Fagus sylvatica L.) is widespread, and is one of the most important broadleaved tree species in Europe; however, its potential for adaptation to climate change is a matter of uncertainty, and little is known about the molecular basis of climate change-relevant traits like bud burst.Diversity among phenology-related genes is predicted to be a contributing factor in local adaptations seen in widely distributed plant species that grow in climatically variable geographic areas, such as forest trees. European beech (We explored single nucleotide polymorphisms (SNP) at candidate genes related to bud burst in beech individuals sampled across 47 populations from Europe. SNP diversity was monitored for 380 candidate genes using a sequence capture approach, providing 2909 unlinked SNP loci. We used two complementary analytical methods to find loci significantly associated with geographic variables, climatic variables , or phenotypic variables . Redundancy analysis (RDA) was used to detect candidate markers across two spatial scales (entire study area and within subregions). We revealed 201 candidate SNPs at the broadest scale, 53.2% of which were associated with phenotypic variables. Additive polygenic scores, which provide a measure of the cumulative signal across significant candidate SNPs, were correlated with a climate variable  related to temperature and precipitation availability, and spring phenology. However, different genotype-environment associations were identified within Southeastern Europe as compared to the entire geographic range of European beech.Environmental conditions play important roles as drivers of genetic diversity of phenology-related genes that could influence local adaptation in European beech. Selection in beech favors genotypes with earlier bud burst under warmer and wetter habitats within its range; however, selection pressures may differ across spatial scales.The online version contains supplementary material available at 10.1186/s12864-021-07907-5. Local adaptation is one of the most important evolutionary mechanisms allowing species to thrive across heterogeneous environments . PopulatThe genetic underpinnings of local adaptation, though, are poorly understood. Local adaptation would be expected to change allelic frequencies of genes affecting fitness in particular environments. However, most phenotypic traits related to adaptation are typically quantitative polygenic traits, which complicates the identification of genetic polymorphisms linked to adaptation , 4. LocaLocal adaptation is best studied in long-lived sessile organisms with large effective population sizes spanning large, variable environments , such asPhenology of tree development is one example of a set of traits thought to be strongly indicative of adaptive fitness. Local population adaptations have been described among tree species for the timing of bud burst in relation to local climatic conditions varying along latitude or altitude –14. As aModifications of the phenology of flowering, bud burst, or bud set have been observed in relation to global climate changes , 20; howQuercus robur), it has been estimated that bud burst phenology, being considered a typical quantitative trait, is controlled by at least 12 unique genes or chromosomal regions . F. F141]. http://www.worldclim.org/) from maps with a spatial resolution of 30 arc-seconds (approximately 1 km2) using DIVA GIS 7.5 software [Environmental data for the locations of the 47 provenances was downloaded from the WorldClim database ; http://software . The chasoftware . PCA is software , and it software .The indices of spring and autumn phenology, tree height at 5 years of age, and survival in each provenance were obtained from Barzdajn and Rzeźnik . These rTotal DNA was extracted from sampled individuals using the GeneMATRIX Plant & Fungi DNA Purification Kit  according to the manufacturer’s protocol with minor modifications. Extracted DNA was quantified using an Eppendorf BioPhotometer and Quantus Fluorometer (Promega). 100 ng of DNA from each tree was used for individual exome capture and sequencing.F. sylvatica transcriptome . These e . Next, F. sylvatica was not available at the time of the experiment set-up, the bait development performed in SeqCap EZ library preparation utilized only the published cDNA sequences for the 485 genes [www.igatechnology.com; Udine, Italy).DNA library preparation and exome capture was performed using the SeqCap EZ Library SR User’s Guide v5.1 (Nimblegen Roche). However, because the reference genome of 85 genes . Followivcf format.Sequence reads were assessed with FastQC software . Adapter2 value was calculated. A cut-off of r2 > 0.5 was used, whereby one of a pair of SNPs was removed from the dataset if the coefficient of determination between the pair was > 0.5, thus removing SNPs showing strong signals of LD.Filtering of variants was carried out using VCFtools  with theSnpEff software  was emplTo improve gene annotations, we performed a manual curation of gene models. While sequence capture technology primarily targets exons, many functional elements are located outside the exonic regions. It is well-known that SNPs located in promoter and UTR regions may regulate gene expression. It is traditionally considered that introns are not as important as exons, but several studies have demonstrated some functional significance for introns , 163. Ma2 copy ratios from targeted capture NGS data based on reads mapped to both on-target and off-target regions. CNVkit has the highest sensitivity and a typical specificity for small CNVs with sizes below 100 kb [batch command after creating a flat reference genome as suggested in the manual using the command reference. A threshold of 0.2 was applied to identify the signals for amplification and deletions of the genes.Copy number variations (CNVs) were identified using CNVkit , a piecew 100 kb . TherefoK) was set from 1 to 10, carried out each time in sets of 10 repeats, with a burn-in period of 50,000 iterations and 100,000 MCMC (Markov Chain Monte Carlo) repeats, and an admixture model with correlated frequencies. The optimal value of K was determined based on the ΔK method [q-matrix) were used to test the correlations with geographic variables of the origin of populations sampled.Genetic diversity was estimated based on expected heterozygosity (He) defined as the probability that two randomly chosen alleles from the population are different using PowerMarker 3.25 software . We inveK method  using thK method . The proBecause phenotypic variables were represented by population averages instead of individual measures, we applied the same approaches (LFMM and RDA) to detect loci with significant effects related to geographic, climate, and phenotypic variables. We used the latent factor mixed model (LFMM) approach  to find K based on the number of distinct genetic clusters identified following the population genetic structure analysis and kept the best out of 10 runs based on a cross-entropy criterion. The MCMC algorithm was used for each of the geographic, climate, and phenotypic variables , using 50,000 steps for burn-in and 100,000 additional steps to compute LFMM parameters (z-scores) for all loci. The number of latent factors was set at the identified value of K. In order to compensate for run-to-run variation, the analysis was repeated over 10 independent runs and z-scores across runs were then combined in R using the LEA package [p-values for multiple testing using the Benjamini–Hochberg procedure, and to calculate the genomic inflation factor to modify z-scores allowing for the control of the FDR, as described in E Frichot and O François [p-values of < 0.001 was then generated for each explanatory variable.We employed an MCMC algorithm for regression analysis whereby potentially confounding population structure is modeled with unobserved (latent) factors . As miss package . The LEAFrançois . A list To test the multivariate relationships between genetic variation with geographic, climate and phenotypic variables, we conducted redundancy analysis (RDA) using the vegan 2.4–5 package in R . RDA is 2. We removed longitude, latitude, and PC2 variables from analyses because they were highly correlated (|r| > 0.7) [RDA was used to estimate the proportion of variance in allele frequencies at SNPs across all sampling locations, which could be explained by geographic, climate, and phenotypic predictors based on the adjusted r| > 0.7)  with PC1| > 0.7) . PredictThe additive polygenic scores approach was used to assess the cumulative signal of candidate loci in response to environmental variation . FirstlyPartial redundancy analyses (pRDA) were performed on a combined dataset comprising outliers detected by both the RDA and the LFMM approaches to examine the relative contribution of geographic, climatic, and phenotypic variables, along with their combinations to adaptive genetic variation. Four different models were considered for partitioning variance components of the RDA Table : Model 1Additional file 1: Table S1. Description of sampled populations. Table S2. List of 485 candidate genes and their functional annotations. Table S3. Correlations between 19 environmental variables (BIO1-BIO19) and the first three principal components. Table S4. Pearson’s correlations  between geographic, climate and phenotypic variables, and canocical correlation coefficients  among groups of variables. Coefficients significant at p < 0.05 are indicated by bold-italic face. Table S5. List of valid gene models and their coordinates in the reference genome of European beech . Table S6. List of outlier loci and their functional annotation across the entire sampled geographic area and within regional groups.Additional file 2: Figure S1. Distribution of coefficients of three first principal components  determined based on 19 bioclimatic variables. Figure S2. Graphical method  allowing for detection of the number of groups K using (A) ΔK and (B) the rate of change of the likelihood distribution . Figure S3. Bar plot of admixture proportions of individuals, inferred using K = 3 based on 2909 SNP loci. Individual’s proportions  are sorted within each cluster , climate  and phenotypic . The solid line represents the regression line of the linear model. Figure S6. Relationships between additive individual polygenic scores based on all 201 outlier markers and each of the explanatory variables: geographic , climate  and phenotypic . The solid line represents the regression line of the quadratic model.


Although genomic selection is being used in many livestock species, it has not yet been considered in local breeds due to the lower population size and the potential less effective impact on the genetic evaluation of these breeds. The current research aims to investigate how genomic data can impact the accuracy of genetic predictions for beef traits in Rendena, a small local cattle breed of the North-East of Italy selected for a dual purpose. Classical animal models using only phenotypic information were compared with two models that integrated genomic data with pedigree information. The genomic models presented better accuracy in estimated breeding values of the animals than the ‘classical’ animal model, especially the ‘simpler’ one assuming homogeneous variances of single nucleotide polymorphisms. Our results show that the inclusion of genomic information can be successfully applied to breeding selection scenarios even in small local cattle breeds such as Rendena.The maintenance of local cattle breeds is key to selecting for efficient food production, landscape protection, and conservation of biodiversity and local cultural heritage. Rendena is an indigenous cattle breed from the alpine North-East of Italy, selected for dual purpose, but with lesser emphasis given to beef traits. In this situation, increasing accuracy for beef traits could prevent detrimental effects due to the antagonism with milk production. Our study assessed the impact of genomic information on estimated breeding values (EBVs) in Rendena performance-tested bulls. Traits considered were average daily gain, in vivo EUROP score, and in vivo estimate of dressing percentage. The final dataset contained 1691 individuals with phenotypes and 8372 animals in pedigree, 1743 of which were genotyped. Using the cross-validation method, three models were compared: (i) Pedigree-BLUP (PBLUP); (ii) single-step GBLUP (ssGBLUP), and (iii) weighted single-step GBLUP (WssGBLUP). Models including genomic information presented higher accuracy, especially WssGBLUP. However, the model with the best overall properties was the ssGBLUP, showing higher accuracy than PBLUP and optimal values of bias and dispersion parameters. Our study demonstrated that integrating phenotypes for beef traits with genomic data can be helpful to estimate EBVs, even in a small local breed. As is the case with many indigenous breeds, a greater genetic diversity than specialized and cosmopolitan breeds is expected also for the Rendena [Rendena is a dual-purpose cattle breed indigenous to the North-East of Italy. This breed is included within the “European Federation of Cattle Breeds of the Alpine System” (FERBA), an organization whose main purpose consists in the preservation and promotion of local cattle breeds of the alpine system  for tra Rendena .For a long time, two major limitations to the genomic selection approach on small populations such as Rendena have been the prohibitive cost of genotyping a sufficient number of single nucleotide polymorphisms (SNPs) per individual and the equations for EBVs’ estimation, which were based on a multistep approach . In factHowever, in the last few years, both limitations preventing the use of the genomic selection approach in small breeds with limited diffusion such as Rendena have subsided. Firstly, the constant decline in prices of SNP platforms has allowed genomic selection to become much more cost-efficient. Secondly, equations such as the single-step genomic best linear unbiased prediction (ssGBLUP) have been developed and found to be suitable even in small-breed contexts . The ssGwww.anare.it, accessed on 18 March 2021). The phenotypes consisted of data recorded on Rendena young bulls during performance tests conducted from 1985 to the present. The phenotypes used were the average daily gain (ADG) obtained by linear regression of weight on age recorded at least 11 times during the stay of bulls at the test performance test station, the mean in vivo fleshiness score (EUROP grade), and the mean in vivo estimate of dressing percentage (DP) evaluated by three skilled classifiers at the end of the test, i.e., about 11 months of age, distribution of phenotype are reported in Phenotypic and pedigree information was provided by the National Breeder Association of Rendena Cattle  and Bovine 150K Array GGPv3 Bead Chip, comprising 138,974 SNPs . The higher density panel was used only in 554 males, whereas the remaining males  and all the females no. = 1253) were genotyped with the LD platform. Males genotyped with LD chips were all animals with at least one father and one full sib genotyped with the HD chip. The two panels shared about 60% of markers. Females with a call rate (CR) lower than 95% and males with a CR lower than 90% were discarded before the analysis. In addition, for both platforms, SNPs with a minor allele frequency (MAF) <0.01 and call rate lower than 0.90 were removed with the plink program . Before 3 were gey is the vector of phenotypes, X represents the incident matrix for systematic fixed effects, and b is the vector of fixed effects. Two cross-classified effects were used as in [Z is the incident matrix of random genetic additive effects, while , where A, which represents the identical by descendent (IBD) matrix constructed from pedigree information. All genetic and genomic prediction models were carried out with the blupf90 suite of programs [The same fixed and random effects were used for all analyzed traits with the following model:ed as in : the conprograms .G⊗A and R⊗I with G and R, are 3 × 3 matrices, respectively, including the additive genetic and the residual (co)variances matrices, ⊗ is the Kronecker product, and A and I are the additive relationships matrix and an identity matrix, respectively. Prior distributions for G and R matrices were independent inverse Wishart. Genetic and residual correlations (ar) were calculated between trait pairs as the ratio of the covariance on the square root of the product of the respective variances.The variance components used in all prediction scenarios were estimated under this model using the univariate approach. In addition, genetic and residual correlation among traits was estimated with multi-traits models. Covariances’ structures were Variances were estimated using Gibbs’s sampling algorithm with gibbs3f90 program . A chainα and β were set to 0.95 and 0.05, respectively. M is a matrix of SNP content centered by twice the current allele frequencies, and pi is the allele frequency for the ith SNP. In addition, variance components were re-estimated under this model to evaluate variances changes by the inclusion of genomic data. Therefore, G in ssGWAS is adjusted so that the average diagonal and off-diagonal matches the averages of A22.In ssGBLUP, the inverse of the IBS matrix lsewhere . G is thposed in :(3)G=MM′wG matrix was built using the following method [δ accounts for the difference in genetic base between the pedigree and genomic relationship.The last method used for genetic prediction was WssGBLUP, which differs from ssGBLUP in the construction of g method :(4)Gw=MD)ssGBLUP  as folloAn iterative algorithm following that reported in  was usedInitial parameters are set to GEBV  and the shrinkage factor (CT). Other strategies such as linear weight [A and G matrix, as reported in A further aim of this study was to identify optimal weight strategies to achieve higher accuracy and less biased genomic prediction.  methods ,30 were r weight ,31 or Bar weight  were notCT were used in this study , considering that values greater than one deviate proportionally from a normal distribution and exhibit grater shrinkage. By default, the postGSf90 program set maximum change in SNPs variance equal to CT equal to 1.105, 1.125, and 1.250. Other scenarios have been explored, setting the maximum change on variance equal to 5.Three values of Estimators of bias, dispersion, and accuracy were adopted to evaluate the different prediction models. The LR cross-validation method was used on this behalf . In thisLR defined bias as disp <1 designate over-dispersion, while disp>1 indicates an under-dispersion.Dispersion was described by the slope of the regression between EBVs in the whole dataset to EBVs in the partial one, i.e., acc) the correlation of breeding values estimated in the two datasets [acc” estimator mean that the EBV estimate of the focal group is mainly influenced by the addition of new phenotypic information with respect to the conditional kinship information. Thus, In this study, we describe as accuracy  and genetic and residual correlations estimated using PBLUP are reported in 2 of 0.335 and 0.392, respectively. In addition, all traits’ pairs, as expected, presented medium to high genetic and residual correlations. ADG presented a medium-positive genetic correlation with the other two traits (0.38 on average), while DP and EUROP were strongly correlated (0.981) to be considered a unique trait.Heritability  presented better accuracy but more under-dispersion and biases. When CT changed from 1.105 to 1.125, accuracy increased by 2% in all phenotypes, and a substantial increase in accuracy was observed, moving to a CT value of 1.250 (+20% on average). When the threshold for maximum SNPs variance was raised up to 5, accuracy increased slightly, especially from the third to tenth iteration.From the previous analysis, for each phenotype, two weighting strategies were retrieved: the one presenting a value of bias close to the optimal value (WssGBLUP_1) and the one with highest accuracy (WssGBLUP_2) The weighting strategies that produced the lowest bias were associated with a CT = 1.105, default value for limit, and iteration 1. On the other hand, as reported previously, CT = 1.250 and limit equal to 5 produced the best results in terms accuracy of prediction. For ADG and DP, maximum accuracy value was found on iteration 4, while for EUROP, iteration 7 was the most successful . Table 4For what concerned the dispersion parameter, in this study, we found that ADG and DP were slightly under-dispersed, while EUROP was a little over-dispersed for PBLUP and WssGBLUP_1, showing values of dispersion <1.When only pedigree information was used, lower accuracy was observed for all traits: ADG presented a value of 0.366, EUROP of 0.464, and DP of 0.506. Lower reliability values were also found in this model. Interesting, PBLUP is the only prediction model in which a marginally negative bias was observed. PBLUP presented similarly biased values as WssGBLUP_1 and ssGBLUP for EUROP and DP , while for ADG, it presented a greater absolute value of bias than for the other two methods.When genomic information was added, a global increase in accuracy and reliability was observed. In ssGBLUP models, increases in accuracy of 0.106, 0.087, and 0.064 were observed for ADG, EUROP, and DP, respectively. Reliability estimators showed the same trend. The ssGBLUP had higher accuracy values with respect to PBLUP, and it also presented bias and dispersion closest to optimal value. As can be seen from The inc_adj estimator represents the increase of accuracy when genomic models were used. ADG is the trait that was most favored from the introduction of genomic data, with a value of 45%, and DP is the one with lower benefits (26.7%). WssGBLUP_1 presents a similar inc_adj value than ssGBLUP, while in WssGBLUP_2, value rises to 4 percentage points in ADG as well as to 5 and 7 percentage points in EUROP and DP.In this study, we evaluated how the use of genomic data can improve the estimates of breeding values in the local dual-purpose Rendena cattle. We used data relative to beef traits collected in the performance tests of young bulls, both because these traits are accounted for in the selection index and also because of the smaller number of genotyped individuals needed with respect to traits such as milk production. In addition, this was the first approach to apply genomic selection in a small local cattle breed.The three performance test phenotypes i.e., ADG, EUROP, and DP, presented medium to high heritability, ranging from 0.30 to 0.40. We recorded little difference from the study of  or beef The usefulness of genomic selection was assessed using LR as a cross-validation method, which provided accuracies, bias, and dispersion of the genetic evaluations. LR presents several advantages : the robResults confirmed that when genomic data were integrated with pedigree, there was a substantial increase in the accuracy of (G)EBVs prediction. Accuracy increased by about 30% on average when switching from BLUP to ssGBLUP. Moreover, an additional increase in accuracy was observed when weighting strategies were applied, i.e., from 0.366 to 0.472 for ADG, from 0.509 to 0.569 for EUROP, and from 0.464 to 0.528 for DP, respectively. These outcomes suggest that the genomic information can potentially capture variation in Mendelian sampling, thus leading to a greater accuracy of prediction when only kinship information is used . A similThe findings of previous studies report that the ssGBLUP led to more accurate predictions than the BLUP. Other research conducted on a different type of beef-related traits presented a substantial increase in breeding value prediction when ssGBLUP was used. However, those investigations were conducted with breeds with much larger population sizes, and results were expressed in terms of reliability ,47,48. IFor that reason, nonlinearA methods were chosen over the other weighting strategies. The consistency of nonlinearA methods in a single-step framework has been reported by . The augDespite this, LR cross-validation methods pointed out that major under-dispersion and bias are observed by applying WssGBLUP. In our study population, proven and/or young animals are evaluated with the rest of the performance test animals. The higher bias present in some of the weighting models led to an inaccurate estimation of genetic trends than in turn led a potentially biased selection decision, i.e., selecting only young animals with respect to the older ones . BecauseOur finding supports the use of genomic data, and in particular the use of ssGBLUP, as the new model for the routinary genetic evaluation on selected bulls of a local breed, the Rendena cattle. In local breeds, genomic information has mainly been used to assess genetic variability or to study specific biological pathways underscoring peculiar traits such as . As mentThe present study shows that genomic imputation and the combination of genotyped and non-genotyped data through ssGBLUP could be a cost-efficiency strategy, compensating for the limited genotype information available on local breeds. This could make genomic selection for limited populations an appealing strategy, as it already is in more cosmopolite breeds such as Holstein  However,All models that included genomic data presented higher accuracy and reliability than the ones using only kinship information. These two estimators were particularly higher in models in which high heterogeneous variances among SNPs had been assumed; however, the same models presented under-dispersion and higher bias, and for that reason, they can be discarded as models to be used in the selection. Models with “best properties” can be identified in the ssGBLUP or in the WssGBLUP, in which weighting strategies presented less shrinkage. Although these two models presented similar proprieties, ssGBLUP could be chosen as the “best” model, because it was neither under- nor over-dispersed, presenting appropriate properties for long-term selection. In conclusion, the present study demonstrated how the use of genomic data in addition to ssGBLUP can lead to a better prediction of genetic effects even with a modest amount of molecular data, as typically happens in local populations. Therefore, we demonstrated how genomic data can be a suitable tool for breeding selection scenarios in local cattle breeds such as Rendena, guaranteeing the competitiveness and thus the conservation of the breed through its improvement of selection’s accuracy.


Accumulating evidence suggests that many psychiatric disorders etiologically represent the extreme end of dimensionally distributed features rather than distinct entities. The extent to which this applies to eating disorders (EDs) is unknown.We investigated if there is similar etiology in (a) the continuous distribution of the Eating Disorder Inventory-2 (EDI-2), (b) the extremes of EDI-2 score, and (c) registered ED diagnoses, in 1481 female twin pairs at age 18 years (born 1992–1999). EDI-2 scores were self-reported at age 18. ED diagnoses were identified through the Swedish National Patient Register, parent-reported treatment and/or self-reported purging behavior of a frequency and duration consistent with DSM-IV criteria. We differentiated between anorexia nervosa (AN) and other EDs.The heritability of the EDI-2 score was 0.65 (95% CI 0.61–0.68). The group heritabilities in DeFries–Fulker extremes analyses were consistent over different percentile-based extreme groups [0.59 (95% CI 0.37–0.81) to 0.65 (95% CI 0.55–0.75)]. Similarly, the heritabilities in liability threshold models were consistent over different levels of severity. In joint categorical-continuous models, the twin-based genetic correlation was 0.52 (95% CI 0.39–0.65) between EDI-2 score and diagnoses of other EDs, and 0.26 (95% CI 0.08–0.42) between EDI-2 score and diagnoses of AN. The non-shared environmental correlations were 0.52 (95% CI 0.32–0.70) and 0.60 (95% CI 0.38–0.79), respectively.Our findings suggest that some EDs can partly be conceptualized as the extreme manifestation of continuously distributed ED features. AN, however, might be more distinctly genetically demarcated from ED features in the general population than other EDs. Eating disorders (EDs) comprise several psychiatric conditions, including anorexia nervosa (AN) and bulimia nervosa (BN), characterized by persistent disturbance of eating or eating-related behaviors. EDs are associated with significant disability and mortality . Taxometric approaches have yielded conflicting results, supporting both categorical and dimensional conceptualizations of EDs . Zygosity was either ascertained using a panel of 48 single nucleotide polymorphisms, or an algorithm of five questions regarding twin similarity. CATSS has ethical approval from the Regional Ethical Review Board in Stockholm.Participants were part of the Child and Adolescent Twin Study in Sweden (CATSS), an ongoing longitudinal twin study We excluded individuals with congenital or early brain damage syndromes, chromosomal syndromes, unknown zygosity, more than 25% missing data on any of the subscales of the ED feature measurement, and pairs where only one twin in a pair had responded . We alsoDrive for Thinness (seven items), Bulimia (seven items) and Body Dissatisfaction (nine items). Drive for Thinness measures excessive concern with dieting, preoccupation with weight, and fear of weight gain; Bulimia measures the tendency to engage in binge eating and to think about purging; and Body Dissatisfaction measures dissatisfaction with one's overall body shape and the size of specific parts of the body. The response options are ‘never’ (1), ‘rarely’ (2), ‘sometimes’ (3), ‘often’ (4), ‘usually’ (5), and ‘always’ (6). One item on the Body Dissatisfaction scale (‘I like the shape of my buttocks’) was not included in the EDI-2 during the first 6 years of CATSS-18 and therefore only available for 26.2% of the sample; however, the reversed item ‘I think my buttocks are too large’ was included. A total EDI-2 score (hereafter EDI-2 score) was computed as the mean of all items on the three subscales when participants had responded to at least 75% of items on each subscale . The EDI-2 has been validated in adult Scandinavian females and in non-Swedish adolescents . It is important to note that the diagnosis of F50.1  in the ICD system was typically given when the amenorrhea criterion in F50.0 was not present. This is different from the current DSM-5 classification of atypical AN . Individuals with DSM-5 atypical AN are not captured by AN/atypical AN in this analysis. We excluded individuals with the following codes: overeating associated with other psychological disturbances (F50.4), vomiting associated with other psychological disturbances (F50.5), other eating disorders (F50.8), feeding disorder of infancy and childhood (F98.2), and Pica of infancy and childhood (F98.3). In total, 93 out of 2962 individuals (3.1%) had an ED diagnosis of interest in the NPR , which includes diagnoses from psychiatric inpatient care from 1973 onwards and from specialized outpatient care from 2001 onwards. NPR diagnoses are coded according to ICD-9 and ICD-10. The CATSS data used in this study were linked to the NPR up until 31 December 2016. As a consequence, the end of follow-up in the NPR in our sample was 17 years for the youngest birth cohort (born 1999) and 24 for the oldest birth cohort (born 1992). EDs in the NPR have not yet been subjected to formal reliability/validity testing; however high validity of the NPR has been reported for a range of other psychiatric disorders . In CATSS-18, the twins were asked: ‘n = 169  and other EDs . The prevalence of EDs was very similar in responders and non-responders , suggesting that attrition did not bias the representativeness of our sample.The total number of individuals identified with any ED by registered diagnoses, parent-reported treatment and self-reported purging was 69 5.7%, . More thData analysis was performed using Stata 15.0 , indicated by higher correlations in MZ pairs than in DZ pairs; non-additive genetic effects (D), indicated when MZ correlations are more than twice as high as DZ correlations; shared environment (C), indicated by DZ correlations more than half as high as the MZ correlations; and non-shared environment (E), evidenced by differences within MZ pairs (and including measurement error). The principles of the twin design are described extensively elsewhere . Twin correlations were estimated by zygosity from a constrained saturated model, in which the means and variances were equated within twin pairs and across zygosity. We then fitted a univariate model to estimate the degree of genetic and environmental influences on the EDI-2 score in the entire sample. The DZ twin correlation was slightly larger than half of the MZ twin correlation, therefore we fitted an ACE model. We tested the significance of individual parameters by constraining them to be equal zero. The best-fitting models were chosen based on the likelihood ratio test . In addition, we used the Bayesian information criterion , since the likelihood ratio test can become oversensitive to small deteriorations in model fit when samples are large and may not select the most appropriate model when data are skewed  were defined using percentile-based cut-offs on the EDI-2, that is, scoring within the 1st, 3rd, 5th, and 10th percentile of the EDI-2 (see the number of probands per percentile in h2g), which estimates the degree to which the genetic influences on extreme scores also influence continuous variation in the same trait. In the classical procedure, a regression equation is fitted to estimate h2g; the equation predicts co-twin scores from the continuous scores of probands. Zygosity is also included as a predictor in the equation; the regression of zygosity on co-twin scores is an estimate of h2g. Here we used a model fitting implementation of the procedure  by modeling an individual's expected score as a function of their co-twin's proband status /[2 × (number of concordant pairs) + number of discordant pairs], indicating the probability that a co-twin of a proband is also a proband . Using lrg) estimates the degree to which genetic influences on one phenotype are shared with those on another phenotype. A genetic correlation of 1.0 (0.0) indicates that all (none) of the additive genetic influence on two phenotypes is shared between them. The genetic correlation, and the heritability of each trait, can be used to calculate bivariate heritability; this refers to the degree to which the genetic factors that overlap across two traits explain the correlation between them. Like the liability threshold model, the categorical component of this model assumes a normal distribution of continuous liability underlying ED diagnosis. In line with the univariate models, we fitted an ACE and its nested models.We used joint categorical-continuous models to estimate the degree to which genetic influences on the EDI-2 score overlapped with genetic influences on AN and OED diagnoses. The joint categorical-continuous model is a hybridization of a liability threshold model (here for AN/OED diagnoses) with a model for a continuous variable (here for EDI-2 score). Initially, the correlations between EDI-2 score in one twin and ED diagnosis in their co-twin were estimated. If these cross-twin cross-trait correlations are greater in MZ than in DZ twins, genetic influence on the covariation of the EDI-2 score with AN/OED diagnoses is suggested. We then fitted a joint categorical-continuous model; this model allowed us to estimate the genetic, shared environmental, and non-shared environmental correlations between EDI-2 score and AN/OED diagnoses. The genetic correlation . The distribution of EDI-2 scores was only slightly skewed (skew = 0.57), nevertheless we transformed the EDI-2 score with the natural logarithm in order to improve the accuracy of the results, as the interpretability of raw scores was not important for the purpose of this study. The prevalence of EDs by and across zygosity is presented in The mean EDI-2 score was 2.66 , AN (h2 = 0.63), and OEDs (h2 = 0.67). The phenotypic correlation was lower for EDI-2 score and AN (rPH = 0.39) than for EDI-2 score and OEDs (rPH = 0.52) (rA = 0.26) than for EDI-2 score and OEDs (rA = 0.52)  . SimilarWe tested whether EDs can be viewed etiologically as the extreme end of a continuous distribution of ED features rather than as distinct disorders. The etiology of ED features, as measured with the EDI-2, appeared to be consistent across differing severity levels. To a moderate degree, the genetic influences on OED diagnoses also influenced continuous ED features. However, this did not hold true for AN, where the genetic influences on the diagnosis influenced continuous ED features to a lesser extent.The genetic correlation between ED features and OED diagnoses was moderate. This result implies that OEDs can be conceptualized as the extreme end of dimensionally distributed ED features, rather than as discrete entities. Our findings extend previous evidence from phenotypic studies that EDs are on a continuum  a higher stability of prevalence of AN over time and cultures compared to OEDs (Keel & Klump, The non-shared environmental correlations between ED features and ED diagnoses were above 0.5. This is in contrast to other dimension-disorder relationships such as in autism spectrum disorder and ADHD, where the non-shared environmental correlations are lower (Taylor et al., Our findings have important implications for future genomic studies of EDs and for how to conceptualize different ED presentations. First, our results suggest that the knowledge derived from studies of ED features may well generalize to diagnosable OEDs, but not necessarily to AN. Second, our data suggest that using ED features in genome-wide association studies may be a viable approach in order to increase sample size and improve statistical power for identifying common genetic variants of OEDs, as has been done for ADHD traits and depressive symptoms in the general population (Demontis et al., v. binge-eating/purging subtype, which precluded our ability to identify factors that would render individuals with AN binge-eating/purging subtype more similar to individuals with BN than those with AN restricting subtype. Related, individuals with self-reported purging and without any NPR diagnosis were collapsed into the OED group; however, a group of these individuals could indeed have had AN of the binge-eating/purging subtype. The effect of this possible misclassification, given the obtained results, would have been an underestimation of the genetic correlation between features and diagnoses of OEDs. Finally, although the overall sample was large, the absolute number of registered ED diagnoses, especially BN, were low. However, we tried to compensate for this by using different methodological approaches as well as including parent- and self-reports in order identify additional cases.Our study has several strengths. We had a large sample size and used a validated measure to assess ED features. Uniquely, the linkage of the data with the NPR enabled us to estimate genetic correlations between diagnosed EDs and ED features, including differentiating between AN and OEDs. Additionally, we used parent- and self-reports to compensate for a likely underestimation of ED diagnoses in the NPR. Our findings also have to be considered in the light of several limitations. First, our results apply to females only, as we were underpowered to conduct the analyses for males. Previous research found evidence for quantitative and qualitative sex differences in genetic and environmental influences on ED features, that is, the magnitude of genetic and environmental effects as well as the type of genetic factors involved differs between males and females (Baker et al., Our data suggest that a moderate proportion of genetic risks associated with OEDs are also associated with continuous variation in ED features, implying that OEDs can be considered the extreme manifestation of etiologically continuous ED features. Molecular genetic studies of OEDs could benefit from complementing the study of categorically defined OEDs with dimensional ED features, in order to increase statistical power to detect genetic variants. However, the evidence was less strong for AN, suggesting that AN might be more genetically distinct from ED features in the general population compared to OEDs.


Accumulating evidences have suggested that high body fat percentage (BF%) often occurs in parallel with cardiovascular diseases (CVDs), implying a common etiology between them. However, the shared genetic etiology underlying BF% and CVDs remains unclear.N = 100,716) and 10 CVD-related traits  with linkage disequilibrium score regression, multi-trait analysis of GWAS, and transcriptome-wide association analysis, and evaluated causal associations using Mendelian randomization.Using large-scale genome-wide association study (GWAS) data, we investigated shared genetics between BF% (P = 1.27 × 10− 22) and coronary artery disease (CAD) . We identified 5 loci and 32 gene-tissue pairs shared between BF% and HF, as well as 16 loci and 28 gene-tissue pairs shared between BF% and CAD. The loci were enriched in blood vessels and brain tissues, while the gene-tissue pairs were enriched in the nervous, cardiovascular, and exo-/endocrine system. In addition, we observed that BF% was causally related with a higher risk of HF  using a MR approach.We found strong positive genetic correlations between BF% and heart failure (HF) (Rg = 0.47, Our findings suggest that BF% and CVDs have shared genetic etiology and targeted reduction of BF% may improve cardiovascular outcomes. This work advances our understanding of the genetic basis underlying co-morbid obesity and CVDs and opens up a new way for early prevention of CVDs. Body fat percentage (BF%) is a proxy for adiposity that is genetically regulated through a leptin or melanocortin pathway in the central nervous system (CNS) . Recent FTO predicts long-term CVD risk (OR = 1.895), suggesting that BF% and CVDs might share genetic architecture [Genetic correlation analysis estimates the correlation of genetic effects between two clinically related traits and highlights the shared etiologies behind such an association. With methodological advances in molecular genetics and the increased number of available GWAS results, it is now feasible for us to investigate genome-wide genetic correlation and identify significant expression-trait associations for complex traits by using genomics resources , we investigated the genetic correlation and causality between BF% and 10 CVD-related traits with the overarching goal of characterizing the specific shared genetic loci and biological pathways. Further, we conducted a large-scale, genome-wide cross-trait analysis to explore novel genetic components among these diseases. The biological effects reflected by shared loci may play important roles in the co-occurrence of high BF% and CVDs.Using genome-wide association study (GWAS) summary-level data from several international consortia  [n = 188,578) for lipids such as high-density lipoprotein (HDL) [The overall study design is shown in Fig. 100,716) ; the Hea100,716) ; the Cor100,716) ; from th100,716) ; from th100,716) ; and froin (HDL) , low-denin (HDL) , total cin (HDL) , and triin (HDL) . Each stin (HDL) . DetailsThe ethical approval and quality control procedures of each consortium have been described in previous studies –14. InfoWe performed a post-GWAS genome-wide genetic correlation analysis for BF% and CVD-related traits using the LDSC software  by assumP value of a single SNP was defined a larger one in two GWASs, and then sorted and divided it into five groups in quartile, to further test the proportion of observed genetic correlation explained by each group and the robustness of our findings.The genetic correlation between BF% and CVDs was partitioned by 13 functional category using partitioned LDSC, including conserved region, DNaseI digital genomic footprinting region (DGF), DNase I hypersensitivity sites (DHSs), fetal DHSs, intron region, repressed region, super enhancers, transcription factor binding site (TFBS), transcribed region, and histone marks H3K4me1, H3K4me3, H3K9ac, and H3K27ac from Roadmap Epigenomics Project , 32. We We applied MTAG , a novelFor each of the shared loci between BF% and HF or CAD that meet the cross-trait meta-analysis significance criteria, we extracted variants within 500 kb of the index SNP and then identified a 99% credible set of causal SNPs using the Bayesian likelihood fine-mapping algorithm , 37. ThiWe first extracted summary statistics for variants within 500 kb of the index SNP at each of the shared loci between BF% and HF or CAD and then used R “coloc” package to perform genetic co-localization analysis to calculate the probability that the two traits shared a common genetic causal variant (H4) . We condPmeta < 1 × 10− 4) to ensure the robustness of TSEA results. In addition, we used Benjamini–Hochberg correction to account for multiple testing [To test if shared gene sets were highly enriched or specific expressed in a tissue, we conducted a tissue-specific expression analysis (TSEA) , 43. The testing .Pmeta < 5 × 10− 08) from cross-trait meta-analysis, we used the PANTHER tool to access enrichment of the gene sets in the Gene Ontology (GO) biological process and Reactome pathway [To obtain biological insights for identified shared genes  with other genetic variants based on European ancestry reference data from the 1000 Genomes Project. We chose the variant with the lowest P value for association with the exposure when genetic variants were in LD. For SNPs that were not available in outcome GWASs, we used the LD proxy search on the online platform (https://snipa.helmholtz-muenchen.de/snipa3/index.php/) to replace them with the proxy SNPs identified in high-LD (r2 > 0.8) or discard them if the proxies were not available.We performed a bidirectional MR analysis between BF% and HF and CAD since they are genetically correlated, using inverse-variance weighted (IVW) as the primary method –52. MediP values of all gene-tissue pairs for each trait, and false discovery rate < 0.05 was considered significant [To identify associations of BF% and CVDs with gene expressions in specific tissues, we conducted a TWAS using FUSION software package based on 44 post-mortal GTEx (version 6) tissue expression weights , 55, 56.nificant .P = 1.27 × 10− 22) and CAD  in large study populations that were predominantly of European ancestry (more than 75%) (Table P = 0.021) and HDL . However, we did not find evidence of genetic correlation between BF% and IS, MI, AF, TC, TG, and LDL   for BF% and HF, which could restore the phenotypic effects of a mutant gene. The correlation estimate for BF% and CAD was highest in the conserved region , where this region remained almost unchanged during evolution , suggesting that our findings were robust     Table S. Among tP < 5 × 10− 08 and single trait P < 0.05). The genomic control parameter (λ) was 1.16 in the cross-trait meta-analysis for BF% and HF, while λ = 1.20 for BF% and CAD . The strongest signal was observed on chromosome 16 at the FTO region , the first GWAS-identified susceptibility gene for obesity [P = 9.86 × 10− 26) and HF (P = 2.99 × 10− 08). The second strongest signal was in close proximity to an intergenic region closest to the GNPDA2 gene on chromosome 4 , a critical gene involved in lipid and glucose metabolism. Previous studies found that the expression level of GNPDA2 alters the transcriptome profile of human adipose-derived mesenchymal stem cells [Pmeta = 3.88 × 10− 09) on LPA are associated with both BF% and HF after meta-analysis. Variation within LPA has shown strong association with Lp(a)-cholesterol levels, which is an independent risk factor for cardiovascular-related events [We identified 5 genome-wide significant independent loci for BF% and HF Table . Out of  obesity , 58. Thiem cells . In addid events .Table 2LPA, HORMAD1, MIA3, WDR12, DGKH, CYP46A1, FES, FTO, IGF2BP1, BCAS3). The most significant locus is characterized by the LPA gene . The second locus  was mapped to the FTO gene. These loci were also found to be significant in the meta-analysis for BF% and HF, showing genetic overlaps between BF% and CVD-related traits. The third strongest signal was observed closest to the MC4R gene , which is involved in the leptin signaling pathway and its disruption is a causal factor of obesity [The cross-trait meta-analysis between BF% and CAD identified 16 genome-wide significant loci Table , 10 of w obesity .Table 3In order to assess whether shared genes between BF% and CVDs are enriched for expression in the disease-relevant tissue, we conducted the TSEA using the GTEx pilot data. We found that shared genes of BF% and HF had three significantly enriched tissues, including blood vessel, brain, and fallopian tube Fig. . The shaThe GO analysis indicated several significant shared biological processes between BF% and HF or CAD  such as glutathione derivative metabolic, glutathione derivative biosynthetic, and nitrobenzene metabolic processes Table S. In addiLists of credible set SNPs in each shared locus for BF% and HF or CAD from fine mapping are shown in Table SC18orf8 and NPC1 are expressed in multiple tissues including brain, nerve, artery, and adipose tissue. Consistent with a previous study [NPC1 was validated as a shared genetic component in lipid metabolism and cardiovascular health from our TWAS results. In addition, the methylation levels of C18orf8 were considered as an epigenetic mechanism in maternal obesity and early life origins of CVD and cancers [ATP5G1, SLC22A3, SNF8, and UBE2Z. ATP5G1 has been shown to encode a subunit of mitochondrial ATP synthase [SLC22A3 have been reported to be expressed at the blood-brain barrier and alters neuronal excitability [SNF8 and UBE2Z play a causal role in the development of CAD through a role in the vasculature [We conducted a TWAS analysis to examine if there are genes whose expression are related with BF%, CAD, and HF, and to determine if these genes are common among these traits. A total of 270 gene-tissue pairs were found across 44 GTEx tissues to be significantly associated with BF% after Benjamini-Hochberg correction  , in addius study , NPC1 wa cancers . Furthersynthase  and SLC2tability . By inteculature , 67.FigP = 4.16 × 10− 04)  Table -S16.TabUsing large-scale GWAS data from international consortia, we analyzed shared genetic etiology and causal associations between BF% and CVD-related traits. First, we found strong positive genetic correlations and further identified novel shared genetic loci between BF% and HF or CAD. Second, we found that the shared genetic loci were enriched mainly in blood vessels and brain tissues and glutathione-related metabolic pathway using functional analysis. Third, we found that the shared genes between BF% and HF or CAD are mostly from nervous, cardiovascular and exo-/endocrine system using TWAS analysis. Finally, we found that BF% was causally associated with HF using bidirectional MR analysis results. Taken together, evaluation of the genetic correlation and causality between BF% and CVDs furthers the understanding of the shared loci and biological mechanisms underlying this comorbidity.NPC1, PMAIP1, and GNPDA2 between BF% and HF, along with NEGR1, HORMAD1, GNPDA2, DGKH, CYP46A1, FES, POC1B, and BCAS3 between BF% and CAD. Most of these loci have significant single-tissue expression quantitative trait loci (eQTL), mainly in the cardiovascular, nervous, and exo-/endocrine system  [PMAIP1, HORMAD1, FES, BCAS3) [The only top loci common to the BF%-HF and BF%-CAD meta-analysis was rs10938397 near  pathway . An anim disease . Other teostasis . NPC1 isn humans , 72. In S Table S. BCAS3 i1, DGKH) –76 and v, BCAS3) –79, indiFTO gene on chromosome 16 was associated with both BF%/HF and BF%/CAD. FTO is an essential regulator in the development of obesity-induced metabolic and vascular changes, which is independent of its known function in regulation of obesity [FTO plays a critical role in cardiac contractile function during homeostasis, remodeling, and regeneration for HF and CAD [NEGR1 gene, which is strongly expressed in the brain, has been reported to affect neuronal control of food intake and promote obesity [NEGR1 gene may have abnormal fat deposition in various peripheral cells, suggesting a potential molecular target for regulating body fat [We found that the shared loci for BF% with HF and CAD were highly expressed in blood vessel/heart tissues, indicating that these traits might be caused by dysfunction of the cardiovascular system. We found that the  obesity . Previou and CAD , 82. Mor obesity . People body fat . In the body fat , 86. Morbody fat , 88. HowIn addition to the cardiovascular and nervous system, our TWAS also reported tissues enrichment from the exo-/endocrine and digestive system suggesting that the shared pathway between BF% and CVDs might have significant functions extending beyond brain and blood vessels. Besides a variety of adaptations/alterations in cardiac structure and function, body fat may affect the risk of HF with an altered metabolic profile in exo-/endocrine system . The incn = 100,280) at 1.32-fold per 1-SD increase in BF% [The bidirectional MR analysis showed a significant association between genetically predicted BF% and increased risk of HF (1.63-fold risk per 1-SD increase in BF%), which was stronger than that observed in a community-based cohort study . Fourth, this study did not assess sex-specific genetic effects using LDSC and MR analysis. Since we conducted the analysis using summary-level GWASs without individual data, it is difficult for us to verify the findings of this study for any variation with sex-specific analysis. Fifth, we could not re-test our hypothesis using additional or alternative large cohort without individual information in the present study. However, the data we used are the largest and latest GWASs for these traits, and the sample sizes of other cohorts are small from which we cannot yield more reliable results. Finally, the current study was limited to assessing shared genetic factors between BF% and CVD-related traits, which could only explain a small proportion of these traits. The effect of gene-environment interaction, in which the genetic variants lead to the occurrence of diseases in high-risk environments, may explain part of the rest variance . AlthougIn summary, our findings provide strong evidence of genetic correlations between BF% and CVDs, including HF and CAD. Additionally, we found a causal association between BF% and HF, which further supports targeted reduction of adiposity for improved cardiovascular outcomes. These results advance our understanding of body fat and provide novel insight into the common genetic basis of BF% and CVDs from molecular and functional levels. Notably, we identified novel genetic loci in both single-trait GWAS and cross-trait meta-analysis for multi-trait GWASs using MTAG. This work reinforces the idea that BF% and CVDs share common biological processes and opens up a new way for early prevention of CVDs.Additional file 1: Table S1. Summary of GWAS data. Table S2. SNP based heritability estimated by LDSC. Table S3. Partitioned genetic correlation between BF% and 2 cardiac traits. Table S4. Novel loci in MTAG for single trait compared with GWAS. Table S5. GO biological process pathway analysis for BF% and HF. Table S6. GO biological process pathway analysis for BF% and CAD. Table S7. Reactome pathways analysis for BF% and HF. Table S8. Reactome pathways analysis for BF% and CAD. Table S9. List of credible set SNPs in each locus from fine mapping. For each of the 5 BF% and HF shared loci, the table lists all SNPs within 500 kb in the 99% credible sets that were calculated. Table S10. List of credible set SNPs in each locus from fine mapping. For each of the 16 BF% and CAD shared loci, the table lists all SNPs within 500 kb in the 99% credible sets that were calculated. Table S11. Genome-wide significant loci by cross-trait meta-analysis at sentinel SNPs associated with BF% and HF with directional effect . Table S12. Genome-wide significant loci by cross-trait meta-analysis at sentinel SNPs associated with BF% and CAD with directional effect . Table S13. Specific characteristics of genetic instruments of MR analysis of BF% levels upon CVD traits. Table S14. Specific characteristics of genetic instruments of MR analysis of CVDs upon BF% levels. Table S15. MR analysis between BF% and cardiac traits in a leave one out approach. Table S16. MR analysis between BF% and cardiac traits for a single snp. Table S17. Significant overlap transcriptome-wide association analysis results between BF% and CVDs. (FDR<0.05). Table S18. Significant single-tissue eQTL from GTEx for 3 representative gene between BF% and HF. Table S19. Significant single-tissue eQTL from GTEx for 8 representative gene between BF% and CAD.Additional file 2: Figure S1. Partitioned genetic correlation between BF% and HF by genetic variants groups with different P value. Figure S2. Partitioned genetic correlation between BF% and CAD by genetic variants groups with different P value. Figure S3. QQ plot of cross-trait meta-analysis between BF% and HF. Figure S4. QQ plot of cross-trait meta-analysis between BF% and CAD.


Days to 50% flowering, number of primary branches/plant, main shoot length, and 1000-seed weight all had a high genotypic coefficient of variability (GCV), broad-sense heritability (h2bs), and genetic advance as percent of the mean (GAM) values, as well as significant and desirable correlations and direct effects on seed yield. As a result, these traits have been recognized as the most critical selection criterion for Indian mustard breeding programs.Understanding the mode of gene action that controls seed yield and Sclerotinia stem rot resistance in Indian mustard is critical for boosting yield potential. In a line × tester mating design, ten susceptible lines and four resistant testers were used to conduct genetic analysis. The significance of general combining ability (GCA) and specific combining ability (SCA) variances revealed that both additive and non-additive gene actions were involved in the inheritance of Sclerotinia stem rot resistance and yield attributing traits. In addition to 1000-seed weight and number of primary and secondary branches/plant, the genotypes RH 1569 (line) and DRMR 2035 (tester) appeared to be the strongest general combiners for Sclerotinia stem rot resistance. RH 1657 × EC 597317 was the only cross among several that demonstrated a significant desired SCA value for Sclerotinia rot resistance. Regarding SCA effects for yield and component traits, the cross RH 1658 × EC 597328 performed best, with a non-significant but acceptable negative SCA effect for resistance. DRMR 2035, RH 1222-28, RH 1569, RH 1599-41, RH 1657, RH 1658, and EC 597328 are promising genotypes to use as parents in future heterosis breeding and for obtaining populations with high yield potential and greater resistance to Sclerotinia stem rot disease in Indian mustard, based on GCA effects of parents,  India is the world’s 4th largest grower and producer of oil-producing crops, accounting for ~19% of worldwide acreage and 2.7% of production. Oilseed crops, just after cereals, play an important role in the Indian agricultural economy. India is on track to become the world’s third-largest consumer market and an importer of edible oils, meeting over 60% of its domestic consumption through imports at the cost of USD 10 billion per year . DomestiBrassica juncea (L.) Czern & Coss.] is the most widely cultivated oilseed crop in India, out of six economically important species of the rapeseed–mustard group, due to its greater sustainability to grow under diverse agro-climatic conditions [Brassica campestris  and Brassica nigra  [Indian mustard [nditions ,6,7. Nat 16, BB) . To meet 16, BB) , current 16, BB) ,15,16,17 16, BB) . Accordi 16, BB) , heterosSclerotinia sclerotiorum (Lib.) de Bary, is the most destructive fungal disease of Indian mustard at the moment, causing yield losses of 32–90% [S. sclerotiorum is a cosmopolitan and widespread phytopathogenic fungus with a broad host range that has gone from being inconsequential to symbolic due to global climatic changes and is currently one of the most devastating diseases of Indian mustard. Sclerotia, the survival structure of the pathogen, can survive in plant detritus for many years and act as the major inoculum for infection. It germinates myceliogenically (soil-borne infection) to produce mycelial hyphae that almost instantly invade the lower portion of plants, including the basal stem. In contrast, its carpogenic germination (airborne infection) has apothecia, which are cup-like structures with a 3–6 mm diameter that release ascospores to infect the upper sections of host plants. Almost all plant parts are affected, including cotyledons, leaves, branches, raceme, siliquae, and stems, with infected tissue displaying typical white fluffy cottony mycelial growth symptoms , and genetic advance as percent of mean (GAM) [2bs, GAM, genetic correlation, and path coefficients for examining the collection of traits [Furthermore, environmental or genotype × environmental interaction may cause variation across genotypes for several traits ,40. As aan (GAM) ,43. Furtan (GAM) ,45. Planf traits .In this context, the current study was carried out to gather information about the nature and extent of gene action, combining ability effects and estimation of selection indices for Sclerotinia stem rot resistance, seed yield, and its component traits in Indian mustard.1 hybrids, 14 parents (10 Lines and 4 Testers), and two standard checks (RH 0749 and RH 725). During the Rabi season of 2019–2020, these plant materials were tested in a Randomized Complete Block Design (RCBD) with three replications at the Research Farm of Oilseeds Section, Department of Genetics and Plant Breeding, CCS HAU, Hisar. The plots were paired rows of 4 m length with a 30 × 10 cm separation (row × plant). Except for any fungicidal treatment to reduce Sclerotinia stem rot, the entire recommended package and practices were followed to establish a healthy crop.The best environmental conditions for Sclerotinia stem rot epidemic development in Indian mustard include high relative humidity (>80%), a temperature range of 5 to 25 °C, and wet soil [The plant materials for this study consisted of 14 different genotypes of Indian mustard chosen for their different responses to Sclerotinia stem rot, seed yield, and its component traits . The parThe seed yield and its component traits were recorded as follow: days to 50% flowering (number of days from sowing to when 50% of the plants had flowered) and days to maturity  both recorded on plot basis, while other phenotypic traits were recorded from ten randomly selected un-inoculated/healthy plants from the center of each row as follows: plant height (measured in cm from the base of the plant to tip of the main raceme), number of primary branches/plant (assessed branches derived from the base of the main stem), number of secondary branches/plant (assessed branches emerged from the primary branches), main shoot length (measured in cm from base of most top primary branch to the tip of the plant), number of siliquae on main shoot (siliquae borne on main raceme), siliqua length , number of seeds/siliqua (counted from the same siliquae used to estimate siliqua length), 1000-seed weight (grams), seed yield/plant (grams), and oil content (determined in % using Soxhlet apparatus method).S. sclerotiorum sclerotia (Hisar isolate) were employed. These sclerotia were first precisely washed in double distilled water, then surface sterilized by soaking for 10–12 s in 0.1% sodium hypochlorite solution. Sclerotia were then gently rinsed 3–4 times with distilled water to remove any remaining germicide before being dehydrated on aseptic blotter paper. Finally, these sclerotia were cut in half using surgical blades and aseptically transferred to Petri-plates containing Potato Dextrose Agar (PDA). These plates were kept in a BOD incubator at 22 ± 1 °C for five days. Using the techniques provided by Li et al. [S. sclerotiorum was used to inoculate the main stems of 10 randomly chosen and labeled plants (other than the 10 representative plants selected for the assessment of yield and its component traits)from each genotype/replication. Mycelial discs (5 mm2) were cut from the borders of pure culture plates, placed on a paraffin wax strip (together with a moist cotton swab) and wrapped tightly around the main stem, as in our prior investigations [For the pure culture preparation, i et al.  and Singigations . Lesion igations , genotypBased on the lesion length (cm) recorded from each plant, the following parameters were computed to determine genotypic response to Sclerotinia stem rot.Mean lesion length (cm) was calculated by averaging the lesion length (cm) measured from the inoculated main stems among all the tested plants by using the following formula:Disease severity index was computed by the following equation described by Ooi :DSI=∑(No2bs), genetic advance (GA), and genetic advance as percent of the mean (GAM) were calculated according to Singh and Chaudhary [The line × tester analysis procedure was utilized to estimate general and specific combining ability effects and variances, as outlined by Kempthorne  and elabhaudhary . Genotyphaudhary . Seed yihaudhary . The stap ≤ 0.01).Total variance from the combined ANOVA of Line × Tester was divided into variances owing to parents, lines , testers , females vs. males (lines vs. testers), crosses, parents vs. crosses, and GCA  and SCA  effects . Parents1 hybrids, and two standard checks  in Sclerotinia stem rot resistance, seed yield, and its attributes. For the majority of the traits tested, all genotypes exhibited significant variation. Days to 50% flowering ranged from 35.0 to 53.0 days, with a mean of 42.7 days, whereas days to maturity have minimum, maximum, and mean values of 137.0, 157.0, and 150.0 days, respectively. The average plant height was 205.5 cm, with a range of 149.5 to 232.0 cm. Number of secondary branches/plant, main shoot length, number of siliquae on the main shoot, siliqua length, number of seeds/siliqua, and 1000-seed weight, among other yield parameters, showed wide variation that may be exploited in a varietal development program. Seed yield varied significantly between genotypes, ranging from 15.6 to 31.4 g/plant. The F1 hybrids viz., RH 1599-44 × DRMR 2035, RH 1599-44 × EC 597317, RH 1658 × EC 597328, and RH 1664 RH × 1222-28 exhibited increased yield potential due to superior performance for traits such as the number of primary and secondary branches/plants, main shoot length, and number of seeds/siliqua. Aside from that, these F1 hybrids had relatively high oil content. The MLL and DSI for Sclerotinia stem rot resistance ranged from 2.28 to 13.96 cm and 0.50 to 3.83, respectively. The parental genotype DRMR 2035 displayed a highly resistant response (MLL < 2.5 cm), whereas RH 1222-28 and EC 597328 exhibited a resistant response (MLL > 2.6 but <5.0 cm). However, most of the genotypes with high resistance were low yielders. Although, it is notable that the F1 hybrid RH 1569 × DRMR 2035 not only outperformed the best-released cultivar (RH 725) in terms of seed and oil yield  but was also reasonably resistant to Sclerotinia stem rot (stem rot . This cop ≤ 0.01) and negative GCA effects for both days to 50% flowering and days to maturity. Only the genotype RH 1569 had a significant and positive GCA effect for the number of primary and secondary branches/plant among all the lines and testers. Lines RH 1658 and PM 26 and testers EC 597328 and EC 597317 showed significantly positive GCA effects on main shoot length. The genotypes RH 1569 (line) and EC 597328 (tester) had a significantly positive GCA effect for number of siliquae on main shoot. Two lines, RH 1633 and RH 1658, and one tester, RH 1222-28, showed significant and positive GCA effects on siliqua length. For 1000-seed weight, lines RH 1569, RH 1633, and RH 1658, as well as testers RH 1222-28, DRMR 2035, and EC 597317, were good general combiners, while lines RH 1599-41 and PM 26 were good general combiners for number of seeds/siliqua. Only the parental lines RH 1569 and RH 1599-44 demonstrated a significantly positive GCA effect on seed yield/plant. The GCA effects for oil content were significant and positive for the lines RH 1566, RH 1599-44, RH 1658, PM 26, and tester DRMR 2035. The female parents, RH 1569, RH 1599-41, RH 1657, and the male parents, RH 1222-28 and DRMR 2035 had significant and desirable negative GCA impacts on MLL and DSI. These parental genotypes were effective Sclerotinia stem rot resistance combiners.1s studied, and were crucial for earliness in Indian mustard. RH 1658 × EC 597328 and RH 1899-53 × DRMR 2035 had significant (p ≤ 0.05) and positive SCA effects for both the number of primary and secondary branches/plants, while RH 1657 × DRMR 2035 had significant (p ≤ 0.05) and positive SCA effects for main shoot length and number of siliquae on main shoot. Four cross combinations, RH 1569 EC × 597317, RH 1599-41 × EC 597317, RH 1633 × EC 597328, and RH 1664 × DRMR 2035, showed significant (p ≤ 0.05) and positive SCA effects on siliqua length. In six and eight distinct crossings, respectively, significant (p ≤ 0.05) and positive SCA effects for the number of seeds/siliqua and 1000-seedweightwas detected. For seed yield/plant, positive SCA crosses are preferred. However, only one cross combination, RH 1658 × EC 597328, demonstrated a significant and positive SCA effect (5.19 for this trait). Sixteen out of forty cross combinations showed significant (p ≤ 0.05) and positive SCA effects for oil content. For Sclerotinia stem rot resistance assessment parameters such as MLL and DSI, the cross RH 1657 × EC 597317 showed significantly negative and desirable SCA effect. In contrast, three crosses, RH 1599-41 × EC 597328, RH 1657 × DRMR 2035, and RH 1664 × EC 597317, showed significantly positive and undesirable SCA effects.p ≤ 0.05), with the main shoot length (31.87) being the largest, followed by the number of siliquae on the main shoot (15.68), and the days to maturity (0.07) and DSI (0.07) being the lowest. Plant height (13.66) had the highest GCA variance, whereas oil content had the lowest (0.05). The proportions of σ2 GCA/σ2 SCA were less than unity (<1) for characters such as number of primary and secondary branches/plant, main shoot length, number of siliquae on main shoot, siliqua length, number of seeds/siliqua, seed yield/plant, and oil content, but were greater than unity (>1) for the remaining characters such as days to 50% flowering, days to maturity, plant height, 1000-seed weight, MLL, and DSI. All examined traits had only marginally larger PCV than GCV, except for seed yield/plant, which had a 1.5-fold higher PCV than GCV value. The GCV and PCV of all the characters in this experiment ranged from low to high. Oil content had the lowest GCV and PCV values (1.48% GCV and 1.51% PCV), whereas MLL had the greatest (25.39% GCV and 31.48% PCV). Estimates of broad-sense heritability (h2bs) ranged from very low (25.31% for days to maturity) to very high (95.55% for oil content). Days to 50% flowering, 1000-seed weight and oil content had high heritability estimates (>80%), while the number of primary and secondary branches/plant, main shoot length, number of siliquae on main shoot, siliqua length, MLL, and DSI had moderately high heritability estimates (60–79%). Days to maturity and seed yield/plant have low heritability estimates (<40%). GAM for days to maturity and DSI ranged from 2.15 to 46.14. For DSI, MLL, 1000-seed weight and days to 50% flowering, GAM was relatively high (>20%).Furthermore, the DSI was negatively associated with days to 50% flowering, plant height, and the number of primary and secondary branches per plant. In contrast, it was positively associated with siliqua length, number of seeds/siliqua, and 1000-seed weight. It revealed that comparatively late and tall genotypes required more days to reach 50% flowering, producing more primary branches/plant and siliquae on the main shoot, but with shorter main shoot lengths and fewer seeds/siliqua. The genetic correlation was subsequently investigated using the path coefficient analysis approach, which partitioned the correlation coefficient into direct and indirect effects via alternative traits. The resultant variable was seed yield/plant, which was the complex outcome of various characters, whereas its component traits were treated as causal variables. 2 and subsequent segregating generations of that cross. The ability to examine the combining ability and heterotic effects in selecting superior parents for the future requires the mean values of parents and F1 combinations. Line × Tester analysis is the best way to examine the potentiality of contrasting lines  and testers  for their combining ability and gene action for different traits [per se performance and combining ability effects, the current study was conducted to identify the best parental genotypes and their cross combinations for Sclerotinia stem rot resistance, seed yield, and component traits. The data from the traits analyzed can establish a helpful breeding strategy for future high-yielding Indian mustard hybrids/varieties with increased Sclerotinia stem rot resistance.Breeding cultivars with high yield potential and resistance to major diseases is a primary goal of crop improvement projects. When hybridization is attempted in a specific mating design, identifying the best parental genotype combinations allows breeders to take advantage of their heterotic effects and shows that superior transgressive segregants are available in the Ft traits . Based op ≤ 0.01) in the analysis of variance (ANOVA), showing that there was a lot of genotypic variation for these characters among the genotypes tested. As a result, line × tester analysis can split total genetic variance into its appropriate orthogonal components. For most of the traits tested, ANOVA for line × tester mating design demonstrated the significance of their orthogonal components, namely, parents, crosses, and parent vs. Crosses. This implies a high level of genetic variability in both males (testers) and females (lines) parents and their F1 hybrids, allowing for a more in-depth investigation of genetic variation by combining ability analysis and the measurement of the extent heterosis for these traits. Kaur et al. [S. sclerotiorum.There must be sufficient genetic variation within the selected lines for any crop breeding program to succeed. The mean square due to genotypes for agronomic traits, MLL, and DSI was highly significant  and DRMR 2035 (tester) looked to be the strongest general combiners for Sclerotinia stem rot resistance and most yield component traits. They should do so well in hybrid combinations with other parents. The greatest criterion for maximizing heterosis in F1 crosses. The SCA effect, which considers loci with non-additive and epistatic gene effects, can also identify high heterotic F1 hybrids. Negative SCA effects for MLL and DSI are desired because they lead to resistance, whereas positive SCA adds to Sclerotinia stem rot susceptibility. RH 1657 × EC 597317 was the only cross that demonstrated a significant desired SCA value for MLL and DSI among other cross combinations, showing the involvement of a particular effect in this hybrid’s resistance expression. It could be because of the good general combiner RH 1657, which has additive effects, and the lousy combiner parent (EC 597317), which has epistatic effects. However, it revealed adverse SCA effects for the majority of yield-related traits. All other crossings, except for cross RH 1657 × EC 597317, had unacceptable and/or insignificant SCA effects for both resistance evaluation criteria, demonstrating that genes/alleles giving Sclerotinia stem rot resistance are recessive over susceptibility. Furthermore, crosses involving both parents with significant GCA effects for resistance had poorer SCA effects, implying the existence of a complex non-allelic gene interaction for resistance and/or that both of these parents may have identical resistance alleles thus could not benefit from fixable gene effects. Similarly, Van Becelaere and Miller [Contrary to what was expected based on the parent’s GCA, significant SCA effects in the desired direction demonstrate positive deflections with regards to the Fd Miller  found thd Miller  found a d Miller  in rice.The contribution to the total variation of the lines, testers, and their interactions support prior results that general effects were more relevant than specific effects for Sclerotinia rot resistance ,61,62. O2bs) into four categories: low (<40%), moderate (40–59%), moderately high (60–79%), and extremely high (>80%). Days to 50% flowering, 1000-seed weight, oil content, number of primary and secondary branches/plant, main shoot length, number of siliquae on main shoot, siliqua length, MLL, and DSI all had moderately high to extremely high heritability values in this study. If these traits are employed to boost seed and oil yield and Sclerotinia stems rot resistance in Indian mustard, the projected gain from the selection will be considerable. The high heritability estimates also suggest that the environment poorly influences certain traits. Heritability estimates for plant height and number of seeds/siliqua were intermediate, indicating that these parameters’ environmental and genetic effects were indistinguishable. Days to maturity and seed yield/plant have low heritability estimates, and direct selection for these traits would be challenging due to the high environmental impact. As a result, component traits with high heritability and strong desirable correlations can be picked concurrently to select these traits. Several studies found moderate to high heritability for yield and its component traits in Indian mustard, similar to the current study [2bs and low GAM [The current study’s PCV values were larger than the GCV values for all traits, showing that environmental effects on the characteristics were more relevant than genotypic effects. Previous publications on higher PCV values than GCV values for yield and component traits in Indian mustard ,73,74,75 high 60–%, and exly high >%. Days tnt study ,82,83. Tnt study ,84,85. Gnt study . High GAnt study . Selecti low GAM . SelectiFor most of the traits tested, the genotypic correlation was higher than the phenotypic correlation, showing genetic relationships. As a result, phenotype-based selection would work ,89,90. SPath coefficient analysis has been widely used in crop breeding projects to discover the relationship between seed yield and component traits . Days toThis study highlighted the rewarding parents and crosses of Indian mustard that can be exploited to launch effective breeding strategies for developing high-yielding and Sclerotinia stem rot-resistant cultivars. Based on results, it is concluded that significant and desirable GCA effects were shown by DRMR 2035 among the testers and RH 1569 among the lines that were considered to be good general combiners for Sclerotinia stem rot resistance, seed yield, and oil content, as well as for most of the yield attributing traits. The cross combination of RH 1657 × EC 597317 and RH 1658 × EC 597328 showed excellent SCA performance for the resistance and yield contributing traits. These parents and hybrids could be utilized as good combiners and isolated to obtain desirable segregate for combining high resistance levels to Sclerotinia stem rot with superior seed yield and oil content in the future Indian mustard breeding program. This study was suggested for a breeding program for which the advantages of both types of variances, namely, additive and non-additive, are chosen. Days to 50% flowering, number of primary branches/plant, main shoot length, and 1000-seed weight and disease severity index are the best selection criterion in the field for use in breeding programs for simultaneous improvement in yield and resistance because of its high heritability and desirable association with seed yield.