napus genome. Integration and comparative analyses of the previously identified resistance QTLs from various mapping studies with the reference genome sequence have detected conserved QTLs on chromosome A9 (22.5–27.5 Mb) and C6 (29.5–36.1 Mb)26. Despite these successes, no fine mapping or map-based cloning for sclerotinia resistance gene has been reported so far, which ultimately circumvents the utilization of identified QTL in the SSR resistance breeding strategy. To date, all identified sclerotinia resistance QTLs only explained a small portion of phenotypic variance and few QTLs could be detected in different populations, different growth stages, or different screening methods. Moreover, bi-parental QTL mapping strategy suffers from low allelic diversity since only two allelic effects were evaluated for a single locus and limited recombination events which leads to the lower mapping resolution27. Apart from bi-parental linkage mapping strategy, genome-wide association study (GWAS) uses natural population originated from non-cross derived lines which offer extensive historical recombination events, shorted linkage disequilibrium (LD) segments thus provides a promising opportunity of having high mapping resolution for the marker-trait-association28.Unfortunately, breeding for SSR resistance is a challenging task as sources of complete resistance to the disease have not been identified in 29 used 84 simple sequence repeat markers to conduct a GWAS study using 152 B. napus accessions at the flowering stage in a controlled environment and identified 34 significantly associated loci of which 21 alleles contributed to the SSR resistance. Wei et al.18 used 30,932 SNP markers to conduct a GWAS study and detected five significant associations on A8, and twelve on C6 using detached stem inoculation method. A total of 26 SNPs associated with SSR resistance were identified on chromosome C4, C6, and C8 from a field study based on detached stem inoculation assay and the genotyping data of 25,573 SNPs by Wu et al.19. However, the effectiveness of GWAS may be limited in detecting common alleles with very small effects, as well as rare variants with small effect30. Similar to GWAS, the GS is performed by employing genome-wide markers distributed throughout the genome. GWAS detects significant SNP-trait associations that account only a small portion of phenotypic variance, indicating that there is a significant amount of genetic information that could be captured with a whole genome modeling. Contrary to GWAS, GS has emerged as a promising genomics-assisted techniques that uses all the molecular markers information and phenotype data of the training population to develop statistical models that predict genomic estimated breeding values (GEBVs) in testing individuals based only on the genotype information32. A number of GS studies have been reported in several crops such as wheat35, and maize38 in the past 10 years. The potential of GS in rapeseed/canola was investigated for various agronomic traits including resistance to blackleg disease40 and concluded as a promising tool for rapeseed breeding. However, the application of genomic prediction for SSR resistance in rapeseed/canola has been limited to date41. Recently, Derbyshire et al.41 implemented GP on adult plant SSR resistance in B. napus and reported that the GS can be used for the improvement of S. sclerotiorum resistance.Recently, the dramatic reduction of sequencing cost and quick turn-around time has led to the development of genome-wide dense molecular markers, which further accelerated the application of GWAS and genomic selection (GS) towards the genetic improvement of complex traits. To highlight the effectiveness of GWAS to improve the enhanced SSR resistance and marker-assisted selection, to date, only a few genetic mapping studies through GWAS for SSR on rapeseed/canola have been reported. Gyawali et al.The objectives of the study were (i) to identify SSR resistant genotypes, (ii) dissect the genetic architecture of SSR resistance, (iii) identify the genomic regions, marker-trait-associations (MTAs), and putative candidate genes conferring SSR resistance, (iv) to explore and evaluate the effectiveness of genomic prediction (GP) for selection of genotypes for SSR resistance.B. napus germplasm accessions and breeding lines originating from 17 countries in the world were collected from North Central Regional Plant Introduction Station (NCRPIS), Ames, Iowa, USA and North Dakota State University (NDSU)  at 22–24 ℃ for 48 h. The canola plants were inoculated at full flowering stage. The main stem of eight arbitrarily selected plants from each row were inoculated by placing a 7 mm agar plug containing actively growing hyphal tips approximately at a height of 40–50 cm above the ground  was recorded from days of seeding to flowering (when 50% of the plants in each replication of each genotype started flowering) during the year of 2019 and 2020 in both locations. In addition to FT, stem diameter (SD) and stem internode length (IL) may have an effect on SSR disease prevalence on B. napus. Therefore, to investigate its effect on SSR resistance, we collected the data on plant SD and IL of the inoculated plant keeping in mind to have the equal number of plants in each plot considering the plant development stage. Data on stem diameter (SD) in both years was measured using a Vernier caliper from the inoculated internodes slightly above where the inoculum was applied at 7 dpi. The internode length (IL) was taken using a measuring scale from the inoculated internode stems at 7 dpi from all the studied environments.Flowering time (FT) is an important developmental stage of the flowering plants in which they switch from the vegetative stage to the reproductive stage and it may have a role in the plant pathogen interactions44. For the environment-wise analyses, genotypes were considered as fixed effects, and replications as random effects. Variance components were used to calculate the broad-sense heritability (H2) asThe four field experiments were designated as 2019 Carrington (CARR_19), 2019 Langdon (LANG_19), 2020 Carrington (CARR_20), and 2020 Osnabrock (OSN_20), were analyzed individually and as a combined set with SAS 9.4 . The best linear unbiased estimates (BLUEs) were calculated within single environment analysis and combined analysis across environments (combENV). For the combined analysis across all environments (combENV), homogeneity of variance was determined by dividing the environment with the highest error mean squares by the environment with lowest error mean squares. If the calculated ratio was less than tenfold, then the data from all environments were combinedH2) for each trait was computed for the combENV set asσ2g is the genotypic variance, σ2ge is the genotype by environment variance, σ2e is the residual error variance, n is the number of environments, and r is the number of replications per environment. Environment-wise and combENV BLUEs for LL and LW phenotypic trait, and only combENV BLUEs for PM_14D and PM_21D were used to perform GWAS analyses. Correlations among all traits were performed by calculating Pearson correlation coefficients in SAS 9.4. To determine whether the collected data was normally distributed, the Shapiro–Wilk test was conducted for all traits in both single environment and a combined dataset. Simple linear regression analysis was performed with stem LL, LW, PM_14D and PM_21D as dependent variables and flowering time as independent variable in R45 (R Development Core Team). The same analysis was performed to examine the relationship between SD and IL as independent variables and stem LL, LW, PM_14D and PM_21D as dependent variables.For the combENV, a mixed linear model was implemented considering genotypes as fixed effects, environment, replication within the environment, and genotype-by-environment interaction as random effects. The broad-sense heritability  alignment tool48 with the default parameters. SNP calling was done using TASSEL 5 GBSv2 pipeline49 and 497,336 unfiltered SNPs were identified. High quality SNP were identified through filtering with VCFtools50 with the following criteria: minor allele frequency (MAF) ≥ 0.05, missing values (max-missing) ≤ 25%, depth (minDP) ≥ 3 and physical distance (thin) ≤ 500 bp. As canola is a self-pollinating crop, more than 25% heterozygous SNP were removed using TASSEL51. The SNPs that were located outside of the chromosomes (i.e. unknown position), were removed. Thus, a total of 38,510 high quality SNPs were retained. For the present study, we utilized the polymorphic SNP markers data with minor allele frequencies greater than 0.05 on 187 genotypes.Fresh young leaf tissues were harvested from each germplasm and lyophilized at -80̊ C until used. The total genomic DNA was extracted from the lyophilized tissues using Qiagene DNeasy kit  following the manufacturer’s protocol. Then the extracted DNA was quantified using NanoDrop 2000/2000c Spectrophotometer (Thermofisher Scientific) and optimized to get the same concentration of DNA. The GBS library was prepared using the 52, and GEMMA-MLM53. In the GAPIT analysis, we computed principal component (PC) analysis for accounting population structure using prcomp  function54 in R and kinship (K) matrix by VanRaden method55 for relationships among individual, both using molecular marker data. The first four PCs were used for model-based clustering analysis to determine the subpopulations using the Mclust package in R. The first four PCA as a population structure and kinship matrix were incorporated in the GWAS models to control false-positives. The single locus mixed linear model (MLM)56, and the multi-locus model fixed and random model circulating probability unification (FarmCPU)57 were implemented in GAPIT. Additionally, the identified significant MTAs were verified by performing GWAS analyses in another commonly used software i.e. GEMMA-MLM (version 0.98.1) through the execution of the command: “gemma -g [genotype file] -a [genotype annotation file] -p [phenotype] -c [first 4PCA] -k [kinship/centered relatedness matrix] -o [output file]. For this analysis, the incorporated first four PCs were the same that we used for FarmCPU, and MLM. The kinship matrix was generated using the centered relatedness procedure in GEMMA, used as a random effect variable in the random model. P-wald test  was calculated for the given model. The significant threshold of P value for the association between SNPs and traits were determined following the method proposed by Li and Ji58. The effective number of independent tests (Meff) among the used 25,809 SNPs were determined by calculating the correlation matrix and eigenvalue decomposition. Then the Bonferroni correction was applied based on the effective number of independent tests (loci). The effective number of independent tests was estimated as 127, thus by applying genome-wide type I error rate at α = 0.05, the determined significant threshold of P = 0.05/127 = 0.0004 or − log10 (P) = 3.4. Therefore, the significant threshold value for the association between SNP and traits were determined by − log10 (P) ≥ 3.4, which is equivalent to P ≤ 0.0004, for FarmCPU, MLM, and GEMMA-MLM. The SNPs detected by at least two models in at least one environment were declared as significant and considered as relatively stable significant SNP. To identify the common significant SNP markers present in more than one environment, a threshold value of − log10 (P) ≥ 3.00 was used, only when those SNPs that had a lower association threshold (P ≤ 0.0004) in one environment were considered common. Manhattan plot and P value distributions by plotting the observed P values against expected P values shown in Q–Q plots were created using the mhplot package in R language.Marker-trait-association analyses were implemented in the GAPIT R package (version 3.0)47. Protein sequences from the gene models were blasted against TAIR 10 protein database to determine the gene annotation. Genes associated with defense response were identified based on the Gene Ontology terms (GO terms) from TAIR website and gene functions found in the previous literature, TAIR 10 and Uniport-KB.Candidate genes were searched for those significant SNPs that were detected in more than two environments, and two or more GWAS models. Genes present within 50 kb upstream and downstream of the significant markers were considered as candidate genes based on the genome and the gene models by ‘ZS11’ reference genome sequencey is the vector of the phenotypic observations, µ is the grand mean, X is the marker genotype matrix, β is the estimated random additive marker effect, and e is the residual error term. Three GS models i.e. ridge regression best linear unbiased prediction rrBLUP59 and two Bayesian models: Bayes C60, and Bayesian Ridge Regression (BRR)31 were used for implementing genomic prediction. All models were analyzed in R language. The GP model rrBLUP, and two Bayesian models were constructed using the package “rrBLUP”59, and BGLR (version 4.0.4)61 package, respectively. For all the analyses, Bayesian models were performed for 5000 Monte Carlo Markov chain iterations with a 1000 burn-iterations. The environment-wise BLUEs of LL, LW, and the combENV BLUEs of LL, LW, PM_14D and PM_21D were used as phenotypic values for subsequent GP analyses. All SNP markers  distributed in the whole genome were employed in the GP. A fivefold cross validation  with 100 iterations or 100 rounds of random sampling were implemented to assess the accuracy and predictive ability of the GP model for each predicting phenotypic traits with the validation populations. Predictive ability was defined as the correlation (Pearson’s r) between genomic estimated breeding values (GEBVs) and the observed phenotypic value. The measure of prediction accuracy is the correlation between GEBV and an estimate of true breeding value. However, true breeding values for this trait are typically unknown. Therefore, the prediction accuracy was indirectly estimated by dividing the correlation between GEBV and observed phenotypic value by an estimate of square root of heritability .The genomic prediction models were constructed with the following formula:h2) was estimated for the combined datasets of all the evaluated traits for each model using all the markers. The additive variance components (Va) and the residual variance components (Ve) were estimated with mixed.solve function in rrBLUP for the rrBLUP model59. Narrow-sense heritability (h2) was assessed by dividing the additive genetic variance (Va) by the total variance estimate . For the Bayesian models, h2 was estimated by taking the average proportion of variance explained by the regression of phenotypes on molecular markers as described by de los Campos et al.63 (https://github.com/gdlc/BGLR-R/blob/master/inst/md/heritability.md).Genomic heritability  on both LL and LW for stem resistance. Similar results were obtained from the ANOVA analysis of the combENV sets for PM_14D, and PM_21D with the exception of the interaction between genotype and environment on PM_21D, which was not-significant (P ≤ 0.05)  ranging from 3.7 to 6.4 , LW and PM_14D , LW and PM_21D   , and similarly a significant positive correlation was also found to be associated between stem LW and IL , LW and PM_14D , LW and PM_21D   , LANG_19 , CARR_20 , OSN_20 , and combined  environments. Highly significant correlations were also reported between stem LL and PM_14D , LL and PM_21D   Fig. . The regLW) Fig. . HoweverLW) Fig. . Regressr =  − 0.39, P =  < 0.0001 for LL; r =  − 0.44, P =  < 0.0001 for LW; r =  − 0.49, P =  < 0.0001 for PM_14D; and r =  − 0.59, P =  < 0.0001 for PM_21D)   Fig. . The reg1D) Fig. . These nAfter eliminating markers with missing data greater than 25%, a total of 25,809 polymorphic SNPs with minor allele frequency (MAF) greater than 5% were obtained and employed for association analysis. The highest proportions of SNPs had MAF between 0.10 and 0.15 (20%) and between 0.05 and 0.10 (20%)  is divided into two parts: fixed effect model (FEM) and a random effect model (REM) and uses them iteratively. The first part (FEM) contains testing maker, one at a time, and multiple associated markers fitted as covariates to control false positives. To avoid the over fitting model problem in FEM, the associated markers are estimated through maximum likelihood method in REM by using them to define kinship57. In the current study, the SNPs detected in any two GWAS models were considered reliable and declared as significant SNP for the studied trait. Significant MTAs were determined on the basis of modified Bonferroni correction by calculating the effective number of independent tests (loci) from the tested 25,809 SNPs by Li and Ji58. The Q–Q plots generated from all GWAS analyses models of the analyzed phenotypic traits showed a sharp deviation from the expected P value distribution in the tail area, indicating that population structure and familial relatedness were well controlled and false positive associations were reduced  in each year and with the combENV BLUEs data to identify best MTAs. To reduce false positive or false negative associations i.e. the chance of committing Type I and Type II errors, three different GWAS algorithms i.e. FarmCPU, MLM, and GEMMA-MLM, were used to identify the true MTAs. The population structures using four PCA and familial relatedness with kinship matrix were incorporated in the implemented MLM and GEMMA-MLM models to control pseudo associations. Incorporation of PCA and kinship matrix in the MLM models as covariates adjusts the correction tests to control false positives, but could not solve the confounding problem between the covariates and test marker, resulting false negatives10 (P) ≥ 3.4; P ≤ 0.0004] by at least two of the GWAS models, and thus were regarded as more reliable. These SNPs were unevenly distributed among the B. napus chromosomes  were used to search for the candidate genes for sclerotinia stem rot resistance using “The three GS models used in this study showed more or less similar results across all traits that we evaluated Fig. . Howeverh2). Therefore, we estimated the narrow-sense heritability based on the whole datasets using the same GS model used to fit the cross-validation sets. The genomic heritability varied, depending on the traits and used GS models as shown in Table Since, the true breeding value of these traits are unknown, we estimate the approximate prediction accuracy to divide the correlation between the phenotypes and the mean predictive ability obtained through cross-validation sets by the square root of heritability  is one of the most economically important and devastating fungal disease of rapeseed/canola that significantly limits seed yield, oil content, and oil quality worldwide. SSR is a highly heritable complex trait, controlled by many genes with minor additive effectsS. sclerotiorum isolates can vary widely in their virulence, or ability to cause damage to plants and consequently, plant genotypes may respond differently to different isolates65. We used a highly virulent S. sclerotiorum isolate, WM031, for this study. This selection allowed us to increase the infection efficiency and effective phenotypic screening for SSR resistance. The effective screening would allow us to the proper evaluation of the corresponding genotypes to produce reliable phenotypic value, which would ultimately improve the GWAS analyses to identify MTAs associated with SSR resistance. To generate stable phenotypic values, we have conducted the study at four location-years with three replications in each location. To mimic the natural sclerotinia infections on rapeseed/canola plants, we used agar-plug stem inoculation method to challenge the plants. This screening method has been implemented successfully by other researchers for the effective identification of SSR resistance66. Plants were inoculated during the flowering, because this is the most prevalent stage for SSR infection in natural field conditions22. The occurrence of SSR at the adult plant stage is a major cause for the seed yield loss and reduced oil content and quality. Therefore, we decided to identify the SSR resistance genotype source and resistance significant MTAs at the mature plant stage in order to incorporate the resistance into the elite canola breeding cultivars and to facilitate the MAS in rapeseed/canola breeding program. Hereafter, we analyzed 187 rapeseed/canola diverse genotypes for SSR resistance under field conditions. This germplasm primarily originates from North America, Europe, Asia and comprises of 26.7, 26.2, and 47.1%, respectively. Gyawali et al.29 carried out GWAS analyses for resistance to SSR under controlled conditions using 152 accessions collected from 18 countries consisting of 0.7% Australia, 69.7% Asia, 20.4% Europe, 7.2% North America, 0.7% South America and 1.3% from the unknown origin. Another GWAS study for SSR resistance conducted by Wu et al.19 used a panel of 448 germplasm accessions comprised of 93.8% (Asia), 1.1% , 1.1% (North America), and 4.0% (Europe) geographical origins. The geographic distribution of the genotypes used in our study provides a good coverage of world-wide germplasm accessions.This study was designed to obtain the most accurate phenotypic and genotypic data possible. 68. The lesion length on the main stem is the most commonly used phenotypic parameter in studies that evaluate the resistant performance of genotypes against SSR in rapeseed/canola66. The association study implemented by Gyawali et al.29 used lesion length and percentage of soft and collapsed lesions on the inoculated stem by noting depth of penetration for the assessment of SSR resistance. However, Wei et al.18, and Wu et al.19 used lesion length as a phenotypic trait for the evaluation of SSR resistance and GWAS analyses. In addition to LL, the LW and PM were also recorded. There were significant differences among the genotypes in relation to LL, LW, and PM at 14 dpi and 21 dpi. Results of this study clearly indicated that LW, PM_14D, PM_21D could be used as alternative phenotypic disease traits for the assessment of SSR resistance in rapeseed/canola. Since, plant mortality is directly related to yield performance, therefore it may be necessary to record plant mortality in addition to the other associated traits for sclerotinia phenotyping. Plant mortality has successfully been used by Shahoveisi et al.24. Li et al.66 evaluated 42 B. napus and 12 B. juncea genotypes for SSR resistance for PM at 21 dpi under field conditions and did not find any significant differences among the genotypes. Interestingly, we have found strong significant correlations among the LL, LW, PM_14D and PM_21D. Therefore, LW and PM could be used as an alternative phenotypic trait/parameter for breeders and pathologists to successfully differentiate and identify the potential SSR resistance genotypes under field conditions. To the best of our knowledge, this is the first to record the disease related LW phenotype to use as an alternative to LL and we demonstrated that there is a significantly high positive correlation between these two traits. The heritability of the stem resistance measured using LL was high which is consistent with previous studies20. Medium to high heritability for LW, PM_14D, and PM_21D was also estimated from the replicated multiple-location trials and combENV analyses, implying that phenotypic variation is mostly derived from genetic variance and phenotypic selection is effective for improving SSR resistance and subsequent association analyses to identify favorable alleles associated with SSR resistance to utilize in MAS.The reaction of the 187 rapeseed/canola diverse genotypes was evaluated and measured in three different ways, lesion length (LL), lesion width (LW), and plant mortality for SSR resistance. In our study, wider phenotypic variability was observed in the stem LL (2.3–9.0 cm), LW (19.3–81.4%), PM_14D (2.0–63%), and PM_21D (16–94%) against the disease infection, indicating that this diversity panel was ideal for performing GWAS. Phenotypic variability together with ideal diversity panel and high number of SNPs would potentially increase the effectiveness and efficiency of significant association detection via marker-trait-associations (MTAs)72. Therefore, the relationships between SSR disease phenotypic traits , and three agronomic traits such as FT, SD, and IL were also explored to assess whether they have direct or indirect effects on the SSR resistance. Results from this study showed that FT and SD had a significant and negative correlation with LL, LW, PM_14D, and PM_21D on B. napus. A similar association was reported in previous studies of this pathosystem72, and implied that early flowering genotypes were more prone to SSR susceptibility with increased stem LL, LW, and plant mortality. A similar association has been reported on other pathosystems, like Arabidopsis-Verticillium dahlia73; Arabidopsis-Fusarium oxysporum74; rice-Pyricularia oryzae75. Studies carried out by Wu et al.71 and Zhang et al.72 detected few co-localized QTL for the SSR resistance and FT in B. napus. They suggested a possible genetic linkage between these two traits. However, the underpinning genetic and molecular mechanisms controlling these associations are still not evident71. In addition to the effect of FT, Qasim et al.20 found a weak negative correlation between LL and SD, which is an agreement with our current study. Li et al.66 observed lower stem LL and plant mortality when the stem diameter was around 10 mm. However, increased lesion length and plant mortality were observed when SD was smaller or larger than 10 mm. Also, significant, positive, but moderate correlations were detected between IL and stem LL, IL and stem LW, IL and PM_14D, and IL and PM_21D. Therefore, this is an indication that evaluation of IL could be another useful parameter for an indirect selection for potential SSR disease resistant genotypes to use in the rapeseed/canola breeding program. Based on the findings from our study as well as from previous studies, effect of agronomic traits i.e. stem IL, and SD need to be taken into careful consideration for the breeders and pathologists to identify and select the accurate promising SSR resistance genotypes phenotypically from the field screening.Several agronomic traits such as plant height, canopy architecture, stem diameter, and flowering time were reported as associated with the sclerotinia disease severity in different crops such as canola, soybean, dry bean etcB. napus in order to identify the genetic loci conferring resistance. GWAS is a powerful genetic mapping strategy for the dissection of complex traits in plants76. Therefore, in this study, GWAS was implemented using three different models for SSR resistance associated traits i.e. LL, LW, PM_14D, and PM_21D with the objectives to maximizing opportunities to identify reliable and stable specific genomic regions and SNPs conferring SSR resistance in rapeseed/canola germplasm. This is the first time to use different single locus (MLM and GEMMA-MLM) and multi-locus (FarmCPU) GWAS models to identify common markers associated with this disease. The purpose of using three different GWAS softwares/algorithms was to reduce the chances of committing type I  and type II  errors. The identification of commonly detected SNPs simultaneously with multiple GWAS models and traits would improve the reliability of the detected MTAs associated with SSR resistance. As the SSR resistance is a quantitatively inherited complex trait and the number of SNP markers is larger than the sample size, it would be necessary to simultaneously use multiple methods for GWAS to identify stable MTAs. Bonferroni-Holm correction77 for multiple testing (α = 0.05) was too conservative, since it assumes that all the tests are independent but in reality, some SNPs may not be independent and they might be in linkage disequilibrium (LD) due to their physical distance or other associated factors. Moreover, with this Bonferroni-type correction, no significant associations between markers and evaluated traits in most of the environments were detected. The use of stringent significant probability threshold reduces the risk of accepting false positives but does not necessarily reduce the risk of rejecting true MTAs. Therefore, the significant threshold value for the association between SNP and traits were estimated by the method proposed by Li and Ji58. In this study, most of the significant SNP markers associated with the SSR resistance traits  detected in the environment-wise and combENV datasets showed small effects, explaining 3.6–12.1% of the observed phenotypic variance. These finding are in an agreement with previous genetic mapping studies (QTL and GWAS) on sclerotinia resistance. This suggests and validates that SSR resistance in B. napus is a complex genetic trait, quantitatively inherited and determined by multiple minor QTL with small effects71. Li et al.66 conducted an integrated and comparative QTL analyses for SSR resistance using previously identified QTLs from various mapping studies with the Darmor-bzh reference genome47 and determined that chromosomes A9 (22.5–27.5 Mb) and C6 (29.5–36.1 Mb) are conserved QTL regions. The putative disease resistance nucleotide-binding-site, leucine-rich-repeat (NBS-LRR) genes were found in this region in a cluster. GWAS analyses from our study revealed a total of 14 significant SNPs located on C6 (22.3–39.4 Mb) genomic regions, and 5 of them were located on chromosome C06 (33.2–34.1 Mb) region that was reported by Li et al.26. However, the SNP markers on chromosome C06 identified in this study have shown overlapping confidence intervals with the QTLs for stem resistance detected by Zhao et al.15, Wu et al.22, Wei et al.18, Wu et al.19, and Qasim et al.20. Identification of the SNPs in our study which aligned with previously identified overlapping genomic regions provide strong evidence that fine-mapping using large segregating mapping population could help us to narrow down the genomic regions. This may lead us to identify the putative candidate gene conferring SSR resistance in rapeseed/canola, and therefore, guide us for the map-based cloning of the sclerotinia resistance gene in future to assist MAS.Bi-parental linkage mapping and association mappings have been used to dissect the complex traits such as SSR resistance in 19 identified five significant SNPs on chromosome A08 (15.09–15.10 Mb) region based on the alignment of Darmor-bzh25 reference genome. We detected six SNPs on chromosome A08 (13.7–22.9 Mb) region based on ‘ZS11’ reference genome sequence47. The ‘ZS11’ reference genome sequence was aligned with ‘Darmor-bzh’ reference genome25. Therefore, our finding was a close agreement with Wu et al.19. We also identified significant SNPs on chromosome C08  which located near or overlapped with the identified QTL genomic regions of chromosome C8  reported by Wu et al.22. A significant SNP (SCM002777.2_46851981) for LL located on chromosome C09 repeatedly detected on multiple environments  were found to be overlapped with the physical interval of Sll19 for stem lesion length using petiole inoculation technique by Zhao et al.15. Moreover, another stable SNP marker (SCM002777.2_48885679) on chromosome C09 identified in almost all the environments and combENV with all the traits except PM_21D located at the physical position of 48.9 Mb in the ‘ZS11’ reference genome sequence. Detection of these stable genomic regions (46.8–48.9) on chromosome C09 in our current study as well as from the previous study provide an exciting opportunity to further explore these regions to develop molecular markers for future MAS in the rapeseed/canola breeding program for SSR resistance. Shahoveisi et al.24 reported QTL SR54.C3.1 associated with SSR resistance in the physical region of (23.4–31.6 Mb) on chromosome C03, the two SNPs  in our study was found in close proximity or within the genomic regions. Moreover, we collected information on the previously identified QTLs, SNPs and their physical positions based on marker information from the past studies and compared it with our findings. In addition to the identification of significant SNPs in the previously detected genomic regions, to best of our knowledge, hereafter we are reporting new genomic regions on chromosome A09 (35.6–45.8 Mb) consisting of ten significant SNP markers, chromosome A03 (28.2–36.2 Mb) with seven significant SNPs, and chromosome A05 (15.9–28.6 Mb) with six markers are associated with SSR resistance.Several QTLs reported here were localized in the vicinity of QTL identified by other researchers. GWAS study conducted by Wu et al.78. Based on these criteria, sixty-nine genes associated with defense response mechanisms were identified. A TIR-NB-LRR gene (LOC106415792) that putatively encodes RPP1 proteins was located in the vicinity of SNP (SCM002777.2_48885679) detected in all the environments and combENV with stem LL, LW, PM_14D traits on chromosome C09. TIR-NB-LRR genes provide defense response against fungi through the activation of the salicylic acid (SA)-dependent resistance pathway79. SA has been known to be involved in the activation of defense response against biotrophic and hemi-biotrophic pathogens. However, recent findings suggest S. sclerotiorum has a brief biotrophic phase followed by a necrotrophic phase81. Our findings are in agreement with Nováková et al.82. Two annotated candidate genes, WRKY transcription factor 33 (WRKY33) and a peroxidase C3-like, were detected 7.1 kb upstream and 4.6 kb downstream of marker SCM002772.2_65359864 on chromosome C4. This marker was detected using stem LL and LW data sets in multiple environments. WRKY33 is involved in defense response to fungus and the camalexin biosynthetic processes which was found to be involved in providing resistance against S. sclerotiorum83. Wang et al.84 demonstrated that overexpression of BnWRKY33 markedly enhanced resistance to S. sclerotiorum in B. napus. Another candidate gene annotated peroxidase C3-like to be involved in defense response. The remaining sixty-six genes reported in this study also encode proteins involved in the disease resistance mechanisms according to TAIR 10 and Uniport-KB.One of the key objectives of GWAS is the identification and utilization of the candidate genes. Thereby we searched for candidate genes associated with disease resistance mechanisms that were located within 50 kb upstream and downstream of significant SNPs detected in at least two environments. We choose 50 kb because LD for this population is low 88. BRR produces homogeneous shrinkage of all marker effects towards zero and yields a normal distribution of the marker effects89. The consistent results were also reported by Derbyshire et al.41,where Bayesian models perform similar or worse than G-BLUP model to predict S. sclerotiorum resistance in B. napus. The predictive ability in this study ranging from medium to high, were comparable or higher than the estimated predictive ability of SSR resistance in two previous studies41. The differences in predictive ability could be attributed due to the difference in populations, diversity, linkage disequilibrium, and trait heritability91. However, the estimated predictive ability was more or less similar with the reported predictive ability by Derbyshire et al.41 when lesion length data was used as a target trait. Interestingly, in our current study, using PM_21D data we achieved 47–56% increase in predictive ability. The results suggest that use of PM_21D data rather than pathogen spreading  could be used as a useful phenotypic trait, which would potentially enable the breeders to achieve higher predictive ability and leading towards the selection of superior genotypes for SSR resistance breeding in rapeseed/canola. GWAS results from this study indicated that most of the significant SNPs explained only 3.6–12.1% of the phenotypic variance. However, most of the identified significant SNPs or QTL from the GWAS and bi-parental linkage mapping studies also showed small effects, explaining less than 10% of the observed phenotypic variance23. Thus, genomic selection offers promising opportunities by capturing the effects of both minor and major genes to exploit the full genetic potential over selection based on few significant markers for the improvement of SSR resistance in rapeseed/canola. In future, this study could be further improved by including more genotypes, as well as integrating other independent biological information.Genomic selection is an effective genomic approach for the improvement of complex traits in cropsIn this study, we have identified resistant genotypes from a genetically diverse resource will serve as a potential donor for improving canola cultivars with SSR resistance at North Dakota State University canola breeding program. The use of multiple phenotypic data sets and GWAS models used on data collected in multiple field environments allowed for the detection of one-hundred thirty-three significant SNPs, some of them were in novel regions of the genome. Some SNPs were detected in multiple datasets and models, suggesting their association with the resistant trait may be stronger than that of others. At the same time, they validate the notion that multiple approaches, e.g., phenotypic data sets and GWAS models, may yield additional information that otherwise would not be captured. The significant stable and new MTAs detected from this study could be used for future MAS of SSR resistance in rapeseed/canola breeding. Further, the strong and significant correlation detected among the phenotypic traits suggested that, stem LW, PM_14D and PM_21D could be used as proxies for stem LL when evaluating genotypes for their reaction to SSR. This study also assessed the potential of GP using different GS models and revealed a medium to high predictive ability depending on various phenotypic traits. Our results suggest that GS holds promise for the improvement of SSR resistance, and its application would enable the breeders for early SSR resistance genotype selection to accelerate the breeding efficiency by reducing the need to phenotype large number of genotypes in the field at maturing stage.Supplementary Information 1.Supplementary Information 2.Supplementary Information 3.Supplementary Information 4.Supplementary Information 5.Supplementary Information 6.Supplementary Information 7.Supplementary Information 8.Supplementary Information 9.


The findings of TDFY and IVOMD heterosis in these sets indicated the potential of improvement of the hybrid cultivars for forage quantity and quality in forage pearl millet. The parental lines single cross parent (SCP)-L02, SCP-L06, and top cross parent (TCP)-T08 found superior in the forage quantity and quality traits can be utilized in the future breeding programs. Most of the forage traits were found to be controlled by using the non-additive gene action. A diverse panel of 105 forage-type hybrid parents (Set-III) genotyped following genotyping by sequencing (GBS) and phenotyped for crude protein (CP) and IVOMD under multi-cuts for 2 years identified one stable significant single nucleotide polymorphism (SNP) on LG4 for CP, and nine SNPs for IVOMD distributed across all the linkage groups except on LG2. The identified loci, once validated, then could be used for the forage quality traits improvement in pearl millet through marker-assisted selection.Pearl millet is an important food and fodder crop cultivated in the arid and semi-arid regions of Africa and Asia, and is now expanding to other regions for forage purpose. This study was conducted to better understand the forage quantity and quality traits to enhance the feed value of this crop. Two sets of pearl millet hybrids (80 single cross hybrids in Set-I and 50 top cross hybrids in Set-II) along with their parents evaluated multi-locationally for the forage-linked traits under multi-cut (two cuts) system revealed significant variability for the forage traits in the hybrids and parents. The mean better parent heterosis (BPH) for total dry forage yield (TDFY) was 136% across all the single cross hybrids and 57% across all the top cross hybrids. The mean BPH for  Pennisetum glaucum (L) R.Br.] is cultivated mainly in the hot and dry agro-ecologies of Africa and Asia. This crop has a high potential for biomass production due to its C4 photosynthetic pathway, and it additionally possesses tolerance to various climatic stresses. Currently, this crop is gaining popularity among the small-holder farmers because of its potential to adapt to the diverse agro-climatic conditions where most of the other crops, such as wheat, rice, sorghum, maize, and barley fail to produce economic yields  and crude protein (CP: 9%) in comparison with sorghum (33 t ha–1 and 6%) and maize (31 t ha–1 and 5.5%) varieties in single cut  system . Severalin vitro organic matter digestibility (IVOMD), and nitrogen content  mapping studies have been conducted in pearl millet for the fodder linked traits, such as for tiller numbers , stover  content . Hash etConsidering that there is limited information on the gene action, heterosis, and mapping of forage traits under multi-cut system in forage type pearl millet, the present study aimed to estimate heterosis, general combining ability (GCA), and specific combining ability (SCA) for the forage traits of single cross and top cross hybrids at two cutting intervals, and to identify the QTLs for the important forage quality traits in pearl millet.In the present study, two sets of test crosses were developed to investigate heterosis, combining ability, and gene effects for the forage traits. The first set consisted of 80 single cross hybrids  generated by crossing 10 seed (A-lines) parents and eight pollinator (R-lines) parents having forage type traits  in a lin6 and above) of the pearl millet breeding program of the International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Patancheru, Telangana, India, was used to examine for the genome wide association study (GWAS) for the two forage quality traits CP and IVOMD.A third set (Set-III) of 105 diverse hybrid parents comprised of 17 seed and 88 pollinator parents [such as parental lines from Set-I  and Set-II (excluding pollinators of set-II which were populations)]  derived Both the single cross (Set-I) and top cross (Set-II) hybrids were evaluated along with the parents and commercial checks at two locations, namely, ICRISAT, Patancheru  and Tamil Nadu Agricultural University (TNAU), Coimbatore, Tamil Nadu, India  during the summer season of 2015. The hybrids and parents were planted side by side in each trial as separate blocks in each replication to avoid any suppressive effect of the hybrids over parents. The parental block included the fertile (B-lines) lines corresponding to their male sterile (A-lines) lines used in the development of hybrids. Four forage pearl millet commercial hybrids popular in India, namely, PAC 981 (Nutrifeed), DFMH 31, Milkon, and Poshan were used as checks in the hybrids block.Both the hybrids and parents were planted in alfisol soils in alpha lattice design with three replications at ICRISAT, Patancheru. Each entry was planted in the four rows of 4 m length with rows spaced 60 cm apart and the plants spaced at 10–12 cm from each other. All the hybrids and parents were planted in black soils in alpha lattice design with the two replications at TNAU, Coimbatore. Each entry was planted in the four rows, each of 4 m length and the rows spaced 45 cm apart. The experiment was conducted during first week of March to the third week of June 2015. The trial was irrigated at 12–15 days interval to avoid any moisture stress, and the crop was protected from the weeds, pests, diseases, and animals at both the locations.The third set (Set-III) of 105 hybrid parents was evaluated at ICRISAT, Patancheru, in alfisol soils during the summer seasons of 2015 and 2016 in partially balanced alpha lattice design with two replications. The plot size, spacing, and field management were same as specified above in the evaluation of first and second sets of trials (of hybrids and parents) at ICRISAT. The experiment was conducted during the first week of March to the third week of June 2015 and 2016.In both Set-I and Set-II trials, the four rows of each entry (hybrids and parents) were harvested manually by cutting at the second node from the bottom of the plant at 50–55 days after planting (at boot stage of plants) as the first cut. At the time of harvest, the plant height  was measured on five random plants from the base of the stem to the tip of the panicle of the main tiller. Fresh weight of the green forage was recorded (kilogram) for each plot. A sub-sample (10–15 plants) of about 1 kg was collected per entry at the time of harvest and recorded for green forage weight, oven dried for 8 h daily for 3–4 days at 60°C in Campbell dryer , and weighed again (dry forage weight in kilogram). The dry matter concentration and dry forage yield (DFY) of each entry was calculated using the formula:–1). The dried sub-samples of the whole plants (10–15 plants) of each entry were chopped into 10–15 mm pieces using a chaff cutter  and ground using Thomas Wiley mill  to pass through 1-mm screen for the chemical analysis. The ground stover samples  were analyzed by near-infrared reflectance spectroscopy (NIRS) for stover nitrogen concentration , neutral detergent fiber (NDF), acid detergent fiber (ADF), acid detergent lignin (ADL), metabolizable energy (ME), and IVOMD as described by –1) were estimated as the sum of the two cuts for each entry in this trial.The GFY and DFY were expressed in units of tons per hectare  over 2 years of evaluation were followed as similar to that of first and second sets of evaluation trials at ICRISAT.® 96 Plant II kit . Normalization of genomic DNA to 10 ng/μl was done in 0.8% agarose gel using lamda DNA . Electrophoresis was performed in Tris acetate-ethylenediaminetetraacetic acid (EDTA) buffer in buffer tank at 90 V for 1 h and the gels were stained with ethidium bromide and visualized in UV light using image analyzer. The DNA of 105 hybrid parents was genotyped using genotyping by sequencing (GBS) . The raw sequencing reads and barcode information were processed for single nucleotide polymorphism (SNP) identification from the published pearl millet reference genome (http://dataverse.icrisat.org/dataset.xhtml?persistentId=doi%3A10.21421%2FD2%2FIWXCUJ&version=DRAFT). These were further filtered for site coverage (90%), minor allele frequency (0.05), and maximum heterozygosity (50%), were identified 34,691 SNPs and were used for the GWAS analysis.The fresh leaf tissues (100 mg) were collected from 20 to 25 seedlings (per accession) of 8 days old plants (days after sowing) planted in the pots in darkroom at 18°C–25°C, and the DNA was isolated using the NucleoSpinng (GBS)  by digese genome  using TAe genome . BarcodeThe complete parental and hybrids data information were available at first cut of single cross hybrids (10 × 8 = 80 hybrids) and top cross hybrids (5 × 10 = 50 hybrids). In Set-I, the missing data (due to poor regeneration ability of parents and hybrids after first cut) from parents [one line and three testers at one site ] resulted into dropping of 35 single cross hybrids. Hence, 9 × 5 = 45 single cross hybrids were analyzed for second cut in Set-I. Whereas, in Set-II, 26 top cross hybrids were removed from further analysis due to the missing data from parents [one line and four testers at one site ] and thus 4 × 6 = 24 top cross hybrids were analyzed at second cut.The genotype means were calculated from the combined ANOVA across the two locations using PROC MIXED . The est1 is the hybrid value, BP (better parent) is either P1 or P2 and CH is the value of the check hybrid. BP was considered to have higher values for PH, GFY, TGFY, DFY, TDFY, CP, ME, and IVOMD, and lower values for NDF, ADF, and ADL. Among the four check hybrids, the PAC 981 (Nutrifeed) is a popular high forage yielding hybrid from the Advanta seed company, which occupies large areas of pearl millet for fodder use in India. Therefore, Nutrifeed was considered as a best check hybrid for calculating the SH in the single cross and top cross hybrids.where, F2GCA/(2σ2GCA + σ2SCA) (The line × tester mating design  was used+ σ2SCA) . The PeaP-value vs. expected − Log10 P-value for all the markers. If there is deviation in the P-values at initial stage, it indicates the existence of population stratification. Manhattan plots were used to visualize the results of GWAS. Negative Log10 of the P-values for each SNPs were plotted over the seven chromosomes for the two traits, CP and IVOMD. The best linear unbiased predictors (BLUPs) were estimated for CP and IVOMD for each hybrid parent across years from the combined analysis (H2) of the traits was estimated by dividing the genotypic variance by the total phenotypic variance  and phenotyped  were used for further statistical analysis. The principal component analysis (PCA) was conducted and the resulted eigenvalue (5) was taken into the consideration to correct the population structure along with kinship matrices (K) values implemented in the multi-locus mixed model (MLMM)  using thanalysis . The Bonanalysis  and its analysis . The genvariance .g2 is the genotypic variance, σge2 is the genotype × environment variance, σe2 is the error variance, r represents the replicates, and “l” represents the locations.where σin vitro dry matter digestibility) in pearl millet.Combined ANOVA exhibited significant differences for most of the forage traits among the genotypes except TDFY, CP, and IVOMD at second cut in the top cross hybrids , indicat–1) in the single cross hybrids was found to be higher than those of the top cross hybrids (TDFY: 11 t ha–1) (The mean TDFY (15 t ha t ha–1) , suggest t ha–1) , indicat t ha–1) , 6, indi t ha–1)  and in t t ha–1)  of pearl t ha–1) , 8.Similarly, the forage quality trait CP ranged from 9 to 14% in single cross hybrids and 9 to 13% in top cross hybrids across cuts, which is still higher than the minimum requirement (about 7%) recommended for feed protein in microbes in the rumen of ruminants . The BPHThe single cross hybrids found to have higher mean values of desirable forage traits, such as TGFY, TDFY, and IVOMD, and low negative mean values of undesirable forage traits, such as NDF, ADF, and ADL at first cut than top cross hybrids . This miSome hybrids from both the single cross and top cross hybrids had higher values for 2–3 forage traits, than the best check hybrid Nutrifeed. For instance, one single cross hybrid (SCH38: L05 × T16) had significant positive standard heterosis (SH) for 12% TDFY, 4% CP and had superior or on par IVOMD with the check hybrid Nutrifeed at first cut . BesidesThe mean squares due to GCA and SCA variances were found to be significant for TDFY in single cross hybrids, CP at first cut in single cross hybrids, and IVOMD at second cut in single cross hybrids, and also at first cut in top cross hybrids, indicating the importance of both the additive and non-additive effects for these traits . Highly The GCA effects of the single cross and top cross parents for the forage quantity and quality traits under different cutting intervals are given in The estimates of SCA effects of the single cross and top cross hybrids for the forage quantity and biochemical quality traits are provided in The SCA effects showed significant positive correlations with hybrid performance and BPH for most of the forage traits across cuts in the single cross and top cross hybrids . The GCABetter parent heterosis (BPH) of forage quantity (TGFY and TDFY) traits did not show correlation with the forage quality (CP and IVOMD) traits across cuts in the single cross and top cross hybrids , indicatThe minimum, maximum, and average values of the two traits, CP and IVOMD, observed at the different cutting intervals over 2 years are shown in p value of 5.63E-07. The same SNP was found to be tightly associated with CP with the p value of 9.05E-07 when analyzed using the pooled hybrid parental data from summer seasons of 2015 and 2016. Six SNPs, one (S6_227902580) on LG6, two (S3_75463586 and S3_291119752) on LG3, and one each on LG4 (S4_46289498), LG1 (S1_180007567) and LG5 (S5_154075820), were found to be significantly associated with IVOMD at first cut during the summer season of 2015, having the p values of 1.57E-17, 4.71E-14, 2.53E-11, 1.16E-10, 1.78E-10, and 5.59E-07, respectively. Also, at second cut, during summer season of 2016, three SNPs S7_104645663 (LG7), S6_192886095 (LG6), and S4_51491754 (LG4) with the p values of 1.06E-09, 4.16E-08, and 2.21E-07, respectively, were tightly linked with IVOMD. Similar to this study, Genome wide association study among 34,691 SNPs and each of the two traits, CP and IVOMD, at two different cutting intervals, detected 10 SNPs which were above the chosen threshold level shown in the Manhatton plots , and theOne SNP was found closely associated with CP and nine SNPs were found associated with IVOMD . The SNPBoth the single cross and top cross hybrids had wide variability for the forage linked traits across cuts. The single cross hybrids had higher forage yielding traits (TGFY and TDFY), and higher IVOMD  and lower NDF, ADF, and ADL  at first cut than top cross hybrids. The mean BPH was higher for TGFY, TDFY, and CP across cuts in single cross hybrids than top cross hybrids. Some single and top cross hybrids outperformed the commercial check hybrid for the forage yield and quality traits, these identified hybrids can be further evaluated in the multi-location trials to confirm their yield potential and stability prior to commercial release. The present study has identified the potential lines and testers for GCA effects, combined for both the forage yield and quality traits, which can offer opportunities for developing hybrids with increased forage productivity in pearl millet. Most of the forage traits across cuts in both the single cross and top cross hybrids were predominantly controlled by the non-additive gene action. No significant correlation was observed between the forage quantity and quality traits indicating that these traits can be improved independently. GWAS identified ten genomic regions associated with the forage quality traits (CP and IVOMD), and thus can be further validated, for improving the pearl millet forage quality traits through marker assisted selection. Significant genomic loci and candidate genes identified from this study lay a foundation for the development of high biomass cultivars with superior forage quality trait in the future forage breeding programs.http://dataverse.icrisat.org/dataset.xhtml?persistentId=doi:10.21421/D2/IWXCUJ.The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: SG, PG, MM, PS, and MB designed the experiments. AV, SK, AR, SS, and RV helped in data analysis. RV involved in genotyping for forage type hybrid parents. MB involved in phenotyping for forage quality traits by NIRS. PG and SK wrote the manuscript. MM and PS reviewed the manuscript. All authors read and approved the final manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The handling editor declared a past collaboration with one of the authors, RV.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.


Even with increasing interest in the ecological importance of intraspecific trait variation (ITV) for better understanding ecological processes, few studies have quantified ITV in seedlings and assessed constraints imposed by trade‐offs and correlations among individual‐level leaf traits. Estimating the amount and role of ITV in seedlings is important to understand tree recruitment and long‐term forest dynamics. We measured ten different size, economics, and whole leaf traits (lamina and petiole) for more than 2,800 seedlings (height ≥ 10 cm and diameter at breast height < 1 cm) in 283 seedling plots and then quantified the amount of ITV and trait correlations across two biological (intraspecific and interspecific) and spatial (within and among plots) scales. Finally, we explored the effects of trait variance and sample size on the strength of trait correlations. We found about 40% (6%–63%) variation in leaf‐level traits was explained by ITV across all traits. Lamina and petiole traits were correlated across biological and spatial scales, whereas leaf size traits  were weakly correlated with economics traits ; lamina mass ratio was strongly related to the petiole length. Trait correlations varied among species, plots, and different scales but there was no evidence that the strength of trait relationships was stronger at broader than finer biological and spatial scales. While larger trait variance increased the strength of correlations, the sample size was the most important factor that was negatively related to the strength of trait correlations. Our results showed that a large amount of trait variation was explained by ITV, which highlighted the importance of considering ITV when using trait‐based approaches in seedling ecology. In addition, sample size was an important factor that influenced the strength of trait correlations, which suggests that comparing trait correlations across studies should consider the differences in sample size. Using individual‐level leaf traits of seedlings, we found intraspecific trait variation was large and correlations were strong between lamina and petiole traits across and within species. Trait correlation strength varied across biological and spatial scales and with sample size. Some of these characteristics are often envisioned by ecologists as functional traits due to their effects on plant growth, survival, and reproduction  greater than 10 cm . Such trait datasets, which include multiple species and a hierarchical sampling method, provide a good opportunity to explore whether trait correlations are weaker at finer scales and influenced by trait variance and sample size. We expect that a substantial amount of ITV exists at the seedling stage because of ontogenetic differences, plastic responses to micro‐environments and because trait variation has yet to be filtered out by abiotic and biotic influences. Specifically, we ask how biological  and spatial  scales explain the amount of trait variation. These two scales are believed to structure ITV in natural systems  forest is the most common vegetation type in Northeast China, and the Liangshui Reserve has a large area of these primary and undisturbed forests. In this Reserve, we established a 9 ha (300 m × 300 m) forest dynamics plot with no disturbance and all woody plants with the diameter at breast height (dbh) ≥ 1 cm were tagged, identified, mapped, and measured (dbh)  in Northeast China. The climate is temperate continental monsoon with most rainfall in summer. The mean annual temperature is −0.3°C, and the mean annual precipitation is 676 mm  seedling plots (referred to as plots thereafter) at the intersections of a 10 m grid , lamina economics traits , petiole size traits , petiole economics traits , and whole leaf economics trait . LT was determined by a micrometer (0.01 mm) and then scanned to estimate LA. Fresh lamina and petiole were weighted by an analytical balance (0.0001 g) and then oven‐dried at 60°C to constant weight. SLA was the LA divided by lamina dry weight, and LDMC was the lamina dry weight divided by lamina fresh weight. Chlorophyll content per area was measured using the SPAD‐502 Plus meter  and multiplied by SLA to generate the Lchl (mass‐based measurement). PL was measured by a ruler (0.1 cm), and PD was determined by a micrometer (0.01 mm). SPL was calculated as PL divided by petiole dry weight, and PDMC was petiole dry weight divided by petiole fresh weight. Finally, LMR was the lamina dry weight divided by the whole leaf dry weight. There were three exceptions to the above methodology that affected how we sampled and processed leaves and trait measurements. First, some seedlings with no leaves or with only unhealthy leaves  were not sampled. Second, if the leaves were partially grazed by herbivores , we used Photoshop CS6 to green the grazed parts to generate a more accurate estimate of LA. This corrected LA values were highly correlated with the original estimates (R2 = .998). Third, for compound leaves, we used the leaflet for the trait measurements. But for LMR, we used all leaflets of a petiole. Before analyses, we excluded the observations with lamina and petiole dry matter < 0.0040 g  and petiole length ≤ 0.2 cm (262 observations). These very small observation values could bias some trait estimates because of the potentially large errors when we measured them using the analytical balance and ruler. Our final analyses included 5,185 leaves of 2,803 seedlings in 30 species in 283 seedling plots  regressions to evaluate the trait correlations illustrated in Figure R2 from SMA regression was same as the square of Pearson’ r. The trait correlations were analyzed (a) between lamina and its corresponding petiole traits; (b) within trait groups; (c) across trait groups; and (d) between LMR and other traits  and within and across plots . Sample size and trait variance were considered as weights separately in the t tests to account for their effects  and (3). Third, we combined the results of correlations at species and plot levels (cases 1 and 3) to evaluate the effects of trait variance and sample size on the strength of correlation using multiple regression. The response variable was R2 values of significant SMA regressions between traits, and the independent variables were variance of both traits, sample size, levels analyzed (species and plot levels), the interaction between trait variance and levels, as well as the interaction between sample size and levels. The trait variance and sample size were log‐transformed and standardized. All leaf traits were log‐transformed before our analyses. All analyses were performed in the R‐3.6.2  and spatial scales   was explained by ITV estimated across either biological or spatial scales. Specifically, the most used leaf trait in ecological analyses, SLA, had more than 50% of its variation explained by ITV. Trait correlations varied largely across different biological and spatial levels. The strength of trait correlation was comparable between intra‐ and interspecific levels but stronger within than across plots. These results did not support the prediction that trait correlations were stronger at broad scales compared to fine scales. For the correlations at the intraspecific and within plot levels, the sample size was the most important factor driving the strength of correlations where correlations were weaker when more individuals were sampled.4.1A large amount of variation  in seedling leaf traits were similar to previous studies that sampled only one species across a broad spatial scale   ; Formal analysis (lead); Investigation (lead); Methodology (lead); Visualization (lead); Writing‐original draft (lead); Writing‐review & editing (lead). Marc W Cadotte: Formal analysis (supporting); Investigation (supporting); Resources (supporting); Supervision (lead); Visualization (supporting); Writing‐original draft (supporting); Writing‐review & editing (lead). Guangze Jin: Conceptualization (supporting); Data curation (lead); Formal analysis (supporting); Funding acquisition (lead); Investigation (supporting); Resources (lead); Supervision (lead); Writing‐original draft (supporting); Writing‐review & editing (supporting).Supplementary MaterialClick here for additional data file.