The discovery of expression quantitative trait loci (eQTLs) and their target genes (eGenes) has not only compensated for the limitations of genome-wide association studies for complex phenotypes but has also provided a basis for predicting gene expression. Efforts have been made to develop analytical methods in statistical genetics, a key discipline in eQTL analysis. In particular, mixed model– and deep learning–based analytical methods have been extremely beneficial in mapping eQTLs and predicting gene expression. Nevertheless, we still face many challenges associated with eQTL discovery. Here, we discuss two key aspects of these challenges: 1, the complexity of eTraits with various factors such as polygenicity and epistasis and 2, the voluminous work required for various types of eQTL profiles. The properties and prospects of statistical methods, including the mixed model method, Bayesian inference, the deep learning method, and the integration method, are presented as future directions for eQTL discovery. This review will help expedite the design and use of efficient methods for eQTL discovery and eTrait prediction. Despite considerable progress in the 20 years since the completion of the Human Genome Project, a complete understanding of the functions of the human genome sequence, comprising >3 billion base pairs, remains still elusive. Genome-wide association studies (GWASs) have identified thousands of quantitative trait loci (QTLs) as association signals for human complex traits. Because the QTLs have mostly been mapped in the noncoding regions of the human genome, knowledge on their underlying genetic mechanisms are limited. Indeed, candidate causal genes corresponding to QTLs are often falsely assigned because the gene closest to a given QTL is suspected as a target. Studies on expression QTL (eQTL) mapping have aimed to identify their target genes (eGenes) and understand the genetic mechanisms underlying their expression (eTraits) and phenotypic traits. However, these studies are just the tip of the iceberg when considering the eQTL studies required in the future. Herein, human eQTL mapping will be discussed, focusing on challenges and prospects. Detailed theories and equations of methodologies will not be discussed in depth in order to appeal to a wider range of geneticists and fields, including those with practical applications.Various approaches have been used in the past decade to develop analytical models and methods for eQTL discovery. In particular, efficient methods have been intensively studied along with advances in technology, from hybridization-based microarray to next-generation sequencing (NGS)–based RNA-seq. Two landmark approaches will be presented herein as examples.The mixed model method for genetic analysis was originally devised using pedigree information to explain the polygenic effects treated as random effects in the analytical model . Here, tMore recently, deep learning has been garnering increased popularity in predicting gene expression. Deep learning does not have a statistical framework, unlike other methods, thus, it offers great potential at gaining results that are difficult to obtain from other eQTL studies. Moreover, the rapid growth of relevant data will vastly increase the contribution of deep learning to eQTL studies. Deep learning generally refers to supervised or unsupervised learning using advanced artificial neural networks, often known as deep neural networks. Supervised learning represents predicting categorical or continuous variables using a training data set, whereas unsupervised learning represents studying intrinsic patterns and clustering them based on pattern similarity. In this respect, supervised deep learning is a good choice for predicting gene expression. In particular, the large amount of data generated by high-throughput techniques can provide an absolute condition for the use of supervised deep learning. For example, convolutional neural networks are feed-forward deep neural networks in which every unit in a layer is connected to all the units in the previous layer without forming any cycle of the unit connections. Convolutional neural networks have been used to predict gene expression using proximal promoter sequences and distal enhancer sequences obtained by HiChIP . RecurreThese approaches have been applied to eQTL studies and have contributed greatly to the discovery of eQTLs for specific genes and offered further insights into general regulation mechanisms of gene expression . Often, eQTL discovery is challenging primarily due to its intrinsic complexity. The expression of a single gene in a cell is regulated by various gene products. Each of the various regulating genes in a cell is also regulated by various gene products. This hierarchical regulatory mechanism suggests that many genes expressed in a cell are generally involved in the expression of a single gene , thus haIn particular, the effect sizes of trans-eQTLs tend to decrease depending on the degree of trans-acting indirection ; therefoLinkages can help researchers find causal sequence variants linked to representative variants associated with gene expression. However, the effect size can be influenced by multiple functional variants in a linkage, often resulting in false positive/negative eQTLs. Many geneticists are therefore reluctant to search for functional sequence variants in the major histocompatibility complex region, where such characteristics resonate extremely well. Epistasis among eQTLs is infrequently examined in eQTL mapping because it requires expensive computing power. Epistasis is a natural mechanism that contributes to gene expression regulation by binding DNA and DNA-derived substances (RNA and proteins). For example, eukaryotic transcription is usually initiated by the interplay among transcription factors, activators, mediators, RNA polymerase, enhancers, and proximal or distal promoters. Population stratification is also ignored in many eQTL analyses, even though this reduces the accuracy of eQTL mapping and can result in spurious eQTLs ,20,21. Spatial expression variability ranges from variations among nearby cells to those among organs. In particular, cellular resolution of eQTLs in the brain might be critical for understanding transcriptional heterogeneity of pyramidal cells in addition to regional functions/misfunctions ,23. LikeThe aforementioned factors prevent eQTL mapping. In particular, eQTL discovery can be difficult if there are small effect sizes because multiple testing is required to reduce false positives. Inference of causality is also interfered by obstacles such as linkage, pleiotropy, and correlated expression . FurtherOwing to the considerable complexity of the eTraits, another critical issue regarding eQTL mapping is the construction of voluminous studies producing a vast amount of data. A genome-wide eQTL analysis produces a genetic profile of regulatory signals for a single gene. In modern studies, eQTL profiles are simultaneously produced for all transcriptome-wide genes. Moreover, integrated analysis of eQTL mapping with GWAS may reveal the regulatory mechanisms underlying phenotypic traits; this is known as a transcriptome-wide association study (TWAS). Profiles can be extended to various types of eQTLs––additive, dominant, recessive, haplotypic, and epistatic. Variations within these profiles can be produced by spatial and temporal gene expression and by various expression molecules across expression processes. Thus, eQTLs are characterized by the expression molecules used for their identification and categorized by expression processes such as transcription and translation . The molThe issues regarding the qualities and quantities of eQTL maps addressed in this article represent important challenges in statistical genetics. Comprehending the complexities of eTraits is a challenge that transcends the currently available mapping facilities and tools. Recently, there have been substantial improvements in computing environments, which have enabled the analysis of considerable amounts of genomic data, and such developments are constantly accelerating. However, the development of high-resolution eQTL maps requires further marked developments in computing memory and speed. Even now, limitations exist in mixed models that can explain background polygenic effects during eQTL mapping . This isIn addition to the abovementioned ones, many other analytical methods have greatly contributed to eQTL mapping for complex eTraits. Nevertheless, the fact that it will likely be impossible to create an ideal eQTL map remains; this is attributed to the great complexity of eQTL maps, as shown in Spurious eQTLs are largely attributed to genetic, environmental, and experimental factors that are ignored or uncounted from the study. Genetic factors include major eQTLs, cis-eQTLs, and trans-eQTLs. A method that can directly explain these genetic factors is the mixed model. As discussed above, the mixed model uses genomic covariance among individuals to explain polygenic effects. The mixed model method reduces residuals unexplained by the analytical model and increases the accuracy of eQTLs ,56. It iExpensive computing costs to invert a large matrix is a stumbling block in the application of the mixed model method to eQTL mapping, and this block has been encountered in many studies. In particular, because a continuous increase in the sample size in eQTL studies is expected with the development of sequencing technologies, methods for solving or avoiding this problem are required. For example, a reduced animal model equivalent to an animal model has been widely used in genetic analyses for animal breeding; these analyses use a mixed model with pedigree information to reduce the size of the numerator relationship matrix and the number of equations to be solved . AnotherBayesian inference has several good properties that make it suitable for eQTL mapping. In particular, the Bayesian approach incorporated with a mixed model may overcome the non-BLUE problem raised from the frequentist approach. For example, the Bayesian approach implemented with Gibbs sampling yields polygenic and residual variance components based on polygenic effects and residuals of individuals at every round of the Gibbs chain, finally providing samples of the eQTL effect. Thus, we can directly obtain various point estimates without assuming any distribution. This provides an empirical Bayes estimate of the eQTL effect, corresponding to the BLUE . NeverthDeep learning is not based on statistical properties; however, it can be used to approach the complexity of eTraits. Deep learning has an advantage in eQTL studies using large amount of data, and it is also a niche approach that is difficult to be used within a statistical framework. However, in the case of deep learning, close attention should be paid to possible issues, such as parameter overfitting, data imbalance, and subtle variances in input data. This is critical to reducing noise and, thus, to unleashing the enormous potential of eQTLs . To applVarious integration algorithms for gene expression analysis have been developed by simultaneously dealing with data from multiple independent studies with comparable designs   and fromFinally, for researchers wanting to apply analytical methods to eQTL analysis, finding a software with an efficient method suitable for the study purpose is critical to reducing computational costs and appropriately inferencing the results. Analytical methods need to be compared and characterized with various conditions to provide updated and proper guidance, and researchers are desperately in need of a thorough review of analytical methods and software prior to data analysis. For example, if a study on eQTL epistasis is planned, considering the eQTL accuracy and time efficiency, a different optimal method may be chosen according to the number of loci in epistasis and the covariates included in the analytical model. Furthermore, the computing environment should be considered.This review has primarily focused on analytical methods from the perspective of statistical genetics. Along with the improvement of methods, increasing sample size and advancing sequencing technology are essential to accelerate the development of eQTL studies. Strategies that prioritize increasing sample size may include establishing standards for data and expanding shareable data repositories. Sequencing techniques such as Hi-C, Capture-C, and 3C-seq may improve eQTL studies for chromatin interactions . Finallyp ≤ 5 × 10−8) for the phenotypic traits corresponded to trans-eQTLs that were recently reported using blood samples of 31,684 individuals by the eQTLGen consortium [Statistical genetics is a key discipline in mapping eQTLs and predicting eTraits. Now, it is time to accelerate the development of analytical methods for solving problems or mitigating limitations. To date, eQTL studies have been strongly biased toward the discovery of cis-eQTLs. This is largely attributable to the easy biological interpretation on direct regulation, the relatively large effect size, and the reduced number of variants to be tested within a certain region. However, we should also make efforts toward developing efficient methods to overcome the limitations of trans-eQTL discovery. Thirty-seven percent of GWAS signals (nsortium , a consiIn conclusion, the genetic architecture of complex eTraits, produced at a higher resolution through a more rational analysis of enormous amounts of gene expression data, will contribute to the understanding of the genetic architecture of complex diseases; this will constitute an important basis in precision medicine.


Mendelian randomization makes use of genetic variants as instrumental variables to eliminate the influence induced by unknown confounders on causal estimation in epidemiology studies. However, with the soaring genetic variants identified in genome-wide association studies, the pleiotropy, and linkage disequilibrium in genetic variants are unavoidable and may produce severe bias in causal inference. In this study, by modeling the pleiotropic effect as a normally distributed random effect, we propose a novel mixed-effects regression model-based method PLDMR, pleiotropy and linkage disequilibrium adaptive Mendelian randomization, which takes linkage disequilibrium into account and also corrects for the pleiotropic effect in causal effect estimation and statistical inference. We conduct voluminous simulation studies to evaluate the performance of the proposed and existing methods. Simulation results illustrate the validity and advantage of the novel method, especially in the case of linkage disequilibrium and directional pleiotropic effects, compared with other methods. In addition, by applying this novel method to the data on Atherosclerosis Risk in Communications Study, we conclude that body mass index has a significant causal effect on and thus might be a potential risk factor of systolic blood pressure. The novel method is implemented in R and the corresponding R code is provided for free download. Conventional epidemiology has made enormous contributions to identifying certain significant exposures associated with common diseases, like fine particle air pollution was found to increase the risk of lung cancer mortality  applies the method of instrumental variables (IVs) to estimate the causal effect of a non-genetic exposure on a disease outcome  and identify the significant causal effect of body mass index (BMI) on systolic blood pressure (SBP). We conclude that incorporating the variance of the pleiotropic effects and LD into MR analyses can efficiently estimate the causal effect and make more credible causal inference.In this paper, we first introduce the mixed-effects regression model inherited from MR-Egger n×m denote their centralized measurement of the m genetic variants, where Gij is the genotype of individual i at the jth variant, 1 ≤ i ≤ n, 1 ≤ j ≤ m. n individuals, respectively. The exposure X is the linear combination of m genotypes and an error term Y is the linear combination of m genotypes, the exposure and an error term X and Y in the correlatedness of the error terms εX and εY. The causal effect of the exposure on the outcome is β in the model, which is of interest. The coefficients m genetic variants on the exposure, and m genetic variants on the outcome. Specifically,Let us first recall the regression models used in MR-Egger , σY ∈ , ρ ∈ , G, εX, and εY  requires γj ≠ 0 for each genetic variant j, which can be met by selecting genetic variants with methods like linear regression; (iii) implies no pleiotropic effect, i.e., α = 0. Our aim is to estimate the causal effect β and then make the statistical inference on it. To this end, we employ the mixed-effects model as described above to relax the requirement in (iii).where σY and the exposure X on the genotype j, respectively, and Let where E0 = 0 on the above regression model yields the inverse-variance weighted (IVW) estimate of the causal effect . For the positive definite matrix (GTG)−1, there exists an m × m orthogonal matrix Q and an m × m diagonal matrix Λ such that (GTG)−1 = QΛQT. Let W−1 as QTW−1Q as Notice that both unknown parameters We call the R package BB  represents the t-distribution with m − 2 degrees of freedom. We use PLDMR for the statistical inference of the causal effect in the presence of pleiotropy and multiple SNPs in LD in Mendelian randomization analyses.which can easily yield the confidence interval of the causal effect β or the n is usually an order of tens of thousands, we have GTG = O(n) and further n, the estimate of the causal effect β and its variance are easily derived from classical simple linear regression. We denote this approximation as PLDMRa. The accuracy of this approximation is illustrated in the simulation study for varied sample sizes from several hundreds to several tens of thousands and varied Considering the sample size α = μα1, or Another special case of our interest is So regressing J is all 1's m × m matrix, and furtherwhere a have closed forms and thus have no computational burden.Again, we can use this normality to construct the confidence interval of β or test the statistical hypothesis of β when the variance of pleiotropic effect is about zero. We refer to this method as LDMR. In contrast to PLDMR, the estimators of LDMR and PLDMRn, the sample size, and a in the two situations, i.e., n, respectively. Additionally, we generate genotype data in LD for every individual i as the steps listed below:To evaluate the proposed methods, a series of scenarios of different parameter settings are designed to conduct the simulation studies. We explore and compare the estimation results and statistical properties of MR-LDP, RAPS, MR-Egger, and LDA MR-Egger with PLDMR in nine combinations of three patterns of pleiotropy  and three magnitudes of linkage disequilibrium . We also vary m × m matrix Σg, i.e., the  cell element is Construct a Toeplitz MVN and calculate Φ(zij), where Φ denotes the cumulative distribution function of N, 1 ≤ i ≤ n, 1 ≤ j ≤ m;Randomly draw j at the jth locus, assign genotype Gij as the Φ(zij) quantile of Binomial, 1 ≤ j ≤ m.For the given minor allele frequency MAFj1, j2 with respect to their “relative distance” |j1 − j2|. Also, we can control the relative strength of linkage disequilibrium by tuning the magnitude of ρg.The Toeplitz matrix used in (i) is able to weaken the correlation of genotypes at two loci j ~ Uniform, γj ~ Uniform, 1 ≤ j ≤ m, and fix σX = σY = 2, ρ = 0.5. We further set ρg = 0, 0.3, and 0.6 to represent no LD, low LD, and high LD; μα = 0, +0.1, and −0.1 to represent balanced pleiotropy, positive pleiotropy, and negative pleiotropy; σα = 0.01, 0.1 and 0.2 to represent different strengths of the pleiotropic effect; n = 1,000, 5,000, 10,000, and 20,000 to represent a wide range of sample sizes. The nominal significance level is 0.05 and the replications are 10,000 for each scenario. For brevity, results of the simulation study with σα = 0.1 and m = 25 are shown in All of the methods are implemented using R software (version 3.6.0). To evaluate the proposed methods comprehensively, we choose MAFniform, γa, we also add PLDMRt, which represents the PLDMR method evaluated at the true values of Four existing two-sample summary-level data methods, i.e., MR-LDP .Now let us look at the performances of the eight methods mentioned above in terms of estimation and testing when the true value of β is 0. As is shown in a, and PLDMRt in the scenarios of balanced pleiotropy (left panel), but are invalid in the scenarios of directional pleiotropy due to its failure in controlling type I error rates. RAPS is the most powerful method for detecting the causal effect in the scenario of balanced pleiotropy and no LD (top-left figure), but is doubtful in other cases. MR-Egger can control type I error rates only when sample size is small and the correlation between SNPs is low , in which cases its power is lower than LDMR, PLDMR, PLDMRa, and PLDMRt. LDA MR-Egger performs better than LDMR, PLDMR, PLDMRa, and PLDMRt when sample size is large and the correlation between SNPs is low. n at 5,000 in this simulation and the variance of the pleiotropic effect is σα = 0.01. MR-LDP still fails to control the type I error rate in the scenarios of directional pleiotropy and RAPS is unable to control type I error rate either when LD exists or directional pleiotropy exists. It can be obviously observed that the type I error rates of MR-Egger inflates when directional pleiotropic effect exists, whereas LDA MR-Egger fails to control type I error rate in the scenarios of directional pleiotropic effect and strong LD. LDMR, PLDMR, PLDMRa, and PLDMRt control type I error rates at 0.05 and show no noticeable changes as m increases. In m, except for MR-LDP and RAPS in the scenarios of directional pleiotropic effect.α in α = 0.01, MR-Egger can control type I error rate at 0.05 in situations of balanced pleiotropy and no LD but still fails in situations of directional pleiotropy, low and high LD groups. LDMR, PLDMR, and PLDMRt control type I error rates well at around 0.05, whereas PLDMRa obviously is conservative, especially in high LD situations. Except for the conservativeness showed by MR-LDP when sample size is small in the scenarios of balanced pleiotropy, the behaviors of MR-LDP and RAPS are almost the same as those when σα = 0.1. When σα = 0.2, the conclusion is similar to that when σα = 0.1  with reference to GWAS Catalog database  and 0.0143 (with standard error 0.00911) for causal effect, respectively. Most importantly, PLDMRa and PLDMR imply a significant causal effect of BMI on SBP with p-values 0.0244 and 0.0272, while MR-Egger and LDMR show no significance in the causal relationship of BMI and SBP . In addition, we conduct MR-LDP, LDA MR-Egger and RAPS methods by randomly selecting 1,000 individuals from the whole dataset to estimate reference LD correlation matrix and splitting the remained 5,782 individuals into two halves to obtain summary statistics. The estimates of the causal effect given by MR-LDP and LDA MR-Egger are 0.00802 and 0.0136, respectively , which show no significance in the causal relationship between BMI and SBP . RAPS estimates the causal effect as 0.0104 (with standard error 0.00362) and implies a significant causal relationship between BMI and SBP .The results of p-value 2 × 10−4 . A large-scale MR study investigating a European population  concluded no significant association of BMI with glucose deterioration with p-value 0.787 . The results of PLDMR are consistent with the findings in the literature.We also investigate the relationship between BMI and GLU . The est2 distribution with one degree of freedom, to detect outliers and exclude them out in further analysis of the summary level data −1 in the variance is associated with n and the diagonal elements of this matrix decrease at rate We have shown in c effect  may be iQ-test in fact focuses on the regression residuals  for the publication of any potentially identifiable images or data included in this article.YW and TL developed the proposed method, designed the simulation study, and wrote the initial draft of manuscript. Y-QH contributed to the development of the method and to drafting the manuscript. LF preprocessed the data and guided data analysis. SY reviewed and approved the final manuscript. All authors contributed to the article and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


We detected genomic regions, which experienced DNA methylation alterations in sperm and were associated with aging and extreme fertility phenotypes . In dynamic hypomethylated regions (HMRs) and partially methylated domains (PMDs), we found genes  that were involved in the embryonic development. We demonstrated that genomic regions, which gained rather than lost methylations during aging, and in animals with low SCR were significantly and selectively enriched for GWAS signals of male fertility traits. Our study discovered 16 genes as the potential candidate markers for male fertility, including SAMD5 and PDE5A. Collectively, this initial effort supported a hypothesis that sperm DNA methylation may contribute to male fertility in cattle and revealed the usefulness of functional annotations in enhancing biological interpretation and genomic prediction for complex traits and diseases.Decreased male fertility is a big concern in both human society and the livestock industry. Sperm DNA methylation is commonly believed to be associated with male fertility. However, due to the lack of accurate male fertility records , few studies have investigated the comprehensive impacts of sperm DNA methylation on male fertility in mammals. In this study, we generated 10 sperm DNA methylomes and performed a preliminary correlation analysis between signals from sperm DNA methylation and signals from large-scale ( Decreased male fertility is a big concern in both human society and the livestock industry ,2. In maHighly-reliable male fertility phenotypes of large-scale Holstein bulls in U.S. provide a valuable source for understanding the genetics of male fertility in mammals, which are estimated based on the performance of both themselves and hundreds of thousands of their offspring. We recently conducted genome-wide association studies (GWAS) for 35 complex phenotypes with a sample size of 27,214 Holstein bulls using imputed sequence variants (~3 millions) to explore their underlying genetic architecture , including 12 fertility, 17 body type, and 6 production traits . Here, wp = 0, t-test) higher than those of young animals, revealing that aging could lead to a significant gain of DNA methylation in sperm globally  higher than those of high-SCR animals  and partially methylated domains (PMDs) in sperm as previously described , due to HMRs: Most HMRs were shared among aged and young animals, and genes whose promoters intersected these shared HMRs were significantly (FDR < 0.05) engaged in translation, peptide biosynthetic, and metabolic processes , which highly intersected CGIs  were significantly and selectively enriched for fertility-related traits. Of specific note were DMRs that gained methylation during aging, which showed a significantly  higher enrichment than those that lost methylation  by analyzing the age-matched animals with extreme phenotypes, which preferred to intersect with CGIs similar to age-induced DMRs  (p < 1 × 10−5) SNPs of reproductive traits in cattle  C. Out ofct test) . Out of n cattle , suggestogenesis . The PDEertility ,33. Our FOXK1 and KCNA7 in sperm induced by aging can be transmitted to the next generation [FOXP1 and FOXN1  could play significant roles in embryonic development, similar for genes in the low-SCR-specific PMDs. These findings were consistent with previous human studies in which sperm methylation patterns were essential for embryonic development ,12,14,16Alterations in epigenome have been proposed to capture and mediate the effects of genetic and environmental factors on complex phenotypes . Mountinp-values for multiple tests . All their GWAS enrichment results were similar. We detected 16 candidate genes for male fertility, and 4 of them were directly associated with SCR, while the others were associated with other male fertility traits  was relatively low. However, Teschendorff et al. (2018) reviewed that DNA methylation alterations associated with complex phenotypes are often much small (~5%) , such asy traits . This isNo animal experiments were performed in this study, and ethics committee approval was therefore not required. We collected ten semen straws from eight healthy and representative Holstein bulls, including six samples collected from six age-matched individuals, among which three had extreme high sire conception rate (SCR) and another three with extreme low SCR, and four samples collected from another two individuals at young and old ages . We isolhttps://www.bioinformatics.babraham.ac.uk/projects/fastqc/) and Trim Galore v 0.4.0  to evaluate the raw data quality and to filter the low-quality data, respectively [We applied FastQC v 0.11.2  to define DMRs for the downstream analyses. We detected DMC with the same procedure. In the following analyses, we focused on DMR. The desirable reasons for analyzing DMRs instead of DMCs have been reviewed in detail [We employed methylKit to conduct DMR/DMC analysis . For DMRM method . Since DM method ,42, we un detail . Brieflyn detail , and altn detail ,45. All the CGs with at least 5× coverage were used for PMD and HMR detection, as described by our earlier study . For PMDTo identify the contiguous HMRs for sperm cells and somatic cells, we used a sliding window approach with a window size of 200 bp and extended the window by 50-bp steps until it contained less than 80% hypomethylated (methylation level <20%) CpGs. Only the HMRs with at least 5 CGs detected with more than 5 × coverage were used for analysis. The GenomicRanges package in R was used to calculate statistics of the overlapped HMRs in different tissues or sperm.https://mmap.github.io/), to conduct single-marker GWAS analysis for each complex trait with the sample size of 27,214 Holstein bulls and imputed sequence variants (~3 million SNPs). The model measured additive effects of genetic variants while accounting for the population structure and individual relatedness with a genomic relationship matrix. All the phenotypes of bulls  used in the current study were carefully calculated based on the performance of many offspring, while accounting for known systematic effects. Details are described in the CDCB website . We have classified the 35 complex traits into three phenotypic categories [Details of the GWAS analysis for the 35 complex traits in cattle were described previously . Brieflytegories , includiDrosophila melanogaster [t that was computed as the variant effect (b) divided by the corresponding standard error. Here, different elements  within a genomic feature  were often not in linkage disequilibrium (LD), as they scattered distantly or even on different chromosomes. This method investigated the genome-wide polygenic signals rather than a subset of variants that passed a certain significance threshold, like linkage disequilibrium (LD) score regression [p-value using one-tailed tests of the proportion of random summary statistics greater than that observed. The current marker-set test method together with multiple quantitative genomic tools are implemented in the QGG package (http://psoerensen.github.io/qgg/).We employed the following sum-based marker-set test approach to examine the enrichment of GWAS signals in genomic features  due to the fact that the complex phenotypes being studied are highly polygenic or even omnigenic . Previounogaster  and livenogaster ,50,51, pgression . It tookgression ,48. In bhttps://bioconductor.org/packages/release/data/annotation/html/org.Bt.eg.db.html) to link genes to GO terms. Here we focused on biological processes terms that comprised of at least 10 directly evidenced genes. We used the following count-based approach to test whether a GO term enriched for DMR or DMC.pi is the p-value for the ith tested window, pi < p0 is satisfied, otherwise it takes zero. Here we arbitrarily used p0 = 0.01, and extended gene regions 10 kb up- and down-stream to include the regulatory regions. Under the null hypothesis , we assumed that Tcount follows a hypergeometric distribution: Tcount~Hyper, where m is the total number of windows being tested in the whole genome, mg is the total number of DMR in the whole genome, and mf is the number of windows being tested in a GO term. Here, we considered a window belonging to a GO term if the window overlapped with genes (± 10 kb up and down-stream) in the GO term. We used the same procedure for DMC-set enrichment analysis.We used Bboconductor package “org.Bt.eg.db” v. 3.3.0 . The GO annotation database can be publicly accessed (https://bioconductor.org/packages/release/data/annotation/html/org.Bt.eg.db.html).The ten cattle sperm methylomes have been submitted to NCBI under GEO accession ID GSE106538 and GSE119263. All genomic annotation files are available for download from Ensembl database (By integrating sperm methylation with a large-scale GWAS of complex traits in cattle, we demonstrated that alterations in sperm DNA methylation were significantly associated with male fertility traits. Our results supported a hypothesis that genes in certain young-specific HMRs and PMDs might play significant roles in embryonic development. We detected 16 genes that gained DNA methylation in sperm during aging and in low-SCR animals, as well as harbored suggestively significant genetic variants of male fertility traits. Our current study provided new hypotheses about the genetic and biological mechanisms underlying male fertility, which will benefit other species, including humans. As functional annotations improve dramatically, such integrative analysis will become more interesting and useful in the near future to better understand the molecular mechanisms underlying diverse complex traits and diseases.


A barrier to the adoption of genomic prediction in small breeding programs is the initial cost of genotyping material. Although decreasing, marker costs are usually higher than field trial costs. In this study we demonstrate the utility of stratifying a narrow‐base biparental oat population genotyped with a modest number of markers to employ genomic prediction at early and later generations. We also show that early generation genotyping data can reduce the number of lines for later phenotyping based on selections of siblings to progress. Using sets of small families selected at an early generation could enable the use of genomic prediction for adaptation to multiple target environments at an early stage in the breeding program. In addition, we demonstrate that mixed marker data can be effectively integrated to combine cheap dominant marker data (including legacy data) with more expensive but higher density codominant marker data in order to make within generation and between lineage predictions based on genotypic information. Taken together, our results indicate that small programs can test and initiate genomic predictions using sets of stratified, narrow‐base populations and incorporating low density legacy genotyping data. This can then be scaled to include higher density markers and a broadened population base. BLUPBest linear unbiased predictionCVCross‐validationDArTDiversity Array TechnologyDiPRDifferentially penalized ridge regressionGBSGenotyping‐by‐sequencingGEBVGenomic estimated breeding valueGSGenomic selectionLDLinkage disequilibriumMCCVMonte Carlo cross‐validationRILRecombinant inbred lineRR‐BLUPRidge regression‐BLUPSSDSingle‐seed descentSNPsingle nucleotide polymorphism1The adoption of affordable genetic markers in breeding programs has expanded the use of accelerated, genomic‐based breeding approaches from genome‐wide information , high levels of long‐range LD  genome  populations using historical trial data on production and quality traits and 223 polymorphic markers. They reported that the simple and computationally efficient best linear unbiased prediction (BLUP) approach was ideally suited to biparental GS. Additionally, extensive LD and large linkage blocks meant that fewer markers were needed for accurate predictions  and phenotyping . Using low‐coverage genotypic information in the early generation, we investigate the recovery of missing phenotypes via genomic prediction, which is required for accurate representation of true phenotypic value and variance. We also extend this to the F7 generation to test prediction of missing yield data. We demonstrate that using mixed marker data is feasible—with both low cost dominant markers and more expensive co‐dominant markers integrated to improve accuracy.Here we report the implementation of genomic prediction within a biparental cross between two cultivated winter oat varieties, ‘Buffalo’ and ‘Tardis’. The population has been previously used to update the oat consensus map based on GBS‐derived, tag‐level haplotypes  and genotyped using 121 polymorphic microsatellites  as reported in Bekele et al., 2 (2005), F3 (2006), and F4 (2007–2010) generation. In addition, the F7 RILs were phenotyped (2010–2014) for both the production characteristics (as previously) and the quality trait grain β‐glucan content at the RIL  generation (Table −1) was applied in a split dose at GS31 and GS35  to examine differential sampling depths from the available population . Within generation predictions were made independently on Tall and Dwarf classes (as determined by F2 genotyping) to account for the known segregating Dw6 gene  before averaging and back‐conversion. In the between‐generation models, where all available marker and phenotype data common to both generations were used to train and predict from the early generation  to RILs, accuracy is reported as the pairwise Pearson correlation within a family. In the between‐generation, between‐lineage models, genotypes were randomly sampled without replacement 100 times according to that k‐fold CV analysis (where k = 2–5). Accuracy is reported for both within and between family Fisher's Z‐transformed mean Pearson correlations with back‐conversion across the 100 per‐k iterations.All prediction accuracies are reported as pairwise Pearson correlations. In the within‐generation models, Fisher's Z‐transformation was used to convert Pearson correlations to a normal distribution  and codominant (microsatellite and GBS) marker types. Markers were thinned at an 2r value of .90 to prevent oversampling and an additive relationship matrix was derived for each marker type. These were linearly combined into a single matrix with separate weighting factors (w and 1−w), between w = 0 and w = 1 in 0.01 steps, to produce a single input to RR‐BLUP, as previously described  genotypes, there were 545 genotyped markers, of which 424 were dominant and 121 codominant. For the RIL (F7) population there were 1,046 codominant genotyped markers. Between the two datasets there were 401 common markers, of which 100 were codominant and 301 were dominant.Across the early generation  was used to predict phenotypes at the RIL level (F7). A total of 15 phenotypes were predicted with 12 predicted at both the early and later RIL generation, and all data are presented in Table 2 genotype level, all the phenotypes were compared across all lines as well as within Dw6 genotypic Tall and Dwarf classes. The accuracy of prediction varied across traits and generations. The traits that were predicted to the highest levels of accuracy across generations were height (range .81–.89), ear emergence (.64–.79), and kernel content (.60–.66). For the majority of traits, the accuracy of prediction was higher when using F7 genotypic and phenotypic data compared to predicting in early generations . Variation was observed for the accuracy of trait prediction when using different phenotyping generations or trial years for some traits including Internode 1 length (.34 from F4 compared to .79 from F2 and .83 from F7 phenotypes) and winter hardiness . Predicting within Dw6 classes gave generally low predictions for all traits when compared to predicting across the full dataset, with the exception of height, grain length, and width in the F4 and the overall low prediction traits .In order to determine the added value of early generation genotyping, within‐generation models were tested. Predictions were made using F3.24 generation for comparative purposes. Nine traits were selected for comparison to assess differences in predictive accuracy between the early and late generations and all data are presented in Table 7) phenotypes with the exception of kernel content . High prediction accuracies were maintained for Internode 1 length for predictions from the F2 to F7 (.58–.76) and F4 to F7 (.54–.77) across genotyping coverage as well as for panicle extrusion, grain yield, and height  and showed a slow pattern of reduction with genotyping density, but predictions from early generations to F7 polytunnel phenotypes were low . This was not the case for height, with predictions stable across both field and polytunnel trials  were all improved through the implementation of DiPR  although their optimal weighting factors varied. Differential weighting for these low‐prediction traits showed that only using codominant markers (DiPR wopt = 0.00) improved maturity and winter hardiness predictions whereas using only dominant markers (wopt = 1.00) optimized prediction of mildew.Dominant markers provide less information than codominant markers but are cheaper to generate, meaning that a greater number are likely to be available (or required) to generate accurate predictions. The DiPR was implemented across nine traits Table  to asses4Triticum aestivum; He et al., Glycine max; Ma et al., 4 capturing 90% of the F8 gains due to an increase in the maximal breeding value of the population  genotype data. In the first instance, F2 genotypes were employed to make models with early generation  phenotypes with 80% of available phenotype data as the training set and 20% as the test set. This simulates lost data in early generation phenotyping when an accurate representation of the cross’ phenotypic value is required for selection. A major limitation to the implementation of GS within small breeding programs is the high upfront genotyping cost , and their derived F4 field phenotypes (based on siblings from F3 rows) are used in conjunction with low‐coverage F2 genotyping, a genomic prediction model could be developed. Following subsequent production and genotyping of fixed RILs, a prediction can be used to select which of the cross’ lineages are likely to perform best and reduce the number of entries into fully replicated field trials, therefore accelerating the breeding cycle  can be used to reduce the costs associated with multi‐environment testing. Bekele et al.  an intermediate optimal solution (wopt = 0.22) was found. This compares to mildew  which had a dominant marker optimum (wopt = 1.00) and winter hardiness  which had a codominant marker optimum (wopt = 0.00). Although dominant markers have been largely superseded by SNP‐based methods of genotyping, our results indicate that for some traits they provide useful information. The low frequency or uneven distribution of SNP markers across the oat genome . We propose that this scheme would permit stronger selection within cross without a loss of accuracy.Supplemental Figure S1. Theoretical program design to increase selection pressure within a cross using genomic prediction based on development of a predictive model from FClick here for additional data file.


Citrofortunella macrocarpa) is an important crop for local economic in Hainan Island. There is no study about Calamansi germplasm evaluation and cultivar development. In this study, Calamansi data were collected from 151 of Calamansi seedling trees, and 37 phenotypic traits were analyzed to investigate their genetic diversities. The cluster analysis and principal component analysis were conducted aiming to provide a theoretical basis for the Calamansi genetic improvement. The results of the diversity analysis revealed: (1) the diversity indexes for qualitative traits were ranged from 0.46–1.39, and the traits with the highest genetic diversity level were fruit shaped and pulp colored (H′ > 1.20); and the diversity indexes for quantitative traits ranged from 0.67–2.10, with the exception of a lower in fruit juice rate (1.08) and lower in number of petals (0.67). (2) The clustering analysis of phenotypic traits have arranged the samples into 4 categories: the first group characterized by fewer flesh Segment number per fruit (SNF) and more Oil cell number (OCN); the second group had 7 samples, all characterized with larger Crown breadth (CB), higher Yield per tree (YPT), the lager leaf, the higher Ascorbic acid (AA), and less Seed number per fruit (SNPF); the third group had 25 samples characterized by smaller Tree foot diameter (TFD),smaller Fruit shape index (FSI) and higher Total soluble solids (TSS) contain; the fourth group had 87 samples, they were characterized by shorter Petiole length (PEL), larger fruit, higher Juice ratio (JR), multiple Stamen number (SN) and longer Pistil length (PIL). (3) The principal component analysis showed the values of the first 9 major components characteristic vectors were all greater than 3, the cumulative contribution rate reach 72.20%, including the traits of single fruit weight, fruit diameter, tree height, tree canopy width etc. Finally, based on the comprehensive main component value of all samples, the Calamansi individuals with higher testing scores were selected for further observation. This study concludes that Calamansi seedling populations in the Hainan Island holds great genetic diversity in varies traits, and can be useful for the Calamansi variety improvements.Calamansi or Philippine lime ( Citrus microcarpa) or Philippine lime, is an important local economic crop in Hainan China. It originated in Southeast Asia, mainly grow in Southeast Asia and tropical regions of China, and it had a long history of cultivation in Hainan Island. Calamansi fruit is rich in vitamins C, aromatic oils, carotenoids and other natural substances which have lots of health benefits for human, such as beneficial effects for human eyes, good for treating cough, asthma, high blood pressure and preventing arteriosclerosis etc3. Calamansi fruit had a fine texture and sour taste. Calamansi juice is widely loved as a delicious fresh condiment. However, the commercially cultivated Calamansi were mostly seedling trees, and their genetic diversity and improvement had not been studied, which causing a series of problems such as no stable commodity supply period and uneven fruit quality. Hainan island is the main growing area of Calamansi in China. The investigations and evaluations of the germplasm of Calamansi in Hainan Island hold great significance for Calamansi genetic improvement with fruit quality.Calamansi  presented a very simple relationship to their common ancestor  showed different traits had range between 0.46–1.39. These traits included Fruit shape (FS) and Pulp color (PC) (H′ > 1.20), consider being high genetic diversity4. The traits included Tree shape (TS) and Tree performance (TP) with lower genetic diversity (H′ < 0.60). The total value of these 8 quality traits diversity were 6.53; there were 4 types of fruit traits with diversity value of 4.29, which accounting for 65.7% of the total traits diversity value.The Shannon–Wiener diversity index , including Branch width(BW) (6.51%), Branch node length (BNL) (8.26%), Leaf lamina length (LLL) (6.76%), Leaf lamina width (LLW) (7.50%), Leaf shape index (LSI) (4.54%), Fruit diameter (FD) (3.86%), Fruit length (FL) (5.60%), Fruit shape index (FSI) (4.05%), Total soluble solids (TSS) (5.18%), Titratable acidity (TA) (8.16%), Total soluble solids/Titratable acidity (TSS/TA) (8.34%), Segment number per fruit (SNF) (6.64%), Peel thickness (PT) (9.57%) and Petal number (PN) (3.09%). These results indicated that these traits hold relatively good genetic stability.Among 29 quantitative traits, the traits like Fruit weight (FW) had the range between 6.15 to 17.02 g, Seed number per fruit (SNPF) ranged between 3 to 11, and the Peel thickness (PT) ranged between 0.66 to 1.52 mm. The values of all the quantitative traits were indicated in Table H′) of 29 quantitative traits were in the range of 0.67–2.10, traits like Juice ratio (JR) (1.08), Petal number (PN) (0.67)and others with lower indexes, indicated that the phenotypic variants of these traits were relatively small, or the distribution of each phenotype was uneven. In this study, except the Juice ratio (JR) and Petal number (PN) these two traits had relatively lower diversity index (H′), other traits all had H′ greater than 1.2, reflecting the rich phenotypes of these traits, and the distribution of each phenotype was relatively uniform.The Shannon–Wiener diversity indexes , Fruit length (FL) and Pistil length (PIL), indicated that the first principal component was mainly affected by traits related to pistil length and fruit size. The contribution rate of the PC2 was 12.97%, and the larger eigenvector values were Tree height (TH) and Crown breadth (CB), indicated that the second principal component was mainly affected by the traits related to the tree. The contribution rate of the PC3 was 7.06%, and the larger eigenvector value was the Titratable acidity (TA), indicated that the third principal component was mainly affected by the titratable acid content. The contribution rate of the PC4 was 6.24%, and the larger eigenvector value was Ascorbic acid (AA). The contribution rate of the PC5 was 5.40%, and the trait with the largest eigenvector value was Branch width (BW). The contribution rate of the PC6 was 4.57%, and the trait with the largest eigenvector value was Branch node length (BNL). The contribution rate of the PC7 was 4.49%, and the traits with the largest eigenvector values were Petal number (PN) and Juice ratio (JR). The contribution rate of the PC8 was 4.03%, and the traits with the largest eigenvector values were Petiole length (PEL). The contribution rate of the PC9 was 3.78%, and the trait with the largest eigenvector value was Leaf shape index (LSI).Comprehensive evaluation results showed that the comprehensive PC values of all samples were distributed between 38.63 and 97.41 , Branch node length (BNL), Leaf lamina length (LLL), Leaf lamina width (LLW), Leaf shape index (LSI), Fruit diameter (FD), Fruit length (FL), Fruit shape index (FSI), Total soluble solids (TSS), Titratable acidity (TA), Total soluble solids/Titratable acidity (TSS/TA), Segment number per fruit (SNF), Peel thickness (PT) and Petal number (PN) were less than 10%, means the genetic performance was relatively stable. Among the quantitative traits, the variation coefficient of the Yield per tree (YPT) was relatively larger; others were distributed between 10.02 and 21.30. The variation of quantitative traits of Calamansi were distributed between 3.09 and 44.00%, indicated that there were large diversity in the quantitative traits among the individual samples, and implied that there was a good breeding potential in the Calamansi population studied. The cluster analysis results had separated the samples into 4 categories: (1) fewer Segment number per fruit (SNF) and more oil cell number (OCN); (2) larger crown breadth (CB), higher yield per tree (YPT), fewer seed number per fruit (SNPF); (3) lower tree foot diameter (TFD) and fruit shape index (FSI), but higher total soluble solids (TSS) and (4) higher titratable acidity (TA), shorter petiole length (PEL), larger fruit diameter (FD) and fruit length (FL), higher juice ratio (JR). This analysis could provide elite individual plant materials to support the Calamansi breeding development. This study was based on the phenotypic traits of Calamansi, using Principal component analysis (PCA), it was found that the cumulative contribution rate of the first nine principal components of Calamansi was 72.20%, which could represent most of the Calamansi, and perhaps implied that those phenotypic traits could be integrated at the same time. Through this analysis, those individuals with higher scores from comprehensive evaluation were selected. This comprehensive evaluation method had been used in the phenotyping and the classifications of other crops20. The results of this study could be used to select Calamansi individuals with outstanding traits.Phenotypic traits are the reflection of the comprehensive effects of the plant genotype and the environmental effects. Phenotype is an important manifestation of genetic variation, and it can directly indicate the abundance of specific genes. Phenotype is the basis for the germplasm innovative and variety improvementIn addition, this research also found that the Calamansi seeds have extremely high level of polyembryonic, but the diversity analysis of the Calamansi population resulted relative higher diversity index, and phenotypic evaluation also showed relative higher diversity among the traits analyzed. This interesting phenomenon might imply high frequency of sprout mutation existing in the Calamansi germplasm population which caused relative high genetic diversity in descendant population after multi-generation of propagation by seeds. Another possibility is that in the long history of cultivation, open-pollinated Calamansi zygotic embryos under the growth pressure, had gradually produced stronger competitive ability than the somatic embryos, and developed into complete individuals, leading to the continuous evolution of Calamansi and phenotypic diversity. Finally, in the process of data collection, it was found that harvested fruits within the commercial standards weight range (10–13 g per fruit) had about twice as more seed numbers than that of fully mature Calamansi fruits. The reasons of this phenomenon and seed number reduction mechanism were unknown at the present time.This study investigated 151 individuals of the Calamansi germplasm resources in Hainan Island, and evaluated various phenotypic traits of cultivated Calamansi. The research provided information for the whole genome association analysis of Calamansi. The resulting data proved to be useful in the subsequent genome-wide association analysis, which built up the connection between Calamansi’s phenotype and the responsible genes.This article is the first research to investigate the germplasm of Calamansi in Hainan Island, China. Hainan Island is a geographically isolated tropic environment. The Calamansi cultivation on the island has several hundred years history, Calamansi has under gone many generations of selections intentionally or unintentionally, the genetic variations (mutations) with advantage to their growth or beneficial to the growers were likely survived and being saved, many genetic variations were saved and cumulated resulted Calamansi’s genetic diversities in the Hainan Island. This study can reflect the genetic characteristics of Calamansi to a certain extent. Calamansi is widely distributed in many countries in Southeast Asia, and is widely used in different culture of life. In the future, all the Calamansi germplasm resources in Southeast Asia will be collected and analyzed, which can more accurately study the genetic characteristics of Calamansi and its genetic information could provide more valuable references for Calamansi breeding and cultivar improvement.In this study, the phenotypic traits of the Calamansi seedling populations in Hainan Island was first time evaluated. The study identified elite individuals for various traits, provided plant materials and data to support the subsequent Calamansi breeding operation. Since Calamansi is a widely cultivated “cash crop” in Hainan Island, it is a plant species that has important role in the local economy, especially for the farmers who only have small scale of land available. In this study, we systematically evaluated 37 phenotypic traits of the seedling populations of Clamansis, and found there were high level of genetic diversity among the Clamansis seedling populations for those traits. Existing Calmansi populations can serve as genetic resource for Calmanis variety development.The samples of this study were collected from the planting groves of Hainan Ming Bo Scientific Technology Co., Ltd. in Quanmei, Wenchang city and Dongchang Farm, Haikou city, China Fig. . The Cal21 were used as standard reference  for quantitative traits, and then from the first level < − 2 s, the tenth level ≥ + 2 s, and every 0.5 s was one level. The correlation between quantitative traits was calculated using Pearson's correlation coefficient, and the principal components of quantitative traits were extracted using dimensionality reduction analysis and factor analysis (SPSS 25.0) Finally, according to the principal component weight, the comprehensive principal component value of the sample was calculated to screen the sample.The data obtained from the phenotype survey were sorted and analyzed using Microsoft Office Excel 2019, and Spss 25.0. The degree of morphological diversity was expressed by Shannon–Wiener index, and the calculation formula wasCitrus clementina12  to obtain the corresponding comparison information. Then GATK 4.0 software was used to perform mutation screening on 100 individuals genome sequences data to obtain the corresponding gvcf files. Finally, all the gvcf files were merged into vcf files, and vcf files were further filtered to obtain SNP site of 100 individual Calamansi. Default parameters were used by all software when processing the data.In this study, after preliminary analysis the phenotypic traits of 151 Calamansi fruit tree samples, 100 fruit trees with rich phenotypic characteristics were selected and subjected to genome sequencing. The library was constructed and sequenced through the Illumina sequencing platform, and 350G raw data were obtained. After acquiring the genomic data of Calamansi, Fastp software was used to perform quality control on the sequencing data, and then quality-controlled data were compared with genomic data of Run_pipeline.pl -Xmx1G -Xmx5G -importGuess all.filtered.snp.vcf -ExportPlugin -saveAS sequence.phy -format Phylip_Interecho -e “sequences.phy\nY” > dnadist.cfgDnadist < dnadist.cfg > dnadist.logecho -e “infile.dist\ny” > neighbor.cfgneighbor < neighbor.cfg > nj.logless infile.dist | tr ‘\n’ ‘|’ | sed ‘s/|/ /g’ | tr ‘|’ ‘\n’ > infile.dist.tableless outtree | tr ‘\n’ ‘|’ | sed ‘s/ //g’ > outtree.nwkThe phylogenetic tree of Calamansi was constructed by Phylip software based on the neighboring method. The specific code is as follows:Finally, the evolutionary tree was obtained by upload obtained outtree.nwk file onto the itol website online.13. Through the software processes, the corresponding value of each trait under each Principle component (PC) can be obtained, and the values are called the characteristic vector of the broad PC. The larger the absolute value of the trait characteristic vector, the greater the influence on the PC. One or several of the traits with the largest absolute value of the characteristic vector under the PC can be considered that this PC is controlled by these traits to a certain extent. The eigenvectors under each PC is added to obtain the eigenvalue (E) of the PC. Through the software calculation, the eigenvalue can be converted into a contribution rate. In theory, the sum of the contribution rates of all PC equals 1, which can fully explain all the information of the original variables.Principal component analysis (PCA), a statistical analysis method that converts multiple variables into a few principal components (PC1–PCn) through dimensionality reduction technology. Principal component analysis was carried out by statistical analysis software SPSS25.0. These PCs can reflect most of the information of the original variables4. The specific scoring formula was as follow: Fn = − 0.042 × 1 − 0.339 × 2 − 0.395 × 3 + …… + 0.681 × 27 + 0.367 × 28 + 0.824 × 29. Then the comprehensive principal component value F was calculated according to the ratio of the characteristic value corresponding to each principal component. In the calculation, the total characteristic value of the extracted principal component served as the weight to sort the comprehensive principal component value F = 0.227 × 1 + 0.140 × 2 + 0.071 × 3 + …… + 0.045 × 7 + 0.040 × 8 + 0.038 × 9.According to the eigenvector matrix and standardized phenotype data, all samples were comprehensively evaluatedThe statistical analysis software SPSS25.0 was used to carry out the cluster analysis, the Ward method was used to conduct cluster analysis of 29 quantitative traits among 151 individuals. The 151 individuals were divided into 4 categories. Ward method is an alternative approach for performing cluster analysis; it looks at cluster analysis as an analysis of variance problem, instead of using distance metrics or measures of association. Ward method involves an agglomerative clustering algorithm, Ward's method starts out with n clusters of size 1 and continues until all the observations are included into one cluster. This method is most appropriate for quantitative variables cluster analysis.Correlation analysis of 29 quantitative traits was carried out by statistical analysis software SPSS25.0 among 151 Calamansi individuals, the directions and levels of the correlation among 29 quantitative traits were indicated in (Fig. The collected plant materials and research activities are in accordance with the laws and regulations of Hainan Province, China.The collection of Calamansi resources has been approved by the grove owner Ming Bo Scientific Technology Co., Ltd.Supplementary Tables.


Mycobacterium avium susbp. paratuberculosis (MAP). Genome-wide association studies (GWAS) have identified single nucleotide polymorphisms (SNPs) significantly associated with susceptibility to bovine PTB. The main objective of this study was to identify quantitative trait loci (QTLs) associated with MAP infection in Spanish Holstein cows (N = 983) using combinations of diagnostic tests and imputed whole-genome sequence (WGS) data. The infection status of these animals was defined by three diagnostic methods including ELISA for MAP-antibodies detection, and tissue culture and PCR for MAP detection. The 983 cows included in this study were genotyped with the Bovine MD SNP50 Bead Chip, and the corresponding genotypes were imputed to WGS using the 1,000 Bull genomes reference population. In total, 33.77 million SNP variants per animal were identified across the genome. Linear mixed models were used to calculate the heritability (h2) estimates for each diagnostic test and test combinations. Next, we performed a case-control GWAS using the imputed WGS datasets and the phenotypes and combinations of phenotypes with h2 estimates > 0.080. After performing the GWAS, the test combinations that showed SNPs with a significant association (PFDR ≤ 0.05), were the ELISA-tissue PCR-tissue culture, ELISA-tissue culture, and ELISA-tissue PCR. A total of twelve quantitative trait loci (QTLs) highly associated with MAP infection status were identified on the Bos taurus autosomes (BTA) 4, BTA5, BTA11, BTA12, BTA14, BTA23, BTA24, and BTA28, and some of these QTLs were linked to immune-modulating genes. The identified QTLs on BTA23 spanning from 18.81 to 22.95 Mb of the Bos taurus genome overlapped with several QTLs previously found to be associated with PTB susceptibility, bovine tuberculosis susceptibility, and clinical mastitis. The results from this study provide more clues regarding the molecular mechanisms underlying susceptibility to PTB infection in cattle and might be used to develop national genetic evaluations for PTB in Spain.Bovine paratuberculosis (PTB) is a chronic inflammatory disease caused by  Mycobacterium avium susbp. paratuberculosis (MAP). PTB is a major problem for animal health and must be notified to the World Organization for Animal Health. In Europe and North America, PTB is considered endemic in dairy cattle, with herd prevalence estimates higher than 50% [Paratuberculosis (PTB) or Johne´s disease is a chronic enteritis of domestic and wild ruminants caused by than 50% . This cathan 50% ,3. The mthan 50% . Howeverthan 50% . Colorecthan 50% ,8.Mycobacterium bovis (Mb) detection tests [Once a cow shows clinical signs of PTB, there is no effective treatment and the animal is culled. Commercial inactivated vaccines against bovine PTB are very successful in reducing MAP presence in feces and tissues, and in increasing both milk production and cattle productive life in infected farms ,10. Howeon tests . Therefoon tests ,13. PTB on tests . Therefo2) estimates of susceptibility to MAP in Friesian cattle ranging from 0.03 to 0.27 [Bos taurus genome associated with MAP infection. However, few QTLs have been identified with consistent association across studies due to multiple factors including differences in genetic backgrounds and variable disease prevalence in the studied populations, different samples size, different statistical models, differences in sensitivity of the various diagnostic methods used for infection status classification, different definitions for the cases and controls phenotypes, and the low heritable nature of these phenotypes. Given that different populations across the world can have differences in genetic backgrounds, it is entirely reasonable for some genes to be monomorphic in some populations (these would therefore not be associated with PTB in these populations) even when they have a major physiological role in the defense against PTB. Previous GWAS in Holstein cattle defined phenotypes based on ante-mortem tests such as serum ELISA, milk ELISA, MAP fecal culture, or fecal PCR. The ELISA test is most commonly used for the detection of MAP-specific antibodies in infected animals. This test is cost-effective and simple to perform as compared with detection by bacteriological culture. However, the drawback of this method is its low sensitivity for detection during the subclinical phase of infection and could yield false-negative results. Tissue culturing is considered to be the gold standard due to its increased sensitivity and ability to detect viable MAP in subclinical stages of PTB that can be missed with other diagnostic methods [Genomic selection could enhance natural resistance to MAP infection and complement existing control strategies . Genetic to 0.27 –18. Geno to 0.27 , medium  to 0.27 –26, and  to 0.27 , as well to 0.27 –32, have methods . However2 estimates, for MAP-specific humoral response and/or tissue MAP infection in Spanish Holstein cattle (N = 983) using different diagnostic methods including ELISA for MAP antibodies detection, and culture and PCR for MAP detection in gut tissues. The second objective was to identify genomic regions and candidate genes associated with MAP infection using imputed WGS data and the individual test results or test combinations with h2 estimates > 0.08.The first objective of the current study was to calculate the genetic parameters, variance, and hin vivo experimentation before stunning for slaughter and, therefore, no specific ethics committee authorization was required. The cows were slaughtered in the Bilbao and Donostia municipal slaughterhouses  under the pertinent Basque (Basque Government Decree 454/1994), Spanish , and European (Council Regulation (EC) No 1099/2009) legislation on animal welfare.Animals used in this study were not submitted to any The Spanish Holstein population included in this study consisted of 983 culled Holstein cattle from several herds located in eight regions: Basque Country , Catalonia , Navarre , Cantabria , Aragon , Castile and Leon , La Rioja  and Asturias . Only cows  were included in the analyses as PTB has a long incubation period and older animals are, therefore, more likely to show clinical signs and positive diagnostic results. The cows were slaughtered in two abattoirs located in the Basque country from March 2007 to May 2010. The infectious status of the animals was determined by ELISA for MAP antibodies detection, and tissue culture and PCR for MAP detection . SamplinBos taurus from Run7.0 of the 1,000 Bull Genomes project and 581,712 SNPs (ASR-UCD1.2). Imputation to the WGS level was then undertaken using a reference population of 2,333 Bos taurus from Run6.0 of the 1,000 Bull Genomes project [Peripheral blood samples were taken at slaughter time and DNA was extracted using the QIAmp DNA Blood Mini Kit according to the manufacturer´s instructions . Purified genomic DNA was quantified spectrophotometrically and subsequently genotyped with the Illumina Bovine MD SNP50 Bead Chip at the molecular genetic laboratory service of the Spanish Federation of Holstein Cattle (CONAFE) using the InfiniumTM iScan software for allele assignation . Individual genotypes were phased using Eagle 2.4  and impu project . In tota2 estimates for MAP infection status explained by all the SNPs were calculated using the genome-wide complex trait analysis (GCTA) software 1.93.2, according to the following formula: G is the additive genetic effect of the individuals and σe is the residual variance [G and σe in the equation were estimated by the genomic-relatedness-based restricted maximum-likelihood (GREML) approach implemented in GCTA. This approach estimates the proportion of phenotypic variation that can be explained by all genome-wide SNPs using an SNP-derived genetic relationship matrix. The concept behind this method is to fit all the SNPs simultaneously as random effects in a mixed linear model to estimate the variance explained by all the SNPs. GCTA implements the method in two steps; first generating the GRM between individuals and then estimating the variance explained by all SNPs by a Restricted Maximum Likelihood (REML) analysis of the phenotypes with the GRM.The variance components, standard errors (SE), and hvariance . The var2 > 0.08 were analyzed in a case-control study using the mlma (mixed linear model) association analysis of the GCTA 1.93.2 which fits the effects of all the SNPs as random effects. The model is y = a + bx + g + e, where y is the phenotype, a is the mean term, b is the additive effect (fixed effect) of the candidate SNP to be tested for association, x is the SNP genotype indicator variable coded as 0, 1 or 2, g is the polygenic effect (random effect) i.e. the accumulated effect of all SNPs , and e is the residual [2 values higher than 50% and were retained, with a mean R2 close to 90%. SNPs with R2 between 50 and 70% were scarce. To account for multiple testing, a 5% chromosome-wise false discovery rate (PFDR ≤ 0.05) was used to determine the probability that associations were not false positives. Uncorrected P-values between P = 5 × 10−5 and P = 5 × 10−7 provided a moderate significance level (α), and uncorrected P-values < 5 × 10−7 were used to identify highly significant associations . The inflation factor (λ) and quantile-quantile plots were calculated to compare observed distributions of–log  to the expected distribution under the no association model for each phenotype. λ value close to 1 suggests appropriate adjustment for potential substructure and λ > 1.2 suggests population stratification.Imputed genotypes and the results from the diagnostic tests with hresidual . Age was−7) were calculated using logistic regression analysis with the WGassociation function of SNPassoc 1.9.2 under five different genetic models  [The odds ratio (OR) and their 95% confidence intervals (CI) for the SNPs associated with a positive ELISA-tissue PCR-tissue culture diagnosis (P < 5 × 10dditive) . The WGa−7) in a given chromosome. The beginning and end of each quantitative trait locus (QTL) were defined in a window of 500,000 base pairs upstream and downstream by the SNPs that were furthest upstream and downstream of the suggestive SNP. Overlapping QTLs were merged and considered as a single QTL. The defined QTLs were further investigated for the presence of candidate genes within 50,000 base pairs to each side of the defined QTL using Ensembl (https://www.ensembl.org). The identified QTLs and candidate genes were compared with the reported cattle QTLs and genes for PTB susceptibility, bovine tuberculosis (bTB) susceptibility, and clinical mastitis , and with human candidate genes for CD, IBD, and colorectal cancer (https://www.ebi.ac.uk/gwas/). In addition, we uploaded the list of candidate genes and search the Innate DB database for innate immunity genes (https://innatedb.com).The location of the significant SNPs was determined with biomaRt 2.44.1 for R  using thThe infection status of each animal was investigated by serological and microbiological methods. 2 estimates were calculated for all the diagnostic tests and their combinations (2 estimates ranged from 0.054 to 0.139 depending on the phenotype. The highest h2 estimate was obtained for the ELISA-tissue PCR-tissue culture (h2 = 0.139) followed by the tissue culture (h2 = 0.101), ELISA-tissue culture (h2 = 0.099), tissue PCR-tissue culture (h2 = 0.086) and ELISA-tissue PCR (h2 = 0.081). When the ELISA was treated as a positive/negative binary trait for MAP status, higher h2 estimates were obtained (h2 = 0.075) when compared with the ELISA (OD) h2 estimates (h2 = 0.057).Variance components along with standard errors (SE) and hinations . The h2 2 > 0.08 was performed. After the GWAS, the phenotypes that showed SNPs with a significant association (PFDR ≤ 0.05) were the ELISA-tissue PCR-tissue culture, ELISA-tissue culture, and ELISA-tissue PCR. The number of SNPs that surpassed the moderate (between P = 5 × 10−5 and P = 5 × 10−7) and the high (P< 5 × 10−7) thresholds are presented in To explore the genetic basis of MAP infection status, a GWAS using the imputed WGS datasets and the phenotypes and combinations of phenotypes with hA total of 3,440 and 159 SNPs were found to be moderate and highly associated with the combination of the ELISA-tissue PCR-tissue culture results (+/-). In this test combination, animals were considered cases when they were positive to all the tests and controls when they were negative to all of them (+/-). Considering the results of the ELISA-tissue culture, the GWAS revealed 4,309 and 238 SNPs moderate, and highly associated with this combination, respectively. As shown in −7), P-values, along with candidate genes located within the defined QTLs are shown in 2, the ELISA-tissue PCR-tissue culture results (+/-), resided within 5 QTLs which were distributed across BTA5 and BTA23. The BTA23 harbored 4 of the 5 QTL regions. The two QTLs that harbored the most genome-wide significantly-associated SNPs were located on BTA23 (15.09–16.86 and 18.81–22.95 Mb). Using the ELISA-Tissue PCR results, we identified the largest QTL (4.12 Mb) on BTA5. Two QTLs located on BTA11 and BTA28 were specifically associated with the combination ELISA-tissue culture, and one QTL on BTA12 was specifically associated with the ELISA-tissue PCR.The GWAS defined a total of 12 QTLs highly associated with MAP infection status on 8 chromosomes including BTA4, BTA5, BTA11, BTA12, BTA14, BTA23, BTA24, and BTA28. A description of the SNPs surpassing the threshold (P < 5 × 10median = 1.007), ELISA-tissue PCR-tissue culture (+/) (λmedian = 1.004), ELISA-tissue culture (+/) (λmedian = 1.006), and ELISA-tissue PCR (+/) (λmedian = 1.006), indicating that significant values were not overestimated due to population stratification or cryptic relatedness. A slight deviation in the upper right tails from the y = x line suggested that some association was present in the data.Quantile-quantile plots comparing the observed distribution of–log  to the expected values under null hypothesis are shown in 2, the ELISA-tissue culture-tissue PCR (+/-), the majority located on BTA23. The OR calculated for all the SNPs associated with a positive ELISA-tissue PCR-tissue culture diagnosis (+/-) (P < 5 × 10−7) were > 1 indicating that the animals with the minor alleles were more likely to have a positive ELISA-tissue PCR-tissue culture result. The OR for the SNPs defining the peak of each QTL are shown in 2 estimates of susceptibility to the disease ranging from < 0.01 (16) to 0.2843 (18). This is comparable with the moderate heritability (h2 = 0.12) estimated for bTb infection even though the phenotype definitions and models used differed [2 estimates were also moderate (ranged from 0.054 to 0.139). Interestingly, we observed that the combination of multiple diagnostic tests increased the h2 estimates, with the highest h2 obtained for the combination of ELISA-tissue PCR-tissue culture (+/-) (h2 = 0.139). For this phenotype, all the common SNPs explained ~14% of the phenotypic variance. Except for the tissue culture (h2 = 0.101), the low h2 estimates obtained for the individual tests (h2 < 0.08) could be attributed to their lack of sensitivity for the detection of animals that are in the subclinical stages of MAP infection . While cows testing ELISA-tissue PCR and tissue culture positive are very likely infected, animals could be test-negative because of a false-negative test result, lack of exposure to MAP, or be truly resistant. While a positive test result is almost certainly associated with disease susceptibility, a negative test does not always reflect resistance. In agreement with this, we observed that all the SNPs associated with the ELISA-tissue PCR-tissue culture diagnosis (+/-) had an OR > 1 and were, therefore, associated with disease susceptibility. This implies that susceptibility to MAP infection can be modified using genetic selection. In our study population, only 9 of the 70 ELISA-positive animals couldn´t be detected by tissue PCR and culture. As above, these cows could be tissue PCR and culture negative because of a false-negative test result or be truly resistant. PTB resistance defined as the ability of the host to clear the pathogen by mounting a protective immune response deserves further studies.Previous studies that estimated genetic parameters for PTB in cattle indicated that susceptibility to MAP infection is heritable, with hdiffered . In our −7) for evidence of an association with MAP infection. These strategies increased the hereditability of the trait and reduced the risk of misclassification. Further studies will demonstrate whether the post-mortem examination of gut tissues and regional lymph nodes can improve, even more, the accuracy of the classification of naturally infected animals and uninfected controls, and provides high h2 estimates. Once a PTB-associated phenotype with a moderate/high h2 estimate is finally selected, genomic predictions will be developed in small, well-recorded reference populations using the genomic BLUP model, and then the prediction equation will be applied to predict genomic estimated breeding values of Holstein cattle that were genotyped but did not have PTB-associated phenotypes themselves.Our study is unique in the definition of cases and controls through the combination of multiple diagnostic tests including ELISA for detection of humoral responses against MAP, and culture and PCR detection of MAP in tissue samples. Moreover, the population size used (N = 983) was bigger than in a previous GWAS using the bovine SNP50 Bead Chip where cases were defined as tissue PCR-tissue culture positives (N = 459) . Althoug−7) on 8 chromosomes . By examining the available cattle QTL database, we observed that the identified QTLs on BTA23 (18.81–22.95 Mb), BTA24 (45.72–46.72 Mb), and BTA12 overlapped with four regions previously associated with PTB susceptibility; QTL14876, QTL166685, QTL139831, and QTL211947 [adhesion G protein-coupled receptor F1 (ADGRF1), adhesion G protein-coupled receptor F5 (ADGRF5), TNF receptor superfamily member 21 (TNFRSF21), defensin β110 (DEFB110) and defensin β114 (DEFB114) are likely associated with MAP infection. Previous studies supported a role for the β-defensins as important host defense effector molecules that are rapidly mobilized by the epithelium upon MAP infection [ADGRF1and ADGRF5, also named GPR110 and GRP116, are G-protein-coupled receptors acting as important regulators in the progression and development of several human inflammatory diseases including IBD, hepatocellular carcinoma, lung cancer, gastric cancer, prostatic cancer, glioma, and colorectal cancer [TNFRSF21 is a member of the TNF/TNFR family and plays a critical role in pathogen recognition, immune response, inflammation, and tumor progression [nuclear factor kappa-B (NF-κβ) and mitogen-activated protein kinase 8  and induces cell apoptosis. Fang et al. demonstrated that the level of TNFRSF21 was dramatically increased in bovine peripheral blood leucocytes of mastitis cows, which underlines its important regulatory role in bovine mastitis inflammation [TNFRSF21 in the regulation of bovine mastitis susceptibility via GWAS and post-transcriptional analysis was discovered [Identifying significant SNPs and QTLs associated with PTB susceptibility is extremely important to understand the molecular mechanisms involved in the pathogenesis of the disease. Our results confirmed that susceptibility to PTB is polygenic with a large number of genetic variants each having a small effect on the regulation of the observed phenotype. Using combinations of diagnostic tests and genotypes imputed to the WGS level, we identified a total of 12 regions associated with PTB susceptibility , invasion and migration of tumors , and in the regulation of the calcineurin-nuclear factor of activated T cells (RCAN2). This region overlapped with QTLs on BTA23 associated with bTb infection in a multi-breed GWAS of Charolais, Limousin, and Holstein Friesian (genetic positions 19.44–22.72 Mb) [RCAN2 gene as well. The RCAN2 is responsible for the regulation of calcineurin 2, and calcineurin activation promotes the survival of Mycobacterium tuberculosis (MTb) within its host by preventing phagocyte maturation which is required to destroy intracellular bacteria [calcineurin 2 may have a pivotal role in the susceptibility of cattle to both bTb and PTB. In addition, a second PTB-associated QTL on BTA23 (11.32–14.19 Mb) overlapped with a bTb-associated QTL (QTL96552) [Other candidate genes identified in BTA23 (18.81–22.95 Mb) include genes involved in the splicing process (2.72 Mb) . Interesbacteria . Our resTL96552) .Forkhead BoxP4 (FOXP4), transcription factor EB (TFEB), the LIM/homeoboxprotein (LHX6), and the mediator of RNA polymerase II transcription subunit 20 (MED2). The TFEB acts as a master regulator of lysosomal biogenesis, autophagy, lysosomal exocytosis, lipid catabolism, energy metabolism, and immune response against intracellular pathogens including MTb [TFEB is a critical regulator of autophagic activation. In the current study, several candidate genes potentially associated with MAP infection were involved in the splicing process, including the U6 spliceosomal RNA and 7SKRNA. Further studies are needed to determine if the identified SNPs are affecting the positional candidate genes or other genes through cis- or trans-regulatory effects [Several positional candidate genes that we identified in other QTLs were important transcriptional regulators, including the ding MTb . Autopha effects .RCAN2 gene was found associated with bTb infection [TNFRSF21 has not been associated with PTB risk in cattle, other members of the TNF receptor superfamily such as TNFRSF18 and TNFRSF4 are known to stimulate the Th1 cell-mediated immune response and have been previously identified as PTB candidate genes [TNFRSF21 was identified to be involved in the regulation of bovine mastitis susceptibility via GWAS [The candidate genes identified in this study are novel in the sense that they have not been associated with PTB risk in cattle before. Interestingly, some of the candidate genes identified in the current study were previously found associated with mastitis and bTb infection. For instance, the nfection  and withnfection . Althougte genes . As mentvia GWAS .TNFRSF21, ADGRF1, FOXP4, Cyclin D3 (CCND3), and Transmembrane Protein 117 (TMEM117) were previously found to be associated with IBD [FOXP4 and CCND3 were also found associated with CD. Recent association analysis identified a new risk locus harboring the TFEB for colorectal cancer susceptibility [riggering Receptor Expressed On Myeloid Cells 1 and 2 , triggering Receptor Expressed On Myeloid Cells like 1 and 2 , Canopy FGF Signaling Regulator 3 (CNPY3), Protein Phosphatase 2 Regulatory Subunit B’Delta (PPP2R5D), Cysteine Rich Secretory Protein 3 (CRISP3), Anoctamin 6 (ANO6), Protein Inhibitor of Activated STAT2 (PIAS2), DAB2 Interacting protein (DAB2IP), and MAPK8.The candidate genes identified in our study were also compared to candidate genes that were previously identified in CD, IBD, and colorectal cancer to determine if there was any overlap. Five of the candidate genes identified in the current study; the with IBD . FOXP4 atibility . Using tIn summary, combining phenotypes and WGS into a joint GWAS improved the power for detecting genetic associations in Spanish Holstein cattle. Our results validated some previously reported associations and identified novel SNPs, QTLs, and candidate genes associated with the antibody response to MAP infection and with MAP detection in infected tissues. Furthermore, the addition to genotyping assays of the SNPs identified in the current study would allow producers to select cattle that are less susceptible to PTB and likely to other bovine diseases as well; ultimately reducing the spread of diseases, preventing further economic losses, and reducing antimicrobial use. Consequently, a reduction of the presence of MAP in the environment may also be beneficial to humans, especially if the link between MAP and human inflammatory diseases is confirmed.S1 FigThe chart depicts the genomic distribution of the SNPs associated with (A) ELISA-tissue culture-tissue PCR (+/-), (B) ELISA-tissue culture-tissue PCR (+/), (C) ELISA-tissue culture (+/), and (D) ELISA-tissue PCR (+/) according to the Ensembl Variant Effect Predictor (VEP).(TIF)Click here for additional data file.S2 Figmedian = 1.007), ELISA- tissue PCR-tissue culture (+/) (λmedian = 1.004), ELISA-tissue culture (+/) (λmedian = 1.006), and ELISA-tissue PCR (+/) (λmedian = 1.006). The red line is the slope expected under no inflation and no true association, the y-axis represents the observed–log , and the x-axis represents the expected–log , under the null hypothesis of no association.The plots showed a distribution close to the expected distribution line for the following phenotypes: ELISA-tissue PCR-tissue culture (+/-) (λ(TIF)Click here for additional data file.


Sus scrofa chromosome (SSC) one was associated with CC and AC in COD pigs, which spans 6.92 Mb (from 170.06 to 176.98 Mb). Moreover, multi-trait GWAS identified 21 significant SNPs associated with the three conformation traits, indicating the multi-trait GWAS is a powerful statistical approach that uncovers pleiotropic locus. Finally, the three candidate genes  were selected that may play a role in the conformation traits. Further bioinformatics analysis indicated that the candidate genes for the three conformation traits mainly participated in sphingolipid metabolism and lysosome pathways. For all we know, this study was the first GWAS for WC in pigs. In general, our findings further reveal the genetic architecture of CC, AC, and WC, which may offer a useful reference for improving the conformation traits in pigs.Chest circumference (CC), abdominal circumference (AC), and waist circumference (WC) are regarded as important indicators for improving economic traits because they can reflect the growth and physiological status in pigs. However, the genetic architecture of CC, AC, and WC is still elusive. Here, we performed single-trait and multi-trait genome-wide association studies (GWASs) for CC, AC, and WC in 2,206 American origin Duroc (AOD) and 2,082 Canadian origin Duroc (COD) pigs. As a result, one novel quantitative trait locus (QTL) on  It has become widely used in humans , plants https://www.animalgenome.org/cgi-bin/QTLdb/SS/index, February 04, 2021). Notably, no QTLs are associated with WC in the pigQTLdb. Furthermore, only a small part of these QTLs were detected by GWAS. Here, we conducted single-trait and multi-trait GWASs for CC, AC, and WC on 2,206 AOD pigs and 2,082 COD pigs. This study aimed to identify more loci associated with CC, AC, and WC traits using single-trait and multi-trait GWASs, further understand the genetic architecture of the three traits in pigs.Few GWAS research on the conformation traits in pigs, especially the CC, AC, and WC traits. To date, 2,492 quantitative trait locus (QTLs) are associated with conformation traits, including 21 and four QTLs are associated with CC and AC in the pig QTL database  and 2,082 COD pigs  were collected from the Guangdong Wens Foodstuff Group Co., Ltd. . All the animals were reared under the same feeding conditions. When the pigs reached the live weight of 100 ± 15 kg and were fasted for 24 h, their CC, AC, and WC were measured by a tape measure. The measuring positions of CC, AC, and WC were shown in From 2013 to 2017, a total of 2,206 AOD pigs  and electrophoresis. Qualified DNA concentration was diluted to 50ng/μl. All animals were genotyped with the GeneSeek Porcine 50K SNP chip  that contains 50,703 SNP across the whole genome. The quality control (QC) was performed by PLINK v 1.9 . Brieflyanalyses .Due to population stratification that may cause false-positive findings in GWAS, principal component analysis (PCA) and quantile-quantile (Q-Q) plots were conducted for two Duroc populations to assess and correct population structure. In this study, PCA and Q-Q plots were generated by software GCTA v1.92.4beta  and R v3GEMMA v0.98 software was used to conduct a univariate linear mixed model (LMM) to detect the association between each SNP and phenotype . The stay denotes a vector of phenotypic values; W refers to the incidence matrices of covariates (fixed effects), including the top five eigenvectors of PCA, sex, and live weight; α is the vector of corresponding coefficients with the intercept; X represents the vector of SNP genotypes; β stands for the corresponding effect size of the SNP; u is the vector of random effects with u ~ MVNn; ε corresponds to the vector of random residuals with ε ~ MVNn. The mvLMM for the approach used in this study was previously studies described , 19. TheHaplotype block analysis was implemented for chromosomal regions with multiple significant SNPs using PLINK v1.9 and Haploview v4.2 . Moreoverp) of the sample via Pearson's correlation coefficient, and the formula for calculating the Pearson's correlation coefficient is given by  analysis. The formula for calculating the genetic correlation coefficient is given by , 22, 23.rg is genetic correlation coefficient between two traits; the subscripts “1” and “2” represent the two traits; σg1g2 refers to the genetic covariance; σg represents square root of the genetic variance for the trait .where The restricted maximum likelihood (REML) method was used to estimate the phenotypic variance explained by the significant SNPs for CC, AC, and CC traits by GCTA software. The model for calculating the phenotypic variance explained by the significant SNPs is given byy is to the vector of phenotype value; β represents a vector of fixed effects, including the top five eigenvectors of PCA; X refers to an incidence matrix for β; g is the vector of the aggregate effects of all the qualified SNPs for the pigs within one population; I is the identity matrix; Ag represents the genetic relationship matrix; where Sus scrofa 11.1 genome version (http://ensembl.org/Sus_scrofa/Info/Index). To further identify potential candidate genes, KOBAS 3.0 (http://kobas.cbi.pku.edu.cn/kobas3) was used to perform Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) analyses  and phenotypic correlation coefficients (r > 0.77) were identified between all traits in the two Duroc pig populations. Given the high genetic correlation and phenotypic correlation coefficients between CC, AC, and WC traits, these traits may be improved together in pig breeding.The statistical distribution and heritability of the traits measured in this study are presented in P-values of Q-Q plots indicated that no overall systematic biases were observed, and the genomic inflation factor (λ) at each trait amounted to 0.977 to 1.04, indicating that there was no population stratification  SNP was associated with AC and WC (P <2.80 × 10−5) (Sus scrofa chromosome (SSC) six was associated with AC and WC in the AOD pigs, and 18 SNPs were associated with CC and AC in the COD pigs  . For COD × 10−5) . However × 10−5) . NotablyCOD pigs . InteresCOD pigs .r = 0.884) and phenotypic correlation coefficients (r = 0.852) between CC and AC in the COD pigs  . These rCOD pigs . In the COD pigs . To examCOD pigs , but thehe model . The samhe model . These rIn this study, we performed multi-triat GWASs for the CC, AC, and WC traits in the two Duroc pig populations, respectively. A total of 21 significant SNPs have pleiotropic effects on CC, AC, and WC by multi-trait GWAS detected. For the AOD pigs, multi-trait GWAS detected two SNPs were associated with the correlated traits, of which one SNP reached genome-wide significant levels . FurtherAmounted to eight candidate genes located within or near the significant SNPs that were associated with the three conformation traits . To furt2 resource population. The results showed that compared to single-trait GWAS, multi-trait GWAS detected a total of 16 newly significant loci for the hematological traits. These results showed that multi-trait GWAS could complement the single-trait GWAS results to increase the statistical power of GWAS.Given that the two Duroc pig populations have different genetic backgrounds, the single-trait GWASs were performed for the CC, AC, and WC traits in the two Duroc populations, respectively. In addition, we found that high phenotype and genetic correlation exist between CC, AC, and WC in the two Duroc pig populations . Multi-t2  pigs, and none of the loci was shared by the three pig populations or two of these populations. Bergfelder et al.  associated with the conformation traits were selected candidates. The ITGA11 gene is near SNP ALGA0006817, and the SNP was identified to be associated with CC. Popova et al.  on SSC1 were associated with CC, and the SNPs are located nearest the TLE3 gene. The TLE3 was identified as a dual function modulator of adipogenesis that augments PPARγ action and inhibits Wnt signaling, and PPARγ and Wnt signaling are central positive and negative regulators of adipogenesis, respectively  may affect skeletal bone mass and lipid metabolism. Finally, three genes were selected as strong candidate genes that may affect the conformation traits. Our findings further reveal the genetic architecture of CC, WC, and AC, which may provide new insights for improving the conformation traits in pigs.This study conducted single-trait and multi-trait GWASs for CC, AC, and WC in 2,206 AOD and 2,082 COD pigs. As a result, we detected a total of 35 newly significant SNPs for CC, AC, and WC on SSC1, SSC4, SSC5, SSC6, SSC7, SSC12, and SSC13. Among these SNPs, single-trait GWAS detected 18 significant SNPs on SSC1 were associated with CC and AC in COD pigs. These 18 SNPs were situated in a QTL region with a 6.92 Mb interval (from 170.06 to 176.98 Mb). Moreover, 21 newly pleiotropic SNPs were identified to be associated with the three conformation traits by multi-trait GWAS, suggesting the multi-trait GWAS is a powerful statistical method to identify pleiotropic locus. According to the bioinformatics analysis and the functions of candidate genes, three genes  can be found at: The animal study was reviewed and approved by the Animal Care and Use Committee of the South China Agricultural University (SCAU) .JY, MY, and ZW conceived and designed the experiments and revised the manuscript. SZ, RD, ZZ, HZ, SW, DR, JW, YQ, and EZ collected the samples, recorded the phenotypes, and performed the experiments. SZ and RD analyzed the data. ZW and GC provided the material. SZ, RD, and MY wrote the manuscript. All authors contributed to the article and approved the submitted version.This study was supported by the Guangdong Yang Fan Innovative and Entrepreneurial Research Team Program (2016YT03H062) and the National and Provincial Fund Cultivation Project of Zhongkai College of Agricultural Engineering.RD, HZ, SW, GC, and ZW were employed by company Guangdong Wens Breeding Swine Technology Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.