Genome-scale metabolic networks for model plants and crops in combination with approaches from the constraint-based modelling framework have been used to predict metabolic traits and design metabolic engineering strategies for their manipulation. With the advances in technologies to generate large-scale genotyping data from natural diversity panels and other populations, genome-wide association and genomic selection have emerged as statistical approaches to determine genetic variants associated with and predictive of traits. Here, we review recent advances in constraint-based approaches that integrate genetic variants in genome-scale metabolic models to characterize their effects on reaction fluxes. Since some of these approaches have been applied in organisms other than plants, we provide a critical assessment of their applicability particularly in crops. In addition, we further dissect the inferred effects of genetic variants with respect to reaction rate constants, abundances of enzymes, and concentrations of metabolites, as main determinants of reaction fluxes and relate them with their combined effects on complex traits, like growth. Through this systematic review, we also provide a roadmap for future research to increase the predictive power of statistical approaches by coupling them with mechanistic models of metabolism. Advances in genotyping have provided unprecedented insights in the genetic variations among individuals of the same species. Allelic variations within a genome of the same species include the differences in the number of tandem repeats at a particular locus, segmental insertions/deletions (indels), and single-nucleotide polymorphisms (SNPs) . Since SDepending on their genomic location, SNPs have the potential to alter all steps of transcription and translation. For instance, if a SNP lies in a transcriptional regulatory element, it can alter mRNA expression; in addition, SNPs that do not lie in protein-coding regions can affect splicing, mRNA degradation, and the sequence of non-coding RNA. If a SNP that lies in a protein-coding region is synonymous, i.e. does not cause amino acid change, it can affect the translation rate and turnover of mRNA, ultimately reflected in changes of the protein abundance; finally, if the SNP is nonsynonymous (missense or nonsense), i.e. leads to amino acid change, it can modify the protein activity. Through their effects on mRNA, enzyme abundance and stability as well as enzyme activity, SNPs have direct effect on metabolic reactions catalysed by the respective enzymes.Metabolism represents the entirety of biochemical reactions through which nutrients are imported from the environment and are transformed into the building blocks of biomass, ensuring growth, as well as other cellular components that support defence, reproduction, and, ultimately, survival . A quantMetabolic reactions do not operate in isolation and jointly affect the temporal change of metabolite concentrations Fig. a. A metamentclass2pt{minim13CO2 are required to infer intracellular reaction fluxes in photoautotrophic growth [Reactions can be divided into extra- and intracellular based on whether or not they facilitate the exchange (i.e. import or export) of metabolites with the environment. Monitoring the change of extracellular metabolite concentrations over time can be readily used to estimate extracellular reaction fluxes . Howeverc growth , 12. TheAs a result, other computational approaches have been developed to predict/estimate fluxes in the constraint-based modelling framework based on the assumption that an organism optimises a cellular task (e.g. growth) under a set of physicochemical constraints  Fig. 1cc1c. ThisHere, we review a collection of recent modelling approaches, which allow the dissection of the genetic basis of reaction fluxes by identifying their association with SNPs that are integrated into metabolic networks. Since these approaches can be grouped based on whether or not they rely on the principles underlying genome-wide association and genomic selection, we also describe the basic methodology underlying these machine-learning and statistical approaches. Focusing on the global effects of SNPs, we also provide a succinct review of studies that examine SNP effects on maximal enzyme activity in model plants and crops. We then offer a perspective for determining local effects of SNPs on Access to a high-quality metabolic model of an organism is key to accurate estimation of fluxes. Genome-scale metabolic models (GEMs) gather the entirety of documented metabolic reactions assembled based on annotation of the genome of an organism . GEMs arArabidopsis thaliana (Arabidopsis) [Oryza sativa (rice) [Zea mays (maize) [Solanum lycopersicum (tomato) [Solanum tuberosum (potato) [Hordeum vulgare (barley) [Brassica napus (oilseed rape) [Medicago truncatula [Glycine max (soybean) [Setaria viridis [Populus trichocarpa [3, and C4 plant species [Efforts in the last decade have resulted in the assembly of high-quality GEMs and metabolic models of central carbon metabolism for key model plants and crops, including: idopsis) –31, Oryza (rice) –37, Zea  (maize) , 38–40, (tomato) , Solanum(potato) , Hordeum(barley) , Brassiced rape) , 45, Meduncatula , Glycinesoybean) , Setaria viridis  and Popuchocarpa , as well species –53  or they species ). Furthe species . They al species . Further species , mesophy species , 50, roo species , 40. The species .Fig. 2OvDetermining the genetic basis of the flux for a given reaction requires that it is quantified in multiple individuals (i.e. genotypes) whose metabolic networks may differ Fig. e. To thir fluxes . Furtherr fluxes  can be uEstablishing a link between genetic markers (e.g. SNPs) and a trait of interest is carried out by application of machine-learning and statistical approaches. Two principal questions can be posed: (1) is the trait statistically associated with a genomic region or position? (2) Are the genetic markers predictive of the trait? (in the sense of predicting a major proportion of the variance). These questions can be used to group the statistical approaches to link genetic markers with (metabolic) traits into those that aim to conduct genetic mapping and those that devise models for genomic selection, respectively.2 populations, backcross, and recombinant inbred lines (RILs)) is termed as quantitative trait loci (QTL) mapping , restriction fragment length polymorphism (RFLP), and simple sequence repeat (SSR), because the recombination blocks in biparental populations are relatively big. This approach has provided powerful means to identify loci that co-segregate with the studied trait in the employed biparental population, due to the smaller number of false positive candidates . HoweverIn contrast to QTL mapping, GWAS has a relatively higher resolution, often down to a single gene level, since it relies on high-density SNPs covering the entire genome . TherefoF test or likelihood ratio test between the model without consideration of the SNP effect and the model that includes the tested SNP  including kinship matrix and population structure was proposed , which ianalysis . The polSNP Fig. b. The teHowever, application of the MLM approach is computationally challenging with the increase in the number of samples and SNPs that are required to improve the resolution and power of the genetic mapping. Several efficient GWAS algorithms have been devised to handle the population structure and kinship by employing elegant matrix transformations (e.g. the efficient mixed model association (EMMA) , genome-From this brief review of computational approaches for genetic mapping based on GWAS, it is evident that they are all based on statistical approaches of association and do not consider mechanistic insights and constraints. Several pressing questions arise: can the coupling of the basic principles of GWAS with mechanistic models of metabolism help in detecting causal SNPs with local effects on reaction fluxes? If so, could this be done with smaller population sizes, without reducing the power of the detected associations? These questions will be addressed in “Genomic selection (GS) is considered the most promising breeding method to speed up the development and release of improved genotypes . It is bIn contrast to GWAS, GS forgoes statistical testing for the effect of SNPs, as the goal is to devise a model of high predictive power. Nevertheless, like GWAS, the accuracy of GS is affected by several factors, including: (i) the sample size, (ii) genetic relationship within and between the training and testing population, (iii) marker density, (iv) heritability of the trait, (v) linkage disequilibrium between markers and quantitative trait loci controlling the trait of interest, and (vi) non-additive genetic effects (e.g. epistasis) , 81. It  y is a vector of phenotype, X is the fixed effect design matrix, 2 norm is equivalent to Based on the machine-learning/statistical techniques employed, GS approaches can be roughly divided into those relying on regression, classification, and deep learning techniques . Ridge rThis approach can shrink all effects toward zero equally across markers, under the assumption that all markers have a common variance. Other approaches, like genomic best linear unbiased prediction (GBLUP), estimate the kinship matrix from genomic markers to represent the pedigree information, then estimate GEBV in a mixed linear model that is equivalent to the rrBLUP model . The GEBLike the GWAS approaches outlined above, GS is based on machine-learning algorithms and the transferability of the resulting models to unseen scenarios, i.e. different population, different environments, and the combination of the two, remains one of the biggest challenges in the application of GS. Therefore, it is of interest to investigate if the prediction accuracy of GS for metabolic traits can be improved if the mentioned approaches are coupled with mechanistic models of metabolism, discussed in “Approach based on genomic selection”.cis position  to the location of the corresponding structural genes , but also in trans position (i.e. on a different chromosome), denoting regulatory QTL.Determining the genetic architecture of metabolism entails genetic mapping of metabolic traits, including: metabolite levels , protein abundances and activities, and reaction fluxes. There are plethora of studies that use GWAS and QTL mapping approaches in diverse plants and crops based on measurement of metabolite levels and protein abundances , 90. Howaturated . Thus, iSaccharomyces cerevisiae (yeast) central carbon metabolism [2-segregants (genotyped by 3727 SNPs) from a cross of two yeast strains [To this end, all the statistical approaches for genetic mapping mentioned above can be readily used to determine the genetic architecture of tabolism  based on strains . These atrans to the respective structural genes, except for single cis-QTL for nitrate reductase, glutamate dehydrogenase, and shikimate dehydrogenase.Since intracellular fluxes are more challenging to quantify see “”, majoricis- and trans-acting control. A tomato introgression population, generated by introgressing segments of the genome of the wild relative Solanum pennellii into the modern tomato cultivar Solanum lycopersicum, was used to investigate QTL for the maximal enzyme activities of 28 enzymes from central carbon metabolism [trans-acting genes.In addition, Mitchell-Olds and Pedersen  performetabolism . To thiscis-QTL of moderate effects for maximal enzyme activity of five enzymes, including UDP-glucose pyrophosphorylase, ADP-glucose pyrophosphorylase, fumarase, and phosphoglucose isomerase. The remaining QTL were trans-acting of smaller effects than the cis-acting, and were found in genomic regions that include components involved in transcriptional and post-translational modifications.The only GWAS with maximal enzyme activities as a trait was carried out in an Arabidopsis diversity panel composed of 349 accessions. To this end, associated SNPs for 24 maximal enzyme activities in central metabolism were detected . The stutrans-acting genes for accurate predictions of their effects on metabolic traits.Genetic mapping of maximal enzyme activities in different plant species demonstrates that genetic variants in both regulatory and structural genes can affect this trait of different enzymes in central metabolism. Therefore, consideration of missense SNPs may only identify a small fraction of the phenotypic variance in this trait. The latter implies that the integration of SNPs into mechanistic models should consider the action of The approaches that integrate SNPs into a metabolic network can be grouped based on two criteria: (i) if they investigate the positioning of SNPs in metabolic network, using the GPR rules and (ii) if they characterize the effect of a SNP on reaction fluxes. With respect to the second criterion, one can further subdivide these approaches based on whether they rely on principles of GWAS or genomic selection, as principal statistical approaches for linking SNPs with traits.A first approach to investigate the role of SNPs in metabolic networks is to characterize their position in the metabolic network. Due to the possibility that a metabolic reaction is catalysed by isoenzymes and protein complexes, as well as due to the promiscuity of some enzymes, whereby they can catalyse multiple reactions , the proOne of the critical factors that determine the power of GWAS is the population size. Integration of SNPs in a metabolic network can facilitate characterisation of their effect on fluxes even with a very small population . Here, wThis constraint-based approach is based on structural sensitivity analysis , wherebyWith these assumptions, the approach is based on representing the genotype-specific flux distribution, Mycobacterium tuberculosis complex with 556 nonsynonymous SNPs, and 88 SNPs were classified as functional with the used threshold value.In this sense, cts; and  only SNPThe approach can be viewed as a multi-locus GWAS , but doeLike the constraint-based approach above, SNPeffect aims to determine whether a SNP is functional or not by characterising its effect on reaction fluxes  Fig. 4cc4c. The e action :\documenAs a result, the flux With measurements of available relative changes in maximal enzyme activities and metabolite levels with respect to a reference genotype, one can obtain lower and upper bounds. Deviation of the steady-state flux is then attributed to (positive/negative) additive effects of SNPs and saturation effects of the enzyme. There are three assumptions on which SNPeffect is based: (i) a SNP is assumed to have consistent effect across all genotypes, i.e. it either increases or decreases reaction fluxes, (ii) the effect of a SNP is allowed to vary across genotypes, (iii) only nonsynonymous SNPs in genes included in the metabolic network are considered. Here, the effect of a SNP are simultaneously determined over all genotypes, by including constraints of steady-state and relative growth rate with respect to the reference genotype. Implementation of the approach clearly requires setting up a reference flux distribution or specifying lower and upper bounds, Populus trichocarpa (poplar) [Like in the structural sensitivity approach, above, SNPeffect can be regarded as a multi-locus GWAS in which the SNPs as present/absent, i.e. without making distinctions between different alleles. Its performance depends on: (i) the optimization function used, which in the existing implementation minimises the effects of the deviations from steady-state flux distribution that respect constraints from relative enzyme activities and metabolite levels, (ii) the reference flux distribution, determined by parsimonious FBA , and (ii(poplar) , and ideAvailability of flux distributions from a population of genotypes whose size is preclusive to conduct GWAS can still be used in GS for reaction fluxes. Tong et al.  developeThe statistical models that are devised in the third step of netGS inherits the shortcomings of GS models. However, through forcing these models to jointly respect physicochemical constraints, netGS aims to improve the model performance for unseen genotypes and in scenarios when there are large differences between training and testing populations. The imposing of these constraints can be regarded as adjusting for epistatic interactions between SNPs, which are otherwise difficult to integrate in a statistical framework due to the large number of SNPs considered. In contrast to the approaches above, netGS is not limited to investigating only nonsynonymous SNPs, but can also consider SNPs which lie in non-coding regions of the genome—which boosts the usage of genomic data. netGS was tested with 67 Arabidopsis accessions for which genotype- and condition-specific biomass reactions were developed based on measurements. The results showed that, in comparison to classical GS, it improves the prediction accuracy of growth within and across nitrogen environments by 32.6% and 51.1%, respectively, as well as from optimal nitrogen to low carbon environment by 50.4%. The approach can readily be applied to any plant species for which metabolic models of high-quality exist and can be coupled with constraints from phenotypic data of specific genotypes.The brief review of the approaches for linking SNPs with metabolic and complex traits highlighted the division of two sets of approaches rooted in different methodologies. On one side, approaches for QTL mapping, GWAS, and genomic selection are solely based on statistics; moreover, genomic selection can be regarded as a black box, machine-learning approach that does not provide mechanistic insights or candidates for further testing. On the other hand, constraint-based approaches are applicable with large-scale models of metabolism and allow to establish a link between fine-grained metabolic processes and complex traits, such as biomass accumulation and growth.Our systematic review indicated the possibility for merging the two complementary types of approaches to overcome their principle drawbacks, namely, the need for large populations, in the case of quantitative genetics approaches, and the need for depicting phenotypic diversity in a population of genotypes, in the case of the constraint-based modelling framework. While these approaches seem to have a great potential, demonstrating their added value necessitates addressing the following issues: first, studies should be planned to compare and contrast the findings between the purely statistical approaches and those based on consideration of SNPs in metabolic models. The existing studies have not performed this comparison due to the small sizes of the populations employed. Such comparative studies would require development of approaches for extraction of genotype-specific metabolic models for which no pipelines are yet freely available. Second, as shown on Fig. A. thaliana Col-0 [The prospects for coupling mechanistic and statistical modelling approaches offer several new research avenues. First, one can aim to determine the statistical significance of a SNP effect obtained from constraint-based approaches. This can be accomplished by usage of permutation tests along with the aforementioned exploration of the space of alternative solutions. As a result, one would not need to rely on arbitrarily set threshold values to classify SNPs as functionally significant. Second, similar to netGS, one can use other types of machine-learning approaches for genomic selection to partition the reactions into active/inactive or into those carrying large or small fluxes, opening the possibility for other modelling directions. Third, with the availability of algorithmic procedures for estimation of turnover numbers of enzymes in a given genotype (e.g. na Col-0 ), one cana Col-0 , MOMENT na Col-0 , or GECKna Col-0 , or by ina Col-0  that areWe envision that these milestones can be achieved in the next 5–10 years of research in metabolic modelling of crops. Altogether, such prospects for a synergistic combination of machine-learning and metabolic models will pave the way for mechanistic modelling of complex traits in populations that involve both inbred and hybrid genotypes.


Pinus radiata D. Don using 622 commercially-used genotypes from 63 families. We used quantitative pedigree-based (Genomic Best Linear Unbiased Prediction or ABLUP) and genomic-based (Genomic Best Linear Unbiased Prediction or GBLUP) approaches to examine the heritability estimates associated with water stress tolerance in P. radiata. Tree seedling growth traits, foliar carbon isotope composition (δ13C), and dark-adapted chlorophyll fluorescence (Y) were monitored before, during and after 10 months of water stress. Height growth showed a constant and moderate heritability level, while the heritability estimate for diameter growth and δ13C decreased with water stress. In contrast, chlorophyll fluorescence exhibited low heritability after 5 and 10 months of water stress. The GBLUP approach provided less breeding value accuracy than ABLUP, however, the relative selection efficiency of GBLUP was greater compared with ABLUP selection techniques. Although there was no significant relationship directly between δ13C and Y, the genetic correlations were significant and stronger for GBLUP. The positive genetic correlations between δ13C and tree biomass traits under water stress indicated that intraspecific variation in δ13C was likely driven by differences in the genotype’s photosynthetic capacity. The results show that foliar δ13C can predict P. radiata genotype tolerance to water stress using ABLUP and GBLUP approaches and that such approaches can provide a faster screening and selection of drought-tolerant genotypes for forestry breeding programs.The selection of drought-tolerant genotypes is globally recognized as an effective strategy to maintain the growth and survival of commercial tree species exposed to future drought periods. New genomic selection tools that reduce the time of progeny trials are required to substitute traditional tree breeding programs. We investigated the genetic variation of water stress tolerance in New Zealand-grown  Global climate change scenarios predict that large areas of planted forests will be at risk in the future due to warmer temperatures and changing precipitation patterns and drought . DroughtPinus radiata D. Don comprise 90% of the commercial forest land and cover 6.4% (1.7 million ha) of New Zealand’s land area  in plant tissues . Leaf δ13C is an attractive parameter that provides a spatial and temporal measure of essential traits related to carbon gain and plant water use . Plant δ13C has been often used as a screening tool in breeding programs to select genotypes with greater WUE and productivity under drought conditions (Drought can be mitigated by increasing water-use efficiency (WUE) at the whole plant level. This is defined as the ratio between biomass production and water consumption. At the leaf level, intrinsic water-use efficiency (iWUE) is described as the ratio of photosynthetic COductance . Variati tissues . A strones e.g., . Measurinditions . A rapidnditions . The meanditions . Plants nditions .13C sometimes leads to negative trade-offs between water use and growth. In conifers, 13C and height and diameter in Araucaria cunninghamii Mudie. Conversely, strong negative genetic correlations (−0.83 to −0.96) were found between tree height and δ13C in Pinus caribaea Morelet  to determine the genetic variation (variance components and heritability estimates) of growth traits, δ13C and Y after exposure to water stress, (2) to estimate the genetic correlations of growth traits, δ13C and Y after exposure to water stress, and (3) to compare the accuracy of ABLUP and GBLUP breeding values and evaluate the relative selection efficiency of GBLUP over ABLUP in the future genomic selection of P. radiata in New Zealand.Genomic selection (GS) refers to the prediction of unobserved phenotypes using genome-wide genetic markers, such as single nucleotide polymorphisms (SNPs). Genomic information can be constructed from pedigree-based relationships among individuals to predict breeding values, known as Best Linear Unbiased Prediction (ABLUP), or from the construction of marker-based relationship matrices and mixed linear models, known as Genomic Best Linear Unbiased Prediction (GBLUP) . As oppo Zealand . In the P. radiata breeding program originated from the Elite germplasm collection  and acclimated to the polyhouse conditions for over 9 months. The polyhouse was located at Scion’s nursery in Rotorua, New Zealand , and provided natural light but no temperature or humidity control. Plants in the polyhouse were not exposed to precipitation and thus plants were irrigated. The climate in the trial location is temperate, with the warmest months being in summer (January to March), and the coolest months being in winter (June to August). The mean daily maximum temperature is 27°C (summer), while the mean daily minimum temperature is 5°C (winter) . We selected a representative population of the third generation of the New Zealand llection . The stu(winter) .I) and height (HI) of each plant were measured. In addition, four fully developed expanded fascicles were collected from under the flushed buds of each ramet. Fresh needles were oven-dried at 65°C and cryo-ground before analyzing carbon isotopic composition (δ13CI) using continuous-flow isotope ratio mass spectrometry  in the Stable Isotope Unit at the University of Waikato . The δ13C values (‰) were expressed relative to the Vienna Peedee Belemnite standard , the basal diameter  and height (H6 and H10) of each plant were measured. An additional δ13C analysis in leaves was performed 10 months after water stress . The maximum quantum yield of Photosystem II (PSII) in the dark-adapted state (Y) was measured after 5 months  and 10 months  of water stress using a pulse-amplitude modulated Portable Chlorophyll Fluorometer . For each measurement, three ramets per genotype were assessed over 3 days to reduce the effect of the diurnal cycle. On the first day, maximum (Fm) and minimum (Fo) chlorophyll fluorescence emissions of dark-adapted needles were measured randomly in all ramets. During the two consecutive days, Fm and Fo measurements were repeated in a different order. The Y values for each plant were calculated as (mF − Fo)/Fm and averaged across 3-day measurements. For each ramet, three Dark Leaf Clips (DLC-8) were simultaneously placed on mature, fully elongated needles. Needles were arranged flat across the opening of the leaf clip and the sliding shutter close 20 to 60 min before the measurement to ensure stable readings. Preliminary testing on a plant subset showed that diurnal variation was not significantly influencing the repeated Y measurements within the 3 days.In early May 2016, three ramets per genotype of similar height and basal diameter were exposed to drought stress at 22–25% WHC. The amount of water required to reach 22–25% WHC was estimated based on the pot weight (including plant and medium). Over the course of the experiment, changes in pot weight due to tree growth were corrected using a linear relationship between pot weight and tree height. The water stress treatment was maintained by calculating the average weight change of 20–30 pots and compensating water loss every week for 11 months (late March 2017). After 6 months (November 2016) and 10 months (March 2017) of water stress, basal diameter  and root dry weight (RDW) as well as total biomass or total dry weight (TDW), were determined after oven-drying the samples at 65°C for at least 72 h.rrBLUP-R package (The genomic information for the 622 genotypes was obtained using the exome-capture genotyping by sequencing (GBS) method . Further package .ASReml-R v.3 R package  algorithm in the  package . Single-Preliminary analyses were used for all traits to compare the performance of a spatial first-order autoregressive mixed model and a mixed model without the autoregressive term. The preliminary analyses showed that the spatial mixed model performed better than the mixed model without the autoregressive term  based on Akaike’s information criterion (AIC). Therefore, only spatial mixed models were used in the current study.The single-trait pedigree-based analysis for all traits was performed using the following model:y is the vector of individual tree observations; β is the vector of fixed effects containing the overall mean; a is the random additive genetic effect which was assumed to be normally distributed ∼NA is the average numerator relationship matrix; pe is the vector of the random non-additive genetic effect of the individual ramet within the genetic material ∼NI is the identity matrix; b is the vector of random incomplete block effect ∼NI is identity matrix; X and Z are incidence matrices of the fixed and random effects of the vector of phenotypes y; e is the random residual deviations of individual trees which was partitioned into a spatial (ξ) and independent (η) residuals as pcol) ⊗ AR1(prow)] + I σ2 where p) represents a first-order autoregressive correlation matrix for rows and columns where ρ is the autocorrelation parameter. The independent residual (η) was assumed to be pairwise independent as Iσ2, where I is the identity matrix, σ2 is uncorrelated residual variance.where A bivariate analysis was employed to estimate the pairwise genetic correlations between each pair of traits. The variance-covariance structure for this model is:icb1 and icb2 represent the incomplete block effects for 1st and 2nd trait; 1 and a2 represent the breeding values for the 1st and 2nd trait; a1a2 is the additive genetic covariance among traits, pe1 and pe2 represent the non-additive genetic effects for the 1st and 2nd trait; e1 and e2 represent the spatially independent residuals; e1e2 is the residual covariance among traits, and I is the identity matrix. The genetic correlations between traits (ra) were estimated aswhere A by the marker-based relationship matrix G implemented based on (M−P)′]is a trace of the matrix defined in the nominator and n is the number of markers (Where  markers .h2) is the proportion of phenotypic variance that is explained by the additive genetic variance  and GBLUP (GBLUP-EBV) models. The accuracy of both models was evaluated using 10-fold cross-validation with ten random replicates. All data were randomly divided into ten equal groups. In each replication, phenotypes from one group were masked and used as a validation dataset. The accuracies of breeding values obtained by estimating the average Pearson’s correlation between predicted breeding values from masked phenotypes and the breeding values estimated using the full dataset obtained from the ten replicates. These breeding values were obtained from the single-trait analysis (Eq. 2).Response to selection was calculated as the ratio between selection accuracy and breeding cycle length in years . Thus reTherefore, the relative efficiency of GBLUP over ABLUP per year is:VGBLUP-EBV is the genomic breeding values for the trees in the validation dataset, FABLUP-EBV is the estimated breeding values from the full dataset, VABLUP-EBV is the estimated breeding values for the trees in the validation dataset, LTS is the length of the breeding cycle for selection based on ABLUP which is approximately 17 years, and LGS is the length of the breeding cycle for selection based on GBLUP, which is approximately 9 years  differed between tree families and genotypes. A correlation was also assessed to test for the relationship between δ13C and Y after 10 months of water stress.One-way analyses of variance (ANOVAs) were conducted, after confirming data met assumptions for normality and homogeneous variance, to test whether means for drought-tolerant traits  was 0.79 with a range of 0.58–0.87  was 0.77 with a range from 0.30 to 0.84  to 0.12 (BD10). In contrast, water stress increased I) to 0.44 (H10). The 13CI was 0.17 compared with 0.07 for δ13C10. The 5) was close to zero due to the small number of observations recorded for this trait (N = 607). However, Y10 was 0.06. The The measured traits showed a wide range of heritability estimates based on either ABLUP  to 0.29 (H10).Heritability estimates based on GBLUP were generally lower than the ABLUP estimates except for δI and BD10 was very strong (I and H10 (r = 0.92). There were a high genetic correlations with H and BD at the beginning and end of the experiment. Genetic correlations between H and BD were moderate to large and ranged from 0.48 to 0.90. Moderate to high genetic correlations were found between biomass and tree growth traits, ranging from 0.54 to 0.99. The genetic correlation between δ13CI and δ13C10 was 0.73. In addition, genetic correlations between δ13C and growth traits were generally positive, with the strongest correlation found between δ13CI and BDI (r = 0.79). Moderate genetic correlations were observed between δ13C and biomass traits, ranging from r = 0.45 to 0.60 regardless of the point in time. The genetic correlation between Y5 and Y10 was strong (r = 0.90). Furthermore, strong and negative genetic correlations were observed between Y5 and BDI (r = −0.92), and between Y5 and HI (r = −0.88). Genetic correlations between Y10 and growth traits, and δ13C were moderate, ranging from r = −0.41 to −0.64. Genetic correlations between traits based on GBLUP showed a similar pattern but were of weaker strength compared with genetic correlations based on ABLUP . On the other hand, the relative efficiency in selection per year (RE year−1) resulted in the overall superiority of GBLUP over ABLUP. When assuming the reduction of time for progeny trials from 17 to 9 years by the genomic selection, the lowest relative selection efficiency per year using GBLUP was obtained for Y10.The RE % of the models showed increased efficiency of GBLUP over ABLUP models . For dia13CI were only observed among families (p < 0.001), but not among genotypes (p = 0.193). In contrast, differences in δ13C10 were significantly influenced by both family (p = 0.002) and genotype (p = 0.005). On the other hand, Y5 differed among families and genotypes , as well as Y10 . Nonetheless, no significant linear relationship was found between δ13C10 and Y10 (p > 0.05) by family , likely due to increasing the additive genetic variance in balance with increasing residual variance. The observed heritability estimates for BDI and HI were in agreement with previous in situ field genetic studies in P. radiata across Australia and New Zealand sites . This decrease was mainly due to the increasing residual variance from BDnd sites , which rstimates .13C increased after water stress and the magnitude of the shift is similar to previous studies with water-stressed P. radiata  compared to wet conditions (0.57). The authors justified this decrease due to the reduced additive genetic variance estimated under water stress conditions. Additionally, We observed that average δ radiata . The incpinaster  and 0.09P. taeda . The decThe estimates of additive genetic variance and narrow-sense heritability showed greater precision when GBLUP was implemented compared to ABLUP. Such a trend was also found in previous studies across different tree species . In summ13C, chlorophyll fluorescence, basal diameter and height growth, and biomass. The moderate to large genetic correlations between δ13C and growth traits, including tree height, diameter and biomass, suggest that fast-growing genotypes under water stress could display greater WUE. However, further studies of genetic correlations between δ13C and growth traits in P. radiata are warranted to confirm these results.This study recorded positive genetic correlations among δ13C suggests that diameter might be an indicator trait for greater WUE of P. radiata breeding populations. The positive genetic correlation between growth and δ13CI or δ13C10 provides evidence that the variation in δ13C (a surrogate of WUE) between genotypes may be possibly due to the differences in the carboxylation efficiency of photosynthesis, rather than changes in stomatal conductance. Further, more drought-tolerant genotypes  may be less sensitive to water stress and able to maintain photosynthetic capacity required for growth with longer periods of open stomata compared with less drought-tolerant genotypes. P. radiata under water stress. 2 concentration. This may be due to the co-evolution of hydraulic and biochemical plant attributes at the genotype level.The findings of this study confirm previous studies with other species that found moderate to strong genetic correlations . The modP. radiata breeding programs.The genetic correlations between diameter or height before and after water stress were strong (0.88–0.99), indicating that genotypes were ranked consistently before and after imposing water stress. These strong genetic correlations indicate that the level of water stress in the current experiment was not strong enough to change the ranking of genotypes. This may be due to the experiment not applying severe enough water stress. Indeed, it is possible that genotype rankings could have changed using lower WHC, which would place the genotypes under more severe water stress deficit. Another theory is that only a few genotypes were sensitive to the water stress. However, results indicate that the best performing genotypes for growth traits will remain the best under water stress conditions, which is important for 13C and Y. That is, genotypes that performed better under water stress presented more negative foliar δ13C values and larger Y values. Physiologically, these results suggest that more drought-tolerant genotypes are more efficient at photosynthesising and have more intact chloroplasts and chlorophyll  under water stress, as suggested by recent studies in P. radiata genotypes  in needles between 70 and 90% (data not shown), which suggests that the application of more severe and/or more prolonged water stress treatments could have led to stronger Y differences among genotypes. Second, Y is a measure of maximum potential quantum yield of chloroplasts under Photosystem II, which may differ of the actual achievable quantum yield and photosynthesis  under ambient light and water stress conditions  Voss  was greater, reaching half of the breeding cycle  Voss , P. pinapinaster ; and P. P. tadea . Accuracng cycle . This is to 181% .In the current study, we used the status number concept (Ns) to estimate the effective population size (Ne). P. radiata drought tolerance assessed by plant growth and needle δ13C (a surrogate trait for WUE). The needle δ13C had low to moderate heritability estimates, however, there were moderate to high genetic correlations with plant growth or biomass traits. Thus, genotypes of P. radiata with a narrow-genetic background can have significant physiological differences related to water-use efficiency traits. Our results suggest that the variation of needle δ13C could be primarily controlled by the photosynthetic capacity and photosynthesis, being the main process driving tree growth under water stress conditions. The results of this study have important practical implications for managing commercial Pinus forests across the globe: First, selection of family and genotype with both greater WUE and better plant growth appears to be practically possible without trade-off. This means that breeding programs using these traits should ensure that Pinus forest productivity is maintained or even improved under some degree of drought stress. Second, the genomic selection for WUE using δ13C is possible with a good degree of accuracy. Carbon stable isotopes are a useful tool to increase the agility of global breeding programs to respond to the challenges of climate change.We’ve demonstrated large genetic variation of https://doi.org/10.5281/zenodo.5768774 (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: .5768774 .AI analyzed the data and wrote the manuscript. HD, JX, DM, and YL conceived and supervised the study, made substantial contributions to the interpretation of the results, and contributed to the revision of the manuscript. K-TB, PB, and MG-G collected the data. MG and JK made substantial contributions to the interpretation of the results and contributed to the revision of the manuscript. All authors contributed to the article and approved the submitted version.AI was employed by Livestock Improvement Corporation (LIC), Hamilton, New Zealand, and Scion, New Zealand. DM, JX, JK, YL, PB, MG, K-TB, ET, and HD were employed by Scion, New Zealand. The remaining author declare that this study received funding from NZ Ministry of Business, Innovation and Employment and the Radiata Pine Breeding Company. The funders were not involved in the study design, analysis, interpretation of data, the writing of this article or the decision to submit it for publication.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Genome-wide association study (GWAS) requires a researcher to perform a multitude of different actions during analysis. From editing and formatting genotype and phenotype information to running the analysis software to summarizing and visualizing the results. A typical GWAS workflow poses a significant challenge of utilizing the command-line, manual text-editing and requiring knowledge of one or more programming/scripting languages, especially for newcomers.vcf2gwas is a package that provides a convenient pipeline to perform all of the steps of a traditional GWAS workflow by reducing it to a single command-line input of a Variant Call Format file and a phenotype data file. In addition, all the required software is installed with the package. vcf2gwas also implements several useful features enhancing the reproducibility of GWAS analysis.vcf2gwas is available under the GNU General Public License. The package can be easily installed using conda. Installation instructions and a manual including tutorials can be accessed on the package website at https://github.com/frankvogt/vcf2gwas.The source code of Bioinformatics online. Genome-wide association study (GWAS) has been proven to be an extremely useful tool to find an association between genetic variants, typically single-nucleotide polymorphisms (SNPs) and a given trait in many organisms. GWAS needs information of traits of a set of individuals under investigation (phenotypes) and genotypes (SNPs) of these individuals obtained through DNA sequencing. During the analysis, genetic variants are tested for the association with the phenotypes using various computational methods implemented in a wide range of software   stands oAlthough GEMMA has a very straightforward command-line interface to carry out the actual association analysis, it requires users to first install necessary software with required dependencies on their machines. Subsequently, users need to prepare the inputs (genotype and phenotype) in a proper format before execution of GEMMA. Similarly, the outputs generated need post-processing of the results for better interpretation and presentation. The entire workflow can be overwhelming especially for inexperienced users. Briefly, this workflow starts with the genotype information in a Variant Call Format (VCF) file. This file has to be converted to the PLINK . vcf2gwas has been successfully tested on machines running a Unix-based OS (macOS/Linux).The bcftools  from Pytvcf2gwas trims, filters and converts input VCF file and formats the phenotype file as required by GEMMA. A basic bash command example to analyze a single phenotype using GEMMA’s linear mixed model providing only the VCF and phenotype file (in the csv format) is:Arabidopsis thaliana host lines (∼900 000 SNPs) when infected with Pseudomonas syringe expressing avrRpm1 gene using the linear mixed model (lmm). Description of the original experiment can be found at https://arapheno.1001genomes.org/phenotype/17/. After the analysis is executed, the results are summarized and visualized with a publication-ready Manhattan plot -based pruning of the SNPs at different R2 thresholds. The wall-clock time and the number of significant SNPs were compared against the number of total SNPs retained after LD-pruning, independently. The results are shown in vcf2gwas with other available GWAS workflows. The comparison is presented in vcf2gwas (for a complete list of features, see Beyond summarizing and plotting the results of the GEMMA analysis, following important additional features are implemented in Dimensionality reduction via PCA or UMAP can be performed on phenotypes and used for analysis.Relevant SNPs can be compared with genes/regions of interest listed in additionally specified file see vcf2gwas is able to analyze several input files with different sets of individuals and multiple phenotypes in an efficient manner due to parallelization, saving the user a lot of time compared with standard GWAS procedure.btab710_Supplementary_DataClick here for additional data file.


This study aimed to estimate the genetic parameters and genetic correlations for related feed efficiency, growth, and carcass traits in Hanwoo cattle.Phenotypic data from 15,279 animals born between 1989 and 2015 were considered. The related feed efficiency traits considered were Kleiber ratio (KR) and relative growth rate (RGR). Carcass traits analyzed were backfat thickness (BT), carcass weight, eye muscle area, and marbling score. Growth traits were assessed by the average daily gain (ADG), metabolic body weight (MBW) at mid-test age from 6 to 24 months, and yearling weight (YW). Variance and covariance components were estimated using restricted maximum likelihood using nine multi-trait animal models.The heritability estimates for related feed efficiency (0.28±0.04 for KR and RGR) and growth traits (0.26±0.02 to 0.33±0.04) were moderate, but the carcass traits tended to be higher (0.38±0.04 to 0.61±0.06). The related feed efficiency traits were positively genetically correlated with all the carcass traits , except for BT, which showed null to weak correlation. Conversely, the genetic correlations of RGR with MBW (−0.36±0.08) and YW (−0.30±0.08) were negative, and those of KR with MBW and YW were close to zero, whereas the genetic correlations of ADG with RGR (0.40±0.08) and KR (0.70±0.05) were positive and relatively moderate to high. The genetic (0.92±0.02) correlations between KR and RGR were very high.Sufficient genetic variability and heritability were observed for traits of interest. Moreover, the inclusion of KR and/or RGR in Hanwoo cattle breeding programs could improve the feed efficiency without producing any unfavorable effects on the carcass traits. The global interest in improving the profitability of beef cattle production systems has recently increased. It is therefore important to identify animals that can efficiently utilize feed resources for reducing feeding costs and improving feed efficiency. Presently, several economically important traits, including body weight, weight gains at specific ages or during specific periods, carcass traits, feed intake, and feed efficiency, are commonly considered as selection criteria in most beef cattle breeding programs –3.Measures of feed efficiency, including residual feed intake , feed coe.g. KR and RGR) and to compute the magnitude of the correlations of these traits with carcass and growth traits, which will allow the construction of an optimal multiple-trait selection index for increasing the profitability of beef production.Feed efficiency has been especially emphasized in Korea since high proportions of feeding resources depend on imports owing to the shortage of roughage and grain production. Nevertheless, the Hanwoo beef industry primarily aims to increase the quantity (carcass weight [CW]) and quality  of the meat, without considering the inclusion of feed efficiency traits in the selection indices for selecting young bulls or/and proven bulls . It is tTo accomplish these goals, we evaluated the genetic parameters for measures of related feed efficiency, including RGR and KR, for the first time, as well as measuring the phenotypic and genetic correlations between i) feed efficiency and growth traits, and ii) feed efficiency and carcass traits in Hanwoo beef cattle.Records of 15,279 animals  born between 1989 and 2015 from the Hanwoo Improvement Center of the National Agricultural Cooperative Federation were used for this study . The pedt) at the termination (t) of the test (body weight at ~12 months) and the previous weight (Wt−1) recorded at a time point (t−1) before (body weight at ~6 months) the termination, according to the equation described by Park et al [Yearling weight (YW) for each animal was determined from the weight , according to the following equation:0 is intercept, β1 is the linear regression coefficient that represents ADG, x is the age in days, and ɛ is the random residual effect.Where y represents the weight of the animal, β0 + β1x*)0.75, respectively; where agemin and agemax are the minima and maximum recorded ages for each animal. RGR was calculated using the following equation:The mid-test age (x*) and the mid-test metabolic body weight (MBW) were obtained by the following equation: in which FBW and IBW represent the final and initial body weights in addition to different maximum and minimum ages is based on year. KR was obtained as a ratio of ADG to mid-test MBW.The national genetic evaluation of Hanwoo considers YW as one of the traits for selecting young bulls at the performance test stage. Therefore, the YW of all the candidate young bulls are recorded, but the body weight traits at 15, 18, and 24 months, and the carcass traits are measured only for the offspring of the young bulls that are selected for producing steers . Hence, y is the vector representing the observations for the traits of interest; b is the vector representing the fixed effects, including batch-test place-sex  and birth place ; batch-test place-slaughter date , birth place , and slaughter age (days from birth to slaughter) as covariates for carcass traits ; u is the vector of random genetic additive effects; e is the vector of random residual effects; and X and Z are incidence matrices related to the fixed and random genetic additive effects, respectively. Var (u) = G⊗A and Var(e) = R⊗I, were assumed, where A is the numerator relationship matrix, I is the identity matrix, and G and R are additive genetic and residual covariance, respectively, for the two or three traits.where Indeed, a multi-trait analysis can be problematic as the number of traits analyzed increase particularly in the presence of wrong starting values for variance-covariance parameters. Hence, the reasonable starting values for genetic and environmental variance-covariance among the nine traits were considered using a series of two-trait analyses according to above the equation. Finally, the pooled estimates of (co)variances were obtained with the option “--pool” in Wombat software  because REMLF90  softwareIn addition, a single-trait analysis was performed using for each trait to obtain initial values of variances and covariances for subsequent restricted maximum likelihood multi-trait analyses.Recently, the characterization of economically important traits and their associations has been of great interest in developing effective genetic improvement programs to increase the overall profitability of beef cattle production systems. In the present study we evaluated, for the first time, the genetic parameters of KR and RGR (indirect measures of feed efficiency) and their correlations with growth and carcass traits in Hanwoo beef cattle.g) for MS and BT.In the present study, the heritability estimates for carcass traits were in accordance with most of the previous studies in Hanwoo cattle ,14,18, eAnalysis of the growth traits revealed that the heritability for YW (0.26) was slightly lower than that (0.30) obtained by Park et al , but higThe heritability estimated for ADG 0.33) was in line with the literatures ,25–28; b3 was in Heritability estimates for the related feed efficiency traits, KR and RGR (0.28), were slightly lower than that those reported by Arthur et al  in CharoAdditionally, the results showed that the heritabilities using single-trait were lower than those obtained by multi-traits for BT, CW, ADG, and MBW; while, similar heritabilities were observed for EMA, MS, and YW . HoweverIn general, our heritability results were in the range of the heritabilities previous reported in Hanwoo cattle and in most of the beef cattle studied. Nevertheless, the differences founded could be attributed to the differences in the number of animals considered, breed, the completeness of the pedigree, precision of recording, environmental variation, and statistical models used for analyses.Analysis of the phenotypic and genetic correlations among the carcass traits agree with the previous study in Hanwoo cattle carry out by Choi et al . HoweverAs depicted in Analysis of the estimated genetic correlations among the growth and carcass traits revealed positive, and low to strong correlations for all the growth traits with both CW and EMA, and especially ADG, which had the greatest correlation with CW (0.94). The results of this study are similar to the earlier observations of Choi et al  concerniAnalysis of the relationship among the related feed efficiency and carcass traits revealed that the genetic correlations of KR and RGR with the three traits, CW, EMA, and MS, were positive but the correlations with BT were weakly negative or close to zero. Hence, selection of animals to improve KR and RGR could lead to increased CW, EMA and MS and reduced BT. Finally, it can be said that the inclusion of KR and/or RGR in the breeding program of Hanwoo cattle could improve feed efficiency without having unfavorable effects on the carcass traits. Hoque et al  reportedThe genetic relationships among the growth traits observed in this study were positive and high, with YW exhibiting a relatively stronger correlation (0.89) with MBW in particular, in comparison to the other traits. Grion et al  observedYB = 2EBVYW+EBVMS, where, EBVYW and EBVMS are the standardized estimated breeding values for YW and MS (parents average), respectively [YB. Increasing the MBW will lead to rising the maintenance requirements and feeding costs, which could be managed by including KR and/or RGR in the young bull selection index.The index for selecting Hanwoo young bulls during performance testing was obtained using SIectively . As mentInformation on the genetic and phenotypic correlations of feed efficiency with growth and carcass traits in Hanwoo cattle is scarce, and our findings, notably, are the first reports of some of these estimates for this breed, and will, therefore, be useful in designing breeding programs aimed to improve these traits.The moderate to high heritability estimates for carcass, growth, and related feed efficiency traits, in addition to the sufficient additive genetic variability observed for some of these traits, indicates that further improvement is possible for these traits. Additionally, favorable genetic correlations of Kleiber ratio and relative growth rate with metabolic body weight and carcass traits along with positive and the low to relatively high genetic correlation with average daily gain observed in this study, suggest that the inclusion of related feed efficiency traits in selection programs for Hanwoo cattle is feasible.


Metabolic syndrome is a group of heritable metabolic traits that are highly associated with type 2 diabetes (T2DM). Classical interventions to T2DM include individual self-management of environmental risk factors, such as improving diet quality, increasing physical activity, and reducing smoking and alcohol consumption, which decreases the risk of developing metabolic syndrome. However, it is poorly understood how the phenotypes of diabetes-related metabolic traits change with respect to lifestyle modifications at the individual level. In the analysis, we used 12 diabetes-related metabolic traits and eight lifestyle covariates from the UK Biobank comprising 288,837 white British participants genotyped for 1,133,273 genome-wide single nucleotide polymorphisms. We found 16 GxE interactions. Modulation of genetic effects by physical activity was seen for four traits  and by alcohol and smoking for three . We also found a number of significant phenotypic modulations by the lifestyle covariates, which were not attributed to the genetic effects in the model. Overall, modulation in the metabolic risk in response to the level of lifestyle covariates was clearly observed, and its direction and magnitude were varied depending on individual differences. We also showed that the metabolic risk inferred by our model was notably higher in T2DM prospective cases than controls. Our findings highlight the importance of individual genetic differences in the prevention and management of diabetes and suggest that the one-size-fits-all approach may not benefit all. Diabetes mellitus is a metabolic disease normally caused by high blood glucose levels, which can lead to complications in kidneys, eyes, and the nervous system . CurrentMetabolic abnormalities and diabetes risk are affected by genetic factors . A recenIn addition to the genetic factors, T2DM risks are also increased by environmental conditions, such as unhealthy diet and physical inactivity . TherefoIn this study, we applied a whole-genome approach  to estimThe UK Biobank’s scientific protocol and operational procedures were reviewed and approved by the North West Multi-Centre Research Ethics Committee (MREC), National Information Governance Board for Health & Social Care (NIGB), and Community Health Index Advisory Group (CHIAG). The access to the UK Biobank data was approved by the UK Biobank based on the application 14575 [“Whole-genome approaches for dissecting (shared) genetic architecture and individual risk prediction of complex traits in human populations”]. Research ethics were approved by the University of South Australia Human Research Ethics Committee (HREC).The UK Biobank consists of more than 500,000 individuals, recruited from 22 assessment centers across the UK between March 2006 and October 2010 . The parAs outcomes, we used the information of diabetes-related metabolic traits, including glucose, HbA1c, total cholesterol (TC), LDL cholesterol (LDL), HDL cholesterol (HDL), CRP, sodium and potassium in urine, BMI, waist–hip ratio (WHR), and systolic and diastolic BP with details on assay and measures given in p-value less than 0.0001, and with minor allele frequency less than 1% were excluded. Duplicated SNPs were also removed. The ratio of discordant SNPs between the initial and second released individuals in the UK Biobank data was calculated, and an additional 29 individuals who had a discordance rate more than 0.05 were excluded. We only used HapMap3 SNPs from the quality-controlled data, which are of high quality and well calibrated to dissect the genetic architecture of complex diseases , have a missing rate ≥5%, have a gender mismatch between self-reported and genetic data, and with putative sex chromosome aneuploidy. One individual from a pair, which has a genomic relationship larger than .05, was randomly selected and excluded. Furthermore, individuals who were population outliers  were excluded. At the SNP level, SNPs with an information score less than .6, with SNP missing rate less than 95%, with Hardy–Weinberg equilibrium diseases . After qp-values across the six groups were reported.Due to the usage of the individual level of genotypic data, which demands high computational resources, we further divided samples into six groups. Of the total samples, 91,472 individuals from the first release of these selected individuals were divided into two groups. Meanwhile, 197,365 individuals from the second release were divided into four groups. Meta-analysis estimates and For the interaction analyses, the phenotypes of the main traits  were adjWhen a lifestyle covariate was used as the second trait in a bivariate model see , the pheFor the diabetes-related metabolic traits in the main analyses, an additional quality control (QC) was applied to the adjusted phenotypes to exclude outliers that are outside the three standard deviations in either direction from the mean of the phenotypic data . The adjp-value threshold was set at 5.21E-04 (= .05/96) after Bonferroni correction to account for 96 tests in total. We declared a significance if the p-value was lower than the significance threshold and the sum of estimated variances of GxE and RxE  overall interaction detected from the comparison between the null and full models, which includes both GxE and RxE interaction, noting that the overlapping section represents the collinearity between estimated GxE and RxE interactions  GxE ractions . It is nractions .Based on the full model see , the expFurthermore, we derived the trajectory of the predicted phenotypes across different levels of lifestyle covariates in each of the 96 analyses (12 traits x 8 covariates). For this, individuals were divided into three groups, that is, the top, middle, and bottom 20% groups according to the estimated interaction effects . To test whether the difference between the groups with cases and controls is statistically significant, we did a paired In addition to the comparison using 69 analyses with the significant overall interactions, we performed the same analyses using 16 analyses that showed significant signals for orthogonal GxE interaction and 58 analyses for orthogonal RxE interaction. The predicted phenotypes were grouped as same as the overall interaction according to the estimated of GxE or RxE interaction effects  or interaction model (MRNM), which can be expressed asAs complementary analyses, we used CAUSE software , which iMethods) and found that 69 out of 96 tests showed significant signals for the interactions when comparing the full and null models  and nongenetic effects (RxE) of 12 diabetes-related metabolic traits are modulated by eight lifestyle covariates (see l models . The sigMethods) . Strong modulations of genetic effects by physical activity were observed for HbA1c and CRP . Although there was no evidence of orthogonal interaction between physical activity and BMI, other lifestyle factors significantly modulated the genetic effects of BMI   . SpecifiALC-BMI) .p-values ranging from 4.32E-96 to 5.51E-12. In the analyses of BMI, a strong risk factor of T2DM, it was shown that the non-genetic effects  against the standardized values of lifestyle factors for three groups  stratified according to the magnitude of estimated GxE interaction , indicating that prospective cases were likely to have higher metabolic risk than controls. However, the slope of predicted risks was not significantly different between prospective cases and controls, showing that the change of metabolic risk in response to lifestyle modification is invariant across prospective cases and controls. Similar results were observed when considering GxE or RxE interaction only in that the intercepts were higher for prospective cases than controls , the variance attributed to unmodeled interactions can be partitioned as residual variance, which results in underestimated heritability, i.e., so-called still-missing heritability . This isIt is well established that phenotypic changes in diabetes-related metabolic traits, such as glucose and HbA1c, are associated with lifestyle modifications , which hPrevious studies have already indicated concerns about the inefficiency of the one-size-fits-all approach . These cOur finding for significant genome-wide GxE interactions across diabetes-related metabolic traits is novel and can be applied in such personalized lifestyle interventions. To our knowledge, no whole-genome GxE interactions have been reported for diabetes-related metabolic traits. Previously, we reported significant GxE and RxE interactions for some metabolic traits, including BMI, BP, cholesterols, and WHR; however, GxE interaction could not be disentangled from RxE because of a small sample size . In thisUnlike standard additive models, MRNM allows us to stratify samples into three groups  according to estimated individual GxE or/and RxE interaction effects. The patterns of the expected phenotypes of the metabolic traits were clearly distinct between the three groups. This shows that the one-size-fits-all approach may not be the best strategy in a T2DM intervention. Importantly, the predicted metabolic risk was significantly higher in T2DM prospective cases than controls. Interestingly, the phenotypic plasticity of metabolic risk in response to lifestyle modification is significantly different between prospective cases and controls only when considering nongenetic effects of metabolic risk .p-value < 10E-100). However, these signals are mostly attributed to RxE interactions  are strongly significant  was used to replicate some of our findings, we note that causality models (such as CAUSE or Mendelian randomization model) are different from MRNM in that they test associations among genetic instruments, exposure, and outcome at the phenotypic level using least squares or similar methods. MRNM, instead, adjusts and removes the phenotypic association because its main interest is to estimate the heterogeneity of genetic and nongenetic variance across different lifestyle values. Therefore, MRNM is probably robust to the assumptions to be made in the causality models . Nonetheless, consistent results from these two very different models can increase the reliability of the findings.In summary, the modulation of diabetes-related metabolic risk in response to the level of lifestyle covariates was clearly observed, and its direction and magnitude varied depending on individual genetic differences. Interestingly, such genetic phenotypic plasticity was invariant across T2DM prospective cases and controls although the overall metabolic genetic risk is significantly higher in T2DM prospective cases than controls. Our findings highlight the importance of individual genetic differences in the prevention and management of diabetes and suggest that the one-size-fits-all approach may not benefit all.


Motivation: Genome-wide association studies (GWAS) have achieved remarkable success in identifying SNP-trait associations in the last decade. However, it is challenging to identify the mechanisms that connect the genetic variants with complex traits as the majority of GWAS associations are in non-coding regions. Methods that integrate genomic and transcriptomic data allow us to investigate how genetic variants may affect a trait through their effect on gene expression. These include CoMM and CoMM-S2, likelihood-ratio-based methods that integrate GWAS and eQTL studies to assess expression-trait association. However, their reliance on individual-level eQTL data render them inapplicable when only summary-level eQTL results, such as those from large-scale eQTL analyses, are available.Result: We develop an efficient probabilistic model, CoMM-S4, to explore the expression-trait association using summary-level eQTL and GWAS datasets. Compared with CoMM-S2, which uses individual-level eQTL data, CoMM-S4 requires only summary-level eQTL data. To test expression-trait association, an efficient variational Bayesian EM algorithm and a likelihood ratio test were constructed. We applied CoMM-S4 to both simulated and real data. The simulation results demonstrate that CoMM-S4 can perform as well as CoMM-S2 and S-PrediXcan, and analyses using GWAS summary statistics from Biobank Japan and eQTL summary statistics from eQTLGen and GTEx suggest novel susceptibility loci for cardiovascular diseases and osteoporosis.Availability and implementation: The developed R package is available at https://github.com/gordonliu810822/CoMM. Howeverequences .Transcriptome-wide association studies (TWAS) aim to provide insights into the specific mechanisms through which variants affect traits. In TWAS, the gene expression of GWAS samples is predicted with the aid of an eQTL dataset; the predicted expression is then analysed for any association with the trait of interest. Unlike approaches that examine gene expression and genetic variants in a pairwise manner, TWAS consider the combinatory effects of all genetic variants within a pre-defined window of the target gene, hence it is especially effective at detecting novel susceptibility loci when multiple variants influence expression. TWAS have proved useful as a stepping stone to generate new insights to a range of complex traits, including schizophrenia , glioma 2 . Compared with CoMM-S2, a major advantage of CoMM-S4 is its ability to use summary-level eQTL data and integrate them with GWAS summary statistics. In CoMM-S4, a joint likelihood is constructed using summary statistics from GWAS and eQTL studies, as well as SNP correlation information from reference panels representative of the GWAS and eQTL populations. We develop an efficient algorithm based on variational Bayes expectation-maximization and parameter expansion (PX-VBEM). To examine the expression-trait association, a likelihood ratio test is constructed.Here we introduce a powerful strategy that integrates eQTL summary statistics (SNP-expression correlation), GWAS summary statistics (SNP-phenotype correlation), and linkage disequilibrium information from reference panels (SNP-SNP correlation) to assess the association between the 4 is assessed in simulated data, and is also applied to traits from the NFBC1966 cohort . To model linkage disequilibrium (LD) in the eQTL and GWAS datasets, we require the SNP correlation matrices j = 1, … , g) estimated using reference panels that correspond to the eQTL and GWAS populations respectively.We have the summary statistics, in the form of z-scores, from the analysis of genetic variant-gene expression pairs in the eQTL dataset. We also have the summary statistics from single-variate analysis in the GWAS dataset. We denote the eQTL z-scores for the j-th gene expression yj and genotype Wj1 is modelled asmj-vector of effect sizes, and n1-vector of independent noise. Similarly, the relationship between trait z and genotype Wj2 is modelled asmj-vector of effect sizes, and n2-vector of independent noise. We further model the GWAS effect size as βj2 = αjβj1, where αj can be interpreted as the effect of gene expression on phenotype under the assumption of no horizontal pleiotropy. To perform a likelihood ratio test for the null hypothesis αj = 0, we first derive the form of the log-likelihood and develop an efficient algorithm to estimate its parameters.The relationship between the i = 1, 2). Details regarding this approximated distribution can also be found in related literature  , the disterature . In pracαj of gene expression on trait. Notably, testing the hypothesis of whether αj = 0 is equivalent to testing whether cj is a positive constant. The accuracy of the above distributional approximations depend on the sample sizes of the eQTL and GWAS datasets, as well as the number of SNPs/genes associated with the gene expression/phenotype. The larger the sample size and the higher the degree of polygenecity, the greater the estimation accuracy.We are primarily interested in the effect τ = 1. In VBEM, the marginal log-likelihood can be decomposed into the evidence lower bound (ELBO) and the Kullback-Liebler (KL) divergence between the variational and true posterior distribution of the latent variable γj:An efficient algorithm is needed to estimate the parameters of the model. Although the EM algorithm is widely used and has a highly stable performance, it requires inverting the matrix ion (PX) . The parτ, we are able to further increase the ELBO and speed up the convergence process. Since the parameter-expanded model reduces to the original model when τ = 1, the original model can be recovered by incorporating τ into the model parameters, as outlined in the We adopt the mean-field form of the variational posterior distributioncj ≠ 0. The test statistic for the j-th gene is4 becomes “there is no expression-trait effect and no horizontal pleitoropy.” As with other TWAS methods, horizontal pleiotropy could produce significant associations and inflation of test statistics  assessing the type-I error of CoMM-S4 under the null hypothesis  comparing the likelihood ratio test statistics from CoMM-S4 with CoMM-S2, the sample sizes of the eQTL and GWAS datasets are n1 = 5, 000 and n2 = 5, 000 respectively. In the power comparison with CoMM-S2 and S-PrediXcan, the sample sizes are n1 = 500 and n2 = 10, 000 respectively. For all simulation scenarios, the sample size of the reference panels for the eQTL and GWAS datasets are n3 = 400 and n4 = 400 respectively.When comparing the test statistic and type-I error of CoMM-Sρ ∈{0.2, 0.5, 0.8} determines the strength of correlations among the SNPs. Subsequently, minor allele frequencies are generated from the uniform distribution A multivariate normal distribution with the covariance structure yj = Wj1γj + e1, where γjk are generated from π and set to 0 with probability 1 − π, where π denotes the sparsity level and k indexes the genetic variants within a pre-defined window of gene j. To simulate distinct scenarios, we choose equally-spaced cellular heritability levels  the eQTL and GWAS populations have the same LD structure, ii) the eQTL and GWAS populations have different LD structures, and iii) the eQTL and GWAS populations have different LD structures and different gene expression architectures, i.e. the set of cis-SNPs for the two populations only partially overlap  and S-PrediXcan (elastic net), respectively  and S-PrediXcan (elastic net), respectively . When the eQTL and GWAS samples have distinct LD structures and different gene expression architectures, there is 67% power for CoMM-S4, compared with 21 and 10% power for S-PrediXcan (ridge) and S-PrediXcan (elastic net), respectively  and S-PrediXcan (elastic net), respectively  identified by CoMM-S4 is between 0 and 64   . We cons eQTLGen  and GTEx eQTLGen , to asse eQTLGen , respect eQTLGen . However eQTLGen , and the studies  suggest p-value < 5 × 10−6) ranged from 2 to 450, and there is a high degree of overlap between the genes identified in the two analyses  terms using Enrichr . The genanalyses , indicatanalysis .p-value < 5 × 10−8) are involved in GO biological processes, and the 23 significantly enriched processes , as well as antigen processing and presentation of peptide antigen via MHC class I (p = 3.14 × 10−8) and via MHC class II (p = 2.76 × 10−6). For rheumatoid arthritis, 137 of the 220 associated genes are involved in GO biological processes, and the 25 significantly enriched processes (p = 1.20 × 10−11), and antigen processing and presentation of exogenous peptide antigen via MHC class II (p = 4.21 × 10−11). For chronic hepatitis B, 91 of the 132 associated genes are involved in GO biological processes, and the 32 significantly enriched processes (p = 2.28 × 10−7) and positive regulation of apoptotic cell clearance (p = 9.21 × 10−6).The TWAS results recapitulate known or proposed biological mechanisms that give rise to the studied traits. GWAS and animal model studies have implicated MHC molecules, interferon-gamma signalling and apoptosis in the development of Graves’ disease , rheumatrocesses  include rocesses  include 4 is able to identify novel susceptibility loci by aggregating the contributions of SNPs with smaller effect sizes. A comparison of the GWAS results with CoMM-S4 results based on the highly-powered eQTLGen study shows that the TWAS signal is larger than the GWAS signal at chr17q12 for congestive heart failure (CHF), chr17p13.1 for peripheral artery disease (PAD), chr17q21.31 for ischemic stroke, and chr6q22.33 for osteoporosis (FBXL20 (p = 1.33 × 10−5), which negatively regulates autophagy , which is involved in autophagy initiation and autophagosome-lysosome fusion , which modulates hypoxia-inducible factor-1 alpha and vascular endothelial growth factor (RNF146 (p = 1.05 × 10−8), which was shown to promote osteoblast development while antagonizing osteoclast differentiation in mice  results for the 9 Biobank Japan traits. For S-PrediXcan, gene expression prediction weights for GTEx (v8) whole blood were obtained from the elastic net model in PredictDB (http://predictdb.org/), and the covariance matrix used to calculate the test statistics is based on Japanese samples from the 1,000 Genomes Project. To allow for fair comparison, we consider only genes that are common to both the CoMM-S4 and S-PrediXcan analyses. Compared with S-PrediXcan (elastic net), CoMM-S4 identifies a similar number of statistically significant genes for 5 Biobank Japan traits , and more statistically significant genes for 4 Biobank Japan traits   . The taint genes . The hig4 and CoMM-S2, and simulation results demonstrate that CoMM-S4 performs as well as CoMM-S2 even though the former uses only summary-level data. Moreover, our analysis of the NFBC1966 cohort has suggested novel susceptibility loci for glucose levels, insulin levels, C-reactive protein, BMI and lipid traits. Our analysis of Biobank Japan traits has similarly suggested novel susceptibility loci for congestive heart failure, ischemic stroke, peripheral artery disease and osteoporosis, and has also recapitulated known and putative mechanisms for Graves’ disease, rheumatoid arthritis, chronic hepatitis B and chronic hepatitis C.In this article, we have developed a collaborative mixed model using both summary statistics from eQTL and GWAS to examine the expression-trait associations in transcriptome-wide association studies. We compared the performance between CoMM-S4 has several advantages over CoMM-S2 and S-PrediXcan. Compared to stage-wise methods like S-PrediXcan, CoMM-S4 accounts for imputation uncertainty, which makes it statistically more powerful in identifying expression-trait associations. Moreover, CoMM-S4 requires only summary-level data (z-scores) from eQTL studies, instead of individual-level data. This allows us to make use of eQTL large-scale studies and meta-analyses where individual-level data may be unavailable.CoMM-S4, we have estimated the parameters using variational inference and parameter expansion. Finally, CoMM-S4, like S-PrediXcan, is not able to distinguish between causal relationship and horizontal pleiotropy. In practice, we can first perform a TWAS to identify regions that show association with the trait of interest, and then apply Mendelian randomization analysis to draw causal conclusions.On the other hand, likelihood-ratio tests are less computationally efficient than score-based tests; the relationship between these tests in the context of individual-level data  are discussed in detail in . To redu


Vitis × Muscadinia hybrids (VM hybrids). The first fruiting bud (FFB), bud fertility (BF), bud fertility coefficient (BFC), number of flowers/flower cluster (N.F/FC), fruit-set efficiency (FSE), number of clusters/vine (N.C/V), and yield/vine (Y/V) traits were evaluated. The FFB trait did not show significant differences among genotypes. The muscadine genotype O28-4-2-2 (1.6 ± 0.2) displayed the FFB closest to the base; however, O17-16-2-1, O18-2-1, and VM A12-10-2 genotypes had the most distant FFB (3.6 ± 0.3). All the other fertility-related traits varied widely among the population. The BF, BFC, N.F/FC, FSE, N.C/V, and Y/V exhibited a range estimated at 35.1%, 81.5%, 259.7, 63.3%, 177 C/V, and 22.3 kg/V, respectively. The muscadine genotypes O42-3-1 (36.7% ± 1.3) and Majesty (34% ± 1.2) exhibited the highest BF; however, the VM A12-10-2 (1.6% ± 0.1) recorded the lowest BF. The VM genotype O15-16-1 (82.8% ± 4.1) displayed the highest BFC; however, the VM A12-10-2 (1.3% ± 0.1) showed the lowest BFC. The muscadine genotypes D7-1-1 (280.3 F/FC ± 21.7) and O17-17-1 (20.7 F/FC ± 5.5) showed the highest and lowest N.F/FC, respectively. The maximum and minimum FSE was observed for the Rosa cultivar (65.7% ± 2.4) and muscadine genotype D7-1-1 (2.4% ± 0.2), respectively. The minimum N.C/V was recorded for VM genotype A12-10-2 (6 C/V ± 0.2) and maximum noted for muscadine genotypes B20-18-2 (183 C/V ± 7.5) and O44-14-1 (176 C/V ± 7.3). Muscadine genotype O23-11-2 (22.6 kg ± 1.1) produced the highest Y/V; however, the lowest yield was recorded for O15-17-1, Fry Seedless, Sugargate, and the VM genotypes and A12-10-2, with an average yield among them estimated at 0.4 kg ± 0.2.In this study, fertility-related traits of 90 muscadine grape genotypes were evaluated. Selected genotypes included 21 standard cultivars, 60 breeding lines, and nine  V. vinifera industry faces severe pressures from pathogens and a rapidly changing environment. As a result, the intensity of agrochemical use has increased and the distribution of regions suitable for grapevine cultivation is becoming limited. Consequently, there is an urgent need to develop new cultivars that carry desirable adaptive traits capable of overcoming the existing and future challenges to the sustainability of the national grape industry. Muscadines (Muscadinia rotundifolia (Michx.) Small) are commonly grown grapes in the southeastern region of the United States due to their high adaptability to diverse biotic/abiotic stresses and unique fruit quality characteristics [Muscadinia species is due to its commercial crop value and as source of germplasm for economically important traits.Stable productivity is an important goal of vineyards and depends on diverse components . Achievieristics . The receristics . Thus, tGrapevine bud fertility is a primary component that influences the ultimate yield quantity and quality . Bud ferEnvironmental factors, such as light intensity, temperature, and water availability, significantly influence bud fertility. High light intensity and temperatures promote the synthesis of gibberellin and cytokinin phytohormones, which stimulate the differentiation of buds into inflorescence primordia . GibbereExcess shoot growth vigor can reduce bud fertility due to bud necrosis . TherefoOverall, bud fertility is a critical trait that determines vineyard yield and must be carefully considered to develop and select new cultivars, and to ensure sustainable production. Therefore, the current study aimed to evaluate the diverse fertility-related traits of 90 muscadine grape genotypes over three production cycles. This study is the first research considering a population evaluation of fertility traits in muscadine grape. Further, we previously evaluated the characteristics of the same muscadine grape genotypes for other traits, including flower structure, cluster- and berry-related traits, biochemical, and color-related traits ,17,18. AThe first fruiting bud (FFB) trait was determined for a genotype based on identifying the first bud from the base that differentiates and develops into a flower cluster. Grapevines are pruned depending on the occurrence of flowering bud events on the new shoots. Spur-pruned grape varieties produce flower clusters on new growth from buds close to the base of one-year canes, near the main stem. However, cane-pruned grape varieties produce flower clusters on new growth from buds towards the end of one-year canes . In geneBud fertility (BF) trait widely varied between muscadine individuals. Among the population, the BF trait had an extensive range of 35.1% . The aveBud fertility coefficient (BFC) trait considerably differed within the population with a wide range of 81.5%. The average BFC level was estimated at 26.4% ± 13.8. The median BFC (~24.7%) divided the population into two equal groups of high and low bud fertility coefficient . The VM The number of flowers/flower cluster (N.F/FC) trait had a broad range of 259.7 N.F/FC among the population. The muscadine D7-1-1 (280.3 F/FC ± 21.7) and the VM genotype O15-16-1 (252.0 F/FC ± 20.0) displayed the highest N.F/FC with insignificant differences between them. However, the five muscadine genotypes O24-19-2 (23.0 F/FC ± 5.0), Majesty (21.7 F/FC ± 4.0), Rosa (21.3 F/FC ± 2.1), B25-14-1 (21.0 F/FC ± 2.6), and O17-17-1 (20.7 F/FC ± 5.5) showed the lowest N.F/FC. The population had an average of 84.1 F/FC ± 56.5. Based on the median N.F/FC (~68.5 F/FC), the population was separated into two halves of high and low N.F/FC. Accordingly, the standard wine cultivars Carlos (186.0 F/FC ± 19.1) and Noble (158.7 F/FC ± 6.5) were classified among muscadine genotypes exhibiting high N.F/FC. However, Fry (32.7 F/FC ± 5.9) and Majesty table cultivars belonged to the group displaying low N.F/FC .The fruit-set efficiency (FSE) trait varied considerably among the population, with a wide range of 63.3%. The maximum FSE was recorded for the Rosa cultivar (65.7% ± 2.4), whereas the lowest FSE was observed in D7-1-1 (2.4% ± 0.2). The average FSE among the population was estimated at 18.7% ± 13.3. The median CWE (~15.2%) divided the population into two groups of high  and low  FSE. The standard commercial table cultivars Fry (45.7% ± 7.1) and Majesty (40.6% ± 5.3) were classified as members of the muscadine group exhibiting high FSE. In contrast, the wine muscadines Noble (7.8% ± 0.2) and Carlos (4.3% ± 0.2) were members of the low FSE group .The average number of clusters/vine (N.C/V) was taken from three different vines/genotype. The N.C/V trait visibly varied among the population and displayed a wide range of 177 C/V . The aveThe average yield/vine (Y/V) was recorded upon reaching optimal harvest characteristics. Among the population, the Y/V trait had a wide range of 22.3 kg . Muscadip > 0.05), excluding the first fruiting bud (FFB) trait (The frequency distribution of fertility-related traits was assessed . All theB) trait A. The diB) trait B–D,F. ByB) trait A,E,G. HoThe BF trait followed a unimodal frequency distribution pattern by which two major BF modes were detected. The two modes were described as low BF (5.2–8.8%) and slightly higher BF (>8.8–12.4%). Each of these two modes was represented by 21 genotypes or ~23.3% of the population B.The distribution pattern of BFC, FSE, and Y/V traits followed a bimodal frequency distribution C,E,G, suThe traits of N.F/FC and N.C/V typically have a trimodal distribution. A trimodal distribution usually indicates three main phenotypes in the studied population D,F. In tFinally, the FFB trait exhibited a multimodal distribution pattern with four phenotypic categories, exhibiting FFB ranges of 1.6–2.0 (seven genotypes or ~7.8% of the population), >2.0–2.4 (five genotypes or ~5.6% of the population), >2.4–3.1 (46 genotypes or ~51.1% of the population), and >3.1–3.7 (32 genotypes or ~35.5% of the population) A. The maTo assess the correlation between the fertility-related traits and the previously evaluated characteristics in the 90 muscadine grape genotypes ,17,18, Pr = 0.224; P = 3.4 × 10−2), CWI , CWE , NB.per.C , and CC . However, the FSE trait was negatively correlated with the number of flowers/flower cluster (NF.per.FC)  and the perfect FS . The previous results suggested that the high fruit-set efficiency is associated with female flower structure and low number of flowers/flower cluster, which obviously influence the number of berries/cluster, cluster size, weight, compactness, and ultimate yield.The correlation plot was separated into five groups based on their significant correlation. The fruit-set efficiency (FSE) was a member of the first group, which also includes NB.per.C, CC, Acid, TAC, and h°. The FSE trait exhibited positive correlation with the traits of yield/vine (Y.per.V) , NC.per.V , perfect FS , CL , NB.per.C , TPC , TFC , and DPPH . However, NF.per.FC trait was negatively correlated with BL , BWI , and BWE . These results indicate that the high number of flowers/flower cluster is associated with perfect flower structure, which enhances bud fertility coefficient, number of clusters/vine, and number of berries/cluster. However, it was associated with reduced berry characteristics in terms of size and weight. In fact, the previous enhanced traits define the general characteristics of muscadine grapes suitable for wine production. Interestingly, these characteristics were found to be associated with accelerated nutraceutical qualities in terms of phenolic/flavonoid content and the antioxidant activity.The NF.per.FC trait was placed among the fourth group along with FS, TFC, TPC, and DPPH traits. The NF.per.FC showed positive correlation with the bud fertility coefficient (BFC) . However, the BFC exhibited a similar positive correlation with both the NC.per.V and Y.per.V , NB.per.C , CL , CWI , and CWE . The NC.per.V trait showed a positive correlation with the Y.per.V trait  and a negative correlation with the BL . Finally, the Y.per.V trait showed a positive correlation with CL , CWI , CWE , NB.per.C , CC , and FF . The previous results suggested a positive cross-correlation between the bud fertility coefficient, the number of clusters/vine, and the yield. However, the increased number of clusters/vine was associated with reduced berry length. The ultimate yield/vine character was the result of accelerated cluster characteristics in terms of size, weight, number of berries/cluster, and berry firmness. However, these characteristics obviously produce compact clusters.All the remaining fertility traits, including first fruiting bud (FFB), bud fertility (BF), bud fertility coefficient (BFC), number of clusters/vine (NC.per.V), and yield/vine (Y.per.V) were grouped together with the color-related traits of L* and C*. The FFB trait did not display a major correlation with any of the evaluated traits. The BF trait showed a positive correlation with only BFC character  over three production cycles in the period between 2017 and 2019.http.//famu.edu/viticulture, accessed on 20 January 2018).Pruning was carried out each winter between January and March. It was performed by leaving the spurs with three to five buds near the primary arm of the vine. Other vineyard management and practices followed the guidelines outlined in the Muscadine Production Guide for Florida written by Florida Agricultural & Mechanical University (FAMU), Center for Viticulture and Small Fruit Research (CVSFR) (Vitis × Muscadinia hybrids (VM hybrids), were selected based on vines’ age with at least 5 year old vines to ensure stable productivity. A complete list of used genotypes is presented . During the phenological phase of visible inflorescences, in shoots with approximately 5 leaves and 10 cm length, according to Coombs [During winter and after pruning time, the total number of buds per vine for each genotype was recorded  was obtained by calculating the ratio between the number of fruiting buds/vine and the total number of buds/vine. However, the bud fertility coefficient (BFC) trait was obtained by calculating the ratio between the number of clusters/vine and the total number of buds/vine. Due to unfavorable weather circumstances of heavy rain during the growing season, which can cause cluster/berry loss, we generated an equation to calculate the yield/vine accurately, as follows:Data of several evaluated traits were collected and analyzed to test the genotype effect using repeated measures of analysis of variance (ANOVA) in SAS  using PROC GLIMMIX. Means for the analyses were determined using the LSMEANS statement and means separation conducted using the Tukey–Kramer adjusted multiple means comparison test. All data are presented as the mean ± SD of 3 biological replicates. The R package corrplot was used to produce the correlation matrix plot .Overall, the results of our study highlight that the evaluated muscadine genotypes are suitable for use in a genome-wide association study to investigate the genetic basis controlling most of the muscadine quality traits assessed in the current and previous studies ,17,18. T