Genomic prediction (GP) based on single nucleotide polymorphisms (SNP) has become a broadly used tool to increase the gain of selection in plant breeding. However, using predictors that are biologically closer to the phenotypes such as transcriptome and metabolome may increase the prediction ability in GP. The objectives of this study were to (i) assess the prediction ability for three yield-related phenotypic traits using different omic datasets as single predictors compared to a SNP array, where these omic datasets included different types of sequence variants , different types of transcriptome  sampled from two tissues, leaf and seedling, and metabolites (M); (ii) investigate the improvement in prediction ability when combining multiple omic datasets information to predict phenotypic variation in barley breeding programs; (iii) explore the predictive performance when using SV, GE, and ePAV from simulated 3’end mRNA sequencing of different lengths as predictors.The prediction ability from genomic best linear unbiased prediction (GBLUP) for the three traits using dSV information was higher than when using tSV, all SV information, or the SNP array. Any predictors from the transcriptome  and metabolome provided higher prediction abilities compared to the SNP array and SV on average across the three traits. In addition, some (di)-similarity existed between different omic datasets, and therefore provided complementary biological perspectives to phenotypic variation. Optimal combining the information of dSV, TE, ePAV, as well as metabolites into GP models could improve the prediction ability over that of the single predictors alone.The use of integrated omic datasets in GP model is highly recommended. Furthermore, we evaluated a cost-effective approach generating 3’end mRNA sequencing with transcriptome data extracted from seedling without losing prediction ability in comparison to the full-length mRNA sequencing, paving the path for the use of such prediction methods in commercial breeding programs.The online version contains supplementary material available at (10.1186/s12864-022-08337-7). Hordeum vulgare L.) is the fourth most important cereal crop in the world  and is used for human nutrition and animal feed [Barley  has emerged as a powerful tool to increase selection gain for complex traits in both livestock and plant breeding programs , 9. ThisTypically, single nucleotide polymorphisms (SNP) serve as predictors in GP –13. SNP Complex biological processes such as transcription, translation, and biochemical cascades resulting in various metabolites occur between DNA sequence and phenotypes , which hTranscription is the first downstream processes after the DNA sequence and, thus, more likely affects the variation of traits compared to SNP. Recently, thanks to technological developments, several studies have proposed to use gene expression (GE) variation as predictor of phenotypic variation in maize , 21, ricArabidopsis thaliana, maize, wheat, and barley reported lower or higher prediction abilities compared to SNP information, depending on the traits and species [Compared to the two previous levels of molecular information (DNA sequence and GE), metabolites (M) have the closest relationship to the expressed phenotype because they are the end-points of upstream biochemical processes , and, th species , 26–29.  species , 21. ThuThe objectives of our study were to (i) assess the prediction ability for three yield-related phenotypic traits  using different omic datasets as single predictors compared to a SNP array, where these omic datasets included different types of sequence variants , different types of transcriptome  sampled from two tissues, leaf and seedling, and metabolites (M); (ii) investigate the improvement in prediction ability when combining multiple omic datasets information to predict phenotypic variation in barley breeding programs; (iii) explore the predictive performance when using SV, GE, and ePAV from simulated 3’end mRNA sequencing of different lengths as predictors.H2) were high and similar for LA (0.91) and HT (0.90) and with 0.83 slightly lower for PH. A total of 192 chemical entities were annotated  were measured for 23 spring barley inbreds in seven environments. The adjusted entry means of the 23 inbreds ranged from 2.52 to 7.07 for LA, 48.75 to 79.75 cm for PH, and 57.31 to 82.23 days for HT . Heritabnnotated  and aftennotated . The hernnotated . The cla∗∗∗), where the correlation coefficients between PH and HT as well as between PH and LA were with about 0.45 considerably lower. Many metabolites were significantly (P<0.05) negatively associated with the assessed phenotypic traits  and seedling (s). GE, TE, and ePAV datasets were similar to each other within the same tissue. This can be explained thereby that the ePAV dataset was derived from GE dataset and the GE dataset was derived from the TE dataset. ePAV ls was, as expected, centered between the ePAV from the individual tissues. Although SNP array, SV, dSV, and tSV clustered together, SNP array was more distant from the cluster of dSV, tSV, and SV which almost overlapped. This was due to that dSV and tSV are a subset of SV. This finding indicated that SNP, expression and metabolite features would provide different layers of biological information and might contribute differently and complementarily to the phenotypic variation.To assess similarity/dissimilarity between these omic datasets, we performed generalized procrustes analysis (GPA)  on the rs and TE s), and metabolite datasets . ePAV ls was chosen as the best compromise across the three traits for further analyses, as it was for none of the three traits in the significance group with the lowest prediction abilities. The TE datasets slightly outperformed the GE datasets for HT and LA, and TE s resulted in the highest prediction ability as single predictor for these traits. In contrast, no difference between TE and GE was observed for PH.The prediction ability of the three phenotypic traits using different single predictors was examined through five-fold cross-validation. Regardless of the predictor, the prediction abilities were higher for traits with higher heritabilities Fig. . Predictets Fig. . The pre0.6) for further analyses.To explore whether the heritability of a metabolite affects the prediction performance, eight classes of metabolites based on different degrees of heritabilities served as predictor. The prediction ability increased when the metabolites with lower heritability (<0.1) were not considered Fig. . Howeverls, were highly correlated  resulted in a higher prediction ability compared to the SNP data produced with the 50K SNP array Fig. . This miSV in gene coding regions can be classified into nsSV and sSV, where the former can change the amino acid sequence of proteins, but not the latter. However, not all amino acid changes lead to significant changes of the protein. This can be explored by the SIFT algorithm in classifying SV into dSV and tSV based on the conversion of amino acid sequences , where tThe predicted phenotypic values based on the three different classes of SV were highly correlated with each other , which cThe features derived from the transcriptome datasets  led to increased prediction abilities by 62.81% compared to SNP array and even SV on average across the three traits and two tissues. This finding was inconsistent with the results of previous studies , 21, whoA single gene can encode multiple distinct transcripts through alternative splicing, which allows organisms to increase the protein diversity based on the same set of genes , and theAll the datasets generated by mRNA sequencing from seedling were well separated from those from leaf Fig. . Similarl, TE l) achieved the same level of prediction ability as that from seedling. One explanation for this finding might be that HT is triggered by environmental factors in later developmental stages and therefore the causal expression features for this trait are more likely to be revealed in leaf than in early developmental stages like seedling.Only for HT, expression information from leaf -similarity between (1) different omic datasets Fig.  and alsols, TE s, and M 0.6 in the joined weighted relationship matrix of GBLUP model maximizing the prediction ability. The highest prediction ability across the three examined traits was observed when more than one predictor was used and, for each of the three traits, without the contribution of the dSV  using an adapted protocol from Lisec et al. [2O:methanol:chloroform (v:v:v) mixture pre-cooled to −20 ∘C, then mixed on a rotator for 10 min and centrifuged at 20,000 g for 2 min (both at 4 ∘C). A total of 30 μl of the supernatant were dried completely in a vacuum concentrator and derivatized in two steps via an MPS-Dual-head autosampler (Gerstel): (1) with 10 μl methoxyamine hydrochloride ) and shaking at 37 ∘C for 90 min, (2) adding 90 μl N-Methyl-N-(trimethylsilyl)trifluoroacetamide  and shaking at 37 ∘C for 30 min. After incubation for 2 hours at room temperature, 1 μl of derivatized compounds was injected at a flow of 1 ml/min with an automatic liner exchange system in conjunction with a cold injection system (Gerstel) in splitless mode (ramping from 50 ∘C to 250 ∘C at 12 ∘C/s) into the GC. Chromatography was performed using a 7890B GC system (Agilent Technologies) with a 30 m long, 0.25 mm internal diameter, HP-5MS column with 5% phenyl methyl siloxane film (Agilent 19091S-433). The oven temperature was held constant at 70 ∘C for 2 min and then ramped at 12.5 ∘C/min to 320 ∘C at which it was held constant for 5 min; resulting in a total run time of 27 minutes.The metabolite profiling of our study was based on leaf samples collected for the 23 barley inbreds with quadruplicates in a greenhouse experiment, where no phenotypic traits were assessed. Seeds of the 23 spring barley inbreds were sown in controlled conditions with 16 hours light and eight hours dark at 22 c et al. . Metabol∘C source temperature and recorded in a mass range of m/z 60 to m/z 800 at 20 scans per second with a 7200 GC-QTOF (Agilent Technologies). Raw data files exported from MassHunter Qualitative  in the mzData format (*mzdata.xml) were converted to the NetCDF format (*.cdf) and baseline-corrected via MetAlign  after conversion of absolute time in retention indices [https://www.ebi.ac.uk/metabolights/MTBLS1561). The compounds corresponding to contaminations, siloxane, ribitol, and dimethylphenylalanine were removed. Furthermore, if several compounds were identified as the same metabolite, the one with the greatest heritabilty, for which the calculation is described below, was retained.Metabolites were ionized with an electron impact source at 70V and 200  041012, ). Quanti 041012, ). After  indices . The rawThe Illumina 50K barley SNP array  was usedhttps://mpgc.mpipz.mpg.de/home/). In addition, two tissue samples of one of the inbreds and one tissue sample of two other inbreds had to be removed during the data cleaning process. Reads were trimmed, adapter and low quality regions were removed. Afterwards, reads were mapped using HISAT2 (version 2.0.5) [l and s were used to separate the leaf  and seedling  samples. For further details see Weisweiler et al. [mRNA was extracted from leaf and seedling samples of the 23 inbreds as described earlier by Weisweiler et al. [n 2.0.5)  to the Mn 2.0.5) . Transcrn 2.0.5) . Newly iGE<10% of the maximum value of GE for a gene-tissue combination (cf. [l), ePAV seedling (ePAV s), and one across both tissues (ePAV ls).For each tissue separately, a presence call was made for each inbred-gene combination in the matrix of presence/absence calls, if GE >0 and an absence call if GE = 0. No presence/absence call (“NA”) was made for the inbreds with 0 <ion (cf. ). Tissueion  and bcftools (version 1.10.2) as described by Weisweiler et al. , and thes was only measured based on the last 200, 250, 300, 350, 400, 450, and 500 bp at the 3’end of each gene. To the same reduced set of sequence data, the ePAV detection procedure and the SV calling procedure has been applied resulting in seven different GE, ePAV, and SV datasets.For the simulation of 3’end mRNA sequencing, GE Based on visual inspections of quantile-quantile (Q-Q) plots of residuals as well as residuals vs. fitted values plots, phenotypic outliers were removed. Each of the phenotypic traits was then analysed across the environments using the following mixed model: yijk was the observed phenotypic value for the ith genotype at the jth environment within the kth replication, μ the general mean, Gi the effect of the ith inbred, Ej the effect of the jth environment, (G×E)ij the interaction between the ith inbred and the jth environment, and εijk the random error. To estimate adjusted entry means for all inbreds, Gi was treated as fixed and the other effects as random. As the samples for metabolites were collected from one environment, the model [where he model  was reduyik was the observed metabolite for the ith inbred within the kth replication, and εik the random error. The resulting adjusted entry means of phenotypic traits and metabolites for each inbred were used in further analyses, where the adjusted entry means of metabolites were designated as M.where Gi as random. The heritability on an entry mean basis for the phenotypic traits and metabolites was then calculated as To estimate the genetic variance  ePAV l, (7) ePAV ls, (8) gene expression in seedling (GE s), (9) gene expression in leaf (GE l), (10) transcript expression in seedling (TE s), (11) transcript expression in leaf (TE l), (12) metabolite (M), was compared based on the most stable and widely used model in GP, genomic best linear unbiased prediction (GBLUP) model [The performance to predict phenotypic variation of different types of predictors: (1) SNP array, (2) sequence variants (SV), (3) deleterious sequence variants (dSV), (4) tolerant sequence variants (tSV), (5) ePAV P) model , which cy is the vector of the adjusted entry means of the examined trait, 1 the unit vector, μ the general mean, Z the incidence matrix of genotypic effects, and u the vector of genotypic effects that are assumed be normal distributed with G denotes the relationship matrix between inbreds and ε is the vector of residuals following a normal distribution where W was defined as a matrix of feature measurements for the respective omic dataset that is designated in the following as predictor. The dimensions of W were the number of barley inbreds (n) times the number of features in the corresponding predictor (m) (Table n=22).For each of the above mentioned omic dataset, the monomorphic features and the features with missing rates >0.2 have been filtered out. m) Table . BecauseG was defined as W∗ is a matrix of feature measurement for the respective predictor, whose columns are centered and standardized to unit variance of W, and W∗. In addition, to assess the impact of the heritability of a metabolite on the prediction performance, only those metabolites with a heritability on an entry mean basis higher than t, where t varied from 0.1 to 0.8 in increments of 0.1, were considered, and the datasets were designated as M 0.1, M 0.2, M 0.3, M 0.4 M 0.5, M 0.6, M 0.7 and M 0.8.For each predictor, the additive relationship matrix G in model (wSNP): the weight of the most representative SNP datasets was determined as the one from the SNP array, SV, tSV, or dSV which has the most stable prediction performance across the three traits (dSV).the weight of SNP (wePAV): the weight of the most representative ePAV datasets was determined as the one from ePAV ls, ePAV s, or ePAV l which has most stable prediction performance across the three traits (ePAV ls).the weight of ePAV (wexpression): the weight of the most representative of the expression datasets was determined as the one from GE s, GE l, TE s, or TE l which has most stable prediction performance across the three traits (TE s).the weight of expression : the weight of the most representative metabolite datasets was determined as the one from M, M 0.1, M 0.2, M 0.3, M 0.4 M 0.5, M 0.6, M 0.7, or M 0.8 which has most stable prediction performance across the three traits (M 0.6).the weight of metabolite  from 0 to 1 in increments of 0.1, resulted in 286 different combinations of joined weighted relationship matrix, where the summation of four weights in each combination must be equal to 1. In addition, the performance of SV, GE s, and ePAV s from simulated 3’end mRNA sequencing of different length as described above was explored.A grid search, varying any weight (y) and predicted Five-fold cross-validation was used to assess the model performance. Prediction abilities were obtained by calculating Pearson correlations between observed . To evaluate similarity/dissimilarity among the various datasets, generalized procrustes analysis (GPA) [Correlations among the three phenotypic traits, and between the three phenotypic traits and the individual metabolites were measured as Pearson correlation coefficient. Principal component analysis (PCA) was performed on each omic dataset   was perfAll analyses have been performed using the statistical software R .Additional file 1 Supplemental Materials.Additional file 2 Supplementary Table S1.Additional file 3 Supplementary Table S2.


To ascertain the role of platelet glycoprotein Ib α‐chain (GPIbα) plasma protein levels in cardiovascular, autoimmune, and autoinflammatory diseases and whether its effects are mediated by platelet count.cis‐acting protein quantitative trait locus (cis‐pQTL) and trans‐pQTL near the GP1BA and BRAP genes as instruments. To assess if platelet count mediated the effect, we then performed a two‐step MR study. Putative associations (GPIbα/platelet count/disease) detected by MR analyses were subsequently assessed using multiple‐trait colocalization analyses.We performed a two‐sample Mendelian randomization (MR) study, using both a P ≤ 2 × 10−3), GPIbα, instrumented by either cis‐pQTL or trans‐pQTL, was causally implicated with an increased risk of oligoarticular and rheumatoid factor (RF)–negative polyarticular juvenile idiopathic arthritis (JIA). These effects of GPIbα appeared to be mediated by platelet count and were supported by strong evidence of colocalization . GPIbα instrumented by cis‐pQTL did not appear to affect cardiovascular risk, although the GPIbα trans‐pQTL was associated with an increased risk of cardiovascular diseases and autoimmune diseases but a decreased risk of autoinflammatory diseases, suggesting that this trans‐acting instrument operates through other pathways.After correction for multiple testing (Bonferroni‐corrected threshold The role of platelets in thrombosis is well‐established; however, our findings provide some novel genetic evidence that platelets may be causally implicated in the development of oligoarticular and RF‐negative polyarticular JIA, and indicate that GPIbα may serve as a putative therapeutic target for these JIA subtypes. Platelet glycoprotein Ib α‐chain (GPIbα) is a platelet surface membrane protein . It funcMendelian randomization (MR) studies utilize genetic variants, randomly allocated during conception, as instruments to infer causality and are less prone to confounding and reverse causation than observational studies . They arTo understand the effects of GPIbα on cardiovascular, autoimmune, and autoinflammatory diseases, and whether these are mediated by platelet count, we conducted a two‐step, two‐sample MR study. We subsequently performed multiple‐trait colocalization analyses  to complement the evidence for causal associations detected in our MR study.MR relies on 3 core assumptions. First, the genetic variant is robustly associated with the exposure. Second, the genetic variant is independent of confounders of the exposure–outcome association. Third, the genetic variant is independent of the outcome except via the exposure .In this study, we first performed a two‐sample MR study to assess the association of GPIbα with cardiovascular, autoimmune, and autoinflammatory disease risk Figure . To furtP < 5 × 10−6), or had an info score of <0.7  associated with GPIbα (P < 5 × 10−8) were selected as instruments.A proteome genome‐wide association study (GWAS) was conducted in 3,301 healthy blood donors of European ancestry . The plaP value having LD r2 < 0.001) associated with platelet count    were selP value, effect allele, other allele, and effect allele frequency) for each outcome from the largest publicly available GWAS at the time of analyses (Arthritis & Rheumatology website at http://onlinelibrary.wiley.com/doi/10.1002/art.41561/abstract).Outcomes included platelet count (per nl), 10 major cardiovascular diseases , and 12 immune‐related diseases. Immune‐related diseases were classified  as autoianalyses , 26, 27 XY), we used the Wald estimate, i.e., the ratio of the genetic association with outcome (βGY) to the genetic association with exposure (βGX) (Arthritis & Rheumatology website at http://onlinelibrary.wiley.com/doi/10.1002/art.41561/abstract). Wald estimates for multiple variants for the same exposure were combined using inverse variance–weighted (IVW) MR with multiplicative random effects  is larger than the variance explained between each variant–outcome effect (RGY2), and therefore whether the instrument primarily influences the outcome through the exposure (and not vice versa) . Several of the traits examined in this study are likely to share clinical and underlying immunopathogenic features despite their distinct phenotypes; therefore, using this stringent correction provides a balance between reducing false positives and providing rigorous results.To estimate the effect of exposure on outcome (βre (βGX)   . Two‐sidR2/K1‐R2N‐K‐1, where R2 indicates the proportion of exposure variability explained by the instrument, K indicates the number of instruments, and N indicates the sample size. R2 was calculated as 2EAF(1 – EAF)β2, where EAF is the effect allele frequency and β is the effect size of the effect allele. Higher F statistic values reflect a lower risk of weak instrument bias . A lead variant‐centric approach was applied, where we extracted effect estimates and allele information for all variants within 1 megabase upstream and downstream of the cis‐acting protein quantitative trait locus (cis‐pQTL) and trans‐acting pQTL for each trait (GPIbα/platelet count/disease), respectively. To provide reliable evidence of colocalization, at least 50 variants , including the causal variant of interest, within the test region for all 3 traits were assessed (−4), 2 traits (p2 = 1 × 10−6), and 3 traits (p3 = 1 × 10−7), as recommended  or just confounded by LD, we subsequently performed multiple‐trait colocalization analyses at each locus . Under tassessed . We assiTwoSampleMR package, and multiple‐trait colocalization analyses were conducted using the moloc package. Results were visualized using the forestplot package in the R software platform .MR analyses were performed using the 2 < 0.001) pQTLs associated with GPIbα were used as instruments: cis‐pQTL (rs72835078 within the GP1BA gene) and trans‐pQTL (rs11065979 near the BRAP gene). The F statistic for cis‐pQTL was 48, with 1.4% of the variance in GPIbα explained by cis‐pQTL, and the F statistic for trans‐pQTL was 50, with 1.5% of the variance in GPIbα explained by trans‐pQTL (Supplementary Table Arthritis & Rheumatology website at http://onlinelibrary.wiley.com/doi/10.1002/art.41561/abstract).Two conditionally uncorrelated , the two‐sample MR analysis  of 2.45  (P = 1.71 × 10−3) . Higher GPIbα level instrumented by trans‐pQTL was also associated with a higher risk of JIA  . When combining the estimates instrumented by both cis‐pQTL and trans‐pQTL, per unit increase in GPIbα level was associated with a 169% higher risk of JIA .Using a Bonferroni‐corrected threshold of s Figure  suggestecis‐pQTL with cardiovascular diseases  and autoimmune diseases  but decreased risk of autoinflammatory diseases   Figure . The SteP = 0.03), among which the effect instrumented by trans‐pQTL  was larger than that instrumented by cis‐pQTL   associated with platelet count (P < 8.31 × 10−9) (Supplementary Table Arthritis & Rheumatology website at http://onlinelibrary.wiley.com/doi/10.1002/art.41561/abstract). Using the IVW method, genome‐wide genetically predicted platelet count was positively associated with the risk of JIA  . Sensitivity analyses restricted to each specific instrument for GPIbα also showed that platelet count increased the risk of JIA ; this association was assessed using multiple‐trait colocalization analysis. The association (GPIbα instrumented by trans‐pQTL/platelet count/JIA) was supported by strong evidence of colocalization (PPA ≥ 0.80), indicating that the same causal variant affects 3 traits. The association of GPIbα instrumented by cis‐pQTL meditated by platelet count with JIA could not be assessed because the causal variant of interest was not available for the outcome (Supplementary Table Arthritis & Rheumatology website at http://onlinelibrary.wiley.com/doi/10.1002/art.41561/abstract).The two‐step MR analyses suggested that GPIbα mediated by platelet count has an impact on JIA  was positively associated with cardiometabolic and autoimmune diseases in overall and sex‐specific analyses , which may also play a role  overlapped between the proteome GWAS and the hematologic GWAS. Nonetheless, bias due to sample overlap is likely to be negligible in this study due to the presence of strong instruments . Second,y a role . Fifth, ns‐genes . Howeverns‐genes , once daUsing two‐step MR and multiple‐trait colocalization approaches, we provide reliable genetic evidence that the genetic variants that regulate GPIbα proteomic pathways, with well‐characterized biology function on platelet count, have a causal etiologic role in oligoarticular and RF‐negative polyarticular JIA. Our findings highlight the active role of platelets in these JIA subtypes, and GPIbα as a putative therapeutic target for these JIA subtypes.All authors were involved in drafting the article or revising it critically for important intellectual content, and all authors approved the final version to be published. Dr. Luo had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.Luo, Clarke, Ramanan, Schooling, Gaunt, Au Yeung, Zheng.Luo, Thompson, Langefeld, Marion, Grom, Gaunt, Zheng.Luo, Clarke, Ramanan, Schooling, Gaunt, Au Yeung, Zheng.Fig S1Click here for additional data file.Table S1‐S8Click here for additional data file.


Background: Atrial fibrillation (AF) is the most common arrhythmia. Genome-wide association studies (GWAS) have identified more than 100 loci associated with AF, but the underlying biological interpretation remains largely unknown. The goal of this study is to identify gene expression and DNA methylation (DNAm) that are pleiotropically or potentially causally associated with AF, and to integrate results from transcriptome and methylome.Methods: We used the summary data-based Mendelian randomization (SMR) to integrate GWAS with expression quantitative trait loci (eQTL) studies and methylation quantitative trait loci (mQTL) studies. The HEIDI (heterogeneity in dependent instruments) test was introduced to test against the null hypothesis that there is a single causal variant underlying the association.Results: We prioritized 22 genes by eQTL analysis and 50 genes by mQTL analysis that passed the SMR & HEIDI test. Among them, 6 genes were overlapped. By incorporating consistent SMR associations between DNAm and AF, between gene expression and AF, and between DNAm and gene expression, we identified several mediation models at which a genetic variant exerted an effect on AF by altering the DNAm level, which regulated the expression level of a functional gene. One example was the genetic variant-cg18693985-CPEB4-AF axis.Conclusion: In conclusion, our integrative analysis identified multiple genes and DNAm sites that had potentially causal effects on AF. We also pinpointed plausible mechanisms in which the effect of a genetic variant on AF was mediated by genetic regulation of transcription through DNAm. Further experimental validation is necessary to translate the identified genes and possible mechanisms into clinical practice. Atrial fibrillation (AF) is the most common sustained arrhythmia. The current global prevalence of AF is between 2 and 4%, and a 2.3-fold rise is expected . Well-knIt is estimated that the impact of common genetic variants on the risk of AF is ≈ 22% . Large-sMost of the AF-associated variants resided in non-coding regions of the genome , 5. Thern = 614) and the Lothian Birth Cohorts of 1921 and 1936 . All the individuals were of European ancestry. We used the annotation file from Price et al. (−8) were included in the SMR analyses. The cis region was defined as within 2 Mb of a probe in either direction.This study was approved by the Institutional Committee on Human Research at the Second Xiangya Hospital. The genetic association data for AF was obtained from the largest GWAS study , which ixy) is the ratio of the estimated effect of the instrument on the outcome (bzy) to that on the exposure (bzx). When testing for a DNAm/transcript–trait association, x, y, and z refer to DNAm/transcript, trait, and the top-associated cis-mQTL/cis-eQTL, respectively. When testing for a DNAm–transcript association, x, y, and z refer to DNAm, transcript, and the top-associated cis-mQTL, respectively. Bear in mind that SMR based on a single genetic variant is unable to distinguish between causality and pleiotropy. Therefore, the pleiotropic association identified should only be interpreted as an inference about causality. The heterogeneity in dependent instruments (HEIDI) test was done to test against the null hypothesis that there is a single causal variant underlying the association. We adopted the default settings in SMR . For SMR & HEIDI test between DNAm and transcript, we only focused on the analysis in the cis-region: only testing for associations between DNAm sites and gene expression probes that were in <2 Mb distance. For the SMR test, we used Bonferroni correction to account for multiple testing.The SMR test was introduced to test the association between the exposure and the outcome using a single genetic variant, usually the single nucleotide polymorphism (SNP), as the instrumental variable . DetaileTo functionally annotate the identified genes, we conducted gene ontology (GO) enrichment analysis using the “ClusterProfiler” package  in R sofP < 0.05/47 = 0.001 because 47 tissue groups were tested.We also conducted tissue-specific enrichment analysis (TSEA) using the “deTS” package  implemenIn the main analyses, we used eQTL and mQTL data from the blood samples as a proxy to heart tissue to maximize power for discovery. To test whether eQTL from blood could well-represent those from AF/heart-related tissues, we further performed the SMR analysis using eQTL data sets of right atrial appendage, left ventricle, and skeletal muscle from the GTEx Consortium.P < 0.05/8507). After the HEIDI test, 23 probes (corresponding to 22 genes) were not rejected at a threshold of 0.01 . In the analysis with DNAm data, we identified 167 DNAm probes (corresponding to 88 closest genes) associated with AF with FDR <0.05 , of which 80 DNAm probes (corresponding to 50 closest genes) passed the HEIDI test. By integrating results from transcriptome and methylome, 66 genes were finally pinpointed and 6 of them existed in both two omics levels  and left ventricle (P = 6.4 × 10−3) after multiple testing and were enriched in skeletal muscle at a threshold of P = 0.05.GO annotation  enrichment analyses were conducted to clarify the biological functions of the 66 identified genes. The enriched GO annotations included muscle system process (GO:0003012) and actin filament-based movement (GO:0030048) in the BP category; cell leading edge (GO:0031252), axolemma (GO:0030673), neuron spine (GO:0044309), and voltage-gated potassium channel complex (GO:0008076) in the CC category; phosphatidylinositol bisphosphate binding (GO:1902936), superoxide-generating NAD(P)H oxidase activity (GO:0016175), voltage-gated potassium channel activity (GO:0005249), and actin-binding (GO:0003779) in the MF category. TSEA analysis showed the identified genes were significantly enriched in right atrial appendage . Thirty-five DNAm probes were detected to show significant pleiotropic associations with 12 gene expression probes at FDR <0.05, of which 13 pairs passed the HEIDI test (corresponding to 10 DNAm and 8 gene probes). These pairs could be explained by the median model  that thein vitro and in vivo in cardiomyocytes and decreased cardiac function  and HEIDI test (P > 0.01) in at least one of the 3 eQTL data sets. The high replication rate verified the feasibility of using blood sample as a proxy to identify genes.To examine whether blood eQTL data are good representatives of eQTL from other AF/heart-related tissues, we conducted the SMR analysis for AF using 3 eQTL data sets from the GTEx, including right atrial appendage, left ventricle, and skeletal muscle. Only 14 of the 22 genes were available in the SMR and HEIDI tests. Of the 14 genes, 10 genes (71.4%) passed both the SMR test (https://www.ebi.ac.uk/gwas/) including CASZ1, CAV2, CDKN1A, CEP68, COG5, CPEB4, HCN4, IL6R, KCNH2, KDM1B, LRIG1, MAPT, NEURL, NKX2-5, PHLDB2, PITX2, PPFIA4, PTK2, RPL3L, RPS2, SOX5, SPATS2L, SSPN, SYNE2, SYNPO2L, and TNFSF13. Our study added their roles in AF at the epigenomic or transcriptomic level. For instance, an increased transcript expression level of interleukin 6 receptor (IL6R) was associated with increased AF risk in the eQTL & GWAS analysis (b_SMR = 0.184). This is consistent with the well-established role of the IL6/IL6R inflammatory signaling as a biomarker of cardiovascular events risk  in cardiac tissue. Decreased expression of KCNIP2 was observed in the right atrial appendage of AF patients  can be found in the article/YL and BL performed the bioinformatic analysis and were the major contributors in writing the manuscript. YM and YH made important modifications to the manuscript. YL, FO, and QL designed the research project and created the final revision of the manuscript. All authors read and approved the final version of the manuscript.This work was supported by grants from the National Natural Science Foundation of China (Grant Nos. 81770337 and 82070356).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.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.


N = 20,190) from the UK Biobank, we provide estimates of the heritability of gray-matter volume in 74 regions of interest (ROIs) in the brain and we map genetic correlations between these ROIs and health-relevant behavioral outcomes, including intelligence. We find four genetically distinct clusters in the brain that are aligned with standard anatomical subdivision in neuroscience. Behavioral traits have distinct genetic correlations with brain morphology which suggests trait-specific relevance of ROIs. These empirical results illustrate how MGREML can be used to estimate internally consistent and high-dimensional genetic correlation matrices in large datasets.Human variation in brain morphology and behavior are related and highly heritable. Yet, it is largely unknown to what extent specific features of brain morphology and behavior are genetically related. Here, we introduce a computationally efficient approach for multivariate genomic-relatedness-based restricted maximum likelihood (MGREML) to estimate the genetic correlation between a large number of phenotypes simultaneously. Using individual-level data ( Ronald de Vlaming and Eric Slob et al. present MGREML, a multivariate tool to estimate pairwise genetic correlations between multiple traits. They apply MGREML to UK Biobank data for 74 brain imaging phenotypes and 8 behavioral traits, demonstrating that these phenotypes have distinct genetic correlations with brain morphology. For example, reduced gray matter density has been implicated in a wide range of neurodegenerative diseases and mental illnesses5. In addition, differences in gray matter volume have been related to cognitive and behavioral phenotypic traits such as fluid intelligence and personality, although results have not always been replicable7.Global and regional gray matter volumes are known to be linked to differences in human behavior and mental health8, that include both the MRI scans and genetic data have enabled recent studies to discover the genetic architecture of human variation in brain morphology and to explore the genetic correlations of brain morphology with behavior and health13. These studies have demonstrated that all features of brain morphology are genetically highly complex traits and that their heritable component is mostly due to the combined influence of many common genetic variants, each with a small effect.Variation in brain morphology can be measured noninvasively using magnetic resonance imaging (MRI). Large-scale data collection efforts, such as the UK Biobank14. As a consequence, the genetic correlations of regional brain volumes with each other, as well as with human behavior and health have remained largely elusive. However, such estimates could advance our understanding of the genetic architecture of the brain, for example, regarding its structure and plasticity. Similarly, a strong genetic overlap of specific features of brain morphology with mental health would provide clues about the neural mechanisms behind the genesis of disease17.A corollary of this insight is that even the currently largest possible genome-wide association studies (GWASs) were only able to identify a small portion of the genetic variants underlying the heritable components of brain morphology: The vast majority of their heritability remains missing19 and comparable multivariate approaches24. Moreover, we show that MGREML allows for stronger statistical inference than methods that are based on GWAS summary statistics, such as bivariate linkage-disequilibrium (LD) score regression (LDSC)26. In short, MGREML yields precise and internally consistent estimates of genetic correlations across a large number of traits when existing approaches applied to the same data are either less precise or computationally unfeasible.We developed multivariate genomic-relatedness-based restricted maximum likelihood (MGREML) to provide a comprehensive map of the genetic architecture of brain morphology. MGREML overcomes several limitations of existing approaches to estimate heritability and genetic correlations from molecular genetic  data. Contrary to existing pairwise bivariate approaches, MGREML guarantees internally consistent  genetic correlation matrices and it yields standard errors that correctly reflect the multivariate structure of the data. The software implementation of MGREML is computationally substantially more efficient than both the traditional bivariate genomic-relatedness-based restricted maximum likelihood (GREML)13. Our analyses are based on data from the UK Biobank brain imaging study27.We leverage the advantages of MGREML by analyzing brain morphology based on MRI-derived gray matter volumes in 74 regions of interest (ROIs). We also estimate the genetic correlations of these ROIs with global measures of brain volume and eight human behavioral traits that have well-known associations with mental and physical health. The anthropometric measures height and body-mass index are also analyzed, because of their relationships with brain size28. Another class of methods is based on individual-level data, such as GREML and variations of this approach33. Methods based on GWAS summary statistics such as LDSC26 and variants thereof34 can leverage the ever-increasing sample sizes of GWAS meta- or mega-analyses35. These methods are computationally efficient and benefit from the fact that GWAS summary statistics are often publicly shared37. However, the computationally more intensive methods based on individual-level data, such as GREML are statistically more powerful38. That is, the resulting estimates are more precise as reflected in the size of the standard errors.Several methods can be used to estimate heritabilities and genetic correlations from molecular genetic data on single-nucleotide polymorphisms (SNPs). One class of these methods is based on GWAS summary statistics39 and educational attainment40). The UK Biobank brain imaging study (Methods) is currently by far the largest available sample that includes both MRI scans and genetic data, often surpassing the sample size of most previous studies in neuroscience by an order of magnitude or more13. Therefore, this dataset is particularly suitable for our individual-level data analysis.Due to the high costs of MRI brain scans, GWAS meta-analysis samples for brain imaging genetics are still relatively small compared to GWAS meta-analysis samples for traits that can be measured at low cost . In mathematical terms, the resulting matrices can be indefinite. Although the correlation between two traits can vary between −1 and +1, their correlations with a third trait are naturally bounded. For a set of three traits, the solution is positive (semi)-definite when the correlations satisfy the following condition: rst denotes the correlation between traits s and t. This condition is violated, for instance, when pairwise correlations are estimated to be r12 = 0.9, r13 = 0.9, and r23 = 0.2. In fact, the genetic correlation matrix in the well-known atlas of genetic correlations is not positive semidefinite25. A second consequence of the pairwise bivariate approach is that the standard errors of the resulting genetic correlation matrix do not adequately reflect the multivariate structure of the data.Irrespective of whether one uses GWAS summary statistics or individual-level data, the use of bivariate methods poses another challenge when computing genetic correlation across more than two traits. In this case, the correlation estimates from bivariate analyses of all pairwise combinations of traits are often simply stacked, to form a ‘grand’ correlation matrix32 guarantees the internal consistency of the estimated genetic correlation matrix by adopting an appropriate factor model for the variance matrices (Supplementary Note 1). An important benefit of this approach is that estimates are always valid, in the sense that the likelihood is defined, even within the optimization procedure. Joint estimation also ensures that the standard errors of the estimated genetic correlations reflect the multivariate structure of the data correctly. Therefore, methods such as genomic structural equation modelling (genomic SEM)42 that use multivariate genetic correlation matrices as input may benefit from using MGREML results, by avoiding the potentially distorting pre-processing step of bending43 an indefinite genetic correlation matrix. To deal with the computational burden and to make MGREML applicable to large data sets in terms of individuals and traits, we derived efficient expressions for the likelihood function and developed a rapid optimization algorithm (Supplementary Note 1). In Supplementary Note 3, we show that MGREML is computationally faster than pairwise bivariate GREML. Moreover, comparisons with ASReml20, BOLT-REML23, GEMMA22, MTG224, and WOMBAT21 highlight the computational gains afforded by MGREML. That is, none of these software packages is able to deal with the dimensionality of our empirical application. Finally, a comparison of results obtained with MGREML with results obtained using LDSC shows that standard errors obtained with MGREML are 32.7–50.6% smaller, illustrating the substantial gains in statistical power afforded by MGREML.Our multivariate extension of GREML estimation44 reveals that ROIs in the heteromodal cortex  are less heritable than primary , subcortical and cerebellar regions .We used MGREML to analyze the heritability of and genetic correlations across 86 traits in 20,190 unrelated ‘white British’ individuals from the UK Biobank  is available in Supplementary Data rg = 0.17) and between the cerebellar and subcortical areas (average rg = 0.15). The largest negative correlations were present between the cerebellar and insular regions (average rg = −0.18) and between the cerebellar and frontal regions (average rg = −0.15)  Fig. . Figure rg = 0.21, corresponding standard error 0.088), a region that has been linked before to emotion regulation47. The genetic correlations of the ROIs with neuroticism . Although the phenotypic correlations between the analyzed ROIs and alcohol consumption are generally negative48, these particular brain regions are among those implicated in the affective response to drug cues based on the perception-valuation-action model49. For educational attainment and intelligence, the strongest correlations are found in the frontal lobe region . Figure 50. Genetic correlations of the ROIs with visual memory . Activity within the middle temporal gyrus has been linked before with reaction time51.We also used MGREML to estimate the genetic correlations between brain morphology and eight human behavioral traits that are known to be related to health and that have previously been studied in large-scale GWASs, as well as the anthropometric measures height and body-mass index. Statistically significant correlations are highlighted in Supplementary Data ism Fig.  and deprism Fig.  are geneory Fig.  are insi6. When analyzing absolute brain volumes of the ROIs rather than relative brain volumes , we indeed observe robust positive relationships between the absolute volumes of the ROIs on the one hand and height and intelligence on the other hand  and traits (T = 86) as in our main analysis. While the resulting estimates are fairly similar -definite genetic correlation matrix.We designed MGREML to estimate high-dimensional genetic correlation matrices from large-scale individual-level genetic data in a computationally efficient manner while guaranteeing the internal consistency of the estimated genetic correlation matrix. For comparison, we used pairwise bivariate GREML to obtain a genetic correlation matrix using the exact same set of individuals N = 0,190 and26 analyses to obtain a genetic correlation matrix using the pairwise bivariate approach for the same empirical application  and has obtained informed consent from its participants.Participants of this study were sourced from UK Biobank. UK Biobank is a prospective cohort study in the UK that collects physical, health, and cognitive measures, and biological samples (including genotype data) in about 500,000 individualsWe selected the 43,691 individuals with available genotype data from the UK Biobank brain imaging study who self-identified as ‘white British’ and with similar genetic ancestry based on a principal component analysis. After stringent quality control (Supplementary Note 4), we estimated pairwise genetic relationships using 1,384,830 autosomal common (Minor Allele Frequency ≥ 0.01) SNPs and retained 37,392 individuals whose pairwise relationship was estimated to be less than 0.025 (approximately corresponding to second- or third-degree cousins or more distant shared ancestry). From these unrelated individuals, we retained the 20,190 individuals  with complete information on all 86 traits in our analyses. The age of these individuals ranges from 40 to 72 years, and the average age is 54.79 years.62 to the intrinsic functional connectivity network44 with the highest overlap. These networks were earlier identified using functional magnetic resonance imaging44.A description of all the variables used in the empirical analyses is available in Supplementary Note 2. Mapping of each cortical region to a network of intrinsic functional connectivity Fig.  is basedy, the effect of single-nucleotide polymorphism (SNP) m on y is modelled as:yj is the phenotype of individual j and m on y, k vector of control variables with k×1 vector of effects β, and uj is the error term.In a genome-wide association study (GWAS) of quantitative trait y has mean zero and/or an intercept is included in the set of control variables, we can assume, without loss of generality, that SNPs are standardized in accordance with their distribution under Hardy–Weinberg equilibrium. That is, we define gjm denotes the standardized genotype for individual j and SNP m, and where fm denotes the empirical allele frequency of the same SNP. Now, gjmαm, where m. In addition, we can consider the contribution of all SNPs jointly using the following model:If mentclass2pt{minimM vector of standardized genotypes for individual j, α is the M×1 vector of effects, and εj is the error term in this model. For a sample of N individuals 32 as implemented in GCTA19, β is assumed to be fixed and SNP effects and errors are assumed to be random, viz., Gα is the total genetic contribution, which follows a A = M−1GG′ is the genomic-relatedness matrix (GRM), capturing genetic similarity between individuals based on all SNPs under consideration , it follows that A = d−1GDG′, with d being the sum of the diagonal elements of D, Eqs.   A in Eq.A, we can write the model in Eq.  is the N×k1 (N×k2) matrix of control variables for trait y1 (y2) with fixed effects β1 (β2), s and t, for s = 1, 2 and t = 1, 2. The Kronecker product (denoted by ‘⊗’) can be used to extend the model in Eq. , as follows63:For two quantitative traits, observed in the same set of t, denoted by rg) between traits s and t , are defined as:In this multivariate model, the SNP-based heritability -definite, by imposing an underlying factor model for both matrices. After optimization, standard errors can be calculated with a time complexity that scales linearly with the number of observations and quadratically with the number of parameters in the model . This optimization procedure is fully incorporated in MGREML, a command-line tool written in Python 3. We recommend using the GCTA-GREML power calculator65 for ex-ante power calculations, because the accuracy of estimates from MGREML and pairwise bivariate GREML is fairly similar ,,VG and VThe empirical results in this study have been obtained using the command-line tool MGREML. Supplementary Note 4 details the analysis pipeline that has been used to obtain the heritability and genetic correlation estimates.Further information on research design is available in the Peer Review FileSupplementary InformationDescription of Additional Supplementary FilesSupplemental Data 1Supplemental Data 2Supplemental Data 3Supplemental Data 4Supplemental Data 5Supplemental Data 6Supplemental Data 7Supplemental Data 8Reporting Summary


P. tomentosa, which has significant phenotypic and genetic variations according to climate region across its natural distribution. In total, we detected 108 significant single-nucleotide polymorphisms, annotated to 88 candidate genes for lenticel, of which 9 causative genes showed significantly different selection signatures among climate regions. Furthermore, PtoNAC083 and PtoMYB46 showed significant association signals and abiotic stress response, so we overexpressed these two genes in Arabidopsis thaliana and found that the number of stem cells in all three overexpression lines was significantly reduced by PtoNAC083 overexpression but slightly increased by PtoMYB46 overexpression, suggesting that both genes are involved in cell division and expansion during lenticel formation. The findings of this study demonstrate the successful application of an integrated strategy for dissecting the genetic basis and landscape genetics of complex adaptive traits, which will facilitate the molecular design of tree ideotypes that may adapt to future climate and environmental changes.The stem lenticel is a highly specialized tissue of woody plants that has evolved to balance stem water retention and gas exchange as an adaptation to local environments. In this study, we applied genome-wide association studies and selective sweeping analysis to characterize the genetic architecture and genome-wide adaptive signatures underlying stem lenticel traits among 303 unrelated accessions of  Populus trichocarpa and altered the frequency of its genomic variations [Many long-lived perennial trees show local adaptation responses to multiple abiotic or biotic factors across large geographic ranges, spanning widely variable spatial environments . A serieriations ,6. A deeriations ,7.Ulmus and Quercus, whereas lenticels are clear and observable in species such as Prunus persica and Populus tomentosa. Anatomically, the lenticel is a type of filling tissue that can be produced annually; it arises from phellogen, which is continuous with the surrounding corky periderm, but more active  are suitable for examining the genetic basis of the complex traits of lenticels ,5,19,20.Populus comprises 25–35 commercially and ecologically important species of deciduous woody plants native to large geographic areas of the Northern Hemisphere [Populus species, but some species display rich phenotypic variation, including P. tomentosa, which typically shows diamond-shaped lenticels on its smooth bark but diverse stem lenticel sizes and numbers across its broad geographic distribution in China  of variation in lenticel morphology in a natural population of 303 unrelated 2), lenticel number (LN), and ratio of the total lenticel area to the total area of a rectangle (RA). All three lenticel traits showed considerable phenotypic diversity in the natural population, with coefficients of variation of 26.2–39.6% (p = 4.86 × 10–40) to 0.549 (LN: p = 4.67 × 10–56) , whereas LA and LN had a weak negative correlation . LA had a weak positive correlation with diameter at breast height  . CorrelaNext, we compared phenotypic variation in the three lenticel traits between subpopulation pairs from three corresponding climate regions, which we presume to have adapted to their different environments . We detePopulus tomentosa accessions. 112 significant associations (p < 5.318 × 10–8) were identified among 108 SNPs and three lenticel traits, and each SNP explained 5.3–17.9% of the phenotypic variation , significantly interacted with five unique SNPs for the three lenticel traits (Ptom.009G.00386 (PtoMYB46), for LA. The genotype combination of T (Chr3_4043365) and G (Chr9_3551378) showed the minimum LA, whereas that of C (Chr3_4043365) and A (Chr9_3551378) showed the maximum LA , which had a significant SNP (G/T) for LA (p = 1.288 × 10–8), showed obvious differences among the subpopulations, in that the mutant allele (T) was dominant in the NE subpopulation but was missing from the NW and S subpopulations  was decreased in frequency in the low-latitude S subpopulation compared with the NE and NW subpopulations. Similar differences were observed in Chr17_4717361, Chr2_18582313, and Chr6_6559040, which are presumably mutant alleles with higher frequencies in northern China , which influences multidimensional cell growth and plant cell wall biogenesis in A. thaliana [KTN1 was strongly associated with LN (p = 7.5907 × 10–9) and was located in the selective signal peak with strong π and FST outliers  or high-LD haplotypes of multiple top-ranked associated SNPs underlying lenticel traits in Populus, which may represent causative signals for each trait (PtoTraB2 (Ptom.007G.00998), which was strongly associated with LN (p = 1.15 × 10–10) and is likely to be a causative gene within the associated signal (TraB1 (Ptom.007G.00997), another member of the TraB family in P. tomentosa, was found to be located 634 bp up.Our GWAS revealed the most significant variants involved in lenticel variation, including the top SNPs  of PtoTraB1 and PtoTraB2, was subjected to candidate gene-based association fine mapping. A newly identified InDel  had the most significant association with the LN (p = 1.67 × 10–8), which was in high LD with the SNP Chr7_10376607 (r2 = 0.96), identified in GWAS (PtoTraB2 than those of the double WT haplotype homozygous (CTTATTCT-A) accessions. The common haplotype heterozygote (CTTATTCT/C-A/C) had moderate LN values compared with the double homozygote groups, which revealed an additive effect of the haplotype on lenticel variation  were examined statistically. Four significant associations were detected in two traits, representing a known SNP (Chr3_4043365) and a new InDel (Chr3_4043376) in PtoNAC083  and RA (p = 5.16 × 10–8). qRT-PCR showed significantly higher PtoNAC83 expression in accessions with common genotype (AGG) than in the accessions with minor genotype (A), revealing that a causal variants InDel Chr3_4043376 is most likely a causal, or is in high LD with the causal variants responsible for the natural of LA, as well as PtoNAC083expression  and drought stress population (constituted with 100 accessions).To identify the possible causative variants within the representative toNAC083 C,D. The pression C,D,F. In, for LA . These t, for LA F, which  regions . We furtomentosa . The expironment . NW is lPtoNAC083 and PtoMYB46 in A. thaliana and measured cell traits in three independent homozygous transformed lines , whereas those of PtoMYB46-overexpressing plants were thicker (119.40–155.71%) than those of WT plants  and net photosynthetic rate of overexpressing plants , with LA  for LA but a positive additive/dominant effect for RA (0.05/0.07) . Thus, tPtoNAC083, had significant epistatic interactions with Chr9_3551378, which is found in PtoMYB46. PtoNAC083 and PtoMYB46 also showed opposite tissue-specific expression patterns, indicating a potential interaction between the two genes, which is consistent with prior studies of A. thaliana reporting that the homologous NAC083  inhibits transcriptional activation of MYB46 in the regulatory network of xylem vessel formation [Epistatic interactions generally define the non-additive interactions between two associated loci . Our epiormation . Epistasormation . Interesormation ,42; thesormation . The fouormation , and theP. tomentosa spanning wide climate gradients with significant spatial and geographic structures [P. tomentosa , which is involved in trehalose catabolism and plays an important role in the plant responses to cold and salinity stresses [In this study, we identified 9 genes with overlapping GWAS and selective sweep signals , suggestbidopsis . The homtabolism . The homstresses . Based ostresses . Therefostresses . Second,stresses .P. tomentosa. Thus, the geographic distribution of favorable alleles sheds light on the potential for tree improvement in future breeding programs.We further investigated population differences in the allele frequency and geographic distribution of gene loci with major effects among candidate genes, with overlapping GWAS and selective sweep signals D–G. The Populus lenticel traits using a single GWAS. These genes may be involved in multiple biological processes, from secondary cell wall biogenesis and stress response to growth regulation and environmental adaptation  was simultaneously associated with LA and RA , selective sweep, gene expression profiling, and al trees . In thisP. tomentosa accessions [n = 102), northwestern , and northeastern  climate regions of China [The association population used in this study consisted of 303 10-year-old accessions that were planted in a common garden in Guan Xian County, Shandong Province, China . In 2009, the association population was asexually propagated via root segments and planted in a randomized complete block design with three replications (blocks). The association population was randomly selected from a collection of 1047 natural cessions , represeof China .2), lenticel number (LN), and ratio of the total LA to the total area of a rectangle (RA) within the sampling area (5 × 10 cm) using ImageJ software [A digital camera (Canon EOS 800D) was used to capture images of lenticels at a tree height of 1.35 m for 303 accessions in July 2019. To control environmental effects, phenotypes of all 303 accessions (each accession contains three replications) were measured from the directions , and the mean value of 12 data (3 replicates × 4 directions) represented the final phenotypes of each accession. A rectangle with a horizontal width of 5 cm and vertical length of 10 cm was used as the standard sampling area (only lenticels within the range are counted). Images were analyzed to estimate the single lenticel area  < 0.2. The resulting 410,171 SNPs were used for kinship and population estimates. Varying levels of K (1–13) in our association population were calculated using the Bayesian clustering program fastStructure, and an optimal K value of 3 was selected using the chooseK.py script. Therefore, we used the K = 3 cluster result for the Q matrix as a covariant in our GWAS. We calculated genome-wide LD across the P. tomentosa genome using all SNPs with a MAF > 0.05 using the command “plink—vcf vcf_file—allow-no-sex—maf 0.05—r2—ld-window 5000—ld-window-r2 0.2—out out_file” in the PLINK software [We filtered the common SNPs using two criteria: minor allele frequency (MAF) < 0.05 and rsoftware .2 > 0.7) in the TASSEL ver. 5.0 software [2 > 0.7) with the following parameters: indep-pairwise 50 5 0.7. The lenticel traits  used in GWAS were transformed by Z-score normalization. The population structure (Q) was evaluated using the STRUCTURE ver. 2.3.4 software [p ≤ 5.318 × 10–8 . Then, we defined 5-kb windows centered on each significant SNP. All gene models of P. tomentosa that overlapped a window were selected as candidate genes. When a window overlapped within more than one gene, we annotated the SNPs with multiple genes. LD was determined using the HAPLOVIEW ver. 4.2 software [We performed mixed linear model association analysis of three lenticel traits using the set of SNPs remaining after the removal of low-frequency SNPs (MAF < 5%) and LD-correlated SNPs  enrichments were determined by AgriGO (http://bioinfo.cau.edu.cn/agriGO/index.php (accessed on 15 August 2019)). Significant GO annotations for a list of genes were determined by FDR threshold p < 0.01.All genes uncovered by GWAS were annotated on the basis of the gene models of the p ≤ 5.318 × 10–8) according to GWAS were used for epistasis analysis. Interactions among different loci were orthogonally decomposed into four components: additive × additive, additive × dominant, dominant × additive, and dominant × dominant interactions.To detect epistatic effects of locus pairs on lenticel traits, we used the EPISNP ver. 2.0 module integrated with the epiSNP software package for Windows, based on the extended Kempthorne model . Only SNP. tomentosa clone ‘741’, with three biological replicates. Total RNA was extracted from shoot apical meristem (peak), leaves, bark, phloem, cambium, developing xylem, mature xylem, and roots using cetyltrimethylammonium bromide lysis buffer and the RNeasy Plant Mini kit  according to the manufacturer’s instructions. High-quality RNA, determined using a nanodrop spectrophotometer , and Qubit 2.0 fluorometer  were used for cDNA library construction according to the sample preparation instructions (Illumina). Paired-end sequencing was performed using the Illumina HiSeq 4000 platform (Illumina) following the user’s manual, and 150-bp paired-end reads were generated.We collected apexes, leaves, bark, phloem, cambium, developing xylem, mature xylem, and roots from the 5-year-old P. tomentosa, nucleotide diversity (π) and genetic differentiation (FST) statistics were calculated using a sliding window approach (5-kb window) in the PLINK [NW/πNE), NW and S populations (πNW/πS), and NE and S populations (πNE/πS) to select windows with the top 5% ratios and the maximum absolute value of FST as candidate regions for further analysis. We performed the same subpopulation comparisons as described in Du et al. (2019) [P. tomentosa [To identify signatures of putative genomic regions under adaptation among three climate regions of he PLINK . We scan. (2019) , becauseomentosa . FinallyP. tomentosa  with different genotypes and were immediately frozen in liquid nitrogen. Lenticel tissue was collected by scraping thin (∼0.5 mm) slices of the lenticel surface at a plant height of 1.35 m. 6510, 6338, and 4411 came from NW climate regions; 1701, 0001, and 0057 came from NE climate regions; 5016, 3717, and 3007 came from S climate regions. Total RNA was extracted from lenticel tissue with cetyltrimethylammonium bromide lysis buffer and the RNeasy Plant Mini kit  according to the manufacturer’s instructions. The gene-specific primers were designed using the Primer Express ver. 5.0 software. We performed qRT-PCR using the 7500 Fast Real-Time PCR System with SYBR Premix Ex Taq, as described in Du et al. (2015) [Populus Actin (EF145577) as the internal control, and the PCR program described in Zhang et al. (2011) [Lenticel tissues were collected from . (2015) . All rea. (2011) .PtoNAC083 and PtoMYB46 from the 1-year-old P. tomentosa clone ‘LM50’ were amplified and inserted into the pCXSN vector under the control of the 35S promoter and introduced into Agrobacterium GV3101. Wild-type (WT) A. thaliana (Col-0) plants were transformed using the floral dipping method. Individuals of the T3 generation were obtained using hygromycin-based selection (30 mmol/L). The expression levels of PtoNAC083 and PtoMYB46 in A. thaliana were measured in three independent homozygous transformed lines by qRT-PCR, and histological sections were used to verify the effects on the cells.Full-length cDNAs of P. tomentosa clone ‘LM50’ were used for anatomical observation of lenticels. A. thaliana samples obtained from the stem at a height of 2 cm were used to verify the effects on the cells (1 cm high in stem). FAA  were used to fix tissue materials. For cell diameter of A. thaliana, twenty cells from each sample were measured and the data averaged. Hypocotyls of 13-day-old A. thaliana were stained with 10 μ/mL propidium iodide (PI) for 1 min and imaged using a confocal laser scanning microscope .Barks from the young stem of 1-year-old 2 assimilation, transpiration rate, and instantaneous leaf water-use efficiency (WUE) (Pn/Tr) of 30-day-old plants were measured by the LI-6400 portable photosynthesis system . The measurements were taken on a fully expanded rosette leaf (six per line) at chamber temperature 23 °C and an ambient CO2 concentration of 360 µmol mol−1.Net CO


ZBED9 associated with HTN in the genome-wide borderline threshold after adjusting for different sets of environmental predictors. Moreover, strong signals on ABHD17C and suggestive signals on FBN1 were detected for DBP and SBP, respectively, while these signals were not consistent in different GWA analysis. Our finding on ZBED9 was confirmed for all BP traits by linkage analysis in an independent sample. We found significant associations with similar direction of effects and allele frequency of genetic variants on ZBED9 with DBP (genome-wide threshold) and HTN  in GWAS summary data of UK Biobank. Although there is no strong evidence to support the function of ZBED9 in blood pressure regulation, it provides new insight into the pleiotropic effects of hypertension and other cardiovascular diseases.High blood pressure is the heritable risk factor for cardiovascular and kidney diseases. Genome-wide association studies(GWAS) on blood pressure traits increase our understanding of its underlying genetic basis. However, a large proportion of GWAS was conducted in Europeans, and some roadblocks deprive other populations to benefit from their results. Iranians population with a high degree of genomic specificity has not been represented in international databases to date, so to fill the gap, we explored the effects of 652,919 genomic variants on Systolic Blood Pressure (SBP), Diastolic Blood Pressure (DBP), and Hypertension (HTN) in 7694 Iranian adults aged 18 and over from Tehran Cardiometabolic Genetic Study (TCGS). We identified consistent signals on  In the analysis of world data, one in three adults is expected to be affected by high blood pressure up to 2025, while macro or microvascular complications are not limited to extreme ranges of systolic and diastolic blood pressures (SBP and DBP)3. Elevated BP is categorized into primary and secondary hypertension based on disease etiology. While the latter is secondary due to other diseases, some medications, and related side effects, the former is known as essential hypertension and is prevalent in all populations5.High blood pressure as a heritable risk factor with direct and indirect effects on the incidence of cardiovascular diseases (CVD), stroke, and chronic renal failure is the leading cause of morbidity and mortality worldwide7. Emerging new genotyping technologies has facilitated studies with Genome-Wide Association (GWA) design in non-communicable and infectious diseases to detect risky or preventive genetic variants after adjusting for environmental risk factors. Promising results from these hypothesis-free studies addressing different pathophysiologic pathways and gene targets for future pharmacologic interventions in complex diseases8.Genetic and environmental risk factors such as age, obesity, and lifestyle are attributed to the heterogeneity of essential hypertension risk in the general population10.The majority of the genomic findings on BP traits show pleiotropic associations. Until recently, these findings were predominantly from Europe-centric studies, have not been translated into understanding of underpinning molecular pathways, new drug development and personalization of HTN treatment in diverse populations with different genetic backgrounds11. A recent review addressed evidence concerning the origin of non-generality of GWAS findings on BP traits in different ethnic groups. Accordingly, a large proportion of GWAS was conducted in individuals with European ancestry, and it was introduced as a major cause of low reproducibility of findings in other populations12.From the viewpoint of genomic architectures, different distribution of risk allele frequencies are contributed to the non-generality of findings between ethnicities14. High blood pressure is main risk factor for CVD events in the Iranian population and its prevalence among CVD cases aged over 18 years is between 17.3% to 20%16. In comparing super populations in 1KG project including, Europeans, East Asians, South Asians, Africans and Hispanics, a large number of novel variants with a higher degree of genomic specificity were identified in the Iranian population17. Heritability estimations on BP traits provide evidence to support role of genetic variants are playing on BP regulation in this population18. However, the Iranian population has not been represented in international genomic databases to date, so there is no evidence concerning the effect(s) of genetic variants in this population. To fill the gap, we conducted the first study using GWA design to investigate the replication or discovery of genetic variants on the Iranian population's BP traits.Iran has experienced the epidemiological transition from communicable to non-communicable diseases during the last four decades, and higher prevalence rates of pre-hypertension (47.3%) and hypertension (22.6%) have been reported in both adults and adolescents in recent years20.The Tehran Cardiometabolic Genetic Study (TCGS) is a family-based genetic analysis of the Tehran Lipid and Glucose Study (TLGS) as the oldest Iranian cohort. In total, more than 20,000 participants are followed in 7 phases since 1999 and underwent a clinical examination on more than 230 metabolic-related traits in each phase of the study. The Medical Ethics Committee of Shahid Beheshti University of Medical Sciences approved this study and all methods were performed in accordance with the relevant guidelines and regulations. All participants gave written informed consent to participate in the original cohort and TCGS. In the case of younger participants, written formal consent was obtained from the parents or guardians. Principles for clinical investigations could be found in the original TCGS and TLGS papersIn the present study, 17,462 participants from the five phases of TCGS (1999–2014) were recruited to investigate hypertension. More details on DNA sample collection, genotype quality control process, phenotype measurements, covariate imputation, case definition, and selection criteria are explained in the supplementary information 1.21 . Moreover, related subjects with Identity By Decent (IBD) ≥ 18.5% were excluded from the study. In the genotype level, variants with minor allele frequency (MAF) < 1%, missing genotype calls > 5%, and Hardy Weinberg Equilibrium (HWE) P value < 1e−6 for quantitative traits, P value < 1e−10 in the HTN cases and P value < 1e−6 in controls were filtered out  of genotyping data was implemented on a per-individual basis before per-marker using PLINK version 1.9 and R2 > 0.8), linear and logistic regression tests were performed. To explore any associations between BP traits and genetic variants, the covariates and top 5 Principal Components(PC) were adjusted and additive and overdominant inheritance models on autosomal chromosomes were checked for GWAS on the quantitative and binary traits in PLINK v1.9, respectively. A conventional genome-wide threshold of 5 × 10–8 considered for a significant P value. The genomic inflation factor was computed for each analysis, and observed versus expected P values were highlighted in the Q–Q plots to check for population stratification. Finally, four regression-based multivariate analyses evaluated the predictive accuracy of initial GWAS outputs for quantitative traits on discovery dataset and also an independent sample of TCGS (N = 2799)24. Accordingly, Polygenic Risk Score (PRS) was calculated after adjusting for the covariates and top 5 PCs, to evaluate proportion of variance, which is explained by genomic variants (R2). We used a standard pipeline to evaluate R2 through calculating PRS on our GWAS summary data. Moreover, discrimination of best fit PRS was assessed for each analysis according to sex. The PRS was computed in the following steps by PLINK and R:i.2 > 0.25 were filter out,Pruning: A window size of 200 variants, sliding across the genome with step size of 50 variants at a time were considered, and any SNPs with rii.2 > 0.2) with P value ≤ 1 and only index SNP captured,Clumping: SNPs within 250 k of the index SNP were considered for clumping. Then, all SNPs are correlated with each other were removed  for inclusion of SNPs in the PRS was extracted,Seven iv.P values for SBP and DBP,PRS generated in different ranges of v.P values thresholds,To find "best-fit" score for the quantitative traits, PRS calculated in different ranges of vi.2 as an index of trait variance explained by genomic variants calculated in different ranges of P values thresholds for two sets of covariates, ,Rvii.2 was plotted by different ranges of P values using "ggplot2",Calculated Rviii.2 by sex using "ggplot2".The best-fit PRS was plotted against quantitative traits based on best result of RAfter checking collinearity for all covariates  with a r2 > 0.2, the effects of significant independent SNPs in the initial GWAS were tested in the presence of the same covariate sets using two-level Haseman-Elston regression model by SAGE version 6.426. This model-free linkage analysis is mathematically equivalent to the likelihood-based score test in variance component linkage analysis, which compute a random effect for genetic variants with T score test.The confirmation study was conducted on 1618 participants in 210 selected TCGS families with an age range of 1 to 93 and familial aggregation of two or more affected (HTN) casesP values less than 1 × 10–4. In the first step, chromosomal coordinates, genes, transcripts, and variants on protein sequence were annotated in Ensemble Variant Effect Predicator27. In the second step, GWAS catalog information was retrieved to identify the association of specified loci on BP traits28. Moreover, we sought to map known BP loci by assessing these loci's functional consequences in Ensemble29. Functional analysis was performed for those variants with consistent results and at least a P value less than 5 × 10–7 after adjusting for different covariate sets. In the case of a similar locus, ldlink browser by National Cancer Institute and LinDA (LINkage Disequilibrium-based Annotation) browser were checked for LD of detected and previously reported variant(s) in all populations using the website http://analysistools.nci.nih.gov/LDlink/ and http://linda.irgb.cnr.it/, respectively. In the third step, a list of detailed information on all loci was retrieved separately in Open Targets POST GWAS, including disease associations, protein interactions, pathways, similar targets based on diseases in common, RNA, and protein baseline expression by the anatomical system and organ30. In the final step, the overall association score was retrieved for each locus and other genes whose protein products interact with new loci through protein interaction networks32. Open Target Platform provides the score from 20 data sources, is ranged from 0 to 1, that the former implies no evidence and later corresponds to the most reliable evidence supporting evidence based on frequency, severity, and significance of association33.Four consecutive steps were followed to explain probable functions and pathophysiologic pathway(s) of discovered variants' effects on BP with Table ABHD17C detected for DBP corresponding to the genome-wide significance threshold. Moreover, suggestive signals within 6p22.1 (ZBED9) and FBN1 with borderline P values detected with the HTN and SBP traits, respectively . The associations of other genetic variants on ZBED9 with QRS amplitude and interval in Europeans and also ABHD17C and FBN1 with BP traits or cardiovascular diseases in Europeans and East Asians were previously reported in GWAS literature. However, there is no correlation (LD) between detected and previously reported genetic variants in the same locus  and HTN  in UK Biobank, while the associations were not significant  in Japanese population (Table 2 > 0.4) and BP traits in the region. Except for Africans, allele frequencies of detected variants on ZBED9 are similar in Europeans, South Asians, Middle East(Qatari) and Iranian population, while the allele frequencies of detected variants on FBN1 and ABHD17C is less than 1% in Europeans  of detected variants on on Table . We chec2 in the independent sample of TCGS was much lower compared to discovery dataset (≤ 1%) and there was low correlation between the PRS and BP traits by sex . In further investigation via sentinel variants, we found rs853684 on ZSCAN31 as a missense consequence in moderate LD (r2 > 0.60) and five missense variants in perfect LD (r2 = 1.0) with detected variants on ZBED9 . Further, the protein interaction network suggests gene subnetworks for ZBED9 with relatively low association with BP and cardiovascular diseases , respectively.The cumulative evidence for association with BP traits for three detected loci was retrieved in the Open Targets POST GWAS. The overall association scores were summarized in Fig. ZBED9, FBN1, and ABHD17C, associated with HTN, SBP, and DBP, respectively. We found significant associations with similar direction of effect and allele frequency of detected variants on ZBED9 with DBP (genome-wide threshold) and HTN  in UK Biobank. However, There is no LD between detected and previously reported genetic variants on three loci in Europeans and other populations including, South Asian, Hispanic or Latin American, East Asian, African American, and Afro-Caribbean populations. The detected signals were not consistent after adjusting for two different sets of covariates on FBN1 and ABHD17C. On the other hand, the association of genetic variants on ZBED9 and BP traits has not been previously reported in other populations, while their effects were consistent after controlling for environmental factors by two different GWA analysis. Further, the confirmed association of genetic variants on ZBED9 in discovery study with SBP, DBP, and HTN in an independent sample of TCGS families substantially increase our knowledge of its effects on HTN in the Iranian population.To our knowledge, this is the first GWAS to examine genetic associations with BP traits in the Iranian population. This study identified suggestive and strong signals on 30.In an effort to explain the function of new loci by RNA and protein baseline expression, the rate of expression for all detected loci was in the range of low to moderate in the circulatory system, including blood, endothelial cells of the umbilical vein, aorta, coronary artery, left heart ventricle, tibial artery, atrium auricular region, and heart muscle, provided by Human Protein Atlas and Expression AtlasZBED9 on BP regulation in the literature. However, GeneHancer-gene association results indicate some known BP loci, including EBF1, NR2F2, SOX5, and PRDM6, are likely acting as transcription factor binding sites or gene targets for ZBED946. In this way, significant association with QRS interval and amplitude as a surrogate of myocardial mass in European population41 and replication with SBP, DBP and also HTN using different criteria based on reference adjusted curves in the Iranian children and adolescents47, who was included in TCGS confirmation study is a signal for further investigations on the functional role of ZBED9 in BP regulation pathway in other populations.There is no evidence to support the functional effects of 48. Accordingly, gene-set-based analysis by Open Target Post GWAS highlighted the probable pleiotropic effects of detected loci, which are likely acting as a common genetic etiology for BP and cardiovascular diseases. Accordingly, the lethal cardiovascular diseases may affect allele frequency of effective variants with advancing age in our study due to survival reduction, so overlook the effect of functional variants in older groups as a competing risk50.High blood pressure is directly associated with vascular mortality due to stroke, cardiomyopathy, aneurysm, cardiac hypertrophy, aorta stenosis, sudden cardiac arrest, and myocardial infarction51. However, there was inadequate statistical power to conduct GWAS by sex after removing related individuals in our study. Moreover, the calculated PRS for the quantitative traits showed similar patterns by sex. Second, despite the high predictive accuracy of PRS by genomic variants and confirm significant SNPs on ZBED9 with family-based regression analysis, it was a case for overfitting due to selecting individuals with similar genetic background from the TCGS cohort. Accordingly, our findings validated in an independent sample of the Iranian population. However, our results still needs to be cross-validated in a different sample of the Iranian population as well52. Third, using a family-based design for GWAS has the advantages of complete robustness against genetic heterogeneity. However, family-based designs are likely biased due to population substructures, and association tests yield at the price of inflated type I error and reduce statistical power54. In this way, we included independent samples from a family bases cohort.We acknowledge that some limitations are evident in our study. First, there is evidence for significant differences in the magnitude of genetic variants effects on high blood pressure between men and women55. Finally, we found signals which were not in LD with previously reported genetic variants on BP traits. Further investigation is necessary to fine mapping of these regions when imputed variants on the Iranian genome become available56.Additionally, there was no association between autozygosity and BP quantitative traits in an international meta-analysis, including TCGS families with a relatively high inbreeding rateZBED9 and BP traits has not been previously reported in other populations. Their effects were consistent after controlling environmental factors by two different GWA analyses and confirmed in a family-based linkage study. Although there is no strong evidence to support the function of ZBED9 in blood pressure regulation by Open Target Post GWAS, its association with QRS interval and QRS amplitude as surrogates of myocardial mass may provide new insight into pleiotropic effects of hypertension and other cardiovascular diseases.We identified three loci, of which two loci were previously reported in individuals with European and Non-European ancestries. The association of genetic variants on Supplementary Information 1.Supplementary Information 2.Supplementary Information 3.


Despite the fact that imbalance between case and control groups is prevalent in genome-wide association studies (GWAS), it is often overlooked. This imbalance is getting more significant and urgent as the rapid growth of biobanks and electronic health records have enabled the collection of thousands of phenotypes from large cohorts, in particular for diseases with low prevalence. The unbalanced binary traits pose serious challenges to traditional statistical methods in terms of both genomic selection and disease prediction. For example, the well-established linear mixed models (LMM) yield inflated type I error rates in the presence of unbalanced case-control ratios. In this article, we review multiple statistical approaches that have been developed to overcome the inaccuracy caused by the unbalanced case-control ratio, with the advantages and limitations of each approach commented. In addition, we also explore the potential for applying several powerful and popular state-of-the-art machine-learning approaches, which have not been applied to the GWAS field yet. This review paves the way for better analysis and understanding of the unbalanced case-control disease data in GWAS. Over the past ten years, genome-wide association studies (GWAS) have shown great potential in investigating the biological and genetic etiology of disease, with the aims of providing better understanding, prevention, and treatment of diseases. As the cost of genotyping decreases, GWAS research moves to a new level, as phenome- wide association studies (PheWAS) enable thousands of phenotypes constructed from electronic health records (EHRs) and biobanks involving tens of millions of variants for hundreds of thousands of participants in large cohorts ,2,3,4. THere we first give a few examples, from slight, moderate, to extreme imbalance ratios: The Wellcome Trust Case Control Consortium (WTCCC) provides a series of GWAS datasets that include 2000 case samples from each of seven common diseases   errFirstly, the error rate has been widely used as an accuracy metric in the classification literature. However, it averages all observations without treating them differently, under the assumption that the samples in minority class have equal importance as those in majority class. As a result, it always favors the majority class . For exaHowever, in the GWAS field with binary traits, people care more about the cases (the disease status) than controls . Therefore, it is more serious to misclassify a case compared to misclassifying a control. To overcome the problem of error rate, weighted loss function or AUC (the area under the ROC curve) has been used . The ROCSecondly, ideally speaking, the training distribution should be the same as the test distribution, but in the unbalanced case, it is relatively more likely to get different distributions between the training and test data when randomly splitting an unbalanced data set. For example, it is possible that the training data are highly unbalanced but that the test data are balanced; or in some extreme cases we may end up with no samples from the case group in training or test data. Under this circumstance, even when equipped with correct accuracy metric a classifier will not work well. To tackle this problem, sampling strategies such as over-sampling/under-sampling have been applied to make the data more balanced. However, over-sampling may increase the model and computation complexity, which is a burden for the GWAS data when millions of genetic variants are involved. Meanwhile, under-sampling may yield less information than we should have. See Zhou (2013)  for a deThirdly, contamination may destroy the sample, and in particular for rare diseases it is always difficult to get enough case samples. When the number of minority classes is too small, we may have insufficient data for the classifier to learn, and thus yield bad results. As pointed out by Sammut and Webb , a ratioLastly, a very low case-control ratio in GWAS data may violate asymptotic assumptions of statistical inferences, such as that of the logistic regression models, which results in an inflated type I error rate . For exap-value, which makes a truly influential variant insignificant.More specifically, unbalanced data may violate assumptions in statistical inferences. If the number of cases is drastically smaller than the number of controls, these cases may be viewed as outliers in most statistical models, and hence it leads to a higher variation for the estimation of coefficients. As a result, it shrinks the absolute value of test statistics and yields a larger X. In a balanced setting, we generate 50 controls from Uniform  distribution, and remaining 50 cases from Uniform . We then generate an intermediate variable W = X + ε where ε follows standard normal distribution. Finally, we connect the response with the only true predictor through an indicator function asLet us illustrate the idea using a simulation example. Suppose our sample size is 100 and we only have one true predictor X in both balanced and unbalanced settings. We repeat 100 times and obtain the standard error and p-value of the coefficient of the true predictor X. From the results demonstrated in p-value for the unbalanced data is almost ten times higher and it leads to a wrong conclusion that X is not significant.In an unbalanced setting, we generate 90 controls from Uniform , 10 cases from Uniform , and follow the same procedure to generate the binary response Linear mixed models (LMMs) has become popular in GWAS for various biomedical traits because of its power in correcting for population structure and genetic relatedness ,43,44,45iπ = P; iy ∈ {1, 0} is the probability for a binary disease status phenotype to be a case for subject i, conditional on their covariates iX, genotype iG, and random effects ib. iX is a 1 × p row vector of covariates for subject i, α is a p × 1 column vector of fixed covariate effects, iG is the genotype of a genetic variant for subject i, and β is the fixed genetic effect. The random effects b = {b1, b2,…, nb}n~N, where τ is a scale variance component parameter and V is usually the genetic relationship matrix (GRM) estimated from a large number of genetic variants.Chen et al.  applied H0:β = 0, which leads to the same null logistic mixed model for all genetic variants: logit(iπ) = iαX + ib. They fit the null logistic mixed model using the penalized quasi-likelihood (PQL) method [α and random effects ib under the null hypothesis until the process reaches convergence. The score of each genetic variant under the null hypothesis is defined as G = T is the n 1 column vector of genotypes, y = T is the n 1 column vector of observed outcomes, and 1, 2,..., n)T is the estimated value of y under H0. The asymptotic p-value of each genetic variant is obtained by assuming that the test statistic T asymptotically follows a Gaussian distribution.Chen et al.  also pro) method  and the ) method  to estimZhou et al.  claimed n. In addition, the SAIGE improved the calculation of the test statistic T. Specifically, the GMMAT test assumes that T asymptotically follows a Gaussian distribution under the null hypothesis, which only considers the first two moments (mean and variance). However, the underlying distribution of T can be substantially different from Gaussian distribution when the case-control ratio is unbalanced. The saddle point approximation is used to approximate the distribution of T using the entire cumulant generating function (CGF). Zhou et al. [p-value than the GMMAT does under the presence of unbalanced binary phenotypes.The SAIGE method still adopted the logistic mixed model structure from the GMMAT, but it improved the variance component and the test statistic to better account for imbalance of binary traits. Specifically, Zhou et al.  applied u et al.  claimed p-value based on a test statistic that was also used in the SAIGE, and they reported a large number of significant markers whose nearby genes have been verified as truly associated with the corresponding phenotypes by other studies. Compared to SAIGE and GMMAT, the logistic regression model used by Dey et al. [In simulation studies, Zhou et al.  showed ty et al.  did not Both the GMMAT and SAIGE are single-SNP methods. The single-SNP method assesses the potential association of each genetic variant in isolation from the others. As a result, multiple limitations have been found for single-SNP methods: they inflate both false-positive and false-negative results ; they haIn the remaining sections, we review several joint methods that have the advantages of considering multiple SNPs simultaneously.p >> n, where sparsity or regularization is expected. The selection is processed by putting sparsity-enforcing priors on the regression coefficients so that the coefficients of non-influential genes can be forced to zero.Various Bayesian approaches have been applied to the GWAS field to make genomic selections ,61,62,63δ0 is a constant, the hyperparameter iφ controls the sparsity of the model, and σ2 is the variance of the regression coefficients of influential genes. For binary traits, the likelihood function for the logistic regression model is maximized as follows: jy ∈ {1, 0} is the observed phenotype for the jth subject, and jp is the probability for a subject to have the disease given his/her genotypes and estimated regression coefficients jp = p  was developed by setting point-normal priors on the regression coefficients:e et al.  calculatp-values in single-SNP models, Bayesian approaches use a binary latent vector, say c, to assess the importance of each SNP, with ic = 1 meaning the ith SNP is influential and ic = 0 meaning the ith SNP is not influential. See Banerjee et al. [Unlike the e et al.  for detae et al.  demonstre et al.  applied However, it is also a legitimate concern that a Bayesian regression is much more computationally inefficient than the single-SNP GWAS methods due to the large number of parameters and the long iterative sampling process. There are many ways to improve the efficiency of a Bayesian algorithm, among which the most standard approach is to use conjugate priors and fast Gibbs sampling applied on the unknown parameters . Some otjr, j = 1,…, n). The result of SVM is interpretable because it tracks which genetic variants are used to construct the separating hyper-plane and classify binary traits. However, SVM may suffer from overfitting and loss of generalizability for high-dimensional data.The Support Vector Machine (SVM)  is a welp >> n), which facilitates the applicability of SVM algorithm to case-control disease data in the GWAS fields. The DWD improves the standard SVM by locating the hyperplane that minimizes the sum of the reciprocals of jr   proposedjr  subjects are increased (decreased) by the same percentage no matter they come from the majority class or from the minority class. In order to support the under-represented minority class and truly address the unbalanced case-control issue, Sun et al.  adjustedFrasca et al.  proposedMunkhdalai et al.  proposedAnother well-known variable selection mechanism for the neural network was the so-called “dropout” proposed by Srivastava et al. , which ip-value can be obtained is crucial to select significant genetic variants. However, the statistical significance study for the state-of-the-art machine-learning approaches is still under-developed. In order to facilitate wide applications of machine-learning approaches into the GWAS field, we refer the readers to the well-established permutation-based significance test skills, which have already been applied to some classifiers to obtain p-values [p-values [p-value for each variant.Performing the hypothesis test and statistical significance for each variant has been the core of the GWAS field, and, therefore, whether the p-values ,85. For p-values  performep-values . As a nop-value of each variant is approximated by counting the fraction of the test statistic scores of permuted data that are larger than that of the original data [p-values depends on the original data  as well as the classifier itself (whether the classifier is able to discover these associations) [The distribution of a test statistic is empirically established by permuting the original data with a large amount of time. Then an empirical nal data ,85. The iations) . Actualliations) . See Ojaiations)  for moreWith the collection of a large-scale of diseases from participants in large cohorts such as biobanks and EHRs, it raises rapidly increasing demands on statistical and machine-learning methods driven by unbalanced case-control GWAS data analysis needs. In this article, we reviewed multiple methods that are designed to address the imbalance in binary traits, including GMMAT and SAIGE that were based on logistic mixed models, the Bayesian variable selection method B-LORE, and machine-learning approaches such as SVM, AdaBoost, and the neural network. Each method has its own advantages as well as limitations, as summarized in n big p” or the curse of dimensionality issue, raise big challenges [In addition to the unbalanced classification issue, there are several other common challenges and concerns in the GWAS literature, to name a few in the following: (1) The GWAS data easily involves millions of SNPs for only thousands of participants, the ultrahigh-dimensionality, “small allenges . (2) LD allenges . (3) Epiallenges ,94,95,96allenges . (5) A mTo overcome the ultrahigh dimensionality challenge, Carlsen et al.  proposedIn addition to genomic selection, phenotypic prediction such as prediction of disease status or population disease prevalence using GWAS data repositories have also attracted a lot of research attention lately ,28,97,98