We compared genomics‐based (GBLUP) and two pedigree‐based (ABLUP: polycross and reconstructed full‐sib [FS] pedigrees) models. Models were extended to estimate dominance genetic effects. Pedigree reconstruction revealed significant unequal male contribution and separated the 26 PX families into 438 FS families. Traditional maternal PX pedigree analysis resulted in up to 51% overestimation in genetic gain and 44% in diversity. Genomic analysis resulted in up to 22% improvement in offspring breeding value (BV) theoretical accuracy, 35% increase in expected genetic gain for forward selection, and doubled selection intensity for backward selection. Overall, all traits showed low to moderate heritability (0.09–0.28), moderate genotype by environment interaction (type‐B genetic correlation: 0.51–0.80), low to high expected genetic gain (6.01%–55%), and no significant negative genetic correlation reflecting no large trade‐offs for multi‐trait selection. Only three traits showed a significant dominance effect. GBLUP resulted in smaller but more accurate heritability estimates for five traits, but larger estimates for the wood traits. Comparison between all, genic‐coding, genic‐non‐coding and intergenic SNPs showed little difference in genetic estimates. In summary, we show that GBLUP overcomes the PX limitations, successfully captures expected historical and hidden relatedness as well as linkage disequilibrium (LD), and results in increased breeding efficiency in WRC.Western redcedar (WRC) is an ecologically and economically important forest tree species characterized by low genetic diversity with high self‐compatibility and high heartwood durability. Using sequence capture genotyping of target genic and non‐genic regions, we genotyped 44 parent trees and 1520 offspring trees representing 26 polycross (PX) families collected from three progeny test sites using 45,378 SNPs. Trees were phenotyped for eight traits related to growth, heartwood and foliar chemistry associated with wood durability and deer browse resistance. We used the  WRC is a slow‐growing species that can typically grow to a height of 60 m and diameters ranging from 150 to 300 cm on a wide variety of sites depending on light availability, elevations, soils, and moisture levels  mating design is one of the most cost‐effective breeding schemes for forest trees, in which a female parent tree (FPT) is pollinated with a pollen mix collected from a group of known males , i.e., non‐random associations of alleles, compared to other conifers . However, the traditional breeding approach in WRC, using pedigree information, cannot capture these two features. Molecular breeding and the use of Genomic‐based Best Linear Unbiased Prediction analysis (GBLUP) may provide strategies for increasing breeding efficiency and genetic gains. GBLUP will also correct for any violations from the maternal HS relationship, and the equal male contribution assumptions of the PX mating design, by using the estimated actual relationship between individuals. GS, which predicts phenotypes from genomic data  has been reported in several studies  examine deviation from the PX assumption of HS relationship and the equal male contribution, and the resulting inflation in genetic estimates; (3) use GBLUP to estimate BVs, heritability, genotype × environment (G × E), dominance effects, and genetic correlations between all traits; (4) quantify the improvement of GBLUP in BV prediction accuracy and expected genetic gain over the PX‐ and FS‐pedigrees; and (5) compare the use of different types and numbers of SNPs  and their effect on heritability.Genomic selection has also brought two major benefits to breeding, increased breeding value (BV) accuracy (when the phenotype is available) and prediction of BV at a younger age for early selection (when the phenotype is absent). In this study, we focus on the first aspect paving the way for the second. Here, we present the use of GBLUP in a WRC breeding program to detect hidden historical relatedness, capture LD, and overcome weaknesses of the PX mating design for forward selections by selecting superior recombinant offspring in maternal families. The specific objectives of the current study were: (1) Apply the 22.1We sampled a 1st generation PX progeny trial established by the BC Ministry of Forests, Lands and Natural Resource Operations and Rural Development. A total of 1000 parent trees were polycrossed with a common set of pollen parents, over an eight‐year period, resulting in seven testing series with four to seven field test sites per series. The 21 males used in the polymix were randomly selected, and the pollen was mixed in unequal proportions to consider different estimates of pollen germination rate  and tree diameter at breast height  were assessed at age 15. All trees from Jordan River and Port McNeill were found to be naturally infected with CLB, which significantly reduced the growth on these two sites Figures  and S2. 2.2.2For foliar monoterpene measurements, current year growth foliage was collected at age 15 and terpenes were quantified at the Glynn Road Analytical Chemistry Lab. Sample extraction, qualitative and quantitative analyses followed the method of Kimball et al.  with som2.2.3At tree age 18 years, increment cores were sampled at breast height (1.3 m) from 1510 trees. Trees were cored three years after sampling of foliage, allowing for a longer time for the expected later expression of wood thujaplicin and lignan compared to growth and foliar traits. Each core represented the full diameter of the tree, intercepting the pith whenever possible, yielding one radius for dendrochronological analysis and the second radius for heartwood extractives analysis. The first radius of each core was dried and sanded to reveal individual cells, scanned at high resolution (1200 DPI), and the annual rings from the bark to pith were visually cross‐dated and counted  and the 21 PX male parent trees (MPTs), note, two parent trees were used as both female and male, and one female did not survive, which reduced the number of genotyped parent trees from 47 to 44). For sequencing of SNPs, we used 0.5 g of tissue from each sample which was lyophilized for 48 h prior to DNA extraction. DNA was isolated according to Xin and Chen . The conFor sequence capture genotyping, 20,858 probes designed from genic and non‐genic regions were used to analyze the 44 parents and 1520 OTs, as described in . After initial biallelic SNP filtering for quality , missing data (sites with more than 40% missing data were removed), depth , and minor allele frequency (MAF of 0.005), hence, 51,638 SNPs were chosen with average mean depth of 31×. Reproducibility was assessed by genotyping replicated control samples and estimated to be between 91% and 96%. The 51,638 SNPs were further filtered using a minimum quality score of 30, MAF of 0.01, maximum mean depth of 90, and sites with more than 20% missing data were removed using vcftools v0.1.17  was constructed using 45,378 SNPs  with the “A.mat” function in the R package “rrBLUP”  correct any error in the female assignment by using the normality properties of the realized relationship coefficient as proposed by Munoz et al. (α = 0.001), and Linkage Disequilibrium (LD) of zero, 1,136 SNPs were retained and used in CERVUS. We run the “parent pair‐sexes known” analysis to verify the identified female and male parents using the G matrix. Two pedigree‐based additive relationship matrices (A matrices) were compared: the original PX‐pedigree (APX) and the reconstructed FS‐pedigree (AFS). Two dominance relationship matrices were compared: the corrected pedigree‐based dominance relationship matrix (Ad matrix) was computed using the R function “Amatrix”, and the realized dominance genomic relationship matrix (Gd matrix) was calculated following the method of Vitezica et al.  and broad‐sense heritability H^2, type‐B additive genetic correlation (r^B), BVs, and Akaike Information Criterion (AIC) from each model.For each trait, we fitted five individual tree models , and total number of rings used as covariates for wood traits only; rs is the vector of random replicate (block) nested within site effect; ibr is the vector of random incomplete block (set) nested within replicate (block) effect; a is the vector of random additive genetic effect (BVs) estimated from APX, AFS, or Gall matrix; sa is the vector of random interaction of site with additive genetic effects; and e is the vector of random heterogenous residuals. X and Zs are incidence matrices relating the phenotypes to the model terms. More detailed descriptions about all the random model terms and their variances distributions are available in Appendix We first fitted the following three mixed‐effect linear models  including the additive effect only:d is the vector of random dominance genetic effect estimated from the Ad or Gd matrix; and sd is the vector of random interaction of site with dominance genetic effects. The other terms are as defined in Equation  was tested in the five models using a likelihood ratio test (LRT) with one degree of freedom (df) between the full model in Equation  for additive genetic effects, as described in Appendix r^), which is the square root of reliability (the correlation between true BV and estimated BV), is used to evaluate models with complete data sets and not from cross‐validation  of FPTs, MPTs, and OTs BVs for the three additive models using the following formula:i is the BV standard error, Fi is the inbreeding coefficient for the ith individual and σ^a2 is the estimated additive genetic variance, and (1 + Fi) is the diagonal of additive relationship matrix . To make r^ comparable between the two pedigree models (ABLUP‐PX and ABLUP‐FS‐A) and the genomic model (GBLUP‐A), we fixed the values of σa2 and σsa2 in Equation  to include the dominance effect and its interaction as follows:y=Xβ+Z1rEquation . We estin Mrode, . We estiEquation .2.6y*  of the eight traits. Instead of performing 28 bivariate models to estimate the genetic correlation, we ran eight multi‐variate GBLUP‐A models  using CORGH variance structure for the additive and residual terms to get an estimate for the additive genetic correlation between the eight traits. The multivariate model is described in Appendix Phenotypic correlations were estimated as the Pearson's correlation between the adjusted phenotypes Equation , but wit2.7N = 75 trees). These estimated gains are considered the maximum possible gain without any optimal selection strategy, as no restrictions were applied for genetic diversity. Then, the status number (Ns) was used as a measure for the genetic diversity of the selected set (top 75 trees), and was estimated following Lindgren et al.'s formula Lindgren et al.  estimated mean BVs, BVs accuracy, and the expected genetic gain % from selecting the top 5% trees for each trait individually Ns=2.8G matrices  versus the corrected A matrix (AFS); (3) calculated the Pearson's correlations between all five additive relationship matrices; APX, AFS, Gall, Ggen‐cod, Ggen‐no‐cod, and Gintergen; and (4) ran three additional GBLUP‐A models to compare between the four genomic models using h^2 and AIC.SNP effects for all 45,378 SNPs were annotated using the Variant Effect Predictor , representing the realized additive genomic relationship between all parents and OTs, is shown in Figure G matrix maternal pedigree corrections and paternity assignments for all offspring with genotyped parents. As expected, it did not assign any parents for all offspring with one or both ungenotyped parents identified in the G matrix (28 OTs with no identified FPT and 77 OTs resulted from pollen contamination).The χ2] = 654.91, df = 20, p‐value < 2.2e‐16). The number of OTs/MPT ranged from 7 to 187  was significantly greater than zero in all of the five models for all eight traits (Table h^2: 0.25–0.34) followed by wood traits (h^2: 0.12–0.29), while growth traits were the least heritable (h^2: 0.07–0.17) (Table h^2 and its SE was observed between ABLUP‐PX and the other models.The additive genetic effect (ts Table . Across h^ind2 than using PX‐pedigree (ABLUP‐PX). An opposite trend was observed for foliar traits. Regardless of whether or not ABLUP‐FS‐A increased or decreased h^2 compared to ABLUP‐PX, the h^2 standard error (SE) was lower by 0.01 (DBH) to 0.05 (W.AT), reflecting more accurate estimates. The better performance of the ABLUP‐FS‐A model, relative to the ABLUP‐PX, is also reflected in the AIC estimates, where ABLUP‐FS‐A estimates were lower than ABLUP‐PX. AIC decreased by 5 (W.TT) to 71 (F.AT) units for all traits except W.AT  gave a smaller h^2 for HT, but SE in GBLUP‐A was one unit smaller, while for DBH the h^2 was one unit larger in GBLUP‐A with the same SE. Foliar traits, however, showed a decrease in h^2 (by 0.06) and SE (by 0.02–0.03) in GBLUP‐A. Surprisingly, wood traits  showed higher h^2 (by 0.03–0.05) with the same SE in the GBLUP‐A, while W.TL showed a one unit decrease in h^2 with the same SE. AIC for GBLUP‐A was smaller than ABLUP‐FS‐A (by 4–17 units) for all traits (except foliar monoterpenes trait), supporting the better performance of GBLUP‐A.GBLUP‐A and ABLUP‐FS‐A yielded the same 3.2.2σd2) was significant for W.AT, W.TT, and W.TE. Meanwhile, the genomic approach (GBLUP‐AD) yielded a significant dominance genetic effect (σd2) for HT, DBH, and W.TL (Tables σsd2) in the GBLUP‐AD model. For HT, DBH, and W.TL, H^2 and its SE were around double the h^2 and its SE. A very small decrease was observed in the h^2 (by 0.01–0.02) in the GBLUP‐AD model compared to GBLUP‐A.LRT showed that by using pedigree (ABLUP‐FS‐AD), the dominance genetic effect , followed by wood traits, while foliar traits were the lowest. ABLUP‐FS‐A, ABLUP‐FS‐AD and GBLUP‐AD gave the same G × E rank as GBLUP‐A  to 0.96 (F.AT and F.TM) Table ; howeverrs Table . Surprisrs Table . Consider^ for male BVs was very similar to that of females  in accuracy when using the FS‐pedigree compared to PX‐pedigree (Table r^ for BVs (from 0.56 to 0.85) than OTs (from 0.41 to 0.67), as expected  were observed within each trait category   to 55% (W.TL) (Table Ns estimate from the PX pedigree was compared to the corrected Ns (estimated from the FS pedigree) to estimate Ns overestimation % from the ABLUP‐PX model . The % of Ns overestimation from the ABLUP‐PX model ranged from 1% (W.TL) to 44% (F.TM)   from the ABLUP‐PX model. Finally, we compared the original gain % estimate from the ABLUP‐PX to the corrected gain % to estimate the gain overestimation % from the ABLUP‐PX model. We followed the same corrected estimates procedure from the ABLUP‐FS model (please see Table el Table . The % gel Table . After eM) Table . GBLUP‐As) Table .3.6Ggen‐no‐cod gave the highest relatedness , genic‐non‐coding , and intergenic . Intergenic SNPs showed the largest disparity in genotype proportions  are more biased by missing parental information and pedigree errors as demonstrated by lower correlation between ABLUP‐PX and ABLUP‐FS/GBLUP‐A  and genomic (GBLUP) models over the traditional PX (ABLUP‐PX) model in terms of heritabilities, G × E estimates, model fit, BV accuracy, and expected genetic gain and diversity, increases the applicability of PX mating design particularly for forward selections. Moreover, it allows for screening more parents (PX pollen donors) and increasing selection intensity for backward selection. GBLUP has the additional advantage of capturing the Mendelian Sampling Term (MST) by using the actual proportion of shared genome between individuals to estimate relationships. It also eliminates the need for paternity assignment and pedigree maternal correction. We also found that traits with lower A Tables  and S8. 4.1G matrix to correct pedigree errors was proposed by Munoz et al.  or over‐ (growth and wood traits) estimated. Moreover, G × E was underestimated in ABLUP‐PX was in agreement with another study using PX in white spruce . Thus, ABLUP‐PX model results in significantly biased genetic estimates which translate to biased genetic gain (up to 51%) and Ns (up to 44%) estimates.Several studies reported the overestimation of 4.24.2.1h^2 values for WRC. For HT growth, Cherry  for 1‐ to 3‐year‐old seedlings. Given that 73% of our PX females (19 out of 26) were selected for growth, and that low h^2 is generally observed for this trait in conifers  were expected. Other forest tree GBLUP studies reported HT and DBH h^2 ranging from zero to 0.25  of all wood traits (189%). We too found that W.TL, which is the sum of plicatic acid and its lactone, gave the highest CV (115%), when compared to other wood extractives (13%–22%). Russell and Daniels  for W.AT, W.TT, and W.TE, could be due to the fact that the G matrix might be successful in capturing linkage disequilibrium (LD) between the SNP marker and some QTLs related to these wood chemical traits. Habier et al.  could be explained by the fact that F.AT is the major monoterpene and represents ~58% of the F.TM, as has been observed in other studies  had significant G × E terms with GBLUP‐A, as opposed to ABLUP‐PX. Considering that lignans are expressed at later ages in WRC wood than thujaplicins  model compared to GBLUP‐A (h^2), there is a no or a very small decrease in the additive variance estimates , further studies in larger FS families are necessary to verify these effects. Despite doubling of heritabilities using the GBLUP‐AD  or 68 (MPT) OTs, while each OT has on average 126 HS and 3.3 FS). This could explain why parental BV SE is larger in GBLUP‐A compared to pedigree models, while having the same BV magnitude, which results in lower theoretical accuracy.The resulting smaller BV r^ as ABLUP‐PX or a small increase (0.01) for five traits but with more variation reflecting higher variability in the corrected maternal family size (12–68) relative to the original size (38–61). This outcome agrees with results reported in others studies  to female BVs, resulting in increased selection intensity for backward selection. However, these estimates showed more variation, due to paternal family size variability (7–187) compared to the corrected maternal family size (12–68). This is consistent with a maritime pine PX study  resulted in the same average FPTs BV nal size 8–61. Thi4.3.2h^2 = 0.09 from GBLUP‐A). A similar increase in GBLUP OT BV accuracy of 14% (wood density traits with larger h^2) to 25%  relative to ABLUP‐PX was reported in white spruce  relative to that of the white spruce study . This may lead us to conclude that when the family size increases, the results from ABLUP approach those of GBLUP. This is further supported by the high Pearson correlation between the BVs from the ABLUP‐FS‐A and GBLUP‐A models  showed more genetic gain improvement  compared to high h^2 foliar traits . This again supports the expectation that traits with low h^2 will benefit more from molecular breeding.In our study, we selected the top 5% from the 1506 OTs to estimate the expected genetic gain without putting any restriction on genetic diversity; however, this would have to be factored in with real selections in the breeding program. GBLUP‐A greatly improved the expected genetic gain over the PX model by 12% (F.AT) to 35% (W.AT). It should be noted that traits with low h^2 (0.14), can be justified as “W.TL” showed the highest amount of phenotypic variation with CV of 117%. However, this extremely high CV can be explained partly by the absence of selection for lignans in the study population and the late expression of lignans in the trees' lifespan  which examined the levels of acetophenone aglycones (AAs) in needles associated with spruce budworm resistance, a compound called “pungenol” showed a very similar distribution to “W.TL” with CV of 103%. In their study, pungenol showed the highest expected genetic gain of 45.4% due to its high h^2 and CV. As expected for foliar traits in our study, given their high h^2 across all traits (0.25–0.28) and that only one female of the 26 was selected for foliar traits, showed the second highest expected genetic gain of 22%–25.5%. The other five traits showed a gain range from 6.01% (HT) to 11% (W.TE). These values are expected given their lower h^2 and the presence of some background selection for these traits already in the study population. Because no previous studies have reported expected gains in WRC, here we compare expected gain estimates for HT and DBH to two studies in 38 PX (N = 892) and 136 FS (N = 1516) families in white spruce that used the same gain calculation method  ranged from 9.43 to 11.85). Here, the gain ranged from 6.01% to 7.44% , similar to those reported before despite the smaller h^2 and number of families. In addition to having a different species, these comparable genetic gain estimates may be also due to our greater selection intensity (N = 1520 vs. 892 in PX spruce). Beaulieu et al. (N = 598), which they found to have higher h^2 and expected genetic gain compared to growth traits, affirming the results of our study.Given the outperformance of GBLUP‐A over the pedigree models, and that it gives the highest expected gain for all traits, this model was used for comparison of genetic gains. The very high estimate of expected genetic gain for “W.TL”, in spite of its small u et al.  also exa4.4G matrices is significant or not. Given the reported inbreeding in western redcedar from previous studies  while intergenic showed the lowest (23%). The opposite trend is generally expected given the fact that heterozygosity is expected to be higher in non‐coding regions; however, targeted sampling of probe regions with variation results in non‐random sampling and so these estimates do not represent the true distribution of heterozygosity across the genome. Considering the relationship coefficient, it is hard to say if this observed difference across the four h^2 and AIC leads to the conclusion that there is no common pattern across the four G matrices to identify the best SNP set. This trait‐specific pattern in term of numbers and types of SNPs was observed by Tan et al.  overcame the limitation of PX mating design for forward selection, increased selection intensity and accuracy for backward selection, and increased the expected genetic gain. Moreover, it eliminates the need of pedigree reconstruction, all of which increases breeding efficiency.Considering that our study population contains a degree of PX family selection for growth and wood traits, we can conclude that a selected advanced breeding population, accompanied with reduced genetic diversity, and low heritability traits in general could benefit more from molecular breeding in future generations.In summary, all traits show low to medium genetic control, acceptable levels of G × E, and no evidence of trade‐off in the expected genetic gain from the three traits  categories. This calls for more studies to investigate the possibility of using growth and total thujaplicins for the indirect selection of the later expressed lignans. As indicated earlier, GS techniques for predicting genetic gains in later‐expressed wood quality traits, such as that of thujaplicins and lignans, would be a cost‐effective approach for improving such traits, because they are difficult to be selected for in the normal time frame of tree breeding operations. Operationally, the next step would be to perform different multi‐trait selection scenarios using the selection index, or independent culling scenarios and look for trees with potential positive gains in all traits. This will result in the greater yield of these secondary metabolites responsible for deer browsing and heartwood rot resistance, as well as faster growing trees. It is worth mentioning that applying GS for multi‐trait early selection operationally in the WRC breeding program, at the seedling phase, is currently in progress.The authors declare that they have no conflict of interestAppendix S1Click here for additional data file.Table S1Click here for additional data file.Figure S1Click here for additional data file.


The selection based on multiple traits enhances the crop cultivars merit to farmers. In this regard, 19 breeding lines as well as two commercial cultivars were studied using a randomized complete block design (RCBD) with three replications in three locations during the 2020–2021 growing season. In this study, to identify the association among different traits and to select the best rapeseed lines based on multiple traits, genotype × trait (GT) and genotype × yield × trait (GYT) biplot analyses were used. The results showed that using GYT biplot is more efficient than GT biplot. Based on the GYT biplot and superiority index (SI), the breeding lines G16 and G18 were considered as superior genotypes in combination with the agronomical traits, that is, 1000‐seed weight, number of seeds per pod, number of pods per plant, number of lateral branches, plant height, and pod length with seed yield, which represents a genetic gain in rapeseed breeding program. Based on seed yield combination with phenological traits (early maturity), the breeding line G15 was selected as the best one. Moreover, the line G2 was defined as the superior one in combination of seed yield with pod length. The results indicated that there is a potential for simultaneous genetic improvement of the characteristics  in rapeseed. Generally, the graphical method of the GYT biplot represented an efficient and practical new way to recognize superior genotypes based on multiple traits in rapeseed breeding programs. In this study, to identify the association among different traits and to select the best rapeseed lines based on multiple traits, genotype × trait (GT) and genotype × yield × trait (GYT) biplot analyses were used. The results showed that using GYT biplot is more efficient than GT biplot. Generally, the graphical method of the GYT biplot represented an efficient and practical new way to recognize superior genotypes based on multiple traits in rapeseed breeding programs. The GT biplot has previously been used in different crops to determine the association between different traits and genotypes such as rapeseed  to examine the association between different traits and (2) to select superior rapeseed lines based on the combination of agronomic traits with SY.It is very difficult to develop superior genotypes considering all studied traits. For this purpose, the GYT biplot method recently has been introduced by Yan and Frégeau‐Reid  for the 2In this study, 19 new rapeseed lines along with Dalgan and RGS003 cultivars were evaluated at three locations with different climates using a randomized complete block design (RCBD) with three replications during growing season 2020–2021). The names and origins of the genotypes are given in Table 020–2021.2.1The data were first analyzed for normality test by Shapiro–Wilk test method using SPSS 19 software SPSS, . The mea2.2The data were graphically analyzed using the first two principal components derived from singular value decomposition (SVD). The GT biplot was carried out by GGEbiplot software  to investigate the interrelationships between different traits and (2) to select superior rapeseed lines based on the yield–trait combinations.33.1Data presented in Table 3.2One of the most useful applications of GT biplot is the polygon view which led to identify the genotypes containing one or more traits with the highest value. The vertex genotype in each sector of the polygon view is the best one in the test trait(s) that falls within that particular sector. According to Figure 3.3The vector view of biplot is one of the GT biplot applications to examine the association between environments. In this vector view, each line connect trait to the origin, called the vector. The angle between the vectors of traits shows the correlation coefficients between traits. If the angles of the two traits vectors are <90° and >90°, it indicated a positive and negative correlation, respectively. Also, if the angle is near 90°, it showed that is no relationship. According to Figure 3.4Principal component analysis indicated that the first and second principal components were accounted for 91.0% of the total variation in the yield–trait combinations data Table . The fir3.5Figure 3.6According to the vector view of the GYT biplot Figure , a posit3.7A ranking biplot that compare genotypes with an ideal genotype is represented in Figure 3.8According to the average tester coordinate (ATC) view of the GYT biplot, lines G16 and G18 followed by G15 were defined as the superior ones in terms of all yield–trait combinations, while the weakest line was G8 Figure . The ran4Oilseeds breeding and cultivation are necessary to increase yield in Iran because the major percent of vegetable oil is imported to the country. Rapeseed is one of the most important oilseed plants that is widely adapted to the climatic conditions of Iran . Also, the line G2 was the superior one in SY combination with PL. In contrast, G8 was ranked the poorest. Selection of the superior genotypes based on multiple traits could enhance the value of crop cultivars to farmers. Hence, the breeding lines G16, G18, and G15 can be recommended for cultivation. Also, according to the GYT biplot, there was a positive correlation between all yield–trait combinations. This is an advantage of the GYT biplot method in comparison with the GT biplot. The other benefit of the GYT biplot is that it could be utilized in excessive features identification that decrease costs in traits measuring of field experiments without compromising accuracy. Therefore, a high positive relationship between NLB and NPP shows that one  of these traits can be used for the selection criterion. Similarly, the high relationship between PH, NSP, DFS, DEF, and DPM indicates that one  of these traits is a suitable selection criterion.5This study is unique, in that the GYT biplot method is used as a comprehensive and effective method for the evaluation and selection of rapeseed lines based on multiple traits. The results indicated that genotype selection could be possible considering multiple traits that led to enhance genetic material in rapeseed breeding programs. The breeding lines G16, G18, and G15 ranked as the superior ones in the combination of all agronomical traits with SY, showing a genetic gain in the rapeseed breeding program. Also our results revealed simultaneous potential genetic improvement of the traits  in rapeseed.This research received no specific grant from any funding agency in the public, commercial, or not‐for‐profit sectors.The authors declare no conflict of interest.This study does not involve any human or animal testing.Written informed consent was obtained from all study participants.Appendix S1Click here for additional data file.


Methods of multivariate analysis is a powerful approach to assist the initial stages of crops genetic improvement, particularly, because it allows many traits to be evaluated simultaneously. In this study, heat-tolerant genotypes have been selected by analyzing phenotypic diversity, direct and indirect relationships among traits were identified, and four selection indices compared. Diversity was estimated using K-means clustering with the number of clusters determined by the Elbow method, and the relationship among traits was quantified by path analysis. Parametric and non-parametric indices were applied to selected genotypes using the magnitude of genotypic variance, heritability, genotypic coefficient of variance, and assigned economic weight as selection criteria. The variability among materials led to the formation of two non-overlapping clusters containing 40 and 154 genotypes. Strong to moderate correlations were found between traits with direct effect of the number of commercial fruit on the mass of commercial fruit. The Smith and Hazel index showed the greatest total gains for all criteria; however, concerning the biochemical traits, the Mulamba and Mock index showed the highest magnitudes of predicted gains. Overall, the K-means clustering, correlation analysis, and path analysis complement the use of selection indices, allowing for selection of genotypes with better balance among the assessed traits. Fragaria × ananassa Duchesne) are popular fruits used for fresh or processing markets3. In 2020, strawberry production was 9 million tons, with China and United States contributing to more than 50% of this world production4. The most common strawberry cultivars in the world are often poorly adapted to tropical conditions as they are mainly developed by breeding programs in temperate regions, such as the United States, Spain, and Italy5. In South America, most strawberry seedlings are grown in nurseries of Argentina and Chile, significantly raising the cost of production in countries like Brazil, where strawberries are grown in 4,300 hectares and more than 75% of seedlings are imported from these nurseries4.World widely grown, strawberries  and the index (I). The H is a linear combination of the analyzed traits, pondered by a coefficient established by the economic weights previously assigned to each trait. The I consist of a linear combination of the “x” values of each trait, pondered by a coefficient to be estimated. The base index19 is established by the linear combination of the average phenotypic values of traits pondered directly by their respective economic weights. The index is estimated by: j is the mean of the jth trait and pj the economic weight. As for the non-parametric indices, the sum of ranks from Mulamba and Mock20 and the genotype-ideotype21 indices were used. The index of Mulamba and Mock ranks genotypes in relation to each trait individually, assigning absolute values, according to the classification direction determined by the breeder, from highest to lowest, or vice versa, depending on how this direction favors the genetic improvement. The values assigned in the classification of each trait are added, resulting in the selection index. The genotype-ideotype index21 estimates the distance of the evaluated genotypes in relation to an ideotype previously defined by the breeder. The first step is to identify the favorable value for improvement based on the average, maximum, and minimum values informed by the statistical computer program. This favorable value, called optimal value (OVj), must be within an upper (UL) and lower (LL) limit for each trait (LLj ≤ Xij ≤ ULj). The OVj is corrected by a constant concerning the depreciation of the genotype average, Cj (Cj = ULj−Lj), resulting in the Yij value. This process guarantees that any value of Xij that is outside the optimal range is not selected. Subsequently, the Yij values obtained with the transformation are standardized and pondered by weights previously assigned by the breeder for each trait. The OVj for each trait is standardized and pondered.This study focused on the classic parametric index presented by Smith22, passion fruit23, soybean25, sweet potato26, among others. Nevertheless, the use of indices in the strawberry genetic improvement process has been just recently shown in the literature13 and still needs to be better understood. The objective of this study was to evaluate and select intraspecific strawberry genotypes, to assess their phenotypic diversity, to compare different selection indices, and determine the direct and indirect relationship among yield and biochemical traits, using multivariate analysis methods.Overall, studies have shown that applying the aforementioned selection indices, which associate information from various characters, increases the success rate in crop improvement programs, including alfalfaThe optimal K value for the population was determined to be 2 according to the Elbow Method Fig. . Two clup < 0.05) among the 21 pairs of traits evaluated  ranged from 366 to 386% (Table 2 (Table 2 (86%) and GCV (81%), followed by the Genotype-Ideotype index, under the criteria h2 (79%) and GCV (75%). The Smith and Hazel index, despite having shown the greatest gains for yield traits, showed the lowest gains of biochemical traits, in which the four indices under the four criteria selected 53 genotypes .According to Dindex Fig. , genotyp5. Nonetheless, these imported cultivars have the potential to be explored in intraspecific crosses aiming to express the existing variability in the species27.Brazilian strawberry production depends almost entirely on cultivars developed in foreign breeding programs that, due to aspects related to genotype × environment interactions, may present lower yield, lower biochemical quality, greater susceptibility to pests and diseases, increasing production costs28. Strawberry also harbors millions of DNA variants of the subgenomes of the species that gave rise to actual strawberry fruit29. In general, strawberry presents great variability in hybrids obtained from crosses, which favors the selection of new cultivars30. Significant variability with identification of superior hybrids has been found in phenotypic analyses for yield and physicochemical traits in populations obtained from crosses between commercial strawberry cultivars in Brazil33. In addition, genetic studies with hybrids and commercial cultivars based on molecular markers have shown that the germplasm of the Brazilian strawberry breeding program has genetic variability and divergence; therefore, it has a high potential for launching new cultivars34.Strawberry is an octoploid species that has gone through various levels of ploidization throughout evolutionary history35 found a direct effect of the total number of fruits (0.81), and an indirect effect of the mass of commercial fruits, via the total number of fruits (0.71), while the average fruit mass showed a direct relationship of 0.22. Authors results corroborate with our study and these positive findings suggest that direct selection via number of commercial fruits has a greater effect on yield and indirectly benefits the average mass of commercial fruits.For the population analyzed in this study, the Elbow method established two clusters, which presented no overlap in the K-means clustering, showing variability in the analyzed population and complete dissimilarity between the two groups formed. The highest phenotypic correlation for the independent variable mass of commercial fruits (MCF) was obtained for the number of commercial fruits (NCF) (0.96), which had a high direct effect (0.88) in the path analysis. The average mass of commercial fruits (AMCF), which also had a medium and positive correlation (0.55) with the MCF, demonstrated in the path analysis that its indirect effect (0.29) on NCF is superior to the direct effect (0.25). Diel et al.6. In the present study, Ratio showed a moderate and positive phenotypic correlation with the mass of commercial fruits (0.52) and number of commercial fruits (0.53); however, when unfolding this correlation, a negative direct effect was observed, while the indirect effect was positive via NCF. In agreement with the present study, Diel35 found a negative direct effect (− 0.10) and a positive indirect effect (0.15) of Ratio via the total number of fruits on the total fruit mass. Direct effects of the number of strawberry fruit on production per plant were also reported by Ara et al.36 and Garg11, while Sighn et al.37 stated that the greatest direct positive effects came from flower number and fruit length. These results evince that the selection of strawberry genotypes for mass of commercial fruits can be directly performed via the number of commercial fruits and that genotypes with numerous fruits, but of medium size, tend to have a better Ratio than genotypes with large fruits.The balance between soluble solids and titratable acidity (Ratio) represents the equilibrium between sweetness and acidity. This balance combined with aroma and other biochemical traits makes up flavor, which has great importance in sensory perception and consumer preference10. The use of selection indices, both parametric and non-parametric, has been useful to identify more balanced hybrids of diverse crops, such as sweet potato26, alfalfa38, soybean40, potato41, maize42, acai43, passion fruit44, and, more recently, strawberry27.Selecting genotypes that balance yield and biochemical traits simultaneously is a complex task45 recommend the use of statistics obtained from the analysis of experimental data as economic weights (EW) since it relates to the genotypic variance, they are dimensionless and maintain a certain proportionality among the evaluated traits. In the present study, the greatest gains for yield traits were obtained by the Smith and Hazel index (330.14%); however, it showed no difference between the statistical criteria or assigned weights. Contrarily, the greatest gains for the biochemical-related traits were obtained by the Mulamba and Mock and Genotype-Ideotype indices, under the criteria of h2 and GCV with 86.34% and 81.06%; 79.41% and 74.87%, respectively. Vieira et al.27, evaluating strawberry genotypes, also reported the greatest increments for yield traits with the Smith and Hazel index and for biochemical characteristics applying the Mulamba and Mock index. It occurs because parametric tests use the distribution parameters to calculate the statistics, while non-parametric tests use ranks assigned to ordered data and are uninfluenced by the probability distribution of the data evaluated46. Thus, the non-parametric Mulamba and Mock index is less sensitive, mathematically, to traits that present wide variance, such as number of fruits.In the present study, the Mulamba and Mock and Genotype-Ideotype indices were more sensitive to the use of different criteria, showing greater differences between gains. Cruz et al.47 analyzed studies in several agronomic crops and concluded that, if the population is large enough, any selection index applied judiciously is useful for the simultaneous improvement of multiple traits, regardless of the method used. Nevertheless, the indices start to select different hybrids for the different criteria with the progress of positions.From the 194 genotypes analyzed, 28 were selected for all indices, under all criteria, in which 27 belong to group 1 of the K-means clustering. The use of different indexes and criteria tend to present very similar results for the initial positions of the selected genotypes. Bernardo et al.28 identified the best hybrids for yield traits in the cross between ‘Camarosa’ × ‘Aromas’. Camarosa has been reported as a highly productive cultivar, with large, firm, and tasty fruits48, being one of the most planted short-day cultivars in the world49. The presence of large number of favorable alleles in ‘Camarosa’ and ‘Aromas’33 and their high productive potential51 make them promising parents for strawberry breeding programs5. Camargo et al.32 also found and selected the best hybrids coming from the crosses ‘Camarosa’ × ‘Aromas’ and ‘Camarosa’ × ‘Sweet Charlie’, concerning biochemical traits.The crosses with the highest number of selected hybrids were ‘Camarosa’ × ‘Aromas’ and ‘Camarosa’ × ‘Sweet Charlie’. Similarly, Galvão et al.The dendrogram generated from the 53 selected genotypes led to the formation of five groups, demonstrating that this population still has variability that can be further investigated.K-means clustering, correlation analysis, and path analysis complement the use of selection indices, leading to the selection of hybrids with better balance between yield- and biochemical-related traits in strawberry. This combined approach is more promising than the direct selection based on only one or a few traits. Furthermore, the multivariate analysis methods were efficient in selecting strawberry genotypes for multi-characters.2 and GCV. The crosses with the highest number of selected hybrids were ‘Camarosa’ × ‘Aromas’ and ‘Camarosa’ × ‘Sweet Charlie’. The selected population of 53 hybrids still has variability with potential to be exploited.The number of commercial fruits was more relevant to the mass of commercial fruits than the average mass of commercial fruits. Therefore, NCF is a trait of greater importance for the selection of strawberry genotypes aiming at yield. The Smith and Hazel index showed the greatest gain for yield traits. Possibly because it is mathematically more influenced by characteristics with greater variability such as yield. The Mulamba and Mock and Genotype-Ideotype indices, both non-parametric, showed the highest estimated gains for biochemical traits under the criteria of hThe material and methods of our study was performed in accordance with the relevant guidelines and regulations. Plant material and replications followed the regulations of the Ministry of Agriculture, Cattle and Supplying of Brazil.52. Hybridization was performed following Chandler et al.53. The choice between cultivars to carry out the crosses to obtain segregating populations was based on the genetic dissimilarity study carried out by Morales et al.34. After crossing, achenes present in the fruits were removed and germinated in vitro, as described by Galvão et al.31. At 60 days after germination, the seedlings were transplanted to 72-cell polypropylene trays containing biostabilized substrate. A total of 2000 plants (about 200 seedlings per population) were transplanted to low-tunnel-covered beds in an augmented block design. Seedlings were transplanted in April 2015 and the genotypes evaluated until November of the same year. Based on agronomic , phytosanitary , firmness traits and distribution of production on cycle, 194 genotypes were selected, grown in the greenhouse, cloned, and transplanted to the experimental field. Strawberry runners were transplanted in trays with substrates to obtain seedlings in sufficient numbers to be used as replications.Ten populations were obtained from biparental crosses among strawberry cultivars traditionally grown in South America Table . All par54. The soil is classified as a typical dystroferric Bruno Latosol55.The experimental area is located in the city of Guarapuava, Paraná, Brazil (25° 23′ 36″ S and 51° 27′ 19″ W). The area has a humid mesothermal subtropical climate, type Cfb, with moderate winter and summer with average temperatures around 22 °C according to the Köppen's classificationSeedlings were obtained from the stolons emitted by the parent plants, kept in a greenhouse. Rooting took place in 46-cell polypropylene trays filled with commercial substrate. At 50 days after planting, seedlings were transplanted in the experimental area, with evaluations occurring between May and November 2016.Strawberry transplanting was performed in a low-tunnel system 0.8 m high with beds 1 m wide × and 0.25 m high surface covered with a black polyethylene film 30-µm thick. To cover the tunnels, 120-µm thick transparent polyethylene film was used. The plant spacing was 0.30 × 0.30 m between plants and 0.40 m between rows.−1 of simple superphosphate, 250 kg ha−1 of potassium chloride, and 295 kg ha−1 of urea, based on the soil chemical analysis in accordance with the recommendations for the strawberry crop52. Nutritional replacement was performed via fertigation twice a week. Irrigation water was provided using a micro-drip system and followed the crop water demand. Additionally, for phytosanitary preventive control, applications of Thiamethoxam and Azoxystrobin + Difenoconazole were carried out. Strawberry fruits were harvested at maturity stage when 75% of fruit were red.Beds were fertilized with 1,650 kg ha1 experimental hybrids and two commercial controls .The experiment was conducted using the randomized block design with three replications and ten plants per plot. There was a total of 194 F−1), number of commercial fruits (NCF) (fruit plant−1), average mass of commercial fruits (AMCF) (g fruit−1), ratio between soluble solids (SS) (Brix°) and titratable acidity (TA) (g citric acid 100 g−1 pulp (Ratio), total pectin , ascorbic acid content (AA) (mg ascorbic acid 100 g−1 pulp), and anthocyanin content (ANT) (mg cyanidin-3-glucoside 100 g−1 pulp).The biochemical traits were assessed in samples of commercial ripe strawberries (above 10 g), stored at − 2 °C right after harvest. Strawberries were thawed, crushed, and homogenized. Using the homogenized pulp, soluble solids content was measured with an Optech bench refractometer. Titratable acidity was determined by the titration method, with aliquots of 10 g of strawberry pulp plus 100 mL of distilled water 0.1 mol L−1NaOH standard solution up to pH 8.2, which corresponds to the turning point of phenolphthalein56. The total pectin was determined by the method described by McCready and McComb57, and calorimetrically determined while using the carbazole reaction, according to the methodology that was described by Bitter and Muir58. Ascorbic acid was obtained by the standard titration method of the Association of Official Analytical Chemists (AOAC), modified by Benassi and Antunes59. Whereas anthocyanin was determined by the differential pH method described by Giusti and Wrosltad60, with adaptations for strawberry. All biochemical analyses were performed in triplicates.Traits that showed significant differences in the analysis of variance were used in the further analyses, namely: mass of commercial fruits (MCF) (g planthttp://cran-rc3sl.ufpr.br). First, the number of clusters was determined by the Elbow Method using the factextra v.1.0.7 package61. A graph was used to indicate the ideal cluster number to represent a data set, where the value of “K” to be used is the point of the curve that looks like an elbow (inflection). Subsequently, the K-means non-hierarchical cluster analysis was performed based on the Euclidean distance, with the stats R Core Team62, dplyr v.0.8.563, ggplot264, and ggfortify65 packages. The relationship between traits was performed using a Pearson correlation map thoughtout the corrplot v.0.84 package66, while a path analysis was performed with agricolae v.1.3-267. The fenotypic correlations were classified as high (r > 0.66), medium (0.33 < r < 0.67) and low (r < 0.33)68.The variability of the 194 genotypes/hybrids and the two controls was analyzed using the R software (70 to estimate genotypic variance (GV), heritability (h2), and genotypic coefficient variation (GCV). Economic weights (EW) were assigned  vector of the economic weights assigned to the traits18.The genotypic aggregate (H) in the classic Smith-Haze index it is obtained by the expression j is the rank of an individual in relation to the j-th variable, and “n” is the number of traits considered in the index. This procedure allows the ranking order of traits to have different weights, as specified by the breeder. Thus, we have that j being the economic weight attributed by the breeder to the j-th trait20.This index of Mulamba and Mock is obtained by the expression: DGI = √1/n Σ(yij − voj)2. The best genotypes were identified, and selection gains were estimated based on IDGI. Based on the values of the ideotype (Yij), the principal components analysis was performed to obtain the eigenvalues and eigenvectors associated with the correlation matrix between the analyzed variables. The distances of the genotypes in relation to the ideotype were estimated. This process allows the selection of genotypes closer to the optimal pattern defined by the breeder (ideotype)21.To obtain the genotype-ideotype index, the values that will express the distance between genotypes and the ideotype are calculated by the expression: I19 were estimated with the following expression: SG (%) = 100 h2 (Xs − Xo)/Xo, where Xs is the average genotypic value of selected hybrids, Xo is the average genotypic value of all hybrids, and h2 is the heritability of the trait of interest. Heritability was obtained by the ratio between genotypic and phenotypic variance, as 19.Selection gains [SG (%)] in the base index71 to generate a circular hierarchical dendrogram created with all selected hybrids and controls, in all parameters and indices using the R packages vegan v.2.5–672, for the standardization of data, ape v.5.073, and cluster v.2.1.074.Lastly, the optimal number of clusters was identified by Dindex index with R package NbClust


Cassava brown streak disease (CBSD) is a major threat to food security in East and central Africa. Breeding for resistance against CBSD is the most economical and sustainable way of addressing this challenge.This study seeks to assess the (1) performance of CBSD incidence and severity; (2) identify genomic regions associated with CBSD traits and (3) candidate genes in the regions of interest, in the Cycle 2 population of the National Crops Resources Research Institute.A total of 302 diverse clones were screened, revealing that CBSD incidence across growing seasons was 44%. Severity scores for both foliar and root symptoms ranged from 1.28 to 1.99 and 1.75 to 2.28, respectively across seasons. Broad sense heritability ranged from low to high (0.15 - 0.96), while narrow sense heritability ranged from low to moderate (0.03 - 0.61). Five QTLs, explaining approximately 19% phenotypic variation were identified for CBSD severity at 3 months after planting on chromosomes 1, 13, and 18 in the univariate GWAS analysis. Multivariate GWAS analysis identified 17 QTLs that were consistent with the univariate analysis including additional QTLs on chromosome 6. Seventy-seven genes were identified in these regions with functions such as catalytic activity, ATP-dependent activity, binding, response to stimulus, translation regulator activity, transporter activity among others.These results suggest variation in virulence in the C2 population, largely due to genetics and annotated genes in these QTLs regions may play critical roles in virus initiation and replication, thus increasing susceptibility to CBSD. Manihot esculenta Crantz) provides the third largest source of calories after maize and rice. The large starchy roots and edible leaves provide food for more than 800 million people   . The lowaxicola)  which maaxicola) .Rapid advances in next-generation sequencing (NGS) and statistical methods have created a platform for implementing modern breeding techniques like marker assisted selection (MAS) and genomic selection (GS) in cassava breeding. Large investments have been made to implement GS which predicts quantitative traits that are often expensive to phenotype using DNA markers across the genome . The abiOther studies have used biparental populations  and haveThe National Crops Resources Research Institute (NaCRRI) in Uganda was one of the first African cassava breeding programs to implement genomic selection (GS) for routine breeding of traits with economic importance . ThroughThe specific objectives are (1) evaluate CBSD trait variability in the C2 population, (2) establish phenotypic and genotypic correlations of CBSD traits and (3) identify genomic regions associated with CBSD traits in univariate and multivariate GWAS analyses to guide marker development for routine breeding, and (4) provide information on the functional annotated genes in the GWAS regions of interest. The results from this study will add to the existing knowledge especially on the genetic architecture of CBSD, providing insights that will be leveraged in breeding for resistance against cassava brown streak viruses.The cycle two (C2) population of genomic selection was developed at the National Crops Resources Research Institute, Uganda. It incorporated two clonal evaluation trials (CETs) that were planted in two locations in 2019/2020 and 2020/2021. Briefly, the C2 population resulted from successive cycles of selection and hybridization of clones selected based on genomic estimated breeding values (GEBVs) from the cycle zero (C0) and cycle one (C1) populations . Ninety-CETs were established in Serere and Namulonge in an augmented incomplete block design with three check varieties  planted in each block. Each plot was made up of ten plants that were planted in a single row with 1m spacing both within and between rows. Spreader rows of TME204 were also included in the CETs to increase disease pressure across both environments. These environments are associated with high CBSD disease pressure, mixture of both viruses and ‘superabundant’ whitefly populations . NamulonWe used the 1-5 visual scoring scale  for bothhttps://www.diversityarrays.com/technology-and-resources/dartreseq/). A total of 28,434 markers were called and these were combined with another imputed genotype dataset that consisted of common SNPs between DArTseq and GBS sequencing platforms  bringing the SNPs to 51,865. Combining both marker datasets improved SNP coverage. To increase the association power and account for the possibility of sequencing error, an additional filtering step was performed on the combined marker dataset to remove genotypes with >10% and SNPs with >5% missing data or with minor allele frequency of less than 5%. A total of 30,846 SNP markers were obtained after filtering and for downstream analyses, SNP markers were converted to the dosage format of 1, 0, -1, which represented alternative allele homozygotes, heterozygotes, and reference allele homozygotes, respectively.Two young top leaves were collected from each seedling of interest, folded, punched using a 5mm hand puncher and placed in 96-well plates. DNA extraction, Genotyping-by-Sequencing and SNP calling were carried out for each sample using DArTseq genotyping platform (lme4 package in R (Two linear mixed effects models were fitted using age in R :ijc = μi:c + gi:c + βj + ri:c(j) + ϵij Full modelyijc = μi:c + gi:c + βj + ϵij Reduced modelyijc was a vector of phenotypic data, μi:c were fixed effects for the three checks and the population mean of the experimental clones with i indexing the checks and c indicating whether yijc is a check or an experimental clone. gi:c are random effects of genotypes i with gi ~ N  are random effects of genotypes nested within year-location-incomplete block combination assumed to have a distribution of ri:c(j) ~ N 2 + gi:c2 was the genotypic variance, σri:c(j)2 variance of genotypes nested within the year-location-incomplete block combination and Where yEMMREML package (Narrow sense heritability was estimated using the function emmreml in the  package  in R.i:c a + Zj b + Zi:c(j) c + ϵijy = μ + Za2), K is the kinship matrix. Vector b and the corresponding Z matrix represented year-location-incomplete block combination with a distribution of b ~ N . Vector c and the corresponding Z matrix represented genotypes nested in year-location-incomplete block combination with a distribution of c ~N , and visualization of the correlation matrices was done using the ‘corrplot’ R package  were evaluated based on phenotypic values, BLUPs, and GEBVs. All analyses were performed using the  package .In the first stage of genomic analysis, deregressed BLUPs were calculated from BLUPs obtained in the full model using the formulagi:c2 variance of the genotypes. Deregressed BLUPs were used to perform univariate and multivariate GWAS for CBSD traits using GEMMA version 0.98.4 with default parameter settings applied  from those of the reduced model for all CBSD traits Table 2.Broad sense heritability estimates for CBSD traits ranged from low to high, 15% to 96% in the combined and year-location specific datasets Table 4.The magnitude of phenotypic and genotypic correlations varied across CBSD traits Table S1via a marker inferred Kinship matrix  was close to the expected -log10 at -log10< 2.0 but at the tail of the distribution the dots deviated from the observed value thus indicating that significant associations were identified  hydrolyzing ATP, (2) interacting with molecules, (3) catalyzing reactions, (4) initiating, activating, perpetuating, or terminating polypeptides synthesis in ribosomes and (5) directed movement of molecules between and within cells. Protein functions identified include (1) modifying DNA, (2) processing and metabolizing RNA, (3) unfolding polypeptides, (4) binding chromatin, (5) forming flexible frameworks for cells to provide attachment points and (6) communication between cells. In addition to regulating transcription of specific sets of genes, docking or fusion of vesicle to cytoplasmic membrane, conversion of small molecules to other forms, covalent modification of proteins, and translation of mRNA to proteins. Three pathway groups were also identified based on the PANTHER classification system that were shown to induce targeted degradation by proteasome machinery thus regulating various protein functions for virus replication and pathogenesis . The mecThis study characterized CBSD in the C2 population. Genotypes explained a large proportion of phenotypic variance with little influence from the environment and genotype by environment interactions. This makes this population a great resource for association mapping. Heritability and correlation estimates were positive and ranged from moderate to high. Observed heritability could be leveraged to reduce costs in the breeding process through indirect selection mainly for CBSD foliar symptoms. This study also identified three genomic regions in univariate analysis on chromosomes 1, 13, and 18 and these were linked to CBSD foliar severity at 3MAP and annotated genes in these regions have been shown to enhance susceptibility to disease. These regions were consistent both in univariate and multivariate GWAS. Identification of these associations is a first step towards pinpointing SNP markers/genomic regions that could be leveraged in developing markers that will be used in marker assisted selection or genomic selection to improve selection efficiency for cassava brown streak disease breeding, thus enhancing food and economic security in Sub Saharan Africa.https://cassavabase.org/, https://cassavabase.org/breeders/trial/6707, https://cassavabase.org/breeders/trial/7071, https://cassavabase.org/breeders/trial/7795, https://cassavabase.org/breeders/trial/7746.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: LN and KR conceived and designed the study. MK and KR collected data. LN and AO performed data analysis. LN wrote the manuscript. LN, AO, KR, MK, and J-LJ reviewed and revised the manuscript. All authors contributed to the article and approved the submitted version.


The Barker Hypothesis posits that adverse intrauterine environments result in fetal growth restriction and increased risk of cardiometabolic disease through developmental compensations. Here we introduce a new statistical model using the genomic SEM software that is capable of simultaneously partitioning the genetic covariation between birthweight and cardiometabolic traits into maternally mediated and offspring mediated contributions. We model the covariance between birthweight and later life outcomes, such as blood pressure, non-fasting glucose, blood lipids and body mass index in the Norwegian HUNT study, consisting of 15,261 mother-eldest offspring pairs with genetic and phenotypic data. Application of this model showed some evidence for maternally mediated effects of systolic blood pressure on offspring birthweight, and pleiotropy between birthweight and non-fasting glucose mediated through the offspring genome. This underscores the importance of genetic links between birthweight and cardiometabolic phenotypes and offer alternative explanations to environmentally based hypotheses for the phenotypic correlation between these variables.The online version contains supplementary material available at 10.1007/s10519-022-10116-9. There is a robust and well-documented relationship between lower birthweight and higher risk of cardiometabolic diseases like type 2 diabetes (T2D) and cardiovascular disease in later life. The Barker Hypothesis posits that adverse intrauterine environments result in fetal growth restriction and increased future risk of cardiometabolic disease through developmental compensations . Evidencin utero as a consequence of nutritional deficiency causes long term developmental compensations that result in increased risk of cardiometabolic disease in later life), whereas the EGG study confirmed a major role for genetics in the genesis of this relationship.In late 2016, the Early Growth Genetics (EGG) Consortium published a GWAS of birthweight using the UK Biobank (UKBB) and several birth cohorts from around the world . This stin utero environment). Indeed, in order to properly understand the meaning of the Horokoshi et al. (2016) [Despite these surprising findings, the Horokoshi et al. (2016)  results . (2016)  results,Two years later, Warrington et al. published one such method based on structural equation modelling (SEM)  and applNevertheless, whilst these results are interesting, they are not definitive. Although the authors were able to partition genetic effects on birthweight into maternal and offspring genetic effects, they did not do the same for cardiometabolic traits , whose GThe HUNT is a large population-based health study of the inhabitants of Trøndelag County in central Norway which commenced in 1984. A comprehensive description of the study population has been previously reported . Approxihttp://genome.ucsc.edu) using BLAT [http://csg.sph.umich.edu/chaolong/LASER/) [http://genome.sph.umich.edu/wiki/Minimac3) [Genotyping, quality control and imputation in the HUNT study have been described in detail elsewhere . In shoring BLAT . Ancestr/LASER/) , 22, usi/LASER/) . The res/LASER/) . Imputatinimac3)  with definimac3) .Identification of genotyped mother-offspring pairs has previously been described in detail . In shorsoftware  which usIndividuals’ own birthweight and mothers reporting offspring birthweight were available for individuals in HUNT after linking with the Medical Birth Registry of Norway (MBRN) . The regDuring the four health surveys (HUNT1-4)  clinicalWe adjusted the blood pressure measurements of individuals who self-reported using blood pressure lowering medication by adding 15 mmHg to their SBP and 10 mmHg to their DBP . LDL choPhenotypic correlations between individuals’ own birthweight and their eight later life traits were estimated using Pearson correlation coefficients. Individuals who had both birthweight and the later life trait available contributed to these analyses . However, because the birth registry commenced in 1967, when health authorities began reporting pregnancy-related data, most of the individuals for whom both measures were available were young. We therefore also specifically calculated correlations in a subgroup of individuals who were 40 years or older (N = 512) on the basis that they were more likely to exhibit signs of cardiometabolic disease and therefore the magnitude of the negative correlation with birthweight might be larger.In the HUNT study, 16 GWAS were performed across eight cardiometabolic phenotypes  in addition to a GWAS of own birthweight and a GWAS of offspring birthweight. Linear mixed models were fit using BOLT-LMM  to accouBecause of the limited number of individuals in the HUNT study with birthweight information, we meta-analysed the HUNT GWAS of own birthweight  and offspring birthweight  with previously published GWAS summary results statistics from the Early Growth Genetics (EGG) Consortium  . We combTo estimate the SNP-heritability and genetic correlation of the traits we used the CTG-VL platform . All sumThe Genomic SEM method  involvesBW, SBP) would emphasize the importance of genetic pleiotropy through an individual’s genome in the genesis of the correlation between birthweight and cardiometabolic risk factors. In contrast, a substantial genetic covariance between the latent genetic factor proxying “offspring birthweight” and the latent genetic factor proxying “offspring cardiometabolic risk factors” , would point to the importance of maternal intrauterine influences and would be consistent with Barker Hypothesis type mechanisms. These different possibilities are described more in Table The genomic structural equation model we used to partition genetic covariances into maternal and offspring components is displayed in Fig. It is important to realise that fitting a complicated SEM like the one in Fig. Summary results statistics files from the GWASs described above were combined using genomic SEM . The sof2 < 0.05, using a European reference panel [Both the full HUNT cohort and offspring in the mother-offspring pairs consisted of 47.3% males Table . Our GWAce panel ) for eacThe magnitude of the phenotypic correlation between individuals’ own birthweight and own later life traits  was low  as we are not estimating maternal genetic effects conditional on offspring genetic effects in this analysis . Although some offspring phenotypes  had significant evidence of heritability (p < 0.05), the Z scores for the test of heritability did not exceed Z > 4. The authors of LD score regression suggest that a Z score of 4 for SNP-heritability is a minimum threshold to obtain reliable estimates of genetic correlations, and consequently suggests that our sample of HUNT mothers may not be large enough to effectively partition the genetic variance/covariance in the data between the different possible sources of variation.There was strong evidence of heritability, estimated by LD score regression, for “own” phenotype, with SNP heritability estimates ranging between 3% (non-fasting glucose) and 20%  Table . HoweverNevertheless, Table In principle, the Genomic SEM model allows us to partition the genetic covariance between traits into offspring and maternally mediated components. However, for most phenotype pairs, this partitioning was uninformative as shown by the wide 95% confidence intervals on the point estimates (Supplementary Tables 3–6). The exceptions were the negative genetic covariance between glucose and birthweight, which appeared to be at least partly mediated through the offspring genome Table . We havevariance in a trait can be informatively decomposed into maternal and offspring components [In this manuscript we introduce a new statistical model that is capable in theory of partitioning the genetic covariation between traits into maternal and offspring contributions. Our model builds upon previous work by our group and others that has demonstrated how the genetic mponents –40. Thesmponents , 41) or mponents  applied mponents , 42.We applied our genomic SEM to summary results GWAS data from the HUNT Study in order to partition the genetic covariance between birthweight and later life cardiometabolic outcomes into maternal and offspring mediated components. Our motivation was that partitioning might be informative with respect to the genesis of the well-known correlation between birthweight and cardiometabolic disease , 7, 43. Overall, although the LD score regression analyses produced evidence for a significant genetic correlation between own/offspring birthweight and several later life traits in the HUNT Study , in most cases our genomic SEM did not have the statistical power to informatively resolve most covariances into maternal and offspring components (despite the large sample size of HUNT). This was likely due to the high negative correlation between competing maternal and fetal parameter estimates that are typical of these sorts of models , as wellIt is noteworthy that the authors of LD score regression suggest that only traits that have strong evidence of SNP heritability (i.e. Z scores > 4) are likely to yield reliable estimates of genetic correlations . Whilst These caveats aside, we did observe some weak evidence for a negative maternally mediated genetic covariance between offspring birthweight and offspring HDL, between SBP and offspring birthweight, and between glucose and own birthweight. A significant (negative) genetic covariance between SBP and offspring birthweight is consistent with a causal relationship between maternal SBP and offspring birthweight, which has been reported using Mendelian randomization in several studies , 49. LikOur genomic SEM involves a number of simplifications. First, we do not include a latent genetic variable for paternal effects. In theory, it would be possible to add latent variables indexing paternal genetics  and the In conclusion, we have developed a new method using genomic SEM that can decompose genetic variances and covariances into maternal and offspring components using summary results data from GWAS of mothers and their offspring. Application of this model to investigate the relationship between birthweight and later life cardiometabolic phenotypes in the HUNT study mostly yielded inconclusive findings due to lack of statistical power. However, we did find some evidence for maternally mediated effects of systolic blood pressure on offspring birthweight, and pleiotropy between birthweight and non-fasting glucose mediated through the offspring genome which is consistent with previous investigations. Our results underscore the genetic links between birthweight and cardiometabolic phenotypes in later life.Below is the link to the electronic supplementary material.Supplementary Material 1Supplementary Material 2Supplementary Material 3Supplementary Material 4


This investigation was carried out to explore G × E interaction for yield and its associated attributes in 30 Bambara groundnut genotypes across four environments in tropical Malaysia. Such evaluations are essential when the breeding program's objective is to choose genotypes with broad adaption and yield potential. Studies of trait relationships, variance components, mean performance, and genetic linkage are needed by breeders when designing, evaluating, and developing selection criteria for improving desired characteristics in breeding programs. The evaluation of breeding lines of Bambara groundnut for high yield across a wide range of environments is important for long-term production and food security. Each site's experiment employed a randomized complete block design with three replicates. Data on vegetative and yield component attributes were recorded. The analysis of variance revealed that there were highly significant (p ≤ 0.01) differences among the 30 genotypes for all variables evaluated. A highly significant and positive correlation was identified between yield per hectare and dry seed weight (0.940), hundred seed weight (0.844), fresh pod weight (0.832), and total pod weight (0.750); the estimated correlation between dry weight of pods and seed yield was 1.0. The environment was more important than genotype and G × E in determining yield and yield components.A total of 49% variation is covered by PC1 (33.9%) and PC2 (15.1%) and the genotypes formed five distinct clusters based on Ward hierarchical clustering (WHC) method. The genotypes S5G1, S5G3, S5G5, S5G6, S5G8, S5G7, S5G2, S5G4, S5G10, S5G13, S5G11, and S5G14 of clusters I, II, and III were closest to the ideal genotype with superior yield across the environments. The PCA variable loadings revealed that an index based on dry pod weight, hundred seed weight, number of total pods and fresh pod weight could be used as a selection criteria to improve seed yield of Bambara groundnut. Vigna subterranea L. (Verdc), Bambara groundnut (2n = 2x = 22) is a legume crop in the Fabaceae family and subfamily Faboideae of the genus Vigna that has received little attention1. It first appeared in West Africa and is now a common food crop in African countries. It is also grown successfully in Asia and South Asia, including Malaysia, Thailand, India, the Philippines, Sri Lanka, India, and Brazil2. Bambara groundnut is the third most common legume in Africa after groundnut and cowpea since it can withstand drought and can be grown in low fertility soils where other crops fail3. By fixing nitrogen, it helps to increase soil fertility4. Surplus Bambara groundnuts are often sold in local markets, raising income for resource-limited farmers5. Bambara groundnut is a reliable source of food for low-income people6. The world's population is continuing to rise at an alarming pace, necessitating an increase in the production of this critical crop to counter potential demographic threats. In Malaysia, average production was 1.18 t ha−17, 0.38 to 1.6 t ha−18, and 0.97 to 3.41 t ha−19 whereas 0.7 to 2.0 t ha−110 was produced in Indonesia. At optimal farming conditions, it has the ability to produce up to 4.0 t ha−111 and 5.0 t ha−13 of the dry pod.12. One of the main constraints to Bambara groundnut production in Malaysia is a scarcity of high-yielding cultivars. Plant breeders have used both conventional and molecular approaches to produce improved Bambara groundnut varieties1. Plant breeders routinely evaluate crop cultivars in broad environment tests using yield and its associated traits, as well as phenotypic expression. The variability in yield over environments (seasons and locations) is related to both biotic and abiotic environmental factors13.Scientists have investigated many ways to increase yields. These include selecting high-yielding varieties well adapted to particular growing areas, quality seed, crop establishment, nursery management, weed management, and post-harvest management. Breeding for high yield has been identified as the most sustainable approach since yield traits have heritability. However, there is high genotype by environment (GE) interaction for grain yield and more information is needed to identify broadly adapted high yielding genotypes. The lack of a modern production system and commercial high-yielding cultivars is the major limitation for this crop expansion; many growers still use traditional landrace varieties14 as certain genotypes have adapted to particular environments. A G × E interaction is characterized by a difference in the relative output of specific traits of two or more genotypes evaluated in two or more environments. This interaction usually changes genotype ranking across environments15. The uncertainty in identifying the target population in ecosystems (TPE), the lack of suitable selection criteria and finding suitable test locations to represent the target environments are all exacerbated by G × E interaction. Therefore, the focus on Bambara groundnut breeding programs has changed from developing genotypes with general adaption to identifying genotypes suited for particular conditions. Higher Bambara grain yield has the potential to improve food security. To discover superior and stable Bambara groundnut genotypes that are suited to several environments, the potential lines must be evaluated across those environments.In most crops, genotype by environment (G × E) interactions are common16 and Oladosu et al.15. Priority should be given to crop management and agronomical issues, especially during the vegetative stage and harvesting, according to Vadiveloo and Phang17, since improved lines generally require better growing conditions to achieve their yield potential. While the focus of any breeding program is to improve yield and grain quality, the identification of correlated traits that contribute to yield stability is important since yield is the net result of many plant processes. The current study aims to evaluate the contribution of several yield-related traits to yield stability in high yielding cultivars across four environments.In order to assess G × E interaction in a multi-environmental yield trial, analysis of variance (ANOVA) is used in this study to test for differences between genotype, location and environment. Among those who have contributed to this work are Zobel et al.V. subterranea were used in this study. Initially, we collected 15 landrace seed samples from Nigeria from which we obtained 150 plants. These were selfed for 3 generations and then evaluated8. From that evaluation, we selected 44 lines which we selfed for 2 more generations and evaluated. We advanced the best 30 of these representing 11 of the 15 original accessions to this experiment. In terms of plant guidelines, we complied with relevant institutional, national, and international guidelines and legislation. We collected the plant seeds or specimens with the proper permission of the institution's authority by following the national and international strategies and deposited them in GenBank, ITAFoS, UPM. We also had appropriate permission from farm or field owners during collection and experimentation. We affirm that during the collection and execution of the experiment, the authors complied with the IUCN Statement on Research Involving Species at Risk of Extinction and the Convention on the Trade in Endangered Species of Wild Fauna and Flora. The name and ID of each accession are listed in Table The research work was conducted with the Institute of Tropical Agriculture and Food Security (ITAFoS), University Putra Malaysia (UPM), Malaysia. A set of 30 accessions of Four field trials were conducted in two nearby fields in two cropping seasons(2020 and 2021) in Malaysia. These four environments represented a range of conditions in temperature, rainfall, soil type, soilstructure, soil pH and management practices. Details of the environmental conditions were presented in Table 8, the distance between plants was 30 cm, row to row was 50 cm, plot to plot was 1.5 m, and the distance between replication was 2.0 m. Recommended intercultural activities such as field planning, land clearing, weeding, irrigation, and fertilizing were used during the growing season. The prescribed fertilizer rates  and all portions of phosphorus and potassium were applied during final land preparation, though, 70% N was added at five weeks after planting18. The field was ploughed following the usual cultural traditions of the local farmers. Where necessary, pest and disease control was carried out. Regular hand weeding was conducted as needed.The experiment was set up as a randomized complete block design (RCBD) with three replications in each environment. The experimental plot consisted of two rows 1.6 m × 0.80 m each. According to Khan et al.19 twenty-seven numerical traits  were performed on the data using SAS version 9.4. The sources of variance tested were genotype (G), environment (E), and genotype by environment (G × E interaction). For each attribute, the mean, range, coefficient of variation (CV) and standard deviation were computed. Mean comparisons were carried out with the least significant difference in consideration (LSD) at 5% probability. Finally, the Pearson correlation was calculated on genotype mean basis using the means reported in Table 2p = σ2g + σ2gE + σ2e. where: σ2g is the genotypic variance, σ2gE is the G × E variance, and σ2e is the mean error variance.The variance components were derived from the expected mean squares in Table 21, the estimations of phenotypic and genotypic coefficients of variation were derived as follows:22, GCV and PCV levels were classified as low (0–10%), moderate (10–20%), and high (≥ 20%).According to Singh and Chaudhary23, defines the formula of heritability as follows:24 heritability percentages are classified as low (0–30%), moderate (30–60%), and high (≥ 60%).The ratio of genotypic variation  set to 5%. Following Johnson et al.24 the genetic advance was classified as modest (0–10%), moderate (10–20%), and high (> 20%).25 the rate is 2.06 at the point when the K is at 5%. The genetic advance (GA) (as a percentage of the mean) was calculated using the Johnson et al.To examine the relationships between the different variables in this study, the correlation coefficient was calculated using SAS software (version 9.4). The correlation heat map was generated using XLSTAT. To show the graphical relationship among principal axis, eigenvalues, and cumulative variance on a single plot, PCA variable, and case loading plot was created using XLSTAT. For two-way (double-dendrogram) clustering and constellation plot, we used JMP ver.16 software based on Ward’s hierarchical clustering (WHC) method. For scatter plot, density plot, and PCA 3D plot we followed NCSS 2021 program.A greater understanding of the contribution of genotypes, environment, and their interaction as sources of heterogeneity is critical for developing more stable genotypes. The combined analysis of variance used for quantifying interactions and defining heterogeneity for agro-morphological traits indicated that the mean square for genotypes, environment and genotype × environment (G × E) demonstrated major variations at p ≤ 0.01 or p ≤ 0.05 or p > 0.05). The mean squares related to the G × E interactions of growth traits from a combined analysis of variance were summarised in Table Days to emergence (DTE) differed significantly (p ≤ 0.01) among environments, genotypes and genotype by environment (G × E). The average emergence period spanned from 9 days (S5G18) to 13 days (S5G12) with an average of 11 days Table . Days toThe number of pods per plant (TNP) was significantly (p ≤ 0.01) different for genotype (G), environment (E) and G × E. The average number of pods per plant was 84, with a range of 73 (S5G24) to 92 (S5G4) as displayed in Table The average hundred seed weight (g) was 176.32 g, with a range of 148.45 g (S5G28) to 197.16 g (S5G5) as indicated in Table The correlations among overall trait means for the vegetative and yield components are shown in Table The correlations among the 15 yield related components over the combined analysis are shown in Table σ2g), genotype by season variance (σ2gs), genotype by location variance (σ2gl), genotype by location by season variance (σ2gls), and error variance (σ2e) as variance components which collectively contribute to phenotypic variance (σ2p). However, the more the variance component lesser the value of heritability and genetic advance26. The PCV spanned from 5.2% for days to maturity to 25.91% for biomass dry weight (g) while, the GCV ranged from 1.15% for shelling percent to 24.57% for biomass dry weight (g). Traits such as number of petiole, number of leaves, biomass fresh and dry weight (g) all had high PCV values of more than 20% whereas, days to emergence, days to 50% flowering (d), number of branches, number of stem, number of nodes, internode length (mm), pod length, pod width, seed length, and seed width all had moderate PCV values ranged from 10 to 20%   were assembled in cluster III while the minimum genotypes (3) were in cluster IV. However, cluster V had four accessions. In the vertical dendrogram, among the 27 traits, ten traits such as TNP, NMP, FPW, SL, PW, DPW, Yield, NSP, DSW, and HSW into cluster I. Cluster II possesses the three vegetative traits such as NNS, NP, and NL whereas eight variables are grouped into cluster III . However, cluster IV assembled the traits like DTE, D50%F, DTM, NS, NIP, and HI. This result had validation based on a correlation test i.e., there was a meaningful relationship among the traits. A heatmap with the cluster represents the chromatic visualization of the relationship between traits and genotypes. In the heatmap, each bar represents the position of accessions and variables intersection point. According to Z-score red and blue colors represent the high and low abundance of traits with accessions. The intensity of the color indicates the magnitude of traits abundance or richness, hence, the more color intensity the more abundance of the components. A constellation diagram  has been widely utilized in agricultural research. In the current study, the first seven principal components (PC) accounted for 83.38% of the total variance Table . The fir26, reported CV = 24.79% for grain yield, CV = 9.32% for hundred seed weight and CV = 31.86% for pods number per plant over six locations. In terms of grain yield and hundred seed weight, our findings revealed highly significant (p ≤ 0.01) variations across genotypes. For almost all characteristics, including dry pod weight, hundred seed weight, and dry seed weight, the G × E interaction was highly significant (p ≤ 0.01), though statistically shelling percent had no meaningful difference. This implies that the genotypes' ordering was not consistent. Similar findings of significance of G × E have been reported by Masindeni26, in Bambara groundnut, Oladosu et al.15 in rice, and Ali et al.27 in groundnut. . In this current research, a significant level of divergence among GE interaction and genotype effect indicated that certain of the presence of diverse multi-environments with different genotypes as well as high yield potential28. According to Yan et al.29, the GEI over a wide extent of mega- environmental trail comprises of two interactions namely, crossover and non-crossover interactions. The yield performance among the tested accessions over multi-environments persistently designates as non-crossover interaction whereas crossover interaction illustrates the comparative alternation in genotypes ranking over a wide range of environments. Plant breeders can either select genotypes for a certain environment or widely adjusted genotypes across the multi-environmental conditions when GEI is subjected to the influence of predictable components30. However, the generation of comparatively constant and stable genotypes over multi-environmental situations is obligatory when GEI is subjected to an unpredictable component31 and for well explanation and realization of GEI, yield stability analysis under multi-location and seasons may prompt both reproducibility and heritability of the traits evaluated32.A significant variation presents in the pooled analysis of variance for the 27 traits of the 30 Bambara groundnut genotypes. Among the vegetative trait, the coefficient of variation ranged from 5.43% for days to maturity to 27.96% for biomass fresh weight (g) however, for the yield component it varied from 6.17% (shelling percent) to 26.24% (hundred seed weight). Masindeni33. Mohammed34 supports the use of correlation matrices in plant breeding as a popular method for determining the degree of relationship between two or more traits. This conclusion is similar to the findings of Pranesh et al.35 and Jonah et al.36 who found a strong and positive significant association between the total number of pods and the attributes such as mature pods number, dry pods weight, seeds number, dry seeds weight, and yield. We found a weak to intermediate and positive relationship between plant height (PH) and yield, biomass fresh and dry weight, and we propose selection based on these qualities may be beneficial for yield increase as well as fodder production for animal feeding. Our findings were corroborated with the research report stated by Mohammed34 in Cote d’ Ivoire and Zenabou et al.37 in Cameroon on Bambara groundnut. Zenabou et al.38 reported days to 50% flowering had a negative correlation with fresh and dry pod weight, which was consistent with our findings due to less variation in planting materials. Plant height was negatiively associated with yield but it was positively associated with shelling percentage, hundred seed weight, fresh pod weight, and these findings agree with the statement of Ahmad39. The number of petiole and leaves per plant expressed a positive correlation with total number of pods, fresh pods weight, dry seed weight, hundred seed weight and shelling percent but a negative correlation with harvest index, yield, a similar statement was noted by Unigwe et al.40. The yield components such as the total number of pods, fresh and dry pods weight , number of seeds, dry seeds weight, hundred seed weight, and harvest index expressed a positively significant correlation with grain yield in our investigation. These results were comparable with the report on Bambara groundnut, published by Khan et al.8, Mohammed34, Pranesh et al.35, Khan et al.41, and Onwubiko et al.42. This suggests that these characteristics might be chosen for to increase Bambara groundnut production.Consideration of the correlation matrix might be a fantastic scale of assessment for improved genotype selection programs25 indicating that trait expression is governed by the environment. According to Sivasubramanian and Madhavamenon22, the proportion of GCV and PCV values is deemed low when the value ranges between 0 and 10%, moderate when the value ranges between 10–20%, and high when the value ranges over 20%. However, because the coefficient of variation is independent of the measurement unit, it is more trustworthy when comparing trials25. The selection may be beneficial to the traits with lower PCV with higher GCV levels to isolate promising cultivars. On Bambara groundnut, Onwubiko et al.43, Gonné et al.44, and Naik45 reported similar findings, as well as in groundnut reported by Ambros46. According to earlier findings, selection may be successful for a specific trait improvement by effectively utilizing genetic variation with the higher degree of heritability48. In a broad sense, heritability refers to the fraction of total variation in phenotypic variables across individuals in a particular group that may be attributed to genetic variation. Higher genotypic coefficient of variation coupled with high heritability as well as high genetic advance gives better clues than the individual measures of variance component49. According to Johnson et al.24, the heritability percentage is deemed low when the value runs between 0 and 30%, moderate when the value runs between 30 and 60%, and high when the value exceeds 60%. Breeders might use high heritability for certain characteristics to choose superior genotypes based on phenotypic observations48. Low heritability, on the other hand, denotes a low heritable component of variation and a greater influence of environmental effects on the expression of such trait, as a result, selection based on such characteristics is futile, according to Ridzuan et al.50. Jaiswal et al.51 on the other hand, emphasized that attributes linked with high heritability do not always result in a higher genetic advance; hence, high heritability coupled with high genetic advance gives a more credible outcome. Furthermore, heredity encompasses both additive and non-additive gene action; hence, heritability should be considered in conjunction with genetic advancement for predicting the selection of superior genotypes52. The proportion of genetic advance is deemed modest, with values ranging from 0 to 10%, moderate (10–20%), and high (> 20%)24. However, in our investigation moderate to high heritability was identified in yield components coupled with moderate to high genetic advance and this is the evidence of medium to high environmental influences on these traits. A similar observation was reported by Masindeni26 and Khaliqi et al.9 in Bambara groundnut. However, using low to medium heritable traits, improvement in the following generation may not provide the expected outcomes since it has been proposed that non-additive gene action i.e., epistatic and the interaction between genotype and environment play a substantial influence in the expression of this trait26. Depending on the variability and heritability estimations, it is possible to deduce that direct selection can improve variables such as hundred seed weight (g), harvest index, biomass fresh and dry weight (g), number of seed, dry seed weight, and yield per hectare in Bambara groundnut. High heritability and genetic advance observed high in biomass fresh weight and biomass dry weight is the representing traits of yield per hectare is supported by Molosiwa49. We recorded Shannon diversity index range from 2.25 to 2.34 indicating that the evaluated genotypes showed a significant level of variation over the environment. Similar findings have been noted by Khan et al.7, who stated a standard scale for Shannon diversity value of 1.5 to 3.5.The values of phenotypic variance is greater than the other components studied namely, genotypic variance, genotype by environment variance etc. for all traits, similar to the findings of earlier reports by Khan et al.8 in Bambara groundnut. Several analyses using various agglomeration approaches were tried to obtain the best possible categorization of accessions. The Ward technique seems to be the greatest agglomeration method for producing the finest results53. The constellation plot  explain the greatest proportion of the overall variance56. Shegro et al.57 used PCA analysis to categorize the 20 Bambara groundnut accessions whereas Mohammed58 found that PC1 and PC2 contributed to the overall variation at 19% and 14%, respectively.Based on the heatmap we observed that the genotypes under clusters I and II captured more red hue with the association of the yield and its contributing traits under cluster I. Our findings were advocated by Khan et al.lot Fig. B arrangeThe combined analysis of variance indicated that genotype (G), environments (E) and genotype by location (G × E interaction) showed extremely significant variations in vegetative, yield, and yield component characteristics. According to the means comparison results, Bambara groundnut genotypes G1, G3, G5, G6, G8, G7, G2, G4, G10, G13, G11, and G14 were the closest to the ideal genotype with superior yield across the environment. These genotypes were grouped into similar clustering according to Ward hierarchical clustering methods which assembled the accessions into five distinct clusters. Considering the pooled data PCA accounted for 49.01% variation contributed by PC1 (33.87%) and PC2 (15.13%). Farmers will reap high yields and steady revenue if better genotypes with the capacity to give a consistently high yield over different conditions are identified and certified for cropping. According to the results shown above, the planting materials have a sufficient level of genetic variation. This indicates that there is enough diversity to be exploited by selection. As a result, greater GCV, broad-sense heritability, and genetic advance are demonstrated by the various yield component features, particularly dry pods weight, hundred seed weight, biomass fresh and dry weight, dry seed weight, harvest index, total number of pods significantly impact the yield. As a result, they would be receptive to positive selection. In deciding yield and yield components, the environment played a greater role than genotype, and G × E interaction accounted for significant variation resulted, complicating genotype selection, an additional statistical analysis is necessary to estimate the stability of each genotype throughout the whole environment. The application of stability statistical measures is recommended to analyze genotype stability that divulges several G × E interaction features, resulting in the detection of stable genotypes across environments. However, statistical techniques such as univariate and multivariate analysis can be more fruitful in unfolding and understanding the G × E interaction alongside variance component analysis. Overall, this result will assist plant breeders in this crop improvement as well as selecting superior lines for the future breeding program.