Zea mays L.), the third most widely cultivated cereal crop in the world, plays a critical role in global food security. To improve the efficiency of selecting superior genotypes in breeding programs, researchers have aimed to identify key genomic regions that impact agronomic traits. In this study, the performance of multi-trait, multi-environment deep learning models was compared to that of Bayesian models , Bayesian Genomic Genotype-Environment Interaction (BGGE), and Bayesian Multi-Trait and Multi-Environment (BMTME)) in terms of the prediction accuracy of flowering-related traits . A tropical maize panel of 258 inbred lines from Brazil was evaluated in three sites  using approximately 290,000 single nucleotide polymorphisms (SNPs). The results demonstrated a 14.4% increase in prediction accuracy when employing multi-trait models compared to the use of a single trait in a single environment approach. The accuracy of predictions also improved by 6.4% when using a single trait in a multi-environment scheme compared to using multi-trait analysis. Additionally, deep learning models consistently outperformed Bayesian models in both single and multiple trait and environment approaches. A complementary genome-wide association study identified associations with 26 candidate genes related to flowering time traits, and 31 marker-trait associations were identified, accounting for 37%, 37%, and 22% of the phenotypic variation of ASI, FF and MF, respectively. In conclusion, our findings suggest that deep learning models have the potential to significantly improve the accuracy of predictions, regardless of the approach used and provide support for the efficacy of this method in genomic selection for flowering-related traits in tropical maize.Maize ( For example, Flowering time is an agriculturally important trait for crop production that can be manipulated by various approaches such as breeding and genetic modifications . In maizOther genetic studies in maize have emphasized the importance of identifying genetic variants (QTLs) controlling flowering time-related traits under a wide range of environmental conditions to improve stress tolerance . The stuVarious studies have highlighted the potential of using GWAS and genomic selection approaches in enhancing crop breeding and developing improved maize varieties e.g., . For exaUni- and Multi-trait , as well as, Uni- and Multi-environment  approaches have been compared keeping fixed the traits  or environments  as one (Uni) or multiple (Multi). However, comparisons among all approaches simultaneously have not been performed yet, particularly for traits exhibiting low or negative correlations. Thus, the present study aimed to evaluate the performance of these four approaches for the genomic prediction of flowering-related traits in tropical maize using the Bayesian and deep learning approaches. To accomplish this, a panel of 258 tropical maize inbred lines was analyzed using SNP markers. In addition, a complementary genome-wide association study, coupled with network-assisted gene prioritization (post-GWAS), was performed to identify potential candidate genes associated with these traits. The results of this study provide insights into the potential of using deep learning models for enhancing prediction accuracy in the context of genomic selection for flowering-related traits in tropical maize.22.1The study utilized a panel of 258 tropical maize inbred lines from the core collection germplasm of the State University of Maringa, Parana State, Brazil, which were derived from three genetic backgrounds: field corn, popcorn, and sweet corn genotypes S1. Geno2.2The experimental design for Cambira and Sabaudia was an alpha-lattice with 24 incomplete blocks and 3 replications per line, while in Iguatemi, the lines were planted according to a partially balanced incomplete block design in a 17x17 square lattice with 4 replications per line. The following flowering-related traits were evaluated: Female Flowering time (FF) measured as the number of days from sowing to visible silks, Male Flowering time (MF) measured as the number of days from sowing to anther extrusion from the tassel glumes, and Anthesis-Silking Interval (ASI) calculated as the difference between MF and FF .2.3The analysis of the phenotypic data was performed using the following Bayesian model available in the package “MCMCglmm”  of R sofy vector corresponds to the adjusted phenotypic observations, which were utilized in the subsequent sections for Genomic Prediction Models and Genome-Wide Association Study.where y is the vector of the phenotypic observations, X and Z are the known incidence matrices that relate the observation vector (y) to the vectors β and f, respectively. β is the vector of replications and block within replications, f is the vector of family effects and ϵ is the vector of residuals or error vector. The Correlations between each pair of traits were calculated using a Bayesian bi-trait model (MCMCglmm), according to where 2.4Genomic DNA was extracted from the leaf tissue of 21-day-old plants using the protocol described by The kinship matrix was calculated using the identity-by-state method  with theThe Linkage Disequilibrium (LD) was estimated using the correlation coefficients of allelic frequencies (r2) calculated for all possible allele combinations. The critical r2 value was determined using the transformation of the square root of the r2 values as proposed by 2.52.5.1In this study, Uni-Trait-Uni-Environment and Multi-Trait-Uni-Environment analyses were implemented according to iy is the vector of the phenotypic values of the traits, iβ and iu are vectors of fixed and random effects associated with trait i, respectively, and ϵi is a vector of error terms, which are independently normally distributed with mean zero and variance iX and iZ are incidence matrices for the fixed and random effects for trait i, respectively. Then mixed model equation (MME) for the above model is:where R and G are covariance matrices associated with the vectors ϵ and u of residuals and random effects, respectively. If R0 is the residual covariance for more than one trait, then R can be calculated as R=R0⊗I (⊗ represent the Kronecker product between R0 and the identity matrix). Similarly, the genetic covariance matrix G can be calculated as G=G0⊗A, where A and G0 are the additive genetic relationship matrix and additive genetic (co)variance matrix, respectively. The MCMCglmm R package , where G is additive relationship matrix calculated using the t is the unstructured covariance matrix of order t x t; 2b is the random genotypic x trait x environment effect matrix distributed as 2b ∼ MN, where ∑e is the unstructured covariance matrix of order e x e. BMTME was performed considering 10,000 burn-in and 100,000 test iterations.where 2.5.4In this study, Deep Learning methods were used to analyze Uni- and Multi-Trait, Uni- and Multi-Environment data, as described in In this study, we employed a neural network architecture with multiple layers to predict flowering traits in tropical maize Figure 1The analytical forms of the model depicted in j=1, …, M neurons) and output  layers, respectively, while pth input variable, and g represents the activation function. In this network, each layer generates the output for each neuron in the subsequent layer, ultimately producing the output for each response variable of interest. The learning process involves adjusting the weights that connect the layers to optimize the model’s performance. The input variables for the multi-trait approach corresponded to the concatenation of environments, markers through the Cholesky decomposition of the genomic relationship matrix, and genotype × environment interaction (G×E). For this purpose, the design matrices of environments (ZE), genotypes (ZG) and G×E (ZGE) were built, followed by the Cholesky decomposition of the genomic relationship matrix (G). Then, the design matrix of genotypes was post-multiplied by the transpose of the upper triangular factor of the Cholesky decomposition (QT), G×E term as the product of the design matrix of the G×E term post-multiplied by the Kronecker product of the identity matrix of order equal to the number of environments and QT, that is, where N denotes the total number of input variables in each layer, In this study, deep learning models were implemented using the R code of 2.5.5The genomic prediction methods were evaluated using four approaches: Uni-Trait-Uni-Environment (UTUE), Uni-Trait-Multi-Environment (MTUE), Multi-Trait-Uni-Environment (MTUE) and Multi-Trait-Multi-Environment (MTME). These approaches were tested in two scenarios: I) randomly selecting independent training (80%) and validation (20%) groups (for each trait in each site), in which 50 cycles of cross-validation were performed, and II) predicting the second season of environment Iguatemi  using the first season of environment Iguatemi (training dataset DT1), other environments , and other environments (Cambira and Sabaudia) plus the first season of Iguatemi (training dataset DT3).The prediction accuracy was evaluated by calculating the average Pearson correlation coefficient between the observed and predicted phenotypes in the validation set for all models .2.6The Genome-Wide Association Study (GWAS) was conducted using the mixed linear model (MLM) in TASSEL 5.2  for the y is the vector of adjusted phenotypic observations, α and v are the vectors of fixed effects of molecular markers and population structure, respectively, μ and ϵ are the vectors of random effects of polygenic effects and residual, respectively. S, Q and Z are the incidence matrices of the associated vectors.where The probability of a locus being associated with two or more traits was evaluated using the Bayes Factor (BF) and Posterior Probability of Association (PPA) . The PPAπ is the significance level of SNP associated with the trait of interest. BF was calculated using Bayesian multivariate regression analysis in the SNPTEST software  and male flowering (MF) remained consistent across all environments Figure 23.1In this study, a Bayesian clustering analysis was conducted on 258 tropical inbred lines, resulting in the grouping of these lines into two genetic clusters . Cluster I and II consisted of 83 (with 82 popcorn and one field corn genotypes) and 175 maize lines  respectively.The t-SNE method was used to visualize the SNP data, and it clearly separated the two clusters through its second dimension (t-SNE2), which was consistent with the results obtained from InStruct Figure 4Linkage disequilibrium (LD) was also estimated at the genome-wide level and for each individual chromosome able S2.3.2The results of the genome-wide association study (GWAS) for the flowering traits in Iguatemi seasons are presented in In season 1 (Iguatemi 2020), the proportion of the phenotypic variance (PV%) explained by SNP markers was 37%, 37%, and 22% of the phenotypic variation of ASI, FF, and FM, respectively Table 1.3.3A total of 26 candidate genes were identified based on the physical position of these SNPs in relation to the maize reference genome B73 , FF (19 genes), and FM (54 genes) S5. The Arabidopsis thaliana, which encode BINDING PROTEIN 3.). The genes GRMZM2G055520 and GRMZM2G161913 have orthologs in Arabidopsis thaliana that encode EARLY FLOWERING 7 and EARLY FLOWERING 8, respectively. These genes are known to play a role in the control of flowering time in plants. Additionally, these four genes  have an ontology associated with the stage of anthesis, or the beginning of flowering, in various cereal plants, including the silking stage in maize and the whole plant flowering stage.The genes GRMZM2G114793 (bip1 - Binding protein homolog 1) and GRMZM2G415007 (bip2 - Binding protein homolog 2) were found to have orthologs in 3.4The performance of four approaches  for predicting flowering traits in tropical maize were compared using Bayesian and deep learning models. For this purpose, the approaches were evaluated in two scenarios: 1) selection of random training and validation datasets in each environment, and 2) prediction of Iguatemi season 2 using other environments as training datasets .3.4.1Predicting accuracy for uni-trait and multi-trait approaches in a single-environment (UTUE and MTUE):The results of the study indicate that the multi-trait approach leads to higher prediction accuracy for all traits in each of the four environments evaluated, compared to the uni-trait approach. According to Predicting accuracies for uni-trait and multi-trait approaches in multi-environments (UTME and MTME):In contrast to the analysis of a single environment, the prediction accuracy of the uni-trait model was found to be higher than that of the multi-trait model in most cases, as shown in 3.4.2The prediction accuracy for scenario II was found to be generally higher than that of scenario I, as shown in 44.1The flowering traits of crops are crucial for yield and seed quality . In thisThe study identified two potential pleiotropic loci that had an impact on both female and male flowering traits. The use of multivariate Bayesian regression , the Deep Learning model in the DT3 (Multi-Trait-Multi-Environment) approach was found to be more effective than models in the DT1 (Uni-Trait-Uni-Environment and Multi-Trait-Uni-Environment) approach. This suggests that Multi-Trait-Multi-Environment approaches may be useful for increasing predictions for primary traits with low heritability when a secondary trait is highly correlated and has high heritability  was found to be more accurate than utilizing information from other environments (DT2 and DT3). This may be due to the high correlation observed between the first and second seasons of Iguatemi for traits such as FF (r = 0.68), MF (r = 0.53), and anthesis-silking interval (ASI: r = 0.30), compared to the correlation between these traits in other environments. Furthermore, the results of this scenario differed from those of scenario I, as the prediction accuracy for the FF trait was found to be superior to that of the MF trait. This may be due to the high correlation observed among all environments and traits for the FF trait . This suggests that the Deep Learning model is less sensitive to random variations among seasons and correlations between traits and that it does not require the consideration of “genotype x environment” interactions and prior information on the covariance matrices of traits  for training and constructing the predictive model . In thisIn this study, it was shown that Deep Learning models based on Uni- or Multi-Trait and Uni- or Multi-Environment approaches outperformed Bayesian Genomic Selection models (MCMCglmm and BMTME). It should be noted that BGGE achieved the same level of prediction accuracy as DL in UTME, however, the computational time required for BGGE was approximately three times longer than that of DL (data not shown). Similar results were observed by 5In conclusion, this study highlights the effectiveness of deep learning models in genomic selection studies for predicting complex flowering-related traits in tropical maize. Deep learning models outperformed other models (except for BGGE in UTME where similar predictions were observed) indicating their superior accuracy across all traits and scenarios. This suggests that Multi-Traits deep learning models are less affected by low or negative correlations among traits. Moreover, these models have the advantage of learning patterns directly from the data without relying on prior assumptions, making them an attractive alternative to traditional Multi-Trait and Multi-Environment based models. Among the deep learning models, the MTUE model consistently demonstrated the highest prediction accuracies on average. Therefore, it is recommended to use this model in breeding programs, especially for predicting traits that are challenging or expensive to phenotype, or those with low levels of correlation. Additionally, deep learning models should be incorporated into the toolkit of plant breeders to accelerate crop breeding progress and improve genetic gain for quantitative traits. On the other hand, this study identified several loci in genomic regions associated with flowering time in tropical maize, which have variable contributions to phenotypic expression. These findings can be utilized in marker-assisted selection programs, where the loci identified can be target to improve breeding outcomes. Additionally, through the co-functional network approach (post-GWAS), orthologs of EARLY FLOWERING genes were identified, which offer potential targets for genome editing programs focused on improving flowering traits. These discoveries provide valuable insights into the genetic architecture and underlying mechanisms of flowering-related traits in tropical maize, which can be incorporated into breeding programs for further advancements.The original contributions presented in the study are included in the article/FM-P, CM, and CS conceived the research plans. LH, CAM, and RU performed the data curation. CM and FM-P analyzed the genomic data and wrote the first draft of the manuscript. CAM, LH, and CS supervised the field experiments. FM-P, CAM, RU, and LH reviewed and edited the final version of manuscript. All authors reviewed and approved the paper for publication. All authors contributed to the article and approved the submitted version.


Nitrogen (N) fertilizer is one of the major inputs for grain crops including barley and its usage is increasing globally. However, N use efficiency (NUE) is low in cereal crops, leading to higher production costs, unfulfilled grain yield potential and environmental hazards. N uptake is initiated from plant root tips but a very limited number of studies have been conducted on roots relevant to NUE specifically. In this review, we used barley, the fourth most important cereal crop, as the primary study plant to investigate this topic. We first highlighted the recent progress and study gaps in genetic analysis results, primarily, the genome-wide association study (GWAS) regarding both biological and statistical considerations. In addition, different factors contributing to NUE are discussed in terms of root morphological and anatomical traits, as well as physiological mechanisms such as N transporter activities and hormonal regulation. N fertilizer is the primary resource to supply soil nutrients to conserve cereal crop production . N fertiHordeum vulgare) is an important cereal and ranks the fourth most grown crop worldwide (Barley (orldwide . Barley orldwide . In addiorldwide . Once a Genome-wide association study (GWAS) is often referred as a “hypothesis-generating” analysis and could serve as a first step to gaining novel understanding of mechanisms of NUE by studying genetic variations of the entire genome instead of only targeting specified genes of interest . AlthougGWAS is a powerful tool to examine the contributions of genetic variants/single nucleotide polymorphisms (SNPs) to the traits of interests . NUE is Several limitations of GWAS impede the implication of GWAS results for improving NUE. One of the major issues is that the identified variants are often unstable across experiments . There iComputing innovation has allowed a more powerful GWAS by increasing the statistical power as well as optimizing the detection of accurate variants. An early GWAS model, described as a univariate model, can only analyze a single phenotype for its association with variants . This moAlthough a few advancements have been achieved, the optimization of both statistical power and computational efficiency when controlling false discoveries was the primary burden. For instance, fixed and random model Circulating Probability Unification (FarmCPU) was developed to resolve the confounding issue owing to population structure and kinship by dissecting the model into fixed effect model and random effect model to separate population structure and kinship, then running the two models iteratively . ThereafMeta-analysis of GWAS (Meta-GWAS) is another model worthen attention. It is to analyze the quantitative combination of summary statistics from independent experiments . SubsequThe accuracy of GWAS counts upon both high density markers and precise phenotyping. With the advancement in genome sequencing and availability of reference genome, the high-density genotyping is no longer a limit. Our understanding on the plant genome has been more advanced than the below-ground phenome in regards of barley NUE . TherefoGrowing conditions have enormous impacts on root growth and development while there are different growth systems in pre-breeding research . The resThe development of high-throughput phenotyping technologies is paramount for root phenotyping since the sample size for detecting QTLs is generally large and processing those samples in conventional ways can be laborious and time-consuming. The most common method is to collect root samples by washing them from soils and then scan clean samples using scanning software such as WinRhizo and RhizoVision . One of It is a widely accepted concept in cereals that larger root biomass can enhance N absorption . Root weRoot system architecture (RSA) is a collection of root features to describe the temporospatial distribution of roots in the soil. Intensive investigations have been done for the root length and it is positively related to N capture . With thRoot cortical senescence (RCS) describes the death of root cortical cells, which can be promoted by severe N scarcity . On the 3−) uptake  uptake . Hence, ) uptake , and thi3−) is widely considered as the most common form absorbed by crops since nitrification is continuously taking place in the soil under the effect of soil bacteria, converting ammonium (NH4+) into NO3−. Therefore, most research has focused on NO3−. After NO3− is taken up by roots, it is reduced into nitrite (NO2−) by NO3− reductase (NR), then further reduced into NH4+ by NO2− reductase (NiR). Subsequently, N in the NH4+ form will undergo several enzymatic reactions and be converted into organic forms, predominantly amino acids , and this process is catalyzed by urease  is a plant hormone responsible for plant cell division, which has been suggested as a key substance communicating N availability with plants and coordinating the metabolism from roots to shoots . The resvia different transporters. Most NUE research focuses on investigating the effects of NO3− transporters. Nevertheless, a more effective uptake of NH4+ and urea can be potentially desirable for barley plants adapting to a wider range of environments and reducing N losses from the system. Lastly, hormonal regulation is a sophisticated mechanism to be examined for communicating roots with above-ground plant responses.In conclusion, it must be acknowledged that there is limited amount of research has examined NUE in barley, especially considering the below-ground performance. First, several classes of candidate NUE genes have been identified in barley while only a few have been proved to be associated with root traits such as NAC domain protein gene. Moreover, most identified root-related QTLs are not consistent across experiments due to statistical limitations, and biological knowledge gaps including phenotyping platforms and the understanding of relevant phenotypes. Given the advances in plant genomic areas such as whole-genome sequencing technologies and the development of meta-analysis algorithm of GWAS, a large gap still exists in the association between plant phenotypes and N levels. At morphological and anatomical levels, future studies should not be limited to root biomass and length but pay more attention to RSA traits and RCS. Moreover, more research into nodal roots is also highly demanded. Regarding physiological aspects, different N forms are absorbed


For the other traits, a preselection of 2000–4000 SNPs, was found to offer the best model fit according to the Akaike information criterion being minimized. But PA-magnitudes from GP using such selections were still similar to that of GP using all markers. Analyses on both real-life and simulated data also showed that the inclusion of a large QTL SNP in the model as a fixed effect could improve PA and accuracy of GP provided that the PVE of the QTL was ≥ 2.5%.Genomic prediction (GP) or genomic selection is a method to predict the accumulative effect of all quantitative trait loci (QTLs) in a population by estimating the realized genomic relationships between the individuals and by capturing the linkage disequilibrium between markers and QTLs. Thus, marker preselection is considered a promising method to capture Mendelian segregation effects. Using QTLs detected in a genome-wide association study (GWAS) may improve GP. Here, we performed GWAS and GP in a population with 904 clones from 32 full-sib families using a newly developed 50 k SNP Norway spruce array. Through GWAS we identified 41 SNPs associated with budburst stage (BB) and the largest effect association explained 5.1% of the phenotypic variation (PVE). For the other five traits such as growth and wood quality traits, only 2 – 13 associations were observed and the PVE of the strongest effects ranged from 1.2% to 2.0%. GP using approximately 100 preselected SNPs, based on the smallest The online version contains supplementary material available at 10.1186/s12864-023-09250-3. Genomic prediction (GP) or genomic selection using genome-wide dense markers has been widely adopted in animal breeding and extensively studied in crops  and treeSeveral studies in trees have shown, that GP using a few thousand randomly selected SNPs may capture most of the variation and achieve a similar GP accuracy as using all available markers –16. It hPicea abies (L.) Karst.) is one of the most important economic species in Europe, especially in the Nordic countries and Northwestern Russia [Norway spruce  dissect the total genetic component into additive and non-additive variances using genomic-based relationship matrix models and compare with the corresponding dissection using traditional pedigree-based relationship models; 2) test the efficiency using different number of SNPs, different number of clones per family with informative marker preselection; 3) investigate the impact of training population size and relatedness between training and validation populations on GP efficiency; and 4) test whether GP could be improved by including the most significant GWAS-marker as a fixed effect in the GP model.We compared the goodness of fit of four models which each could utilize traditional pedigree data (PBLUP) or genomic data (GBLUP). In brief, the models included one simple additive model , one additive model where a residual genotypic  effect was included , one model where a dominance term was included on top of the additive and residual clonal terms  and finally a model where one specific epistatic term also was added . Please see Material and Methods section Eqs. –4, TableWe found that the additive genetic variance under PBLUP-AR and GBLUP-AR decreased compared to that under PBLUP-A and GBLUP-A for HT6, HT12, DBH, and BB, respectively  within each chromosome varied from 33.7 kb in chromosome 2 to 54.6 kb in chromosome 9, with an average of 42.9 kb for the whole genome based on the SNP array in the studied full-sib family population .We initially estimated predictive abilities (PAs) by a ten times tenfold cross-validations where PBLUP and GBLUP models were trained by the randomly selected portions of the population where phenotypic clonal means were made available whereas the validation was made on the other portion of the population where phenotypic data was kept hidden. The PA-estimates were then compared to the square root of the clone mean narrow-sense heritability  with 100–200 preselected SNPs with the smallest p-values did not outperform random marker selection unless the numbers of SNPs selected were less than a few hundred. Furthermore, for these traits preselecting more markers beyond a few hundred still increased the PA with the highest value being reached when all markers were used  whereas for the other traits more markers resulted in higher within-family PA. AIC values for all traits except BB showed a similar trend , we found that the PA of GP using a preselected set of SNPs with the smallest Ga-matrix  in GWAS as an additional fixed regression effect in the model (GBLUP-F). For HT12, BB, and FD, the GBLUP-F model produced a slightly higher PA than GBLUP (by 0.02 – 0.03) but for DBH, HT6, and PILO, GBLUP-F and GBLUP showed similar values. Furthermore, we calculated an approximation of cross-validation accuracy in the absence of phenotypic data as the PA of PBLUP and GBLUP divided by hc and we found that the overall accuracy of GP for all traits ranged from 0.58 for HT6 to 0.81 for BB.As indicated in Fig. ix Table . For allTo verify whether including a major gene locus as a fixed effect in the GBLUP model (GBLUP-F) would increase PA of GP, we also performed 10 repeats of tenfold cross-validations on simulated data where single SNPs exerting a range of PVE were included Table . SimulatThe within-family accuracy of the GBLUP-F model increased from a very low level (0.10) when the fitted fixed-effect SNP explained 0% of the variation  to 0.42 for a SNP with a PVE at 5%  within families, we performed tenfold cross-validations using GBLUP within the largest ten families only , 2) 100 preselected SNPs for BB and 2000 preselected SNPs for the rest of the six traits based on significant SNP-trait associations (GBLUP-S), and 3) all markers for the Ga -matrix plus the marker with the highest GWAS significance fitted as a fixed regression effect in the model (GBLUP-F). We found that marker preselection increased PAs for some traits , but not for others (HT6 and DBH). The PA-estimates across families were very low for most traits (0.03 to 0.11) but for BB the GBLUP PA was 0.19, increased to 0.24 when fitting the most powerful SNP as a fixed effect (GBLUP-F), and increased further to 0.25 when using GBLUP-S . As previously, we performed the GBLUP with 1) all markers for In tree or conifer breeding programs, a control-pollinated clonal test provides an opportunity to simultaneously estimate the additive, non-additive, dominance, and epistatic variances using pedigree. However, most heritability estimates in many traditional tree breeding programs were based on open-pollinated or control-pollinated progeny trials without vegetative propagation. In this context, such estimates of additive genetic variation may be biased due to the difficulty of separating the additive genetic variance from parts of the dominance and epistatic variances . Using vEucalyptus population. For Pinus contorta Douglas ex Loudon var. latifolia, Cappa et al. (2022) [Recently, several studies on tree species reported that selecting markers with particular influence over a trait could improve PA . Tan and. (2022)  reportedThe recently developed 50 k SNP array for Norway spruce included several QTLs per trait detected in our previous GWAS –40. In tGa-matrix built from 100 preselected markers resulted in GBLUP-models having lower AIC values and being better at predicting the genetic value in the absence of phenotypic data than did a Ga-matrix model using all available markers. Such preselected SNPs were also observed to capture a considerable amount of within-family variation/Mendelian segregation effects. However, for other more polygenic traits, genomic models using ca. 4000 preselected markers showed a comparable PA compared to the model using all markers, even though such models exhibited lower AIC values. This indicates that the preselection of influential markers is more likely to be successful when applied to a limited set of markers showing highly significant associations to a trait where a relatively limited number of trait-associated SNPs are indicated to exhibit considerable individual effects (PVE > 1%).For BB, we found that a In this study, we also investigated a GP model for BB where a large-size QTL with a PVE of ca. 5% was explicitly included as a fixed regression effect and we observed a 4.4% improvement in overall PA Table  in compaModel training using higher numbers of clones per family, is usually expected to capture more Mendelian segregation effects  and imprPinus taeda L. [Ga-matrix of the model  or by fitting the most significant marker as a fixed effect in the model .Forward-selection tree breeding usually entails the selection of a few elite candidates within a progeny test population (F1) and the candidates are in turn crossed to produce a new batch of progenies (F2). Thus, when using existing F1 as a training dataset to predict F2, the average relationship between F1 and F2 will be lower compared with relationships within the same F1 generation as was used for tenfold random cross-validations in this study. We therefore also investigated the situation where training and validation datasets contained individuals from separate families, and we produced a 32-fold cross-validation scheme by removing phenotypic data for one family at a time. The relationships between the validation family and the training dataset were thus restricted to the level of half-sibs or weaker. Thus, the PA across families was considerably lower than for conventional random cross-validation Table , which wtaeda L. . HoweverMarker density (i.e. the number of markers) is usually considered as one of the most important factors affecting GP performance . Howeverr2 ≥ 0.2) was observed to be 42.9 kb when based on the 50 k SNP array used in this population . But for the other traits, approximately 2000–4000 preselected SNPs, indicated by the smallest Akaike information criterion to offer the best model fit, still resulted in PA being similar to that of GP models using all markers. Analyses on both real-life and simulated data also showed that the inclusion of a large QTL SNP in the model as a fixed effect could improve PA and the accuracy of GP provided that the PVE of the QTL was ≥ 2.5%. Currently, most of the published marker resources designed for genomic selection in tree species, such as exome capture, genotyping-by-sequencing (GBS), and commonly designed SNP arrays did not consider the potential redundancy of some of the markers. Therefore, within such marker panels, many markers could be in strong LD with each other, but still not being closely associated to QTLs. Therefore, using all available markers in a genomic model will not necessarily improve genomic prediction, and may even decrease the prediction power, such as was observed for the studied trait BB. Thus, we encourage performing a marker preselection step for genomic prediction, especially when whole genome sequencing data or whole genome-imputed markers data becomes available. Meanwhile, the inclusion of a large QTL SNP in the model as a fixed effect is also recommended in genomic selection.Genomic selection with a marker preselection is considered an efficient approach in animal and tree breeding. For the budburst stage (BB), whose character within this study appeared to be oligogenic, a preselection of approximately 100 SNPs based on the smallest A Norway spruce breeding population using 32 control-pollinated families from 49 parents was established in 2007 at four different field sites. A total of 1430 unique clones were derived from the 32 families with an average of about 45 clones per family and about three ramets per genotype were originally planted in each site. The detailed descriptions of the four sites in this breeding population were presented in the published paper . GeneralTree height was measured at field age six HT6) at all four sites and at twelve years (HT12) at one of the sites. Diameter at breast height (DBH) was measured at field age twelve at all four sites. Budburst stage (BB) was scored at field age six at three sites based on eight categories . Pilodyn at all fPiab50K SNP array chip [Newly fresh needles were sampled from 904 clones in the spring of 2018. Total genomic DNA was extracted using the Qiagen plant DNA extraction protocol with DNA quantification performed using the Qubit® ds DNA Broad Range Assay Kit . Genotypic data were generated using the Norway spruce ray chip . Genotypray chip . Here, mray chip .A and Ga, respectively) were detected for some individuals, we performed a pedigree correction for this population based on A and a heatmap of Ga. The number of parents increased from 49 based on the documented pedigree to 55 based on Ga and the number of families also increased from the original 32 to 56  model was used to fit the row and column directions for phenotypic data from each site using ASReml v4.1 . Block effects were estimated simultaneously. Adjusted data, where all significant block and spatial effects were removed, were used for downstream analyses.y is the vector of adjusted phenotypic observations of a single trait; d are the vectors of random additive and dominance effects, respectively;ds are the vectors of random additive-by-site and dominance-by-site effects, respectively; r is the vector of residual genotypic effects, referring to an un-dissectable combination of dominance and epistatic effects in Eq.  and genomic-based best linear unbiased prediction (GBLUP) as following:entclass1pt{minimaPedigree-based BLUP (PBLUP) models based on Eqs.  to 4) a a4) are A) and dominance (D) relationship matrices were produced using the AGHmatix package [tr) [M/(2n) were filtered when calculating all the genomic matrices for GBLUP-models, where M is the harmonic number of clones per family and n is the total population size in each GP model. When applied to the whole population as in this case (n = 904), we filtered away SNPs with a MAF < 0.006. More detailed descriptions of the matrices are shown in Supplementary methods SThe pedigree-based additive ( package . The genribed by  for \doca and by  for \docage in R . The reln R [tr) . Due to entclass1pt{minimaλ, which is defined as the median of the resulting χ2 test statistics based on p-values from GWAS divided by the expected median of the χ2 distribution [To check the additive genetic architecture of the six traits in the breeding population, we also performed GWAS based on clone mean values across-site for all traits using the multi-locus BLINK model  conducteribution ) to checribution . The perribution .Piab50K SNP array, 43,267 SNPs were successfully mapped and were evenly distributed across the 12 chromosomes  was conducted in the F1 full-sib progeny population. All SNPs were mapped onto the Norway spruce genome v2.0 (In preparation). Out of the 47,445 SNPs available from the Due to the negligible effects of dominance, dominance-by-site, and also first-order epistatic effects for all traits Table S, clone my’ is the vector  of the clonal means across-site, X and Z1 are the matrices related to the a. The random additive effects (A will be replaced by Ga in the GBLUP-A model.where hc).The prediction ability (PA) was defined as the Pearson correlation between predicted breeding values (EBVs) and clonal means. Furthermore, we calculated the PA for within-family predictions as Pearson correlations between the EBVs deviation from EBV family mean and the clonal mean deviation from the family means used as benchmark validation values. An estimate of the selection accuracy was calculated by dividing PA with the square root of the pedigree-based narrow-sense clonal mean heritability  and in a subset population with 482 clones from the 10 largest families. The general model fit was evaluated by inspecting their Aikake Information Criterion values (AIC) and the subset sample of markers showing the smallest AIC-values were selected for further investigations . In the subset population (n = 482), we filtered SNPs according to the previously mentioned principles thus filtering out all SNPs with MAF < 0.025. Consequently, 42,665 SNPs were kept for the GWAS used for marker preselection.To test the impact of the number of SNPs on the overall and within-family PA of GBLUP, we performed GPs using 14 subsets of SNPs  and using two different types of sampling strategies: 1) randomly selected SNP subsets and 2) SNP subsets selected based on the smallest PBLUP-C: the traditional pedigree-based BLUP including pedigree correction.Ga) estimated from all markers.GBLUP: a genome-based BLUP with a relationship matrix  which instead was included as a fixed regression effect. This single marker was selected from GWAS results based on each training population.GBLUP-F: a genome-based BLUP with a For in depth investigations of the impact of marker preselection, we tested and compared the efficacy for four different modelling approaches:ASReml-R v4.0.5 was used to perform cross-validations for the above four models. The Wald-F statistics was used to test the significance of the fixed regression/covariate effect. In addition to the four approaches mentioned above, we also performed several extra strategies.Compared with the previously random cross-validation strategy, another validation method entailed the prediction of genomic breeding values of all progenies within a specific full-sib family based on model training using all the other families using GBLUP. In all, there were 32 such across-family cross-validation repeats and the relationships between training and validation sets were thus consistently weak . The PA within the validated family therefore largely depends on whether the model could capture the Mendelian segregation effects.To test if an increase in family size could improve the PA of PBLUP-C and GBLUP, we randomly selected five to 30 clones per family as a training set for the largest ten families with 48–56 clones per family , 0.25% (minor effect), 1.25%, 2.5% and 5% (major true QTL). GP-models (according to Eq. To verify whether the inclusion of a major-effect locus as a fixed effect in the model would improve PA, we conducted finite-locus model simulations of a simplified breeding population undergoing one generation of directional selection. We used the software Metagene  to simulAdditional file 1. Supplementary figuresAdditional file 2. Supplementary methodsAdditional file 3. Supplementary table S1-S6


Existing methods for simulating synthetic genotype and phenotype datasets have limited scalability, constraining their usability for large-scale analyses. Moreover, a systematic approach for evaluating synthetic data quality and a benchmark synthetic dataset for developing and evaluating methods for polygenic risk scores are lacking.We present HAPNEST, a novel approach for efficiently generating diverse individual-level genotypic and phenotypic data. In comparison to alternative methods, HAPNEST shows faster computational speed and a lower degree of relatedness with reference panels, while generating datasets that preserve key statistical properties of real data. These desirable synthetic data properties enabled us to generate 6.8 million common variants and nine phenotypes with varying degrees of heritability and polygenicity across 1 million individuals. We demonstrate how HAPNEST can facilitate biobank-scale analyses through the comparison of seven methods to generate polygenic risk scoring across multiple ancestry groups and different genetic architectures.https://www.ebi.ac.uk/biostudies/studies/S-BSST936. The HAPNEST software for generating synthetic datasets is available as Docker/Singularity containers and open source Julia and C code at https://github.com/intervene-EU-H2020/synthetic_data.A synthetic dataset of 1 008 000 individuals and nine traits for 6.8 million common variants is available at  With the emergence of large-scale biobanks, methods to analyse common genetic variants [single-nucleotide polymorphisms (SNPs)] across diverse human populations are in growing demand. This is especially the case for polygenic risk scoring (PRS) methods, which quantify an individual’s genetic risk for a disease or other phenotypic trait , but somBroadly, two main approaches have been used to simulate individual level genetic data. Coalescence-based methods, such as Hudson’s ms and msprime , that diTo address these limitations, we introduce HAPNEST, a user-friendly tool for generating synthetic datasets for genotypes and phenotypes, evaluating synthetic data quality, and analysing the behaviour of model parameters with respect to the evaluation metrics. HAPNEST simulates genotypes by resampling a set of existing reference genomes, according to a stochastic model that approximates the underlying processes of coalescent, recombination and mutation. Like HAPGEN2, HAPNEST is also based on the Li and Stephens model of LD , but HAPhttps://github.com/intervene-EU-H2020/synthetic_data, and has also been distributed as Docker and Singularity containers. We have generated 6.8 million common variants and nine phenotypes with varying degrees of heritability and polygenicity across 1 million individuals and made this large synthetic dataset available at https://www.ebi.ac.uk/biostudies/studies/S-BSST936 to encourage standardized evaluation of new statistical methods by the genomic research community.We compare the performance of HAPNEST with current state-of-the-art genotype and phenotype simulation tools in terms of data quality and computational speed. Furthermore, as a demonstration of the utility of our tool, we show the application of our diverse, biobank-scale synthetic data for evaluating the performance of various PRS methods under different disease models. Our open-source software tool is available at T, when determining genetic distances, HAPNEST simulates pairs of synthetic haplotypes, where each haplotype is constructed as a mosaic of segments of various lengths imperfectly copied from a reference set of real haplotypes . This rent model  and motik SNPs for s .Synthetic data quality is evaluated based on a workflow implemented in the HAPNEST software tool for measuring the fidelity, diversity, and generalizability of synthetic datasets. We compare HAPNEST with three alternative methods: HAPGEN2 , and SimFidelity is measured as the similarity between the real (reference) and synthetic datasets for four properties: minor allele frequency (MAF) distribution, population structure in terms of alignment of the principal components (PCs), LD decay and nearest neighbour adversarial accuracy , HAPNEST (multiobjective), and Sim1000G .In We evaluate preservation of population structure of HAPNEST and other state of the art simulators. We consider privacy-preserving metrics by calculating the nearest neighbour adversarial accuracy score, which averages the true positive rate and true negative rate for distinguishing real and synthetic data. Adversarial accuracy scores closest to 0.5 are observed for the G2P and HAPNEST (multiobjective) methods, indicating that these synthetic samples are more indistinguishable from the real data .Nsyn = 25 000, k > 1 00 000), HAPNEST achieves comparable performance in the LD metric and better performance in some metrics such as PC alignment, compared with the smaller datasets. Similar results were observed for both the European and African ancestry experiments.Overall, our analysis indicates that no one method performs best across all evaluation metrics, but instead there are tradeoffs that end users should consider, depending on the priorities of their use case. The correlation in the performance of metrics across methods is weak overall, but a lower LD decay distance is generally associated with a higher PC alignment, suggesting that methods with better preservation of LD structure also preserve the population structure of the reference data well . The neaDiversity is measured by the degree of genetic relatedness (kinship) within the synthetic dataset and generalizability is measured by the degree of genetic relatedness between the real and synthetic datasets. HAPNEST (multiobjective) reached the best generalizability and diversity of all methods evaluated  when conThe scalability of HAPNEST is validated by measuring the computational speed of generating genotype datasets for a varying number of synthetic samples, SNPs and computing threads. The experiments compare a smaller panel of 18 267 HapMap3 variants and a larger panel of 109 673 variants with non-zero MAF on chromosome 21. The analyses were conducted using a CentOS server with Intel Xeon E5 2680 v3 2.50 GHz processors and 32GB RAM for both single- and multithreaded setups, and use fixed input parameters, x times faster than HAPGEN2 (for between 18 267 and 109 673 SNPs) for a single thread and this increased to 5.3–24x times faster for multiple threads (tested for eight threads). This demonstrates that HAPNEST uses parallelization to achieve gains in computational efficiency for larger synthetic datasets. We note that HAPGEN2 does not have a command-line option for multiple threads and that similar results for both the single and multithreaded experiments (x times less RAM for 100 000 synthetic samples on a single thread) and uses multithreading to more efficiently utilize the available computing resources.We observe that while generation times are similar for smaller datasets, HAPNEST is increasingly faster than HAPGEN2 for more variants and larger sample sizes  which aperiments  indicateA continuous or binary phenotype can be assigned to each sample as an aggregation of genetic effect, user-input covariate effect (if any) and environmental noise. The genetic component is generated as a weighted sum of causal allele counts . For eaca, b, and c reflect strength of negative selection on each aspect and we used extensive empirical observations  in the population. Thus, IBD segments degrade over time, which also shows an impact on LD  segments in population genetics . As showCompared with other methods, HAPNEST-generated genotypes demonstrated better diversity and generalizability, which are essential features when scaling to large sample sizes. While the genetic relatedness analysis indicated that the genotypes are sufficiently different from the reference data, a nearest-neighbour adversarial accuracy close to 0.5 indicates that statistically speaking, it would be difficult to discern a synthetic genotype from a real genotype. These properties of synthetic datasets are desirable in the context of data privacy, where we may want to create a synthetic twin of sensitive data that preserves key statistical properties of the real data, but cannot be traced back to real individuals. However, we note that we have not tested the HAPNEST method for differential privacy guarantees and for this reason, we advise to use HAPNEST, or any of the reference-based generation methods, only on publicly available genomics datasets.Once individual level genotypes have been generated, we can subsequently assign phenotypes to each sample as an aggregation of polygenic effects, non-genetic effects and environmental noise. We also implemented population-specific phenotypic effects by assuming shared causal variants across populations with distinct but correlated effect sizes, and multitrait simulation allowing for different genetic correlation and pleiotropy models.We believe our tool can benefit the community especially for GWAS-related method development, for which one of the examples can be PRS computation and evaluation. HAPNEST allows researchers to assess the validity of genetic scoring methods under a broad variety of setups, including cross-ancestry, trans-diagnostic, and different genetic architectures. Here, as a demonstration of its utility, we applied PRSpipe [PRSpipe is a Snakemake pipeline developed to calculate and evaluate polygenic risk scores from GWAS summary statistics. It implements and extends the GenoPred (btad535_Supplementary_DataClick here for additional data file.


The results indicated a potential causal association between 22 gut microbiota genera and dyslipidemia in humans. Furthermore, these findings suggested that the impact of gut microbiota on dyslipidemia regulation is dependent on the specific phylum, family, and genus. Bacillota phylum demonstrated the greatest diversity, with 15 distinct genera distributed among eight families. Notably, gut microbiota-derived from the Lachnospiraceae and Lactobacillaceae families exhibit statistically significant associations with lipid levels that contribute to overall health (p < 0.05). The sensitivity analysis indicated that our findings possess robustness (p > 0.05). The findings of our investigation provide compelling evidence that substantiates a causal association between the gut microbiota and dyslipidemia in the human body. It is noteworthy to highlight the significant influence of the Bacillota phylum as a crucial regulator of lipid levels, and the families Lachnospiraceae and Lactobacillaceae should be recognized as probiotics that significantly contribute to this metabolic process.The determination of a causal association between gut microbiota and a range of dyslipidemia remains uncertain. To clarify these associations, we employed a two-sample Mendelian randomization (MR) analysis utilizing the inverse-variance weighted (IVW) method. This comprehensive analysis investigated the genetic variants that exhibited a significant association ( The gut microecosystem, consisting of approximately 1014 microorganisms . It assuDyslipidemia is presently characterized in clinical settings by the presence of anomalies in various lipid types, including high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), triglyceride (TG), total cholesterol (TC), apolipoprotein A1 (APOA1), and apolipoprotein B (APOB) concentrations ,44,45,46Mendelian randomization (MR) analysis is a prevalent approach employed in population studies to evaluate causality, wherein genetic variation is utilized to ascertain the coherence between observed associations linking risk factors and outcomes ,70. The −8 (genetic variants must exhibit a strong association with the exposure), clump = TRUE, r2 = 0.01, kb = 5000 (IVs with linkage disequilibrium were removed to ensure the independence of the selected genetic variations) [Genetic variants that exhibit a robust association with distinct genera of gut microbiota were identified using a comprehensive genome-wide association study (GWAS) conducted on individuals of European descent, as documented in the OpenGWAS database ,77. The iations) ,81. A cohttp://www.phenos-canner.medschl.cam.ac.uk/phenoscanner, accessed on 5 September 2023). If a specific SNP exhibits a direct correlation with lipid abnormalities in the host or with abnormalities in liver function (that may impact lipid synthesis), host fat distribution, fatty acid metabolism, and body mass index (BMI), it is deemed to possess potential confounding effects, leading to its exclusion from the study. Then, we combined the two sets of data for subsequent MR analysis.SNPs associated with dyslipidemia  were also obtained from the OpenGWAS database, and the population structure is also dominated by European . If therThe present study utilized publicly accessible GWAS summary statistics data sourced from the OpenGWAS database. This database obtained informed consent from all participating studies in accordance with the protocols approved by their respective institutional review boards. Consequently, the submission of a dedicated ethics statement is unnecessary.p value (less than 0.05) derived from the Q statistic. In cases where heterogeneity was present, the effect evaluation was estimated using the random-effects IVW method, while the fixed-effects model was employed in the absence of heterogeneity [p value is less than 0.05, it suggests the presence of horizontal pleiotropy. In such cases, an alternative MR method was employed to report the findings [p value was less than 0.05.The standard inverse-variance weighted (IVW) method was employed for primary two-sample Mendelian randomization (MR) analyses, which were further enhanced by incorporating the weighted median and MR Egger methods available in the TwoSampleMR package ,84. The ogeneity ,87. Sensogeneity ,89,90. Sogeneity ,91. Whenfindings ,92. For p value of IVW < 0.05) can be outlined as follows: Coprobacter and Olsenella for HDL-C  is as follows: Coprococcus2, Lachnospiraceae NK4A136 group, Lactobacillus, and Parabacteroides for HDL-C  in the causal associations between Olsenella and TG, Anaerotruncus, Ruminococcaceae UCG009, and APOA1, and the effect size for these associations was estimated using the random effect model of the IVW method, while a fixed effects model was employed to assess other causal effect sizes. The ultimate findings demonstrated that all effect values were statistically significant  from the Oscillospiraceae family, each playing distinct roles in lipid regulation. Ruminococcus2 and Anaerotruncus had the potential to increase lipid levels in the body, whereas other bacteria, such as Oscillospira, Butyricicoccus, and the Ruminococcus torques group genus, demonstrated the ability to decrease lipid levels. Oscillospiraceae is a bacterial family classified within the phylum Bacillota, consisting of obligate anaerobes. Despite the variation in shapes among its members, including rod-shaped and cocci forms , the OscAccording to reports, there is a significant association between a decrease in Butyricicoccus and the occurrence of inflammatory bowel disease (IBD) . IBD encIn relation to the Ruminococcaceae family, we have identified two gut microbiota genera that exhibit distinct effects on lipid levels in the body. Specifically, the Ruminococcaceae UCG009 genus appears to decrease APOA1 levels, while the Ruminococcaceae UCG010 genus appears to decrease APOB levels. The Ruminococcaceae family is known to play a role in energy metabolism, insulin signaling, and inflammatory processes. Moreover, an increase in the relative abundance of Ruminococcaceae has been found to increase the risk of gestational diabetes mellitus (GDM) development . In a stThe genera of gut microbiota belonging to the families Lachnospiraceae, Lactobacillaceae, and Peptococcaceae within the Bacillota phylum have been found to have significant positive effects on lipid levels in the human body. Notably, the genera Dorea, Coprococcus2, and Lachnospiraceae NK4A136 group from the Lachnospiraceae family, Lactobacillus from the Lactobacillaceae family, and Peptococcus from the Peptococcaceae family have demonstrated a dual role in regulating lipid metabolism. These gut microbiota have the ability to reduce harmful lipids (APOB and LDL-C) while also promoting the evaluation of beneficial lipids (HDL-C and APOA1) in the body. Lachnospiraceae, a prominent taxon in the human gut microbiota, has been found to potentially mitigate colon cancer in humans using the production of butyric acid ,117,118.In addition, our investigation revealed that several families within the Bacillota phylum, such as the Eubacterium coprostanoligenes group from the Eubacteriaceae family, Lactococcus from the Streptococcaceae family, and Terrisporobacter from the Peptostreptococcaceae family, exert a noteworthy influence on the augmentation of lipid levels in the human organism. Significantly, these gut microbiota species demonstrated substantial effects on TC and LDL-C levels. The significance of this particular family resides in its ability to generate diverse strains that produce short-chain fatty acids, particularly butyric acid. These short-chain fatty acids are widely acknowledged for their pivotal functions in maintaining human health, including their function as specialized nutrients and energy sources for the intestinal epithelium, preserving the integrity of the intestinal mucosal barrier, reducing inflammation levels in humans, and enhancing gastrointestinal motility ,127. LacIn addition, our findings reveal the presence of additional phyla in the observed data, including three gut microbiota belonging to the Actinomycetota phylum, which are distributed among two families, and two gut microbiota belonging to the Bacteroidota phylum are distributed across two families. Moreover, within the Euryarchaeota and Pseudomonadota phylum, two distinct gut microbiota genera are identified, each belonging to their respective autonomous families. The Actinomycetota genus is prevalent in the microbiome of human infants  and is kIn conclusion, the influence of gut microbiota on lipid metabolism varies depending on the specific types of gut microbiota. Our study demonstrates that the predominant phylum of gut microbiota in humans also encompasses the most diverse microbial group responsible for regulating lipid metabolism. The families Lachnospiraceae and Lactobacillaceae are of notable importance and should be recognized as crucial microbiota in ameliorating dyslipidemia within the human body. Furthermore, it is imperative to recognize that individuals with hyperlipidemia should abstain from consuming cheese. Our research findings elucidate the wide-ranging and ubiquitous influence of gut microbiota on the regulation of lipid metabolism levels, thereby enhancing our comprehension of the interplay between gut microbiota and diseases associated with dyslipidemia. These results provide novel evidence that contributes to a more comprehensive understanding of how gut microbiota modulates bodily functions and metabolism.Our study possesses several notable strengths, such as the implementation of the MR approach, which effectively mitigates certain confounding factors frequently encountered in epidemiological studies. Additionally, we have employed a homogenous population, thereby reducing the inherent heterogeneity often encountered when individuals from diverse ancestral backgrounds are included in genetic studies. Stratified analyses were employed to assess the causal associations between various genera of gut microbiota and distinct dyslipidemia types. Additionally, sensitivity analysis was conducted on the subgroup analysis outcomes, yielding statistically robust results. Nevertheless, it is important to note that the inclusion of exclusively European individuals in our analyses may limit the generalizability of these findings to other ancestral populations.Our findings demonstrate a definitive causal link between gut microbiota and dyslipidemia within the human organism. Notably, the Bacillota phylum emerges as the most influential regulator of body lipid levels. The families Lachnospiraceae and Lactobacillaceae assume a noteworthy role in ameliorating lipid metabolism abnormalities and should be recognized as crucial gut microbiota in this process. Additionally, it is recommended that individuals with hyperlipidemia are advised to exercise caution when consuming cheese, as the prevalence of Lactococcus in this food item may potentially exert detrimental effects on their lipid profile.


Carcass traits are crucial for broiler ducks, but carcass traits can only be measured postmortem. Genomic selection (GS) is an effective approach in animal breeding to improve selection and reduce costs. However, the performance of genomic prediction in duck carcass traits remains largely unknown.2 population of ducks. Most of the cut weight traits and intestine length traits were estimated to be high and moderate heritabilities, respectively, while the heritabilities of percentage slaughter traits were dynamic. The reliability of genome prediction using GBLUP increased by an average of 0.06 compared to the conventional BLUP method. The Permutation studies revealed that 50K markers had achieved ideal prediction reliability, while 3K markers still achieved 90.7% predictive capability would further reduce the cost for duck carcass traits. The genomic relationship matrix normalized by our true variance method instead of the widely used In this study, we estimated the genetic parameters, performed GS using different models and marker densities, and compared the estimation performance between GS and conventional BLUP on 35 carcass traits in an FThis study demonstrates genomic selection for duck carcass traits is promising. The genomic prediction can be further improved by modifying the genomic relationship matrix using our proposed true variance method and several Bayesian models. Permutation study provides a theoretical basis for the fact that low-density arrays can be used to reduce genotype costs in duck genome selection.The online version contains supplementary material available at 10.1186/s40104-023-00875-8. Ducks play a considerable role in the structure of the waterfowl meat market in Asia and some European countries. Duck is also the third largest meat consumption in China after pork and chicken. Duck cuts, such as breasts and legs, offer more options for diet-conscious consumers. Duck meat is generally regarded as flavorsome, rich in amino acids and polyunsaturated fatty acids, and relatively low in fat. Duck meat has a higher number of muscle fibers , lipid cSeveral efforts have been made to improve duck productive traits in duck breeding programs using pedigree and phenotypic information . AlthougGenomic selection (GS) is an effective approach in animal breeding to improve selection and reduce costs, which has been widely used in livestock , poultryIn ducks, a commercial single nucleotide polymorphism (SNP) array has not been developed, the only way to get genotype information is from whole or reduced-representation genome sequencing. A lack of low-cost SNP arrays would increase the breed cost and delay the application of genomic selection. Marker density is an important factor affecting the accuracy of genome prediction and breed cost . AlthougThe choice of statistical models also has a noticeable impact on the prediction accuracy of GS , 15. TheDuck meat production is based mainly on commercial crossbreeds of different Pekin strains. Hence, the objectives of this study were: (1) to calculate genetic parameters of duck carcass traits, (2) to estimate reliability gains from using genomic evaluations instead of traditional BLUP evaluations, (3) to document how the density of markers affect predictive ability of GS, (4) to propose the best strategies to improve genomic predictions for duck carcass traits.The phenotypes, pedigrees, and genotypes of ducks were conducted in an experimental cross-population of Pecking duck × mallard. Phenotypes for 35 carcass composition traits were measured in 988 animals with age of 8 weeks. Table y − the vector of phenotype value; β − the vector of fixed effects, including sex, reciprocal crosses and feed room; a − the vector of additive genetic effects and assumed that a∼N minus the mean of marker across individuals 2Z = M − 2The linear mixed models are formulated ass method , the forZZ' should be divided by The GRM construction method proposed by VanRaden  was wideThe second method was commonly used by human genetics studies, such as Yang et al.’s GCTA , 31, as Z is mentioned above, D − a diagonal matrix with m is the number of SNP markers. This method assumed that each marker has a different variance of genotype across individuals. Each marker should be scaled by itself variance th SNP with two alleles, one allele with frequencies Both of the above two methods used the The expectation th SNP genotypes 1 and 2 were However, in most cases, the SNPs could be influenced by artificial selection, inbreed, mutation, genetic drift, etc., which were not fully satisfied for the Hardy–Weinberg principle. Using The true expectation When the SNP marker across individuals obeys the Hardy–Weinberg principle, its MAF Using The modified Yang et al.’s formula should be:D matrix should be:The markers’ diagonal p(1 − p), SP), Yang’s model (Independent 2p(1 − p), IP), VanRaden’s model with true variance  and Yang et al.’s model with true variance .Therefore, we generated four different GRM models used for estimating SNP heritability and genome prediction reliability: VanRaden’s model  . BayesR +π3+π4=1 . The unk+π3+π4=1 .The prediction reliability of the models was estimated based on a fivefold cross-validation. In fivefold cross-validation, the phenotypes of 20% of the animals were masked and then estimated using the phenotypes and genotypes of the remaining 80% of animals. The dataset of genotyped animals with phenotypes was randomly divided into five subsets, predicting one subset at a time and using the phenotypes of the remaining four subsets. Genomic prediction reliability was calculated as the Pearson correlation coefficient between adjusted phenotypic values and genomic predicted genetic values. The mean correlation value was used as the reliability for each trait.To evaluate the influence of marker density on the SNP heritability estimation and genome prediction, we randomly selected 0.5K, 1K, 3K, 5K, 10K, 50K, 100K and 500K from the original 1.04 million (M) markers. We built the genomic relationship matrix, and estimate SNP heritabilities and genomic breeding values using the GBLUP model for each selected marker. The prediction reliability of the models was estimated based on a fivefold cross-validation. We repeated this process 30 times to obtain stable results for each marker density. The predictive capability was equal to the current reliability divided by the best performance of reliability within the nine different density markers for a given trait.rg = 0.998 ± 0.002, rp = 0.976 ± 0.004), carcass weight and eviscerated weight . The negative correlation between some of the percentage traits was due to their competitive ratio on whole carcass. Interestingly, we found the gaps between genetic and phenotype correlation were considerable in several trait pairs , such as eviscerated percentage and liver , lean meat percentage and skeleton weight , and dressed rate and shank length .The phenotype information of 35 traits was described in Table P < 0.001, paired t-test). For each trait, we observed 32 out of 35 traits were increased by genome prediction. The increment in predictive ability obtained using GBLUP respect to pedigree BLUP was more noticeable in the neck weight (0.12), dressing rate (0.12), and carcass weight (0.11). The average increment of predictive reliability between GBLUP and BLUP was 0.06 (ranged from −0.02 to 0.12) across all 35 traits.The genomic predictive reliability for carcass traits using pedigree BLUP and GBLUP methods are summarized in Table SWe randomly selected 0.5K, 1K, 3K, 5K, 10K, 50K, 100K, and 500K, from the original sequencing markers, each permutation was repeated 30 times. To check whether the marker density could affect the genomic relationships, we calculated the Pearson correlation coefficients between permutations in each density group after building the GRM. We found that the correlation coefficients between permutations rapidly increased from 0.5K to 3K density, and moderately increased from 3 to 50K density Fig. . The preZZ'. VanRaden [t-test P < 2.29E−05) and GBLUP reliability (paired t-test P < 1.78E−05) than sum t-test P < 4.16E−23 for sum variance, and P < 2.72E−22 for independent variance). The average of SNP heritability gained was 0.039 for both sum and independent true variance methods. For the prediction, we also found a significant improvement in predictive reliability of GS for most traits in both sum (paired t-test P < 4.77E−9) and independent (paired t-test P < 7.86E−11) methods by true variance  than for the GBLUP models in most traits, while the advantage of Bayesian models in percentage traits was meager Fig. A. For ea2 population of ducks.Genomic selection expects to speed up genetic progress in animal breeding programs . Practic2 crossbreed of Pekin type ducks study (0.47–0.75) [There are few reports on the genetic parameters of duck carcass traits , 36–38. 47–0.75) . The duc47–0.75) , but hig47–0.75) .Both phenotypic and genetic correlations of weight traits were usually positive and high. Similar results for duck populations were generally reported by previous studies , 40, 41.The high predictive ability of GS in skin and subcutaneous fat weight, abdominal fat weight, carcass weight, skin and subcutaneous fat percentage, eviscerated weight, and gizzard percentage, suggests that the better performance of GS could be found in traits with high heritability. Similar results were seen in other research where there was a significant association between trait heritability and prediction reliability , 43. ThiIn Bayesian models, the differences among methods are the assumptions on the genetic marker effects, which outperform GBLUP when the number of quantitative trait loci (QTLs) underlying the trait is smaller than the number of independent chromosome segments . In thisAn increase in marker densities generally resulted in raised accuracy predicted. In our study, the marker density of 1K could achieve the predictive ability of the traditional BLUP breeding strategy, which indicated GS has great potential in broiler duck breeding. The predictive ability was dramatically increased when marker density was below 3K, then the increase of predictive ability was slowed down. With a marker density of 50K, the prediction accuracy for most carcass traits reached a plateau. A similar phenomenon was found in other species although the threshold might be different , 53. TheZZ' should be corrected by In recent years, GBLUP has been a widely used method for genomic evaluation in livestock. The predictive ability of genomic breeding values estimated by GBLUP can be affected by the characteristics of GRM, which was significantly affected by the number of markers, markers’ weights and standardized methods , 52. AltOur results demonstrate that genomic prediction is a feasible approach for accurate selection in duck breeding programs, especially for these traits which are difficult to be measured such as carcass traits. The genomic prediction can be further improved by modifying the GRM using our true variance method, which is worth promoting in GS. Several Bayesian models, especially for BayesN, could bring more noticeable improvement in the predictive ability of GS, which need attention. The permutation studies of density markers indicate 50K markers achieved ideal prediction accuracy, while 3K markers still achieved 90.7% predictive capability, which would be promised to further reduce cost in duck breeding. In conclusion, our findings offer some useful strategies for the optimizing predictive ability of GS and provide theoretical foundations for designing a low-density panel in ducks.Additional file 1: Table S1. Estimation of phenotypic correlation and genetic correlation for the 35 carcass traits.Additional file 2: Table S2. Estimation of SNP heritabilities for duck carcass traits using different marker densities.Additional file 3: Table S3. The predictive reliability of genomic breeding values for duck carcass traits using different marker densities by GBLUP model.Additional file 4: Table S4. The SNP heritabilities and prednishiictive reliability of genomic breeding values for duck carcass traits using four different GRM methods.Additional file 5: Table S5. The predictive reliability of genomic breeding values for duck carcass traits using Bayesian models.Additional file 6.Additional file 7: Fig. S1. The permutation of marker density affects the estimation of SNP heritability of GS in duck carcass traits. (A–C) The SNP heritability changes by the various markers' density across each trait for weight traits (A), length traits (B) and percentage traits (C).Additional file 8: Fig. S2. The predictive capability of genomic breeding values for duck carcass traits using different marker densities. The color of each box represents a high capability (red) or a low capability (blue).


Can we identify genetic variants associated with ectopic pregnancy by undertaking the first genome-wide association study (GWAS) leveraging two large-scale biobank initiatives?MUC1 (mucin 1) as the most plausible affected gene.We identified two novel genome-wide significant associations with ectopic pregnancy, highlighting Ectopic pregnancy is an important cause of maternal morbidity and mortality worldwide. Despite being a common early pregnancy complication, the genetic predisposition to this condition remains understudied and no large scale genetic studies have been performed so far.A GWAS meta-analysis including 7070 women with ectopic pregnancy and 248 810 controls from Estonian Biobank and the FinnGen study.We identified ectopic pregnancy cases from national registers by ICD  codes (ICD-10 O00), and all remaining women were considered controls. We carried out standard GWAS meta-analysis and additionally annotated GWAS signals, analysed co-localization with quantitative trait loci, estimated genetic correlations and identified associated phenotypes to characterize the genetic signals, as well as to analyse the genetic and phenotypic relationships with the condition.P = 5.32−9) and 10  potentially associated with ectopic pregnancy. Follow-up analyses propose MUC1, which codes for an epithelial glycoprotein with an important role in barrier function, as the most likely candidate gene for the association on chromosome 1. We also characterize the phenotypic and genetic correlations with other phenotypes, identifying a genetic correlation with smoking and diseases of the (genito)urinary and gastrointestinal system, and phenotypic correlations with various reproductive health diagnoses, reflecting the previously known epidemiological associations.We identified two genome-wide significant loci on chromosomes 1 .The main limitation is that the findings are based on European-based ancestry populations, with limited data on other populations, and we only captured maternal genomes. Additionally, further larger meta-analysis or independent studies are needed to validate these findings.This study encourages the use of large-scale genetic datasets to unravel genetic factors linked to ectopic pregnancy, which is difficult to study in experimental settings. Increased sample size might bring additional genetic factors associating with ectopic pregnancy and inform its heritability. Altogether, our results provide more insight into the biology of ectopic pregnancy and, accordingly, the biological processes governing embryo implantation.N.P.G. was supported by MATER Marie Sklodowska-Curie which received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No. 813707. This study was funded by European Union through the European Regional Development Fund Project No. 2014-2020.4.01.15-0012 GENTRANSMED. Computations were performed in the High-Performance Computing Center of University of Tartu. The authors declare no competing interests. An ectopic pregnancy is a pregnancy complication where the fertilized oocyte implants and grows outside the uterine cavity, mostly (95% of cases) in the Fallopian tube. Studies suggest that tubal ectopic pregnancy is a result of a combination of retention of the embryo within the Fallopian tube due to impaired embryo-tubal transport and alterations in the tubal environment allowing premature implantation  meta-analysis in 7070 ectopic pregnancy cases and 248 810 controls. We identify two genome-wide significant signals and thus provide the first evidence of a genetic susceptibility component to ectopic pregnancy in humans.Our analyses included a total of 7070 women with ectopic pregnancy and 248 810 female controls of European ancestry from two studies: summary level statistics from the FinnGen R7 data release  for the phenotype O15_PREG_ECTOP, and individual level data from the Estonian Biobank  . In the The EstBB is a population-based biobank with over 200 000 participants , currently including around 135 000 women. All biobank participants have signed a broad informed consent form and analyses were carried out under ethical approval 1.1-12/624 from the Estonian Committee on Bioethics and Human Research  and data release N05 from the EstBB. Individuals with ectopic pregnancy were identified using the ICD-10 code O00 , and all female biobank participants who did not have this diagnosis were considered as controls , which resulted in 2544 cases and 124 263 female controls . The age range of the selected controls was 49.7 ± 14.2 years. Information on ICD codes is obtained via regular linking with the National Health Insurance Fund and other relevant databases , and minor allele frequency <1%. Same analyses were conducted for association analysis and imputation of chromosome X, except for the HWE filter, which was not applied. Pre-phasing was conducted using Eagle v2.3 software (Details of EstBB genotyping procedure have been described previously (software . The popsoftware . In EstBhttps://r7.risteys.finngen.fi/phenocode/O15_PREG_ECTOP). The summary statistics were downloaded as described here: https://www.finngen.fi/en/access_results. The FinnGen cohort and the relevant genotyping/data analysis details have been described in For FinnGen, we used GWAS summary statistics from the R7 data release and thus did not have individual level data. In the FinnGen data, cases were defined as women having a respective ICD8 (631), ICD9 (633), or ICD10 (O00) code for ectopic pregnancy, while controls were women who did not have any of the respective codes .We conducted an inverse variance weighted fixed-effects meta-analysis with genomic control using GWAMA (v2.1) . The genP-value <5 × 10−8 and linkage disequilibrium (LD) r2<0.1, based on 1000G European reference) and independent significant SNPs and each risk locus . Then FUMA identifies potential candidate SNPs that are in LD with any of the identified independent significant SNPs and annotates them via linking with several databases (ANNOVAR (We used FUMA (v1.4.0) ((ANNOVAR .https://www.ebi.ac.uk/gwas/) for previous associations between the identified candidate SNPs and studies in the GWAS catalog. The results of this look-up are presented in FUMA also links with the GWAS catalogue (https://genome.sph.umich.edu/wiki/LiftOver#Binary_liftOver_tool).We used HyPrColoc (P < 5 × 10−8) GWAS loci identified we extracted the ±500 kb of its top hit from QTL datasets and ran the co-localization analysis against eQTL Catalogue traits. For each eQTL Catalogue dataset, we included all the QTL features which shared at least 80% of tested variants with the variants present in our GWAS region. We used the default settings for HyPrColoc analyses and did not specify any sample overlap argument, because HyPrColoc paper , we were not able to include this dataset in a formal meta-analysis.We looked up the association results for our European ancestry meta-analysis lead variants with summary statistics publicly available from Biobank Japan (h2SNP) of the ectopic pregnancy meta-analysis. To convert observed heritability estimates to liability scale, we used a transformation tool which is suitable for low prevalence biobank phenotypes  (We used LD-Score (LDSC) regression (https://genoma.io/) to calculate genetic correlations between our ectopic pregnancy meta-analysis and 1335 traits. We applied a multiple testing correction  to determine statistical significance using the p.adjust function in R 3.6.3. Full results of the genetic correlation analysis are presented in We used the Complex Traits Genetics Virtual Lab  , IL8 (interleukin 8), IL6 (interleukin 6), ESR1 (estrogen receptor 1), and EGFR  (Gene-based testing was carried out with MAGMA v1.08 implemented in FUMA (eceptor) . ResultsP-value threshold—2.5 × 10−5). Results were visualized using the PheWAS library 0.99.5-4. All analyses were carried out in R 3.6.3. The results of this analysis are presented in Using the individual level data in the EstBB, we conducted an analysis to find ICD10 diagnosis codes associated with the O00 diagnosis. We tested the association between ectopic pregnancy (defined as ICD10 O00) and other ICD10 codes in a logistic regression framework, adjusting for age and 10 genetic PCs. Since the EstBB includes a large proportion of relatives and inclusion of relatives might inflate the association statistics, we excluded all first- and second-degree relatives  in pairwise comparisons, keeping the index cases, if possible, to not lose in power. This resulted in 2370 cases and 42 970 controls for the analysis. Bonferroni correction was applied to select statistically significant associations . There was no evidence of inflation (λ = 1.0285) in the GWAS meta-analysis (LDSC intercept = 0.9706 (SE 0.0067)). The observed SNP heritability estimate was 0.0106 (SE 0.0019), which corresponds to a liability scale SNP heritability of 7.03% (SE 0.013).The meta-analysis identified two loci for ectopic pregnancy, with three independent lead signals significantly associated with EP (P = 5.32 × 10−9), in the exon of a non-coding transcript KRTCAP2 (P = 1.38 × 10−8) in the GBAP1 pseudogene. As can be seen from MUC1 (mucin 1). Co-localization analysis showed our GWAS signal overlaps with QTL signals associated with both MUC1 expression and specific transcripts , a splice acceptor variant in the second exon of MUC1 that in our meta-analysis has a P-value of 1.87 × 10−7  = 1.10 95% CI 1.06–1.13). According to the GWAS catalogue look-up, this region has previously been associated with gastric cancer and several biomarker levels such as liver enzyme levels , red blood cell count, platelet count, haematocrit percentage, urate levels, urinary albumin-to-creatinine ratio, serum uric acid levels, cystatin C levels, serum phosphate levels, urea levels, magnesium levels, serum CC16 levels, and creatinine levels  on chromosome 1  between the ASB13 and TASOR2 genes. Of the candidate SNPs at this locus, rs74599685 in the intron of ASB13 has a RegulomeDB score of 2b, which indicates evidence for a regulatory effect.We identified a second signal on chromosome 10, where the lead SNP is rs11598956 , which adds reliability to the described genetic associations and proves the homogeneity of cases definition criteria between studies.A sensitivity analysis carried out in the EstBB using controls who have been pregnant yielded similar results (data not shown), indicating that the use of unselected controls does not have an effect on the results. When comparing the two studies, none of the presented lead variants showed heterogeneity of effects between the two studies meta-analysed .Using GARFIELD .RP11-201K10.3, THBS3, KRTCAP2, TRIM46, MTX1, and EFNA1. Look-up of genes associated with ectopic pregnancy in previous literature (ADGRD1 (GPR113), VEGFA, IL8, IL6, ESR1, and EGFR) revealed that none of them passed the Bonferroni correction threshold and only GPR113  and ESR1 were nominally significant . Full results are shown in Gene-based analysis implemented in MAGMA identified six associations significant after Bonferroni correction (0.05/18895 protein coding genes), all on chromosome 1—rg) between ectopic pregnancy and 1335 other traits (https://genoma.io/). We found 93 significant (FDR < 0.05) genetic correlations with European-ancestry ectopic pregnancy meta-analysis. Selected genetic correlations with phenotypes related to smoking, overall and reproductive health, anthropometrics, and education are presented in We tested pairwise genetic correlations ), procreative management ), salpingitis ), posthemorrhage anaemia ), miscarriage ; O03, OR = 2.6 (2.3–3.0)), other female pelvic inflammatory disease ), chlamydial infection ), bleeding in early pregnancy ), and abdominal pain ). In addition, we found increased odds of peritoneal disorders ), habitual abortion ), excessive, frequent, and irregular menstruation ), complications associated with artificial insemination ), ovarian dysfunction ), endometriosis ), hydatidiform mole ), and three ICD codes related to the respiratory system—acute nasopharyngitis (J00), acute tonsillitis (J03), and acute bronchitis (J20). In total, 61 diagnosis codes with a significantly different prevalence in cases and controls were identified .We conducted the first GWAS meta-analysis for ectopic pregnancy in 7070 women with ectopic pregnancy and 248 810 female controls from two European-ancestry biobanks. We identify two genetic risk loci providing evidence of a genetic susceptibility component to this common early pregnancy complication. Our results inform the genetic background and provide clues to the etiopathogenesis of ectopic pregnancy.We also characterize the phenotypic and genetic correlations with other phenotypes. The genetic correlation analyses provide further support for the association between ectopic pregnancy and smoking, which has been shown to be an important risk factor of ectopic pregnancy in past epidemiological studies . HoweverRecent years have seen numerous studies that have begun to clarify the genetic susceptibility to pregnancy complications such as pre-eclampsia  of rs4072037 results in a 27 bp/9 amino acid deletion in the 2nd exon of MUC1. Previous studies have shown that this may lead to changes in intracellular trafficking, glycosylation, and folding of the protein, which all may affect the function of MUC1, or alternatively, it has been shown that the same variant may also affect the transcriptional activity of the The association with the second locus on chromosome 10 is less clear and our GWAS follow-up analyses failed to provide any solid evidence to support the role of any specific gene. Given the low frequency of this variant , further replication studies are needed in populations where it is more common to confirm its association with ectopic pregnancy and propose potential explanations about the mechanisms.While our study of more than 7000 cases identifies two genetic risk loci for ectopic pregnancy, further larger meta-analyses or independent studies are needed to validate these findings, especially regarding the rare variant on chromosome 10. Additionally, the limited available data from other ancestries hinders full assessment of the transferability of these findings across diverse populations. Moreover, as with other reproductive phenotypes, the lack of sufficiently sized relevant tissue data  in commonly used gene expression and other databases (such as the GTeX), hinders the proper interpretation of GWAS findings and highlights the need for large-scale gene expression studies in female reproductive tissues. Additional studies are also needed to evaluate whether smoking, an important risk factor of ectopic pregnancy, somehow mediates the genetic effects, along with studies evaluating additional risk factors and their interaction with ectopic pregnancy.In conclusion, the first GWAS meta-analysis in ectopic pregnancy provides genetic evidence to support the involvement of the MUC1 epithelial glycoprotein and maps genetic and phenotypic associations with other phenotypes, providing input for further studies.dead217_Supplementary_Figure_S1Click here for additional data file.dead217_Supplementary_Figure_S2Click here for additional data file.dead217_Supplementary_Table_S1Click here for additional data file.dead217_Supplementary_Table_S2Click here for additional data file.dead217_Supplementary_Table_S3Click here for additional data file.dead217_Supplementary_Table_S4Click here for additional data file.dead217_Supplementary_Table_S5Click here for additional data file.dead217_Supplementary_Table_S6Click here for additional data file.dead217_Supplementary_Table_S7Click here for additional data file.